mirror of
https://github.com/agentscope-ai/ReMe.git
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rm memoryscope
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jinli.yl
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# __ __ ____
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# | \/ | ___ _ __ ___ ___ _ __ _ _/ ___| ___ ___ _ __ ___
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# | |\/| |/ _ \ '_ ` _ \ / _ \| '__| | | \___ \ / __/ _ \| '_ \ / _ \
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# | | | | __/ | | | | | (_) | | | |_| |___) | (_| (_) | |_) | __/
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# |_| |_|\___|_| |_| |_|\___/|_| \__, |____/ \___\___/| .__/ \___|
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# |___/ |_|
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# Instruction
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# To construct docker image:
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# sudo docker build --network=host -t memoryscope .
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# To run docker image:
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# sudo docker run -it --rm --memory=4G --net=host memoryscope
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# To run docker image with arguments (refer to memoryscope/core/config/arguments.py):
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# sudo docker run -it --rm --memory=4G --net=host -e "OPENAI_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" -e "language=en" -e "human_name=superman" -e "generation_backend=openai_generation" -e "generation_model=gpt-4o" -e "embedding_backend=openai_embedding" -e "embedding_model=text-embedding-3-small" -e "enable_ranker=False" memoryscope
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FROM python:3.11
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# (Not necessary) Change pip source
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RUN echo '[global]' > /etc/pip.conf && \
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echo 'index-url = https://mirrors.aliyun.com/pypi/simple/' >> /etc/pip.conf && \
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echo 'trusted-host = mirrors.aliyun.com' >> /etc/pip.conf
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# Install Elastic Search
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RUN useradd -m elastic_search_user
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USER elastic_search_user
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WORKDIR /home/elastic_search_user/elastic_search
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# COPY elasticsearch-8.15.0-linux-x86_64.tar.gz ./elasticsearch-8.15.0-linux-x86_64.tar.gz
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RUN wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-8.15.0-linux-x86_64.tar.gz
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RUN tar -xzf elasticsearch-8.15.0-linux-x86_64.tar.gz
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WORKDIR /home/elastic_search_user/elastic_search/elasticsearch-8.15.0
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ENV DISCOVERY_TYPE=single-node \
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XPACK_SECURITY_ENABLED=false \
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XPACK_LICENSE_SELF_GENERATED_TYPE=trial
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# Change user back to root and fix ownership
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USER root
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RUN chown -R elastic_search_user:elastic_search_user /home/elastic_search_user/
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WORKDIR /memory_scope_project
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# (Not necessary) Install the majority of deps, using docker build cache to accelerate future building
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COPY requirements.txt ./
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RUN pip3 install -r requirements.txt
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# Enter working dir
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WORKDIR /memory_scope_project
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COPY . .
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# RUN pip3 install poetry
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# RUN poetry install
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RUN pip3 install -r requirements.txt
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# Launch!
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# CMD ["bash"]
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CMD ["bash", "examples/docker/entrypoint.sh"]
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@ -1,56 +0,0 @@
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# __ __ ____
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# | \/ | ___ _ __ ___ ___ _ __ _ _/ ___| ___ ___ _ __ ___
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# | |\/| |/ _ \ '_ ` _ \ / _ \| '__| | | \___ \ / __/ _ \| '_ \ / _ \
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# | | | | __/ | | | | | (_) | | | |_| |___) | (_| (_) | |_) | __/
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# |_| |_|\___|_| |_| |_|\___/|_| \__, |____/ \___\___/| .__/ \___|
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# |___/ |_|
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# Instruction
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# To construct docker image:
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# sudo docker build --network=host -t memoryscope .
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|
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# To run docker image:
|
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# sudo docker run -it --rm --memory=4G --net=host memoryscope
|
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# To run docker image with arguments (refer to memoryscope/core/config/arguments.py):
|
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# sudo docker run -it --rm --memory=4G --net=host -e "OPENAI_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" -e "language=en" -e "human_name=superman" -e "generation_backend=openai_generation" -e "generation_model=gpt-4o" -e "embedding_backend=openai_embedding" -e "embedding_model=text-embedding-3-small" -e "enable_ranker=False" memoryscope
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#docker run -it --rm ghcr.io/modelscope/memoryscope_arm /bin/bash
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FROM python:3.11
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|
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# (Not necessary) Change pip source
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RUN echo '[global]' > /etc/pip.conf && \
|
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echo 'index-url = https://mirrors.aliyun.com/pypi/simple/' >> /etc/pip.conf && \
|
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echo 'trusted-host = mirrors.aliyun.com' >> /etc/pip.conf
|
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|
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# Install Elastic Search
|
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RUN useradd -m elastic_search_user
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USER elastic_search_user
|
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WORKDIR /home/elastic_search_user/elastic_search
|
||||
RUN wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-8.15.2-linux-aarch64.tar.gz
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RUN tar -xzf elasticsearch-8.15.2-linux-aarch64.tar.gz
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RUN mv /home/elastic_search_user/elastic_search/elasticsearch-8.15.2 /home/elastic_search_user/elastic_search/elasticsearch-8.15.0
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WORKDIR /home/elastic_search_user/elastic_search/elasticsearch-8.15.0
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ENV DISCOVERY_TYPE=single-node \
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XPACK_SECURITY_ENABLED=false \
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XPACK_LICENSE_SELF_GENERATED_TYPE=trial
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|
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# Change user back to root and fix ownership
|
||||
USER root
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RUN chown -R elastic_search_user:elastic_search_user /home/elastic_search_user/
|
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WORKDIR /memory_scope_project
|
||||
|
||||
# (Not necessary) Install the majority of deps, using docker build cache to accelerate future building
|
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COPY requirements.txt ./
|
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RUN pip3 install -r requirements.txt
|
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|
||||
# Enter working dir
|
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WORKDIR /memory_scope_project
|
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COPY . .
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# RUN pip3 install poetry
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# RUN poetry install
|
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RUN pip3 install -r requirements.txt
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|
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# Launch!
|
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# CMD ["bash"]
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CMD ["bash", "examples/docker/entrypoint.sh"]
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@ -1,131 +0,0 @@
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English | [**中文**](./README_ZH.md) | [**日本語**](./README_JP.md)
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# MemoryScope
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<p align="center">
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<img src="./docs/images/logo.png" alt="MemoryScopeLogo" width="75%">
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</p>
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Equip your LLM chatbot with a powerful and flexible long term memory system.
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[](https://pypi.org/project/memoryscope/)
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[](https://pypi.org/project/memoryscope/)
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[](./LICENSE)
|
||||
[](https://modelscope.github.io/MemoryScope/en/index.html#welcome-to-memoryscope-tutorial)
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[](https://modelscope.github.io/MemoryScope/en/docs/api.html)
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||||
[](https://modelscope.github.io/MemoryScope/en/docs/contribution.html)
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|
||||
----
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## 📰 News
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- **[2024-09-10]** We release MemoryScope v0.1.1.0 now, which is also available in [PyPI](https://pypi.org/simple/memoryscope/)!
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----
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## 🌟 What is MemoryScope？
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MemoryScope provides LLM chatbots with powerful and flexible long-term memory capabilities, offering a framework for building such abilities.
|
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It can be applied to scenarios like personal assistants and emotional companions, continuously learning through long-term memory to remember users' basic information as well as various habits and preferences.
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This allows users to gradually experience a sense of "understanding" when using the LLM.
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|
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### Demo
|
||||
<p align="center">
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<img src="https://github.com/user-attachments/assets/1754c814-1342-4288-a8a3-74d0b40f59a6" alt="en_demo" width="75%">
|
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</p>
|
||||
|
||||
### Framework
|
||||
<p align="center">
|
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<img src="./docs/images/framework.png" alt="Framework" width="75%">
|
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</p>
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|
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💾 Memory Database: MemoryScope is equipped with a vector database (default is *ElasticSearch*) to store all memory fragments recorded in the system.
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🔧 Worker Library: MemoryScope atomizes the capabilities of long-term memory into individual workers, including over 20 workers for tasks such as query information filtering, observation extraction, and insight updating.
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🛠️ Operation Library: Based on the worker pipeline, it constructs the operations for memory services, realizing key capabilities such as memory retrieval and memory consolidation.
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- Memory Retrieval: Upon arrival of a user query, this operation returns the semantically related memory pieces
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and/or those from the corresponding time if the query involves reference to time.
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- Memory Consolidation: This operation takes in a batch of user queries and returns important user information
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extracted from the queries as consolidated *observations* to be stored in the memory database.
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- Reflection and Re-consolidation: At regular intervals, this operation performs reflection upon newly recorded *observations*
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to form and update *insights*. Then, memory re-consolidation is performed to ensure contradictions and repetitions
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among memory pieces are properly handled.
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|
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⚙️ Best Practices:
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|
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- Based on the core capabilities of long-term memory, MemoryScope has implemented a dialogue interface (API) with long-term memory and a command-line dialogue practice (CLI) with long-term memory.
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- MemoryScope combines currently popular agent frameworks (AutoGen, AgentScope) to provide best practices.
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### Main Features
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⚡ Low response-time (RT) for the user:
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- Backend operations (Memory Consolidation, Reflection and Re-consolidation) are decoupled from the frontend operation
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(Memory Retrieval) in the system.
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- While backend operations are usually (and are recommended to be) queued or executed at regular intervals, the
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system's response time (RT) for the user depends solely on the frontend operation, which is only ~500ms.
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🌲 Hierarchical and coherent memory:
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- The memory pieces stored in the system are in a hierarchical structure, with *insights* being the high level information
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from the aggregation of similarly-themed *observations*.
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- Contradictions and repetitions among memory pieces are handled periodically to ensure coherence of memory.
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- Fictitious contents from the user are filtered out to avoid hallucinations by the LLM.
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⏰ Time awareness:
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- The system is time sensitive when performing both Memory Retrieval and Memory Consolidation. Therefore, it can retrieve
|
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accurate relevant information when the query involves reference to time.
|
||||
|
||||
----
|
||||
|
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## 💼 Supported Model API
|
||||
|
||||
| Backend | Task | Some Supported Models |
|
||||
|-------------------|------------|------------------------------------------------------------------------|
|
||||
| openai_backend | Generation | gpt-4o, gpt-4o-mini, gpt-4, gpt-3.5-turbo |
|
||||
| | Embedding | text-embedding-ada-002, text-embedding-3-large, text-embedding-3-small |
|
||||
| dashscope_backend | Generation | qwen-max, qwen-plus, qwen-plus, qwen2-72b-instruct |
|
||||
| | Embedding | text-embedding-v1, text-embedding-v2 |
|
||||
| | Reranker | gte-rerank |
|
||||
|
||||
In the future, we will support more model interfaces and local deployment of LLM and embedding services.
|
||||
|
||||
|
||||
## 🚀 Installation
|
||||
For installation, please refer to [Installation.md](docs/installation.md).
|
||||
|
||||
|
||||
## 🍕 Quick Start
|
||||
- [Simple Usages (Quick Start)](./examples/api/simple_usages.ipynb)
|
||||
- [With AutoGen](./examples/api/autogen_example.md)
|
||||
- [CLI with a MemoryScope Chatbot](./examples/cli/CLI_README.md)
|
||||
- [Advanced Customization](./examples/advance/custom_operator.md)
|
||||
|
||||
## 💡 Contribute
|
||||
|
||||
Contributions are always encouraged!
|
||||
|
||||
We highly recommend install pre-commit hooks in this repo before committing pull requests.
|
||||
These hooks are small house-keeping scripts executed every time you make a git commit,
|
||||
which will take care of the formatting and linting automatically.
|
||||
```shell
|
||||
pip install -e .
|
||||
pre-commit install
|
||||
```
|
||||
|
||||
Please refer to our [Contribution Guide](./docs/contribution.md) for more details.
|
||||
|
||||
## 📖 Citation
|
||||
|
||||
Reference to cite if you use MemoryScope in a paper:
|
||||
|
||||
```
|
||||
@software{MemoryScope,
|
||||
author = {Li Yu and
|
||||
Tiancheng Qin and
|
||||
Qingxu Fu and
|
||||
Sen Huang and
|
||||
Xianzhe Xu and
|
||||
Zhaoyang Liu and
|
||||
Boyin Liu},
|
||||
month = {09},
|
||||
title = {{MemoryScope}},
|
||||
url = {https://github.com/modelscope/MemoryScope},
|
||||
year = {2024}
|
||||
}
|
||||
```
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@ -1,121 +0,0 @@
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[**English**](./README.md) | [**中文**](./README_ZH.md) | 日本語
|
||||
|
||||
# MemoryScope
|
||||
<p align="center">
|
||||
<img src="./docs/images/logo.png" alt="MemoryScopeLogo" width="75%">
|
||||
</p>
|
||||
あなたのLLMチャットボットに強力で柔軟な長期記憶システムを装備しましょう。
|
||||
|
||||
[](https://pypi.org/project/memoryscope/)
|
||||
[](https://pypi.org/project/memoryscope/)
|
||||
[](./LICENSE)
|
||||
[](https://modelscope.github.io/MemoryScope/en/index.html#welcome-to-memoryscope-tutorial)
|
||||
[](https://modelscope.github.io/MemoryScope/en/docs/api.html)
|
||||
[](https://modelscope.github.io/MemoryScope/en/docs/contribution.html)
|
||||
|
||||
----
|
||||
## 📰 ニュース
|
||||
|
||||
- **[2024-09-10]** MemoryScope v0.1.1.0をリリースしました。 [PyPI](https://pypi.org/simple/memoryscope/)でも入手可能です！
|
||||
----
|
||||
## 🌟 MemoryScopeとは？
|
||||
MemoryScopeは、LLMチャットボットに強力で柔軟な長期記憶能力を提供し、その能力を構築するためのフレームワークを提供します。
|
||||
個人アシスタントや感情的な伴侶などのシナリオに適用でき、長期記憶を通じてユーザーの基本情報やさまざまな習慣や好みを覚え続けることができます。
|
||||
これにより、ユーザーはLLMを使用する際に徐々に「理解されている」感覚を体験することができます。
|
||||
|
||||
### デモ
|
||||
<p align="center">
|
||||
<img src="https://github.com/user-attachments/assets/1754c814-1342-4288-a8a3-74d0b40f59a6" alt="en_demo" width="75%">
|
||||
</p>
|
||||
|
||||
### フレームワーク
|
||||
<p align="center">
|
||||
<img src="./docs/images/framework.png" alt="Framework" width="75%">
|
||||
</p>
|
||||
|
||||
💾 メモリデータベース: MemoryScopeは、システム内に記録されたすべての記憶片を保存するためのベクトルデータベース（デフォルトは*ElasticSearch*）を備えています。
|
||||
|
||||
🔧 ワーカーライブラリ: MemoryScopeは、長期記憶の能力を個々のワーカーに原子化し、クエリ情報のフィルタリング、観察の抽出、洞察の更新など、20以上のワーカーを含みます。
|
||||
|
||||
🛠️ オペレーションライブラリ: ワーカーパイプラインに基づいて、メモリサービスのオペレーションを構築し、メモリの取得やメモリの統合などの主要な機能を実現します。
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||||
|
||||
- メモリの取得: ユーザークエリが到着すると、この操作は意味的に関連する記憶片を返します。
|
||||
クエリが時間に言及している場合は、対応する時間の記憶片も返します。
|
||||
- メモリの統合: この操作は、一連のユーザークエリを受け取り、クエリから抽出された重要なユーザー情報を統合された*観察*としてメモリデータベースに保存します。
|
||||
- 反映と再統合: 定期的に、この操作は新たに記録された*観察*を反映し、*洞察*を形成および更新します。
|
||||
その後、メモリの再統合を実行して、記憶片間の矛盾や重複が適切に処理されるようにします。
|
||||
|
||||
⚙️ ベストプラクティス:
|
||||
|
||||
- MemoryScopeは、長期記憶のコア機能に基づいて、長期記憶を持つ対話インターフェース（API）と長期記憶を持つコマンドライン対話の実践（CLI）を実装しています。
|
||||
- MemoryScopeは、現在人気のあるエージェントフレームワーク（AutoGen、AgentScope）を組み合わせて、ベストプラクティスを提供します。
|
||||
|
||||
### 主な特徴
|
||||
|
||||
⚡ 低い応答時間（RT）:
|
||||
- システム内のバックエンド操作（メモリの統合、反映と再統合）は、フロントエンド操作（メモリの取得）と分離されています。
|
||||
- バックエンド操作は通常（および推奨される）キューに入れられるか、定期的に実行されるため、システムのユーザー応答時間（RT）はフロントエンド操作のみに依存し、約500ミリ秒です。
|
||||
|
||||
🌲 階層的で一貫性のある記憶:
|
||||
- システムに保存される記憶片は階層構造になっており、*洞察*は同様のテーマの*観察*の集約から得られる高レベルの情報です。
|
||||
- 記憶片間の矛盾や重複は定期的に処理され、一貫性が保たれます。
|
||||
- ユーザーの虚偽の内容はフィルタリングされ、LLMの幻覚を避けることができます。
|
||||
|
||||
⏰ 時間感覚:
|
||||
- メモリの取得とメモリの統合を実行する際に時間感覚があり、クエリが時間に言及している場合に正確な関連情報を取得できます。
|
||||
|
||||
----
|
||||
|
||||
## 💼 サポートされているモデルAPI
|
||||
|
||||
| バックエンド | タスク | サポートされているモデルの一部 |
|
||||
|-------------------|------------|------------------------------------------------------------------------|
|
||||
| openai_backend | Generation | gpt-4o, gpt-4o-mini, gpt-4, gpt-3.5-turbo |
|
||||
| | Embedding | text-embedding-ada-002, text-embedding-3-large, text-embedding-3-small |
|
||||
| dashscope_backend | Generation | qwen-max, qwen-plus, qwen-plus, qwen2-72b-instruct |
|
||||
| | Embedding | text-embedding-v1, text-embedding-v2 |
|
||||
| | Reranker | gte-rerank |
|
||||
|
||||
将来的には、より多くのモデルインターフェースとローカルデプロイメントのLLMおよび埋め込みサービスをサポートする予定です。
|
||||
|
||||
## 🚀 インストール
|
||||
インストール方法については、[Installation.md](docs/installation.md)を参照してください。
|
||||
|
||||
## 🍕 クイックスタート
|
||||
- [簡単な使用法（クイックスタート）](./examples/api/simple_usages.ipynb)
|
||||
- [AutoGenとの連携](./examples/api/autogen_example.md)
|
||||
- [MemoryScopeチャットボットとのCLI](./examples/cli/README.md)
|
||||
- [高度なカスタマイズ](./examples/advance/custom_operator.md)
|
||||
|
||||
## 💡 貢献
|
||||
|
||||
貢献は常に奨励されています！
|
||||
|
||||
プルリクエストをコミットする前に、このリポジトリにpre-commitフックをインストールすることを強くお勧めします。
|
||||
これらのフックは、gitコミットを行うたびに実行される小さなハウスキーピングスクリプトであり、フォーマットとリンティングを自動的に処理します。
|
||||
```shell
|
||||
pip install -e .
|
||||
pre-commit install
|
||||
```
|
||||
|
||||
詳細については、[貢献ガイド](./docs/contribution.md)を参照してください。
|
||||
|
||||
## 📖 引用
|
||||
|
||||
MemoryScopeを論文で使用する場合は、以下の引用を追加してください：
|
||||
|
||||
```
|
||||
@software{MemoryScope,
|
||||
author = {Li Yu and
|
||||
Tiancheng Qin and
|
||||
Qingxu Fu and
|
||||
Sen Huang and
|
||||
Xianzhe Xu and
|
||||
Zhaoyang Liu and
|
||||
Boyin Liu},
|
||||
month = {09},
|
||||
title = {{MemoryScope}},
|
||||
url = {https://github.com/modelscope/MemoryScope},
|
||||
year = {2024}
|
||||
}
|
||||
```
|
||||
|
|
@ -1,124 +0,0 @@
|
|||
[**English**](./README.md) | 中文 | [**日本語**](./README_JP.md)
|
||||
|
||||
# MemoryScope
|
||||
<p align="center">
|
||||
<img src="./docs/images/logo.png" alt="MemoryScopeLogo" width="75%">
|
||||
</p>
|
||||
为您的大语言模型聊天机器人配备强大且灵活的长期记忆系统。
|
||||
|
||||
[](https://pypi.org/project/memoryscope/)
|
||||
[](https://pypi.org/project/memoryscope/)
|
||||
[](./LICENSE)
|
||||
[](https://modelscope.github.io/MemoryScope/zh/index.html#id1)
|
||||
[](https://modelscope.github.io/MemoryScope/zh/docs/api.html)
|
||||
[](https://modelscope.github.io/MemoryScope/zh/docs/contribution.html)
|
||||
|
||||
<!-- 创空间
|
||||
[](https://modelscope.cn/studios?name=memoryscope&page=1&sort=latest)
|
||||
-->
|
||||
|
||||
|
||||
----
|
||||
## 📰 新闻
|
||||
|
||||
- **[2024-09-10]** 我们现在发布了 MemoryScope v0.1.1.0，该版本也可以在 [PyPI](https://pypi.org/simple/memoryscope/) 上获取！
|
||||
----
|
||||
|
||||
## 🌟 什么是MemoryScope？
|
||||
MemoryScope可以为LLM聊天机器人提供强大且灵活的长期记忆能力，并提供了构建长期记忆能力的框架。
|
||||
MemoryScope可以用于个人助理、情感陪伴等记忆场景，通过长期记忆能力来不断学习，记得用户的基础信息以及各种习惯和喜好，使得用户在使用LLM时逐渐感受到一种“默契”。
|
||||
|
||||
### Demo
|
||||
<p align="center">
|
||||
<img src="https://github.com/user-attachments/assets/57519274-8c01-4d88-bcd2-0ebce3551e5d" alt="zh_demo" width="75%">
|
||||
</p>
|
||||
|
||||
|
||||
### 核心框架：
|
||||
<p align="center">
|
||||
<img src="./docs/images/framework.png" alt="Framework" width="75%">
|
||||
</p>
|
||||
|
||||
💾 记忆数据库: MemoryScope配备了向量数据库(默认是*ElasticSearch*)，用于存储系统中记录的所有记忆片段。
|
||||
|
||||
🔧 核心worker库: MemoryScope将长期记忆的能力原子化，抽象成单独的worker，包括query信息过滤，observation抽取，insight更新等20+worker。
|
||||
|
||||
🛠️ 核心Operation库: MemoryScope将workers组织为工作流(workflow)，构建了处理记忆的完善Operation库，实现了记忆检索，记忆巩固等核心能力。
|
||||
|
||||
- 记忆检索：当用户输入对话，此操作返回语义相关的记忆片段。如果输入对话包含对时间的指涉，则同时返回相应时间中的记忆片段。
|
||||
- 记忆巩固：此操作接收一批用户的输入对话，并从对话中提取重要的用户信息，将其作为 *observation* 形式的记忆片段存储在记忆数据库中。
|
||||
- 反思与再巩固：每隔一段时间，此操作对新记录的 *observations* 进行反思，以形成和更新 *insight* 形式的记忆片段。然后执行记忆再巩固，以确保记忆片段之间的矛盾和重复得到妥善处理。
|
||||
|
||||
⚙️ 最佳实践:
|
||||
|
||||
- MemoryScope在构建了长期记忆核心能力的基础上，实现了带长期记忆的对话接口(API)和带长期记忆的命令行对话实践(CLI)。
|
||||
- MemoryScope结合了目前流行的Agent框架（AutoGen、AgentScope），给出了最佳实践。
|
||||
|
||||
### 🤝主要特点
|
||||
|
||||
⚡ 极低的线上时延（RT）:
|
||||
- 系统中后端操作（记忆巩固、反思和再巩固）与前端操作（记忆检索）相互独立。
|
||||
- 由于后端操作通常（并且推荐）通过队列或每隔固定间隔执行，系统的用户时延（RT）完全取决于前端操作，仅为约500毫秒。
|
||||
|
||||
🌲 记忆存储的层次结构和内容的连贯一致性:
|
||||
- 系统中存储的记忆片段采用分层结构，通过汇总主题相似的 *observations* 生成高层次的 *insights* 信息。
|
||||
- 定期处理记忆片段之间的矛盾和重复，以保证记忆内容的连贯一致性。
|
||||
- 过滤掉用户输入的虚构内容，以避免LLM产生幻觉。
|
||||
|
||||
⏰ 时间敏感性:
|
||||
- 在执行记忆检索和记忆巩固时具备时间敏感性，因此在输入对话包含对时间的指涉时，可以检索到准确的相关信息。
|
||||
|
||||
----
|
||||
|
||||
## 💼 支持的模型API
|
||||
|
||||
| Backend | Task | Some Supported Models |
|
||||
|-------------------|------------|------------------------------------------------------------------------|
|
||||
| openai_backend | Generation | gpt-4o, gpt-4o-mini, gpt-4, gpt-3.5-turbo |
|
||||
| | Embedding | text-embedding-ada-002, text-embedding-3-large, text-embedding-3-small |
|
||||
| dashscope_backend | Generation | qwen-max, qwen-plus, qwen-plus, qwen2-72b-instruct |
|
||||
| | Embedding | text-embedding-v1, text-embedding-v2 |
|
||||
| | Reranker | gte-rerank |
|
||||
|
||||
未来将支持更多的模型接口和支持本地部署的LLM和emb服务
|
||||
|
||||
----
|
||||
## 🚀 安装
|
||||
完整的安装方法请参考[安装指南](docs/installation_zh.md)。
|
||||
|
||||
## 🍕 快速开始
|
||||
- [简易用法（快速开始）](./examples/api/simple_usages_zh.ipynb)
|
||||
- [在命令行与MemoryScope聊天机器人交互](./examples/cli/CLI_README_ZH.md)
|
||||
- [进阶自定义用法](./examples/advance/custom_operator.md)
|
||||
- [结合AutoGen使用](./examples/api/autogen_example.md)
|
||||
|
||||
|
||||
## 💡 代码贡献
|
||||
欢迎社区的代码贡献。
|
||||
我们非常推荐每一个贡献者在代码提交前，安装`pre-commit`钩子工具，
|
||||
能够帮助在每一次git提交的时候，进行自动化的代码格式校验。
|
||||
```shell
|
||||
pip install -e .
|
||||
pre-commit install
|
||||
```
|
||||
请参阅我们的[贡献指南](./docs/contribution_zh.md) 了解更多详细信息。
|
||||
|
||||
## 📖 引用
|
||||
|
||||
如果您在论文中有使用该项目，请添加以下引用：
|
||||
|
||||
```
|
||||
@software{MemoryScope,
|
||||
author = {Li Yu and
|
||||
Tiancheng Qin and
|
||||
Qingxu Fu and
|
||||
Sen Huang and
|
||||
Xianzhe Xu and
|
||||
Zhaoyang Liu and
|
||||
Boyin Liu},
|
||||
month = {09},
|
||||
title = {{MemoryScope}},
|
||||
url = {https://github.com/modelscope/MemoryScope},
|
||||
year = {2024}
|
||||
}
|
||||
```
|
||||
|
|
@ -1,24 +0,0 @@
|
|||
"""
|
||||
Warning!
|
||||
|
||||
This script purges the entire vector store !
|
||||
|
||||
"""
|
||||
|
||||
from memoryscope import MemoryScope, Arguments
|
||||
|
||||
arguments = Arguments(
|
||||
language="en",
|
||||
human_name="user",
|
||||
assistant_name="AI",
|
||||
memory_chat_class="api_memory_chat",
|
||||
generation_backend="openai_generation",
|
||||
generation_model="gpt-4o",
|
||||
embedding_backend="openai_embedding",
|
||||
embedding_model="text-embedding-3-small",
|
||||
enable_ranker=False,
|
||||
)
|
||||
|
||||
ms = MemoryScope(arguments=arguments)
|
||||
es_store = ms.context.memory_store.es_store
|
||||
es_store.sync_delete_all()
|
||||
|
|
@ -1,16 +0,0 @@
|
|||
services:
|
||||
memory_scope_main:
|
||||
image: ghcr.io/modelscope/memoryscope_arm:main
|
||||
# image: ghcr.io/modelscope/memoryscope_arm:main # For ARM architecture
|
||||
environment:
|
||||
DASHSCOPE_API_KEY: "sk-0000000000"
|
||||
# OPENAI_API_KEY: "sk-0000000000"
|
||||
volumes:
|
||||
- ./memoryscope/core/config:/memory_scope_project/memoryscope/memoryscope/core/config
|
||||
deploy:
|
||||
resources:
|
||||
limits:
|
||||
memory: 4G
|
||||
stdin_open: true
|
||||
tty: true
|
||||
# Please execute `docker compose run memory_scope_main` instead of `docker compose up`
|
||||
|
|
@ -1,19 +0,0 @@
|
|||
# MemoryScope Documentation
|
||||
|
||||
## Build Documentation
|
||||
|
||||
Please use the following commands to build sphinx doc of MemoryScope.
|
||||
|
||||
```shell
|
||||
# step 1: Install dependencies
|
||||
pip install sphinx sphinx-autobuild sphinx_rtd_theme myst-parser sphinxcontrib-mermaid
|
||||
|
||||
# step 2: go into the sphinx_doc dir
|
||||
cd docs/sphinx_doc
|
||||
|
||||
# step 3: build the sphinx doc
|
||||
./build_sphinx_doc.sh
|
||||
|
||||
# step 4: view sphinx_doc/build/html/index.html using your browser
|
||||
cd docs/sphinx_doc/build/html && python -m http.server 8899
|
||||
```
|
||||
|
|
@ -1,42 +0,0 @@
|
|||
# Contribute to MemoryScope
|
||||
Our community thrives on the diverse ideas and contributions of its members. Whether you're fixing a bug, adding a new feature, improving the documentation, or adding examples, your help is welcome. Here's how you can contribute:
|
||||
## Report Bugs and Ask For New Features?
|
||||
Did you find a bug or have a feature request? Please first check the issue tracker to see if it has already been reported. If not, feel free to open a new issue. Include as much detail as possible:
|
||||
- A descriptive title
|
||||
- Clear description of the issue
|
||||
- Steps to reproduce the problem
|
||||
- Version of the MemoryScope you are using
|
||||
- Any relevant code snippets or error messages
|
||||
## Contribute to Codebase
|
||||
### Fork and Clone the Repository
|
||||
To work on an issue or a new feature, start by forking the MemoryScope repository and then cloning your fork locally.
|
||||
```bash
|
||||
git clone https://github.com/your-username/memoryscope.git
|
||||
cd memoryscope
|
||||
```
|
||||
### Create a New Branch
|
||||
Create a new branch for your work. This helps keep proposed changes organized and separate from the `main` branch.
|
||||
```bash
|
||||
git checkout -b your-feature-branch-name
|
||||
```
|
||||
### Making Changes
|
||||
With your new branch checked out, you can now make your changes to the code. Remember to keep your changes as focused as possible. If you're addressing multiple issues or features, it's better to create separate branches and pull requests for each.
|
||||
We provide a developer version with additional `pre-commit` hooks to perform format checks compared to the official version:
|
||||
```bash
|
||||
# Install the developer version
|
||||
pip install -e .
|
||||
# Install pre-commit hooks
|
||||
pre-commit install
|
||||
```
|
||||
### Commit Your Changes
|
||||
Once you've made your changes, it's time to commit them. Write clear and concise commit messages that explain your changes.
|
||||
```bash
|
||||
git add -A
|
||||
git commit -m "A brief description of the changes"
|
||||
```
|
||||
You might get some error messages raised by `pre-commit`. Please resolve them according to the error code and commit again.
|
||||
### Submit a Pull Request
|
||||
When you're ready for feedback, submit a pull request to the MemoryScope `main` branch. In your pull request description, explain the changes you've made and any other relevant context.
|
||||
We will review your pull request. This process might involve some discussion, additional changes on your part, or both.
|
||||
### Code Review
|
||||
Wait for us to review your pull request. We may suggest some changes or improvements. Keep an eye on your GitHub notifications and be responsive to any feedback.
|
||||
|
|
@ -1,51 +0,0 @@
|
|||
# 贡献到MemoryScope
|
||||
我们的社区因其成员的多样化思想和贡献而兴旺发展。无论是修复一个错误，添加一个新功能，改进文档，还是添加示例，我们都欢迎您的帮助。以下是您做出贡献的方法：
|
||||
## 报告错误和提出新功能
|
||||
当您发现一个错误或者有一个功能请求，请首先检查问题跟踪器，查看它是否已经被报告。如果没有，随时可以开设一个新的问题。请包含尽可能多的细节:
|
||||
- 简明扼要的标题
|
||||
- 清晰地描述问题
|
||||
- 提供重现问题的步骤
|
||||
- 提供所使用的MemoryScope版本
|
||||
- 提供所有相关代码片段或错误信息
|
||||
## 对代码库做出贡献
|
||||
### Fork和Clone仓库
|
||||
要处理一个问题或新功能，首先要Fork仓库，然后将你的Fork克隆到本地。
|
||||
```bash
|
||||
git clone git@github.com:modelscope/MemoryScope.git
|
||||
cd MemoryScope
|
||||
```
|
||||
### 创建一个新分支
|
||||
为您的工作创建一个新分支。这有助于保持拟议更改的组织性，并与`main`分支分离。
|
||||
```bash
|
||||
git checkout -b your-feature-branch-name
|
||||
```
|
||||
### 做出修改
|
||||
我们非常推荐每一个贡献者在代码提交前，安装`pre-commit`钩子工具，
|
||||
能够帮助在每一次git提交的时候，进行自动化的代码格式校验。
|
||||
```bash
|
||||
# 安装开发者版本
|
||||
pip install -e .
|
||||
# 安装 pre-commit 钩子
|
||||
pre-commit install
|
||||
```
|
||||
|
||||
### 提交您的修改
|
||||
|
||||
修改完成之后就是提交它们的时候了。请提供清晰而简洁的提交信息，以解释您的修改内容。
|
||||
|
||||
```bash
|
||||
git add -A
|
||||
git commit -m "修改内容的简要描述"
|
||||
```
|
||||
|
||||
运行时您可能会收到 `pre-commit` 给出的错误信息。请根据错误信息修改您的代码然后再次提交。
|
||||
|
||||
### 提交 Pull Request
|
||||
|
||||
当您准备好您的修改分支后，向MemoryScope的 `main` 分支提交一个Pull Request。在您的Pull Request描述中，解释您所做的修改以及其他相关的信息。
|
||||
|
||||
我们将审查您的Pull Request。这个过程可能涉及一些讨论以及额外的代码修改。
|
||||
|
||||
### 代码审查
|
||||
|
||||
等待我们审核您的Pull Request。我们可能会提供一些更改或改进建议。请留意您的GitHub通知，并对反馈做出响应。
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 221 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 73 KiB |
|
|
@ -1,126 +0,0 @@
|
|||
# Installing MemoryScope
|
||||
|
||||
## I. Install with docker [Recommended] [x86_64]
|
||||
|
||||
1. Clone the repository and edit settings
|
||||
```bash
|
||||
# clone project
|
||||
git clone https://github.com/modelscope/memoryscope
|
||||
cd memoryscope
|
||||
# edit configuration, e.g. add api keys
|
||||
vim memoryscope/core/config/demo_config.yaml
|
||||
```
|
||||
|
||||
2. Build Docker image
|
||||
```bash
|
||||
sudo docker build --network=host -t memoryscope .
|
||||
```
|
||||
If you are using arm-based computers, modify command above into: `sudo docker build -f DockerfileArm --network=host -t memoryscope .`
|
||||
|
||||
3. Launch Docker container
|
||||
```bash
|
||||
sudo docker run -it --rm --net=host memoryscope
|
||||
```
|
||||
|
||||
> [!Important]
|
||||
> To inspect memory shift during the conversation, modify command in step 3 to `sudo docker run -it --name=memoryscope_container --rm --net=host memoryscope`;<br/>
|
||||
> Then start a new terminal window and execute `sudo docker exec -it memoryscope_container python quick-start-demo.py --config_path=memoryscope/core/config/demo_config_zh.yaml`;<br/>
|
||||
> In the second window, input `/list_memory refresh_time=5` to inspect memory
|
||||
|
||||
## II. Install with docker compose [Recommended] [x86_64]
|
||||
|
||||
1. Clone the repository and edit settings
|
||||
```bash
|
||||
# clone project
|
||||
git clone https://github.com/modelscope/memoryscope
|
||||
cd memoryscope
|
||||
# edit configuration, e.g. add api keys
|
||||
vim memoryscope/core/config/demo_config.yaml
|
||||
```
|
||||
|
||||
2. Edit `docker-compose.yml` to change environment variable.
|
||||
```
|
||||
OPENAI_API_KEY: "sk-0000000000"
|
||||
```
|
||||
|
||||
3. Run `docker-compose run memory_scope_main` to build and launch the memory-scope cli interface. (For ARM architecture, you should edit `docker-compose.yml`, changing `image: ghcr.io/modelscope/memoryscope:main` to `image: ghcr.io/modelscope/memoryscope_arm:main`)
|
||||
|
||||
|
||||
## III. Install from PyPI
|
||||
|
||||
1. Install from PyPI
|
||||
```bash
|
||||
pip install memoryscope
|
||||
```
|
||||
|
||||
2. Run Elasticsearch service, refer to [elasticsearch documents](https://www.elastic.co/guide/en/elasticsearch/reference/current/getting-started.html).
|
||||
The docker method is recommended:
|
||||
```
|
||||
sudo docker run -p 9200:9200 \
|
||||
-e "discovery.type=single-node" \
|
||||
-e "xpack.security.enabled=false" \
|
||||
-e "xpack.license.self_generated.type=trial" \
|
||||
docker.elastic.co/elasticsearch/elasticsearch:8.13.2
|
||||
```
|
||||
|
||||
3. Test Chinese / Dashscope Configuration
|
||||
```bash
|
||||
export DASHSCOPE_API_KEY="sk-0000000000"
|
||||
memoryscope --language="cn" \
|
||||
--memory_chat_class="cli_memory_chat" \
|
||||
--human_name="用户" \
|
||||
--assistant_name="AI" \
|
||||
--generation_backend="dashscope_generation" \
|
||||
--generation_model="qwen-max" \
|
||||
--embedding_backend="dashscope_embedding" \
|
||||
--embedding_model="text-embedding-v2" \
|
||||
--enable_ranker=True \
|
||||
--rank_backend="dashscope_rank" \
|
||||
--rank_model="gte-rerank"
|
||||
```
|
||||
|
||||
4. Test English / OpenAI Configuration
|
||||
```bash
|
||||
export OPENAI_API_KEY="sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
|
||||
memoryscope --language="en" \
|
||||
--memory_chat_class="cli_memory_chat" \
|
||||
--human_name="User" \
|
||||
--assistant_name="AI" \
|
||||
--generation_backend="openai_generation" \
|
||||
--generation_model="gpt-4o" \
|
||||
--embedding_backend="openai_embedding" \
|
||||
--embedding_model="text-embedding-3-small" \
|
||||
--enable_ranker=False
|
||||
```
|
||||
|
||||
## IV. Install from source
|
||||
|
||||
1. Clone the repository and edit settings
|
||||
```bash
|
||||
# clone project
|
||||
git clone https://github.com/modelscope/memoryscope
|
||||
cd memoryscope
|
||||
# edit configuration, e.g. add api keys
|
||||
vim memoryscope/core/config/demo_config.yaml
|
||||
```
|
||||
|
||||
2. Install
|
||||
```bash
|
||||
pip install -e .
|
||||
```
|
||||
|
||||
3. Run Elasticsearch service, refer to [elasticsearch documents](https://www.elastic.co/guide/en/elasticsearch/reference/current/getting-started.html).
|
||||
The docker method is recommended:
|
||||
```
|
||||
sudo docker run -p 9200:9200 \
|
||||
-e "discovery.type=single-node" \
|
||||
-e "xpack.security.enabled=false" \
|
||||
-e "xpack.license.self_generated.type=trial" \
|
||||
docker.elastic.co/elasticsearch/elasticsearch:8.13.2
|
||||
```
|
||||
|
||||
4. Launch memoryscope, also refer to [cli documents](../examples/cli/CLI_README.md)
|
||||
```bash
|
||||
export OPENAI_API_KEY="sk-0000000000"
|
||||
python quick-start-demo.py --config_path=memoryscope/core/config/demo_config_zh.yaml
|
||||
```
|
||||
|
|
@ -1,129 +0,0 @@
|
|||
# MemoryScope 安装指南
|
||||
|
||||
## 一、使用 Docker 安装 [推荐]
|
||||
|
||||
1. 克隆仓库并编辑配置
|
||||
```bash
|
||||
# 克隆项目
|
||||
git clone https://github.com/modelscope/memoryscope
|
||||
cd memoryscope
|
||||
# 编辑配置，例如添加 API 密钥
|
||||
vim memoryscope/core/config/demo_config_zh.yaml
|
||||
```
|
||||
|
||||
2. 构建 Docker 镜像
|
||||
```bash
|
||||
sudo docker build --network=host -t memoryscope .
|
||||
```
|
||||
备注：如果是arm架构的电脑，则必须使用另一个命令：`sudo docker build -f DockerfileArm --network=host -t memoryscope .`
|
||||
|
||||
3. 启动 Docker 容器
|
||||
```bash
|
||||
sudo docker run -it --rm --net=host memoryscope
|
||||
```
|
||||
|
||||
|
||||
> [!Important]
|
||||
> 如果需要观察Memory的变化请调整第3步的运行命令。首先执行 `sudo docker run -it --name=memoryscope_container --rm --net=host memoryscope`启动memoryscope；<br/>
|
||||
> 然后新建命令行窗口，运行`sudo docker exec -it memoryscope_container python quick-start-demo.py --config_path=memoryscope/core/config/demo_config_zh.yaml`；<br/>
|
||||
> 在第二个窗口，继续输入`/list_memory refresh_time=5`来检查实时的memory
|
||||
|
||||
## 二、使用 Docker Compose 安装 [推荐] [x86_64]
|
||||
|
||||
1. 克隆仓库并编辑配置
|
||||
```bash
|
||||
# 克隆项目
|
||||
git clone https://github.com/modelscope/memoryscope
|
||||
cd memoryscope
|
||||
# 编辑配置，例如添加 API 密钥
|
||||
vim memoryscope/core/config/demo_config_zh.yaml
|
||||
```
|
||||
|
||||
2. 编辑 `docker-compose.yml` 文件以更改环境变量。
|
||||
```
|
||||
DASHSCOPE_API_KEY: "sk-0000000000"
|
||||
```
|
||||
|
||||
3. 运行 `docker-compose run memory_scope_main` 命令来构建并启动 MemoryScope CLI 界面。(备注：如果是arm架构，还需要手动将docker-compose.yml中的`ghcr.io/modelscope/memoryscope:main`修改成`ghcr.io/modelscope/memoryscope_arm:main`)
|
||||
|
||||
|
||||
## 三、通过 PYPI 安装
|
||||
|
||||
1. 从 PyPI 安装：
|
||||
```bash
|
||||
pip install memoryscope
|
||||
```
|
||||
|
||||
2. 运行 Elasticsearch 服务，参照 [Elasticsearch 文档](https://www.elastic.co/guide/cn/elasticsearch/reference/current/getting-started.html)。
|
||||
推荐使用 Docker 方法：
|
||||
```
|
||||
sudo docker run -p 9200:9200 \
|
||||
-e "discovery.type=single-node" \
|
||||
-e "xpack.security.enabled=false" \
|
||||
-e "xpack.license.self_generated.type=trial" \
|
||||
docker.elastic.co/elasticsearch/elasticsearch:8.13.2
|
||||
```
|
||||
|
||||
3. 测试中文 / Dashscope 对话配置：
|
||||
```bash
|
||||
export DASHSCOPE_API_KEY="sk-0000000000"
|
||||
memoryscope --language="cn" \
|
||||
--memory_chat_class="cli_memory_chat" \
|
||||
--human_name="用户" \
|
||||
--assistant_name="AI" \
|
||||
--generation_backend="dashscope_generation" \
|
||||
--generation_model="qwen-max" \
|
||||
--embedding_backend="dashscope_embedding" \
|
||||
--embedding_model="text-embedding-v2" \
|
||||
--enable_ranker=True \
|
||||
--rank_backend="dashscope_rank" \
|
||||
--rank_model="gte-rerank"
|
||||
```
|
||||
|
||||
4. 测试英文 / OpenAI 对话配置：
|
||||
```bash
|
||||
export OPENAI_API_KEY="sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
|
||||
memoryscope --language="en" \
|
||||
--memory_chat_class="cli_memory_chat" \
|
||||
--human_name="User" \
|
||||
--assistant_name="AI" \
|
||||
--generation_backend="openai_generation" \
|
||||
--generation_model="gpt-4o" \
|
||||
--embedding_backend="openai_embedding" \
|
||||
--embedding_model="text-embedding-3-small" \
|
||||
--enable_ranker=False
|
||||
```
|
||||
|
||||
|
||||
## 四、从源码安装
|
||||
|
||||
1. 克隆仓库并编辑设置
|
||||
```bash
|
||||
# 克隆项目
|
||||
git clone https://github.com/modelscope/memoryscope
|
||||
cd memoryscope
|
||||
# 编辑配置，例如添加 API 密钥
|
||||
vim memoryscope/core/config/demo_config_zh.yaml
|
||||
```
|
||||
|
||||
2. 安装依赖
|
||||
```bash
|
||||
pip install -e .
|
||||
```
|
||||
|
||||
3. 运行 Elasticsearch 服务，参照 [Elasticsearch 文档](https://www.elastic.co/guide/cn/elasticsearch/reference/current/getting-started.html)。
|
||||
推荐使用 Docker 方法：
|
||||
```
|
||||
sudo docker run -p 9200:9200 \
|
||||
-e "discovery.type=single-node" \
|
||||
-e "xpack.security.enabled=false" \
|
||||
-e "xpack.license.self_generated.type=trial" \
|
||||
docker.elastic.co/elasticsearch/elasticsearch:8.13.2
|
||||
```
|
||||
|
||||
4. 启动 MemoryScope，同时参考 [CLI 文档](../examples/cli/CLI_README_ZH.md)
|
||||
```bash
|
||||
export DASHSCOPE_API_KEY="sk-0000000000"
|
||||
python quick-start-demo.py --config_path=memoryscope/core/config/demo_config_zh.yaml
|
||||
```
|
||||
|
||||
|
|
@ -1,32 +0,0 @@
|
|||
# Makefile
|
||||
|
||||
SPHINXBUILD = sphinx-build
|
||||
SPHINXPROJ = MemoryScope-Doc
|
||||
ASSETSDIR = assets
|
||||
BUILDDIR = build/html
|
||||
SOURCEDIR_EN = en/source
|
||||
BUILDDIR_EN = build/html/en
|
||||
SOURCEDIR_ZH = zh/source
|
||||
BUILDDIR_ZH = build/html/zh
|
||||
|
||||
# English document
|
||||
en:
|
||||
@$(SPHINXBUILD) -b html "$(SOURCEDIR_EN)" "$(BUILDDIR_EN)"
|
||||
@echo
|
||||
@echo "Build finished. The HTML pages are in $(BUILDDIR_EN)"
|
||||
|
||||
# Chinese document
|
||||
zh:
|
||||
@$(SPHINXBUILD) -b html "$(SOURCEDIR_ZH)" "$(BUILDDIR_ZH)"
|
||||
@echo
|
||||
@echo "Build finished. The HTML pages are in $(BUILDDIR_ZH)"
|
||||
|
||||
index:
|
||||
@cp "$(ASSETSDIR)/redirect.html" "$(BUILDDIR)/index.html"
|
||||
|
||||
%: Makefile
|
||||
@$(SPHINXBUILD) -M $@ "$(SOURCEDIR_EN)" "$(BUILDDIR_EN)" $(O)
|
||||
|
||||
all: en zh index
|
||||
|
||||
.PHONY: all en zh index
|
||||
|
|
@ -1,12 +0,0 @@
|
|||
<!DOCTYPE html>
|
||||
<html>
|
||||
<head>
|
||||
<meta charset="utf-8" />
|
||||
<meta http-equiv="refresh" content="0; url=en/index.html" />
|
||||
<title>MemoryScope Documentation</title>
|
||||
</head>
|
||||
<body>
|
||||
<p>Redirecting to English documentation...</p>
|
||||
<p>If you are not redirected, <a href="en/index.html">click here</a>.</p>
|
||||
</body>
|
||||
</html>
|
||||
|
|
@ -1,55 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
# remove build
|
||||
rm -rf build/html/*
|
||||
rm -rf en/source/memoryscope*.rst
|
||||
rm -rf zh/source/memoryscope*.rst
|
||||
rm -rf ja/source/memoryscope*.rst
|
||||
|
||||
# copy related files
|
||||
cd ../../
|
||||
|
||||
cp README.md docs/sphinx_doc/en/source/README.md
|
||||
cp docs/installation.md docs/sphinx_doc/en/source/docs/installation.md
|
||||
cp docs/contribution.md docs/sphinx_doc/en/source/docs/contribution.md
|
||||
cp -r docs/images docs/sphinx_doc/en/source/docs/images
|
||||
cp -r examples docs/sphinx_doc/en/source/examples
|
||||
|
||||
cp README_ZH.md docs/sphinx_doc/zh/source/README.md
|
||||
cp docs/installation_zh.md docs/sphinx_doc/zh/source/docs/installation.md
|
||||
cp docs/contribution_zh.md docs/sphinx_doc/zh/source/docs/contribution.md
|
||||
cp -r docs/images docs/sphinx_doc/zh/source/docs/images
|
||||
cp -r examples docs/sphinx_doc/zh/source/examples
|
||||
|
||||
cp README_JP.md docs/sphinx_doc/ja/source/README.md
|
||||
cp docs/installation_jp.md docs/sphinx_doc/ja/source/docs/installation.md
|
||||
cp docs/contribution_jp.md docs/sphinx_doc/ja/source/docs/contribution.md
|
||||
cp -r docs/images docs/sphinx_doc/ja/source/docs/images
|
||||
cp -r examples docs/sphinx_doc/ja/source/examples
|
||||
|
||||
# build
|
||||
cd docs/sphinx_doc
|
||||
sphinx-apidoc -f -o en/source ../../memoryscope -t template -e
|
||||
sphinx-apidoc -f -o zh/source ../../memoryscope -t template -e
|
||||
sphinx-apidoc -f -o ja/source ../../memoryscope -t template -e
|
||||
|
||||
# clear redundant files
|
||||
make clean all
|
||||
|
||||
rm en/source/README.md
|
||||
rm en/source/docs/installation.md
|
||||
rm en/source/docs/contribution.md
|
||||
rm -rf en/source/docs/images
|
||||
rm -rf en/source/examples
|
||||
|
||||
rm zh/source/README.md
|
||||
rm zh/source/docs/installation.md
|
||||
rm zh/source/docs/contribution.md
|
||||
rm -rf zh/source/docs/images
|
||||
rm -rf zh/source/examples
|
||||
|
||||
rm ja/source/README.md
|
||||
rm ja/source/docs/installation.md
|
||||
rm ja/source/docs/contribution.md
|
||||
rm -rf ja/source/docs/images
|
||||
rm -rf ja/source/examples
|
||||
|
|
@ -1,4 +0,0 @@
|
|||
.language-selector a {
|
||||
color: white;
|
||||
width: 20px;
|
||||
}
|
||||
|
|
@ -1,5 +0,0 @@
|
|||
<!-- language_selector.html -->
|
||||
<div class="language-selector">
|
||||
<a href="{{ pathto('../en/' + pagename) }}">English</a></li> |
|
||||
<a href="{{ pathto('../zh/' + pagename) }}">中文</a></li>
|
||||
</div>
|
||||
|
|
@ -1,3 +0,0 @@
|
|||
<!-- layout.html -->
|
||||
{% extends "!layout.html" %} {% block sidebartitle %} {{ super() }} {% include
|
||||
"language_selector.html" %} {% endblock %}
|
||||
|
|
@ -1,105 +0,0 @@
|
|||
# -*- coding: utf-8 -*-
|
||||
# Configuration file for the Sphinx documentation builder.
|
||||
#
|
||||
# This file only contains a selection of the most common options. For a full
|
||||
# list see the documentation:
|
||||
# https://www.sphinx-doc.org/en/master/usage/configuration.html
|
||||
|
||||
# -- Path setup --------------------------------------------------------------
|
||||
|
||||
# If extensions (or modules to document with autodoc) are in another directory,
|
||||
# add these directories to sys.path here. If the directory is relative to the
|
||||
# documentation root, use os.path.abspath to make it absolute, like shown here.
|
||||
#
|
||||
import os
|
||||
import sys
|
||||
|
||||
sys.path.insert(0, os.path.abspath("../../../../../MemoryScope"))
|
||||
|
||||
|
||||
# -- Project information -----------------------------------------------------
|
||||
|
||||
language = "en"
|
||||
|
||||
project = "MemoryScope"
|
||||
copyright = "2024, Alibaba Tongyi Lab"
|
||||
author = "EcoML team of Alibaba Tongyi Lab"
|
||||
|
||||
|
||||
# -- General configuration ---------------------------------------------------
|
||||
|
||||
# Add any Sphinx extension module names here, as strings. They can be
|
||||
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
|
||||
# ones.
|
||||
extensions = [
|
||||
"sphinx.ext.autodoc",
|
||||
"sphinx.ext.autosummary",
|
||||
"sphinx.ext.viewcode",
|
||||
"sphinx.ext.napoleon",
|
||||
"sphinxcontrib.mermaid",
|
||||
"myst_parser",
|
||||
"sphinx.ext.autosectionlabel",
|
||||
"sphinxcontrib.autodoc_pydantic",
|
||||
"nbsphinx"
|
||||
]
|
||||
|
||||
autodoc_pydantic_model_show_json = True
|
||||
autodoc_pydantic_settings_show_json = True
|
||||
|
||||
# Prefix document path to section labels, otherwise autogenerated labels would
|
||||
# look like 'heading' rather than 'path/to/file:heading'
|
||||
autosectionlabel_prefix_document = True
|
||||
autosummary_generate = True
|
||||
autosummary_ignore_module_all = False
|
||||
|
||||
autodoc_member_order = "bysource"
|
||||
|
||||
# If true, '()' will be appended to :func: etc. cross-reference text.
|
||||
add_function_parentheses = False
|
||||
|
||||
# If true, the current module name will be prepended to all description
|
||||
# unit titles (such as .. function::).
|
||||
add_module_names = True
|
||||
|
||||
autodoc_default_flags = ["members"]
|
||||
|
||||
autodoc_default_options = {
|
||||
"members": True,
|
||||
"member-order": "bysource",
|
||||
"special-members": "__init__",
|
||||
}
|
||||
# Add any paths that contain templates here, relative to this directory.
|
||||
templates_path = ["_templates"]
|
||||
|
||||
# List of patterns, relative to source directory, that match files and
|
||||
# directories to ignore when looking for source files.
|
||||
# This pattern also affects html_static_path and html_extra_path.
|
||||
exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
|
||||
|
||||
# -- Options for HTML output -------------------------------------------------
|
||||
|
||||
# The theme to use for HTML and HTML Help pages. See the documentation for
|
||||
# a list of builtin themes.
|
||||
#
|
||||
html_theme = "sphinx_rtd_theme"
|
||||
|
||||
# html_logo = "_static/logo.png"
|
||||
|
||||
# Add any paths that contain custom static files (such as style sheets) here,
|
||||
# relative to this directory. They are copied after the builtin static files,
|
||||
# so a file named "default.css" will overwrite the builtin "default.css".
|
||||
html_static_path = ["_static"]
|
||||
|
||||
html_theme_options = {
|
||||
# "logo_only": True,
|
||||
"navigation_depth": 4,
|
||||
}
|
||||
|
||||
source_suffix = {
|
||||
".rst": "restructuredtext",
|
||||
".md": "markdown",
|
||||
}
|
||||
|
||||
html_css_files = [
|
||||
"custom.css",
|
||||
]
|
||||
|
|
@ -1,68 +0,0 @@
|
|||
.. _api:
|
||||
|
||||
|
||||
MemoryScope API Documentation
|
||||
|
||||
|
||||
Enumeration
|
||||
===========
|
||||
|
||||
.. automodule:: memoryscope.enumeration
|
||||
:members:
|
||||
|
||||
Scheme
|
||||
======
|
||||
.. automodule:: memoryscope.scheme
|
||||
:members:
|
||||
|
||||
Config
|
||||
======
|
||||
.. automodule:: memoryscope.core.config
|
||||
:members:
|
||||
|
||||
|
||||
Models
|
||||
======
|
||||
.. automodule:: memoryscope.core.models
|
||||
:members:
|
||||
|
||||
|
||||
|
||||
Storage
|
||||
=======
|
||||
.. automodule:: memoryscope.core.storage
|
||||
:members:
|
||||
|
||||
|
||||
Worker
|
||||
======
|
||||
Base
|
||||
----
|
||||
|
||||
.. automodule:: memoryscope.core.worker
|
||||
:members:
|
||||
|
||||
Frontend
|
||||
--------
|
||||
.. automodule:: memoryscope.core.worker.frontend
|
||||
:members:
|
||||
|
||||
Backend
|
||||
--------
|
||||
.. automodule:: memoryscope.core.worker.backend
|
||||
:members:
|
||||
|
||||
Operation
|
||||
=========
|
||||
.. automodule:: memoryscope.core.operation
|
||||
:members:
|
||||
|
||||
Service
|
||||
=======
|
||||
.. automodule:: memoryscope.core.service
|
||||
:members:
|
||||
|
||||
Chat
|
||||
====
|
||||
.. automodule:: memoryscope.core.chat
|
||||
:members:
|
||||
|
|
@ -1,57 +0,0 @@
|
|||
.. MemoryScope documentation master file, created by
|
||||
sphinx-quickstart on Fri Jan 5 17:53:54 2024.
|
||||
You can adapt this file completely to your liking, but it should at least
|
||||
contain the root `toctree` directive.
|
||||
|
||||
:github_url: https://github.com/modelscope/memoryscope
|
||||
|
||||
MemoryScope Documentation
|
||||
=========================
|
||||
|
||||
Welcome to MemoryScope Tutorial
|
||||
-------------------------------
|
||||
|
||||
.. image:: docs/images/logo.png
|
||||
:align: center
|
||||
|
||||
MemoryScope provides LLM chatbots with powerful and flexible long-term memory capabilities, offering a framework for building such abilities.
|
||||
It can be applied to scenarios like personal assistants and emotional companions, continuously learning through long-term memory to remember users' basic information as well as various habits and preferences.
|
||||
This allows users to gradually experience a sense of "understanding" when using the LLM.
|
||||
|
||||
.. image:: docs/images/framework.png
|
||||
:align: center
|
||||
|
||||
Framework
|
||||
^^^^^^^^^^^^^^^^^^^^
|
||||
|
||||
💾 Memory Database: MemoryScope is equipped with a vector database (default is *ElasticSearch*) to store all memory fragments recorded in the system.
|
||||
|
||||
🔧 Worker Library: MemoryScope atomizes the capabilities of long-term memory into individual workers, including over 20 workers for tasks such as query information filtering, observation extraction, and insight updating.
|
||||
|
||||
🛠️ Operation Library: Based on the worker pipeline, it constructs the operations for memory services, realizing key capabilities such as memory retrieval and memory consolidation.
|
||||
|
||||
- Memory Retrieval: Upon arrival of a user query, this operation returns the semantically related memory pieces
|
||||
and/or those from the corresponding time if the query involves reference to time.
|
||||
- Memory Consolidation: This operation takes in a batch of user queries and returns important user information
|
||||
extracted from the queries as consolidated *observations* to be stored in the memory database.
|
||||
- Reflection and Re-consolidation: At regular intervals, this operation performs reflection upon newly recorded *observations*
|
||||
to form and update *insights*. Then, memory re-consolidation is performed to ensure contradictions and repetitions
|
||||
among memory pieces are properly handled.
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:caption: MemoryScope Tutorial
|
||||
|
||||
About MemoryScope <README.md>
|
||||
Installation <docs/installation.md>
|
||||
Cli Client <examples/cli/CLI_README.md>
|
||||
Simple Usages <examples/api/simple_usages.ipynb>
|
||||
Advanced usage <examples/advance/custom_operator.md>
|
||||
Contribution <docs/contribution.md>
|
||||
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 6
|
||||
:caption: MemoryScope API Reference
|
||||
|
||||
API <docs/api.rst>
|
||||
|
|
@ -1,7 +0,0 @@
|
|||
memoryscope
|
||||
===========
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 4
|
||||
|
||||
memoryscope
|
||||
|
|
@ -1,55 +0,0 @@
|
|||
.. MemoryScope documentation master file, created by
|
||||
sphinx-quickstart on Fri Jan 5 17:53:54 2024.
|
||||
You can adapt this file completely to your liking, but it should at least
|
||||
contain the root `toctree` directive.
|
||||
|
||||
:github_url: https://github.com/modelscope/memoryscope
|
||||
|
||||
MemoryScope ドキュメント
|
||||
=========================
|
||||
|
||||
MemoryScopeに関するドキュメントへようこそ
|
||||
-------------------------------
|
||||
|
||||
.. image:: ./docs/images/logo.png
|
||||
:align: center
|
||||
|
||||
MemoryScopeは、LLMチャットボットに強力で柔軟な長期記憶能力を提供し、長期記憶能力を構築するためのフレームワークを提供します。
|
||||
MemoryScopeは、個人アシスタントや感情的な伴侶などの記憶シナリオに使用でき、長期記憶能力を通じてユーザーの基本情報やさまざまな習慣や好みを覚え続けることができます。
|
||||
これにより、ユーザーはLLMを使用する際に徐々に「理解されている」感覚を体験することができます。
|
||||
|
||||
.. image:: docs/images/framework.png
|
||||
:align: center
|
||||
|
||||
フレームワーク
|
||||
^^^^^^^^^^^^^^^^^^^^
|
||||
|
||||
💾 メモリデータベース: MemoryScopeは、システム内に記録されたすべての記憶片を保存するためのベクトルデータベース（デフォルトは*ElasticSearch*）を備えています。
|
||||
|
||||
🔧 ワーカーライブラリ: MemoryScopeは、長期記憶の能力を個々のワーカーに原子化し、クエリ情報のフィルタリング、観察の抽出、洞察の更新など、20以上のワーカーを含みます。
|
||||
|
||||
🛠️ オペレーションライブラリ: ワーカーパイプラインに基づいて、メモリサービスのオペレーションを構築し、メモリの取得やメモリの統合などの主要な機能を実現します。
|
||||
|
||||
- メモリの取得: ユーザークエリが到着すると、この操作は意味的に関連する記憶片を返します。
|
||||
クエリが時間に言及している場合は、対応する時間の記憶片も返します。
|
||||
- メモリの統合: この操作は、一連のユーザークエリを受け取り、クエリから抽出された重要なユーザー情報を統合された*観察*としてメモリデータベースに保存します。
|
||||
- 反映と再統合: 定期的に、この操作は新たに記録された*観察*を反映し、*洞察*を形成および更新します。
|
||||
その後、メモリの再統合を実行して、記憶片間の矛盾や重複が適切に処理されるようにします。
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:caption: MemoryScope チュートリアル
|
||||
|
||||
MemoryScopeについて <README.md>
|
||||
インストール <docs/installation.md>
|
||||
CLIクライアント <examples/cli/CLI_README.md>
|
||||
簡単な使用法 <examples/api/simple_usages.ipynb>
|
||||
高度な使用法 <examples/advance/custom_operator.md>
|
||||
貢献 <docs/contribution.md>
|
||||
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 6
|
||||
:caption: MemoryScope APIリファレンス
|
||||
|
||||
API <docs/api.rst>
|
||||
|
|
@ -1,13 +0,0 @@
|
|||
loguru
|
||||
tiktoken
|
||||
pillow
|
||||
requests
|
||||
openai
|
||||
numpy
|
||||
sphinx
|
||||
sphinx-autobuild
|
||||
sphinx_rtd_theme
|
||||
sphinxcontrib-mermaid
|
||||
myst-parser
|
||||
autodoc_pydantic
|
||||
nbsphinx
|
||||
|
|
@ -1,4 +0,0 @@
|
|||
.language-selector a {
|
||||
color: white;
|
||||
width: 20px;
|
||||
}
|
||||
|
|
@ -1,5 +0,0 @@
|
|||
<!-- language_selector.html -->
|
||||
<div class="language-selector">
|
||||
<a href="{{ pathto('../en/' + pagename) }}">English</a></li> |
|
||||
<a href="{{ pathto('../zh/' + pagename) }}">中文</a></li>
|
||||
</div>
|
||||
|
|
@ -1,3 +0,0 @@
|
|||
<!-- layout.html -->
|
||||
{% extends "!layout.html" %} {% block sidebartitle %} {{ super() }} {% include
|
||||
"language_selector.html" %} {% endblock %}
|
||||
|
|
@ -1,105 +0,0 @@
|
|||
# -*- coding: utf-8 -*-
|
||||
# Configuration file for the Sphinx documentation builder.
|
||||
#
|
||||
# This file only contains a selection of the most common options. For a full
|
||||
# list see the documentation:
|
||||
# https://www.sphinx-doc.org/en/master/usage/configuration.html
|
||||
|
||||
# -- Path setup --------------------------------------------------------------
|
||||
|
||||
# If extensions (or modules to document with autodoc) are in another directory,
|
||||
# add these directories to sys.path here. If the directory is relative to the
|
||||
# documentation root, use os.path.abspath to make it absolute, like shown here.
|
||||
#
|
||||
import os
|
||||
import sys
|
||||
|
||||
sys.path.insert(0, os.path.abspath("../../../../../MemoryScope"))
|
||||
|
||||
|
||||
# -- Project information -----------------------------------------------------
|
||||
|
||||
language = "zh"
|
||||
|
||||
project = "MemoryScope"
|
||||
copyright = "2024, Alibaba Tongyi Lab"
|
||||
author = "EcoML team of Alibaba Tongyi Lab"
|
||||
|
||||
|
||||
# -- General configuration ---------------------------------------------------
|
||||
|
||||
# Add any Sphinx extension module names here, as strings. They can be
|
||||
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
|
||||
# ones.
|
||||
extensions = [
|
||||
"sphinx.ext.autodoc",
|
||||
"sphinx.ext.autosummary",
|
||||
"sphinx.ext.viewcode",
|
||||
"sphinx.ext.napoleon",
|
||||
"sphinxcontrib.mermaid",
|
||||
"myst_parser",
|
||||
"sphinx.ext.autosectionlabel",
|
||||
"sphinxcontrib.autodoc_pydantic",
|
||||
"nbsphinx"
|
||||
]
|
||||
|
||||
autodoc_pydantic_model_show_json = True
|
||||
autodoc_pydantic_settings_show_json = True
|
||||
|
||||
# Prefix document path to section labels, otherwise autogenerated labels would
|
||||
# look like 'heading' rather than 'path/to/file:heading'
|
||||
autosectionlabel_prefix_document = True
|
||||
autosummary_generate = True
|
||||
autosummary_ignore_module_all = False
|
||||
|
||||
autodoc_member_order = "bysource"
|
||||
|
||||
# If true, '()' will be appended to :func: etc. cross-reference text.
|
||||
add_function_parentheses = False
|
||||
|
||||
# If true, the current module name will be prepended to all description
|
||||
# unit titles (such as .. function::).
|
||||
add_module_names = True
|
||||
|
||||
autodoc_default_flags = ["members"]
|
||||
|
||||
autodoc_default_options = {
|
||||
"members": True,
|
||||
"member-order": "bysource",
|
||||
"special-members": "__init__",
|
||||
}
|
||||
# Add any paths that contain templates here, relative to this directory.
|
||||
templates_path = ["_templates"]
|
||||
|
||||
# List of patterns, relative to source directory, that match files and
|
||||
# directories to ignore when looking for source files.
|
||||
# This pattern also affects html_static_path and html_extra_path.
|
||||
exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
|
||||
|
||||
# -- Options for HTML output -------------------------------------------------
|
||||
|
||||
# The theme to use for HTML and HTML Help pages. See the documentation for
|
||||
# a list of builtin themes.
|
||||
#
|
||||
html_theme = "sphinx_rtd_theme"
|
||||
|
||||
# html_logo = "_static/logo.png"
|
||||
|
||||
# Add any paths that contain custom static files (such as style sheets) here,
|
||||
# relative to this directory. They are copied after the builtin static files,
|
||||
# so a file named "default.css" will overwrite the builtin "default.css".
|
||||
html_static_path = ["_static"]
|
||||
|
||||
html_theme_options = {
|
||||
# "logo_only": True,
|
||||
"navigation_depth": 4,
|
||||
}
|
||||
|
||||
source_suffix = {
|
||||
".rst": "restructuredtext",
|
||||
".md": "markdown",
|
||||
}
|
||||
|
||||
html_css_files = [
|
||||
"custom.css",
|
||||
]
|
||||
|
|
@ -1,68 +0,0 @@
|
|||
.. _api:
|
||||
|
||||
|
||||
MemoryScope API 接口文档
|
||||
|
||||
|
||||
Enumeration
|
||||
===========
|
||||
|
||||
.. automodule:: memoryscope.enumeration
|
||||
:members:
|
||||
|
||||
Scheme
|
||||
======
|
||||
.. automodule:: memoryscope.scheme
|
||||
:members:
|
||||
|
||||
Config
|
||||
======
|
||||
.. automodule:: memoryscope.core.config
|
||||
:members:
|
||||
|
||||
|
||||
Models
|
||||
======
|
||||
.. automodule:: memoryscope.core.models
|
||||
:members:
|
||||
|
||||
|
||||
|
||||
Storage
|
||||
=======
|
||||
.. automodule:: memoryscope.core.storage
|
||||
:members:
|
||||
|
||||
|
||||
Worker
|
||||
======
|
||||
Base
|
||||
----
|
||||
|
||||
.. automodule:: memoryscope.core.worker
|
||||
:members:
|
||||
|
||||
Frontend
|
||||
--------
|
||||
.. automodule:: memoryscope.core.worker.frontend
|
||||
:members:
|
||||
|
||||
Backend
|
||||
--------
|
||||
.. automodule:: memoryscope.core.worker.backend
|
||||
:members:
|
||||
|
||||
Operation
|
||||
=========
|
||||
.. automodule:: memoryscope.core.operation
|
||||
:members:
|
||||
|
||||
Service
|
||||
=======
|
||||
.. automodule:: memoryscope.core.service
|
||||
:members:
|
||||
|
||||
Chat
|
||||
====
|
||||
.. automodule:: memoryscope.core.chat
|
||||
:members:
|
||||
|
|
@ -1,53 +0,0 @@
|
|||
.. MemoryScope documentation master file, created by
|
||||
sphinx-quickstart on Fri Jan 5 17:53:54 2024.
|
||||
You can adapt this file completely to your liking, but it should at least
|
||||
contain the root `toctree` directive.
|
||||
|
||||
:github_url: https://github.com/modelscope/memoryscope
|
||||
|
||||
MemoryScope 文档
|
||||
=========================
|
||||
|
||||
欢迎浏览MemoryScope相关文档
|
||||
-------------------------------
|
||||
|
||||
.. image:: ./docs/images/logo.png
|
||||
:align: center
|
||||
|
||||
MemoryScope可以为LLM聊天机器人提供强大且灵活的长期记忆能力，并提供了构建长期记忆能力的框架。
|
||||
MemoryScope可以用于个人助理、情感陪伴等记忆场景，通过长期记忆能力来不断学习，记得用户的基础信息以及各种习惯和喜好，使得用户在使用LLM时逐渐感受到一种“默契”。
|
||||
|
||||
.. image:: docs/images/framework.png
|
||||
:align: center
|
||||
|
||||
核心框架
|
||||
^^^^^^^^^^^^^^^^^^^^
|
||||
|
||||
💾 记忆数据库: MemoryScope配备了向量数据库(默认是*ElasticSearch*)，用于存储系统中记录的所有记忆片段。
|
||||
|
||||
🔧 核心worker库: MemoryScope将长期记忆的能力原子化，抽象成单独的worker，包括query信息过滤，observation抽取，insight更新等20+worker。
|
||||
|
||||
🛠️ 核心Op库: 并基于worker的pipeline构建了memory服务的核心operation，实现了记忆检索，记忆巩固等核心能力。
|
||||
|
||||
- 记忆检索：当用户输入对话，此操作返回语义相关的记忆片段。如果输入对话包含对时间的指涉，则同时返回相应时间中的记忆片段。
|
||||
- 记忆巩固：此操作接收一批用户的输入对话，并从对话中提取重要的用户信息，将其作为 *observation* 形式的记忆片段存储在记忆数据库中。
|
||||
- 反思与再巩固：每隔一段时间，此操作对新记录的 *observations* 进行反思，以形成和更新 *insight*
|
||||
形式的记忆片段。然后执行记忆再巩固，以确保记忆片段之间的矛盾和重复得到妥善处理。
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:caption: MemoryScope 教程
|
||||
|
||||
关于 MemoryScope <README.md>
|
||||
安装 <docs/installation.md>
|
||||
命令行终端 <examples/cli/CLI_README_ZH.md>
|
||||
简单案例 <examples/api/simple_usages_zh.ipynb>
|
||||
高级用法 <examples/advance/custom_operator_zh.md>
|
||||
贡献 <docs/contribution.md>
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 6
|
||||
:caption: MemoryScope 接口
|
||||
|
||||
API <docs/api.rst>
|
||||
|
||||
|
|
@ -1,7 +0,0 @@
|
|||
memoryscope
|
||||
===========
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 4
|
||||
|
||||
memoryscope
|
||||
|
|
@ -1,49 +0,0 @@
|
|||
# Custom Operator and Worker
|
||||
|
||||
1. Create a new worker named `example_query_worker.py` in the `contrib` directory:
|
||||
```bash
|
||||
vim memoryscope/contrib/example_query_worker.py
|
||||
```
|
||||
|
||||
2. Write the program for the new custom worker. Note that the class name must match the filename, which is `ExampleQueryWorker`:
|
||||
```python
|
||||
import datetime
|
||||
from memoryscope.constants.common_constants import QUERY_WITH_TS
|
||||
from memoryscope.core.worker.memory_base_worker import MemoryBaseWorker
|
||||
|
||||
class ExampleQueryWorker(MemoryBaseWorker):
|
||||
def _run(self):
|
||||
timestamp = int(datetime.datetime.now().timestamp()) # Current timestamp as default
|
||||
assert "query" in self.chat_kwargs
|
||||
query = self.chat_kwargs["query"]
|
||||
if not query:
|
||||
query = ""
|
||||
else:
|
||||
query = query.strip() + "\n You must add a `meow~` at the end of each of your answers."
|
||||
# Store the determined query and its timestamp in the context
|
||||
self.set_workflow_context(QUERY_WITH_TS, (query, timestamp))
|
||||
```
|
||||
|
||||
3. Create a YAML startup file (copying `demo_config.yaml`):
|
||||
```
|
||||
cp memoryscope/core/config/demo_config.yaml examples/advance/replacement.yaml
|
||||
vim examples/advance/replacement.yaml
|
||||
```
|
||||
|
||||
4. At the bottom, insert the definition for the new worker and replace the previous default `set_query` worker, and update the operation's workflow:
|
||||
```
|
||||
rewrite_query:
|
||||
class: contrib.example_query_worker
|
||||
generation_model: generation_model
|
||||
```
|
||||
```
|
||||
retrieve_memory:
|
||||
class: core.operation.frontend_operation
|
||||
workflow: rewrite_query,[extract_time|retrieve_obs_ins,semantic_rank],fuse_rerank
|
||||
description: "retrieve long-term memory"
|
||||
```
|
||||
|
||||
5. Verify:
|
||||
```
|
||||
python quick-start-demo.py --config examples/advance/replacement.yaml
|
||||
```
|
||||
|
|
@ -1,53 +0,0 @@
|
|||
# 自定义 Operator 和 Worker
|
||||
|
||||
1. 在 `contrib` 路径下创建新worker，命名为 `example_query_worker.py`:
|
||||
```bash
|
||||
vim memoryscope/contrib/example_query_worker.py
|
||||
```
|
||||
|
||||
2. 写入新的自定义worker的程序，注意`class`的命名需要与文件名保持一致，为`ExampleQueryWorker`：
|
||||
```python
|
||||
import datetime
|
||||
from memoryscope.constants.common_constants import QUERY_WITH_TS
|
||||
from memoryscope.core.worker.memory_base_worker import MemoryBaseWorker
|
||||
|
||||
class ExampleQueryWorker(MemoryBaseWorker):
|
||||
|
||||
def _run(self):
|
||||
|
||||
timestamp = int(datetime.datetime.now().timestamp()) # Current timestamp as default
|
||||
|
||||
assert "query" in self.chat_kwargs
|
||||
query = self.chat_kwargs["query"]
|
||||
if not query:
|
||||
query = ""
|
||||
else:
|
||||
query = query.strip() + "\n You must add a `meow~` at the end of each of your answer."
|
||||
|
||||
# Store the determined query and its timestamp in the context
|
||||
self.set_workflow_context(QUERY_WITH_TS, (query, timestamp))
|
||||
```
|
||||
|
||||
3. 创建yaml启动文件（复制demo_config_zh.yaml）
|
||||
```
|
||||
cp memoryscope/core/config/demo_config_zh.yaml examples/advance/replacement.yaml
|
||||
vim examples/advance/replacement.yaml
|
||||
```
|
||||
|
||||
4. 在最下面插入新worker的定义，并且取代之前的默认`set_query`worker，并替换operation的workflow
|
||||
```
|
||||
rewrite_query:
|
||||
class: contrib.example_query_worker
|
||||
generation_model: generation_model
|
||||
```
|
||||
```
|
||||
retrieve_memory:
|
||||
class: core.operation.frontend_operation
|
||||
workflow: rewrite_query,[extract_time|retrieve_obs_ins,semantic_rank],fuse_rerank
|
||||
description: "retrieve long-term memory"
|
||||
```
|
||||
|
||||
5. 验证：
|
||||
```
|
||||
python quick-start-demo.py --config examples/advance/replacement.yaml
|
||||
```
|
||||
|
|
@ -1,185 +0,0 @@
|
|||
global:
|
||||
language: en
|
||||
thread_pool_max_workers: 5
|
||||
logger_name: memoryscope
|
||||
logger_name_time_suffix: "%Y%m%d_%H%M%S"
|
||||
logger_to_screen: false
|
||||
enable_ranker: false
|
||||
enable_today_contra_repeat: true
|
||||
enable_long_contra_repeat: false
|
||||
output_memory_max_count: 20
|
||||
|
||||
memory_chat:
|
||||
cli_memory_chat:
|
||||
class: core.chat.cli_memory_chat
|
||||
memory_service: memoryscope_service
|
||||
generation_model: generation_model
|
||||
stream: true
|
||||
|
||||
memory_service:
|
||||
memoryscope_service:
|
||||
class: core.service.memory_scope_service
|
||||
human_name: user
|
||||
assistant_name: AI
|
||||
memory_operations:
|
||||
read_message:
|
||||
class: core.operation.frontend_operation
|
||||
workflow: read_message
|
||||
description: "read short memory"
|
||||
|
||||
retrieve_memory:
|
||||
class: core.operation.frontend_operation
|
||||
workflow: rewrite_query,[extract_time|retrieve_obs_ins,semantic_rank],fuse_rerank
|
||||
description: "retrieve long-term memory"
|
||||
|
||||
list_memory:
|
||||
class: core.operation.frontend_operation
|
||||
workflow: set_query,retrieve_top_memory,print_memory
|
||||
description: "read all long-term memory of the user, use `refresh_time=5` to refresh screen every 5 seconds."
|
||||
|
||||
delete_memory:
|
||||
class: core.operation.frontend_operation
|
||||
workflow: set_query,retrieve_all_memory,delete_memory
|
||||
description: "delete a single long-term memory"
|
||||
|
||||
delete_all:
|
||||
class: core.operation.frontend_operation
|
||||
workflow: set_query,retrieve_all_memory,delete_all
|
||||
description: "delete all long-term memory"
|
||||
|
||||
add_memory:
|
||||
class: core.operation.frontend_operation
|
||||
workflow: add_memory
|
||||
description: "add a single observation"
|
||||
|
||||
consolidate_memory:
|
||||
class: core.operation.consolidate_memory_op
|
||||
workflow: info_filter,[get_observation|get_observation_with_time|load_today_memory],contra_repeat,store_memory
|
||||
description: "summary user's observation memory, run backend."
|
||||
interval_time: 1
|
||||
|
||||
reflect_and_reconsolidate:
|
||||
class: core.operation.backend_operation
|
||||
workflow: load_obs_and_insight,get_reflection_subject,update_insight,long_contra_repeat,store_memory
|
||||
description: "summary user's insight memory, run backend."
|
||||
interval_time: 15
|
||||
|
||||
worker:
|
||||
dummy:
|
||||
class: core.worker.dummy_worker
|
||||
generation_model: generation_model
|
||||
embedding_model: embedding_model
|
||||
rank_model: rank_model
|
||||
read_message:
|
||||
class: core.worker.frontend.read_message_worker
|
||||
set_query:
|
||||
class: core.worker.frontend.set_query_worker
|
||||
rewrite_query:
|
||||
class: contrib.example_query_worker
|
||||
generation_model: generation_model
|
||||
retrieve_obs_ins:
|
||||
class: core.worker.frontend.retrieve_memory_worker
|
||||
retrieve_obs_top_k: 100
|
||||
retrieve_ins_top_k: 100
|
||||
extract_time:
|
||||
class: core.worker.frontend.extract_time_worker
|
||||
generation_model: generation_model
|
||||
semantic_rank:
|
||||
class: core.worker.frontend.semantic_rank_worker
|
||||
rank_model: rank_model
|
||||
fuse_rerank:
|
||||
class: core.worker.frontend.fuse_rerank_worker
|
||||
fuse_score_threshold: 0.01
|
||||
fuse_ratio_dict:
|
||||
conversation: 0.5
|
||||
observation: 1
|
||||
obs_customized: 1.2
|
||||
insight: 2.0
|
||||
fuse_time_ratio: 2.0
|
||||
retrieve_top_memory:
|
||||
class: core.worker.frontend.retrieve_memory_worker
|
||||
retrieve_obs_top_k: 100
|
||||
retrieve_ins_top_k: 100
|
||||
retrieve_expired_top_k: 100
|
||||
print_memory:
|
||||
class: core.worker.frontend.print_memory_worker
|
||||
retrieve_all_memory:
|
||||
class: core.worker.frontend.retrieve_memory_worker
|
||||
retrieve_obs_top_k: 1000
|
||||
retrieve_ins_top_k: 1000
|
||||
retrieve_expired_top_k: 1000
|
||||
delete_memory:
|
||||
class: core.worker.backend.update_memory_worker
|
||||
method: delete_memory
|
||||
delete_all:
|
||||
class: core.worker.backend.update_memory_worker
|
||||
method: delete_all
|
||||
add_memory:
|
||||
class: core.worker.backend.update_memory_worker
|
||||
method: from_query
|
||||
info_filter:
|
||||
class: core.worker.backend.info_filter_worker
|
||||
generation_model: generation_model
|
||||
load_today_memory:
|
||||
class: core.worker.backend.load_memory_worker
|
||||
retrieve_today_top_k: 100
|
||||
get_observation:
|
||||
class: core.worker.backend.get_observation_worker
|
||||
generation_model: generation_model
|
||||
get_observation_with_time:
|
||||
class: core.worker.backend.get_observation_with_time_worker
|
||||
generation_model: generation_model
|
||||
contra_repeat:
|
||||
class: core.worker.backend.contra_repeat_worker
|
||||
generation_model: generation_model
|
||||
store_memory:
|
||||
class: core.worker.backend.update_memory_worker
|
||||
method: from_memory_key
|
||||
memory_key: all
|
||||
load_obs_and_insight:
|
||||
class: core.worker.backend.load_memory_worker
|
||||
retrieve_not_reflected_top_k: 100
|
||||
retrieve_not_updated_top_k: 100
|
||||
retrieve_insight_top_k: 100
|
||||
get_reflection_subject:
|
||||
class: core.worker.backend.get_reflection_subject_worker
|
||||
generation_model: generation_model
|
||||
reflect_obs_cnt_threshold: 5
|
||||
update_insight:
|
||||
class: core.worker.backend.update_insight_worker
|
||||
generation_model: generation_model
|
||||
rank_model: rank_model
|
||||
embedding_model: embedding_model
|
||||
update_insight_threshold: 0.01
|
||||
enable_parallel: false
|
||||
long_contra_repeat:
|
||||
class: core.worker.backend.long_contra_repeat_worker
|
||||
generation_model: generation_model
|
||||
long_contra_repeat_threshold: 0.5
|
||||
|
||||
model:
|
||||
generation_model:
|
||||
class: core.models.llama_index_generation_model
|
||||
module_name: dashscope_generation
|
||||
model_name: qwen-max
|
||||
max_tokens: 2000
|
||||
temperature: 0.01
|
||||
embedding_model:
|
||||
class: core.models.llama_index_embedding_model
|
||||
module_name: dashscope_embedding
|
||||
model_name: text-embedding-v2
|
||||
rank_model:
|
||||
class: core.models.llama_index_rank_model
|
||||
module_name: dashscope_rank
|
||||
model_name: gte-rerank
|
||||
top_n: 500
|
||||
|
||||
memory_store:
|
||||
class: core.storage.llama_index_es_memory_store
|
||||
embedding_model: embedding_model
|
||||
index_name: memory_index
|
||||
es_url: http://localhost:9200
|
||||
retrieve_mode: dense
|
||||
|
||||
monitor:
|
||||
class: core.storage.dummy_monitor
|
||||
|
|
@ -1,22 +0,0 @@
|
|||
# Working with AgentScope
|
||||
|
||||
1. First, make sure that you have installed AutoGen as well as memoryscope.
|
||||
```
|
||||
pip install agentscope memoryscope
|
||||
```
|
||||
|
||||
|
||||
2. Then, ensure that es is up and running. [elasticsearch documents](https://www.elastic.co/guide/en/elasticsearch/reference/current/getting-started.html).
|
||||
The docker method is recommended:
|
||||
```
|
||||
sudo docker run -p 9200:9200 \
|
||||
-e "discovery.type=single-node" \
|
||||
-e "xpack.security.enabled=false" \
|
||||
-e "xpack.license.self_generated.type=trial" \
|
||||
docker.elastic.co/elasticsearch/elasticsearch:8.13.2
|
||||
```
|
||||
|
||||
3. Finally, we can start the autogen demo.
|
||||
```
|
||||
python examples/api/agentscope_example.py
|
||||
```
|
||||
|
|
@ -1,72 +0,0 @@
|
|||
from typing import Optional, Union, Sequence
|
||||
|
||||
import agentscope
|
||||
from agentscope.agents import AgentBase, UserAgent
|
||||
from agentscope.message import Msg
|
||||
|
||||
from memoryscope import MemoryScope, Arguments
|
||||
|
||||
|
||||
class MemoryScopeAgent(AgentBase):
|
||||
def __init__(self, name: str, arguments: Arguments, **kwargs) -> None:
|
||||
# Disable AgentScope memory and use MemoryScope memory instead
|
||||
super().__init__(name, use_memory=False, **kwargs)
|
||||
|
||||
# Create a memory client in MemoryScope
|
||||
self.memory_scope = MemoryScope(arguments=arguments)
|
||||
self.memory_chat = self.memory_scope.default_memory_chat
|
||||
|
||||
def reply(self, x: Optional[Union[Msg, Sequence[Msg]]] = None) -> Msg:
|
||||
# Generate response
|
||||
response = self.memory_chat.chat_with_memory(query=x.content)
|
||||
|
||||
# Wrap the response in a message object in AgentScope
|
||||
msg = Msg(name=self.name, content=response.message.content, role="assistant")
|
||||
|
||||
# Print/speak the message in this agent's voice
|
||||
self.speak(msg)
|
||||
|
||||
return msg
|
||||
|
||||
def close(self):
|
||||
# Close the backend service of MemoryScope
|
||||
self.memory_scope.close()
|
||||
|
||||
|
||||
def main():
|
||||
# Setting of MemoryScope
|
||||
arguments = Arguments(
|
||||
language="cn",
|
||||
human_name="用户",
|
||||
assistant_name="AI",
|
||||
memory_chat_class="api_memory_chat",
|
||||
generation_backend="dashscope_generation",
|
||||
generation_model="qwen-max",
|
||||
embedding_backend="dashscope_embedding",
|
||||
embedding_model="text-embedding-v2",
|
||||
rank_backend="dashscope_rank",
|
||||
rank_model="gte-rerank")
|
||||
|
||||
# Initialize AgentScope
|
||||
agentscope.init(project="MemoryScope")
|
||||
|
||||
memoryscope_agent = MemoryScopeAgent(name="Assistant", arguments=arguments)
|
||||
|
||||
user_agent = UserAgent()
|
||||
|
||||
# Dialog
|
||||
msg = None
|
||||
while True:
|
||||
# User input
|
||||
msg = user_agent(msg)
|
||||
if msg.content == "exit":
|
||||
break
|
||||
# Agent speaks
|
||||
msg = memoryscope_agent(msg)
|
||||
|
||||
# End memory
|
||||
memoryscope_agent.close()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -1,22 +0,0 @@
|
|||
# Working with AutoGen
|
||||
|
||||
1. First, make sure that you have installed AutoGen as well as memoryscope.
|
||||
```
|
||||
pip install pyautogen memoryscope
|
||||
```
|
||||
|
||||
|
||||
2. Then, ensure that es is up and running. [elasticsearch documents](https://www.elastic.co/guide/en/elasticsearch/reference/current/getting-started.html).
|
||||
The docker method is recommended:
|
||||
```
|
||||
sudo docker run -p 9200:9200 \
|
||||
-e "discovery.type=single-node" \
|
||||
-e "xpack.security.enabled=false" \
|
||||
-e "xpack.license.self_generated.type=trial" \
|
||||
docker.elastic.co/elasticsearch/elasticsearch:8.13.2
|
||||
```
|
||||
|
||||
3. Finally, we can start the autogen demo.
|
||||
```
|
||||
python examples/api/autogen_example.py
|
||||
```
|
||||
|
|
@ -1,79 +0,0 @@
|
|||
from typing import Optional, Union, Literal, Dict, List, Any, Tuple
|
||||
|
||||
from autogen import Agent, ConversableAgent, UserProxyAgent
|
||||
|
||||
from memoryscope import MemoryScope, Arguments
|
||||
|
||||
|
||||
class MemoryScopeAgent(ConversableAgent):
|
||||
def __init__(
|
||||
self,
|
||||
name: str = "assistant",
|
||||
system_message: Optional[str] = "",
|
||||
human_input_mode: Literal["ALWAYS", "NEVER", "TERMINATE"] = "NEVER",
|
||||
llm_config: Optional[Union[Dict, bool]] = None,
|
||||
arguments: Arguments = None,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__(
|
||||
name=name,
|
||||
system_message=system_message,
|
||||
human_input_mode=human_input_mode,
|
||||
llm_config=llm_config,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
# Create a memory client in MemoryScope
|
||||
self.memory_scope = MemoryScope(arguments=arguments)
|
||||
self.memory_chat = self.memory_scope.default_memory_chat
|
||||
|
||||
self.register_reply([Agent, None], MemoryScopeAgent.generate_reply_with_memory, remove_other_reply_funcs=True)
|
||||
|
||||
def generate_reply_with_memory(
|
||||
self,
|
||||
messages: Optional[List[Dict]] = None,
|
||||
sender: Optional[Agent] = None,
|
||||
config: Optional[Any] = None,
|
||||
) -> Tuple[bool, Union[str, Dict, None]]:
|
||||
# Generate response
|
||||
|
||||
contents = []
|
||||
for message in messages:
|
||||
if message.get("role") != self.name:
|
||||
contents.append(message.get("content", ""))
|
||||
|
||||
query = contents[-1]
|
||||
response = self.memory_chat.chat_with_memory(query=query)
|
||||
return True, response.message.content
|
||||
|
||||
def close(self):
|
||||
self.memory_scope.close()
|
||||
|
||||
|
||||
def main():
|
||||
# Create the agent of MemoryScope
|
||||
arguments = Arguments(
|
||||
language="cn",
|
||||
human_name="用户",
|
||||
assistant_name="AI",
|
||||
memory_chat_class="api_memory_chat",
|
||||
generation_backend="dashscope_generation",
|
||||
generation_model="qwen-max",
|
||||
embedding_backend="dashscope_embedding",
|
||||
embedding_model="text-embedding-v2",
|
||||
rank_backend="dashscope_rank",
|
||||
rank_model="gte-rerank"
|
||||
)
|
||||
|
||||
assistant = MemoryScopeAgent("assistant", arguments=arguments)
|
||||
|
||||
# Create the agent that represents the user in the conversation.
|
||||
user_proxy = UserProxyAgent("user", code_execution_config=False)
|
||||
|
||||
# Let the assistant start the conversation. It will end when the user types exit.
|
||||
assistant.initiate_chat(user_proxy, message="有什么需要帮忙的吗？")
|
||||
assistant.close()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -1,105 +0,0 @@
|
|||
from memoryscope import MemoryScope, Arguments
|
||||
|
||||
arguments = Arguments(
|
||||
language="cn",
|
||||
human_name="用户",
|
||||
assistant_name="AI",
|
||||
memory_chat_class="api_memory_chat",
|
||||
generation_backend="dashscope_generation",
|
||||
generation_model="qwen-max",
|
||||
embedding_backend="dashscope_embedding",
|
||||
embedding_model="text-embedding-v2",
|
||||
rank_backend="dashscope_rank",
|
||||
rank_model="gte-rerank",
|
||||
enable_ranker=True)
|
||||
|
||||
|
||||
def chat_example1():
|
||||
with MemoryScope(config_path="memoryscope/core/config/demo_config_zh.yaml") as ms:
|
||||
memory_chat = ms.default_memory_chat
|
||||
response = memory_chat.chat_with_memory(query="我的爱好是弹琴。")
|
||||
print("回答：\n" + response.message.content)
|
||||
print("记忆：\n" + response.meta_data["memories"])
|
||||
|
||||
|
||||
def chat_example2():
|
||||
with MemoryScope(arguments=arguments) as ms:
|
||||
memory_chat = ms.default_memory_chat
|
||||
response = memory_chat.chat_with_memory(query="我的爱好是弹琴。")
|
||||
print("回答：\n" + response.message.content)
|
||||
print("记忆：\n" + response.meta_data["memories"])
|
||||
|
||||
|
||||
def chat_example3():
|
||||
with MemoryScope(arguments=arguments) as ms:
|
||||
memory_chat = ms.default_memory_chat
|
||||
response = memory_chat.chat_with_memory(
|
||||
query="你知道我消灭了几个星系吗？",
|
||||
temporary_memories="张三是一只哥斯拉，这是真是的，不是虚构的。\n前天张三消灭了星系0x4be？\n大前天张三消灭了星系0x5be？")
|
||||
print("回答：\n" + response.message.content)
|
||||
|
||||
|
||||
def chat_example4():
|
||||
with MemoryScope(arguments=arguments) as ms:
|
||||
memory_chat = ms.default_memory_chat
|
||||
memory_chat.run_service_operation("delete_all")
|
||||
|
||||
response = memory_chat.chat_with_memory(query="我的爱好是弹琴。")
|
||||
print("回答1：\n" + response.message.content)
|
||||
result = memory_chat.run_service_operation("consolidate_memory")
|
||||
print(result)
|
||||
|
||||
response = memory_chat.chat_with_memory(query="你知道我的乐器爱好是什么？", history_message_strategy=None)
|
||||
print("回答2：\n" + response.message.content)
|
||||
print("记忆2：\n" + response.meta_data["memories"])
|
||||
|
||||
|
||||
def chat_example5():
|
||||
with MemoryScope(arguments=arguments) as ms:
|
||||
memory_service = ms.default_memory_service
|
||||
memory_service.init_service()
|
||||
|
||||
result = memory_service.list_memory()
|
||||
print(f"list_memory result={result}")
|
||||
|
||||
result = memory_service.retrieve_memory()
|
||||
print(f"retrieve_memory result={result}")
|
||||
|
||||
result = memory_service.consolidate_memory()
|
||||
print(f"consolidate_memory result={result}")
|
||||
|
||||
|
||||
def chat_example6():
|
||||
with MemoryScope(arguments=arguments) as ms:
|
||||
memory_chat = ms.default_memory_chat
|
||||
memory_chat.run_service_operation("delete_all", "张三")
|
||||
memory_chat.run_service_operation("delete_all", "李四")
|
||||
|
||||
print("李四=========================")
|
||||
response = memory_chat.chat_with_memory(query="我的爱好是弹琴。", role_name="李四")
|
||||
print("回答1：\n" + response.message.content)
|
||||
result = memory_chat.run_service_operation("consolidate_memory", role_name="李四")
|
||||
print(result)
|
||||
response = memory_chat.chat_with_memory(query="你知道我的乐器爱好是什么？", role_name="李四",
|
||||
history_message_strategy=None)
|
||||
print("回答2：\n" + response.message.content)
|
||||
print("记忆2：\n" + response.meta_data["memories"])
|
||||
|
||||
print("张三=========================")
|
||||
response = memory_chat.chat_with_memory(query="我的爱好是打羽毛球。", role_name="张三")
|
||||
print("回答1：\n" + response.message.content)
|
||||
result = memory_chat.run_service_operation("consolidate_memory", role_name="张三")
|
||||
print(result)
|
||||
response = memory_chat.chat_with_memory(query="你知道我的运动爱好是什么？", role_name="张三",
|
||||
history_message_strategy=None)
|
||||
print("回答2：\n" + response.message.content)
|
||||
print("记忆2：\n" + response.meta_data["memories"])
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# chat_example1()
|
||||
# chat_example2()
|
||||
# chat_example3()
|
||||
chat_example4()
|
||||
# chat_example5()
|
||||
# chat_example6()
|
||||
|
|
@ -1,509 +0,0 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Example usages of **chat** and **service** interfaces\n",
|
||||
"This notebook shows simple usages of MemoryScope's **chat** and **service** interfaces, along with its main features.\n",
|
||||
"\n",
|
||||
"Before running this notebook, follow the [**Installation**](../../docs/installation.md#iii-install-from-pypi) guidelines in Readme, and start the Docker image first."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Initiate a MemoryScope instance\n",
|
||||
"First, we need to specify a configuration and initiate a MemoryScope instance."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from memoryscope import MemoryScope, Arguments\n",
|
||||
"arguments = Arguments(\n",
|
||||
" language=\"en\",\n",
|
||||
" human_name=\"User\",\n",
|
||||
" assistant_name=\"AI\",\n",
|
||||
" memory_chat_class=\"api_memory_chat\",\n",
|
||||
" generation_backend=\"dashscope_generation\",\n",
|
||||
" generation_model=\"qwen2-72b-instruct\",\n",
|
||||
" embedding_backend=\"dashscope_embedding\",\n",
|
||||
" embedding_model=\"text-embedding-v2\",\n",
|
||||
" rank_backend=\"dashscope_rank\",\n",
|
||||
" rank_model=\"gte-rerank\",\n",
|
||||
" enable_ranker=True,\n",
|
||||
" worker_params={\"get_reflection_subject\": {\"reflect_num_questions\": 3}}\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"ms = MemoryScope(arguments=arguments)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Chat without memory\n",
|
||||
"MemoryScope comes with a default **chat** interface, so it's very easy to start chatting, just as what you'll do with any LLM chatbot."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Response 1: \n",
|
||||
"That's wonderful! Playing the piano is a beautiful and expressive hobby that can bring joy, relaxation, and a sense of achievement. It engages both the creative and technical aspects of your mind, enhancing cognitive skills and fostering emotional expression. Whether you enjoy classical pieces, modern compositions, or improvisation, the piano offers a vast repertoire to explore. Keep practicing and enjoying your musical journey!\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"memory_chat = ms.default_memory_chat\n",
|
||||
"memory_chat.run_service_operation(\"delete_all\")\n",
|
||||
"response = memory_chat.chat_with_memory(query=\"My hobby is to play piano.\")\n",
|
||||
"print(\"Response 1: \\n\" + response.message.content)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"----\n",
|
||||
"You can choose to chat with or without multi-round conversation contexts. However, since **Memory Consolidation** has not been called, there's no memory pieces in the system yet."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Response 2: \n",
|
||||
"Based on our conversation, you have mentioned that your hobby is to play the piano. Therefore, yes, you play a musical instrument – the piano.\n",
|
||||
"Response 3: \n",
|
||||
"I'm sorry, but as MemoryScope, I don't have access to personal information about individuals unless it has been shared with me during our conversation. Therefore, I cannot determine if you play any musical instruments. If you do play an instrument, feel free to share that information with me!\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"response = memory_chat.chat_with_memory(query=\"Do you know if I play any musical instruments?\")\n",
|
||||
"print(\"Response 2: \\n\" + response.message.content)\n",
|
||||
"response = memory_chat.chat_with_memory(query=\"Do you know if I play any musical instruments?\",\n",
|
||||
" history_message_strategy=None)\n",
|
||||
"print(\"Response 3: \\n\" + response.message.content)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Memory Consolidation\n",
|
||||
"Now, we do a bit more chatting and then try out **Memory Consolidation**."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Response 4: \n",
|
||||
"Thank you for letting me know! Working for Meta (formerly known as Facebook) implies that you're part of a company at the forefront of technology, social media, and innovation. Meta is known for its influential platforms like Facebook, Instagram, and WhatsApp, as well as its work in virtual reality with Oculus and efforts towards building the metaverse. It's exciting to be part of a company shaping the future of digital communication and interaction.\n",
|
||||
"Response 5: \n",
|
||||
"Eating fruit is a great way to nourish your body and satisfy your taste buds. For this afternoon, consider having a refreshing and nutritious option like a juicy orange, a sweet apple, a handful of berries (such as strawberries, blueberries, or raspberries), or a slice of refreshing watermelon. These fruits are not only delicious but also packed with vitamins, antioxidants, and fiber to keep you energized throughout the day. Choose the one that appeals to you most or mix a few for a colorful fruit salad!\n",
|
||||
"Response 6: \n",
|
||||
"Watermelon is an excellent choice! It's hydrating, low in calories, and rich in nutrients like vitamin C, vitamin A, and lycopene. Its high water content makes it perfect for a refreshing snack on a warm day. Enjoy your watermelon; it's a tasty and healthy way to treat yourself this afternoon.\n",
|
||||
"Response 7: \n",
|
||||
"\"Happy Birthday, dear [friend's name]! May your special day be filled with love, laughter, and unforgettable moments. May the coming year bring you joy, success, and adventures that make your heart sing. Cheers to another year of friendship and wonderful memories. Enjoy your day to the fullest!\"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"response = memory_chat.chat_with_memory(query=\"I work for Meta\")\n",
|
||||
"print(\"Response 4: \\n\" + response.message.content)\n",
|
||||
"response = memory_chat.chat_with_memory(query=\"What fruit should I eat this afternoon?\")\n",
|
||||
"print(\"Response 5: \\n\" + response.message.content)\n",
|
||||
"response = memory_chat.chat_with_memory(query=\"I like watermelon。\")\n",
|
||||
"print(\"Response 6: \\n\" + response.message.content)\n",
|
||||
"response = memory_chat.chat_with_memory(query=\"Help me write a short birthday wish for a friend.\")\n",
|
||||
"print(\"Response 7: \\n\" + response.message.content)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"consolidate_memory result=[MEMORY ACTIONS]:\n",
|
||||
"new observation: user's hobby is playing the piano (valid)\n",
|
||||
"new observation: user works for meta (valid)\n",
|
||||
"new observation: user likes watermelon (valid)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"memory_service = ms.default_memory_service\n",
|
||||
"memory_service.init_service()\n",
|
||||
"result = memory_service.consolidate_memory()\n",
|
||||
"print(f\"consolidate_memory result={result}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"----\n",
|
||||
"**Memory Consolidation** extracted 3 *observations* out of the 7 chat messages from the user, with the uninformative ones being filtered out.\n",
|
||||
"\n",
|
||||
"We try more cases to test its time awareness and the ability to filter out fictitious contents from the user."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Response 8: \n",
|
||||
"Prospects at Amazon can be quite promising, given its status as one of the world's leading tech companies. You can expect competitive compensation, career growth opportunities, a dynamic work environment, and the chance to work on innovative projects. However, actual experiences may vary based on role, team, and individual performance. It's always a good idea to research specific positions, company culture, and employee reviews to get a more comprehensive understanding.\n",
|
||||
"Response 9: \n",
|
||||
"Great, planning a business trip to Seattle can be exciting. Some key points to consider for your trip:\n",
|
||||
"\n",
|
||||
"1. **Weather**: Check the weather forecast for Seattle to pack appropriately. Seattle is known for its mild, marine climate with possible rainfall throughout the year.\n",
|
||||
"\n",
|
||||
"2. **Transportation**: Familiarize yourself with transportation options like Sea-Tac Airport, light rail, buses, and ride-sharing services for getting around.\n",
|
||||
"\n",
|
||||
"3. **Accommodation**: Book a hotel near your meeting location or in a convenient area like Downtown Seattle for easy access to amenities.\n",
|
||||
"\n",
|
||||
"4. **Meetings**: Confirm all meeting schedules and locations in advance. Consider any time differences if coming from outside the Pacific Time Zone.\n",
|
||||
"\n",
|
||||
"5. **Business Etiquette**: Brush up on general business etiquette, especially if you're meeting with new clients or partners.\n",
|
||||
"\n",
|
||||
"6. **Networking**: Look out for industry events or networking opportunities during your stay.\n",
|
||||
"\n",
|
||||
"7. **Dining**: Research dining options for client meetings or personal meals, Seattle offers a diverse culinary scene.\n",
|
||||
"\n",
|
||||
"8. **Leisure**: If time permits, plan to explore local attractions like Pike Place Market, Space Needle, or take a stroll along Puget Sound.\n",
|
||||
"\n",
|
||||
"Safe travels and have a productive trip!\n",
|
||||
"Response 10: \n",
|
||||
"That sounds like a nice plan! Catching up with Liam over a meal after his return from Google in New York will likely be an opportunity to exchange experiences and stories. Here are a few tips for your meet-up:\n",
|
||||
"\n",
|
||||
"1. **Choose a Venue**: Pick a restaurant that suits both your tastes and preferences, perhaps somewhere central or with a special significance to your friendship.\n",
|
||||
"\n",
|
||||
"2. **Schedule**: Coordinate a date and time that works well for both of your schedules, considering Liam might need time to adjust after his return.\n",
|
||||
"\n",
|
||||
"3. **Conversation Topics**: Prepare some conversation starters about his experience at Google, life in New York, and any shared interests or memories from your time in class together.\n",
|
||||
"\n",
|
||||
"4. **Professional Insights**: Liam might have valuable insights from his work at Google that could be beneficial for your own career growth, especially considering your interest in the tech industry.\n",
|
||||
"\n",
|
||||
"5. **Personal Updates**: Share your own updates too – your work at Meta, your love for watermelon, and your piano hobby could spark interesting discussions.\n",
|
||||
"\n",
|
||||
"Enjoy your reunion and the chance to learn from each other's experiences!\n",
|
||||
"Response 11: \n",
|
||||
"As Cynthia hugged her tightly, User felt a bittersweet pang. \"Promise we'll stay close,\" User whispered, staring at the luggage adorned with University of Iowa stickers. Cynthia smiled, eyes glistening, \"Distance means nothing when hearts are intertwined.\" They laughed, remembering late-night study sessions fueled by watermelon slices, and the echoes of piano melodies that filled User's apartment. As the cab honked, their hands reluctantly parted, but their dreams intertwined, ready for chapters anew.\n",
|
||||
"Response 12: \n",
|
||||
"I'm unable to provide real-time information or look up specific companies without prior data. If SMCI is a hypothetical or fictional company within our conversation context, I don't have details on it. For actual companies, it would be best to search online or refer to official sources for the most accurate and up-to-date information on their nature and activities.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"response = memory_chat.chat_with_memory(query=\"What are the prospects like if I go to work at Amazon?\")\n",
|
||||
"print(\"Response 8: \\n\" + response.message.content)\n",
|
||||
"response = memory_chat.chat_with_memory(query=\"Note: I am planning a business trip to Seattle next week.\")\n",
|
||||
"print(\"Response 9: \\n\" + response.message.content)\n",
|
||||
"response = memory_chat.chat_with_memory(query=\"My classmate Liam is currently working at Google. He will return to New York next month, and I plan to have a meal with him.\")\n",
|
||||
"print(\"Response 10: \\n\" + response.message.content)\n",
|
||||
"response = memory_chat.chat_with_memory(query=\"Cynthia is my best friend, and she has decided to go to university in Iowa. Write an 80-word micro drama starting with this.\")\n",
|
||||
"print(\"Response 11: \\n\" + response.message.content)\n",
|
||||
"response = memory_chat.chat_with_memory(query=\"What kind of company is SMCI, and what do they do?\")\n",
|
||||
"print(\"Response 12: \\n\" + response.message.content)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"consolidate_memory result=[MEMORY ACTIONS]:\n",
|
||||
"new observation: user is interested in career prospects at amazon (valid)\n",
|
||||
"new observation: user is inquiring about smci's nature and business (valid)\n",
|
||||
"new observation: user is planning a business trip to seattle next week (Inference time: next week after august 2, 2024) (valid)\n",
|
||||
"new observation: user plans to meet classmate liam in new york next month (Inference time: next month after august 2, 2024) (valid)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"result = memory_service.consolidate_memory()\n",
|
||||
"print(f\"consolidate_memory result={result}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"----\n",
|
||||
"We can see **Memory Consolidation** successfully filtered out fictitious contents, and shows good time sensitivity.\n",
|
||||
"\n",
|
||||
"We try more cases to test its resolution of conflicting contents."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Response 13: \n",
|
||||
"You might enjoy eating watermelon this afternoon since you like it.\n",
|
||||
"Response 14: \n",
|
||||
"Great choice! Enjoy your mangoes today.\n",
|
||||
"Response 15: \n",
|
||||
"Congratulations on your new job at Apple! How are you finding the experience so far?\n",
|
||||
"Response 16: \n",
|
||||
"That's wonderful! Peaches and apples are both delicious and nutritious choices. Enjoy snacking on them whenever you like.\n",
|
||||
"Response 17: \n",
|
||||
"No problem at all, everyone has their preferences. If you don't like coconuts, there are plenty of other fruits to enjoy.\n",
|
||||
"Response 18: \n",
|
||||
"Sounds like an exciting plan! Florida offers great surfing spots with its warm waters and waves. Have a fantastic time surfing next month.\n",
|
||||
"Response 19：\n",
|
||||
"Happy Birthday for tomorrow! May your day be filled with joy, laughter, and memorable moments.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"response = memory_chat.chat_with_memory(query=\"What fruit should I eat this afternoon?\")\n",
|
||||
"print(\"Response 13: \\n\" + response.message.content)\n",
|
||||
"response = memory_chat.chat_with_memory(query=\"Watermelon is really good, but I also like mangoes. Today, I want to eat mangoes.\")\n",
|
||||
"print(\"Response 14: \\n\" + response.message.content)\n",
|
||||
"response = memory_chat.chat_with_memory(query=\"I recently switched jobs and joined Apple.\")\n",
|
||||
"print(\"Response 15: \\n\" + response.message.content)\n",
|
||||
"response = memory_chat.chat_with_memory(query=\"I also like eating peaches and apples.\")\n",
|
||||
"print(\"Response 16: \\n\" + response.message.content)\n",
|
||||
"response = memory_chat.chat_with_memory(query=\"I don't like eating coconuts.\")\n",
|
||||
"print(\"Response 17: \\n\" + response.message.content)\n",
|
||||
"response = memory_chat.chat_with_memory(query=\"I am planning to go surfing in Florida next month.\")\n",
|
||||
"print(\"Response 18: \\n\" + response.message.content)\n",
|
||||
"response = memory_chat.chat_with_memory(query=\"Tomorrow is my birthday\")\n",
|
||||
"print(\"Response 19：\\n\" + response.message.content)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"consolidate_memory result=[MEMORY ACTIONS]:\n",
|
||||
"new observation: user likes mangoes and wants to eat them today (valid)\n",
|
||||
"new observation: user is planning to go surfing in florida next month (valid)\n",
|
||||
"new observation: user's birthday is tomorrow (valid)\n",
|
||||
"new observation: user recently switched jobs and joined apple (valid)\n",
|
||||
"new observation: user likes eating peaches and apples (valid)\n",
|
||||
"new observation: user doesn't like eating coconuts (valid)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"result = memory_service.consolidate_memory()\n",
|
||||
"print(f\"consolidate_memory result={result}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Reflection and Re-Consolidation\n",
|
||||
"Now, we have accumulated enough new *observations* in the system, so we can call **Reflection and Re-Consolidation**, let's see what will it get."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"consolidate_memory result=[MEMORY ACTIONS]:\n",
|
||||
"new insight: The Travel plans of User: travel plans include a business trip to seattle next week after august 2, 2024, meeting a classmate in new york next month after august 2, 2024, and going surfing in florida next month (valid)\n",
|
||||
"new insight: The Fruit preferences of User: fruit preferences: watermelon, peaches, apples, mangoes; dislikes coconuts (valid)\n",
|
||||
"new insight: The Career interest of User: amazon (valid)\n",
|
||||
"modified observation: user is planning a business trip to seattle next week (Inference time: next week after august 2, 2024) (valid)\n",
|
||||
"modified observation: user likes mangoes and wants to eat them today (valid)\n",
|
||||
"modified observation: user is interested in career prospects at amazon (valid)\n",
|
||||
"modified observation: user is inquiring about smci's nature and business (valid)\n",
|
||||
"modified observation: user recently switched jobs and joined apple (valid)\n",
|
||||
"modified observation: user's hobby is playing the piano (valid)\n",
|
||||
"modified observation: user is planning to go surfing in florida next month (valid)\n",
|
||||
"modified observation: user's birthday is tomorrow (valid)\n",
|
||||
"modified observation: user likes watermelon (valid)\n",
|
||||
"modified observation: user works for meta (valid)\n",
|
||||
"modified observation: user plans to meet classmate liam in new york next month (Inference time: next month after august 2, 2024) (valid)\n",
|
||||
"modified observation: user likes eating peaches and apples (valid)\n",
|
||||
"modified observation: user doesn't like eating coconuts (valid)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"result = memory_service.reflect_and_reconsolidate()\n",
|
||||
"print(f\"consolidate_memory result={result}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Low response-time (RT) for the user\n",
|
||||
"Finally, we test the RT of MemoryScope system for the user. Specifically, we test the difference of RT when responding with and without retrieving memory pieces from the system."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import time\n",
|
||||
"\n",
|
||||
"start_time = time.time()\n",
|
||||
"response = memory_chat.chat_with_memory(query=\"Do you know if I play any musical instruments?\",\n",
|
||||
" history_message_strategy=None)\n",
|
||||
"end_time = time.time()\n",
|
||||
"total_time = end_time - start_time\n",
|
||||
"print(\"With memory retrieval\\nResponse 20: \\n\" + response.message.content + f\"\\n RT: {total_time} seconds\\n\")\n",
|
||||
"\n",
|
||||
"start_time = time.time()\n",
|
||||
"response = memory_chat.chat_with_memory(query=\"Do you know if I have any plans in the next month?\",\n",
|
||||
" history_message_strategy=None)\n",
|
||||
"end_time = time.time()\n",
|
||||
"total_time = end_time - start_time\n",
|
||||
"print(\"With memory retrieval\\nResponse 21: \\n\" + response.message.content + f\"\\n RT: {total_time} seconds\\n\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"With memory retrieval\n",
|
||||
"Response 20: \n",
|
||||
"Yes, you play the piano.\n",
|
||||
" RT: 1.494797706604004 seconds\n",
|
||||
"\n",
|
||||
"With memory retrieval\n",
|
||||
"Response 21: \n",
|
||||
"Yes, you have the following plans in the next month:\n",
|
||||
"\n",
|
||||
"1. A business trip to Seattle sometime after August 2, 2024.\n",
|
||||
"2. Meeting a classmate in New York next month, also after August 2, 2024.\n",
|
||||
"3. Going surfing in Florida next month.\n",
|
||||
" RT: 6.400442123413086 seconds\n",
|
||||
"\n",
|
||||
"Without memory retrieval\n",
|
||||
"Response 22: \n",
|
||||
"I'm sorry, but as MemoryScope, I don't have access to personal information about individuals unless it has been shared with me during our conversation. Therefore, I cannot determine if you play any musical instruments without you providing that information. Have you mentioned anything about your musical abilities before?\n",
|
||||
" RT: 3.8412117958068848 seconds\n",
|
||||
"\n",
|
||||
"Without memory retrieval\n",
|
||||
"Response 23: \n",
|
||||
"I'm sorry, but as an AI, I don't have access to personal schedules or information unless it has been shared with me during our conversation, which we haven't had. I cannot access external databases or personal calendars. I'm here to provide general information and assistance based on the knowledge I've been trained on.\n",
|
||||
" RT: 6.7565529346466064 seconds\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"memory_chat.run_service_operation(\"delete_all\")\n",
|
||||
"start_time = time.time()\n",
|
||||
"response = memory_chat.chat_with_memory(query=\"Do you know if I play any musical instruments?\",\n",
|
||||
" history_message_strategy=None)\n",
|
||||
"end_time = time.time()\n",
|
||||
"total_time = end_time - start_time\n",
|
||||
"print(\"Without memory retrieval\\nResponse 22: \\n\" + response.message.content + f\"\\n RT: {total_time} seconds\\n\")\n",
|
||||
"\n",
|
||||
"start_time = time.time()\n",
|
||||
"response = memory_chat.chat_with_memory(query=\"Do you know if I have any plans in the next month?\\n\",\n",
|
||||
" history_message_strategy=None)\n",
|
||||
"end_time = time.time()\n",
|
||||
"total_time = end_time - start_time\n",
|
||||
"print(\"Without memory retrieval\\nResponse 23: \\n\" + response.message.content + f\"\\n RT: {total_time} seconds\\n\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"----\n",
|
||||
"We can see responding with retrieving memory pieces from MemoryScope does not increase RT."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## More Examples\n",
|
||||
"We direct the reader to [Advanced Customization](../advance/custom_operator.md) for guidance on customizing the various settings of the MemoryScope system. It is also possible to create or customize your own MemoryScope **operations** by specifying a **workflow** and the corresponding **workers** that best meet your specific needs.\n",
|
||||
"\n",
|
||||
"Additionally, you can also try out the [CLI with a MemoryScope Chatbot](../cli/CLI_README.md). We have implemented the chatbot so that the **Memory Consolidation** and **Reflection and Re-Consolidation** operations are always run asynchronously in the backend, ensuring that they do not incur any response time for the user.\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 2
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython2"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
|
|
@ -1,488 +0,0 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# chat 和 service 接口的示例用法\n",
|
||||
"这个笔记本展示了 MemoryScope 的 **chat** 和 **service** 接口的简单用法，以及它的主要功能。\n",
|
||||
"\n",
|
||||
"在运行这个笔记本之前，请先按照 Readme 中的 [**Installation**](../../docs/installation_zh.md#三通过-pypi-安装) 指南进行安装，并启动 Docker 镜像。"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 初始化一个 MemoryScope 实例\n",
|
||||
"首先，我们需要指定一个配置并初始化一个 MemoryScope 实例。"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from memoryscope import MemoryScope, Arguments\n",
|
||||
"arguments = Arguments(\n",
|
||||
" language=\"cn\",\n",
|
||||
" human_name=\"用户\",\n",
|
||||
" assistant_name=\"AI\",\n",
|
||||
" memory_chat_class=\"api_memory_chat\",\n",
|
||||
" generation_backend=\"dashscope_generation\",\n",
|
||||
" generation_model=\"qwen-max\",\n",
|
||||
" embedding_backend=\"dashscope_embedding\",\n",
|
||||
" embedding_model=\"text-embedding-v2\",\n",
|
||||
" rank_backend=\"dashscope_rank\",\n",
|
||||
" rank_model=\"gte-rerank\",\n",
|
||||
" enable_ranker=True)\n",
|
||||
"\n",
|
||||
"ms = MemoryScope(arguments=arguments)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 聊天（不含记忆）\n",
|
||||
"MemoryScope 配有默认的 chat 接口，因此开始聊天非常容易，就像使用任何大型语言模型聊天机器人一样。"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"回答1：\n",
|
||||
"很高兴了解到您的爱好是弹琴，这是一种既能陶冶情操又能提升音乐技能的美妙艺术形式。无论是古典钢琴、爵士乐还是现代流行曲目，每一种风格都能带来不同的享受和挑战。希望您在弹琴的过程中能够持续发现乐趣，创造出更多动人的旋律。\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"memory_chat = ms.default_memory_chat\n",
|
||||
"memory_chat.run_service_operation(\"delete_all\")\n",
|
||||
"response = memory_chat.chat_with_memory(query=\"我的爱好是弹琴。\")\n",
|
||||
"print(\"回答1：\\n\" + response.message.content)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"----\n",
|
||||
"你可以选择进行含有或不含有多轮对话上下文的聊天。然而，由于尚未调用**记忆巩固**功能，系统中还没有任何记忆片段。"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"回答2：\n",
|
||||
"是的，您提到过您的爱好是弹琴，所以我认为您对键盘乐器，特别是钢琴有一定的爱好。\n",
|
||||
"回答3：\n",
|
||||
"作为基于当前对话的MemoryScope智能助理，我没有之前关于您乐器爱好的信息。请告诉我，您喜欢哪种乐器？这样我就可以记住并提供相关帮助了。\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"response = memory_chat.chat_with_memory(query=\"你知道我有什么乐器爱好吗？\")\n",
|
||||
"print(\"回答2：\\n\" + response.message.content)\n",
|
||||
"response = memory_chat.chat_with_memory(query=\"你知道我有什么乐器爱好吗？\",\n",
|
||||
" history_message_strategy=None)\n",
|
||||
"print(\"回答3：\\n\" + response.message.content)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## **记忆巩固**\n",
|
||||
"现在，我们再聊多几句，然后尝试**记忆巩固**功能。"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"回答4：\n",
|
||||
"了解，您在阿里巴巴工作。阿里巴巴集团是一家总部位于中国杭州的全球领先的电子商务和科技公司，以其电子商务平台如淘宝、天猫闻名，同时在云计算、数字媒体及娱乐、金融科技等领域也有广泛布局。如果您有关于工作、技术或公司文化方面的问题，欢迎随时询问。\n",
|
||||
"回答5：\n",
|
||||
"选择水果可以根据个人口味、营养需求以及季节来决定。夏天，一些清爽解暑的水果会是不错的选择，比如西瓜、哈密瓜、葡萄、桃子或者李子，它们都含有丰富的水分和维生素，有助于消暑降温。如果您想要补充纤维素，火龙果或者猕猴桃也是很好的选择。最终，选择哪种水果，还是要看您自己的喜好和身体状况。\n",
|
||||
"回答6：\n",
|
||||
"西瓜是夏季的理想选择，它不仅清凉解渴，还含有大量的水分和电解质，可以帮助身体补充流失的水分。西瓜还富含维生素C、A和抗氧化剂，如番茄红素，对皮肤健康和心血管系统都有益处。享用美味的西瓜时，记得切块后冷藏一下，口感会更加清爽哦！\n",
|
||||
"回答7：\n",
|
||||
"\"生日快乐，愿你的每一天都如蛋糕般甜蜜，笑容比烛光更灿烂！\"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"response = memory_chat.chat_with_memory(query=\"我在阿里巴巴干活\")\n",
|
||||
"print(\"回答4：\\n\" + response.message.content)\n",
|
||||
"response = memory_chat.chat_with_memory(query=\"今天下午吃什么水果好？\")\n",
|
||||
"print(\"回答5：\\n\" + response.message.content)\n",
|
||||
"response = memory_chat.chat_with_memory(query=\"我喜欢吃西瓜。\")\n",
|
||||
"print(\"回答6：\\n\" + response.message.content)\n",
|
||||
"response = memory_chat.chat_with_memory(query=\"帮我写一句给朋友的生日祝福语，简短一点。\")\n",
|
||||
"print(\"回答7：\\n\" + response.message.content)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"consolidate_memory result=[MEMORY ACTIONS]:\n",
|
||||
"new observation: 用户爱好弹琴。 (valid)\n",
|
||||
"new observation: 用户在阿里巴巴工作。 (valid)\n",
|
||||
"new observation: 用户喜欢吃西瓜。 (valid)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"memory_service = ms.default_memory_service\n",
|
||||
"memory_service.init_service()\n",
|
||||
"result = memory_service.consolidate_memory()\n",
|
||||
"print(f\"consolidate_memory result={result}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"----\n",
|
||||
"**记忆巩固**从用户的7条聊天消息中提取了3条 *observations* ，其余无效的信息被过滤掉了。\n",
|
||||
"\n",
|
||||
"我们尝试更多的情况，以测试其时间感知能力和过滤用户虚构的内容的能力。"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"回答8：\n",
|
||||
"京东作为中国领先的电商平台之一，拥有广泛的业务范围和良好的行业声誉。加入京东工作，您可能会享受到以下几方面的前景优势：\n",
|
||||
"\n",
|
||||
"1. **职业发展**：京东提供多元化的职业路径和晋升机会，您可以在电商、物流、科技、金融等多个领域找到适合自己的位置和发展空间。\n",
|
||||
"\n",
|
||||
"2. **技术与创新**：京东持续投入于技术创新，尤其是在人工智能、大数据、云计算等方面，为员工提供了接触和学习前沿技术的机会。\n",
|
||||
"\n",
|
||||
"3. **企业文化**：京东强调“正道成功”的企业文化，注重诚信、团队合作与社会责任，有利于塑造积极向上的工作氛围。\n",
|
||||
"\n",
|
||||
"4. **福利待遇**：京东通常为员工提供有竞争力的薪酬福利体系，包括但不限于健康保险、员工培训、股权激励等。\n",
|
||||
"\n",
|
||||
"5. **行业影响力**：作为行业巨头，京东的从业经历对您的职业生涯将是一大亮点，有助于提升个人品牌和未来的职业选择灵活性。\n",
|
||||
"\n",
|
||||
"当然，具体前景还取决于您的职位、个人能力、行业趋势及个人职业规划等因素。建议您深入了解目标岗位的具体要求，评估个人与岗位的匹配度，并关注行业动态，为自己的职业发展做出合理规划。\n",
|
||||
"回答9：\n",
|
||||
"好的，已记录您计划下周去北京出差。请随时告诉我如果您需要关于北京的出行建议、天气预报或是其他相关信息。祝您出差顺利！\n",
|
||||
"回答10：\n",
|
||||
"已记录您的安排，您将于下个月与在亚马逊工作的同学李亚平在上海共进晚餐。临近日期时，不妨提前联系李亚平确认具体时间和地点，以便愉快地进行聚会。希望你们有个美好的重聚！\n",
|
||||
"回答11：\n",
|
||||
"【场景：傍晚，公园长椅】 \n",
|
||||
"小亮是我最好的朋友，他决定去山西上大学。夕阳下，我们肩并肩坐着。 \n",
|
||||
"我：“山西的面食可出名了，你这小吃货有福了！” \n",
|
||||
"小亮笑：“那必须的，说好你放假就来找我，咱们一起吃遍山西！” \n",
|
||||
"我点头，心中泛起不舍：“一言为定，别忘了，那里还有千年古城等你探索。” \n",
|
||||
"小亮看向远方，眼里闪烁着梦想的光：“新旅程，我们一起加油！” \n",
|
||||
"【画面渐暗，友情的力量温暖而坚定】\n",
|
||||
"回答12：\n",
|
||||
"SMCI可能指代的是Super Micro Computer, Inc.（超微电脑股份有限公司），简称Supermicro。这是一家总部位于美国加利福尼亚州圣何塞的公司，成立于1993年。Supermicro主要设计、制造和销售高性能服务器和技术解决方案，包括服务器、存储系统、主板以及支持云计算、数据中心、企业IT、高性能计算（HPC）和嵌入式系统的其他硬件组件。它们的产品以高效率、灵活性和定制化选项著称，在全球范围内服务于各种规模的企业和组织。\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"response = memory_chat.chat_with_memory(query=\"假如我去京东工作，前景怎么样？\")\n",
|
||||
"print(\"回答8：\\n\" + response.message.content)\n",
|
||||
"response = memory_chat.chat_with_memory(query=\"记一下，下周我准备去北京出差\")\n",
|
||||
"print(\"回答9：\\n\" + response.message.content)\n",
|
||||
"response = memory_chat.chat_with_memory(query=\"我同学李亚平现在在亚马逊工作，他下个月回上海，我要和他吃个饭\")\n",
|
||||
"print(\"回答10：\\n\" + response.message.content)\n",
|
||||
"response = memory_chat.chat_with_memory(query=\"小亮是我最好的朋友，他决定去山西上大学。以这个为开头写一个80字的微剧本。\")\n",
|
||||
"print(\"回答11：\\n\" + response.message.content)\n",
|
||||
"response = memory_chat.chat_with_memory(query=\"SMCI是什么公司，做什么的？\")\n",
|
||||
"print(\"回答12：\\n\" + response.message.content)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"consolidate_memory result=[MEMORY ACTIONS]:\n",
|
||||
"new observation: 用户计划2024年8月9日去北京出差。 (推断时间： 2024年8月9日) (valid)\n",
|
||||
"new observation: 用户的同学李亚平下个月回上海，用户将与其见面吃饭。 (推断时间： 2024年9月) (valid)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"result = memory_service.consolidate_memory()\n",
|
||||
"print(f\"consolidate_memory result={result}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"----\n",
|
||||
"我们可以看到，**记忆巩固**成功过滤掉了虚假内容，并展示了良好的时间敏感性。\n",
|
||||
"\n",
|
||||
"我们尝试更多的情况，以测试其解决冲突内容的能力。"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"回答13：\n",
|
||||
"既然您喜欢吃西瓜，今天下午吃西瓜是个不错的选择。\n",
|
||||
"回答14：\n",
|
||||
"那太好了，如果您今天想换换口味，吃芒果也是很好的选择，享受它的香甜吧！\n",
|
||||
"回答15：\n",
|
||||
"恭喜您加入美团！希望您的新工作一切顺利，有新的挑战和机遇。\n",
|
||||
"回答16：\n",
|
||||
"了解了，桃子和苹果都是既营养又美味的选择，多吃水果对身体有益，您可以根据季节和个人喜好来挑选。\n",
|
||||
"回答17：\n",
|
||||
"好的，知道您不喜欢椰子，以后在推荐水果时会留意这一点。\n",
|
||||
"回答18：\n",
|
||||
"听起来很有趣！去海南冲浪是个很棒的计划，下个月那边的天气应该很适合水上活动，祝您玩得开心！别忘了做好防晒哦。\n",
|
||||
"回答19：\n",
|
||||
"生日快乐！希望您明天能度过一个特别且难忘的一天，满满的祝福给您！有任何庆祝计划吗？\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"response = memory_chat.chat_with_memory(query=\"今天下午吃什么水果好？\")\n",
|
||||
"print(\"回答13：\\n\" + response.message.content)\n",
|
||||
"response = memory_chat.chat_with_memory(query=\"西瓜确实不错，但是我也喜欢吃芒果。我今天想吃芒果。\")\n",
|
||||
"print(\"回答14：\\n\" + response.message.content)\n",
|
||||
"response = memory_chat.chat_with_memory(query=\"我最近跳槽去了美团。\")\n",
|
||||
"print(\"回答15：\\n\" + response.message.content)\n",
|
||||
"response = memory_chat.chat_with_memory(query=\"我还喜欢吃桃子和苹果。\")\n",
|
||||
"print(\"回答16：\\n\" + response.message.content)\n",
|
||||
"response = memory_chat.chat_with_memory(query=\"我不喜欢吃椰子。\")\n",
|
||||
"print(\"回答17：\\n\" + response.message.content)\n",
|
||||
"response = memory_chat.chat_with_memory(query=\"我准备下个月去海南冲浪。\")\n",
|
||||
"print(\"回答18：\\n\" + response.message.content)\n",
|
||||
"response = memory_chat.chat_with_memory(query=\"明天是我生日。\")\n",
|
||||
"print(\"回答19：\\n\" + response.message.content)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"consolidate_memory result=[MEMORY ACTIONS]:\n",
|
||||
"new observation: 用户喜欢吃桃子和苹果。 (valid)\n",
|
||||
"new observation: 用户不喜欢吃椰子。 (valid)\n",
|
||||
"new observation: 用户喜欢吃芒果。 (valid)\n",
|
||||
"new observation: 用户计划2024年9月去海南冲浪。 (推断时间： 2024年9月) (valid)\n",
|
||||
"new observation: 用户的生日是每年8月3日。 (推断时间： 每年8月3日) (valid)\n",
|
||||
"modified observation: 用户在阿里巴巴工作。 (expired)\n",
|
||||
"modified observation: 用户最近跳槽至美团。 (expired)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"result = memory_service.consolidate_memory()\n",
|
||||
"print(f\"consolidate_memory result={result}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## **反思与再巩固**\n",
|
||||
"现在，我们在系统中已经积累了足够多的新的 *observations* ，因此我们可以调用**反思与再巩固**功能，让我们看看会得到什么。"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"consolidate_memory result=[MEMORY ACTIONS]:\n",
|
||||
"new insight: 用户的出差计划： 2024年8月9日去北京出差 (valid)\n",
|
||||
"new insight: 用户的生日： 每年8月3日 (valid)\n",
|
||||
"new insight: 用户的水果偏好： 喜欢桃子、苹果、西瓜、芒果，不喜欢吃椰子 (valid)\n",
|
||||
"modified observation: 用户计划2024年8月9日去北京出差。 (推断时间： 2024年8月9日) (valid)\n",
|
||||
"modified observation: 用户的生日是每年8月3日。 (推断时间： 每年8月3日) (valid)\n",
|
||||
"modified observation: 用户计划2024年9月去海南冲浪。 (推断时间： 2024年9月) (valid)\n",
|
||||
"modified observation: 用户喜欢吃芒果。 (valid)\n",
|
||||
"modified observation: 用户喜欢吃桃子和苹果。 (valid)\n",
|
||||
"modified observation: 用户爱好弹琴。 (valid)\n",
|
||||
"modified observation: 用户喜欢吃西瓜。 (valid)\n",
|
||||
"modified observation: 用户不喜欢吃椰子。 (valid)\n",
|
||||
"modified observation: 用户的同学李亚平下个月回上海，用户将与其见面吃饭。 (推断时间： 2024年9月) (valid)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"result = memory_service.reflect_and_reconsolidate()\n",
|
||||
"print(f\"consolidate_memory result={result}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 低用户时延（RT）\n",
|
||||
"\n",
|
||||
"最后，我们测试 MemoryScope 系统对用户的响应时间 (RT)。具体来说，我们测试在有和没有从系统中检索记忆片段时聊天的响应时间的差异。"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import time\n",
|
||||
"\n",
|
||||
"start_time = time.time()\n",
|
||||
"response = memory_chat.chat_with_memory(query=\"你知道我的乐器爱好是什么吗？\",\n",
|
||||
" history_message_strategy=None)\n",
|
||||
"end_time = time.time()\n",
|
||||
"total_time = end_time - start_time\n",
|
||||
"print(\"使用记忆检索\\n回答20：\\n\" + response.message.content + f\"\\n 耗时：{total_time}秒\\n\")\n",
|
||||
"\n",
|
||||
"start_time = time.time()\n",
|
||||
"response = memory_chat.chat_with_memory(query=\"你知道我接下去的一个月内有什么计划吗？\",\n",
|
||||
" history_message_strategy=None)\n",
|
||||
"end_time = time.time()\n",
|
||||
"total_time = end_time - start_time\n",
|
||||
"print(\"使用记忆检索\\n回答21：\\n\" + response.message.content + f\"\\n 耗时：{total_time}秒\\n\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"使用记忆检索\n",
|
||||
"回答20：\n",
|
||||
"您喜欢弹琴。\n",
|
||||
" 耗时：1.3783161640167236秒\n",
|
||||
"\n",
|
||||
"使用记忆检索\n",
|
||||
"回答21：\n",
|
||||
"您接下来一个月内的计划包括：\n",
|
||||
"- 2024年8月9日去北京出差。\n",
|
||||
"- 计划在2024年9月去海南冲浪。\n",
|
||||
"- 2024年9月，您的同学李亚平回上海，您将与他见面吃饭。\n",
|
||||
" 耗时：6.538439035415649秒\n",
|
||||
"\n",
|
||||
"不使用记忆检索\n",
|
||||
"回答20：\n",
|
||||
"对不起，我没有记录您的个人信息，包括您的乐器爱好。如果您告诉我，我可以帮您记住。\n",
|
||||
" 耗时：2.597784996032715秒\n",
|
||||
"\n",
|
||||
"不使用记忆检索\n",
|
||||
"回答21：\n",
|
||||
"对不起，作为基于当前会话的MemoryScope智能助理，我无法获取或存储您的个人日程信息。如果您需要查询自己的计划，建议您查看自己的日历或者备忘录。\n",
|
||||
" 耗时：5.246160984039307秒\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"memory_chat.run_service_operation(\"delete_all\")\n",
|
||||
"start_time = time.time()\n",
|
||||
"response = memory_chat.chat_with_memory(query=\"你知道我的乐器爱好是什么吗？\",\n",
|
||||
" history_message_strategy=None)\n",
|
||||
"end_time = time.time()\n",
|
||||
"total_time = end_time - start_time\n",
|
||||
"print(\"不使用记忆检索\\n回答20：\\n\" + response.message.content + f\"\\n 耗时：{total_time}秒\\n\")\n",
|
||||
"\n",
|
||||
"start_time = time.time()\n",
|
||||
"response = memory_chat.chat_with_memory(query=\"你知道我接下去的一个月内有什么计划吗？\\n\",\n",
|
||||
" history_message_strategy=None)\n",
|
||||
"end_time = time.time()\n",
|
||||
"total_time = end_time - start_time\n",
|
||||
"print(\"不使用记忆检索\\n回答21：\\n\" + response.message.content + f\"\\n 耗时：{total_time}秒\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"----\n",
|
||||
"我们可以看到，从 MemoryScope 检索记忆片段不会增加聊天的响应时间。"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 更多用法\n",
|
||||
"我们建议读者参考[进阶自定义用法](../advance/custom_operator_zh.md)来对MemoryScope系统进行各种自定义设置。您还可以通过自定义**workflow**和对应的**worker**来创建或定制满足您特定需求的**operation**。\n",
|
||||
"\n",
|
||||
"此外，您还可以尝试使用[在命令行与MemoryScope聊天机器人交互](../cli/CLI_README_ZH.md)。我们在这里实现了始终在后台异步运行**记忆巩固**和**反思与再巩固**这两个操作，从而使得它们不会增加聊天的响应时间。"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 2
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython2"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
|
|
@ -1,64 +0,0 @@
|
|||
# The Cli Interface of MemoryScope
|
||||
|
||||
## Usage
|
||||
Before running, follow the [**Installation**](../../docs/installation.md#iii-install-from-pypi) guidelines in Readme, and start the Docker image first.
|
||||
MemoryScope can be launched in two different ways:
|
||||
|
||||
### 1. Using YAML Configuration File
|
||||
|
||||
If you prefer to configure your settings via a YAML file, you can do so by providing the path to the configuration file as follows:
|
||||
```bash
|
||||
memoryscope --config_path=memoryscope/core/config/demo_config.yaml
|
||||
```
|
||||
|
||||
### 2. Using Command Line Arguments
|
||||
|
||||
Alternatively, you can specify all the parameters directly on the command line:
|
||||
|
||||
```bash
|
||||
# Chinese / Dashscope
|
||||
memoryscope --language="cn" \
|
||||
--memory_chat_class="cli_memory_chat" \
|
||||
--human_name="用户" \
|
||||
--assistant_name="AI" \
|
||||
--generation_backend="dashscope_generation" \
|
||||
--generation_model="qwen-max" \
|
||||
--embedding_backend="dashscope_embedding" \
|
||||
--embedding_model="text-embedding-v2" \
|
||||
--enable_ranker=True \
|
||||
--rank_backend="dashscope_rank" \
|
||||
--rank_model="gte-rerank"
|
||||
# English / OpenAI
|
||||
memoryscope --language="en" \
|
||||
--memory_chat_class="cli_memory_chat" \
|
||||
--human_name="user" \
|
||||
--assistant_name="AI" \
|
||||
--generation_backend="openai_generation" \
|
||||
--generation_model="gpt-4o" \
|
||||
--embedding_backend="openai_embedding" \
|
||||
--embedding_model="text-embedding-3-small" \
|
||||
--enable_ranker=False
|
||||
```
|
||||
|
||||
Here are the available options that can be set through either method:
|
||||
|
||||
- `--language`: The language used for the conversation.
|
||||
- `--memory_chat_class`: The class name for managing the chat history.
|
||||
- `--human_name`: The name of the human user.
|
||||
- `--assistant_name`: The name of the AI assistant.
|
||||
- `--generation_backend`: The backend used for generating responses.
|
||||
- `--generation_model`: The model used for generating responses.
|
||||
- `--embedding_backend`: The backend used for text embeddings.
|
||||
- `--embedding_model`: The model used for creating text embeddings.
|
||||
- `--enable_ranker`: A boolean indicating whether to use a dummy ranker (default is `False`).
|
||||
- `--rank_backend`: The backend used for ranking responses.
|
||||
- `--rank_model`: The model used for ranking responses.
|
||||
|
||||
### 3. View Memory
|
||||
You can open two command line windows following the method in the second step.
|
||||
In one command line window, you can have a conversation with the AI, while in the other, you can check the AI's long-term memory about the user.
|
||||
Use /help to open the command line help, and find the command /list_memory along with the corresponding auto-refresh instruction.
|
||||
```
|
||||
/list_memory refresh_time=5
|
||||
```
|
||||
Then you can enjoy a pleasant conversation with the AI!
|
||||
|
|
@ -1,64 +0,0 @@
|
|||
# MemoryScope 的命令行接口
|
||||
|
||||
## 使用方法
|
||||
在运行之前，请先按照 Readme 中的 [**Installation**](../../docs/installation_zh.md#三通过-pypi-安装) 指南进行安装，并启动 Docker 镜像。
|
||||
MemoryScope 可以通过两种不同的方式启动：
|
||||
|
||||
### 1. 使用 YAML 配置文件
|
||||
|
||||
如果您更喜欢通过 YAML 文件配置设置，可以通过提供配置文件的路径来实现：
|
||||
```bash
|
||||
memoryscope --config_path=memoryscope/core/config/demo_config_zh.yaml
|
||||
```
|
||||
|
||||
### 2. 使用命令行参数
|
||||
|
||||
或者，您可以直接在命令行上指定所有参数：
|
||||
|
||||
```
|
||||
# 中文
|
||||
memoryscope --language="cn" \
|
||||
--memory_chat_class="cli_memory_chat" \
|
||||
--human_name="用户" \
|
||||
--assistant_name="AI" \
|
||||
--generation_backend="dashscope_generation" \
|
||||
--generation_model="qwen-max" \
|
||||
--embedding_backend="dashscope_embedding" \
|
||||
--embedding_model="text-embedding-v2" \
|
||||
--enable_ranker=True \
|
||||
--rank_backend="dashscope_rank" \
|
||||
--rank_model="gte-rerank"
|
||||
# 英文
|
||||
memoryscope --language="en" \
|
||||
--memory_chat_class="cli_memory_chat" \
|
||||
--human_name="User" \
|
||||
--assistant_name="AI" \
|
||||
--generation_backend="openai_generation" \
|
||||
--generation_model="gpt-4o" \
|
||||
--embedding_backend="openai_embedding" \
|
||||
--embedding_model="text-embedding-3-small" \
|
||||
--enable_ranker=False
|
||||
```
|
||||
|
||||
以下是可以通过任一方法设置的可用选项：
|
||||
|
||||
- `--language`: 对话中使用的语言。
|
||||
- `--memory_chat_class`: 管理聊天记录的类名。
|
||||
- `--human_name`: 人类用户的名字。
|
||||
- `--assistant_name`: AI 助手的名字。
|
||||
- `--generation_backend`: 用于生成回复的后端。
|
||||
- `--generation_model`: 用于生成回复的模型。
|
||||
- `--embedding_backend`: 用于文本嵌入的后端。
|
||||
- `--embedding_model`: 用于创建文本嵌入的模型。
|
||||
- `--enable_ranker`: 一个布尔值，指示是否使用排名器（默认为 False）。
|
||||
- `--rank_backend`: 用于排名回复的后端。
|
||||
- `--rank_model`: 用于排名回复的模型。
|
||||
|
||||
### 3. 查看记忆
|
||||
按照第二步的方式可以打开两个命令行的窗口。
|
||||
其中一个命令行窗口可以和AI进行对话，另一个命令行窗口可以查看AI关于用户的长期记忆
|
||||
使用/help打开命令行帮助，找到/list_memory的命令和对应自动刷新的指令。
|
||||
```
|
||||
/list_memory refresh_time=5
|
||||
```
|
||||
接下来就可以和AI进行愉快地交流啦。
|
||||
|
|
@ -1,2 +0,0 @@
|
|||
sh examples/docker/run_elastic_search.sh
|
||||
python quick-start-demo.py --config_path=memoryscope/core/config/demo_config_zh.yaml
|
||||
|
|
@ -1 +0,0 @@
|
|||
su - elastic_search_user -c "/home/elastic_search_user/elastic_search/elasticsearch-8.15.0/bin/elasticsearch -E xpack.security.enabled=false -E discovery.type=single-node -E xpack.license.self_generated.type=trial -d"
|
||||
|
|
@ -1,10 +0,0 @@
|
|||
""" Version of MemoryScope."""
|
||||
__version__ = "0.1.1.0"
|
||||
import fire
|
||||
|
||||
from memoryscope.core.config.arguments import Arguments # noqa: F401
|
||||
from memoryscope.core.memoryscope import MemoryScope # noqa: F401
|
||||
|
||||
|
||||
def cli():
|
||||
fire.Fire(MemoryScope.cli_memory_chat)
|
||||
|
|
@ -1,8 +0,0 @@
|
|||
from . import common_constants
|
||||
from . import language_constants
|
||||
|
||||
|
||||
__all__ = [
|
||||
"common_constants",
|
||||
"language_constants"
|
||||
]
|
||||
|
|
@ -1,48 +0,0 @@
|
|||
# common_constants.py
|
||||
# This module defines constants used as keys throughout the application to maintain a consistent reference
|
||||
# for data structures related to workflow management, chat interactions, context storage, memory operations,
|
||||
# node processing, and temporal inference functionalities.
|
||||
|
||||
WORKFLOW_NAME = "workflow_name"
|
||||
|
||||
MEMORYSCOPE_CONTEXT = "memoryscope_context"
|
||||
|
||||
RESULT = "result"
|
||||
|
||||
MEMORIES = "memories"
|
||||
|
||||
CHAT_MESSAGES = "chat_messages"
|
||||
|
||||
CHAT_MESSAGES_SCATTER = "chat_messages_scatter"
|
||||
|
||||
CHAT_KWARGS = "chat_kwargs"
|
||||
|
||||
USER_NAME = "user_name"
|
||||
|
||||
TARGET_NAME = "target_name"
|
||||
|
||||
MEMORY_MANAGER = "memory_manager"
|
||||
|
||||
QUERY_WITH_TS = "query_with_ts"
|
||||
|
||||
RETRIEVE_MEMORY_NODES = "retrieve_memory_nodes"
|
||||
|
||||
RANKED_MEMORY_NODES = "ranked_memory_nodes"
|
||||
|
||||
NOT_REFLECTED_NODES = "not_reflected_nodes"
|
||||
|
||||
NOT_UPDATED_NODES = "not_updated_nodes"
|
||||
|
||||
EXTRACT_TIME_DICT = "extract_time_dict"
|
||||
|
||||
NEW_OBS_NODES = "new_obs_nodes"
|
||||
|
||||
NEW_OBS_WITH_TIME_NODES = "new_obs_with_time_nodes"
|
||||
|
||||
INSIGHT_NODES = "insight_nodes"
|
||||
|
||||
TODAY_NODES = "today_nodes"
|
||||
|
||||
MERGE_OBS_NODES = "merge_obs_nodes"
|
||||
|
||||
TIME_INFER = "time_infer"
|
||||
|
|
@ -1,215 +0,0 @@
|
|||
from memoryscope.enumeration.language_enum import LanguageEnum
|
||||
|
||||
# This dictionary maps languages to lists of words related to datetime expressions.
|
||||
# It aids in recognizing and processing datetime mentions in text, enhancing the system's ability to understand
|
||||
# temporal context across different languages.
|
||||
DATATIME_WORD_LIST = {
|
||||
LanguageEnum.CN: [
|
||||
"天",
|
||||
"周",
|
||||
"月",
|
||||
"年",
|
||||
"星期",
|
||||
"点",
|
||||
"分钟",
|
||||
"小时",
|
||||
"秒",
|
||||
"上午",
|
||||
"下午",
|
||||
"早上",
|
||||
"早晨",
|
||||
"晚上",
|
||||
"中午",
|
||||
"日",
|
||||
"夜",
|
||||
"清晨",
|
||||
"傍晚",
|
||||
"凌晨",
|
||||
"岁",
|
||||
],
|
||||
LanguageEnum.EN: [
|
||||
# Units of Time
|
||||
"year", "yr",
|
||||
"month", "mo",
|
||||
"week", "wk",
|
||||
"day", "d",
|
||||
"hour", "hr",
|
||||
"minute", "min",
|
||||
"second", "sec",
|
||||
|
||||
# Days of the Week
|
||||
"Monday", "Mon",
|
||||
"Tuesday", "Tue", "Tues",
|
||||
"Wednesday", "Wed",
|
||||
"Thursday", "Thu", "Thur", "Thurs",
|
||||
"Friday", "Fri",
|
||||
"Saturday", "Sat",
|
||||
"Sunday", "Sun",
|
||||
|
||||
# Months of the Year
|
||||
"January", "Jan",
|
||||
"February", "Feb",
|
||||
"March", "Mar",
|
||||
"April", "Apr",
|
||||
"May", "May",
|
||||
"June", "Jun",
|
||||
"July", "Jul",
|
||||
"August", "Aug",
|
||||
"September", "Sep", "Sept",
|
||||
"October", "Oct",
|
||||
"November", "Nov",
|
||||
"December", "Dec",
|
||||
|
||||
# Relative Time References
|
||||
"Today",
|
||||
"Tomorrow", "Tmrw",
|
||||
"Yesterday", "Yday",
|
||||
"Now",
|
||||
"Morning", "AM", "a.m.",
|
||||
"Afternoon", "PM", "p.m.",
|
||||
"Evening",
|
||||
"Night",
|
||||
"Midnight",
|
||||
"Noon",
|
||||
|
||||
# Seasonal References
|
||||
"Spring",
|
||||
"Summer",
|
||||
"Autumn", "Fall",
|
||||
"Winter",
|
||||
|
||||
# General Time References
|
||||
"Century", "cent.",
|
||||
"Decade",
|
||||
"Millennium",
|
||||
"Quarter", "Q1", "Q2", "Q3", "Q4",
|
||||
"Semester",
|
||||
"Fortnight",
|
||||
"Weekend"
|
||||
]
|
||||
}
|
||||
|
||||
# A mapping of weekdays for each supported language, facilitating calendar-related operations and understanding
|
||||
# within the application.
|
||||
WEEKDAYS = {
|
||||
LanguageEnum.CN: [
|
||||
"周一",
|
||||
"周二",
|
||||
"周三",
|
||||
"周四",
|
||||
"周五",
|
||||
"周六",
|
||||
"周日"
|
||||
],
|
||||
LanguageEnum.EN: [
|
||||
"Monday",
|
||||
"Tuesday",
|
||||
"Wednesday",
|
||||
"Thursday",
|
||||
"Friday",
|
||||
"Saturday",
|
||||
"Sunday",
|
||||
]
|
||||
}
|
||||
|
||||
MONTH_DICT = {
|
||||
LanguageEnum.CN: [
|
||||
"1月",
|
||||
"2月",
|
||||
"3月",
|
||||
"4月",
|
||||
"5月",
|
||||
"6月",
|
||||
"7月",
|
||||
"8月",
|
||||
"9月",
|
||||
"10月",
|
||||
"11月",
|
||||
"12月",
|
||||
],
|
||||
LanguageEnum.EN: [
|
||||
"January",
|
||||
"February",
|
||||
"March",
|
||||
"April",
|
||||
"May",
|
||||
"June",
|
||||
"July",
|
||||
"August",
|
||||
"September",
|
||||
"October",
|
||||
"November",
|
||||
"December",
|
||||
]
|
||||
}
|
||||
|
||||
# Constants for the word 'none' in different languages
|
||||
NONE_WORD = {
|
||||
LanguageEnum.CN: "无",
|
||||
LanguageEnum.EN: "none"
|
||||
}
|
||||
|
||||
# Constants for the word 'repeated' in different languages
|
||||
REPEATED_WORD = {
|
||||
LanguageEnum.CN: "重复",
|
||||
LanguageEnum.EN: "repeated"
|
||||
}
|
||||
|
||||
# Constants for the word 'contradictory' in different languages
|
||||
CONTRADICTORY_WORD = {
|
||||
LanguageEnum.CN: "矛盾",
|
||||
LanguageEnum.EN: "contradiction"
|
||||
}
|
||||
|
||||
# Constants for the phrase 'included' in different languages
|
||||
CONTAINED_WORD = {
|
||||
LanguageEnum.CN: "被包含",
|
||||
LanguageEnum.EN: "contained"
|
||||
}
|
||||
|
||||
# Constants for the symbol ':' in different languages' representations
|
||||
COLON_WORD = {
|
||||
LanguageEnum.CN: "：",
|
||||
LanguageEnum.EN: ":"
|
||||
}
|
||||
|
||||
# Constants for the symbol ',' in different languages' representations
|
||||
COMMA_WORD = {
|
||||
LanguageEnum.CN: "，",
|
||||
LanguageEnum.EN: ","
|
||||
}
|
||||
|
||||
# Default human name placeholders for different languages
|
||||
DEFAULT_HUMAN_NAME = {
|
||||
LanguageEnum.CN: "用户",
|
||||
LanguageEnum.EN: "user"
|
||||
}
|
||||
|
||||
# Mapping of datetime terms from natural language to standardized keys for each supported language
|
||||
DATATIME_KEY_MAP = {
|
||||
LanguageEnum.CN: {
|
||||
"年": "year",
|
||||
"月": "month",
|
||||
"日": "day",
|
||||
"周": "week",
|
||||
"星期几": "weekday",
|
||||
},
|
||||
LanguageEnum.EN: {
|
||||
"Year": "year",
|
||||
"Month": "month",
|
||||
"Day": "day",
|
||||
"Week": "week",
|
||||
"Weekday": "weekday",
|
||||
}
|
||||
}
|
||||
|
||||
# Phrase for indicating inferred time in different languages
|
||||
TIME_INFER_WORD = {
|
||||
LanguageEnum.CN: "推断时间",
|
||||
LanguageEnum.EN: "Inference time"
|
||||
}
|
||||
|
||||
USER_NAME_EXPRESSION = {
|
||||
LanguageEnum.CN: "用户姓名是{name}。",
|
||||
LanguageEnum.EN: "User's name is {name}."
|
||||
}
|
||||
|
|
@ -1,86 +0,0 @@
|
|||
import datetime
|
||||
|
||||
from memoryscope.constants.common_constants import QUERY_WITH_TS
|
||||
from memoryscope.constants.language_constants import NONE_WORD
|
||||
from memoryscope.core.worker.memory_base_worker import MemoryBaseWorker
|
||||
from memoryscope.enumeration.message_role_enum import MessageRoleEnum
|
||||
|
||||
|
||||
class ExampleQueryWorker(MemoryBaseWorker):
|
||||
# NOTE: If you want to utilize the capabilities of the prompt handler, please be sure to include this sentence.
|
||||
file_path: str = __file__
|
||||
|
||||
def _parse_params(self, **kwargs):
|
||||
self.rewrite_history_count: int = kwargs.get("rewrite_history_count", 2)
|
||||
self.generation_model_kwargs: dict = kwargs.get("generation_model_kwargs", {})
|
||||
|
||||
def rewrite_query(self, query: str) -> str:
|
||||
chat_messages = self.chat_messages_scatter
|
||||
if len(chat_messages) <= 1:
|
||||
return query
|
||||
|
||||
if chat_messages[-1].role == MessageRoleEnum.USER:
|
||||
chat_messages = chat_messages[:-1]
|
||||
chat_messages = chat_messages[-self.rewrite_history_count:]
|
||||
|
||||
# get context
|
||||
context_list = []
|
||||
for message in chat_messages:
|
||||
context = message.content
|
||||
if len(context) > 200:
|
||||
context = context[:100] + context[-100:]
|
||||
if message.role == MessageRoleEnum.USER:
|
||||
context_list.append(f"{self.target_name}: {context}")
|
||||
elif message.role == MessageRoleEnum.ASSISTANT:
|
||||
context_list.append(f"Assistant: {context}")
|
||||
|
||||
if not context_list:
|
||||
return query
|
||||
|
||||
system_prompt = self.prompt_handler.rewrite_query_system
|
||||
user_query = self.prompt_handler.rewrite_query_query.format(query=query,
|
||||
context="\n".join(context_list))
|
||||
rewrite_query_message = self.prompt_to_msg(system_prompt=system_prompt,
|
||||
few_shot="",
|
||||
user_query=user_query)
|
||||
self.logger.info(f"rewrite_query_message={rewrite_query_message}")
|
||||
|
||||
# Invoke the LLM to generate a response
|
||||
response = self.generation_model.call(messages=rewrite_query_message,
|
||||
**self.generation_model_kwargs)
|
||||
|
||||
# Handle empty or unsuccessful responses
|
||||
if not response.status or not response.message.content:
|
||||
return query
|
||||
|
||||
response_text = response.message.content
|
||||
self.logger.info(f"rewrite_query.response_text={response_text}")
|
||||
|
||||
if not response_text or response_text.lower() == self.get_language_value(NONE_WORD):
|
||||
return query
|
||||
|
||||
return response_text
|
||||
|
||||
def _run(self):
|
||||
query = "" # Default query value
|
||||
timestamp = int(datetime.datetime.now().timestamp()) # Current timestamp as default
|
||||
|
||||
if "query" in self.chat_kwargs:
|
||||
# set query if exists
|
||||
query = self.chat_kwargs["query"]
|
||||
if not query:
|
||||
query = ""
|
||||
query = query.strip()
|
||||
|
||||
# set ts if exists
|
||||
_timestamp = self.chat_kwargs.get("timestamp")
|
||||
if _timestamp and isinstance(_timestamp, int):
|
||||
timestamp = _timestamp
|
||||
|
||||
if self.rewrite_history_count > 0:
|
||||
t_query = self.rewrite_query(query=query)
|
||||
if t_query:
|
||||
query = t_query
|
||||
|
||||
# Store the determined query and its timestamp in the context
|
||||
self.set_workflow_context(QUERY_WITH_TS, (query, timestamp))
|
||||
|
|
@ -1,21 +0,0 @@
|
|||
rewrite_query_system:
|
||||
cn: |
|
||||
任务: 消除指代问题并重写
|
||||
要求: 检查提供的问题是否存在指代。如果存在指代，通过上下文信息重写问题，使其信息充足，能够单独回答。如果没有指代问题，则回答“无”。
|
||||
en: |
|
||||
Task: Eliminate referencing issues and rewrite
|
||||
Requirements: Check the provided questions for any references. If references exist, rewrite the questions using contextual information to make them sufficiently informative so they can be answered independently. If there are no referencing issues, respond with "None".
|
||||
|
||||
rewrite_query_query:
|
||||
cn: |
|
||||
上下文：
|
||||
{context}
|
||||
问题：{query}
|
||||
重写：
|
||||
|
||||
en: |
|
||||
Context:
|
||||
{context}
|
||||
Question: {query}
|
||||
Rewrite:
|
||||
|
||||
|
|
@ -1,7 +0,0 @@
|
|||
from .memoryscope import MemoryScope
|
||||
from .memoryscope_context import MemoryscopeContext
|
||||
|
||||
__all__ = [
|
||||
"MemoryScope",
|
||||
"MemoryscopeContext"
|
||||
]
|
||||
|
|
@ -1,9 +0,0 @@
|
|||
from .api_memory_chat import ApiMemoryChat
|
||||
from .base_memory_chat import BaseMemoryChat
|
||||
from .cli_memory_chat import CliMemoryChat
|
||||
|
||||
__all__ = [
|
||||
"ApiMemoryChat",
|
||||
"BaseMemoryChat",
|
||||
"CliMemoryChat"
|
||||
]
|
||||
|
|
@ -1,201 +0,0 @@
|
|||
from typing import List, Optional, Literal
|
||||
|
||||
from memoryscope.constants.common_constants import MEMORIES
|
||||
from memoryscope.constants.language_constants import DEFAULT_HUMAN_NAME, USER_NAME_EXPRESSION
|
||||
from memoryscope.core.chat.base_memory_chat import BaseMemoryChat
|
||||
from memoryscope.core.memoryscope_context import MemoryscopeContext
|
||||
from memoryscope.core.models.base_model import BaseModel
|
||||
from memoryscope.core.service.base_memory_service import BaseMemoryService
|
||||
from memoryscope.core.utils.datetime_handler import DatetimeHandler
|
||||
from memoryscope.core.utils.prompt_handler import PromptHandler
|
||||
from memoryscope.enumeration.message_role_enum import MessageRoleEnum
|
||||
from memoryscope.scheme.message import Message
|
||||
from memoryscope.scheme.model_response import ModelResponse, ModelResponseGen
|
||||
|
||||
|
||||
class ApiMemoryChat(BaseMemoryChat):
|
||||
|
||||
def __init__(self,
|
||||
memory_service: str,
|
||||
generation_model: str,
|
||||
context: MemoryscopeContext,
|
||||
stream: bool = False,
|
||||
**kwargs):
|
||||
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self._memory_service: BaseMemoryService | str = memory_service
|
||||
self._generation_model: BaseModel | str = generation_model
|
||||
self.context: MemoryscopeContext = context
|
||||
self.stream: bool = stream
|
||||
self.generation_model_kwargs: dict = kwargs.pop("generation_model_kwargs", {})
|
||||
|
||||
self._prompt_handler: PromptHandler | None = None
|
||||
|
||||
@property
|
||||
def prompt_handler(self) -> PromptHandler:
|
||||
"""
|
||||
Lazy initialization property for the prompt handler.
|
||||
|
||||
This property ensures that the `_prompt_handler` attribute is only instantiated when it is first accessed.
|
||||
It uses the current file's path and additional keyword arguments for configuration.
|
||||
|
||||
Returns:
|
||||
PromptHandler: An instance of the PromptHandler configured for this CLI session.
|
||||
"""
|
||||
if self._prompt_handler is None:
|
||||
self._prompt_handler = PromptHandler(__file__,
|
||||
language=self.context.language,
|
||||
prompt_file="memory_chat_prompt",
|
||||
**self.kwargs)
|
||||
return self._prompt_handler
|
||||
|
||||
@property
|
||||
def memory_service(self) -> BaseMemoryService:
|
||||
"""
|
||||
Property to access the memory service. If the service is initially set as a string,
|
||||
it will be looked up in the memory service dictionary of context, initialized,
|
||||
and then returned as an instance of `BaseMemoryService`. Ensures the memory service
|
||||
is properly started before use.
|
||||
|
||||
Returns:
|
||||
BaseMemoryService: An active memory service instance.
|
||||
|
||||
Raises:
|
||||
ValueError: If the declaration of memory service is not found in the memory service dictionary of context.
|
||||
"""
|
||||
if isinstance(self._memory_service, str):
|
||||
if self._memory_service not in self.context.memory_service_dict:
|
||||
raise ValueError(f"Missing declaration of memory_service in context: {self._memory_service}")
|
||||
|
||||
self._memory_service: BaseMemoryService = self.context.memory_service_dict[self._memory_service]
|
||||
# init service & update kwargs
|
||||
self._memory_service.init_service()
|
||||
return self._memory_service
|
||||
|
||||
@property
|
||||
def human_name(self):
|
||||
return self.memory_service.human_name
|
||||
|
||||
@property
|
||||
def assistant_name(self):
|
||||
return self.memory_service.assistant_name
|
||||
|
||||
@property
|
||||
def generation_model(self) -> BaseModel:
|
||||
"""
|
||||
Property to get the generation model. If the model is set as a string, it will be resolved from the global
|
||||
context's model dictionary.
|
||||
|
||||
Raises:
|
||||
ValueError: If the declaration of generation model is not found in the model dictionary of context .
|
||||
|
||||
Returns:
|
||||
BaseModel: An actual generation model instance.
|
||||
"""
|
||||
if isinstance(self._generation_model, str):
|
||||
if self._generation_model not in self.context.model_dict:
|
||||
raise ValueError(f"Missing declaration of generation model in yaml config: {self._generation_model}")
|
||||
self._generation_model = self.context.model_dict[self._generation_model]
|
||||
return self._generation_model
|
||||
|
||||
def iter_response(self,
|
||||
remember_response: bool,
|
||||
resp: ModelResponseGen,
|
||||
memories: str,
|
||||
query_message: Message) -> ModelResponseGen:
|
||||
|
||||
model_response: ModelResponse | None = None
|
||||
for model_response in resp:
|
||||
yield model_response
|
||||
|
||||
if remember_response:
|
||||
if model_response and model_response.message:
|
||||
model_response.message.role_name = self.assistant_name
|
||||
model_response.meta_data[MEMORIES] = memories
|
||||
self.memory_service.add_messages_pair([query_message, model_response.message])
|
||||
else:
|
||||
self.logger.warning("model_response or model_response.message is empty!")
|
||||
|
||||
def chat_with_memory(self,
|
||||
query: str,
|
||||
role_name: Optional[str] = None,
|
||||
system_prompt: Optional[str] = None,
|
||||
memory_prompt: Optional[str] = None,
|
||||
temporary_memories: Optional[str] = None,
|
||||
history_message_strategy: Literal["auto", None] | int = "auto",
|
||||
remember_response: bool = True,
|
||||
**kwargs):
|
||||
|
||||
chat_messages: List[Message] = []
|
||||
|
||||
# prepare query message
|
||||
if not role_name:
|
||||
role_name = self.human_name
|
||||
query_message = Message(role=MessageRoleEnum.USER.value, role_name=role_name, content=query)
|
||||
|
||||
# To retrieve memory, prepare the query timestamp and role name by adding query_message.
|
||||
memories: str = self.memory_service.retrieve_memory(query=query_message.content,
|
||||
role_name=query_message.role_name,
|
||||
timestamp=query_message.time_created)
|
||||
|
||||
# format system_message with memories
|
||||
system_prompt_list = []
|
||||
if system_prompt:
|
||||
system_prompt_list.append(system_prompt)
|
||||
else:
|
||||
dt_handler = DatetimeHandler()
|
||||
date_time = dt_handler.datetime_format("%Y-%m-%d %H:%M:%S")
|
||||
weekday = dt_handler.get_dt_info_dict(self.context.language)["weekday"]
|
||||
system_prompt_list.append(self.prompt_handler.system_prompt.format(date_time=f"{date_time} {weekday}"))
|
||||
|
||||
if memories:
|
||||
# add memory prompt
|
||||
if memory_prompt:
|
||||
system_prompt_list.append(memory_prompt)
|
||||
else:
|
||||
system_prompt_list.append(self.prompt_handler.memory_prompt)
|
||||
|
||||
if self.human_name != DEFAULT_HUMAN_NAME[self.context.language]:
|
||||
system_prompt_list.append(USER_NAME_EXPRESSION[self.context.language].format(name=self.human_name))
|
||||
system_prompt_list.append(memories)
|
||||
|
||||
if temporary_memories:
|
||||
system_prompt_list.extend(temporary_memories)
|
||||
|
||||
system_prompt_join = "\n".join([x.strip() for x in system_prompt_list])
|
||||
system_message = Message(role=MessageRoleEnum.SYSTEM.value, content=system_prompt_join)
|
||||
chat_messages.append(system_message)
|
||||
|
||||
# Include past conversation history in the message list
|
||||
if history_message_strategy:
|
||||
history_messages = []
|
||||
|
||||
if history_message_strategy == "auto":
|
||||
history_messages = self.memory_service.read_message()
|
||||
|
||||
elif isinstance(history_message_strategy, int):
|
||||
history_messages = self.memory_service.get_chat_messages_scatter(history_message_strategy)
|
||||
|
||||
if history_messages:
|
||||
assert isinstance(history_messages[0], Message)
|
||||
chat_messages.extend(history_messages)
|
||||
|
||||
# Append the current user's message to the conversation context
|
||||
chat_messages.append(query_message)
|
||||
self.logger.info(f"chat_messages={chat_messages}")
|
||||
|
||||
resp = self.generation_model.call(messages=chat_messages, stream=self.stream, **self.generation_model_kwargs)
|
||||
if self.stream:
|
||||
return self.iter_response(remember_response, resp, memories, query_message)
|
||||
|
||||
else:
|
||||
model_response: ModelResponse = resp
|
||||
if remember_response:
|
||||
if model_response and model_response.message:
|
||||
model_response.message.role_name = self.assistant_name
|
||||
model_response.meta_data[MEMORIES] = memories
|
||||
self.memory_service.add_messages_pair([query_message, model_response.message])
|
||||
else:
|
||||
self.logger.warning("model_response or model_response.message is empty!")
|
||||
return model_response
|
||||
|
|
@ -1,75 +0,0 @@
|
|||
from abc import ABCMeta, abstractmethod
|
||||
from typing import Optional, Literal
|
||||
|
||||
from memoryscope.core.service.base_memory_service import BaseMemoryService
|
||||
from memoryscope.core.utils.logger import Logger
|
||||
|
||||
|
||||
class BaseMemoryChat(metaclass=ABCMeta):
|
||||
"""
|
||||
An abstract base class representing a chat system integrated with memory services.
|
||||
It outlines the method to initiate a chat session leveraging memory data, which concrete subclasses must implement.
|
||||
"""
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
self.kwargs: dict = kwargs
|
||||
self.logger = Logger.get_logger()
|
||||
|
||||
@property
|
||||
def memory_service(self) -> BaseMemoryService:
|
||||
"""
|
||||
Abstract property to access the memory service.
|
||||
|
||||
Raises:
|
||||
NotImplementedError: This method should be implemented in a subclass.
|
||||
"""
|
||||
raise NotImplementedError
|
||||
|
||||
@abstractmethod
|
||||
def chat_with_memory(self,
|
||||
query: str,
|
||||
role_name: Optional[str] = None,
|
||||
system_prompt: Optional[str] = None,
|
||||
memory_prompt: Optional[str] = None,
|
||||
temporary_memories: Optional[str] = None,
|
||||
history_message_strategy: Literal["auto", None] | int = "auto",
|
||||
remember_response: bool = True,
|
||||
**kwargs):
|
||||
"""
|
||||
The core function that carries out conversation with memory accepts user queries through query and returns the
|
||||
conversation results through model_response. The retrieved memories are stored in the memories within meta_data.
|
||||
Args:
|
||||
query (str): User's query, includes the user's question.
|
||||
role_name (str, optional): User's role name.
|
||||
system_prompt (str, optional): System prompt. Defaults to the system_prompt in "memory_chat_prompt.yaml".
|
||||
memory_prompt (str, optional): Memory prompt, It takes effect when there is a memory and will be placed in
|
||||
front of the retrieved memory. Defaults to the memory_prompt in "memory_chat_prompt.yaml".
|
||||
temporary_memories (str, optional): Manually added user memory in this function.
|
||||
history_message_strategy ("auto", None, int):
|
||||
- If it is set to "auto"， the history messages in the conversation will retain those that have not
|
||||
yet been summarized. Default to "auto".
|
||||
- If it is set to None， no conversation history will be saved.
|
||||
- If it is set to an integer value "n", recent "n" message-pair[user, assistant] will be retained.
|
||||
remember_response (bool, optional): Flag indicating whether to save the AI's response to memory.
|
||||
Defaults to False.
|
||||
Returns:
|
||||
- ModelResponse: In non-streaming mode, returns a complete AI response.
|
||||
- ModelResponseGen: In streaming mode, returns a generator yielding AI response parts.
|
||||
- Memories: To obtain the memory by invoking the method of model_response.meta_data[MEMORIES]
|
||||
"""
|
||||
raise NotImplementedError
|
||||
|
||||
def start_backend_service(self, **kwargs):
|
||||
self.memory_service.start_backend_service(**kwargs)
|
||||
|
||||
def run_service_operation(self, name: str, role_name: Optional[str] = None, **kwargs):
|
||||
return self.memory_service.run_operation(name, role_name=role_name, **kwargs)
|
||||
|
||||
def run(self):
|
||||
"""
|
||||
Abstract method to run the chat system.
|
||||
|
||||
This method should contain the logic to initiate and manage the chat process,
|
||||
utilizing the memory service as needed. It must be implemented by subclasses.
|
||||
"""
|
||||
pass
|
||||
|
|
@ -1,209 +0,0 @@
|
|||
import os
|
||||
import time
|
||||
from typing import Optional, Literal
|
||||
|
||||
import questionary
|
||||
|
||||
from memoryscope.core.chat.api_memory_chat import ApiMemoryChat
|
||||
from memoryscope.core.utils.tool_functions import char_logo
|
||||
|
||||
|
||||
class CliMemoryChat(ApiMemoryChat):
|
||||
"""
|
||||
Command-line interface for chatting with an AI that integrates memory functionality.
|
||||
Allows users to interact, manage chat history, adjust streaming settings, and view commands' help.
|
||||
"""
|
||||
USER_COMMANDS = {
|
||||
"exit": "Exit the CLI.",
|
||||
"clear": "Clear the command history.",
|
||||
"help": "Display available CLI commands and their descriptions.",
|
||||
"stream": "Toggle between getting streamed responses from the model."
|
||||
}
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
self._logo = char_logo("MemoryScope")
|
||||
|
||||
def print_logo(self):
|
||||
"""
|
||||
Prints the logo of the CLI application to the console.
|
||||
|
||||
The logo is composed of multiple lines, which are iterated through
|
||||
and printed one by one to provide a visual identity for the chat interface.
|
||||
"""
|
||||
for line in self._logo:
|
||||
print(line)
|
||||
|
||||
def chat_with_memory(self,
|
||||
query: str,
|
||||
role_name: Optional[str] = None,
|
||||
system_prompt: Optional[str] = None,
|
||||
memory_prompt: Optional[str] = None,
|
||||
temporary_memories: Optional[str] = None,
|
||||
history_message_strategy: Literal["auto", None] | int = "auto",
|
||||
remember_response: bool = True,
|
||||
**kwargs):
|
||||
resp = super().chat_with_memory(query=query,
|
||||
role_name=role_name,
|
||||
system_prompt=system_prompt,
|
||||
memory_prompt=memory_prompt,
|
||||
temporary_memories=temporary_memories,
|
||||
history_message_strategy=history_message_strategy,
|
||||
remember_response=remember_response,
|
||||
**kwargs)
|
||||
|
||||
if self.stream:
|
||||
for _resp in resp:
|
||||
questionary.print(_resp.delta, end="")
|
||||
questionary.print("")
|
||||
else:
|
||||
questionary.print(resp.message.content)
|
||||
|
||||
@staticmethod
|
||||
def parse_query_command(query: str):
|
||||
"""
|
||||
Parses the user's input query command, separating it into the command and its associated keyword arguments.
|
||||
|
||||
Args:
|
||||
query (str): The raw input string from the user which includes the command and its arguments.
|
||||
|
||||
Returns:
|
||||
tuple: A tuple containing the command (str) as the first element and a dictionary (kwargs) of keyword
|
||||
arguments as the second element.
|
||||
"""
|
||||
query_split = query.lstrip("/").lower().split(" ") # Split and preprocess the input command
|
||||
command = query_split[0] # Extract the command
|
||||
args = query_split[1:] # Extract the arguments following the command
|
||||
kwargs = {} # Initialize dictionary to hold keyword arguments
|
||||
|
||||
for arg in args:
|
||||
# Skip if no arguments exist (unnecessary check due to prior assignment, but retained as per original)
|
||||
if not args:
|
||||
continue
|
||||
arg_split = arg.split("=") # Split argument into key-value pair
|
||||
if len(arg_split) >= 2: # Ensure there's both a key and value
|
||||
k = arg_split[0] # Extract key
|
||||
v = arg_split[1] # Extract value
|
||||
if k and v: # Only add to kwargs if both key and value are non-empty
|
||||
kwargs[k] = v
|
||||
|
||||
return command, kwargs # Return the parsed command and keyword arguments
|
||||
|
||||
def process_commands(self, query: str) -> bool:
|
||||
"""
|
||||
Parses and executes commands from user input in the CLI chat interface.
|
||||
Supports operations like exiting, clearing screen, showing help, toggling stream mode,
|
||||
executing predefined memory operations, and handling unknown commands.
|
||||
|
||||
Args:
|
||||
query (str): The user's input command string.
|
||||
|
||||
Returns:
|
||||
bool: Indicates whether to continue running the CLI after processing the command.
|
||||
"""
|
||||
continue_run = True
|
||||
command, kwargs = self.parse_query_command(query)
|
||||
|
||||
# Print prompt for AI's response
|
||||
questionary.print("> ", end="", style="fg:yellow")
|
||||
questionary.print(f"{self.assistant_name}: ", end="", style="bold")
|
||||
|
||||
if command == "exit":
|
||||
self.memory_service.stop_backend_service()
|
||||
continue_run = False
|
||||
|
||||
elif command == "clear":
|
||||
os.system("clear")
|
||||
|
||||
elif command == "help":
|
||||
questionary.print("CLI commands", "bold")
|
||||
for cmd, desc in self.USER_COMMANDS.items():
|
||||
questionary.print(text=f" /{cmd}:", style="bold")
|
||||
questionary.print(text=f" {desc}")
|
||||
|
||||
elif command == "stream":
|
||||
self.stream = not self.stream
|
||||
questionary.print(f"set stream: {self.stream}")
|
||||
|
||||
elif command in self.memory_service.op_description_dict:
|
||||
refresh_time = kwargs.pop("refresh_time", "")
|
||||
if refresh_time and refresh_time.isdigit():
|
||||
refresh_time = int(refresh_time)
|
||||
self.memory_service.stop_backend_service()
|
||||
while True:
|
||||
result = self.memory_service.run_operation(name=command, **kwargs)
|
||||
if os.name == 'nt':
|
||||
os.system('cls')
|
||||
else:
|
||||
os.system('clear')
|
||||
self.print_logo()
|
||||
if result:
|
||||
if isinstance(result, list):
|
||||
result = "\n".join([str(x) for x in result])
|
||||
questionary.print(result)
|
||||
else:
|
||||
questionary.print(f"command={command} result is empty! kwargs={kwargs}")
|
||||
time.sleep(refresh_time)
|
||||
|
||||
else:
|
||||
result = self.memory_service.run_operation(name=command, **kwargs)
|
||||
if result:
|
||||
if isinstance(result, list):
|
||||
result = "\n".join([str(x) for x in result])
|
||||
questionary.print(result)
|
||||
else:
|
||||
questionary.print(f"command={command} result is empty! kwargs={kwargs}")
|
||||
|
||||
else:
|
||||
questionary.print(f"Unknown command={command} received.")
|
||||
|
||||
return continue_run
|
||||
|
||||
def run(self):
|
||||
"""
|
||||
Runs the CLI chat loop, which handles user input, processes commands,
|
||||
communicates with the AI model, manages conversation memory, and controls
|
||||
the chat session including streaming responses, command execution, and error handling.
|
||||
|
||||
The loop continues until the user explicitly chooses to exit.
|
||||
"""
|
||||
self.print_logo()
|
||||
self.USER_COMMANDS.update(self.memory_service.op_description_dict)
|
||||
|
||||
while True:
|
||||
try:
|
||||
query = questionary.text(message=f"{self.human_name}:", multiline=False, qmark=">").unsafe_ask()
|
||||
if not query:
|
||||
continue
|
||||
|
||||
query: str = query.strip()
|
||||
|
||||
# Handle special commands prefixed with '/'
|
||||
if query.startswith("/"):
|
||||
if self.process_commands(query=query):
|
||||
continue
|
||||
else:
|
||||
break
|
||||
|
||||
# Print prompt for AI's response
|
||||
questionary.print("> ", end="", style="fg:yellow")
|
||||
questionary.print(f"{self.assistant_name}: ", end="", style="bold")
|
||||
|
||||
# Fetch and display AI's response
|
||||
self.start_backend_service()
|
||||
self.chat_with_memory(query=query)
|
||||
|
||||
except KeyboardInterrupt:
|
||||
# Handle user interruption and confirm exit
|
||||
questionary.print("User interrupt occurred.")
|
||||
is_exit = questionary.confirm("Continue exit?").unsafe_ask()
|
||||
if is_exit:
|
||||
self.memory_service.stop_backend_service()
|
||||
break
|
||||
|
||||
except Exception as e:
|
||||
# Log and handle any unanticipated exceptions
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
self.logger.exception(f"An exception occurred when running cli memory chat. args={e.args}.")
|
||||
continue
|
||||
|
|
@ -1,11 +0,0 @@
|
|||
system_prompt:
|
||||
cn: |
|
||||
你是一个名为MemoryScope的智能助理，请用中文简洁地回答用户问题。当前时间是{date_time}。
|
||||
en: |
|
||||
You are a helpful assistant named MemoryScope, please answer questions concisely in English. The current time is {date_time}.
|
||||
|
||||
memory_prompt:
|
||||
cn: |
|
||||
在回答用户问题时，请尽量忘记大部分不相关的信息。只有当信息与用户问题或对话内容非常相关时，才记住这些信息并加以使用。请确保你的回答简洁、准确，并聚焦于用户问题或对话主题。信息：
|
||||
en: |
|
||||
When responding to user questions, please try to forget most of the irrelevant information. Only remember and use the information if it is highly relevant to the current question or conversation. Ensure that your answers are concise, accurate, and focused on the user's current question or the topic of discussion. Information:
|
||||
|
|
@ -1,7 +0,0 @@
|
|||
from .arguments import Arguments
|
||||
from .config_manager import ConfigManager
|
||||
|
||||
__all__ = [
|
||||
"Arguments",
|
||||
"ConfigManager",
|
||||
]
|
||||
|
|
@ -1,81 +0,0 @@
|
|||
from dataclasses import dataclass, field
|
||||
from typing import Literal, Dict
|
||||
|
||||
|
||||
@dataclass
|
||||
class Arguments(object):
|
||||
language: Literal["cn", "en"] = field(default="cn", metadata={"help": "support en & cn now"})
|
||||
|
||||
thread_pool_max_workers: int = field(default=5, metadata={"help": "thread pool max workers"})
|
||||
|
||||
memory_chat_class: str = field(default="cli_memory_chat", metadata={
|
||||
"help": "cli_memory_chat(Command-line interaction), api_memory_chat(API interface interaction), etc."})
|
||||
|
||||
chat_stream: bool | None = field(default=None, metadata={
|
||||
"help": "In the case of cli_memory_chat, stream mode is recommended. For api_memory_chat mode, "
|
||||
"please use non-stream. If set to None, the value will be automatically determined."})
|
||||
|
||||
human_name: str = field(default="user", metadata={"help": "Human user's name"})
|
||||
|
||||
assistant_name: str = field(default="AI", metadata={"help": "assistant' name"})
|
||||
|
||||
consolidate_memory_interval_time: int | None = field(default=1, metadata={
|
||||
"help": "Memory backend service: If you feel that the token consumption is relatively high, "
|
||||
"please increase the time interval. When set to None, the value will not be updated."})
|
||||
|
||||
reflect_and_reconsolidate_interval_time: int | None = field(default=15, metadata={
|
||||
"help": "Memory backend service: If you feel that the token consumption is relatively high, "
|
||||
"please increase the time interval. When set to None, the value will not be updated."})
|
||||
|
||||
worker_params: Dict[str, dict] = field(default_factory=lambda: {}, metadata={
|
||||
"help": "dict format: worker_name -> param_key -> param_value"})
|
||||
|
||||
generation_backend: str = field(default="dashscope_generation", metadata={
|
||||
"help": "global generation backend: openai_generation, dashscope_generation, etc."})
|
||||
|
||||
generation_model: str = field(default="qwen-max", metadata={
|
||||
"help": "global generation model: gpt-4o, gpt-4o-mini, gpt-4-turbo, qwen-max, etc."})
|
||||
|
||||
generation_params: dict = field(default_factory=lambda: {}, metadata={
|
||||
"help": "global generation params: max_tokens, top_p, temperature, etc."})
|
||||
|
||||
embedding_backend: str = field(default="dashscope_generation", metadata={
|
||||
"help": "global embedding backend: openai_embedding, dashscope_embedding, etc."})
|
||||
|
||||
embedding_model: str = field(default="text-embedding-v2", metadata={
|
||||
"help": "global embedding model: text-embedding-3-large, text-embedding-3-small, text-embedding-ada-002, "
|
||||
"text-embedding-v2, etc."})
|
||||
|
||||
embedding_params: dict = field(default_factory=lambda: {})
|
||||
|
||||
rank_backend: str = field(default="dashscope_rank", metadata={"help": "global rank backend: dashscope_rank, etc."})
|
||||
|
||||
rank_model: str = field(default="gte-rerank", metadata={"help": "global rank model: gte-rerank, etc."})
|
||||
|
||||
rank_params: dict = field(default_factory=lambda: {})
|
||||
|
||||
es_index_name: str = field(default="memory_index")
|
||||
|
||||
es_url: str = field(default="http://localhost:9200")
|
||||
|
||||
retrieve_mode: str = field(default="dense", metadata={
|
||||
"help": "retrieve_mode: dense, sparse(not implemented), hybrid(not implemented)"})
|
||||
|
||||
enable_ranker: bool = field(default=False, metadata={
|
||||
"help": "If a semantic ranking model is not available, MemoryScope will use cosine similarity scoring as a "
|
||||
"substitute. However, the ranking effectiveness will be somewhat compromised.",
|
||||
"map_yaml": "global->enable_ranker"})
|
||||
|
||||
enable_today_contra_repeat: bool = field(default=True, metadata={
|
||||
"help": "Whether enable conflict resolution and deduplication for the day? "
|
||||
"Note that enabling this will increase token consumption.",
|
||||
"map_yaml": "global->enable_today_contra_repeat"})
|
||||
|
||||
enable_long_contra_repeat: bool = field(default=False, metadata={
|
||||
"help": "Whether to enable long-term conflict resolution and deduplication. "
|
||||
"Note that enabling this will increase token consumption.",
|
||||
"map_yaml": "global->enable_long_contra_repeat"})
|
||||
|
||||
output_memory_max_count: int = field(default=20, metadata={
|
||||
"help": "The maximum number of memories retrieved during memory recall.",
|
||||
"map_yaml": "global->output_memory_max_count"})
|
||||
|
|
@ -1,197 +0,0 @@
|
|||
import json
|
||||
import os
|
||||
from dataclasses import fields
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
from typing import Optional, Literal
|
||||
|
||||
import yaml
|
||||
|
||||
from memoryscope.core.config.arguments import Arguments
|
||||
from memoryscope.core.utils.logger import Logger
|
||||
|
||||
|
||||
class ConfigManager(object):
|
||||
|
||||
def __init__(self,
|
||||
config_path: Optional[str] = None,
|
||||
arguments: Optional[Arguments] = None,
|
||||
demo_config_name: str = "demo_config_zh.yaml",
|
||||
**kwargs):
|
||||
self.config: dict = {}
|
||||
self.kwargs = kwargs
|
||||
self.logger = Logger.get_logger("memoryscope")
|
||||
|
||||
if not (config_path or kwargs or arguments):
|
||||
raise RuntimeError("can not init config manager without kwargs or --config_path!")
|
||||
|
||||
if config_path:
|
||||
self.read_config(config_path)
|
||||
else:
|
||||
self.read_config((Path(__file__).parent / demo_config_name).__str__())
|
||||
|
||||
kwargs = {k: v for k, v in kwargs.items() if k in [x.name for x in fields(Arguments)]}
|
||||
kwargs_padding = {x.name: None for x in fields(Arguments) if x.name not in kwargs}
|
||||
kwargs.update(kwargs_padding)
|
||||
|
||||
# (high) when there are environment variables, read them and merge into kwargs
|
||||
kwargs_from_env = {x.name:os.environ.get(x.name, None) for x in fields(Arguments) if os.environ.get(x.name, None) is not None}
|
||||
kwargs.update(kwargs_from_env)
|
||||
|
||||
# generate argument dataclass
|
||||
if not arguments:
|
||||
arguments = Arguments(**kwargs)
|
||||
else:
|
||||
# (highest) when arguments is passed into the memoryscope
|
||||
arguments = arguments
|
||||
|
||||
self.update_config_by_arguments(arguments)
|
||||
self.logger.info("\n" + self.dump_config())
|
||||
|
||||
def read_config(self, config_path: str):
|
||||
if config_path.endswith(".yaml"):
|
||||
with open(config_path) as f:
|
||||
self.config = yaml.load(f, yaml.FullLoader)
|
||||
|
||||
elif config_path.endswith(".json"):
|
||||
with open(config_path) as f:
|
||||
self.config = json.load(f)
|
||||
|
||||
@staticmethod
|
||||
def update_ignore_none(config, new_config_dict):
|
||||
update_dict = {k:v for k, v in new_config_dict.items() if v is not None}
|
||||
config.update(update_dict)
|
||||
return
|
||||
|
||||
@staticmethod
|
||||
def update_global_by_arguments(config: dict, arguments: Arguments):
|
||||
ConfigManager.update_ignore_none(
|
||||
config,
|
||||
{
|
||||
"language": arguments.language,
|
||||
"thread_pool_max_workers": arguments.thread_pool_max_workers,
|
||||
"enable_ranker": arguments.enable_ranker,
|
||||
"enable_today_contra_repeat": arguments.enable_today_contra_repeat,
|
||||
"enable_long_contra_repeat": arguments.enable_long_contra_repeat,
|
||||
"output_memory_max_count": arguments.output_memory_max_count,
|
||||
}
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def update_memory_chat_by_arguments(config: dict, arguments: Arguments):
|
||||
if arguments.memory_chat_class is not None:
|
||||
memory_chat_class_split = config["class"].split(".")
|
||||
stream = arguments.chat_stream
|
||||
if stream is None:
|
||||
stream = arguments.memory_chat_class in ["cli_memory_chat", ]
|
||||
config.update(
|
||||
{
|
||||
"class": ".".join(memory_chat_class_split[:-1] + [arguments.memory_chat_class]),
|
||||
"stream": stream,
|
||||
}
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def update_memory_service_by_arguments(config: dict, arguments: Arguments):
|
||||
ConfigManager.update_ignore_none(config, {
|
||||
"human_name": arguments.human_name,
|
||||
"assistant_name": arguments.assistant_name,
|
||||
})
|
||||
if arguments.consolidate_memory_interval_time is not None:
|
||||
config["memory_operations"]["consolidate_memory"]["interval_time"] = \
|
||||
arguments.consolidate_memory_interval_time
|
||||
|
||||
if arguments.reflect_and_reconsolidate_interval_time is not None:
|
||||
config["memory_operations"]["reflect_and_reconsolidate"]["interval_time"] = \
|
||||
arguments.reflect_and_reconsolidate_interval_time
|
||||
|
||||
@staticmethod
|
||||
def update_worker_by_arguments(config: dict, arguments: Arguments):
|
||||
if arguments.worker_params is not None:
|
||||
for worker_name, kv_dict in arguments.worker_params.items():
|
||||
if worker_name not in config:
|
||||
continue
|
||||
config[worker_name].update(kv_dict)
|
||||
|
||||
@staticmethod
|
||||
def update_model_by_arguments(config: dict, arguments: Arguments):
|
||||
ConfigManager.update_ignore_none(config["generation_model"], {
|
||||
"module_name": arguments.generation_backend,
|
||||
"model_name": arguments.generation_model,
|
||||
})
|
||||
if isinstance(arguments.generation_params, dict):
|
||||
ConfigManager.update_ignore_none(config["generation_model"], {
|
||||
**arguments.generation_params,
|
||||
})
|
||||
|
||||
ConfigManager.update_ignore_none(config["embedding_model"], {
|
||||
"module_name": arguments.embedding_backend,
|
||||
"model_name": arguments.embedding_model,
|
||||
})
|
||||
if isinstance(arguments.embedding_params, dict):
|
||||
ConfigManager.update_ignore_none(config["embedding_model"], {
|
||||
**arguments.embedding_params,
|
||||
})
|
||||
|
||||
ConfigManager.update_ignore_none(config["rank_model"], {
|
||||
"module_name": arguments.rank_backend,
|
||||
"model_name": arguments.rank_model,
|
||||
})
|
||||
if isinstance(arguments.rank_params, dict):
|
||||
ConfigManager.update_ignore_none(config["rank_model"], {
|
||||
**arguments.rank_params,
|
||||
})
|
||||
|
||||
@staticmethod
|
||||
def update_memory_store_by_arguments(config: dict, arguments: Arguments):
|
||||
ConfigManager.update_ignore_none(config, {
|
||||
"index_name": arguments.es_index_name,
|
||||
"es_url": arguments.es_url,
|
||||
"retrieve_mode": arguments.retrieve_mode}
|
||||
)
|
||||
|
||||
def update_config_by_arguments(self, arguments: Arguments):
|
||||
# prepare global
|
||||
self.update_global_by_arguments(self.config["global"], arguments)
|
||||
|
||||
# prepare memory chat
|
||||
memory_chat_conf_dict = self.config["memory_chat"]
|
||||
memory_chat_config = list(memory_chat_conf_dict.values())[0]
|
||||
self.update_memory_chat_by_arguments(memory_chat_config, arguments)
|
||||
|
||||
# prepare memory service
|
||||
memory_service_conf_dict = self.config["memory_service"]
|
||||
memory_service_config = list(memory_service_conf_dict.values())[0]
|
||||
self.update_memory_service_by_arguments(memory_service_config, arguments)
|
||||
|
||||
# prepare worker
|
||||
self.update_worker_by_arguments(self.config["worker"], arguments)
|
||||
|
||||
# prepare model
|
||||
self.update_model_by_arguments(self.config["model"], arguments)
|
||||
|
||||
# prepare memory store
|
||||
self.update_memory_store_by_arguments(self.config["memory_store"], arguments)
|
||||
|
||||
def add_node_object(self, node: str, name: str, config: dict):
|
||||
self.config[node][name] = config
|
||||
|
||||
def pop_node_object(self, node: str, name: str):
|
||||
return self.config[node].pop(name, None)
|
||||
|
||||
def clear_node_all(self, node: str):
|
||||
self.config[node].clear()
|
||||
|
||||
def dump_config(self, file_type: Literal["json", "yaml"] = "yaml", file_path: Optional[str] = None) -> str:
|
||||
if file_type == "json":
|
||||
content = json.dumps(self.config, indent=2, ensure_ascii=False)
|
||||
elif file_type == "yaml":
|
||||
content = yaml.dump(self.config, indent=2, allow_unicode=True)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
if file_path:
|
||||
with open(file_path, "w") as f:
|
||||
f.write(content)
|
||||
|
||||
return content
|
||||
|
|
@ -1,179 +0,0 @@
|
|||
global:
|
||||
language: en
|
||||
thread_pool_max_workers: 5
|
||||
enable_ranker: false
|
||||
enable_today_contra_repeat: true
|
||||
enable_long_contra_repeat: false
|
||||
output_memory_max_count: 20
|
||||
|
||||
memory_chat:
|
||||
cli_memory_chat:
|
||||
class: core.chat.cli_memory_chat
|
||||
memory_service: memoryscope_service
|
||||
generation_model: generation_model
|
||||
stream: true
|
||||
|
||||
memory_service:
|
||||
memoryscope_service:
|
||||
class: core.service.memory_scope_service
|
||||
human_name: user
|
||||
assistant_name: AI
|
||||
memory_operations:
|
||||
read_message:
|
||||
class: core.operation.frontend_operation
|
||||
workflow: read_message
|
||||
description: "read short memory"
|
||||
|
||||
retrieve_memory:
|
||||
class: core.operation.frontend_operation
|
||||
workflow: set_query,[extract_time|retrieve_obs_ins,semantic_rank],fuse_rerank
|
||||
description: "retrieve long-term memory"
|
||||
|
||||
list_memory:
|
||||
class: core.operation.frontend_operation
|
||||
workflow: set_query,retrieve_top_memory,print_memory
|
||||
description: "read all long-term memory of the user, use `refresh_time=5` to refresh screen every 5 seconds."
|
||||
|
||||
delete_memory:
|
||||
class: core.operation.frontend_operation
|
||||
workflow: set_query,retrieve_all_memory,delete_memory
|
||||
description: "delete a single long-term memory"
|
||||
|
||||
delete_all:
|
||||
class: core.operation.frontend_operation
|
||||
workflow: set_query,retrieve_all_memory,delete_all
|
||||
description: "delete all long-term memory"
|
||||
|
||||
add_memory:
|
||||
class: core.operation.frontend_operation
|
||||
workflow: add_memory
|
||||
description: "add a single observation"
|
||||
|
||||
consolidate_memory:
|
||||
class: core.operation.consolidate_memory_op
|
||||
workflow: info_filter,[get_observation|get_observation_with_time|load_today_memory],contra_repeat,store_memory
|
||||
description: "summary user's observation memory, run backend."
|
||||
interval_time: 1
|
||||
|
||||
reflect_and_reconsolidate:
|
||||
class: core.operation.backend_operation
|
||||
workflow: load_obs_and_insight,get_reflection_subject,update_insight,long_contra_repeat,store_memory
|
||||
description: "summary user's insight memory, run backend."
|
||||
interval_time: 15
|
||||
|
||||
worker:
|
||||
dummy:
|
||||
class: core.worker.dummy_worker
|
||||
generation_model: generation_model
|
||||
embedding_model: embedding_model
|
||||
rank_model: rank_model
|
||||
read_message:
|
||||
class: core.worker.frontend.read_message_worker
|
||||
set_query:
|
||||
class: core.worker.frontend.set_query_worker
|
||||
retrieve_obs_ins:
|
||||
class: core.worker.frontend.retrieve_memory_worker
|
||||
retrieve_obs_top_k: 100
|
||||
retrieve_ins_top_k: 100
|
||||
extract_time:
|
||||
class: core.worker.frontend.extract_time_worker
|
||||
generation_model: generation_model
|
||||
semantic_rank:
|
||||
class: core.worker.frontend.semantic_rank_worker
|
||||
rank_model: rank_model
|
||||
fuse_rerank:
|
||||
class: core.worker.frontend.fuse_rerank_worker
|
||||
fuse_score_threshold: 0.01
|
||||
fuse_ratio_dict:
|
||||
conversation: 0.5
|
||||
observation: 1
|
||||
obs_customized: 1.2
|
||||
insight: 2.0
|
||||
fuse_time_ratio: 2.0
|
||||
retrieve_top_memory:
|
||||
class: core.worker.frontend.retrieve_memory_worker
|
||||
retrieve_obs_top_k: 100
|
||||
retrieve_ins_top_k: 100
|
||||
retrieve_expired_top_k: 100
|
||||
print_memory:
|
||||
class: core.worker.frontend.print_memory_worker
|
||||
retrieve_all_memory:
|
||||
class: core.worker.frontend.retrieve_memory_worker
|
||||
retrieve_obs_top_k: 1000
|
||||
retrieve_ins_top_k: 1000
|
||||
retrieve_expired_top_k: 1000
|
||||
delete_memory:
|
||||
class: core.worker.backend.update_memory_worker
|
||||
method: delete_memory
|
||||
delete_all:
|
||||
class: core.worker.backend.update_memory_worker
|
||||
method: delete_all
|
||||
add_memory:
|
||||
class: core.worker.backend.update_memory_worker
|
||||
method: from_query
|
||||
info_filter:
|
||||
class: core.worker.backend.info_filter_worker
|
||||
generation_model: generation_model
|
||||
load_today_memory:
|
||||
class: core.worker.backend.load_memory_worker
|
||||
retrieve_today_top_k: 100
|
||||
get_observation:
|
||||
class: core.worker.backend.get_observation_worker
|
||||
generation_model: generation_model
|
||||
get_observation_with_time:
|
||||
class: core.worker.backend.get_observation_with_time_worker
|
||||
generation_model: generation_model
|
||||
contra_repeat:
|
||||
class: core.worker.backend.contra_repeat_worker
|
||||
generation_model: generation_model
|
||||
store_memory:
|
||||
class: core.worker.backend.update_memory_worker
|
||||
method: from_memory_key
|
||||
memory_key: all
|
||||
load_obs_and_insight:
|
||||
class: core.worker.backend.load_memory_worker
|
||||
retrieve_not_reflected_top_k: 100
|
||||
retrieve_not_updated_top_k: 100
|
||||
retrieve_insight_top_k: 100
|
||||
get_reflection_subject:
|
||||
class: core.worker.backend.get_reflection_subject_worker
|
||||
generation_model: generation_model
|
||||
reflect_obs_cnt_threshold: 6
|
||||
update_insight:
|
||||
class: core.worker.backend.update_insight_worker
|
||||
generation_model: generation_model
|
||||
rank_model: rank_model
|
||||
embedding_model: embedding_model
|
||||
update_insight_threshold: 0.01
|
||||
enable_parallel: false
|
||||
long_contra_repeat:
|
||||
class: core.worker.backend.long_contra_repeat_worker
|
||||
generation_model: generation_model
|
||||
long_contra_repeat_threshold: 0.5
|
||||
|
||||
model:
|
||||
generation_model:
|
||||
class: core.models.llama_index_generation_model
|
||||
module_name: openai_generation
|
||||
model_name: gpt-4o
|
||||
max_tokens: 2000
|
||||
temperature: 0.01
|
||||
embedding_model:
|
||||
class: core.models.llama_index_embedding_model
|
||||
module_name: openai_embedding
|
||||
model_name: text-embedding-3-small
|
||||
rank_model:
|
||||
class: core.models.llama_index_rank_model
|
||||
module_name: dashscope_rank
|
||||
model_name: gte-rerank
|
||||
top_n: 500
|
||||
|
||||
memory_store:
|
||||
class: core.storage.llama_index_es_memory_store
|
||||
embedding_model: embedding_model
|
||||
index_name: memory_index
|
||||
es_url: http://localhost:9200
|
||||
retrieve_mode: dense
|
||||
|
||||
monitor:
|
||||
class: core.storage.dummy_monitor
|
||||
|
|
@ -1,179 +0,0 @@
|
|||
global:
|
||||
language: cn
|
||||
thread_pool_max_workers: 5
|
||||
enable_ranker: true
|
||||
enable_today_contra_repeat: true
|
||||
enable_long_contra_repeat: false
|
||||
output_memory_max_count: 20
|
||||
|
||||
memory_chat:
|
||||
cli_memory_chat:
|
||||
class: core.chat.cli_memory_chat
|
||||
memory_service: memoryscope_service
|
||||
generation_model: generation_model
|
||||
stream: true
|
||||
|
||||
memory_service:
|
||||
memoryscope_service:
|
||||
class: core.service.memory_scope_service
|
||||
human_name: 用户
|
||||
assistant_name: AI
|
||||
memory_operations:
|
||||
read_message:
|
||||
class: core.operation.frontend_operation
|
||||
workflow: read_message
|
||||
description: "read short memory"
|
||||
|
||||
retrieve_memory:
|
||||
class: core.operation.frontend_operation
|
||||
workflow: set_query,[extract_time|retrieve_obs_ins,semantic_rank],fuse_rerank
|
||||
description: "retrieve long-term memory"
|
||||
|
||||
list_memory:
|
||||
class: core.operation.frontend_operation
|
||||
workflow: set_query,retrieve_top_memory,print_memory
|
||||
description: "read all long-term memory of the user, use `refresh_time=5` to refresh screen every 5 seconds."
|
||||
|
||||
delete_memory:
|
||||
class: core.operation.frontend_operation
|
||||
workflow: set_query,retrieve_all_memory,delete_memory
|
||||
description: "delete a single long-term memory"
|
||||
|
||||
delete_all:
|
||||
class: core.operation.frontend_operation
|
||||
workflow: set_query,retrieve_all_memory,delete_all
|
||||
description: "delete all long-term memory"
|
||||
|
||||
add_memory:
|
||||
class: core.operation.frontend_operation
|
||||
workflow: add_memory
|
||||
description: "add a single observation"
|
||||
|
||||
consolidate_memory:
|
||||
class: core.operation.consolidate_memory_op
|
||||
workflow: info_filter,[get_observation|get_observation_with_time|load_today_memory],contra_repeat,store_memory
|
||||
description: "summary user's observation memory, run backend."
|
||||
interval_time: 1
|
||||
|
||||
reflect_and_reconsolidate:
|
||||
class: core.operation.backend_operation
|
||||
workflow: load_obs_and_insight,get_reflection_subject,update_insight,long_contra_repeat,store_memory
|
||||
description: "summary user's insight memory, run backend."
|
||||
interval_time: 15
|
||||
|
||||
worker:
|
||||
dummy:
|
||||
class: core.worker.dummy_worker
|
||||
generation_model: generation_model
|
||||
embedding_model: embedding_model
|
||||
rank_model: rank_model
|
||||
read_message:
|
||||
class: core.worker.frontend.read_message_worker
|
||||
set_query:
|
||||
class: core.worker.frontend.set_query_worker
|
||||
retrieve_obs_ins:
|
||||
class: core.worker.frontend.retrieve_memory_worker
|
||||
retrieve_obs_top_k: 100
|
||||
retrieve_ins_top_k: 100
|
||||
extract_time:
|
||||
class: core.worker.frontend.extract_time_worker
|
||||
generation_model: generation_model
|
||||
semantic_rank:
|
||||
class: core.worker.frontend.semantic_rank_worker
|
||||
rank_model: rank_model
|
||||
fuse_rerank:
|
||||
class: core.worker.frontend.fuse_rerank_worker
|
||||
fuse_score_threshold: 0.01
|
||||
fuse_ratio_dict:
|
||||
conversation: 0.5
|
||||
observation: 1
|
||||
obs_customized: 1.2
|
||||
insight: 2.0
|
||||
fuse_time_ratio: 2.0
|
||||
retrieve_top_memory:
|
||||
class: core.worker.frontend.retrieve_memory_worker
|
||||
retrieve_obs_top_k: 100
|
||||
retrieve_ins_top_k: 100
|
||||
retrieve_expired_top_k: 100
|
||||
print_memory:
|
||||
class: core.worker.frontend.print_memory_worker
|
||||
retrieve_all_memory:
|
||||
class: core.worker.frontend.retrieve_memory_worker
|
||||
retrieve_obs_top_k: 1000
|
||||
retrieve_ins_top_k: 1000
|
||||
retrieve_expired_top_k: 1000
|
||||
delete_memory:
|
||||
class: core.worker.backend.update_memory_worker
|
||||
method: delete_memory
|
||||
delete_all:
|
||||
class: core.worker.backend.update_memory_worker
|
||||
method: delete_all
|
||||
add_memory:
|
||||
class: core.worker.backend.update_memory_worker
|
||||
method: from_query
|
||||
info_filter:
|
||||
class: core.worker.backend.info_filter_worker
|
||||
generation_model: generation_model
|
||||
load_today_memory:
|
||||
class: core.worker.backend.load_memory_worker
|
||||
retrieve_today_top_k: 100
|
||||
get_observation:
|
||||
class: core.worker.backend.get_observation_worker
|
||||
generation_model: generation_model
|
||||
get_observation_with_time:
|
||||
class: core.worker.backend.get_observation_with_time_worker
|
||||
generation_model: generation_model
|
||||
contra_repeat:
|
||||
class: core.worker.backend.contra_repeat_worker
|
||||
generation_model: generation_model
|
||||
store_memory:
|
||||
class: core.worker.backend.update_memory_worker
|
||||
method: from_memory_key
|
||||
memory_key: all
|
||||
load_obs_and_insight:
|
||||
class: core.worker.backend.load_memory_worker
|
||||
retrieve_not_reflected_top_k: 100
|
||||
retrieve_not_updated_top_k: 100
|
||||
retrieve_insight_top_k: 100
|
||||
get_reflection_subject:
|
||||
class: core.worker.backend.get_reflection_subject_worker
|
||||
generation_model: generation_model
|
||||
reflect_obs_cnt_threshold: 6
|
||||
update_insight:
|
||||
class: core.worker.backend.update_insight_worker
|
||||
generation_model: generation_model
|
||||
rank_model: rank_model
|
||||
embedding_model: embedding_model
|
||||
update_insight_threshold: 0.01
|
||||
enable_parallel: false
|
||||
long_contra_repeat:
|
||||
class: core.worker.backend.long_contra_repeat_worker
|
||||
generation_model: generation_model
|
||||
long_contra_repeat_threshold: 0.5
|
||||
|
||||
model:
|
||||
generation_model:
|
||||
class: core.models.llama_index_generation_model
|
||||
module_name: dashscope_generation
|
||||
model_name: qwen-max
|
||||
max_tokens: 2000
|
||||
temperature: 0.01
|
||||
embedding_model:
|
||||
class: core.models.llama_index_embedding_model
|
||||
module_name: dashscope_embedding
|
||||
model_name: text-embedding-v2
|
||||
rank_model:
|
||||
class: core.models.llama_index_rank_model
|
||||
module_name: dashscope_rank
|
||||
model_name: gte-rerank
|
||||
top_n: 500
|
||||
|
||||
memory_store:
|
||||
class: core.storage.llama_index_es_memory_store
|
||||
embedding_model: embedding_model
|
||||
index_name: memory_index
|
||||
es_url: http://localhost:9200
|
||||
retrieve_mode: dense
|
||||
|
||||
monitor:
|
||||
class: core.storage.dummy_monitor
|
||||
|
|
@ -1,117 +0,0 @@
|
|||
from concurrent.futures import ThreadPoolExecutor
|
||||
from datetime import datetime
|
||||
|
||||
from memoryscope.core.chat.base_memory_chat import BaseMemoryChat
|
||||
from memoryscope.core.config.config_manager import ConfigManager
|
||||
from memoryscope.core.memoryscope_context import MemoryscopeContext
|
||||
from memoryscope.core.service.base_memory_service import BaseMemoryService
|
||||
from memoryscope.core.utils.tool_functions import init_instance_by_config
|
||||
from memoryscope.enumeration.language_enum import LanguageEnum
|
||||
from memoryscope.enumeration.model_enum import ModelEnum
|
||||
|
||||
|
||||
class MemoryScope(ConfigManager):
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
self._context: MemoryscopeContext = MemoryscopeContext()
|
||||
self._context.memory_scope_uuid = datetime.now().strftime(r"%Y%m%d_%H%M%S")
|
||||
super().__init__(**kwargs)
|
||||
self._init_context_by_config()
|
||||
|
||||
def _init_context_by_config(self):
|
||||
# set global config
|
||||
global_conf = self.config["global"]
|
||||
self._context.language = LanguageEnum(global_conf["language"])
|
||||
self._context.thread_pool = ThreadPoolExecutor(max_workers=global_conf["thread_pool_max_workers"])
|
||||
self._context.meta_data.update({
|
||||
"enable_ranker": global_conf["enable_ranker"],
|
||||
"enable_today_contra_repeat": global_conf["enable_today_contra_repeat"],
|
||||
"enable_long_contra_repeat": global_conf["enable_long_contra_repeat"],
|
||||
"output_memory_max_count": global_conf["output_memory_max_count"],
|
||||
})
|
||||
|
||||
if not global_conf["enable_ranker"]:
|
||||
self.logger.warning("If a semantic ranking model is not available, MemoryScope will use cosine similarity "
|
||||
"scoring as a substitute. However, the ranking effectiveness will be somewhat "
|
||||
"compromised.")
|
||||
|
||||
# init memory_chat
|
||||
memory_chat_conf_dict = self.config["memory_chat"]
|
||||
if memory_chat_conf_dict:
|
||||
for name, conf in memory_chat_conf_dict.items():
|
||||
self._context.memory_chat_dict[name] = init_instance_by_config(conf, name=name, context=self._context)
|
||||
|
||||
# set memory_service
|
||||
memory_service_conf_dict = self.config["memory_service"]
|
||||
assert memory_service_conf_dict
|
||||
for name, conf in memory_service_conf_dict.items():
|
||||
self._context.memory_service_dict[name] = init_instance_by_config(conf, name=name, context=self._context)
|
||||
|
||||
# init model
|
||||
model_conf_dict = self.config["model"]
|
||||
assert model_conf_dict
|
||||
for name, conf in model_conf_dict.items():
|
||||
self._context.model_dict[name] = init_instance_by_config(conf, name=name)
|
||||
|
||||
# init memory_store
|
||||
memory_store_conf = self.config["memory_store"]
|
||||
assert memory_store_conf
|
||||
emb_model_name: str = memory_store_conf[ModelEnum.EMBEDDING_MODEL.value]
|
||||
embedding_model = self._context.model_dict[emb_model_name]
|
||||
self._context.memory_store = init_instance_by_config(memory_store_conf, embedding_model=embedding_model)
|
||||
|
||||
# init monitor
|
||||
monitor_conf = self.config["monitor"]
|
||||
if monitor_conf:
|
||||
self._context.monitor = init_instance_by_config(monitor_conf)
|
||||
|
||||
# set worker config
|
||||
self._context.worker_conf_dict = self.config["worker"]
|
||||
|
||||
def close(self):
|
||||
# wait service to stop
|
||||
for _, service in self._context.memory_service_dict.items():
|
||||
service.stop_backend_service(wait_service=True)
|
||||
|
||||
self._context.thread_pool.shutdown()
|
||||
|
||||
self._context.memory_store.close()
|
||||
|
||||
if self._context.monitor:
|
||||
self._context.monitor.close()
|
||||
|
||||
self.logger.close()
|
||||
|
||||
def __enter__(self):
|
||||
return self
|
||||
|
||||
def __exit__(self, exc_type, exc_val, exc_tb):
|
||||
if exc_type is not None:
|
||||
self.logger.warning(f"An exception occurred: {exc_type.__name__}: {exc_val}\n{exc_tb}")
|
||||
self.close()
|
||||
|
||||
@property
|
||||
def context(self):
|
||||
return self._context
|
||||
|
||||
@property
|
||||
def memory_chat_dict(self):
|
||||
return self._context.memory_chat_dict
|
||||
|
||||
@property
|
||||
def memory_service_dict(self):
|
||||
return self._context.memory_service_dict
|
||||
|
||||
@property
|
||||
def default_memory_chat(self) -> BaseMemoryChat:
|
||||
return list(self.memory_chat_dict.values())[0]
|
||||
|
||||
@property
|
||||
def default_memory_service(self) -> BaseMemoryService:
|
||||
return list(self.memory_service_dict.values())[0]
|
||||
|
||||
@classmethod
|
||||
def cli_memory_chat(cls, **kwargs):
|
||||
with cls(**kwargs) as ms:
|
||||
memory_chat = ms.default_memory_chat
|
||||
memory_chat.run()
|
||||
|
|
@ -1,53 +0,0 @@
|
|||
from concurrent.futures import ThreadPoolExecutor
|
||||
from dataclasses import dataclass, field
|
||||
|
||||
from memoryscope.enumeration.language_enum import LanguageEnum
|
||||
from memoryscope.core.utils.singleton import singleton
|
||||
|
||||
@singleton
|
||||
@dataclass
|
||||
class MemoryscopeContext(object):
|
||||
"""
|
||||
The context class archives all configs utilized by store, monitor, services and workers.
|
||||
"""
|
||||
|
||||
language: LanguageEnum = LanguageEnum.EN
|
||||
|
||||
thread_pool: ThreadPoolExecutor | None = None
|
||||
|
||||
memory_store = None
|
||||
|
||||
monitor = None
|
||||
|
||||
memory_chat_dict: dict = field(default_factory=lambda: {}, metadata={"help": "name -> memory_chat"})
|
||||
|
||||
memory_service_dict: dict = field(default_factory=lambda: {}, metadata={"help": "name -> memory_service"})
|
||||
|
||||
model_dict: dict = field(default_factory=lambda: {}, metadata={"help": "name -> model"})
|
||||
|
||||
worker_conf_dict: dict = field(default_factory=lambda: {}, metadata={"help": "name -> worker_conf"})
|
||||
|
||||
meta_data: dict = field(default_factory=lambda: {})
|
||||
|
||||
memory_scope_uuid: str = ""
|
||||
|
||||
print_workflow_dynamic: bool = False
|
||||
|
||||
log_elasticsearch_dynamic: bool = False
|
||||
|
||||
|
||||
def get_memoryscope_uuid():
|
||||
ms_context = MemoryscopeContext()
|
||||
if ms_context.memory_scope_uuid:
|
||||
return ms_context.memory_scope_uuid
|
||||
else:
|
||||
# raise RuntimeError("MemoryscopeContext is not initialized yet. Please initialize it first.")
|
||||
return "memory_scope_uuid_not_registered"
|
||||
|
||||
def get_memoryscope_context():
|
||||
ms_context = MemoryscopeContext()
|
||||
if ms_context.memory_scope_uuid:
|
||||
return ms_context
|
||||
else:
|
||||
# raise RuntimeError("MemoryscopeContext is not initialized yet. Please initialize it first.")
|
||||
return "memory_scope_uuid_not_registered"
|
||||
|
|
@ -1,13 +0,0 @@
|
|||
from .base_model import BaseModel
|
||||
from .dummy_generation_model import DummyGenerationModel
|
||||
from .llama_index_embedding_model import LlamaIndexEmbeddingModel
|
||||
from .llama_index_generation_model import LlamaIndexGenerationModel
|
||||
from .llama_index_rank_model import LlamaIndexRankModel
|
||||
|
||||
__all__ = [
|
||||
"BaseModel",
|
||||
"DummyGenerationModel",
|
||||
"LlamaIndexEmbeddingModel",
|
||||
"LlamaIndexGenerationModel",
|
||||
"LlamaIndexRankModel"
|
||||
]
|
||||
|
|
@ -1,121 +0,0 @@
|
|||
import inspect
|
||||
import time
|
||||
import os
|
||||
from abc import abstractmethod, ABCMeta
|
||||
from typing import Any
|
||||
|
||||
from memoryscope.core.utils.logger import Logger
|
||||
from memoryscope.core.utils.registry import Registry
|
||||
from memoryscope.core.utils.timer import Timer
|
||||
from memoryscope.enumeration.model_enum import ModelEnum
|
||||
from memoryscope.scheme.model_response import ModelResponse, ModelResponseGen
|
||||
from memoryscope.core.memoryscope_context import MemoryscopeContext
|
||||
from memoryscope.core.memoryscope_context import get_memoryscope_uuid
|
||||
|
||||
MODEL_REGISTRY = Registry("models")
|
||||
|
||||
|
||||
class BaseModel(metaclass=ABCMeta):
|
||||
m_type: ModelEnum | None = None
|
||||
|
||||
def __init__(self,
|
||||
model_name: str,
|
||||
module_name: str,
|
||||
timeout: int = None,
|
||||
max_retries: int = 3,
|
||||
retry_interval: float = 1.0,
|
||||
kwargs_filter: bool = True,
|
||||
raise_exception: bool = True,
|
||||
**kwargs):
|
||||
|
||||
self.model_name: str = model_name
|
||||
self.module_name: str = module_name
|
||||
self.timeout: int = timeout
|
||||
self.max_retries: int = max_retries
|
||||
self.retry_interval: float = retry_interval
|
||||
self.kwargs_filter: bool = kwargs_filter
|
||||
self.raise_exception: bool = raise_exception
|
||||
self.context: MemoryscopeContext = get_memoryscope_uuid()
|
||||
self.kwargs: dict = kwargs
|
||||
|
||||
self._model: Any = None
|
||||
self.logger = Logger.get_logger("base_model")
|
||||
|
||||
@property
|
||||
def model(self):
|
||||
if self._model is None:
|
||||
if self.module_name not in MODEL_REGISTRY.module_dict:
|
||||
raise RuntimeError(f"method_type={self.module_name} is not supported!")
|
||||
obj_cls = MODEL_REGISTRY[self.module_name]
|
||||
if 'openai' in self.module_name:
|
||||
if os.environ.get('OPENAI_API_KEY', None) is None:
|
||||
raise ValueError("Missing openai api key!")
|
||||
|
||||
if self.kwargs_filter:
|
||||
allowed_kwargs = list(inspect.signature(obj_cls.__init__).parameters.keys())
|
||||
kwargs = {key: value for key, value in self.kwargs.items() if key in allowed_kwargs}
|
||||
else:
|
||||
kwargs = self.kwargs
|
||||
self._model = obj_cls(**kwargs)
|
||||
|
||||
return self._model
|
||||
|
||||
@abstractmethod
|
||||
def before_call(self, model_response: ModelResponse, **kwargs):
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def after_call(self, model_response: ModelResponse, **kwargs) -> ModelResponse | ModelResponseGen:
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def _call(self, model_response: ModelResponse, stream: bool = False, **kwargs):
|
||||
pass
|
||||
|
||||
def call(self, stream: bool = False, **kwargs) -> ModelResponse | ModelResponseGen:
|
||||
with Timer(self.__class__.__name__, time_log_type="none") as t:
|
||||
model_response = ModelResponse(m_type=self.m_type)
|
||||
|
||||
self.before_call(stream=stream, model_response=model_response, **kwargs)
|
||||
for i in range(self.max_retries):
|
||||
if self.raise_exception:
|
||||
self._call(stream=stream, model_response=model_response, **kwargs)
|
||||
else:
|
||||
try:
|
||||
self._call(stream=stream, model_response=model_response, **kwargs)
|
||||
except Exception as e:
|
||||
model_response.status = False
|
||||
model_response.details = e.args
|
||||
|
||||
if isinstance(model_response, ModelResponse) and not model_response.status:
|
||||
self.logger.warning(f"call model={self.model_name} failed! {t.cost_str} retry_cnt={i} "
|
||||
f"details={model_response.details}", stacklevel=2)
|
||||
time.sleep(i * self.retry_interval)
|
||||
else:
|
||||
return self.after_call(stream=stream, model_response=model_response, **kwargs)
|
||||
|
||||
@abstractmethod
|
||||
async def _async_call(self, model_response: ModelResponse, **kwargs) -> ModelResponse:
|
||||
pass
|
||||
|
||||
async def async_call(self, **kwargs) -> ModelResponse:
|
||||
with Timer(self.__class__.__name__, time_log_type="none") as t:
|
||||
model_response = ModelResponse(m_type=self.m_type)
|
||||
|
||||
self.before_call(model_response=model_response, **kwargs)
|
||||
for i in range(self.max_retries):
|
||||
if self.raise_exception:
|
||||
await self._async_call(model_response=model_response, **kwargs)
|
||||
else:
|
||||
try:
|
||||
await self._async_call(model_response=model_response, **kwargs)
|
||||
except Exception as e:
|
||||
model_response.status = False
|
||||
model_response.details = e.args
|
||||
|
||||
if not model_response.status:
|
||||
self.logger.warning(f"async_call model={self.model_name} failed! {t.cost_str} retry_cnt={i} "
|
||||
f"details={model_response.details}", stacklevel=2)
|
||||
time.sleep(i * self.retry_interval)
|
||||
else:
|
||||
return self.after_call(model_response=model_response, **kwargs)
|
||||
|
|
@ -1,93 +0,0 @@
|
|||
import time
|
||||
from typing import List
|
||||
|
||||
from llama_index.core.base.llms.types import ChatMessage
|
||||
|
||||
from memoryscope.core.models.base_model import BaseModel, MODEL_REGISTRY
|
||||
from memoryscope.enumeration.message_role_enum import MessageRoleEnum
|
||||
from memoryscope.enumeration.model_enum import ModelEnum
|
||||
from memoryscope.scheme.message import Message
|
||||
from memoryscope.scheme.model_response import ModelResponse, ModelResponseGen
|
||||
|
||||
|
||||
class DummyGenerationModel(BaseModel):
|
||||
"""
|
||||
The `DummyGenerationModel` class serves as a placeholder model for generating responses.
|
||||
It processes input prompts or sequences of messages, adapting them into a structure compatible
|
||||
with chat interfaces. It also facilitates the generation of mock (dummy) responses for testing,
|
||||
supporting both immediate and streamed output.
|
||||
"""
|
||||
m_type: ModelEnum = ModelEnum.GENERATION_MODEL
|
||||
|
||||
MODEL_REGISTRY.register("dummy_generation", object)
|
||||
|
||||
def before_call(self, model_response: ModelResponse, **kwargs):
|
||||
"""
|
||||
Prepares the input data before making a call to the language model.
|
||||
It accepts either a 'prompt' directly or a list of 'messages'.
|
||||
If 'prompt' is provided, it sets the data accordingly.
|
||||
If 'messages' are provided, it constructs a list of ChatMessage objects from the list.
|
||||
Raises an error if neither 'prompt' nor 'messages' are supplied.
|
||||
|
||||
Args:
|
||||
model_response: model_response
|
||||
**kwargs: Arbitrary keyword arguments including 'prompt' and 'messages'.
|
||||
|
||||
Raises:
|
||||
RuntimeError: When both 'prompt' and 'messages' inputs are not provided.
|
||||
"""
|
||||
prompt: str = kwargs.pop("prompt", "")
|
||||
messages: List[Message] | List[dict] = kwargs.pop("messages", [])
|
||||
|
||||
if prompt:
|
||||
data = {"prompt": prompt}
|
||||
elif messages:
|
||||
if isinstance(messages[0], dict):
|
||||
data = {"messages": [ChatMessage(role=msg["role"], content=msg["content"]) for msg in messages]}
|
||||
else:
|
||||
data = {"messages": [ChatMessage(role=msg.role, content=msg.content) for msg in messages]}
|
||||
else:
|
||||
raise RuntimeError("prompt and messages are both empty!")
|
||||
data.update(**kwargs)
|
||||
model_response.meta_data["data"] = data
|
||||
|
||||
def after_call(self,
|
||||
model_response: ModelResponse,
|
||||
stream: bool = False,
|
||||
**kwargs) -> ModelResponse | ModelResponseGen:
|
||||
"""
|
||||
Processes the model's response post-call, optionally streaming the output or returning it as a whole.
|
||||
|
||||
This method modifies the input `model_response` by resetting its message content and, based on the `stream`
|
||||
parameter, either yields the response in a generated stream or returns the complete response directly.
|
||||
|
||||
Args:
|
||||
model_response (ModelResponse): The initial response object to be processed.
|
||||
stream (bool, optional): Flag indicating whether to stream the response. Defaults to False.
|
||||
**kwargs: Additional keyword arguments (not used in this implementation).
|
||||
|
||||
Returns:
|
||||
ModelResponse | ModelResponseGen: If `stream` is True, a generator yielding updated `ModelResponse` objects;
|
||||
otherwise, a modified `ModelResponse` object with the complete content.
|
||||
"""
|
||||
model_response.message = Message(role=MessageRoleEnum.ASSISTANT, content="")
|
||||
|
||||
call_result = ["-" for _ in range(10)]
|
||||
if stream:
|
||||
def gen() -> ModelResponseGen:
|
||||
for delta in call_result:
|
||||
model_response.message.content += delta
|
||||
model_response.delta = delta
|
||||
time.sleep(0.1)
|
||||
yield model_response
|
||||
|
||||
return gen()
|
||||
else:
|
||||
model_response.message.content = "".join(call_result)
|
||||
return model_response
|
||||
|
||||
def _call(self, model_response: ModelResponse, stream: bool = False, **kwargs):
|
||||
return model_response
|
||||
|
||||
async def _async_call(self, model_response: ModelResponse, **kwargs):
|
||||
return model_response
|
||||
|
|
@ -1,87 +0,0 @@
|
|||
from typing import List
|
||||
|
||||
from llama_index.embeddings.dashscope import DashScopeEmbedding
|
||||
from llama_index.embeddings.openai import OpenAIEmbedding
|
||||
|
||||
from memoryscope.core.models.base_model import BaseModel, MODEL_REGISTRY
|
||||
from memoryscope.enumeration.model_enum import ModelEnum
|
||||
from memoryscope.scheme.model_response import ModelResponse
|
||||
from memoryscope.core.utils.logger import Logger
|
||||
|
||||
|
||||
class LlamaIndexEmbeddingModel(BaseModel):
|
||||
"""
|
||||
Manages text embeddings utilizing the DashScopeEmbedding within the LlamaIndex framework,
|
||||
facilitating embedding operations for both sync and async modes, inheriting from BaseModel.
|
||||
"""
|
||||
m_type: ModelEnum = ModelEnum.EMBEDDING_MODEL
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
self.logger = Logger.get_logger("llama_index_embedding_model")
|
||||
|
||||
@classmethod
|
||||
def register_model(cls, model_name: str, model_class: type):
|
||||
"""
|
||||
Registers a new embedding model class with the model registry.
|
||||
|
||||
Args:
|
||||
model_name (str): The name to register the model under.
|
||||
model_class (type): The class of the model to register.
|
||||
"""
|
||||
MODEL_REGISTRY.register(model_name, model_class)
|
||||
|
||||
MODEL_REGISTRY.register("dashscope_embedding", DashScopeEmbedding)
|
||||
MODEL_REGISTRY.register("openai_embedding", OpenAIEmbedding)
|
||||
|
||||
def before_call(self, model_response: ModelResponse, **kwargs):
|
||||
text: str | List[str] = kwargs.pop("text", "")
|
||||
if isinstance(text, str):
|
||||
text = [text]
|
||||
model_response.meta_data["data"] = dict(texts=text)
|
||||
self.logger.info("Embedding Model:\n" + text[0])
|
||||
|
||||
def after_call(self, model_response: ModelResponse, **kwargs) -> ModelResponse:
|
||||
embeddings = model_response.raw
|
||||
if not embeddings:
|
||||
model_response.details = "empty embeddings"
|
||||
model_response.status = False
|
||||
return model_response
|
||||
if len(embeddings) == 1:
|
||||
# return list[float]
|
||||
embeddings = embeddings[0]
|
||||
|
||||
model_response.embedding_results = embeddings
|
||||
return model_response
|
||||
|
||||
def _call(self, model_response: ModelResponse, **kwargs):
|
||||
"""
|
||||
Executes a synchronous call to generate embeddings for the input data.
|
||||
|
||||
This method utilizes the `get_text_embedding_batch` method of the encapsulated model,
|
||||
passing the processed data from `self.data`. The result is then packaged into a
|
||||
`ModelResponse` object with the model type specified by `self.m_type`.
|
||||
|
||||
Args:
|
||||
**kwargs: Additional keyword arguments that might be used in the embedding process.
|
||||
|
||||
Returns:
|
||||
ModelResponse: An object containing the embedding results and the model type.
|
||||
"""
|
||||
model_response.raw = self.model.get_text_embedding_batch(**model_response.meta_data["data"])
|
||||
|
||||
async def _async_call(self, model_response: ModelResponse, **kwargs):
|
||||
"""
|
||||
Executes an asynchronous call to generate embeddings for the input data.
|
||||
|
||||
Similar to `_call`, but uses the asynchronous `aget_text_embedding_batch` method
|
||||
of the model. It handles the input data asynchronously and packages the result
|
||||
within a `ModelResponse` instance.
|
||||
|
||||
Args:
|
||||
**kwargs: Additional keyword arguments for the embedding process, if any.
|
||||
|
||||
Returns:
|
||||
ModelResponse: An object encapsulating the embedding output and the model's type.
|
||||
"""
|
||||
model_response.raw = await self.model.aget_text_embedding_batch(**model_response.meta_data["data"])
|
||||
|
|
@ -1,128 +0,0 @@
|
|||
from typing import List
|
||||
|
||||
from llama_index.core.base.llms.types import ChatMessage, ChatResponse, CompletionResponse
|
||||
from llama_index.llms.dashscope import DashScope
|
||||
from llama_index.llms.openai import OpenAI
|
||||
|
||||
from memoryscope.core.models.base_model import BaseModel, MODEL_REGISTRY
|
||||
from memoryscope.enumeration.message_role_enum import MessageRoleEnum
|
||||
from memoryscope.enumeration.model_enum import ModelEnum
|
||||
from memoryscope.scheme.message import Message
|
||||
from memoryscope.scheme.model_response import ModelResponse, ModelResponseGen
|
||||
from memoryscope.core.utils.logger import Logger
|
||||
|
||||
|
||||
class LlamaIndexGenerationModel(BaseModel):
|
||||
"""
|
||||
This class represents a generation model within the LlamaIndex framework,
|
||||
capable of processing input prompts or message histories, selecting an appropriate
|
||||
language model service from a registry, and generating text responses, with support
|
||||
for both streaming and non-streaming modes. It encapsulates logic for formatting
|
||||
these interactions within the context of a memory scope management system.
|
||||
"""
|
||||
|
||||
m_type: ModelEnum = ModelEnum.GENERATION_MODEL
|
||||
|
||||
MODEL_REGISTRY.register("dashscope_generation", DashScope)
|
||||
MODEL_REGISTRY.register("openai_generation", OpenAI)
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
self.logger = Logger.get_logger("llama_index_generation_model")
|
||||
|
||||
def before_call(self, model_response: ModelResponse, **kwargs):
|
||||
"""
|
||||
Prepares the input data before making a call to the language model.
|
||||
It accepts either a 'prompt' directly or a list of 'messages'.
|
||||
If 'prompt' is provided, it sets the data accordingly.
|
||||
If 'messages' are provided, it constructs a list of ChatMessage objects from the list.
|
||||
Raises an error if neither 'prompt' nor 'messages' are supplied.
|
||||
|
||||
Args:
|
||||
model_response: model_response
|
||||
**kwargs: Arbitrary keyword arguments including 'prompt' and 'messages'.
|
||||
|
||||
Raises:
|
||||
RuntimeError: When both 'prompt' and 'messages' inputs are not provided.
|
||||
"""
|
||||
prompt: str = kwargs.pop("prompt", "")
|
||||
messages: List[Message] | List[dict] = kwargs.pop("messages", [])
|
||||
|
||||
if prompt:
|
||||
data = {"prompt": prompt}
|
||||
elif messages:
|
||||
if isinstance(messages[0], dict):
|
||||
data = {"messages": [ChatMessage(role=msg["role"], content=msg["content"]) for msg in messages]}
|
||||
else:
|
||||
data = {"messages": [ChatMessage(role=msg.role, content=msg.content) for msg in messages]}
|
||||
else:
|
||||
raise RuntimeError("prompt and messages are both empty!")
|
||||
data.update(**kwargs)
|
||||
model_response.meta_data["data"] = data
|
||||
|
||||
def after_call(self,
|
||||
model_response: ModelResponse,
|
||||
stream: bool = False,
|
||||
**kwargs) -> ModelResponse | ModelResponseGen:
|
||||
model_response.message = Message(role=MessageRoleEnum.ASSISTANT, content="")
|
||||
|
||||
call_result = model_response.raw
|
||||
if stream:
|
||||
def gen() -> ModelResponseGen:
|
||||
for response in call_result:
|
||||
delta = response.delta if response.delta else ""
|
||||
model_response.message.content += delta
|
||||
model_response.delta = response.delta
|
||||
yield model_response
|
||||
self.logger.info(self.logger.format_chat_message(model_response))
|
||||
return gen()
|
||||
else:
|
||||
if isinstance(call_result, CompletionResponse):
|
||||
model_response.message.content = call_result.text
|
||||
elif isinstance(call_result, ChatResponse):
|
||||
model_response.message.content = call_result.message.content
|
||||
else:
|
||||
raise NotImplementedError
|
||||
self.logger.info(self.logger.format_chat_message(model_response))
|
||||
return model_response
|
||||
|
||||
def _call(self, model_response: ModelResponse, stream: bool = False, **kwargs):
|
||||
data = model_response.meta_data["data"]
|
||||
# FIXME: special case for OpenAI model, is this necessary?
|
||||
data.pop("stream")
|
||||
if "prompt" in data:
|
||||
if stream:
|
||||
model_response.raw = self.model.stream_complete(**data)
|
||||
else:
|
||||
model_response.raw = self.model.complete(**data)
|
||||
elif "messages" in data:
|
||||
if stream:
|
||||
model_response.raw = self.model.stream_chat(**data)
|
||||
else:
|
||||
model_response.raw = self.model.chat(**data)
|
||||
else:
|
||||
raise RuntimeError("prompt or messages is missing!")
|
||||
|
||||
async def _async_call(self, model_response: ModelResponse, **kwargs):
|
||||
"""
|
||||
Asynchronously calls the language model with the provided prompt or message history,
|
||||
and packages the raw response into a ModelResponse object.
|
||||
|
||||
This method checks if the input data contains a 'prompt' or 'messages' key to decide
|
||||
which method to call on the model instance. It uses 'acomplete' for simple prompts and
|
||||
'achat' for chat-based message histories.
|
||||
|
||||
Args:
|
||||
**kwargs: Additional keyword arguments that might be used in the model call.
|
||||
|
||||
Returns:
|
||||
ModelResponse: An object containing the raw response from the language model.
|
||||
"""
|
||||
data = model_response.meta_data["data"]
|
||||
|
||||
if "prompt" in data:
|
||||
model_response.raw = await self.model.acomplete(**data)
|
||||
elif "messages" in data:
|
||||
model_response.raw = await self.model.achat(**data)
|
||||
else:
|
||||
raise RuntimeError("prompt or messages is missing!")
|
||||
|
|
@ -1,101 +0,0 @@
|
|||
from typing import List
|
||||
|
||||
from llama_index.core.data_structs import Node
|
||||
from llama_index.core.schema import NodeWithScore
|
||||
from llama_index.postprocessor.dashscope_rerank import DashScopeRerank
|
||||
|
||||
from memoryscope.core.models.base_model import BaseModel, MODEL_REGISTRY
|
||||
from memoryscope.enumeration.model_enum import ModelEnum
|
||||
from memoryscope.scheme.model_response import ModelResponse
|
||||
from memoryscope.core.utils.logger import Logger
|
||||
|
||||
|
||||
class LlamaIndexRankModel(BaseModel):
|
||||
"""
|
||||
The LlamaIndexRankModel class is designed to rerank documents according to their relevance
|
||||
to a provided query, utilizing the DashScope Rerank model. It transforms document lists
|
||||
and queries into a compatible format for ranking, manages the ranking process, and allocates
|
||||
rank scores to individual documents.
|
||||
"""
|
||||
m_type: ModelEnum = ModelEnum.RANK_MODEL
|
||||
|
||||
MODEL_REGISTRY.register("dashscope_rank", DashScopeRerank)
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
self.logger = Logger.get_logger("llama_index_rank_model")
|
||||
|
||||
def before_call(self, model_response: ModelResponse, **kwargs):
|
||||
"""
|
||||
Prepares necessary data before the ranking call by extracting the query and documents,
|
||||
ensuring they are valid, and initializing nodes with dummy scores.
|
||||
|
||||
Args:
|
||||
model_response: model response
|
||||
**kwargs: Keyword arguments containing 'query' and 'documents'.
|
||||
"""
|
||||
query: str = kwargs.pop("query", "")
|
||||
documents: List[str] = kwargs.pop("documents", [])
|
||||
if isinstance(documents, str):
|
||||
documents = [documents]
|
||||
assert query and documents and all(documents), \
|
||||
f"query or documents is empty! query={query}, documents={len(documents)}"
|
||||
assert len(documents) < 500, \
|
||||
"The input documents of Dashscope rerank model should not larger than 500!"
|
||||
# Using -1.0 as dummy scores
|
||||
nodes = [NodeWithScore(node=Node(text=doc), score=-1.0) for doc in documents]
|
||||
|
||||
model_response.meta_data.update({
|
||||
"data": {"nodes": nodes, "query_str": query, "top_n": len(documents)},
|
||||
"documents_map": {doc: idx for idx, doc in enumerate(documents)},
|
||||
})
|
||||
|
||||
def after_call(self, model_response: ModelResponse, **kwargs) -> ModelResponse:
|
||||
"""
|
||||
Processes the model response post-ranking, assigning calculated rank scores to each document
|
||||
based on their index in the original document list.
|
||||
|
||||
Args:
|
||||
model_response (ModelResponse): The initial response from the ranking model.
|
||||
**kwargs: Additional keyword arguments (unused).
|
||||
|
||||
Returns:
|
||||
ModelResponse: Updated response with rank scores assigned to documents.
|
||||
"""
|
||||
if not model_response.rank_scores:
|
||||
model_response.rank_scores = {}
|
||||
|
||||
documents_map = model_response.meta_data["documents_map"]
|
||||
for node in model_response.raw:
|
||||
text = node.node.text
|
||||
idx = documents_map[text]
|
||||
model_response.rank_scores[idx] = node.score
|
||||
|
||||
self.logger.info(self.logger.format_rank_message(model_response))
|
||||
return model_response
|
||||
|
||||
def _call(self, model_response: ModelResponse, **kwargs):
|
||||
"""
|
||||
Executes the ranking process by passing prepared data to the model's postprocessing method.
|
||||
|
||||
Args:
|
||||
**kwargs: Keyword arguments (unused).
|
||||
|
||||
Returns:
|
||||
ModelResponse: A response object encapsulating the ranked nodes.
|
||||
"""
|
||||
self.model.top_n = model_response.meta_data["data"]["top_n"]
|
||||
model_response.meta_data["data"].pop("top_n")
|
||||
model_response.raw = self.model.postprocess_nodes(**model_response.meta_data["data"])
|
||||
|
||||
async def _async_call(self, **kwargs) -> ModelResponse:
|
||||
"""
|
||||
Asynchronous wrapper for the `_call` method, maintaining the same functionality.
|
||||
|
||||
Args:
|
||||
**kwargs: Keyword arguments (unused).
|
||||
|
||||
Returns:
|
||||
ModelResponse: A response object encapsulating the ranked nodes.
|
||||
"""
|
||||
raise NotImplementedError
|
||||
|
|
@ -1,13 +0,0 @@
|
|||
from .backend_operation import BackendOperation
|
||||
from .base_operation import BaseOperation
|
||||
from .base_workflow import BaseWorkflow
|
||||
from .consolidate_memory_op import ConsolidateMemoryOp
|
||||
from .frontend_operation import FrontendOperation
|
||||
|
||||
__all__ = [
|
||||
"BackendOperation",
|
||||
"BaseOperation",
|
||||
"BaseWorkflow",
|
||||
"ConsolidateMemoryOp",
|
||||
"FrontendOperation"
|
||||
]
|
||||
|
|
@ -1,83 +0,0 @@
|
|||
import time
|
||||
import threading
|
||||
|
||||
from memoryscope.core.operation.base_operation import OPERATION_TYPE
|
||||
from memoryscope.core.operation.frontend_operation import FrontendOperation
|
||||
|
||||
|
||||
class BackendOperation(FrontendOperation):
|
||||
"""
|
||||
BaseBackendOperation serves as an abstract base class for defining backend operations.
|
||||
It manages operation status, loop control, and integrates with a global context for thread management.
|
||||
"""
|
||||
operation_type: OPERATION_TYPE = "backend"
|
||||
|
||||
def __init__(self, interval_time: int, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self._interval_time: int = interval_time
|
||||
|
||||
self._operation_status_run: bool = False
|
||||
self._loop_switch: bool = False
|
||||
self._backend_task = None
|
||||
|
||||
def init_workflow(self, **kwargs):
|
||||
"""
|
||||
Initializes the workflow by setting up workers with provided keyword arguments.
|
||||
|
||||
Args:
|
||||
**kwargs: Arbitrary keyword arguments to be passed during worker initialization.
|
||||
"""
|
||||
self.init_workers(is_backend=True, **kwargs)
|
||||
|
||||
def _loop_operation(self, **kwargs):
|
||||
"""
|
||||
Loops until _loop_switch is False, sleeping for 1 second in each interval.
|
||||
At each interval, it checks if _loop_switch is still True, and if so, executes the operation.
|
||||
"""
|
||||
while self._loop_switch:
|
||||
for _ in range(self._interval_time):
|
||||
if self._loop_switch:
|
||||
time.sleep(1)
|
||||
else:
|
||||
break
|
||||
|
||||
if self._loop_switch:
|
||||
if self._operation_status_run:
|
||||
continue
|
||||
|
||||
self._operation_status_run = True
|
||||
|
||||
if len(self.target_names) > 1:
|
||||
self.logger.warning("current version is not stable under target_names.size > 1!")
|
||||
|
||||
for target_name in self.target_names:
|
||||
try:
|
||||
self.run_operation(target_name=target_name, **kwargs)
|
||||
except Exception as e:
|
||||
self.logger.exception(f"op_name={self.name} target_name={target_name} encounter exception. "
|
||||
f"args={e.args}")
|
||||
|
||||
self._operation_status_run = False
|
||||
|
||||
def start_operation_backend(self, **kwargs):
|
||||
"""
|
||||
Initiates the background operation loop if it's not already running.
|
||||
Sets the _loop_switch to True and submits the _loop_operation to a thread from the global thread pool.
|
||||
"""
|
||||
if not self._loop_switch:
|
||||
self._loop_switch = True
|
||||
self._backend_task = self.thread_pool.submit(self._loop_operation, **kwargs)
|
||||
self.logger.info(f"start operation={self.name}...")
|
||||
|
||||
def stop_operation_backend(self, wait_operation: bool = False):
|
||||
"""
|
||||
Stops the background operation loop by setting the _loop_switch to False.
|
||||
"""
|
||||
self._loop_switch = False
|
||||
if self._backend_task:
|
||||
if wait_operation:
|
||||
self._backend_task.result()
|
||||
self.logger.info(f"stop operation={self.name}...")
|
||||
else:
|
||||
self.logger.info(f"send stop signal to operation={self.name}...")
|
||||
|
|
@ -1,72 +0,0 @@
|
|||
from abc import ABCMeta, abstractmethod
|
||||
from typing import Literal, List
|
||||
|
||||
from memoryscope.scheme.message import Message
|
||||
|
||||
OPERATION_TYPE = Literal["frontend", "backend"]
|
||||
|
||||
|
||||
class BaseOperation(metaclass=ABCMeta):
|
||||
"""
|
||||
An abstract base class representing an operation that can be categorized as either frontend or backend.
|
||||
|
||||
Attributes:
|
||||
operation_type (OPERATION_TYPE): Specifies the type of operation, defaulting to "frontend".
|
||||
name (str): The name of the operation.
|
||||
description (str): A description of the operation.
|
||||
"""
|
||||
|
||||
operation_type: OPERATION_TYPE = "frontend"
|
||||
|
||||
def __init__(self,
|
||||
name: str,
|
||||
user_name: str,
|
||||
target_names: List[str],
|
||||
chat_messages: List[List[Message]],
|
||||
description: str):
|
||||
"""
|
||||
Initializes a new instance of the BaseOperation.
|
||||
"""
|
||||
self.name: str = name
|
||||
self.user_name: str = user_name
|
||||
self.target_names: List[str] = target_names
|
||||
self.chat_messages: List[List[Message]] = chat_messages
|
||||
self.description: str = description
|
||||
|
||||
def init_workflow(self, **kwargs):
|
||||
"""
|
||||
Initialize the workflow with additional keyword arguments if needed.
|
||||
|
||||
Args:
|
||||
**kwargs: Additional parameters for initializing the workflow.
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def run_operation(self, target_name: str, **kwargs):
|
||||
"""
|
||||
Abstract method to define the operation to be run.
|
||||
Subclasses must implement this method.
|
||||
|
||||
Args:
|
||||
target_name (str): target_name(human name).
|
||||
**kwargs: Keyword arguments for running the operation.
|
||||
|
||||
Raises:
|
||||
NotImplementedError: If the subclass does not implement this method.
|
||||
"""
|
||||
raise NotImplementedError
|
||||
|
||||
def start_operation_backend(self, **kwargs):
|
||||
"""
|
||||
Placeholder method for running an operation specific to the backend.
|
||||
Intended to be overridden by subclasses if backend operations are required.
|
||||
"""
|
||||
pass
|
||||
|
||||
def stop_operation_backend(self, wait_operation: bool = False):
|
||||
"""
|
||||
Placeholder method to stop any ongoing backend operations.
|
||||
Should be implemented in subclasses where backend operations are managed.
|
||||
"""
|
||||
pass
|
||||
|
|
@ -1,207 +0,0 @@
|
|||
import re
|
||||
import threading
|
||||
from concurrent.futures import ThreadPoolExecutor, as_completed
|
||||
from itertools import zip_longest
|
||||
from typing import Dict, Any, List
|
||||
from rich.console import Console
|
||||
|
||||
from memoryscope.constants.common_constants import WORKFLOW_NAME
|
||||
from memoryscope.core.memoryscope_context import MemoryscopeContext
|
||||
from memoryscope.core.utils.logger import Logger
|
||||
from memoryscope.core.utils.timer import Timer
|
||||
from memoryscope.core.utils.tool_functions import init_instance_by_config
|
||||
from memoryscope.core.worker.base_worker import BaseWorker
|
||||
|
||||
class BaseWorkflow(object):
|
||||
|
||||
def __init__(self,
|
||||
name: str,
|
||||
memoryscope_context: MemoryscopeContext,
|
||||
workflow: str = "",
|
||||
**kwargs):
|
||||
|
||||
self.name: str = name
|
||||
self.memoryscope_context: MemoryscopeContext = memoryscope_context
|
||||
self.thread_pool: ThreadPoolExecutor = self.memoryscope_context.thread_pool
|
||||
self.workflow: str = workflow
|
||||
self.kwargs = kwargs
|
||||
|
||||
self.workflow_worker_list: List[List[List[str]]] = []
|
||||
self.worker_dict: Dict[str, BaseWorker | bool] = {}
|
||||
self.workflow_context: Dict[str, Any] = {}
|
||||
self.context_lock = threading.Lock()
|
||||
|
||||
self.logger: Logger = Logger.get_logger("workflow")
|
||||
|
||||
if self.workflow:
|
||||
self.workflow_worker_list = self._parse_workflow()
|
||||
self._print_workflow()
|
||||
|
||||
def workflow_print_console(self, *args, **kwargs):
|
||||
if self.memoryscope_context.print_workflow_dynamic:
|
||||
Console().print(*args, **kwargs)
|
||||
return
|
||||
|
||||
def _parse_workflow(self):
|
||||
"""
|
||||
Parses the workflow string to configure worker threads and organizes them into execution order.
|
||||
|
||||
The workflow string format supports complex configurations with optional multi-threading indications.
|
||||
E.g., `[task1,task2|task3],task4` denotes task1 and task2 can run in parallel to task3, followed by task4.
|
||||
|
||||
Returns:
|
||||
List[List[List[str]]]: A nested list representing the execution plan, including parallel groups and tasks.
|
||||
"""
|
||||
# Regular expression to match components of the workflow, handling both plain items and grouped items.
|
||||
pattern = r"(\[[^\]]*\]|[^,]+)"
|
||||
# Find all matches in the workflow string based on the pattern.
|
||||
workflow_split = re.findall(pattern, self.workflow)
|
||||
|
||||
for workflow_part in workflow_split:
|
||||
# e.g., [d,e,f|g,h]
|
||||
workflow_part = workflow_part.strip()
|
||||
if '[' in workflow_part or ']' in workflow_part:
|
||||
workflow_part = workflow_part.replace('[', '').replace(']', '')
|
||||
|
||||
# Split the part by '|' to identify potential parallel task groups.
|
||||
line_split = [x.strip() for x in workflow_part.split("|") if x]
|
||||
|
||||
# Skip if no valid tasks are identified after splitting.
|
||||
if len(line_split) <= 0:
|
||||
continue
|
||||
|
||||
# Determine if the current part involves multi-threading based on the number of groups.
|
||||
is_multi_thread: bool = len(line_split) > 1
|
||||
|
||||
# e.g., ["d","e","f"]
|
||||
line_split_split: List[List[str]] = []
|
||||
for sub_line_split in line_split:
|
||||
sub_split = [x.strip() for x in sub_line_split.split(",")]
|
||||
line_split_split.append(sub_split)
|
||||
# add workers
|
||||
for sub_item in sub_split:
|
||||
self.worker_dict[sub_item] = is_multi_thread
|
||||
|
||||
# Append the parsed and structured tasks to the workflow execution plan.
|
||||
self.workflow_worker_list.append(line_split_split)
|
||||
|
||||
# Return the fully constructed workflow execution plan.
|
||||
return self.workflow_worker_list
|
||||
|
||||
def _print_workflow(self):
|
||||
"""
|
||||
Prints the workflow stages in a structured format. Each stage of the workflow
|
||||
is detailed with its constituent parts, either single elements or grouped
|
||||
elements separated by ' | '.
|
||||
|
||||
The method iterates over the workflow parts, handling both singular steps
|
||||
and parallel steps (where elements are zipped together).
|
||||
"""
|
||||
self.logger.info(f"----- workflow.{self.name}.print.begin -----")
|
||||
i: int = 0
|
||||
for workflow_part in self.workflow_worker_list:
|
||||
if len(workflow_part) == 1:
|
||||
# Handles workflow parts with single elements
|
||||
for w in workflow_part[0]:
|
||||
self.logger.info(f"stage{i}: {w}")
|
||||
i += 1
|
||||
else:
|
||||
# Handles workflow parts with multiple parallel elements (zipped)
|
||||
for w_zip in zip_longest(*workflow_part, fillvalue="-"):
|
||||
self.logger.info(f"stage{i}: {' | '.join(w_zip)}")
|
||||
i += 1
|
||||
# Skips placeholder '-' used for uneven lists in zip_longest
|
||||
for w in w_zip:
|
||||
if w == "-":
|
||||
continue
|
||||
self.logger.info(f"----- workflow.{self.name}.print.end -----")
|
||||
|
||||
def init_workers(self, is_backend: bool = False, **kwargs):
|
||||
"""
|
||||
Initializes worker instances based on the configuration for each worker defined in `G_CONTEXT.worker_config`.
|
||||
Each worker can be set to run in a multithreaded mode depending on the `is_backend` flag or the worker's
|
||||
individual configuration.
|
||||
|
||||
Args:
|
||||
is_backend (bool, optional): A flag indicating whether the workers should be initialized in a
|
||||
backend context. Defaults to False.
|
||||
**kwargs: Additional keyword arguments to be passed during worker initialization.
|
||||
|
||||
Raises:
|
||||
RuntimeError: If a worker mentioned in `self.worker_dict` does not exist in `G_CONTEXT.worker_config`.
|
||||
|
||||
Note:
|
||||
This method modifies `self.worker_dict` in-place, replacing the keys with actual worker instances.
|
||||
"""
|
||||
for name in list(self.worker_dict.keys()):
|
||||
if name not in self.memoryscope_context.worker_conf_dict:
|
||||
raise RuntimeError(f"worker={name} is not exists in worker config!")
|
||||
|
||||
# note: shared context object in all workers
|
||||
self.worker_dict[name] = init_instance_by_config(
|
||||
config=self.memoryscope_context.worker_conf_dict[name],
|
||||
name=name,
|
||||
is_multi_thread=is_backend or self.worker_dict[name],
|
||||
context=self.workflow_context,
|
||||
memoryscope_context=self.memoryscope_context,
|
||||
context_lock=self.context_lock,
|
||||
thread_pool=self.thread_pool,
|
||||
**kwargs)
|
||||
|
||||
def _run_sub_workflow(self, worker_list: List[str]) -> bool:
|
||||
for name in worker_list:
|
||||
worker = self.worker_dict[name]
|
||||
worker.run()
|
||||
if not worker.continue_run:
|
||||
self.logger.warning(f"worker={worker.name} stop workflow!")
|
||||
return False
|
||||
return True
|
||||
|
||||
def run_workflow(self, **kwargs):
|
||||
"""
|
||||
Executes the workflow by orchestrating the steps defined in `self.workflow_worker_list`.
|
||||
This method supports both sequential and parallel execution of sub-workflows based on the structure
|
||||
of `self.workflow_worker_list`.
|
||||
|
||||
If a workflow part consists of a single item, it is executed sequentially. For parts with multiple items,
|
||||
they are submitted for parallel execution using a thread pool. The workflow will stop if any sub-workflow
|
||||
returns False.
|
||||
|
||||
Args:
|
||||
**kwargs: Additional keyword arguments to be passed to context.
|
||||
"""
|
||||
with Timer(f"workflow.{self.name}", time_log_type="wrap"):
|
||||
log_buf = f"Operation: {self.name}"
|
||||
self.logger.info(log_buf)
|
||||
self.workflow_print_console(log_buf, style="bold red")
|
||||
self.workflow_context.clear()
|
||||
self.workflow_context.update({WORKFLOW_NAME: self.name, **kwargs})
|
||||
n_stage = len(self.workflow_worker_list)
|
||||
# Iterate over each part of the workflow
|
||||
for index, workflow_part in enumerate(self.workflow_worker_list):
|
||||
# self.logger.info(self.logger.format_current_context(self.workflow_context))
|
||||
# Sequential execution for single-item parts
|
||||
if len(workflow_part) == 1:
|
||||
log_buf = f"\t- Operation: {self.name} | {index+1}/{n_stage}: {workflow_part[0]}"
|
||||
self.logger.info(log_buf)
|
||||
self.workflow_print_console(log_buf, style="bold red")
|
||||
if not self._run_sub_workflow(workflow_part[0]):
|
||||
break
|
||||
# Parallel execution for multi-item parts
|
||||
else:
|
||||
t_list = []
|
||||
# Submit tasks to the thread pool
|
||||
n_sub_stage = len(workflow_part)
|
||||
for sub_index, sub_workflow in enumerate(workflow_part):
|
||||
log_buf = f"\t- Operation: {self.name} | {index+1}/{n_stage} | sub workflow {sub_index+1}/{n_sub_stage}: {str(sub_workflow)}"
|
||||
self.logger.info(log_buf)
|
||||
self.workflow_print_console(log_buf, style="red")
|
||||
t_list.append(self.thread_pool.submit(self._run_sub_workflow, sub_workflow))
|
||||
|
||||
# Check results; if any task returns False, stop the workflow
|
||||
flag = True
|
||||
for future in as_completed(t_list):
|
||||
if not future.result():
|
||||
flag = False
|
||||
if not flag:
|
||||
break
|
||||
|
|
@ -1,81 +0,0 @@
|
|||
from typing import List
|
||||
|
||||
from memoryscope.constants.common_constants import CHAT_KWARGS, CHAT_MESSAGES, RESULT, TARGET_NAME, USER_NAME
|
||||
from memoryscope.core.operation.backend_operation import BackendOperation
|
||||
from memoryscope.scheme.message import Message
|
||||
|
||||
|
||||
class ConsolidateMemoryOp(BackendOperation):
|
||||
|
||||
def __init__(self,
|
||||
message_lock,
|
||||
contextual_msg_min_count: int = 0,
|
||||
**kwargs):
|
||||
super().__init__(**kwargs)
|
||||
self.message_lock = message_lock
|
||||
self.contextual_msg_min_count: int = contextual_msg_min_count
|
||||
|
||||
def run_operation(self, target_name: str, **kwargs):
|
||||
"""
|
||||
Executes an operation after preparing the chat context, checking message memory status,
|
||||
and updating workflow status accordingly.
|
||||
|
||||
If the number of not-memorized messages is less than the contextual message count,
|
||||
the operation is skipped. Otherwise, it sets up the chat context, runs the workflow,
|
||||
captures the result, and updates the memory status.
|
||||
|
||||
Args:
|
||||
target_name (str): target_name(human name).
|
||||
**kwargs: Keyword arguments for chat operation configuration.
|
||||
|
||||
Returns:
|
||||
Any: The result obtained from running the workflow.
|
||||
"""
|
||||
|
||||
chat_messages: List[List[Message]] = []
|
||||
for messages in self.chat_messages:
|
||||
if not messages:
|
||||
continue
|
||||
|
||||
if messages[0].memorized:
|
||||
continue
|
||||
|
||||
contain_flag = False
|
||||
|
||||
for msg in messages:
|
||||
if msg.role_name == target_name:
|
||||
contain_flag = True
|
||||
break
|
||||
|
||||
if contain_flag:
|
||||
chat_messages.append(messages)
|
||||
|
||||
if not chat_messages:
|
||||
self.logger.info(f"empty not_memorized chat_messages for target_name={target_name}.")
|
||||
return
|
||||
|
||||
if len(chat_messages) < self.contextual_msg_min_count:
|
||||
self.logger.info(f"not_memorized_size={len(chat_messages)} < {self.contextual_msg_min_count}, skip.")
|
||||
return
|
||||
|
||||
# prepare kwargs
|
||||
workflow_kwargs = {
|
||||
CHAT_MESSAGES: chat_messages,
|
||||
CHAT_KWARGS: {**kwargs, **self.kwargs},
|
||||
TARGET_NAME: target_name,
|
||||
USER_NAME: self.user_name,
|
||||
}
|
||||
|
||||
# Execute the workflow with the prepared context
|
||||
self.run_workflow(**workflow_kwargs)
|
||||
|
||||
# Retrieve the result from the context after workflow execution
|
||||
result = self.workflow_context.get(RESULT)
|
||||
|
||||
# set message memorized
|
||||
with self.message_lock:
|
||||
for messages in chat_messages:
|
||||
for msg in messages:
|
||||
msg.memorized = True
|
||||
|
||||
return result
|
||||
|
|
@ -1,61 +0,0 @@
|
|||
from typing import List
|
||||
|
||||
from memoryscope.constants.common_constants import RESULT, CHAT_MESSAGES, CHAT_KWARGS, TARGET_NAME, USER_NAME
|
||||
from memoryscope.core.operation.base_operation import BaseOperation, OPERATION_TYPE
|
||||
from memoryscope.core.operation.base_workflow import BaseWorkflow
|
||||
from memoryscope.scheme.message import Message
|
||||
|
||||
|
||||
class FrontendOperation(BaseWorkflow, BaseOperation):
|
||||
operation_type: OPERATION_TYPE = "frontend"
|
||||
|
||||
def __init__(self,
|
||||
name: str,
|
||||
user_name: str,
|
||||
target_names: List[str],
|
||||
chat_messages: List[List[Message]],
|
||||
description: str,
|
||||
**kwargs):
|
||||
super().__init__(name=name, **kwargs)
|
||||
BaseOperation.__init__(self,
|
||||
name=name,
|
||||
user_name=user_name,
|
||||
target_names=target_names,
|
||||
chat_messages=chat_messages,
|
||||
description=description)
|
||||
|
||||
def init_workflow(self, **kwargs):
|
||||
"""
|
||||
Initializes the workflow by setting up workers with provided keyword arguments.
|
||||
|
||||
Args:
|
||||
**kwargs: Arbitrary keyword arguments to be passed during worker initialization.
|
||||
"""
|
||||
self.init_workers(**kwargs)
|
||||
|
||||
def run_operation(self, target_name: str, **kwargs):
|
||||
"""
|
||||
Executes the main operation of reading recent chat messages, initializing workflow,
|
||||
and returning the result of the workflow execution.
|
||||
|
||||
Args:
|
||||
target_name (str): target_name(human name).
|
||||
**kwargs: Additional keyword arguments used in the operation context.
|
||||
|
||||
Returns:
|
||||
Any: The result obtained from executing the workflow.
|
||||
"""
|
||||
|
||||
# prepare kwargs
|
||||
workflow_kwargs = {
|
||||
CHAT_MESSAGES: self.chat_messages,
|
||||
CHAT_KWARGS: {**kwargs, **self.kwargs},
|
||||
TARGET_NAME: target_name,
|
||||
USER_NAME: self.user_name,
|
||||
}
|
||||
|
||||
# Execute the workflow with the prepared context
|
||||
self.run_workflow(**workflow_kwargs)
|
||||
|
||||
# Retrieve the result from the context after workflow execution
|
||||
return self.workflow_context.get(RESULT)
|
||||
|
|
@ -1,7 +0,0 @@
|
|||
from .base_memory_service import BaseMemoryService
|
||||
from .memory_scope_service import MemoryScopeService
|
||||
|
||||
__all__ = [
|
||||
"BaseMemoryService",
|
||||
"MemoryScopeService"
|
||||
]
|
||||
|
|
@ -1,99 +0,0 @@
|
|||
from abc import ABCMeta, abstractmethod
|
||||
from typing import List, Dict
|
||||
|
||||
from memoryscope.constants.language_constants import DEFAULT_HUMAN_NAME
|
||||
from memoryscope.core.memoryscope_context import MemoryscopeContext
|
||||
from memoryscope.core.operation.base_operation import BaseOperation
|
||||
from memoryscope.core.utils.logger import Logger
|
||||
from memoryscope.scheme.message import Message
|
||||
|
||||
|
||||
class BaseMemoryService(metaclass=ABCMeta):
|
||||
"""
|
||||
An abstract base class for managing memory operations within a multithreaded context.
|
||||
It sets up the infrastructure for operation handling, message storage, and synchronization,
|
||||
along with logging capabilities and customizable configurations.
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
memory_operations: Dict[str, dict],
|
||||
context: MemoryscopeContext,
|
||||
assistant_name: str = None,
|
||||
human_name: str = None,
|
||||
**kwargs):
|
||||
"""
|
||||
Initializes the BaseMemoryService with operation definitions, keys for memory access,
|
||||
and additional keyword arguments for flexibility.
|
||||
|
||||
Args:
|
||||
memory_operations (Dict[str, dict]): A dictionary defining available memory operations.
|
||||
context (MemoryscopeContext): runtime context.
|
||||
human_name (str): human name.
|
||||
assistant_name (str): assistant name.
|
||||
**kwargs: Additional parameters to customize service behavior.
|
||||
"""
|
||||
self._operations_conf: Dict[str, dict] = memory_operations
|
||||
self._context: MemoryscopeContext = context
|
||||
self._human_name: str = human_name
|
||||
self._assistant_name: str = assistant_name
|
||||
self._kwargs = kwargs
|
||||
|
||||
if not self._human_name:
|
||||
self._human_name = DEFAULT_HUMAN_NAME[self._context.language]
|
||||
if not self._assistant_name:
|
||||
self._assistant_name = "AI"
|
||||
|
||||
self._operation_dict: Dict[str, BaseOperation] = {}
|
||||
self._chat_messages: List[List[Message]] = []
|
||||
self._role_names: List[str] = []
|
||||
|
||||
self.logger = Logger.get_logger()
|
||||
|
||||
@property
|
||||
def human_name(self) -> str:
|
||||
return self._human_name
|
||||
|
||||
@property
|
||||
def assistant_name(self) -> str:
|
||||
return self._assistant_name
|
||||
|
||||
def get_chat_messages_scatter(self, recent_n_pair: int) -> List[Message]:
|
||||
chat_messages_scatter: List[Message] = []
|
||||
for messages in self._chat_messages[-recent_n_pair:]:
|
||||
chat_messages_scatter.extend(messages)
|
||||
return chat_messages_scatter
|
||||
|
||||
@property
|
||||
def op_description_dict(self) -> Dict[str, str]:
|
||||
"""
|
||||
Property to retrieve a dictionary mapping operation keys to their descriptions.
|
||||
Returns:
|
||||
Dict[str, str]: A dictionary where keys are operation identifiers and values are their descriptions.
|
||||
"""
|
||||
return {k: v.description for k, v in self._operation_dict.items()}
|
||||
|
||||
@abstractmethod
|
||||
def add_messages_pair(self, messages: List[Message]):
|
||||
raise NotImplementedError
|
||||
|
||||
@abstractmethod
|
||||
def register_operation(self, name: str, operation_config: dict, **kwargs):
|
||||
raise NotImplementedError
|
||||
|
||||
@abstractmethod
|
||||
def init_service(self, **kwargs):
|
||||
raise NotImplementedError
|
||||
|
||||
def start_backend_service(self, name: str = None, **kwargs):
|
||||
pass
|
||||
|
||||
def stop_backend_service(self, wait_service: bool = False):
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def run_operation(self, name: str, role_name: str = "", **kwargs):
|
||||
raise NotImplementedError
|
||||
|
||||
def __getattr__(self, name: str):
|
||||
assert name in self._operation_dict, f"operation={name} is not registered!"
|
||||
return lambda **kwargs: self.run_operation(name=name, **kwargs)
|
||||
|
|
@ -1,134 +0,0 @@
|
|||
import threading
|
||||
from typing import List
|
||||
|
||||
from memoryscope.core.operation.base_operation import BaseOperation
|
||||
from memoryscope.core.service.base_memory_service import BaseMemoryService
|
||||
from memoryscope.core.utils.tool_functions import init_instance_by_config
|
||||
from memoryscope.scheme.message import Message
|
||||
|
||||
|
||||
class MemoryScopeService(BaseMemoryService):
|
||||
def __init__(self,
|
||||
history_msg_count: int = 100,
|
||||
contextual_msg_max_count: int = 10,
|
||||
contextual_msg_min_count: int = 0,
|
||||
**kwargs):
|
||||
"""
|
||||
init function.
|
||||
Args:
|
||||
history_msg_count (int): The conversation history in memory, control the quantity, and reduce memory usage.
|
||||
contextual_msg_max_count (int): The maximum context length in a conversation. If it exceeds this length,
|
||||
it will not be included in the context to prevent token overflow.
|
||||
contextual_msg_min_count (int): The minimum context length in a conversation. If it is shorter than this
|
||||
length, no conversation summary will be made and no long-term memory will be generated.
|
||||
kwargs (dict): Additional parameters to customize service behavior.
|
||||
"""
|
||||
super().__init__(**kwargs)
|
||||
|
||||
assert history_msg_count >= contextual_msg_max_count >= contextual_msg_min_count
|
||||
self._history_msg_count: int = history_msg_count
|
||||
self._contextual_msg_max_count: int = contextual_msg_max_count
|
||||
self._contextual_msg_min_count: int = contextual_msg_min_count
|
||||
|
||||
self._message_lock = threading.Lock()
|
||||
|
||||
def add_messages_pair(self, messages: List[Message]):
|
||||
"""
|
||||
Adds a list of messages to the chat history, it can be a pair [user_message, assistant_message].
|
||||
Ensuring the message list remains sorted by creation time and does not exceed the maximum history message count.
|
||||
|
||||
Args:
|
||||
messages (List[Message] | Message): A single message instance or a list of message instances
|
||||
to be added to the chat history.
|
||||
"""
|
||||
assert messages, "messages should not be empty!"
|
||||
|
||||
with self._message_lock:
|
||||
# Append the sorted messages to the chat history
|
||||
self._chat_messages.append(messages)
|
||||
|
||||
# Sort the messages by their creation time to maintain chronological order
|
||||
self._chat_messages.sort(key=lambda x: x[0].time_created)
|
||||
|
||||
# If the chat history exceeds the allowed message count, remove the oldest messages
|
||||
if len(self._chat_messages) > self._history_msg_count:
|
||||
gap_size = len(self._chat_messages) - self._history_msg_count
|
||||
for _ in range(gap_size):
|
||||
self._chat_messages.pop(0)
|
||||
|
||||
for message in messages:
|
||||
if message.role_name and message.role_name != self.assistant_name \
|
||||
and message.role_name not in self._role_names:
|
||||
self._role_names.append(message.role_name)
|
||||
|
||||
def register_operation(self, name: str, operation_config: dict, **kwargs):
|
||||
if name in self._operation_dict:
|
||||
self.logger.warning(f"op_name={name} is registered before!")
|
||||
return
|
||||
|
||||
operation: BaseOperation = init_instance_by_config(
|
||||
config=operation_config,
|
||||
name=name,
|
||||
user_name=self._assistant_name,
|
||||
target_names=self._role_names,
|
||||
chat_messages=self._chat_messages,
|
||||
message_lock=self._message_lock,
|
||||
memoryscope_context=self._context,
|
||||
contextual_msg_max_count=self._contextual_msg_max_count,
|
||||
contextual_msg_min_count=self._contextual_msg_min_count)
|
||||
|
||||
# Initialize workflow for each operation
|
||||
operation.init_workflow(**kwargs)
|
||||
self._operation_dict[name] = operation
|
||||
self.logger.info(f"service={self.__class__.__name__} init operation={name}")
|
||||
|
||||
def init_service(self, **kwargs):
|
||||
for name, operation_config in self._operations_conf.items():
|
||||
self.register_operation(name, operation_config, **kwargs)
|
||||
|
||||
def run_operation(self, name: str, role_name: str = "", **kwargs):
|
||||
"""
|
||||
Executes a specific operation by its name with provided keyword arguments.
|
||||
|
||||
Args:
|
||||
name (str): The name of the operation to execute.
|
||||
role_name (str): The name of the operation to execute.
|
||||
**kwargs: Keyword arguments for the operation's execution.
|
||||
|
||||
Returns:
|
||||
The result of the operation execution, if any. Otherwise, None.
|
||||
|
||||
Raises:
|
||||
Warning: If the operation name is not initialized in `_operation_dict`.
|
||||
"""
|
||||
if name not in self._operation_dict:
|
||||
self.logger.warning(f"operation={name} is not registered!")
|
||||
return
|
||||
|
||||
target_name = self._human_name
|
||||
if role_name:
|
||||
target_name = role_name
|
||||
if role_name not in self._role_names:
|
||||
self._role_names.append(role_name)
|
||||
|
||||
return self._operation_dict[name].run_operation(target_name=target_name, **kwargs)
|
||||
|
||||
def start_backend_service(self, name: str = None, **kwargs):
|
||||
"""
|
||||
Start all backend operations.
|
||||
"""
|
||||
for op_name, operation in self._operation_dict.items():
|
||||
if name:
|
||||
if op_name == name:
|
||||
operation.start_operation_backend(**kwargs)
|
||||
else:
|
||||
if operation.operation_type == "backend":
|
||||
operation.start_operation_backend(**kwargs)
|
||||
|
||||
def stop_backend_service(self, wait_service: bool = False):
|
||||
"""
|
||||
Stops all backend operations that are currently running.
|
||||
"""
|
||||
for _, operation in self._operation_dict.items():
|
||||
if operation.operation_type == "backend":
|
||||
operation.stop_operation_backend(wait_operation=wait_service)
|
||||
|
|
@ -1,23 +0,0 @@
|
|||
from .base_memory_store import BaseMemoryStore
|
||||
from .base_monitor import BaseMonitor
|
||||
from .dummy_memory_store import DummyMemoryStore
|
||||
from .dummy_monitor import DummyMonitor
|
||||
from .llama_index_es_memory_store import LlamaIndexEsMemoryStore
|
||||
from .llama_index_sync_elasticsearch import (
|
||||
# get_elasticsearch_client,
|
||||
# _mode_must_match_retrieval_strategy,
|
||||
# _to_elasticsearch_filter,
|
||||
# _to_llama_similarities,
|
||||
ESCombinedRetrieveStrategy,
|
||||
SyncElasticsearchStore
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
"BaseMemoryStore",
|
||||
"BaseMonitor",
|
||||
"DummyMemoryStore",
|
||||
"DummyMonitor",
|
||||
"LlamaIndexEsMemoryStore",
|
||||
"ESCombinedRetrieveStrategy",
|
||||
"SyncElasticsearchStore"
|
||||
]
|
||||
|
|
@ -1,79 +0,0 @@
|
|||
from abc import ABCMeta, abstractmethod
|
||||
from typing import Dict, List
|
||||
|
||||
from memoryscope.scheme.memory_node import MemoryNode
|
||||
|
||||
|
||||
class BaseMemoryStore(metaclass=ABCMeta):
|
||||
"""
|
||||
An abstract base class defining the interface for a memory store which handles memory nodes.
|
||||
It outlines essential operations like retrieval, updating, flushing, and closing of memory scopes.
|
||||
"""
|
||||
|
||||
@abstractmethod
|
||||
def retrieve_memories(self,
|
||||
query: str = "",
|
||||
top_k: int = 3,
|
||||
filter_dict: Dict[str, List[str]] = None) -> List[MemoryNode]:
|
||||
"""
|
||||
Retrieves a list of MemoryNode objects that are most relevant to the query,
|
||||
considering a filter dictionary for additional constraints. The number of nodes returned
|
||||
is limited by top_k.
|
||||
|
||||
Args:
|
||||
query (str): The query string used to find relevant memories.
|
||||
top_k (int): The maximum number of MemoryNode objects to return.
|
||||
filter_dict (Dict[str, List[str]]): A dictionary with keys representing filter fields
|
||||
and values as lists of strings for filtering criteria.
|
||||
|
||||
Returns:
|
||||
List[MemoryNode]: A list of MemoryNode objects sorted by relevance to the query,
|
||||
limited to top_k items.
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
async def a_retrieve_memories(self,
|
||||
query: str = "",
|
||||
top_k: int = 3,
|
||||
filter_dict: Dict[str, List[str]] = None) -> List[MemoryNode]:
|
||||
"""
|
||||
Asynchronously retrieves a list of MemoryNode objects that best match the query,
|
||||
respecting a filter dictionary, with the result size capped at top_k.
|
||||
|
||||
Args:
|
||||
query (str): The text to search for in memory nodes.
|
||||
top_k (int): Maximum number of nodes to return.
|
||||
filter_dict (Dict[str, List[str]]): Filters to apply on memory nodes.
|
||||
|
||||
Returns:
|
||||
List[MemoryNode]: A list of up to top_k MemoryNode objects matching the criteria.
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def batch_insert(self, nodes: List[MemoryNode]):
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def batch_update(self, nodes: List[MemoryNode], update_embedding: bool = True):
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def batch_delete(self, nodes: List[MemoryNode]):
|
||||
pass
|
||||
|
||||
def flush(self):
|
||||
"""
|
||||
Flushes any pending memory updates or operations to ensure data consistency.
|
||||
This method should be overridden by subclasses to provide the specific flushing mechanism.
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def close(self):
|
||||
"""
|
||||
Closes the memory store, releasing any resources associated with it.
|
||||
Subclasses must implement this method to define how the memory store is properly closed.
|
||||
"""
|
||||
pass
|
||||
|
|
@ -1,47 +0,0 @@
|
|||
from abc import ABCMeta, abstractmethod
|
||||
|
||||
|
||||
class BaseMonitor(metaclass=ABCMeta):
|
||||
"""
|
||||
An abstract base class defining the interface for monitor classes.
|
||||
Subclasses should implement the methods defined here to provide concrete monitoring behavior.
|
||||
"""
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def add(self):
|
||||
"""
|
||||
Abstract method to add data or events to the monitor.
|
||||
This method should be implemented by subclasses to define how data is added into the monitoring system.
|
||||
|
||||
:return: None
|
||||
"""
|
||||
|
||||
@abstractmethod
|
||||
def add_token(self):
|
||||
"""
|
||||
Abstract method to add a token or a specific type of identifier to the monitor.
|
||||
Subclasses should implement this to specify how tokens are managed within the monitoring context.
|
||||
|
||||
:return: None
|
||||
"""
|
||||
|
||||
def flush(self):
|
||||
"""
|
||||
Method to flush any buffered data in the monitor.
|
||||
Intended to ensure that all pending recorded data is processed or written out.
|
||||
|
||||
:return: None
|
||||
"""
|
||||
pass
|
||||
|
||||
def close(self):
|
||||
"""
|
||||
Method to close the monitor, performing necessary cleanup operations.
|
||||
This could include releasing resources, closing files, or any other termination tasks.
|
||||
|
||||
:return: None
|
||||
"""
|
||||
pass
|
||||
|
|
@ -1,48 +0,0 @@
|
|||
from typing import Dict, List
|
||||
|
||||
from memoryscope.core.models.base_model import BaseModel
|
||||
from memoryscope.core.storage.base_memory_store import BaseMemoryStore
|
||||
from memoryscope.scheme.memory_node import MemoryNode
|
||||
|
||||
|
||||
class DummyMemoryStore(BaseMemoryStore):
|
||||
"""
|
||||
Placeholder implementation of a memory storage system interface. Defines methods for querying, updating,
|
||||
and closing memory nodes with asynchronous capabilities, leveraging an embedding model for potential
|
||||
semantic retrieval. Actual storage operations are not implemented.
|
||||
"""
|
||||
|
||||
def __init__(self, embedding_model: BaseModel, **kwargs):
|
||||
"""
|
||||
Initializes the DummyMemoryStore with an embedding model and additional keyword arguments.
|
||||
|
||||
Args:
|
||||
embedding_model (BaseModel): The model used to embed data for potential similarity-based retrieval.
|
||||
**kwargs: Additional keyword arguments for configuration or future expansion.
|
||||
"""
|
||||
self.embedding_model: BaseModel = embedding_model
|
||||
self.kwargs = kwargs
|
||||
|
||||
def retrieve_memories(self,
|
||||
query: str = "",
|
||||
top_k: int = 3,
|
||||
filter_dict: Dict[str, List[str]] = None) -> List[MemoryNode]:
|
||||
pass
|
||||
|
||||
async def a_retrieve_memories(self,
|
||||
query: str = "",
|
||||
top_k: int = 3,
|
||||
filter_dict: Dict[str, List[str]] = None) -> List[MemoryNode]:
|
||||
pass
|
||||
|
||||
def batch_insert(self, nodes: List[MemoryNode]):
|
||||
pass
|
||||
|
||||
def batch_update(self, nodes: List[MemoryNode], update_embedding: bool = True):
|
||||
pass
|
||||
|
||||
def batch_delete(self, nodes: List[MemoryNode]):
|
||||
pass
|
||||
|
||||
def close(self):
|
||||
pass
|
||||
|
|
@ -1,30 +0,0 @@
|
|||
from memoryscope.core.storage.base_monitor import BaseMonitor
|
||||
|
||||
|
||||
class DummyMonitor(BaseMonitor):
|
||||
"""
|
||||
DummyMonitor serves as a placeholder or mock class extending BaseMonitor,
|
||||
providing empty method bodies for 'add', 'add_token', and 'close' operations.
|
||||
This can be used for testing or in situations where a full monitor implementation is not required.
|
||||
"""
|
||||
|
||||
def add(self):
|
||||
"""
|
||||
Placeholder for adding data to the monitor.
|
||||
This method currently does nothing.
|
||||
"""
|
||||
pass
|
||||
|
||||
def add_token(self):
|
||||
"""
|
||||
Placeholder for adding a token to the monitored data.
|
||||
This method currently does nothing.
|
||||
"""
|
||||
pass
|
||||
|
||||
def close(self):
|
||||
"""
|
||||
Placeholder for closing the monitor and performing any necessary cleanup.
|
||||
This method currently does nothing.
|
||||
"""
|
||||
pass
|
||||
|
|
@ -1,177 +0,0 @@
|
|||
import random
|
||||
import pickle
|
||||
from typing import Dict, List
|
||||
|
||||
from llama_index.core import VectorStoreIndex
|
||||
from llama_index.core.schema import TextNode, NodeWithScore, QueryBundle
|
||||
|
||||
from memoryscope.core.models.base_model import BaseModel
|
||||
from memoryscope.core.storage.base_memory_store import BaseMemoryStore
|
||||
from memoryscope.core.storage.llama_index_sync_elasticsearch import (SyncElasticsearchStore,
|
||||
ESCombinedRetrieveStrategy,
|
||||
_to_elasticsearch_filter)
|
||||
from memoryscope.core.utils.logger import Logger
|
||||
from memoryscope.scheme.memory_node import MemoryNode
|
||||
|
||||
|
||||
class LlamaIndexEsMemoryStore(BaseMemoryStore):
|
||||
|
||||
def __init__(self,
|
||||
embedding_model: BaseModel,
|
||||
index_name: str,
|
||||
es_url: str,
|
||||
retrieve_mode: str = "dense",
|
||||
hybrid_alpha: float = None,
|
||||
**kwargs):
|
||||
self.emb_dims = None
|
||||
self.index_name = index_name
|
||||
self.embedding_model: BaseModel = embedding_model
|
||||
retrieval_strategy = ESCombinedRetrieveStrategy(retrieve_mode=retrieve_mode, hybrid_alpha=hybrid_alpha)
|
||||
self.es_store = SyncElasticsearchStore(index_name=index_name,
|
||||
es_url=es_url,
|
||||
retrieval_strategy=retrieval_strategy,
|
||||
**kwargs)
|
||||
|
||||
# TODO The llamaIndex utilizes some deprecated functions, hence langchain logs warning messages. By
|
||||
# adding the following lines of code, the display of deprecated information is suppressed.
|
||||
self.index = VectorStoreIndex.from_vector_store(vector_store=self.es_store,
|
||||
embed_model=self.embedding_model.model)
|
||||
|
||||
self.logger = Logger.get_logger("es_memory_store")
|
||||
|
||||
def retrieve_memories(self,
|
||||
query: str = "",
|
||||
top_k: int = 3,
|
||||
filter_dict: Dict[str, List[str]] | Dict[str, str] = None) -> List[MemoryNode]:
|
||||
# if index is not created, return []
|
||||
exists = self.es_store.client.indices.exists(index=self.index_name)
|
||||
if not exists:
|
||||
return []
|
||||
|
||||
if filter_dict is None:
|
||||
filter_dict = {}
|
||||
|
||||
es_filter = _to_elasticsearch_filter(filter_dict)
|
||||
retriever = self.index.as_retriever(vector_store_kwargs={"es_filter": es_filter, "fields": ['embedding']},
|
||||
similarity_top_k=top_k,
|
||||
sparse_top_k=top_k)
|
||||
|
||||
if query:
|
||||
text_nodes = retriever.retrieve(query)
|
||||
if text_nodes and text_nodes[0].embedding:
|
||||
self.emb_dims = len(text_nodes[0].embedding)
|
||||
else:
|
||||
text_nodes = self.es_store.sync_search_all_with_filter(es_filter, ['embedding'])
|
||||
self.logger.log_dictionary_info({
|
||||
"action": "retrieve_memories",
|
||||
"query": query,
|
||||
"text_nodes": [f"ID: {n.node_id} |Text: {n.text}" for n in text_nodes]
|
||||
})
|
||||
return [self._text_node_2_memory_node(n) for n in text_nodes]
|
||||
|
||||
async def a_retrieve_memories(self,
|
||||
query: str = "",
|
||||
top_k: int = 3,
|
||||
filter_dict: Dict[str, List[str]] | Dict[str, str] = None) -> List[MemoryNode]:
|
||||
raise NotImplementedError
|
||||
|
||||
def batch_insert(self, nodes: List[MemoryNode]):
|
||||
self.index.insert_nodes([self._memory_node_2_text_node(node) for node in nodes])
|
||||
|
||||
def batch_update(self, nodes: List[MemoryNode], update_embedding: bool = True):
|
||||
if update_embedding:
|
||||
for node in nodes:
|
||||
node.vector = []
|
||||
|
||||
self.batch_delete(nodes)
|
||||
self.batch_insert(nodes)
|
||||
|
||||
def batch_delete(self, nodes: List[MemoryNode]):
|
||||
# TODO batch_delete
|
||||
for node in nodes:
|
||||
self.delete(node)
|
||||
|
||||
def insert(self, node: MemoryNode):
|
||||
self.index.insert_nodes([self._memory_node_2_text_node(node)])
|
||||
self.logger.log_dictionary_info({
|
||||
"action": "insert",
|
||||
"node": f"ID: {node.memory_id} | Text: {node.content} | Key: {node.key} | Type: {node.memory_type}"
|
||||
})
|
||||
|
||||
def delete(self, node: MemoryNode):
|
||||
self.logger.log_dictionary_info({
|
||||
"action": "delete",
|
||||
"id": node.memory_id,
|
||||
})
|
||||
return self.es_store.delete(node.memory_id)
|
||||
|
||||
def update(self, node: MemoryNode, update_embedding: bool = True):
|
||||
if update_embedding:
|
||||
node.vector = []
|
||||
self.delete(node)
|
||||
self.insert(node)
|
||||
|
||||
def close(self):
|
||||
"""
|
||||
Closes the Elasticsearch store, releasing any resources associated with it.
|
||||
"""
|
||||
self.es_store.close()
|
||||
|
||||
def dummy_query_vector(self):
|
||||
random_floats = [random.uniform(0, 1) for _ in range(self.emb_dims)]
|
||||
return random_floats
|
||||
|
||||
@staticmethod
|
||||
def _memory_node_2_text_node(memory_node: MemoryNode) -> TextNode:
|
||||
"""
|
||||
Converts a MemoryNode object into a TextNode object.
|
||||
|
||||
Args:
|
||||
memory_node (MemoryNode): The MemoryNode to be converted.
|
||||
|
||||
Returns:
|
||||
TextNode: The converted TextNode with content and metadata from the MemoryNode.
|
||||
"""
|
||||
embedding = memory_node.vector
|
||||
key_vector_str = pickle.dumps(memory_node.key_vector).decode('latin1')
|
||||
if not embedding:
|
||||
embedding = None
|
||||
metadatas = memory_node.model_dump(exclude={"content",
|
||||
"vector",
|
||||
"key_vector",
|
||||
"score_recall",
|
||||
"score_rank",
|
||||
"score_rerank"})
|
||||
metadatas["key_vector"] = key_vector_str
|
||||
return TextNode(id_=memory_node.memory_id,
|
||||
text=memory_node.content,
|
||||
embedding=embedding,
|
||||
text_template="{content}",
|
||||
metadata=metadatas)
|
||||
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _text_node_2_memory_node(text_node: NodeWithScore) -> MemoryNode:
|
||||
"""
|
||||
Converts a NodeWithScore object into a MemoryNode object.
|
||||
|
||||
Args:
|
||||
text_node (NodeWithScore): The NodeWithScore to be converted, typically retrieved from search results.
|
||||
|
||||
Returns:
|
||||
MemoryNode: The converted MemoryNode with text and metadata from the NodeWithScore.
|
||||
"""
|
||||
|
||||
if text_node.metadata.get("key_vector", None):
|
||||
key_vector = pickle.loads(text_node.metadata["key_vector"].encode('latin1'))
|
||||
else:
|
||||
key_vector = []
|
||||
text_node.metadata["key_vector"] = key_vector
|
||||
|
||||
text_node.metadata["vector"] = text_node.embedding if text_node.embedding else []
|
||||
|
||||
if hasattr(text_node, "score"):
|
||||
text_node.metadata["score_recall"] = text_node.score
|
||||
|
||||
return MemoryNode(content=text_node.text, **text_node.metadata)
|
||||
|
|
@ -1,795 +0,0 @@
|
|||
"""Elasticsearch vector store."""
|
||||
|
||||
from typing import Any, Callable, Dict, List, Literal, Optional, Union, cast
|
||||
|
||||
import nest_asyncio
|
||||
import numpy as np
|
||||
from memoryscope.core.utils.logger import Logger
|
||||
from memoryscope.core.memoryscope_context import get_memoryscope_context
|
||||
|
||||
from elasticsearch import AsyncElasticsearch, Elasticsearch
|
||||
from elasticsearch.helpers.vectorstore import (
|
||||
AsyncBM25Strategy,
|
||||
AsyncSparseVectorStrategy,
|
||||
AsyncDenseVectorStrategy,
|
||||
AsyncRetrievalStrategy,
|
||||
DistanceMetric,
|
||||
)
|
||||
from elasticsearch.helpers.vectorstore import VectorStore
|
||||
from llama_index.core.bridge.pydantic import PrivateAttr
|
||||
from llama_index.core.schema import BaseNode, MetadataMode, TextNode
|
||||
from llama_index.core.vector_stores.types import (
|
||||
BasePydanticVectorStore,
|
||||
VectorStoreQuery,
|
||||
VectorStoreQueryMode,
|
||||
VectorStoreQueryResult,
|
||||
)
|
||||
from llama_index.core.vector_stores.utils import (
|
||||
metadata_dict_to_node,
|
||||
node_to_metadata_dict,
|
||||
)
|
||||
from llama_index.vector_stores.elasticsearch.utils import (
|
||||
get_user_agent,
|
||||
)
|
||||
|
||||
DISTANCE_STRATEGIES = Literal[
|
||||
"COSINE",
|
||||
"DOT_PRODUCT",
|
||||
"EUCLIDEAN_DISTANCE",
|
||||
]
|
||||
|
||||
|
||||
def get_elasticsearch_client(
|
||||
url: Optional[str] = None,
|
||||
cloud_id: Optional[str] = None,
|
||||
api_key: Optional[str] = None,
|
||||
username: Optional[str] = None,
|
||||
password: Optional[str] = None,
|
||||
use_async: Optional[bool] = False,
|
||||
) -> AsyncElasticsearch:
|
||||
if url and cloud_id:
|
||||
raise ValueError(
|
||||
"Both es_url and cloud_id are defined. Please provide only one."
|
||||
)
|
||||
|
||||
connection_params: Dict[str, Any] = {}
|
||||
|
||||
if url:
|
||||
connection_params["hosts"] = [url]
|
||||
elif cloud_id:
|
||||
connection_params["cloud_id"] = cloud_id
|
||||
else:
|
||||
raise ValueError("Please provide either elasticsearch_url or cloud_id.")
|
||||
|
||||
if api_key:
|
||||
connection_params["api_key"] = api_key
|
||||
elif username and password:
|
||||
connection_params["basic_auth"] = (username, password)
|
||||
if use_async:
|
||||
es_client = AsyncElasticsearch(
|
||||
**connection_params, headers={"user-agent": get_user_agent()}
|
||||
)
|
||||
else:
|
||||
es_client = Elasticsearch(
|
||||
**connection_params, headers={"user-agent": get_user_agent()}
|
||||
)
|
||||
|
||||
es_client.info() # use sync client so don't have to 'await' to just get info
|
||||
|
||||
return es_client
|
||||
|
||||
|
||||
def _to_llama_similarities(scores: List[float]) -> List[float]:
|
||||
"""
|
||||
Converts a list of similarity scores into a normalized form for LlamaIndex compatibility.
|
||||
The normalization involves an exponential transformation based on the maximum score in the list.
|
||||
|
||||
Args:
|
||||
scores (List[float]): A list of raw similarity scores.
|
||||
|
||||
Returns:
|
||||
List[float]: A list of normalized similarity scores suitable for LlamaIndex.
|
||||
"""
|
||||
if scores is None or len(scores) == 0:
|
||||
return []
|
||||
|
||||
scores_to_norm: np.ndarray = np.array(scores)
|
||||
# Normalize scores by subtracting the max score and applying the exponential function
|
||||
return np.exp(scores_to_norm - np.max(scores_to_norm)).tolist()
|
||||
|
||||
|
||||
def _mode_must_match_retrieval_strategy(
|
||||
mode: VectorStoreQueryMode, retrieval_strategy: AsyncRetrievalStrategy
|
||||
) -> None:
|
||||
"""
|
||||
Different retrieval strategies require different ways of indexing that must be known at the
|
||||
time of adding data. The query mode is known at query time. This function checks if the
|
||||
retrieval strategy (and way of indexing) is compatible with the query mode and raises and
|
||||
exception in the case of a mismatch.
|
||||
"""
|
||||
if mode == VectorStoreQueryMode.DEFAULT:
|
||||
# it's fine to not specify an explicit other mode
|
||||
return
|
||||
|
||||
mode_retrieval_dict = {
|
||||
VectorStoreQueryMode.SPARSE: AsyncSparseVectorStrategy,
|
||||
VectorStoreQueryMode.TEXT_SEARCH: AsyncBM25Strategy,
|
||||
VectorStoreQueryMode.HYBRID: AsyncDenseVectorStrategy,
|
||||
}
|
||||
|
||||
required_strategy = mode_retrieval_dict.get(mode)
|
||||
if not required_strategy:
|
||||
raise NotImplementedError(f"query mode {mode} currently not supported")
|
||||
|
||||
if not isinstance(retrieval_strategy, required_strategy):
|
||||
raise ValueError(
|
||||
f"query mode {mode} incompatible with retrieval strategy {type(retrieval_strategy)}, "
|
||||
f"expected {required_strategy}"
|
||||
)
|
||||
|
||||
if mode == VectorStoreQueryMode.HYBRID and not retrieval_strategy.hybrid:
|
||||
raise ValueError(f"to enable hybrid mode, it must be set in retrieval strategy")
|
||||
|
||||
|
||||
class ESCombinedRetrieveStrategy(AsyncDenseVectorStrategy):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
distance: DistanceMetric = DistanceMetric.COSINE,
|
||||
model_id: Optional[str] = None,
|
||||
retrieve_mode: str = "dense",
|
||||
rrf: Union[bool, Dict[str, Any]] = True,
|
||||
text_field: Optional[str] = "text_field",
|
||||
hybrid_alpha: Optional[float] = None,
|
||||
):
|
||||
if retrieve_mode == "dense":
|
||||
self.alpha = 1.0
|
||||
elif retrieve_mode == "sparse":
|
||||
# self.alpha = 0.0
|
||||
raise NotImplementedError
|
||||
elif retrieve_mode == "hybrid":
|
||||
# self.alpha = hybrid_alpha
|
||||
raise NotImplementedError
|
||||
|
||||
super().__init__(distance=distance, model_id=model_id, hybrid=True, rrf=rrf, text_field=text_field)
|
||||
|
||||
def _hybrid(self, query: str, knn: Dict[str, Any], filter: List[Dict[str, Any]], top_k: int) -> Dict[str, Any]:
|
||||
# Add a query to the knn query.
|
||||
# RRF is used to even the score from the knn query and text query
|
||||
# RRF has two optional parameters: {'rank_constant':int, 'window_size':int}
|
||||
# https://www.elastic.co/guide/en/elasticsearch/reference/current/rrf.html
|
||||
if not query:
|
||||
query_body = {
|
||||
"query": {
|
||||
"bool": {
|
||||
"filter": filter,
|
||||
}
|
||||
},
|
||||
}
|
||||
else:
|
||||
query_body = {
|
||||
"knn": knn,
|
||||
"query": {
|
||||
"bool": {
|
||||
"must": [
|
||||
{
|
||||
"match": {
|
||||
self.text_field: {
|
||||
"query": query,
|
||||
"boost": (1 - self.alpha) if self.alpha is not None else 1.0,
|
||||
}
|
||||
},
|
||||
}
|
||||
],
|
||||
"filter": filter,
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
if self.alpha is None and isinstance(self.rrf, Dict):
|
||||
query_body["rank"] = {"rrf": self.rrf}
|
||||
elif self.alpha is None and isinstance(self.rrf, bool) and self.rrf is True:
|
||||
query_body["rank"] = {"rrf": {"window_size": top_k}}
|
||||
|
||||
return query_body
|
||||
|
||||
def es_query(
|
||||
self,
|
||||
*,
|
||||
query: Optional[str],
|
||||
query_vector: Optional[List[float]],
|
||||
text_field: str,
|
||||
vector_field: str,
|
||||
k: int,
|
||||
num_candidates: int,
|
||||
filter: List[Dict[str, Any]] = None,
|
||||
) -> Dict[str, Any]:
|
||||
if filter is None:
|
||||
filter = []
|
||||
|
||||
knn = {
|
||||
"filter": filter,
|
||||
"field": vector_field,
|
||||
"k": k,
|
||||
"num_candidates": num_candidates,
|
||||
"boost": self.alpha if self.alpha is not None else 1.0,
|
||||
}
|
||||
|
||||
if query_vector is not None:
|
||||
knn["query_vector"] = query_vector
|
||||
else:
|
||||
# Inference in Elasticsearch. When initializing we make sure to always have
|
||||
# a model_id if we don't have an embedding_service.
|
||||
knn["query_vector_builder"] = {
|
||||
"text_embedding": {
|
||||
"model_id": self.model_id,
|
||||
"model_text": query,
|
||||
}
|
||||
}
|
||||
|
||||
if self.hybrid:
|
||||
return self._hybrid(query=cast(str, query), knn=knn, filter=filter, top_k=k)
|
||||
|
||||
return {"knn": knn}
|
||||
|
||||
|
||||
def before_index_creation(
|
||||
self, *, client: AsyncElasticsearch, text_field: str, vector_field: str
|
||||
) -> None:
|
||||
if self.model_id:
|
||||
from elasticsearch.helpers.vectorstore._async._utils import model_must_be_deployed
|
||||
import asyncio
|
||||
print('before_index_creation')
|
||||
asyncio.run(model_must_be_deployed(client, self.model_id))
|
||||
print('before_index_creation 2')
|
||||
|
||||
|
||||
def _to_elasticsearch_filter(standard_filters: Dict[str, List[str]]) -> Dict[str, Any]:
|
||||
"""
|
||||
Converts standard Llama-index filters into a format compatible with Elasticsearch.
|
||||
|
||||
This function transforms dictionary-based filters, where each key represents a field and
|
||||
the value is a list of strings, into an Elasticsearch query structure. It supports both
|
||||
list values (interpreted as 'should' clauses for OR logic) and single values (interpreted
|
||||
as 'must' clauses for AND logic).
|
||||
|
||||
Args:
|
||||
standard_filters (Dict[str, List[str]]): A dictionary containing filter criteria,
|
||||
where keys are field names and values are lists of strings or single string values
|
||||
representing filter values.
|
||||
|
||||
Returns:
|
||||
Dict[str, Any]: A dictionary structured as an Elasticsearch filter query.
|
||||
"""
|
||||
result = {
|
||||
"bool": {}
|
||||
}
|
||||
for key, value in standard_filters.items():
|
||||
if isinstance(value, list):
|
||||
operands = []
|
||||
for v in value:
|
||||
key_str = f"metadata.{key}.keyword" if isinstance(v, str) else f"metadata.{key}"
|
||||
operands.append(
|
||||
{
|
||||
"term":
|
||||
{
|
||||
key_str: {"value": v}
|
||||
}
|
||||
}
|
||||
)
|
||||
result['bool'].update({"should": operands}) # Add 'should' clause for OR logic
|
||||
result['bool'].update({"minimum_should_match": 1}) # Ensure at least one 'should' match
|
||||
else:
|
||||
key_str = f"metadata.{key}.keyword" if isinstance(value, str) else f"metadata.{key}"
|
||||
operand = [{
|
||||
"term": {
|
||||
key_str: {
|
||||
"value": value,
|
||||
}
|
||||
}
|
||||
}]
|
||||
if "must" in result['bool']:
|
||||
result['bool']['must'].extend(operand) # Extend existing 'must' clause for AND logic
|
||||
else:
|
||||
result['bool'].update({"must": operand}) # Initialize 'must' clause if not present
|
||||
return result
|
||||
|
||||
|
||||
class SyncElasticsearchStore(BasePydanticVectorStore):
|
||||
"""
|
||||
Elasticsearch vector store.
|
||||
|
||||
Args:
|
||||
index_name: Name of the Elasticsearch index.
|
||||
es_client: Optional. Pre-existing AsyncElasticsearch client.
|
||||
es_url: Optional. Elasticsearch URL.
|
||||
es_cloud_id: Optional. Elasticsearch cloud ID.
|
||||
es_api_key: Optional. Elasticsearch API key.
|
||||
es_user: Optional. Elasticsearch username.
|
||||
es_password: Optional. Elasticsearch password.
|
||||
text_field: Optional. Name of the Elasticsearch field that stores the text.
|
||||
vector_field: Optional. Name of the Elasticsearch field that stores the
|
||||
embedding.
|
||||
batch_size: Optional. Batch size for bulk indexing. Defaults to 200.
|
||||
distance_strategy: Optional. Distance strategy to use for similarity search.
|
||||
Defaults to "COSINE".
|
||||
retrieval_strategy: Retrieval strategy to use. AsyncBM25Strategy /
|
||||
AsyncSparseVectorStrategy / AsyncDenseVectorStrategy / AsyncRetrievalStrategy.
|
||||
Defaults to AsyncDenseVectorStrategy.
|
||||
|
||||
Raises:
|
||||
ConnectionError: If AsyncElasticsearch client cannot connect to Elasticsearch.
|
||||
ValueError: If neither es_client nor es_url nor es_cloud_id is provided.
|
||||
|
||||
Examples:
|
||||
`pip install llama-index-vector-stores-elasticsearch`
|
||||
|
||||
```python
|
||||
from llama_index.vector_stores import ElasticsearchStore
|
||||
|
||||
# Additional setup for ElasticsearchStore class
|
||||
index_name = "my_index"
|
||||
es_url = "http://localhost:9200"
|
||||
es_cloud_id = "<cloud-id>" # Found within the deployment page
|
||||
es_user = "elastic"
|
||||
es_password = "<password>" # Provided when creating deployment or can be reset
|
||||
es_api_key = "<api-key>" # Create an API key within Kibana (Security -> API Keys)
|
||||
|
||||
# Connecting to ElasticsearchStore locally
|
||||
es_local = ElasticsearchStore(
|
||||
index_name=index_name,
|
||||
es_url=es_url)
|
||||
|
||||
# Connecting to Elastic Cloud with username and password
|
||||
es_cloud_user_pass = ElasticsearchStore(
|
||||
index_name=index_name,
|
||||
es_cloud_id=es_cloud_id,
|
||||
es_user=es_user,
|
||||
es_password=es_password)
|
||||
|
||||
# Connecting to Elastic Cloud with API Key
|
||||
es_cloud_api_key = ElasticsearchStore(
|
||||
index_name=index_name,
|
||||
es_cloud_id=es_cloud_id,
|
||||
es_api_key=es_api_key,
|
||||
)
|
||||
```
|
||||
|
||||
"""
|
||||
|
||||
class Config:
|
||||
# allow pydantic to tolarate its inability to validate AsyncRetrievalStrategy
|
||||
arbitrary_types_allowed = True
|
||||
|
||||
stores_text: bool = True
|
||||
index_name: str
|
||||
es_client: Optional[Any]
|
||||
es_url: Optional[str]
|
||||
es_cloud_id: Optional[str]
|
||||
es_api_key: Optional[str]
|
||||
es_user: Optional[str]
|
||||
es_password: Optional[str]
|
||||
text_field: str = "content"
|
||||
vector_field: str = "embedding"
|
||||
batch_size: int = 200
|
||||
distance_strategy: Optional[DISTANCE_STRATEGIES] = "COSINE"
|
||||
retrieval_strategy: AsyncRetrievalStrategy
|
||||
logger: Logger = None
|
||||
log_elasticsearch_dynamic: bool = False
|
||||
|
||||
_store = PrivateAttr()
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
index_name: str,
|
||||
es_client: Optional[Any] = None,
|
||||
es_url: Optional[str] = None,
|
||||
es_cloud_id: Optional[str] = None,
|
||||
es_api_key: Optional[str] = None,
|
||||
es_user: Optional[str] = None,
|
||||
es_password: Optional[str] = None,
|
||||
text_field: str = "content",
|
||||
vector_field: str = "embedding",
|
||||
batch_size: int = 200,
|
||||
distance_strategy: Optional[DISTANCE_STRATEGIES] = "COSINE",
|
||||
retrieval_strategy: Optional[AsyncRetrievalStrategy] = None,
|
||||
) -> None:
|
||||
nest_asyncio.apply()
|
||||
|
||||
if not es_client:
|
||||
es_client = get_elasticsearch_client(
|
||||
url=es_url,
|
||||
cloud_id=es_cloud_id,
|
||||
api_key=es_api_key,
|
||||
username=es_user,
|
||||
password=es_password,
|
||||
)
|
||||
|
||||
if retrieval_strategy is None:
|
||||
retrieval_strategy = AsyncDenseVectorStrategy(
|
||||
distance=DistanceMetric[distance_strategy]
|
||||
)
|
||||
|
||||
metadata_mappings = {
|
||||
"document_id": {"type": "keyword"},
|
||||
"doc_id": {"type": "keyword"},
|
||||
"ref_doc_id": {"type": "keyword"},
|
||||
}
|
||||
|
||||
self._store = VectorStore(
|
||||
user_agent=get_user_agent(),
|
||||
client=es_client,
|
||||
index=index_name,
|
||||
retrieval_strategy=retrieval_strategy,
|
||||
text_field=text_field,
|
||||
vector_field=vector_field,
|
||||
metadata_mappings=metadata_mappings,
|
||||
)
|
||||
|
||||
super().__init__(
|
||||
index_name=index_name,
|
||||
es_client=es_client,
|
||||
es_url=es_url,
|
||||
es_cloud_id=es_cloud_id,
|
||||
es_api_key=es_api_key,
|
||||
es_user=es_user,
|
||||
es_password=es_password,
|
||||
text_field=text_field,
|
||||
vector_field=vector_field,
|
||||
batch_size=batch_size,
|
||||
distance_strategy=distance_strategy,
|
||||
retrieval_strategy=retrieval_strategy,
|
||||
)
|
||||
|
||||
self.logger = Logger.get_logger("elastic_search")
|
||||
self.log_elasticsearch_dynamic = get_memoryscope_context().log_elasticsearch_dynamic
|
||||
|
||||
@property
|
||||
def client(self) -> Any:
|
||||
"""
|
||||
Get the asynchronous Elasticsearch client.
|
||||
|
||||
Returns:
|
||||
Any: The asynchronous Elasticsearch client instance configured for this store.
|
||||
"""
|
||||
return self._store.client
|
||||
|
||||
def close(self) -> None:
|
||||
return self._store.close()
|
||||
|
||||
def add(
|
||||
self,
|
||||
nodes: List[BaseNode],
|
||||
*,
|
||||
create_index_if_not_exists: bool = True,
|
||||
**add_kwargs: Any,
|
||||
) -> List[str]:
|
||||
"""
|
||||
Adds a list of nodes, each containing embeddings, to an Elasticsearch index.
|
||||
Optionally creates the index if it does not already exist.
|
||||
|
||||
Args:
|
||||
nodes (List[BaseNode]): A list of node objects, each encapsulating an embedding.
|
||||
create_index_if_not_exists (bool, optional):
|
||||
A flag indicating whether to create the Elasticsearch index if it's not present.
|
||||
Defaults to True.
|
||||
|
||||
Returns:
|
||||
List[str]: A list of node IDs that have been successfully added to the index.
|
||||
|
||||
Raises:
|
||||
ImportError: If the 'elasticsearch[async]' Python package is not installed.
|
||||
BulkIndexError: If there is a failure during the asynchronous bulk indexing with AsyncElasticsearch.
|
||||
|
||||
Note:
|
||||
This method delegates the actual operation to the `sync_add` method.
|
||||
"""
|
||||
add_res = self.sync_add(nodes, create_index_if_not_exists=create_index_if_not_exists)
|
||||
self.log_vector_store_brief(title='after add')
|
||||
return add_res
|
||||
|
||||
|
||||
def sync_add(
|
||||
self,
|
||||
nodes: List[BaseNode],
|
||||
*,
|
||||
create_index_if_not_exists: bool = True,
|
||||
**add_kwargs: Any,
|
||||
) -> List[str]:
|
||||
"""
|
||||
Asynchronously adds a list of nodes, each containing an embedding, to the Elasticsearch index.
|
||||
|
||||
This method processes each node to extract its ID, embedding, text content, and metadata,
|
||||
preparing them for batch insertion into the index. It ensures the index is created if not present
|
||||
and respects the dimensionality of the embeddings for consistency.
|
||||
|
||||
Args:
|
||||
nodes (List[BaseNode]): A list of node objects, each encapsulating an embedding.
|
||||
create_index_if_not_exists (bool, optional): A flag indicating whether to create the Elasticsearch
|
||||
index if it does not already exist. Defaults to True.
|
||||
**add_kwargs (Any): Additional keyword arguments passed to the underlying add_texts method
|
||||
for customization during the indexing process.
|
||||
|
||||
Returns:
|
||||
List[str]: A list of node IDs that were successfully added to the index.
|
||||
|
||||
Raises:
|
||||
ImportError: If the Elasticsearch Python client is not installed.
|
||||
BulkIndexError: If there's a failure during the asynchronous bulk indexing operation.
|
||||
"""
|
||||
if len(nodes) == 0:
|
||||
return []
|
||||
|
||||
# Extract necessary components from each node
|
||||
embeddings: List[List[float]] = [] # Embedding vectors
|
||||
texts: List[str] = [] # Textual contents of nodes
|
||||
metadatas: List[dict] = [] # Metadata associated with nodes
|
||||
ids: List[str] = [] # Unique identifiers for nodes
|
||||
|
||||
for node in nodes:
|
||||
ids.append(node.node_id) # Node identifier
|
||||
embeddings.append(node.get_embedding()) # Node's embedding vector
|
||||
texts.append(node.get_content(metadata_mode=MetadataMode.NONE)) # Node's raw text content
|
||||
metadatas.append(node_to_metadata_dict(node, remove_text=True)) # Convert node to metadata dictionary
|
||||
|
||||
# Initialize the number of dimensions in the store if not set
|
||||
if not self._store.num_dimensions:
|
||||
self._store.num_dimensions = len(embeddings[0]) # Set based on the first node's embedding size
|
||||
|
||||
# Add the prepared data to the Elasticsearch index asynchronously
|
||||
return self._store.add_texts(
|
||||
texts=texts,
|
||||
metadatas=metadatas,
|
||||
vectors=embeddings,
|
||||
ids=ids,
|
||||
create_index_if_not_exists=create_index_if_not_exists,
|
||||
bulk_kwargs=add_kwargs,
|
||||
)
|
||||
|
||||
def delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None:
|
||||
"""
|
||||
Deletes a node from the Elasticsearch index using the provided reference document ID.
|
||||
|
||||
Optionally, extra keyword arguments can be supplied to customize the deletion behavior,
|
||||
which are passed directly to Elasticsearch's `delete_by_query` operation.
|
||||
|
||||
Args:
|
||||
ref_doc_id (str): The unique identifier of the node/document to be deleted.
|
||||
delete_kwargs (Any): Additional keyword arguments for Elasticsearch's
|
||||
`delete_by_query`. These might include query filters,
|
||||
timeouts, or other operational configurations.
|
||||
|
||||
Raises:
|
||||
Exception: If the deletion operation via Elasticsearch's `delete_by_query` fails.
|
||||
|
||||
Note:
|
||||
This method internally calls a synchronous delete method (`sync_delete`)
|
||||
to execute the deletion operation against Elasticsearch.
|
||||
"""
|
||||
del_res = self.sync_delete(ref_doc_id, **delete_kwargs)
|
||||
self.log_vector_store_brief(title='after delete')
|
||||
return del_res
|
||||
|
||||
def sync_delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None:
|
||||
"""
|
||||
Synchronously deletes a node from the Elasticsearch index based on the reference document ID.
|
||||
|
||||
Args:
|
||||
ref_doc_id (str): The unique identifier of the node/document to be deleted.
|
||||
delete_kwargs (Any): Optional keyword arguments to be passed
|
||||
to the delete_by_query operation of AsyncElasticsearch,
|
||||
allowing for additional customization of the deletion process.
|
||||
|
||||
Raises:
|
||||
Exception: If the deletion operation via AsyncElasticsearch's delete_by_query fails.
|
||||
|
||||
Note:
|
||||
The function directly uses '_id' field to match the document for deletion instead of 'metadata.ref_doc_id',
|
||||
ensuring targeted removal based on the document's unique identifier within Elasticsearch.
|
||||
"""
|
||||
# The original commented line suggests an alternative query using 'metadata.ref_doc_id',
|
||||
# but the active code line performs the deletion based on '_id', which typically aligns with 'ref_doc_id'.
|
||||
return self._store.delete(query={"term": {"_id": ref_doc_id}}, **delete_kwargs)
|
||||
|
||||
def query(
|
||||
self,
|
||||
query: VectorStoreQuery,
|
||||
custom_query: Optional[
|
||||
Callable[[Dict, Union[VectorStoreQuery, None]], Dict]
|
||||
] = None,
|
||||
es_filter: Optional[List[Dict]] = None,
|
||||
**kwargs: Any,
|
||||
) -> VectorStoreQueryResult:
|
||||
"""
|
||||
Executes a query against the Elasticsearch index to retrieve the top k most similar nodes
|
||||
based on the input query embedding. Supports customization of the query process and
|
||||
application of Elasticsearch filters.
|
||||
|
||||
Args:
|
||||
query (VectorStoreQuery): The query containing the embedding and other parameters.
|
||||
custom_query (Callable[[Dict, Union[VectorStoreQuery, None]], Dict], optional):
|
||||
An optional custom function to modify the Elasticsearch query body, allowing for
|
||||
additional query parameters or logic. Defaults to None.
|
||||
es_filter (Optional[List[Dict]], optional): An optional Elasticsearch filter list to
|
||||
apply to the query. If a filter is directly included in the `query`, this argument
|
||||
will not be used. Defaults to None.
|
||||
**kwargs (Any): Additional keyword arguments that might be used in the query process.
|
||||
|
||||
Returns:
|
||||
VectorStoreQueryResult: The result of the query operation, including the most similar nodes.
|
||||
|
||||
Raises:
|
||||
Exception: If an error occurs during the Elasticsearch query execution.
|
||||
|
||||
"""
|
||||
|
||||
q_res = self.sync_query(query, custom_query, es_filter, **kwargs)
|
||||
self.logger.log_dictionary_info({
|
||||
"action": "query",
|
||||
"query": query.query_str,
|
||||
"result": [tn.text for tn in q_res.nodes]
|
||||
})
|
||||
return q_res
|
||||
|
||||
def sync_delete_all(self):
|
||||
try:
|
||||
self._store.client.delete_by_query(index=[self.index_name], body={"query": {"match_all": {}}})
|
||||
except: # elasticsearch.NotFoundError
|
||||
pass
|
||||
|
||||
def sync_search_all(self):
|
||||
search_res = self._store.client.search(index=[self.index_name], body={"query": {"match_all": {}}})
|
||||
return search_res
|
||||
|
||||
def log_vector_store_brief(self, title="current vector store content"):
|
||||
if not self.log_elasticsearch_dynamic:
|
||||
return "Dynamic elasticsearch logging is disabled to enhance performance."
|
||||
search_res = self.sync_search_all()
|
||||
|
||||
brief = {
|
||||
f"{hit['_source']['metadata']['memory_id']}({hit['_source']['metadata']['user_name']}/{hit['_source']['metadata']['target_name']}/{hit['_source']['metadata']['memory_type']})":
|
||||
hit['_source']['content']
|
||||
for hit in search_res["hits"]["hits"]
|
||||
}
|
||||
self.logger.log_dictionary_info(brief, title=title)
|
||||
|
||||
return brief
|
||||
|
||||
def sync_search_all_with_filter(self, es_filter, fields):
|
||||
query_body = {'query': {'bool': {'filter': es_filter}}}
|
||||
k = 1000
|
||||
fields = ['embedding', 'metadata', 'content']
|
||||
response = self.client.search(
|
||||
index=self.index_name,
|
||||
**query_body,
|
||||
size=k,
|
||||
source=True,
|
||||
source_includes=fields,
|
||||
)
|
||||
res = []
|
||||
for hit in response["hits"]["hits"]:
|
||||
tn = TextNode(
|
||||
id_=hit['_id'],
|
||||
text=hit['_source']['content'],
|
||||
embedding=hit['_source']['embedding'],
|
||||
text_template="{content}",
|
||||
metadata=hit['_source']['metadata']
|
||||
)
|
||||
res.append(
|
||||
tn
|
||||
)
|
||||
return res
|
||||
|
||||
def sync_query(
|
||||
self,
|
||||
query: VectorStoreQuery,
|
||||
custom_query: Optional[
|
||||
Callable[[Dict, Union[VectorStoreQuery, None]], Dict]
|
||||
] = None,
|
||||
es_filter: Optional[List[Dict]] = None,
|
||||
fields: List[str] = [],
|
||||
) -> VectorStoreQueryResult:
|
||||
"""
|
||||
Asynchronously queries the Elasticsearch index for the top k most similar nodes
|
||||
based on the provided query embedding. Supports custom query modifications
|
||||
and application of Elasticsearch filters.
|
||||
|
||||
Args:
|
||||
query (VectorStoreQuery): The query containing the embedding and other details.
|
||||
custom_query (Callable[[Dict, Union[VectorStoreQuery, None]], Dict], optional):
|
||||
A custom function to modify the Elasticsearch query body. Defaults to None.
|
||||
es_filter (List[Dict], optional): Additional filters to apply during the query.
|
||||
If filters are present in the query, these filters will not be used. Defaults to None.
|
||||
fields (List[str], optional): .
|
||||
|
||||
Returns:
|
||||
VectorStoreQueryResult: The result of the query, including nodes, their IDs,
|
||||
and similarity scores.
|
||||
|
||||
Raises:
|
||||
Exception: If the Elasticsearch query encounters an error.
|
||||
|
||||
Note:
|
||||
The mode of the query must align with the retrieval strategy set for this store.
|
||||
In case of legacy metadata, a warning is logged and nodes are constructed accordingly.
|
||||
"""
|
||||
_mode_must_match_retrieval_strategy(query.mode, self.retrieval_strategy)
|
||||
|
||||
if query.filters is not None and len(query.filters.legacy_filters()) > 0:
|
||||
filter = [_to_elasticsearch_filter(query.filters)]
|
||||
else:
|
||||
filter = es_filter or []
|
||||
num_candidates = query.similarity_top_k * 10 if query.similarity_top_k <= 1000 else query.similarity_top_k
|
||||
|
||||
hits = self._store.search(
|
||||
query=query.query_str,
|
||||
query_vector=query.query_embedding,
|
||||
k=query.similarity_top_k,
|
||||
num_candidates=num_candidates, # query.similarity_top_k * 10,
|
||||
filter=filter,
|
||||
custom_query=custom_query,
|
||||
fields=fields,
|
||||
)
|
||||
|
||||
return self.post_process_hits(hits)
|
||||
|
||||
|
||||
def post_process_hits(self, hits: List[Dict[str, Any]]) -> VectorStoreQueryResult:
|
||||
top_k_nodes = []
|
||||
top_k_ids = []
|
||||
top_k_scores = []
|
||||
|
||||
for hit in hits:
|
||||
source = hit["_source"]
|
||||
metadata = source.get("metadata", None)
|
||||
embedding = source.get("embedding", None)
|
||||
text = source.get(self.text_field, None)
|
||||
node_id = hit["_id"]
|
||||
|
||||
try:
|
||||
# Attempt to parse metadata using the standard method
|
||||
node = metadata_dict_to_node(metadata)
|
||||
node.text = text
|
||||
node.embedding = embedding
|
||||
except Exception:
|
||||
# Legacy support for old metadata format
|
||||
self.logger.warning(
|
||||
f"Could not parse metadata from hit {hit['_source']['metadata']}"
|
||||
)
|
||||
node_info = source.get("node_info")
|
||||
relationships = source.get("relationships", {})
|
||||
start_char_idx = None
|
||||
end_char_idx = None
|
||||
if isinstance(node_info, dict):
|
||||
start_char_idx = node_info.get("start", None)
|
||||
end_char_idx = node_info.get("end", None)
|
||||
|
||||
node = TextNode(
|
||||
text=text,
|
||||
metadata=metadata,
|
||||
id_=node_id,
|
||||
embedding=embedding,
|
||||
start_char_idx=start_char_idx,
|
||||
end_char_idx=end_char_idx,
|
||||
relationships=relationships,
|
||||
)
|
||||
top_k_nodes.append(node)
|
||||
top_k_ids.append(node_id)
|
||||
top_k_scores.append(hit.get("_rank", hit["_score"]))
|
||||
|
||||
if (
|
||||
isinstance(self.retrieval_strategy, AsyncDenseVectorStrategy)
|
||||
and self.retrieval_strategy.hybrid
|
||||
):
|
||||
# total_rank = sum(top_k_scores)
|
||||
top_k_scores = [rank for rank in top_k_scores]
|
||||
# top_k_scores = [(total_rank - rank) / total_rank for rank in top_k_scores]
|
||||
# top_k_scores = [total_rank - rank / total_rank for rank in top_k_scores]
|
||||
|
||||
return VectorStoreQueryResult(
|
||||
nodes=top_k_nodes,
|
||||
ids=top_k_ids,
|
||||
# similarities=_to_llama_similarities(top_k_scores),
|
||||
similarities=top_k_scores
|
||||
)
|
||||
|
|
@ -1,33 +0,0 @@
|
|||
from .datetime_handler import DatetimeHandler
|
||||
from .logger import Logger
|
||||
from .prompt_handler import PromptHandler
|
||||
from .registry import Registry
|
||||
from .response_text_parser import ResponseTextParser
|
||||
from .timer import Timer
|
||||
from .tool_functions import (
|
||||
underscore_to_camelcase,
|
||||
camelcase_to_underscore,
|
||||
init_instance_by_config,
|
||||
prompt_to_msg,
|
||||
char_logo,
|
||||
md5_hash,
|
||||
contains_keyword,
|
||||
cosine_similarity
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
"DatetimeHandler",
|
||||
"Logger",
|
||||
"PromptHandler",
|
||||
"Registry",
|
||||
"ResponseTextParser",
|
||||
"Timer",
|
||||
"underscore_to_camelcase",
|
||||
"camelcase_to_underscore",
|
||||
"init_instance_by_config",
|
||||
"prompt_to_msg",
|
||||
"char_logo",
|
||||
"md5_hash",
|
||||
"contains_keyword",
|
||||
"cosine_similarity"
|
||||
]
|
||||
|
|
@ -1,318 +0,0 @@
|
|||
import datetime
|
||||
import re
|
||||
from typing import List
|
||||
|
||||
from memoryscope.constants.language_constants import WEEKDAYS, DATATIME_WORD_LIST, MONTH_DICT
|
||||
from memoryscope.enumeration.language_enum import LanguageEnum
|
||||
|
||||
|
||||
class DatetimeHandler(object):
|
||||
"""
|
||||
Handles operations related to datetime such as parsing, extraction, and formatting,
|
||||
with support for both Chinese and English contexts including weekday names and
|
||||
specialized text parsing for date components.
|
||||
"""
|
||||
|
||||
|
||||
def __init__(self, dt: datetime.datetime | str | int | float = None):
|
||||
"""
|
||||
Initialize the DatetimeHandler instance with a datetime object, string, integer, or float representation
|
||||
of a timestamp. If no argument is provided, the current time is used.
|
||||
|
||||
Args:
|
||||
dt (datetime.datetime | str | int | float, optional):
|
||||
The datetime to be handled. Can be a datetime object, a timestamp string, or a numeric timestamp.
|
||||
Defaults to None, which sets the instance to the current datetime.
|
||||
|
||||
Attributes:
|
||||
self._dt (datetime.datetime): The internal datetime representation of the input.
|
||||
self._dt_info_dict (dict | None): A dictionary containing parsed datetime information, defaults to None.
|
||||
"""
|
||||
if isinstance(dt, str | int | float):
|
||||
if isinstance(dt, str):
|
||||
dt = float(dt)
|
||||
self._dt: datetime.datetime = datetime.datetime.fromtimestamp(dt)
|
||||
elif isinstance(dt, datetime.datetime):
|
||||
self._dt: datetime.datetime = dt
|
||||
else:
|
||||
self._dt: datetime.datetime = datetime.datetime.now()
|
||||
|
||||
self._dt_info_dict: dict | None = None
|
||||
|
||||
def _parse_dt_info(self, language: LanguageEnum):
|
||||
"""
|
||||
Parses the datetime object (_dt) into a dictionary containing detailed date and time components,
|
||||
including language-specific weekday representation.
|
||||
|
||||
Returns:
|
||||
dict: A dictionary with keys representing date and time parts such as 'year', 'month',
|
||||
'day', 'hour', 'minute', 'second', 'week', and 'weekday' with respective values.
|
||||
The 'weekday' value is translated based on the current language context.
|
||||
"""
|
||||
return {
|
||||
"year": self._dt.year,
|
||||
"month": MONTH_DICT[language][self._dt.month - 1],
|
||||
"day": self._dt.day,
|
||||
"hour": self._dt.hour,
|
||||
"minute": self._dt.minute,
|
||||
"second": self._dt.second,
|
||||
"week": self._dt.isocalendar().week,
|
||||
"weekday": WEEKDAYS[language][self._dt.isocalendar().weekday - 1],
|
||||
}
|
||||
|
||||
def get_dt_info_dict(self, language: LanguageEnum):
|
||||
"""
|
||||
Property method to get the dictionary containing parsed datetime information.
|
||||
If None, initialize using `_parse_dt_info`.
|
||||
|
||||
Returns:
|
||||
dict: A dictionary with parsed datetime information.
|
||||
"""
|
||||
if self._dt_info_dict is None:
|
||||
self._dt_info_dict = self._parse_dt_info(language=language)
|
||||
return self._dt_info_dict
|
||||
|
||||
@classmethod
|
||||
def extract_date_parts_cn(cls, input_string: str) -> dict:
|
||||
"""
|
||||
Extracts various components of a date (year, month, day, etc.) from an input string based on Chinese formats.
|
||||
|
||||
This method identifies year, month, day, weekday, and hour components within the input
|
||||
string based on predefined patterns. It supports relative terms like '每' (every) and
|
||||
translates weekday names into numeric representations.
|
||||
|
||||
Args:
|
||||
input_string (str): The Chinese text containing date and time information.
|
||||
|
||||
Returns:
|
||||
dict: A dictionary with keys 'year', 'month', 'day', 'weekday', and 'hour',
|
||||
each holding the corresponding extracted value. If a component is not found,
|
||||
it will not be included in the dictionary. For relative terms like '每' (every),
|
||||
the value is set to -1.
|
||||
|
||||
"""
|
||||
# Extending our pattern to handle every/每 as a possible value.
|
||||
patterns = {
|
||||
"year": r"(\d+|每)年",
|
||||
"month": r"(\d+|每)月",
|
||||
"day": r"(\d+|每)日",
|
||||
"weekday": r"周([一二三四五六日])",
|
||||
"hour": r"(\d+)点"
|
||||
}
|
||||
weekday_dict = {"一": 1, "二": 2, "三": 3, "四": 4, "五": 5, "六": 6, "日": 7}
|
||||
extracted_data = {}
|
||||
|
||||
# Search for patterns in the input string and populate the dictionary
|
||||
for key, pattern in patterns.items():
|
||||
match = re.search(pattern, input_string)
|
||||
if match: # If there is a match, include it in the output dictionary
|
||||
if match.group(1) == "每":
|
||||
extracted_data[key] = -1
|
||||
elif match.group(1) in weekday_dict.keys():
|
||||
extracted_data[key] = weekday_dict[match.group(1)]
|
||||
else:
|
||||
extracted_data[key] = int(match.group(1))
|
||||
return extracted_data
|
||||
|
||||
@classmethod
|
||||
def extract_date_parts_en(cls, input_string: str) -> dict:
|
||||
"""
|
||||
Extracts various components of a date (year, month, day, etc.) from an input string based on English formats.
|
||||
|
||||
This method employs regex patterns to identify and parse different date and time elements within the provided
|
||||
text. It supports extraction of year, month name, day, 12-hour and 24-hour time formats, and weekdays.
|
||||
|
||||
Args:
|
||||
input_string (str): The English text containing date and time information.
|
||||
|
||||
Returns:
|
||||
dict: A dictionary containing the extracted date parts with default values of -1 where components are not
|
||||
found. Keys include 'year', 'month', 'day', 'hour', 'minute', 'second', and 'weekday'.
|
||||
"""
|
||||
date_info = {
|
||||
"year": -1,
|
||||
"month": -1,
|
||||
"day": -1,
|
||||
"hour": -1,
|
||||
"minute": -1,
|
||||
"second": -1,
|
||||
"weekday": -1
|
||||
}
|
||||
|
||||
# Patterns to extract the parts of the date/time
|
||||
patterns = {
|
||||
"year": r"\b(\d{4})\b",
|
||||
"month": r"\b(January|February|March|April|May|June|July|August|September|October|November|December)\b",
|
||||
"day_month_year": r"\b(?P<month>January|February|March|April|May|June|July|August|September|October"
|
||||
r"|November|December) (?P<day>\d{1,2}),? (?P<year>\d{4})\b",
|
||||
"day_month": r"\b(?P<month>January|February|March|April|May|June|July|August|September|October|November"
|
||||
r"|December) (?P<day>\d{1,2})\b",
|
||||
"hour_12": r"\b(\d{1,2})\s*(AM|PM|am|pm)\b",
|
||||
"hour_24": r"\b(\d{1,2}):(\d{2}):(\d{2})\b"
|
||||
}
|
||||
|
||||
month_mapping = {
|
||||
"January": 1, "February": 2, "March": 3, "April": 4, "May": 5, "June": 6, "July": 7, "August": 8,
|
||||
"September": 9, "October": 10, "November": 11, "December": 12
|
||||
}
|
||||
|
||||
weekday_mapping = {
|
||||
"Monday": 1, "Tuesday": 2, "Wednesday": 3, "Thursday": 4, "Friday": 5, "Saturday": 6, "Sunday": 7
|
||||
}
|
||||
|
||||
# Attempt to match full date (day month year)
|
||||
day_month_year_match = re.search(patterns["day_month_year"], input_string)
|
||||
if day_month_year_match:
|
||||
date_info["year"] = int(day_month_year_match.group("year"))
|
||||
date_info["month"] = month_mapping[day_month_year_match.group("month")]
|
||||
date_info["day"] = int(day_month_year_match.group("day"))
|
||||
|
||||
# If year wasn't found, try matching day and month without year
|
||||
elif date_info["year"] == -1:
|
||||
day_month_match = re.search(patterns["day_month"], input_string)
|
||||
if day_month_match:
|
||||
date_info["month"] = month_mapping[day_month_match.group("month")]
|
||||
date_info["day"] = int(day_month_match.group("day"))
|
||||
|
||||
# Extract year if not already found
|
||||
if date_info["year"] == -1:
|
||||
year_match = re.search(patterns["year"], input_string)
|
||||
if year_match:
|
||||
date_info["year"] = int(year_match.group(0))
|
||||
|
||||
# Extract month if not already found
|
||||
if date_info["month"] == -1:
|
||||
month_match = re.search(patterns["month"], input_string)
|
||||
if month_match:
|
||||
date_info["month"] = month_mapping[month_match.group(0)]
|
||||
|
||||
# Extract 12-hour format time
|
||||
hour_12_match = re.search(patterns["hour_12"], input_string)
|
||||
if hour_12_match:
|
||||
hour, period = int(hour_12_match.group(1)), hour_12_match.group(2).lower()
|
||||
if period == 'pm' and hour != 12:
|
||||
hour += 12
|
||||
elif period == 'am' and hour == 12:
|
||||
hour = 0
|
||||
date_info["hour"] = hour
|
||||
|
||||
# Identify weekday
|
||||
for week_day, value in weekday_mapping.items():
|
||||
if week_day in input_string:
|
||||
date_info["weekday"] = value
|
||||
break
|
||||
|
||||
return date_info
|
||||
|
||||
@classmethod
|
||||
def extract_date_parts(cls, input_string: str, language: LanguageEnum) -> dict:
|
||||
"""
|
||||
Extracts various date components from the input string based on the current language context.
|
||||
|
||||
This method dynamically selects a language-specific function to parse the input string and extract
|
||||
date parts such as year, month, day, etc. If the function for current language context does not exist,
|
||||
a warning is logged and an empty dictionary is returned.
|
||||
|
||||
Args:
|
||||
input_string (str): The string containing date information to be parsed.
|
||||
language (str): current language.
|
||||
|
||||
Returns:
|
||||
dict: A dictionary containing extracted date components, or an empty dictionary if parsing fails.
|
||||
"""
|
||||
func_name = f"extract_date_parts_{language.value}"
|
||||
if not hasattr(cls, func_name):
|
||||
# cls.logger.warning(f"language={language.value} needs to complete extract_date_parts func!")
|
||||
return {}
|
||||
return getattr(cls, func_name)(input_string=input_string)
|
||||
|
||||
@classmethod
|
||||
def has_time_word_cn(cls, query: str, datetime_word_list: List[str]) -> bool:
|
||||
"""
|
||||
Check if the input query contains any datetime-related words based on the cn language context.
|
||||
|
||||
Args:
|
||||
query (str): The input string to check for datetime-related words.
|
||||
datetime_word_list (list[str]): datetime keywords
|
||||
|
||||
Returns:
|
||||
bool: True if the query contains at least one datetime-related word, False otherwise.
|
||||
"""
|
||||
contain_datetime = False
|
||||
# TODO use re
|
||||
for datetime_word in datetime_word_list:
|
||||
if datetime_word in query:
|
||||
contain_datetime = True
|
||||
break
|
||||
return contain_datetime
|
||||
|
||||
@classmethod
|
||||
def has_time_word_en(cls, query: str, datetime_word_list: List[str]) -> bool:
|
||||
"""
|
||||
Check if the input query contains any datetime-related words based on the en language context.
|
||||
|
||||
Args:
|
||||
query (str): The input string to check for datetime-related words.
|
||||
datetime_word_list (list[str]): datetime keywords
|
||||
|
||||
Returns:
|
||||
bool: True if the query contains at least one datetime-related word, False otherwise.
|
||||
"""
|
||||
contain_datetime = False
|
||||
for datetime_word in datetime_word_list:
|
||||
datetime_word = datetime_word.lower()
|
||||
# TODO fix strip
|
||||
if datetime_word in [x.strip().lower().strip(",").strip(".").strip("?").strip(":")
|
||||
for x in query.split(" ")]:
|
||||
contain_datetime = True
|
||||
break
|
||||
return contain_datetime
|
||||
|
||||
@classmethod
|
||||
def has_time_word(cls, query: str, language: LanguageEnum) -> bool:
|
||||
func_name = f"has_time_word_{language.value}"
|
||||
if not hasattr(cls, func_name):
|
||||
# cls.logger.warning(f"language={language.value} needs to complete has_time_word function!")
|
||||
return False
|
||||
|
||||
if language not in DATATIME_WORD_LIST:
|
||||
# cls.logger.warning(f"language={language.value} is missing in DATATIME_WORD_LIST!")
|
||||
return False
|
||||
|
||||
datetime_word_list = DATATIME_WORD_LIST[language]
|
||||
return getattr(cls, func_name)(query=query, datetime_word_list=datetime_word_list)
|
||||
|
||||
def datetime_format(self, dt_format: str = "%Y%m%d") -> str:
|
||||
"""
|
||||
Format the stored datetime object into a string based on the provided format.
|
||||
|
||||
Args:
|
||||
dt_format (str, optional): The datetime format string. Defaults to "%Y%m%d".
|
||||
|
||||
Returns:
|
||||
str: A formatted datetime string.
|
||||
"""
|
||||
return self._dt.strftime(dt_format)
|
||||
|
||||
def string_format(self, string_format: str, language: LanguageEnum) -> str:
|
||||
"""
|
||||
Format the datetime information stored in the instance using a custom string format.
|
||||
|
||||
Args:
|
||||
string_format (str): A format string where placeholders are keys from `dt_info_dict`.
|
||||
language (str): current language.
|
||||
|
||||
Returns:
|
||||
str: A formatted datetime string.
|
||||
"""
|
||||
return string_format.format(**self.get_dt_info_dict(language=language))
|
||||
|
||||
@property
|
||||
def timestamp(self) -> int:
|
||||
"""
|
||||
Get the timestamp representation of the stored datetime.
|
||||
|
||||
Returns:
|
||||
int: A timestamp value.
|
||||
"""
|
||||
return int(self._dt.timestamp())
|
||||
|
|
@ -1,248 +0,0 @@
|
|||
import os
|
||||
import logging
|
||||
import pprint
|
||||
from logging.handlers import RotatingFileHandler
|
||||
from pathlib import Path
|
||||
from rich.console import Console
|
||||
from rich.panel import Panel
|
||||
from rich.text import Text
|
||||
|
||||
LOG_FORMAT = "%(asctime)s %(levelname)s [%(module)s:%(lineno)d] %(message)s"
|
||||
DATE_FORMAT = "%Y-%m-%d %H:%M:%S"
|
||||
|
||||
LOGGER_DICT = {}
|
||||
|
||||
def rich2text(rich_table):
|
||||
console = Console(width=150)
|
||||
with console.capture() as capture:
|
||||
console.print(rich_table)
|
||||
return '\n' + str(Text.from_ansi(capture.get()))
|
||||
|
||||
def append_memoryscope_uuid(dir_path):
|
||||
from memoryscope.core.memoryscope_context import get_memoryscope_uuid # pylint: disable=import-outside-toplevel
|
||||
dir_path = os.path.join(dir_path, get_memoryscope_uuid())
|
||||
return dir_path
|
||||
|
||||
class Logger(logging.Logger):
|
||||
"""
|
||||
The `Logger` class handle the stream of information or errors in activities.
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
name: str,
|
||||
level: int = logging.INFO,
|
||||
format_style: str = LOG_FORMAT,
|
||||
date_format_style: str = DATE_FORMAT,
|
||||
to_stream: bool = False,
|
||||
to_file: bool = True,
|
||||
file_mode: str = "w",
|
||||
file_type: str = "log",
|
||||
dir_path: str = "log",
|
||||
max_bytes: int = 1024 * 1024 * 1024,
|
||||
backup_count: int = 10):
|
||||
"""
|
||||
Initializes the Logger instance, setting up handlers for console and file logging based on provided parameters.
|
||||
|
||||
Args:
|
||||
name (str): Identifier for the logger.
|
||||
level (int, optional): Logging level. Defaults to logging.INFO.
|
||||
format_style (str, optional): Log message format. Defaults to LOG_FORMAT constant.
|
||||
date_format_style (str, optional): Date format for logs. Defaults to DATE_FORMAT constant.
|
||||
to_stream (bool, optional): Enables console logging. Defaults to True.
|
||||
to_file (bool, optional): Enables file logging. Defaults to True.
|
||||
file_mode (str, optional): File open mode. Defaults to 'w'.
|
||||
file_type (str, optional): Log file extension type. Defaults to 'log'.
|
||||
dir_path (str, optional): Directory for log files. Defaults to 'log'.
|
||||
max_bytes (int, optional): Maximum log file size before rotation. Defaults to 1GB.
|
||||
backup_count (int, optional): Number of rotated log files to retain. Defaults to 10.
|
||||
"""
|
||||
super(Logger, self).__init__(name, level)
|
||||
|
||||
self.formatter = logging.Formatter(format_style, date_format_style)
|
||||
self.date_format_style = date_format_style
|
||||
self.to_stream: bool = to_stream
|
||||
self.to_file: bool = to_file
|
||||
self.file_mode: str = file_mode
|
||||
self.file_type: str = file_type
|
||||
self.dir_path: str = dir_path
|
||||
|
||||
self.max_bytes: int = max_bytes
|
||||
self.backup_count: int = backup_count
|
||||
|
||||
self.trace_id: str = ""
|
||||
|
||||
if self.to_stream:
|
||||
self._add_stream_handler() # Adds a handler to output logs to the console
|
||||
if self.to_file:
|
||||
self._add_file_handler() # Adds a handler to output logs to a file
|
||||
|
||||
self.info(f"logger={name} is inited.") # Logs an initialization message
|
||||
|
||||
def log_dictionary_info(self, dictionary, title=""):
|
||||
self.info(self.format_current_context(dictionary, title))
|
||||
|
||||
def format_current_context(self, context, title=""):
|
||||
pp = pprint.PrettyPrinter()
|
||||
pretty_string = pp.pformat(context)
|
||||
if title:
|
||||
pretty_string = f"{title}\n{pretty_string}"
|
||||
return self.wrap_in_box(pretty_string)
|
||||
|
||||
def wrap_in_box(self, context):
|
||||
return rich2text(Panel(context, width=128))
|
||||
|
||||
def format_chat_message(self, message):
|
||||
buf = []
|
||||
buf.append('\n')
|
||||
buf.append(f"LM Input:\n")
|
||||
for chat_message in message.meta_data['data']['messages']:
|
||||
buf.append(chat_message.content)
|
||||
buf.append('\n')
|
||||
buf.append(f"------------------------------------------\n")
|
||||
buf.append(f"LM Output:\n")
|
||||
buf.append(message.message.content)
|
||||
buf.append('\n')
|
||||
buf.append('\n')
|
||||
return self.wrap_in_box(''.join(buf))
|
||||
|
||||
def format_rank_message(self, model_response):
|
||||
buf = []
|
||||
buf.append('\n')
|
||||
buf.append(f"Query Input:\n")
|
||||
buf.append(model_response.meta_data['data']['query_str'])
|
||||
buf.append('\n')
|
||||
buf.append(f"------------------------------------------\n")
|
||||
buf.append(f"Rank:\n")
|
||||
rank = 0
|
||||
for index, score in model_response.rank_scores.items():
|
||||
rank += 1
|
||||
node = model_response.meta_data['data']['nodes'][index]
|
||||
node_text = node.text
|
||||
buf.append(f"Score {score} | Rank {rank} | {node_text}\n")
|
||||
buf.append('\n')
|
||||
buf.append('\n')
|
||||
return self.wrap_in_box(''.join(buf))
|
||||
|
||||
def _add_file_handler(self):
|
||||
"""
|
||||
Adds a file handler to the logger which logs messages to a rotating file.
|
||||
|
||||
The file is stored in a specified directory with a name derived from the logger's name and type.
|
||||
The file handler is set up to rotate when it reaches a certain size and keeps a defined number of backups.
|
||||
|
||||
This method ensures the directory exists before creating the file handler and sets the formatter
|
||||
for consistent log message formatting.
|
||||
"""
|
||||
file_path = Path().joinpath(self.dir_path, f"{self.name}.{self.file_type}")
|
||||
os.makedirs(file_path.parent, exist_ok=True) # Ensure the directory exists
|
||||
file_name = file_path.as_posix() # Get the absolute path as a string
|
||||
if not hasattr(Logger, 'notice_print'):
|
||||
Console().print(f"\nRegistering loggers at: {os.path.abspath(os.path.dirname(file_name))}. System logs can be found in this directory.\n", style="bold red")
|
||||
Logger.notice_print = True
|
||||
# Instantiate a rotating file handler with specified parameters
|
||||
file_handler = RotatingFileHandler(
|
||||
filename=file_name,
|
||||
maxBytes=self.max_bytes, # Maximum size of the log file before rotation
|
||||
backupCount=self.backup_count, # Number of backup files to keep
|
||||
encoding="utf-8") # Set the encoding to UTF-8
|
||||
file_handler.setFormatter(self.formatter) # Apply the logger's formatter to the handler
|
||||
self.addHandler(file_handler) # Add the file handler to this logger instance
|
||||
|
||||
def _add_stream_handler(self):
|
||||
"""
|
||||
Adds a stream handler to the logger for console output. The handler is configured
|
||||
with the logger's formatter and set to use UTF-8 encoding.
|
||||
"""
|
||||
stream_handler = logging.StreamHandler()
|
||||
stream_handler.setFormatter(self.formatter) # Configure the handler with the logger's formatter
|
||||
stream_handler.encoding = 'utf-8' # Set the handler's encoding to UTF-8
|
||||
self.addHandler(stream_handler) # Add the handler to the logger
|
||||
|
||||
def close(self):
|
||||
"""
|
||||
Closes all handlers associated with this logger instance.
|
||||
|
||||
This method iterates over the handlers attached to the logger and
|
||||
calls their `close` method to ensure that any system resources used
|
||||
by the handlers are freed properly.
|
||||
"""
|
||||
for handler in self.handlers:
|
||||
# Close each handler to release resources
|
||||
handler.close()
|
||||
|
||||
def clear(self):
|
||||
"""
|
||||
Clears all handlers from the logger.
|
||||
"""
|
||||
self.handlers.clear()
|
||||
|
||||
def set_trace_id(self, trace_id: str):
|
||||
"""
|
||||
Sets the trace ID for the logger. If the provided trace ID is longer than 8 characters,
|
||||
it will be truncated to the first 8 characters.
|
||||
|
||||
Args:
|
||||
trace_id (str): The trace identifier to be associated with the logs.
|
||||
"""
|
||||
self.trace_id: str = trace_id
|
||||
if len(self.trace_id) >= 8:
|
||||
self.trace_id = self.trace_id[:8]
|
||||
|
||||
def makeRecord(self, name, level, fn, lno, msg, args, exc_info,
|
||||
func=None, extra=None, sinfo=None):
|
||||
"""
|
||||
Creates a log record with additional trace_id included in the extra information.
|
||||
|
||||
This method extends the default behavior of creating a log record by adding
|
||||
a trace_id from the logger instance to the record's extra data, allowing
|
||||
for traceability within logged data.
|
||||
|
||||
Args:
|
||||
name (str): The name of the logger.
|
||||
level (int): The logging level of the record.
|
||||
fn (str): The name of the function containing the logging call.
|
||||
lno (int): The line number at which the logging call was made.
|
||||
msg (str): The logged message, before formatting.
|
||||
args (tuple): The arguments to the log message.
|
||||
exc_info (tuple): Exception information or None.
|
||||
func (function): The function where the logging call was made. Defaults to None.
|
||||
extra (dict): Additional information for the log record. Defaults to None.
|
||||
sinfo (str): Stack trace information or None.
|
||||
|
||||
Returns:
|
||||
logging.LogRecord: The created log record with potentially enriched 'extra' field.
|
||||
"""
|
||||
if extra is None:
|
||||
extra = {}
|
||||
if self.trace_id:
|
||||
extra["trace_id"] = self.trace_id # Include trace_id from the logger in the log record extra data
|
||||
return super().makeRecord(name, level, fn, lno, msg, args, exc_info, func, extra, sinfo)
|
||||
|
||||
@classmethod
|
||||
def get_logger(cls, name: str = None, **kwargs):
|
||||
"""
|
||||
Retrieves or creates a logger instance with the specified name and configurations.
|
||||
|
||||
If no name is provided, it defaults to the first registered logger's name or 'default' if none exist.
|
||||
This method ensures that only one logger instance exists per name by reusing existing instances
|
||||
stored in `LOGGER_DICT`.
|
||||
|
||||
Args:
|
||||
name (str, optional): The name of the logger. Defaults to None, which triggers auto-naming logic.
|
||||
**kwargs: Additional keyword arguments to configure the logger.
|
||||
|
||||
Returns:
|
||||
Logger: The requested or newly created logger instance.
|
||||
"""
|
||||
if name is None:
|
||||
if LOGGER_DICT:
|
||||
name = list(LOGGER_DICT.keys())[0]
|
||||
else:
|
||||
name = "default"
|
||||
|
||||
if name not in LOGGER_DICT:
|
||||
logger_dir = kwargs.get('dir_path', 'log')
|
||||
logger_dir = append_memoryscope_uuid(logger_dir)
|
||||
LOGGER_DICT[name] = Logger(name=name, dir_path=logger_dir, **kwargs)
|
||||
|
||||
return LOGGER_DICT[name]
|
||||
|
|
@ -1,164 +0,0 @@
|
|||
import json
|
||||
import os.path
|
||||
from pathlib import Path
|
||||
from typing import Dict
|
||||
|
||||
import yaml
|
||||
|
||||
from memoryscope.enumeration.language_enum import LanguageEnum
|
||||
|
||||
|
||||
class PromptHandler(object):
|
||||
"""
|
||||
The `PromptHandler` class manages prompt messages by loading them from YAML or JSON files and dictionaries,
|
||||
supporting language selection based on a context, and providing dictionary-like access to the prompt messages.
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
class_path: str,
|
||||
language: LanguageEnum | str,
|
||||
prompt_file: str = "",
|
||||
prompt_dict: dict = None,
|
||||
**kwargs):
|
||||
"""
|
||||
Initializes the PromptHandler with paths to prompt sources and additional keyword arguments.
|
||||
|
||||
Args:
|
||||
class_path (str): The path to the class where prompts are utilized.
|
||||
prompt_file (str, optional): The path to an external file containing prompts. Defaults to "".
|
||||
prompt_dict (dict, optional): A dictionary directly containing prompt definitions. Defaults to None.
|
||||
language (LanguageEnum, str): context language.
|
||||
**kwargs: Additional keyword arguments that might be used in prompt handling.
|
||||
"""
|
||||
class_path: Path = Path(class_path)
|
||||
self._class_dir: Path = class_path.parent
|
||||
self._class_name: str = class_path.stem
|
||||
self._language_enum: LanguageEnum = LanguageEnum(language)
|
||||
self.kwargs = kwargs
|
||||
|
||||
self._prompt_dict: Dict[str, str] = {}
|
||||
|
||||
self.add_prompt_file((self._class_dir / self._class_name).__str__(), raise_exception=False)
|
||||
if prompt_file:
|
||||
self.add_prompt_file((self._class_dir / prompt_file).__str__())
|
||||
if prompt_dict:
|
||||
self.add_prompt_dict(prompt_dict)
|
||||
|
||||
@staticmethod
|
||||
def file_path_completion(file_path: str, raise_exception: bool = True) -> str:
|
||||
"""
|
||||
Attempts to complete the given file path by appending either a `.yaml` or `.json` extension
|
||||
based on the existence of the respective file. If neither exists, an exception is raised.
|
||||
|
||||
Args:
|
||||
file_path (str): The base path of the file to be completed.
|
||||
raise_exception (bool): If the file cannot be found, report an error.
|
||||
|
||||
Returns:
|
||||
str: The completed file path with the appropriate extension.
|
||||
|
||||
Raises:
|
||||
RuntimeError: If neither the `.yaml` nor `.json` file exists at the given path.
|
||||
"""
|
||||
if file_path.endswith(".yaml") or file_path.endswith(".json"):
|
||||
return file_path
|
||||
|
||||
if os.path.exists(f"{file_path}.yaml"):
|
||||
return f"{file_path}.yaml"
|
||||
|
||||
if os.path.exists(f"{file_path}.json"):
|
||||
return f"{file_path}.json"
|
||||
|
||||
if raise_exception:
|
||||
raise RuntimeError(f"{file_path}/yaml/json is not exists!")
|
||||
|
||||
def add_prompt_file(self, file_path: str, raise_exception: bool = True):
|
||||
"""
|
||||
Adds prompt messages from a YAML or JSON file to the internal dictionary.
|
||||
|
||||
This method supports loading prompts from files ending with '.yaml' or '.json'.
|
||||
It uses the respective libraries to parse the content and merge it into the current prompt dictionary.
|
||||
|
||||
Args:
|
||||
file_path (str): The path to the YAML or JSON file containing the prompts.
|
||||
raise_exception (bool): If the file cannot be found, report an error.
|
||||
"""
|
||||
file_path = self.file_path_completion(file_path, raise_exception=raise_exception)
|
||||
if not file_path:
|
||||
return
|
||||
|
||||
prompt_dict = {}
|
||||
|
||||
if file_path.endswith(".yaml"):
|
||||
# Load prompts from a YAML file
|
||||
with open(file_path) as f:
|
||||
prompt_dict = yaml.load(f, yaml.FullLoader)
|
||||
|
||||
elif file_path.endswith(".json"):
|
||||
# Load prompts from a JSON file (corrected file handling)
|
||||
with open(file_path) as f:
|
||||
prompt_dict = json.load(f)
|
||||
|
||||
# Merge the loaded prompts into the existing dictionary
|
||||
self.add_prompt_dict(prompt_dict)
|
||||
|
||||
def add_prompt_dict(self, prompt_dict: dict):
|
||||
"""
|
||||
Adds prompt messages from a dictionary, ensuring each message has a valid entry for the current language.
|
||||
|
||||
Args:
|
||||
prompt_dict (dict): A dictionary where keys represent prompt identifiers and values are nested dictionaries
|
||||
containing language-specific prompt messages.
|
||||
|
||||
Raises:
|
||||
RuntimeError: If a prompt message for the current language is not found.
|
||||
"""
|
||||
for key, language_dict in prompt_dict.items():
|
||||
prompts = language_dict.get(self._language_enum.value)
|
||||
if not prompts:
|
||||
raise RuntimeError(f"{key}.prompt.{self._language_enum.value} is empty!")
|
||||
self._prompt_dict[key] = prompts.strip()
|
||||
|
||||
@property
|
||||
def prompt_dict(self) -> dict:
|
||||
"""
|
||||
Retrieves the internal dictionary containing all prompt messages.
|
||||
|
||||
Returns:
|
||||
dict: The dictionary of prompt messages with keys as identifiers and values as prompt strings.
|
||||
"""
|
||||
return self._prompt_dict
|
||||
|
||||
def __getitem__(self, key: str) -> str:
|
||||
"""
|
||||
Enables accessing prompt messages using dictionary-like indexing.
|
||||
|
||||
Args:
|
||||
key (str): The identifier for the prompt message.
|
||||
|
||||
Returns:
|
||||
str: The prompt message corresponding to the given key.
|
||||
"""
|
||||
return self._prompt_dict[key]
|
||||
|
||||
def __setitem__(self, key: str, value: str):
|
||||
"""
|
||||
Allows setting prompt messages using dictionary-like item assignment.
|
||||
|
||||
Args:
|
||||
key (str): The identifier for the prompt message.
|
||||
value (str): The new prompt message content.
|
||||
"""
|
||||
self._prompt_dict[key] = value
|
||||
|
||||
def __getattr__(self, key: str) -> str:
|
||||
"""
|
||||
Overrides attribute access to provide prompt messages dynamically.
|
||||
|
||||
Args:
|
||||
key (str): The identifier for the prompt message attempted to access as an attribute.
|
||||
|
||||
Returns:
|
||||
str: The prompt message corresponding to the given attribute-like key.
|
||||
"""
|
||||
return self._prompt_dict[key]
|
||||
|
|
@ -1,81 +0,0 @@
|
|||
"""
|
||||
Registry for different modules.
|
||||
Init class according to the class name and verify the input parameters.
|
||||
"""
|
||||
from typing import Dict, Any, List
|
||||
|
||||
|
||||
class Registry(object):
|
||||
"""
|
||||
A registry to manage and instantiate various modules by their names, ensuring the uniqueness of registered entries.
|
||||
It supports both individual and bulk registration of modules, as well as retrieval of modules by name.
|
||||
|
||||
Attributes:
|
||||
name (str): The name of the registry.
|
||||
module_dict (Dict[str, Any]): A dictionary holding registered modules where keys are module names and values are
|
||||
the modules themselves.
|
||||
"""
|
||||
|
||||
def __init__(self, name: str):
|
||||
"""
|
||||
Initializes the Registry with a given name.
|
||||
|
||||
Args:
|
||||
name (str): The name to identify this registry.
|
||||
"""
|
||||
self.name: str = name
|
||||
self.module_dict: Dict[str, Any] = {}
|
||||
|
||||
def register(self, module_name: str = None, module: Any = None):
|
||||
"""
|
||||
Registers module in the registry in a single call.
|
||||
|
||||
Args:
|
||||
module_name (str): The name of module to be registered.
|
||||
module (List[Any] | Dict[str, Any]): The module to be registered.
|
||||
|
||||
Raises:
|
||||
NotImplementedError: If the input is already registered.
|
||||
"""
|
||||
assert module is not None
|
||||
if module_name is None:
|
||||
module_name = module.__name__
|
||||
|
||||
if module_name in self.module_dict:
|
||||
raise KeyError(f'{module_name} is already registered in {self.name}')
|
||||
self.module_dict[module_name] = module
|
||||
|
||||
def batch_register(self, modules: List[Any] | Dict[str, Any]):
|
||||
"""
|
||||
Registers multiple modules in the registry in a single call. Accepts either a list of modules or a dictionary
|
||||
mapping names to modules.
|
||||
|
||||
Args:
|
||||
modules (List[Any] | Dict[str, Any]): A list of modules or a dictionary mapping module names to the modules.
|
||||
|
||||
Raises:
|
||||
NotImplementedError: If the input is neither a list nor a dictionary.
|
||||
"""
|
||||
if isinstance(modules, list):
|
||||
module_name_dict = {m.__name__: m for m in modules}
|
||||
elif isinstance(modules, dict):
|
||||
module_name_dict = modules
|
||||
else:
|
||||
raise NotImplementedError("Input must be a list or a dictionary.")
|
||||
self.module_dict.update(module_name_dict)
|
||||
|
||||
def __getitem__(self, module_name: str):
|
||||
"""
|
||||
Retrieves a registered module by its name using index notation.
|
||||
|
||||
Args:
|
||||
module_name (str): The name of the module to retrieve.
|
||||
|
||||
Returns:
|
||||
A registered module corresponding to the given name.
|
||||
|
||||
Raises:
|
||||
AssertionError: If the specified module is not found in the registry.
|
||||
"""
|
||||
assert module_name in self.module_dict, f"{module_name} not found in {self.name}"
|
||||
return self.module_dict[module_name]
|
||||
|
|
@ -1,60 +0,0 @@
|
|||
import re
|
||||
from typing import List
|
||||
|
||||
from memoryscope.constants.language_constants import NONE_WORD
|
||||
from memoryscope.core.utils.logger import Logger
|
||||
from memoryscope.enumeration.language_enum import LanguageEnum
|
||||
|
||||
|
||||
class ResponseTextParser(object):
|
||||
"""
|
||||
The `ResponseTextParser` class is designed to parse and process response texts. It provides methods to extract
|
||||
patterns from the text and filter out unnecessary information, while also logging the processing steps and outcomes.
|
||||
"""
|
||||
|
||||
PATTERN_V1 = re.compile(r"<(.*?)>") # Regular expression pattern to match content within angle brackets
|
||||
|
||||
def __init__(self, response_text: str, language: LanguageEnum, logger_prefix: str = ""):
|
||||
# Strips leading and trailing whitespace from the response text
|
||||
self.response_text: str = response_text.strip()
|
||||
self.language: LanguageEnum = language
|
||||
|
||||
# The prefix of log. Defaults to "".
|
||||
self.logger_prefix: str = logger_prefix
|
||||
|
||||
# Initializes a logger instance for logging parsing activities
|
||||
self.logger: Logger = Logger.get_logger()
|
||||
|
||||
def parse_v1(self) -> List[List[str]]:
|
||||
"""
|
||||
Extract specific patterns from the text which match content within angle brackets.
|
||||
|
||||
Returns:
|
||||
Contents match the specific patterns.
|
||||
"""
|
||||
result = []
|
||||
for line in self.response_text.split("\n"):
|
||||
line = line.strip()
|
||||
if not line:
|
||||
continue
|
||||
matches = [match.group(1) for match in self.PATTERN_V1.finditer(line)]
|
||||
if matches:
|
||||
result.append(matches)
|
||||
self.logger.info(f"{self.logger_prefix} response_text={self.response_text} result={result}", stacklevel=2)
|
||||
return result
|
||||
|
||||
def parse_v2(self) -> List[str]:
|
||||
"""
|
||||
Extract lines which contain NONE_WORD.
|
||||
|
||||
Returns:
|
||||
Contents match the specific patterns.
|
||||
"""
|
||||
result = []
|
||||
for line in self.response_text.split("\n"):
|
||||
line = line.strip()
|
||||
if not line or line.lower() == NONE_WORD.get(self.language):
|
||||
continue
|
||||
result.append(line)
|
||||
self.logger.info(f"{self.logger_prefix} response_text={self.response_text} result={result}", stacklevel=2)
|
||||
return result
|
||||
|
|
@ -1,9 +0,0 @@
|
|||
def singleton(cls):
|
||||
_instance = {}
|
||||
|
||||
def _singleton(*args, **kargs):
|
||||
if cls not in _instance:
|
||||
_instance[cls] = cls(*args, **kargs)
|
||||
return _instance[cls]
|
||||
|
||||
return _singleton
|
||||
|
|
@ -1,130 +0,0 @@
|
|||
import time
|
||||
from typing import Literal
|
||||
|
||||
from memoryscope.core.utils.logger import Logger
|
||||
|
||||
TIME_LOG_TYPE = Literal["end", "wrap", "none"]
|
||||
|
||||
|
||||
class Timer(object):
|
||||
"""
|
||||
A class used to measure the execution time of code blocks. It supports logging the elapsed time and can be
|
||||
customized to display time in seconds or milliseconds.
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
name: str,
|
||||
time_log_type: TIME_LOG_TYPE = "end",
|
||||
use_ms: bool = True,
|
||||
stack_level: int = 2,
|
||||
float_precision: int = 4,
|
||||
**kwargs):
|
||||
|
||||
"""
|
||||
Initializes the `Timer` instance with the provided args and sets up a logger
|
||||
|
||||
Args:
|
||||
name (str): The log name.
|
||||
time_log_type (str): The log type. Defaults to 'End'.
|
||||
use_ms (bool): Use 'ms' as the timescale or not. Defaults to True.
|
||||
stack_level (int): The stack level of log. Defaults to 2.
|
||||
float_precision (int): The precision of cost time. Defaults to 4.
|
||||
|
||||
"""
|
||||
|
||||
self.name: str = name
|
||||
self.time_log_type: TIME_LOG_TYPE = time_log_type
|
||||
self.use_ms: bool = use_ms
|
||||
self.stack_level: int = stack_level
|
||||
self.float_precision: int = float_precision
|
||||
self.kwargs: dict = kwargs
|
||||
|
||||
# time recorder
|
||||
self.t_start = 0
|
||||
self.t_end = 0
|
||||
self.cost = 0
|
||||
|
||||
self.logger = Logger.get_logger()
|
||||
|
||||
def _set_cost(self):
|
||||
"""
|
||||
Accumulate the cost time.
|
||||
"""
|
||||
self.t_end = time.time()
|
||||
self.cost = self.t_end - self.t_start
|
||||
if self.use_ms:
|
||||
self.cost *= 1000
|
||||
|
||||
@property
|
||||
def cost_str(self):
|
||||
"""
|
||||
Represent the cost time into a formatted string.
|
||||
"""
|
||||
self._set_cost()
|
||||
if self.use_ms:
|
||||
return f"cost={self.cost:.4f}ms"
|
||||
else:
|
||||
return f"cost={self.cost:.4f}s"
|
||||
|
||||
def __enter__(self, *args, **kwargs):
|
||||
"""
|
||||
Begin timing.
|
||||
"""
|
||||
self.t_start = time.time()
|
||||
if self.time_log_type == "wrap":
|
||||
self.logger.info(f"----- {self.name}.begin -----")
|
||||
return self
|
||||
|
||||
def __exit__(self, exc_type, exc_value, exc_tb):
|
||||
"""
|
||||
End timing and print the formatted log.
|
||||
"""
|
||||
if self.time_log_type == "none":
|
||||
return
|
||||
|
||||
lines = []
|
||||
if self.time_log_type == "wrap":
|
||||
lines.append(f"----- {self.name}.end -----")
|
||||
else:
|
||||
lines.append(self.name)
|
||||
|
||||
lines.append(self.cost_str)
|
||||
|
||||
if self.kwargs:
|
||||
for k, v in self.kwargs.items():
|
||||
if isinstance(v, float):
|
||||
float_style = f".{self.float_precision}f"
|
||||
line = f"{k}={v:{float_style}}"
|
||||
else:
|
||||
line = f"{k}={v}"
|
||||
lines.append(line)
|
||||
|
||||
self.logger.info(" ".join(lines), stacklevel=self.stack_level)
|
||||
|
||||
|
||||
def timer(func):
|
||||
"""
|
||||
A decorator function that measures the execution time of the wrapped function.
|
||||
|
||||
Args:
|
||||
func (Callable): The function to be wrapped and timed.
|
||||
|
||||
Returns:
|
||||
Callable: The wrapper function that includes timing functionality.
|
||||
"""
|
||||
|
||||
def wrapper(*args, **kwargs):
|
||||
"""
|
||||
The wrapper function that manages the timing of the original function.
|
||||
|
||||
Args:
|
||||
*args: Variable length argument list for the decorated function.
|
||||
**kwargs: Arbitrary keyword arguments for the decorated function.
|
||||
|
||||
Returns:
|
||||
Any: The result of the decorated function.
|
||||
"""
|
||||
with Timer(name=func.__name__, **kwargs):
|
||||
return func(*args, **kwargs)
|
||||
|
||||
return wrapper
|
||||
|
|
@ -1,220 +0,0 @@
|
|||
import hashlib
|
||||
import random
|
||||
import re
|
||||
import time
|
||||
from copy import deepcopy
|
||||
from importlib import import_module
|
||||
from typing import List
|
||||
|
||||
import numpy as np
|
||||
import pyfiglet
|
||||
from termcolor import colored
|
||||
|
||||
from memoryscope.enumeration.message_role_enum import MessageRoleEnum
|
||||
from memoryscope.scheme.message import Message
|
||||
|
||||
ALL_COLORS = ["red", "green", "yellow", "blue", "magenta", "cyan", "light_grey", "light_red", "light_green",
|
||||
"light_yellow", "light_blue", "light_magenta", "light_cyan", "white"]
|
||||
|
||||
|
||||
def underscore_to_camelcase(name: str, is_first_title: bool = True) -> str:
|
||||
"""
|
||||
Converts an underscore_notation string to CamelCase.
|
||||
|
||||
Args:
|
||||
name (str): The underscore_notation string to be converted.
|
||||
is_first_title (bool): Title the first word or not. Defaults to True
|
||||
|
||||
Returns:
|
||||
str: A CamelCase formatted string.
|
||||
"""
|
||||
name_split = name.split("_")
|
||||
if is_first_title:
|
||||
return "".join(x.title() for x in name_split)
|
||||
else:
|
||||
return name_split[0] + ''.join(x.title() for x in name_split[1:])
|
||||
|
||||
|
||||
def camelcase_to_underscore(name: str) -> str:
|
||||
"""
|
||||
Converts a CamelCase string to underscore_notation.
|
||||
|
||||
Args:
|
||||
name (str): The CamelCase formatted string to be converted.
|
||||
|
||||
Returns:
|
||||
str: A converted string in underscore_notation.
|
||||
"""
|
||||
return re.sub(r'(?<!^)(?=[A-Z])', '_', name).lower()
|
||||
|
||||
|
||||
def init_instance_by_config(config: dict, default_class_dir: str = "memoryscope", **kwargs):
|
||||
"""
|
||||
Initialize an instance of a class specified in the configuration dictionary.
|
||||
|
||||
This function dynamically imports a class from a module path, allowing for
|
||||
user-defined classes or default paths. It supports adding a suffix to the
|
||||
class name, merging additional keyword arguments with the config, and handling
|
||||
nested module paths.
|
||||
|
||||
Args:
|
||||
config (dict): A dictionary containing the configuration, including
|
||||
the 'class' key that specifies the class's module path.
|
||||
default_class_dir (str, optional): The default module path prefix
|
||||
to use if not explicitly defined in
|
||||
'config'. Defaults to "memory_scope".
|
||||
**kwargs: Additional keyword arguments to pass to the class constructor.
|
||||
|
||||
Returns:
|
||||
instance: An instance initialized with the provided config and kwargs.
|
||||
"""
|
||||
|
||||
config_copy = deepcopy(config)
|
||||
origin_class_path: str = config_copy.pop("class")
|
||||
if not origin_class_path:
|
||||
raise RuntimeError("empty class path!")
|
||||
user_defined: bool = config_copy.pop("user_defined", False)
|
||||
|
||||
class_path_list = []
|
||||
if not user_defined and default_class_dir and not origin_class_path.startswith(default_class_dir):
|
||||
class_path_list.append(default_class_dir)
|
||||
|
||||
class_path_split = origin_class_path.split(".")
|
||||
class_file_name: str = class_path_split[-1]
|
||||
|
||||
class_name = underscore_to_camelcase(class_file_name)
|
||||
if class_name == class_file_name:
|
||||
class_path_list.extend(class_path_split[-1:])
|
||||
else:
|
||||
class_path_list.extend(class_path_split)
|
||||
|
||||
module = import_module(".".join(class_path_list))
|
||||
config_copy.update(kwargs)
|
||||
return getattr(module, class_name)(**config_copy)
|
||||
|
||||
|
||||
def prompt_to_msg(system_prompt: str,
|
||||
few_shot: str,
|
||||
user_query: str,
|
||||
concat_system_prompt: bool = True) -> List[Message]:
|
||||
"""
|
||||
Converts input strings into a structured list of message objects suitable for AI interactions.
|
||||
|
||||
Args:
|
||||
system_prompt (str): The system-level instruction or context.
|
||||
few_shot (str): An example or demonstration input, often used for illustrating expected behavior.
|
||||
user_query (str): The actual user query or prompt to be processed.
|
||||
concat_system_prompt(bool): Concat system prompt again or not in the user message.
|
||||
A simple method to improve the effectiveness for some LLMs. Defaults to True.
|
||||
|
||||
Returns:
|
||||
List[Message]: A list of Message objects, each representing a part of the conversation setup.
|
||||
"""
|
||||
|
||||
system_message = Message(role=MessageRoleEnum.SYSTEM.value, content=system_prompt.strip())
|
||||
if concat_system_prompt:
|
||||
user_content_list = [system_prompt, few_shot, user_query]
|
||||
else:
|
||||
user_content_list = [few_shot, user_query]
|
||||
user_message = Message(role=MessageRoleEnum.USER.value, content="\n".join([x.strip() for x in user_content_list]))
|
||||
return [system_message, user_message]
|
||||
|
||||
|
||||
def char_logo(words: str, seed: int = time.time_ns(), color=None):
|
||||
"""
|
||||
Render the context of logo with colors
|
||||
|
||||
Args:
|
||||
words: The context of logo.
|
||||
seed: The random seed which generates colors if there is no specific color. Defaults to the current timestamp.
|
||||
color: The specific color. Defaults to None.
|
||||
|
||||
Returns:
|
||||
A rendered logo
|
||||
"""
|
||||
font = pyfiglet.Figlet()
|
||||
rendered_text = font.renderText(words)
|
||||
colored_lines = []
|
||||
all_colors = ALL_COLORS.copy()
|
||||
random.seed = seed
|
||||
for line in rendered_text.splitlines():
|
||||
line_color = color
|
||||
if line_color is None:
|
||||
random.shuffle(all_colors)
|
||||
line_color = all_colors[0]
|
||||
colored_line = ""
|
||||
for char in line:
|
||||
colored_char = colored(char, line_color, attrs=['bold'])
|
||||
colored_line += colored_char
|
||||
colored_lines.append(colored_line)
|
||||
return colored_lines
|
||||
|
||||
|
||||
def md5_hash(input_string: str) -> str:
|
||||
"""
|
||||
Computes a MD5 hash of the given input string.
|
||||
|
||||
Args:
|
||||
input_string (str): The string for which the MD5 hash needs to be computed.
|
||||
|
||||
Returns:
|
||||
str: A hexadecimal MD5 hash representation.
|
||||
"""
|
||||
m = hashlib.md5()
|
||||
m.update(input_string.encode('utf-8'))
|
||||
return m.hexdigest()
|
||||
|
||||
|
||||
def contains_keyword(text, keywords) -> bool:
|
||||
"""
|
||||
Checks if the given text contains any of the specified keywords, ignoring case.
|
||||
|
||||
Args:
|
||||
text (str): The text to search within.
|
||||
keywords (List[str]): A list of keywords to look for in the text.
|
||||
|
||||
Returns:
|
||||
bool: True if any keyword is found in the text, False otherwise.
|
||||
"""
|
||||
escaped_keywords = map(re.escape, keywords)
|
||||
pattern = re.compile('|'.join(escaped_keywords), re.IGNORECASE)
|
||||
return pattern.search(text) is not None
|
||||
|
||||
|
||||
def cosine_similarity(query: List[float], documents: List[List[float]]):
|
||||
query = np.array(query)
|
||||
documents = np.array(documents)
|
||||
|
||||
query_norm = np.linalg.norm(query)
|
||||
if query_norm == 0:
|
||||
raise ValueError("Query vector norm is zero, which will result in a division by zero")
|
||||
|
||||
documents_norm = np.linalg.norm(documents, axis=1)
|
||||
if np.any(documents_norm == 0):
|
||||
raise ValueError("One of the document vectors has zero norm, which will result in a division by zero")
|
||||
|
||||
dot_product = np.dot(documents, query)
|
||||
|
||||
cosine_similarities = dot_product / (query_norm * documents_norm)
|
||||
return cosine_similarities.tolist()
|
||||
|
||||
|
||||
def cosine_similarity_matrix(query: List[List[float]]):
|
||||
query = np.array(query)
|
||||
|
||||
documents_norm = np.linalg.norm(query, axis=1)
|
||||
if np.any(documents_norm == 0):
|
||||
raise ValueError("One of the document vectors has zero norm, which will result in a division by zero")
|
||||
|
||||
n_query = query.shape[0]
|
||||
query_expanded = np.expand_dims(query, axis=0)
|
||||
query_triplicated = np.repeat(query_expanded, repeats=n_query, axis=0)
|
||||
query_transpose = query_triplicated.swapaxes(0, 1)
|
||||
|
||||
q = np.expand_dims(documents_norm, axis=0)
|
||||
norm_dot = q.transpose() * q
|
||||
dot_product = (query_triplicated*query_transpose).sum(-1) / norm_dot
|
||||
|
||||
return dot_product
|
||||
|
||||
|
||||
|
|
@ -1,11 +0,0 @@
|
|||
from .base_worker import BaseWorker
|
||||
from .dummy_worker import DummyWorker
|
||||
from .memory_base_worker import MemoryBaseWorker
|
||||
from .memory_manager import MemoryManager
|
||||
|
||||
__all__ = [
|
||||
"BaseWorker",
|
||||
"DummyWorker",
|
||||
"MemoryBaseWorker",
|
||||
"MemoryManager"
|
||||
]
|
||||
|
|
@ -1,21 +0,0 @@
|
|||
from .contra_repeat_worker import ContraRepeatWorker
|
||||
from .get_observation_with_time_worker import GetObservationWithTimeWorker
|
||||
from .get_observation_worker import GetObservationWorker
|
||||
from .get_reflection_subject_worker import GetReflectionSubjectWorker
|
||||
from .info_filter_worker import InfoFilterWorker
|
||||
from .load_memory_worker import LoadMemoryWorker
|
||||
from .long_contra_repeat_worker import LongContraRepeatWorker
|
||||
from .update_insight_worker import UpdateInsightWorker
|
||||
from .update_memory_worker import UpdateMemoryWorker
|
||||
|
||||
__all__ = [
|
||||
"ContraRepeatWorker",
|
||||
"GetObservationWithTimeWorker",
|
||||
"GetObservationWorker",
|
||||
"GetReflectionSubjectWorker",
|
||||
"InfoFilterWorker",
|
||||
"LoadMemoryWorker",
|
||||
"LongContraRepeatWorker",
|
||||
"UpdateInsightWorker",
|
||||
"UpdateMemoryWorker"
|
||||
]
|
||||
|
|
@ -1,129 +0,0 @@
|
|||
from typing import List
|
||||
|
||||
from memoryscope.constants.common_constants import NEW_OBS_NODES, NEW_OBS_WITH_TIME_NODES, MERGE_OBS_NODES, TODAY_NODES
|
||||
from memoryscope.constants.language_constants import NONE_WORD, CONTRADICTORY_WORD, CONTAINED_WORD
|
||||
from memoryscope.core.utils.response_text_parser import ResponseTextParser
|
||||
from memoryscope.core.worker.memory_base_worker import MemoryBaseWorker
|
||||
from memoryscope.enumeration.store_status_enum import StoreStatusEnum
|
||||
from memoryscope.scheme.memory_node import MemoryNode
|
||||
|
||||
|
||||
class ContraRepeatWorker(MemoryBaseWorker):
|
||||
"""
|
||||
The `ContraRepeatWorker` class specializes in processing memory nodes to identify and handle
|
||||
contradictory and repetitive information. It extends the base functionality of `MemoryBaseWorker`.
|
||||
|
||||
Responsibilities:
|
||||
- Collects observation nodes from various memory categories.
|
||||
- Constructs a prompt with these observations for language model analysis.
|
||||
- Parses the model's response to detect contradictions or redundancies.
|
||||
- Adjusts the status of memory nodes based on the analysis.
|
||||
- Persists the updated node statuses back into memory.
|
||||
"""
|
||||
file_path: str = __file__
|
||||
|
||||
def _parse_params(self, **kwargs):
|
||||
self.generation_model_kwargs: dict = kwargs.get("generation_model_kwargs", {})
|
||||
self.retrieve_top_k: int = kwargs.get("retrieve_top_k", 30)
|
||||
self.contra_repeat_max_count: int = kwargs.get("contra_repeat_max_count", 50)
|
||||
self.enable_today_contra_repeat: bool = self.memoryscope_context.meta_data["enable_today_contra_repeat"]
|
||||
|
||||
def _run(self):
|
||||
"""
|
||||
Executes the primary routine of the ContraRepeatWorker which involves fetching memory nodes,
|
||||
constructing a prompt, querying a language model, parsing the response to identify nodes for merging,
|
||||
updating node statuses, and saving the updated nodes back to memory.
|
||||
|
||||
Steps:
|
||||
1. Retrieves new observation nodes and nodes observed on the current day.
|
||||
2. Optionally combines today's nodes with the new ones, sorts, and limits the list by a predefined count.
|
||||
3. Constructs a prompt using the combined nodes, system prompt, and a few-shot example.
|
||||
4. Queries a language model with the constructed prompt.
|
||||
5. Parses the model's response to identify nodes to merge or exclude based on contradiction or redundancy.
|
||||
6. Updates the status of nodes accordingly.
|
||||
7. Persists the changes back to memory storage.
|
||||
"""
|
||||
if not self.enable_today_contra_repeat:
|
||||
self.logger.warning("today_contra_repeat is not enabled!")
|
||||
return
|
||||
|
||||
all_obs_nodes: List[MemoryNode] = self.memory_manager.get_memories([NEW_OBS_NODES, NEW_OBS_WITH_TIME_NODES])
|
||||
if not all_obs_nodes:
|
||||
self.logger.info("all_obs_nodes is empty!")
|
||||
# self.continue_run = False
|
||||
return
|
||||
|
||||
today_obs_nodes: List[MemoryNode] = self.memory_manager.get_memories(TODAY_NODES)
|
||||
|
||||
if today_obs_nodes:
|
||||
all_obs_nodes.extend(today_obs_nodes)
|
||||
all_obs_nodes = sorted(all_obs_nodes, key=lambda x: x.timestamp, reverse=True)[:self.contra_repeat_max_count]
|
||||
|
||||
if len(all_obs_nodes) == 1:
|
||||
self.logger.info("all_obs_nodes.size=1, stop.")
|
||||
return
|
||||
|
||||
# build prompt
|
||||
user_query_list = []
|
||||
for i, n in enumerate(all_obs_nodes):
|
||||
user_query_list.append(f"{i + 1} {n.content}")
|
||||
|
||||
system_prompt = self.prompt_handler.contra_repeat_system.format(num_obs=len(user_query_list),
|
||||
user_name=self.target_name)
|
||||
few_shot = self.prompt_handler.contra_repeat_few_shot.format(user_name=self.target_name)
|
||||
user_query = self.prompt_handler.contra_repeat_user_query.format(user_query="\n".join(user_query_list))
|
||||
contra_repeat_message = self.prompt_to_msg(system_prompt=system_prompt, few_shot=few_shot,
|
||||
user_query=user_query)
|
||||
self.logger.info(f"contra_repeat_message={contra_repeat_message}")
|
||||
|
||||
# call LLM
|
||||
response = self.generation_model.call(messages=contra_repeat_message, **self.generation_model_kwargs)
|
||||
|
||||
# return if empty
|
||||
if not response.status or not response.message.content:
|
||||
return
|
||||
response_text = response.message.content
|
||||
|
||||
# parse text
|
||||
idx_merge_obs_list = ResponseTextParser(response_text, self.language, self.__class__.__name__).parse_v1()
|
||||
if len(idx_merge_obs_list) <= 0:
|
||||
self.logger.warning("idx_merge_obs_list is empty!")
|
||||
return
|
||||
|
||||
# add merged obs
|
||||
merge_obs_nodes: List[MemoryNode] = []
|
||||
for obs_content_list in idx_merge_obs_list:
|
||||
if not obs_content_list:
|
||||
continue
|
||||
|
||||
# Expecting a pair [index, flag]
|
||||
if len(obs_content_list) != 2:
|
||||
self.logger.warning(f"obs_content_list={obs_content_list} is invalid!")
|
||||
continue
|
||||
|
||||
idx, keep_flag = obs_content_list
|
||||
|
||||
if not idx.isdigit():
|
||||
self.logger.warning(f"idx={idx} is invalid!")
|
||||
continue
|
||||
|
||||
# index number needs to be corrected to -1
|
||||
idx = int(idx) - 1
|
||||
if idx >= len(all_obs_nodes):
|
||||
self.logger.warning(f"idx={idx} is invalid!")
|
||||
continue
|
||||
|
||||
# judge flag
|
||||
keep_flag = keep_flag.lower()
|
||||
if keep_flag not in self.get_language_value([NONE_WORD, CONTRADICTORY_WORD, CONTAINED_WORD]):
|
||||
self.logger.warning(f"keep_flag={keep_flag} is invalid!")
|
||||
continue
|
||||
|
||||
node: MemoryNode = all_obs_nodes[idx]
|
||||
if keep_flag != self.get_language_value(NONE_WORD):
|
||||
node.store_status = StoreStatusEnum.EXPIRED.value
|
||||
self.logger.info(f"contra_repeat stage: {node.content} {node.store_status} {node.action_status}")
|
||||
merge_obs_nodes.append(node)
|
||||
|
||||
# save context
|
||||
self.memory_manager.set_memories(MERGE_OBS_NODES, merge_obs_nodes, log_repeat=False)
|
||||
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