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chore(version): bump version to 0.10.4
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jinli.yl
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6 changed files with 33 additions and 34 deletions
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@ -4,7 +4,7 @@
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<p align="center">
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<a href="https://pypi.org/project/reme-ai/"><img src="https://img.shields.io/badge/python-3.12+-blue" alt="Python Version"></a>
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<a href="https://pypi.org/project/reme-ai/"><img src="https://img.shields.io/badge/pypi-v0.1.10.3-blue?logo=pypi" alt="PyPI Version"></a>
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<a href="https://pypi.org/project/reme-ai/"><img src="https://img.shields.io/badge/pypi-v0.1.10.4-blue?logo=pypi" alt="PyPI Version"></a>
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<a href="./LICENSE"><img src="https://img.shields.io/badge/license-Apache--2.0-black" alt="License"></a>
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<a href="https://github.com/modelscope/ReMe"><img src="https://img.shields.io/github/stars/modelscope/ReMe?style=social" alt="GitHub Stars"></a>
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</p>
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@ -28,7 +28,7 @@ Personal memory helps "**understand user preferences**", task memory helps agent
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## 📰 Latest Updates
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- **[2025-10]** 🚀 ReMe v0.1.10.3 released! Core enhancement: direct Python import support. You can now use ReMe without starting an HTTP or MCP service - simply `from reme_ai import ReMeApp` and call methods directly in your Python code.
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- **[2025-10]** 🚀 ReMe v0.1.10.4 released! Core enhancement: direct Python import support. You can now use ReMe without starting an HTTP or MCP service - simply `from reme_ai import ReMeApp` and call methods directly in your Python code.
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- **[2025-10]** 🔧 Tool Memory support is now available! Enables data-driven tool selection and parameter optimization through historical performance tracking. Check out the [Tool Memory Guide](docs/tool_memory/tool_memory.md) and [benchmark results](docs/tool_memory/tool_bench.md).
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- **[2025-09]** 🎉 ReMe v0.1.9 has been officially released, adding support for asynchronous operations. It has also been
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integrated into the memory service of agentscope-runtime.
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@ -17,7 +17,7 @@ kernelspec:
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<div class="flex justify-center space-x-3">
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<a href="https://pypi.org/project/reme-ai/"><img src="https://img.shields.io/badge/python-3.12+-blue" alt="Python Version"></a>
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<a href="https://pypi.org/project/reme-ai/"><img src="https://img.shields.io/badge/pypi-v0.1.10.3-blue?logo=pypi" alt="PyPI Version"></a>
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<a href="https://pypi.org/project/reme-ai/"><img src="https://img.shields.io/badge/pypi-v0.1.10.4-blue?logo=pypi" alt="PyPI Version"></a>
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<a href="./LICENSE"><img src="https://img.shields.io/badge/license-Apache--2.0-black" alt="License"></a>
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<a href="https://github.com/modelscope/ReMe"><img src="https://img.shields.io/github/stars/modelscope/ReMe?style=social" alt="GitHub Stars"></a>
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</div>
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@ -12,7 +12,7 @@ kernelspec:
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name: python3
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---
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# 🚀 Vector Store API Guide
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# Vector Store API Guide
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This guide covers the vector store implementations available in ReMe, their APIs, and how to use them effectively.
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@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
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[project]
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name = "reme_ai"
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version = "0.1.10.3"
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version = "0.1.10.4"
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description = "Remember me"
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authors = [
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{ name = "jinli.yl", email = "jinli.yl@alibaba-inc.com" },
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@ -24,7 +24,7 @@ classifiers = [
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keywords = ["llm", "memory", "experience", "memoryscope", "ai", "mcp", "http"]
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dependencies = [
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"flowllm[reme]>=0.1.11.3",
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"flowllm[reme]>=0.1.11.4",
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]
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[project.optional-dependencies]
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@ -2,7 +2,7 @@ import os
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os.environ["FLOW_APP_NAME"] = "ReMe"
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__version__ = "0.1.10.3"
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__version__ = "0.1.10.4"
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from reme_ai.app import ReMeApp
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from . import agent
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@ -28,12 +28,12 @@ class ReMeApp(FlowLLMApp):
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"""
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def __init__(self,
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*args,
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llm_api_key: str = None,
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llm_api_base: str = None,
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embedding_api_key: str = None,
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embedding_api_base: str = None,
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config_path: str = None,
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*args,
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**kwargs):
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"""
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Initialize ReMeApp with configuration for LLM, embeddings, and vector stores.
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@ -59,6 +59,29 @@ class ReMeApp(FlowLLMApp):
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Both approaches accept the same configuration parameters and produce identical results.
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Args:
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*args: Additional command-line style arguments passed to parser.
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These parameters are identical to the command-line startup parameters in README.
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Common configuration examples:
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For complete configuration reference, see: reme_ai/config/default.yaml
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LLM Configuration:
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- "llm.default.model_name=qwen3-30b-a3b-thinking-2507" - Set LLM model
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- "llm.default.backend=openai_compatible" - Set LLM backend type
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- "llm.default.params={'temperature': '0.6'}" - Set model parameters
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Embedding Configuration:
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- "embedding_model.default.model_name=text-embedding-v4" - Set embedding model
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- "embedding_model.default.backend=openai_compatible" - Set embedding backend
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- "embedding_model.default.params={'dimensions': 1024}" - Embedding parameters
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Vector Store Configuration:
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- "vector_store.default.backend=local" - Use local vector store
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- "vector_store.default.backend=memory" - Use memory vector store
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- "vector_store.default.backend=qdrant" - Use Qdrant vector store
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- "vector_store.default.backend=elasticsearch" - Use Elasticsearch
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- "vector_store.default.embedding_model=default" - Link vector store to embedding model
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- "vector_store.default.params={'collection_name': 'my_memories'}" - Vector store parameters
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llm_api_key: API key for LLM service (e.g., OpenAI, Claude).
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If provided, this will override the FLOW_LLM_API_KEY environment variable.
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Environment variable: FLOW_LLM_API_KEY
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@ -76,30 +99,6 @@ class ReMeApp(FlowLLMApp):
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config_path: Path to custom configuration YAML file. If provided, loads configuration from this file.
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Example: "path/to/my_config.yaml"
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This overrides the default configuration with your custom settings.
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*args: Additional command-line style arguments passed to parser.
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These parameters are identical to the command-line startup parameters in README.
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Common configuration examples:
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For complete configuration reference, see: reme_ai/config/default.yaml
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LLM Configuration:
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- "llm.default.model_name=qwen3-30b-a3b-thinking-2507" - Set LLM model
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- "llm.default.backend=openai_compatible" - Set LLM backend type
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- "llm.default.params={'temperature': '0.6'}" - Set model parameters
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Embedding Configuration:
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- "embedding_model.default.model_name=text-embedding-v4" - Set embedding model
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- "embedding_model.default.backend=openai_compatible" - Set embedding backend
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- "embedding_model.default.params={'dimensions': 1024}" - Embedding parameters
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Vector Store Configuration:
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- "vector_store.default.backend=local" - Use local vector store
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- "vector_store.default.backend=memory" - Use memory vector store
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- "vector_store.default.backend=qdrant" - Use Qdrant vector store
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- "vector_store.default.backend=elasticsearch" - Use Elasticsearch
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- "vector_store.default.embedding_model=default" - Link vector store to embedding model
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- "vector_store.default.params={'collection_name': 'my_memories'}" - Vector store parameters
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**kwargs: Additional keyword arguments passed to parser. Same format as args but as key-value pairs.
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Example: model_name="gpt-4", temperature=0.7
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@ -118,7 +117,8 @@ class ReMeApp(FlowLLMApp):
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- README.md "Environment Configuration" for environment variable setup
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- example.env for all available environment variables
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"""
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super().__init__(llm_api_key=llm_api_key,
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super().__init__(*args,
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llm_api_key=llm_api_key,
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llm_api_base=llm_api_base,
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embedding_api_key=embedding_api_key,
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embedding_api_base=embedding_api_base,
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@ -126,7 +126,6 @@ class ReMeApp(FlowLLMApp):
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parser=ConfigParser,
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config_path=config_path,
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load_default_config=True,
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args=args,
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**kwargs)
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async def async_execute(self, name: str, **kwargs) -> dict:
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