up

README.md

@@ -1,10 +1,23 @@
 # ExperienceMaker
+<p align="center">
+ <img src="cookbook/material/ExperienceMaker.png" alt="ExperienceMakerLogo" width="50%">
+</p>
 
-# 🌟 What is ExperienceMaker?
+[![](https://img.shields.io/badge/python-3.12+-blue)](https://pypi.org/project/experiencemaker/)
+[![](https://img.shields.io/badge/pypi-v0.1.0-blue?logo=pypi)](https://pypi.org/project/experiencemaker/)
+[![](https://img.shields.io/badge/license-Apache--2.0-black)](./LICENSE)
+
+----
+## 📰 News
+- **[2025-08]** We release ExperienceMaker v0.1.0 now, which is also available in [PyPI](https://pypi.org/simple/experiencemaker/)!
+
+----
+## 🌟 What is ExperienceMaker?
 ExperienceMaker provides agents with robust capabilities for experience generation and reuse. 
 By summarizing agents' past trajectories into experiences, it enables these experiences to be applied to subsequent tasks. 
 Through the continuous accumulation of experience, agents can keep learning and progressively become more skilled in performing tasks.
 
+
 ### Core Features
 - **Experience Generation**: Generate successful or failed experiences by summarizing the agent's historical trajectories.
 - **Experience Reuse**: Apply experiences to new tasks by retrieving them from a vector store, helping the agent improve through practice. During RL training, Experience allows the agent to maintain state information, enabling more efficient rollouts.
@@ -70,35 +83,74 @@ curl -fsSL https://elastic.co/start-local | sh
 ```
 - Elasticsearch [quick start](./cookbook/)
 - chroma
+-
 
-## Call
+## Call Summarizer Service
+
+```python
+import json
+
+import requests
+
+base_url = "http://0.0.0.0:8001/"
+workspace_id = "test_workspace1"
 
 
+def run_summary(messages: list, dump_experience: bool = True):
+  response = requests.post(url=base_url + "summarizer", json={
+    "workspace_id": workspace_id,
+    "traj_list": [
+      {"messages": messages, "score": 1.0}
+    ]
+  })
 
+  if response.status_code != 200:
+    print(response.text)
+    return
 
-
-
-## 💡 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 .
+  response = response.json()
+  experience_list = response["experience_list"]
+  if dump_experience:
+    with open("experience.jsonl", "w") as f:
+      f.write(json.dumps(experience_list, indent=2, ensure_ascii=False))
 ```
 
-Please refer to our [Contribution Guide](./docs/contribution.md) for more details.
+## Call Summarizer Service
+
+```python
+import requests
+
+base_url = "http://0.0.0.0:8001/"
+workspace_id = "test_workspace1"
+
+
+def run_retriever(query: str):
+  response = requests.post(url=base_url + "retriever", json={
+    "workspace_id": workspace_id,
+    "query": query,
+  })
+
+  if response.status_code != 200:
+    print(response.text)
+    return ""
+
+  response = response.json()
+  experience_merged: str = response["experience_merged"]
+  print(f"experience_merged={experience_merged}")
+  return experience_merged
+```
+
+For more details, please refer to the simple_demo
 
 ## 📖 Citation
-Reference to cite if you use ExperienceMaker in a paper:
+
+Reference to cite if you use `ExperienceMaker` in a paper:
 ```
-@software{ExperiperienceMaker,
-author = {///},
-month = {0715},
-title = {{ExperiperienceMaker}},
+@software{
+title = {ExperiperienceMaker},
+author = {The ExperiperienceMaker Team},
 url = {https://github.com/modelscope/ExperiperienceMaker},
+month = {08},
 year = {2025}
 }
 ```

cookbook/vector_store_quick_start.md

@@ -0,0 +1,286 @@
+# 🚀 Vector Store Quick Start Guide
+
+This comprehensive guide covers all available vector store implementations in ExperienceMaker, their differences, use cases, and setup instructions.
+
+## 📋 Overview
+
+ExperienceMaker supports multiple vector store backends for different use cases and deployment scenarios:
+
+- **FileVectorStore** (`backend=local_file`) - 📁 Local file-based storage for development and small datasets
+- **ChromaVectorStore** (`backend=chroma`) - 🔮 Embedded vector database for local development and moderate scale
+- **EsVectorStore** (`backend=elasticsearch`) - 🔍 Elasticsearch-based storage for production and large scale
+
+## ⚡ Vector Store Implementations
+
+### 1. 📁 FileVectorStore (`backend=local_file`)
+
+A simple file-based vector store that saves data to local JSONL files. Perfect for development, testing, and small datasets.
+
+#### 💡 When to Use
+- **Development and testing** - No external dependencies required 🛠️
+- **Small datasets** - Suitable for datasets with < 10,000 vectors 📊
+- **Single-user applications** - No concurrent access support 👤
+- **Prototyping** - Quick setup without infrastructure ⚡
+
+#### ✨ Features
+- ✅ No external dependencies
+- ✅ Simple file-based persistence
+- ✅ Built-in cosine similarity search
+- ❌ No concurrent access support
+- ❌ Limited scalability
+- ❌ No advanced filtering
+
+#### ⚙️ Configuration Parameters
+```python
+from experiencemaker.vector_store import FileVectorStore
+from experiencemaker.embedding_model.openai_compatible_embedding_model import OpenAICompatibleEmbeddingModel
+
+embedding_model = OpenAICompatibleEmbeddingModel(
+    dimensions=1536,
+    model_name="text-embedding-3-small"
+)
+
+vector_store = FileVectorStore(
+    embedding_model=embedding_model,
+    store_dir="./file_vector_store",  # Directory to store JSONL files
+    batch_size=1024                   # Batch size for operations
+)
+```
+
+#### 💻 Example Usage
+```python
+# Create workspace and insert data
+workspace_id = "my_workspace"
+vector_store.create_workspace(workspace_id)
+
+nodes = [
+    VectorNode(
+        workspace_id=workspace_id,
+        content="Artificial intelligence is revolutionizing technology",
+        metadata={"category": "tech", "source": "article1"}
+    ),
+    VectorNode(
+        workspace_id=workspace_id,
+        content="Machine learning enables data-driven insights",
+        metadata={"category": "tech", "source": "article2"}
+    )
+]
+
+vector_store.insert(nodes, workspace_id)
+
+# Search
+results = vector_store.search("What is AI?", workspace_id, top_k=2)
+```
+
+### 2. 🔮 ChromaVectorStore (`backend=chroma`)
+
+An embedded vector database that provides persistent storage with advanced features while remaining easy to deploy.
+
+#### 💡 When to Use
+- **Local development** with persistence requirements 🏠
+- **Medium-scale applications** (10K - 1M vectors) 📈
+- **Multi-user applications** with moderate concurrency 👥
+- **Applications requiring metadata filtering** 🔍
+- **Docker deployments** without external database dependencies 🐳
+
+#### ✨ Features
+- ✅ Persistent embedded database
+- ✅ Advanced metadata filtering
+- ✅ Built-in vector indexing (HNSW)
+- ✅ HTTP API support
+- ✅ Concurrent access support
+- ✅ Collection management
+- ❌ Limited horizontal scaling
+
+#### ⚙️ Configuration Parameters
+```python
+from experiencemaker.vector_store import ChromaVectorStore
+from experiencemaker.embedding_model.openai_compatible_embedding_model import OpenAICompatibleEmbeddingModel
+
+embedding_model = OpenAICompatibleEmbeddingModel(
+    dimensions=1536,
+    model_name="text-embedding-3-small"
+)
+
+vector_store = ChromaVectorStore(
+    embedding_model=embedding_model,
+    store_dir="./chroma_vector_store",  # Directory for Chroma database
+    batch_size=1024                     # Batch size for operations
+)
+```
+
+#### 💻 Example Usage
+```python
+workspace_id = "chroma_workspace"
+
+# Check if workspace exists
+if not vector_store.exist_workspace(workspace_id):
+    vector_store.create_workspace(workspace_id)
+
+# Insert with metadata
+nodes = [
+    VectorNode(
+        workspace_id=workspace_id,
+        content="Deep learning models require large datasets",
+        metadata={"category": "AI", "difficulty": "advanced", "topic": "deep_learning"}
+    )
+]
+
+vector_store.insert(nodes, workspace_id)
+
+# Search with results
+results = vector_store.search("deep learning", workspace_id, top_k=5)
+for result in results:
+    print(f"Content: {result.content}")
+    print(f"Metadata: {result.metadata}")
+```
+
+### 3. 🔍 EsVectorStore (`backend=elasticsearch`)
+
+Production-grade vector search using Elasticsearch with advanced filtering, scaling, and enterprise features.
+
+#### 💡 When to Use
+- **Production environments** requiring high availability 🏭
+- **Large-scale applications** (1M+ vectors) 🚀
+- **High-throughput scenarios** with many concurrent users ⚡
+- **Complex filtering requirements** on metadata 🎯
+- **Distributed deployments** across multiple nodes 🌐
+- **Enterprise environments** with existing Elasticsearch infrastructure 🏢
+
+#### ✨ Features
+- ✅ Horizontal scaling
+- ✅ High availability and fault tolerance
+- ✅ Advanced filtering and aggregations
+- ✅ Real-time indexing and search
+- ✅ Cluster management
+- ✅ Enterprise security features
+- ✅ Monitoring and analytics
+- ❌ Complex setup and maintenance
+- ❌ Higher resource requirements
+
+#### 🛠️ Setup Elasticsearch
+
+Before using EsVectorStore, you need to set up Elasticsearch. Choose one of the following methods:
+
+##### Option 1: All-in-One Script (Recommended for Development) 🎯
+```bash
+curl -fsSL https://elastic.co/start-local | sh
+```
+
+##### Option 2: Docker Run with HTTP Host 🐳
+```bash
+# Pull the latest Elasticsearch image
+docker pull docker.elastic.co/elasticsearch/elasticsearch-wolfi:9.0.0
+
+# Run Elasticsearch container
+docker run -p 9200:9200 \
+  -e "discovery.type=single-node" \
+  -e "xpack.security.enabled=false" \
+  -e "xpack.license.self_generated.type=trial" \
+  -e "http.host=0.0.0.0" \
+  docker.elastic.co/elasticsearch/elasticsearch-wolfi:9.0.0
+```
+
+##### 🔧 Environment Configuration
+Set the Elasticsearch hosts environment variable:
+```bash
+export ES_HOSTS=http://localhost:9200
+```
+
+#### ⚙️ Configuration Parameters
+```python
+from experiencemaker.vector_store import EsVectorStore
+from experiencemaker.embedding_model.openai_compatible_embedding_model import OpenAICompatibleEmbeddingModel
+import os
+
+embedding_model = OpenAICompatibleEmbeddingModel(
+    dimensions=1536,
+    model_name="text-embedding-3-small"
+)
+
+vector_store = EsVectorStore(
+    embedding_model=embedding_model,
+    hosts=os.getenv("ES_HOSTS", "http://localhost:9200"),  # Elasticsearch hosts
+    basic_auth=None,                                       # ("username", "password") for auth
+    batch_size=1024,                                      # Batch size for bulk operations
+    retrieve_filters=[]                                   # Pre-configured filters
+)
+```
+
+#### 🎯 Advanced Filtering
+EsVectorStore supports advanced filtering capabilities:
+
+```python
+# Add term filters
+vector_store.add_term_filter("metadata.category", "technology")
+vector_store.add_term_filter("metadata.language", "en")
+
+# Add range filters
+vector_store.add_range_filter("metadata.score", gte=0.8)
+vector_store.add_range_filter("metadata.timestamp", gte="2024-01-01", lte="2024-12-31")
+
+# Search with filters applied
+results = vector_store.search("machine learning", workspace_id, top_k=10)
+
+# Clear filters for next search
+vector_store.clear_filter()
+```
+
+#### 💻 Example Usage
+```python
+import os
+from experiencemaker.schema.vector_node import VectorNode
+
+# Configure connection
+workspace_id = "production_workspace"
+
+# Create workspace with custom mapping
+if not vector_store.exist_workspace(workspace_id):
+    vector_store.create_workspace(workspace_id)
+
+# Insert with rich metadata
+nodes = [
+    VectorNode(
+        workspace_id=workspace_id,
+        content="Transformer architecture revolutionized NLP",
+        metadata={
+            "category": "AI",
+            "subcategory": "NLP",
+            "author": "research_team",
+            "timestamp": "2024-01-15",
+            "confidence": 0.95,
+            "tags": ["transformer", "nlp", "attention"]
+        }
+    )
+]
+
+# Insert with refresh for immediate availability
+vector_store.insert(nodes, workspace_id, refresh=True)
+
+# Advanced search with filters
+vector_store.add_term_filter("metadata.category", "AI")
+vector_store.add_range_filter("metadata.confidence", gte=0.9)
+
+results = vector_store.search("transformer models", workspace_id, top_k=5)
+
+for result in results:
+    print(f"Score: {result.metadata.get('_score', 'N/A')}")
+    print(f"Content: {result.content}")
+    print(f"Metadata: {result.metadata}")
+```
+
+## 📊 Comparison Matrix
+
+| Feature | FileVectorStore | ChromaVectorStore | EsVectorStore |
+|---------|----------------|------------------|---------------|
+| **Setup Complexity** | ⭐ Very Easy | ⭐⭐ Easy | ⭐⭐⭐⭐ Complex |
+| **Scalability** | < 10K vectors | < 1M vectors | 10M+ vectors |
+| **Concurrency** | Single user | Moderate | High |
+| **Persistence** | JSONL files | SQLite/DuckDB | Distributed |
+| **Filtering** | Basic | Advanced | Enterprise |
+| **Performance** | Good for small | Good for medium | Excellent for large |
+| **Resource Usage** | Minimal | Low-Medium | High |
+| **Maintenance** | None | Low | High |
+| **Production Ready** | ❌ | ⚠️ Limited | ✅ Yes |
+
+🎉 This guide provides everything you need to get started with vector stores in ExperienceMaker. Choose the implementation that best fits your use case and scale up as needed! ✨