# ExperienceMaker Quick Start Guide
This guide will help you get started with ExperienceMaker quickly using practical examples.

## 🚀 What You'll Learn
- How to set up ExperienceMaker service
- Run an agent and generate experiences
- Retrieve and apply experiences to new tasks
- Build experience-enhanced agents

## 📋 Prerequisites
- Python 3.12+
- LLM API access (OpenAI or compatible)
- Embedding model API access

## 🛠️ Installation

### Option 1: Install from PyPI (Recommended)

```bash
pip install experiencemaker
```

### Option 2: Install from Source

```bash
git clone https://github.com/modelscope/ExperienceMaker.git
cd ExperienceMaker
pip install .
```

## ⚙️ Environment Setup
Create a `.env` file in your project directory:

```bash
# Required: LLM API configuration
LLM_API_KEY="sk-xxx"
LLM_BASE_URL="https://xxx.com/v1"

# Required: Embedding model configuration  
EMBEDDING_MODEL_API_KEY="sk-xxx"
EMBEDDING_MODEL_BASE_URL="https://xxx.com/v1"

# Optional: Elasticsearch configuration (if using Elasticsearch backend)

```

## 🚀 Start the Service
For testing, use the `local_file` backend:
```bash
experiencemaker \
  http_service.port=8001 \
  llm.default.model_name=qwen3-32b \
  embedding_model.default.model_name=text-embedding-v4 \
  vector_store.default.backend=local_file
```
The service will start on `http://localhost:8001`

### Elasticsearch Backend
```bash
experiencemaker \
  llm.default.model_name=qwen3-32b \
  embedding_model.default.model_name=text-embedding-v4 \
  vector_store.default.backend=elasticsearch
```

**Setup Elasticsearch:**
```bash
export ES_HOSTS="http://localhost:9200"
# Quick setup using Elastic's official script
curl -fsSL https://elastic.co/start-local | sh
```
📖 **Need Help?** Refer to [Vector Store Setup](./doc/vector_store_setup.md) for comprehensive deployment guidance.

## 📝 Your First ExperienceMaker Script

Here's how to get started!
- The `load_dotenv()` function loads environment variables from your `.env` file, or you can manually export them.
- The `base_url` points to your ExperienceMaker service.
- The `workspace_id` serves as your experience storage namespace. Experiences in different workspaces remain completely
  isolated and cannot access each other.

```python
import requests
from dotenv import load_dotenv

load_dotenv()
base_url = "http://0.0.0.0:8001/"
workspace_id = "test_workspace"
```

### 📊 Call Summarizer Examples

Batch summarize the trajectory list, where each trajectory consists of a message and a score.

- The message is the conversation history.
- The score represents the rating between 0 and 1, with 0 typically indicating failure and 1 indicating success.

```python
response = requests.post(url=base_url + "summarizer", json={
  "workspace_id": workspace_id,
  "traj_list": [
    {"messages": messages, "score": 1.0}
  ]
})

response = response.json()
experience_list = response["experience_list"]
for experience in experience_list:
  print(experience)
```

### 🔍 Call Retriever Examples
Retrieve the top_k={top_k} experiences related to {query} in workspace=test_workspace, and finally accept the assembled context. 
Alternatively, you can also accept the raw experience_list parameter and assemble the context yourself.

```python
response = requests.post(url=base_url + "retriever", json={
  "workspace_id": workspace_id,
  "query": query,
  "top_k": 1,
})

response = response.json()
experience_merged: str = response["experience_merged"]
print(f"experience_merged={experience_merged}")
```

### 💾 Dump Experiences From Vector Store
Dump the experience with workspace_id from the vector store into the {path}/{workspace_id}.jsonl file.

```python
response = requests.post(url=base_url + "vector_store", json={
  "workspace_id": workspace_id,
  "action": "dump",
  "path": "./",
})
print(response.json())
```

### 📥 Load Experiences To Vector Store
Load the {path}/{workspace_id}.jsonl file into the vector store, workspace_id={workspace_id}.

```python
response = requests.post(url=base_url + "vector_store", json={
  "workspace_id": "test_workspace1",
  "action": "load",
  "path": "./",
})

print(response.json())
```

🎭 **Want to See It in Action?** We've prepared a [simple react agent](./cookbook/simple_demo/simple_demo.py) that demonstrates how to enhance agent capabilities by integrating summarizer and retriever components, achieving significantly better performance.

## 🐛 Common Issues

### Service Won't Start
- Check if port 8001 is available
- Verify your API keys in `.env` file
- Ensure Python version is 3.12+

### No Experiences Retrieved
- Make sure you've run the summarizer first
- Check if workspace_id matches between operations
- Verify vector store backend is properly configured

### API Connection Errors
- Confirm LLM_BASE_URL and API keys are correct
- Test API access independently
- Check network connectivity

---

🎯 **You're all set!** You now have a working ExperienceMaker setup that can learn from interactions and improve over time.