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bugfix
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jinli.yl
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2 changed files with 85 additions and 138 deletions
106
README.md
106
README.md
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@ -152,38 +152,37 @@ curl -fsSL https://elastic.co/start-local | sh
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## 📝 Your First ExperienceMaker Script
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Here's how to get started!
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- The `load_dotenv()` function loads environment variables from your `.env` file, or you can manually export them.
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- The `base_url` points to your ExperienceMaker service.
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- The `workspace_id` serves as your experience storage namespace. Experiences in different workspaces remain completely
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isolated and cannot access each other.
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```python
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import requests
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from dotenv import load_dotenv
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load_dotenv()
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base_url = "http://0.0.0.0:8001/"
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workspace_id = "test_workspace"
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```
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### 📊 Call Summarizer Examples
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Batch summarize the trajectory list, where each trajectory consists of a message and a score.
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- The message is the conversation history.
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- The score represents the rating between 0 and 1, with 0 typically indicating failure and 1 indicating success.
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```python
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import requests
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from dotenv import load_dotenv
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response = requests.post(url=base_url + "summarizer", json={
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"workspace_id": workspace_id,
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"traj_list": [
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{"messages": messages, "score": 1.0}
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]
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})
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load_dotenv()
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base_url = "http://0.0.0.0:8001/"
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workspace_id = "test_workspace"
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def run_summary(messages: list):
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response = requests.post(url=base_url + "summarizer", json={
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"workspace_id": workspace_id,
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"traj_list": [
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{"messages": messages, "score": 1.0}
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]
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})
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response = response.json()
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experience_list = response["experience_list"]
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for experience in experience_list:
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print(experience)
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response = response.json()
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experience_list = response["experience_list"]
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for experience in experience_list:
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print(experience)
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```
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### 🔍 Call Retriever Examples
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@ -191,67 +190,40 @@ Retrieve the top_k={top_k} experiences related to {query} in workspace=test_work
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Alternatively, you can also accept the raw experience_list parameter and assemble the context yourself.
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```python
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import requests
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from dotenv import load_dotenv
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response = requests.post(url=base_url + "retriever", json={
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"workspace_id": workspace_id,
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"query": query,
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"top_k": 1,
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})
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load_dotenv()
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base_url = "http://0.0.0.0:8001/"
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workspace_id = "test_workspace"
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def run_retriever(query: str):
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response = requests.post(url=base_url + "retriever", json={
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"workspace_id": workspace_id,
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"query": query,
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"top_k": 1,
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})
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response = response.json()
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experience_merged: str = response["experience_merged"]
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print(f"experience_merged={experience_merged}")
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response = response.json()
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experience_merged: str = response["experience_merged"]
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print(f"experience_merged={experience_merged}")
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```
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### 💾 Dump Experiences From Vector Store
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Dump the experience with workspace_id from the vector store into the {path}/{workspace_id}.jsonl file.
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```python
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import requests
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from dotenv import load_dotenv
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load_dotenv()
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base_url = "http://0.0.0.0:8001/"
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workspace_id = "test_workspace1"
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def dump_experience():
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response = requests.post(url=base_url + "vector_store", json={
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"workspace_id": workspace_id,
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"action": "dump",
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"path": "./",
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})
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print(response.json())
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response = requests.post(url=base_url + "vector_store", json={
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"workspace_id": workspace_id,
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"action": "dump",
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"path": "./",
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})
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print(response.json())
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```
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### 📥 Load Experiences To Vector Store
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Load the {path}/{workspace_id}.jsonl file into the vector store, workspace_id={workspace_id}.
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```python
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import requests
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from dotenv import load_dotenv
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response = requests.post(url=base_url + "vector_store", json={
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"workspace_id": "test_workspace1",
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"action": "load",
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"path": "./",
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})
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load_dotenv()
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base_url = "http://0.0.0.0:8001/"
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workspace_id = "test_workspace1"
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def load_experience():
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response = requests.post(url=base_url + "vector_store", json={
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"workspace_id": "test_workspace1",
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"action": "load",
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"path": "./",
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})
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print(response.json())
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print(response.json())
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```
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🎭 **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.
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@ -74,105 +74,80 @@ curl -fsSL https://elastic.co/start-local | sh
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## 📝 Your First ExperienceMaker Script
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Here's how to get started!
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- The `load_dotenv()` function loads environment variables from your `.env` file, or you can manually export them.
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- The `base_url` points to your ExperienceMaker service.
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- The `workspace_id` serves as your experience storage namespace. Experiences in different workspaces remain completely
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isolated and cannot access each other.
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### Call Summarizer Examples
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Batch summarize the trajectory list, where each trajectory consists of a message and a score.
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```python
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import requests
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from dotenv import load_dotenv
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load_dotenv()
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base_url = "http://0.0.0.0:8001/"
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workspace_id = "test_workspace"
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```
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### 📊 Call Summarizer Examples
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Batch summarize the trajectory list, where each trajectory consists of a message and a score.
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- The message is the conversation history.
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- The score represents the rating between 0 and 1, with 0 typically indicating failure and 1 indicating success.
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```python
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import requests
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from dotenv import load_dotenv
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response = requests.post(url=base_url + "summarizer", json={
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"workspace_id": workspace_id,
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"traj_list": [
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{"messages": messages, "score": 1.0}
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]
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})
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load_dotenv()
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base_url = "http://0.0.0.0:8001/"
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workspace_id = "test_workspace"
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def run_summary(messages: list):
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response = requests.post(url=base_url + "summarizer", json={
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"workspace_id": workspace_id,
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"traj_list": [
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{"messages": messages, "score": 1.0}
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]
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})
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response = response.json()
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experience_list = response["experience_list"]
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for experience in experience_list:
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print(experience)
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response = response.json()
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experience_list = response["experience_list"]
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for experience in experience_list:
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print(experience)
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```
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### Call Retriever Examples
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### 🔍 Call Retriever Examples
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Retrieve the top_k={top_k} experiences related to {query} in workspace=test_workspace, and finally accept the assembled context.
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Alternatively, you can also accept the raw experience_list parameter and assemble the context yourself.
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```python
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import requests
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from dotenv import load_dotenv
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response = requests.post(url=base_url + "retriever", json={
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"workspace_id": workspace_id,
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"query": query,
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"top_k": 1,
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})
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load_dotenv()
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base_url = "http://0.0.0.0:8001/"
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workspace_id = "test_workspace"
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def run_retriever(query: str):
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response = requests.post(url=base_url + "retriever", json={
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"workspace_id": workspace_id,
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"query": query,
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})
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response = response.json()
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experience_merged: str = response["experience_merged"]
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print(f"experience_merged={experience_merged}")
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response = response.json()
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experience_merged: str = response["experience_merged"]
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print(f"experience_merged={experience_merged}")
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```
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### Dump Experiences
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### 💾 Dump Experiences From Vector Store
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Dump the experience with workspace_id from the vector store into the {path}/{workspace_id}.jsonl file.
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```python
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import requests
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from dotenv import load_dotenv
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load_dotenv()
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base_url = "http://0.0.0.0:8001/"
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workspace_id = "test_workspace1"
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def dump_experience():
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response = requests.post(url=base_url + "vector_store", json={
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"workspace_id": workspace_id,
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"action": "dump",
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"path": "./",
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})
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print(response.json())
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response = requests.post(url=base_url + "vector_store", json={
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"workspace_id": workspace_id,
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"action": "dump",
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"path": "./",
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})
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print(response.json())
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```
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### Load Experiences
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### 📥 Load Experiences To Vector Store
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Load the {path}/{workspace_id}.jsonl file into the vector store, workspace_id={workspace_id}.
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```python
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import requests
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from dotenv import load_dotenv
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response = requests.post(url=base_url + "vector_store", json={
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"workspace_id": "test_workspace1",
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"action": "load",
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"path": "./",
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})
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load_dotenv()
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base_url = "http://0.0.0.0:8001/"
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workspace_id = "test_workspace1"
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def load_experience():
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response = requests.post(url=base_url + "vector_store", json={
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"workspace_id": "test_workspace2",
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"action": "load",
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"path": "./",
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})
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print(response.json())
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print(response.json())
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```
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🎭 **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.
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