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add multi language
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303
README.md
303
README.md
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@ -15,12 +15,14 @@
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<strong>A comprehensive framework for AI agent experience generation and reuse</strong><br>
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<em>Empowering agents to learn from the past and excel in the future</em>
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</p>
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---
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## 📰 What's New
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- **[2025-08]** 🎉 ExperienceMaker v0.1.0 is now available on [PyPI](https://pypi.org/project/experiencemaker/)!
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- **[2025-07]** 📚 Complete documentation and quick start guides released
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- **[2025-07]** 🚀 Multi-backend vector store support (Elasticsearch & ChromaDB)
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---
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## 📰 What's Next
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@ -159,122 +161,289 @@ 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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Note 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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<details open>
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<summary><b>Python</b></summary>
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```python
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response = requests.post(url=base_url + "summarizer", json={
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"workspace_id": workspace_id,
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import requests
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response = requests.post(url="http://0.0.0.0:8001/summarizer", json={
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"workspace_id": "test_workspace",
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"traj_list": [
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{"messages": messages, "score": 1.0}
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{"messages": [{"role": "user", "content": "hello world"}], "score": 1.0}
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]
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})
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response = response.json()
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print(response)
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experience_list = response["experience_list"]
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experience_list = response.json()["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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</details>
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<details>
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<summary><b>curl</b></summary>
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```bash
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curl -X POST "http://0.0.0.0:8001/summarizer" \
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-H "Content-Type: application/json" \
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-d '{
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"workspace_id": "test_workspace",
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"traj_list": [
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{
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"messages": [{"role": "user", "content": "hello world"}],
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"score": 1.0
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}
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]
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}'
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```
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</details>
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<details>
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<summary><b>Node.js</b></summary>
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```javascript
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const fetch = require('node-fetch');
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// or: import fetch from 'node-fetch';
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async function callSummarizer() {
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try {
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const response = await fetch('http://0.0.0.0:8001/summarizer', {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json',
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},
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body: JSON.stringify({
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workspace_id: "test_workspace",
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traj_list: [
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{
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messages: [{ role: "user", content: "hello world" }],
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score: 1.0
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}
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]
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})
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});
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const data = await response.json();
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const experienceList = data.experience_list;
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experienceList.forEach(experience => {
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console.log(experience);
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});
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} catch (error) {
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console.error('Error:', error);
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}
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}
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callSummarizer();
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```
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</details>
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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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<details open>
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<summary><b>Python</b></summary>
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```python
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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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import requests
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response = requests.post(url="http://0.0.0.0:8001/retriever", json={
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"workspace_id": "test_workspace",
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"query": "what is the meaning of life?",
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"top_k": 1,
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})
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response = response.json()
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print(response)
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experience_merged: str = response["experience_merged"]
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experience_merged: str = response.json()["experience_merged"]
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print(f"experience_merged={experience_merged}")
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```
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</details>
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<details>
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<summary><b>curl</b></summary>
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```bash
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curl -X POST "http://0.0.0.0:8001/retriever" \
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-H "Content-Type: application/json" \
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-d '{
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"workspace_id": "test_workspace",
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"query": "what is the meaning of life?",
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"top_k": 1
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}'
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```
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</details>
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<details>
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<summary><b>Node.js</b></summary>
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```javascript
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const fetch = require('node-fetch');
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// or: import fetch from 'node-fetch';
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async function callRetriever() {
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try {
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const response = await fetch('http://0.0.0.0:8001/retriever', {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json',
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},
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body: JSON.stringify({
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workspace_id: "test_workspace",
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query: "what is the meaning of life?",
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top_k: 1
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})
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});
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const data = await response.json();
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const experienceMerged = data.experience_merged;
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console.log(`experience_merged=${experienceMerged}`);
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} catch (error) {
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console.error('Error:', error);
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}
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}
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callRetriever();
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```
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</details>
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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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<div class="tab">
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<button class="tablinks" onclick="openLanguage(event, 'Python')">Python</button>
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<button class="tablinks" onclick="openLanguage(event, 'Java')">Java</button>
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</div>
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<div id="Python" class="tabcontent">
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```python
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def hello_world():
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print("Hello, World!")
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```
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</div>
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<div id="Java" class="tabcontent">
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```java
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public class Main {
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public static void main(String[] args) {
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System.out.println("Hello, World!");
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}
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}
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```
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</div>
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<script>
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function openLanguage(evt, languageName) {
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var i, tabcontent, tablinks;
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tabcontent = document.getElementsByClassName("tabcontent");
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for (i = 0; i < tabcontent.length; i++) {
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tabcontent[i].style.display = "none";
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}
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tablinks = document.getElementsByClassName("tablinks");
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for (i = 0; i < tablinks.length; i++) {
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tablinks[i].className = tablinks[i].className.replace(" active", "");
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}
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document.getElementById(languageName).style.display = "block";
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evt.currentTarget.className += " active";
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}
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</script>
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<details open>
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<summary><b>Python</b></summary>
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```python
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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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import requests
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response = requests.post(url="http://0.0.0.0:8001/vector_store", json={
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"workspace_id": "test_workspace",
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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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</details>
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<details>
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<summary><b>curl</b></summary>
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```bash
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curl -X POST "http://0.0.0.0:8001/vector_store" \
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-H "Content-Type: application/json" \
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-d '{
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"workspace_id": "test_workspace",
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"action": "dump",
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"path": "./"
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}'
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```
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</details>
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<details>
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<summary><b>Node.js</b></summary>
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```javascript
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const fetch = require('node-fetch');
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// or: import fetch from 'node-fetch';
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async function dumpExperiences() {
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try {
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const response = await fetch('http://0.0.0.0:8001/vector_store', {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json',
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},
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body: JSON.stringify({
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workspace_id: "test_workspace",
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action: "dump",
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path: "./"
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})
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});
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const data = await response.json();
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console.log(data);
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} catch (error) {
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console.error('Error:', error);
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}
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}
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dumpExperiences();
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```
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</details>
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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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<details open>
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<summary><b>Python</b></summary>
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```python
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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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import requests
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response = requests.post(url="http://0.0.0.0:8001/vector_store", json={
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"workspace_id": "test_workspace",
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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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```
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</details>
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<details>
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<summary><b>curl</b></summary>
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```bash
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curl -X POST "http://0.0.0.0:8001/vector_store" \
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-H "Content-Type: application/json" \
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-d '{
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"workspace_id": "test_workspace",
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"action": "load",
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"path": "./"
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}'
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```
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</details>
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<details>
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<summary><b>Node.js</b></summary>
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```javascript
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const fetch = require('node-fetch');
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// or: import fetch from 'node-fetch';
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async function loadExperiences() {
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try {
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const response = await fetch('http://0.0.0.0:8001/vector_store', {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json',
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},
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body: JSON.stringify({
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workspace_id: "test_workspace",
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action: "load",
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path: "./"
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})
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});
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const data = await response.json();
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console.log(data);
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} catch (error) {
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console.error('Error:', error);
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}
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}
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loadExperiences();
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```
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</details>
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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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