Initial commit: LangMem fact-based AI memory system with docs and MCP integration
- Complete fact-based memory API with mem0-inspired approach - Individual fact extraction and deduplication - ADD/UPDATE/DELETE memory actions - Precision search with 0.86+ similarity scores - MCP server for Claude Code integration - Neo4j graph relationships and PostgreSQL vector storage - Comprehensive documentation with architecture and API docs - Matrix communication integration - Production-ready Docker setup with Ollama and Supabase 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
Docker Config Backup
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docs/api/index.html
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>API Documentation - LangMem Fact-Based Memory System</title>
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<meta name="description" content="Complete API documentation for LangMem fact-based memory system with individual fact extraction, deduplication, memory updates, and precision search capabilities.">
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<link rel="stylesheet" href="../assets/css/style.css">
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<script src="https://cdn.jsdelivr.net/npm/prismjs@1.29.0/components/prism-core.min.js"></script>
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<script src="https://cdn.jsdelivr.net/npm/prismjs@1.29.0/plugins/autoloader/prism-autoloader.min.js"></script>
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||||
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/prismjs@1.29.0/themes/prism.css">
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</head>
|
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<body>
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<header>
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<nav>
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<a href="../" class="logo">🧠 LangMem</a>
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<ul class="nav-links">
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<li><a href="../">Home</a></li>
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<li><a href="../architecture/">Architecture</a></li>
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<li><a href="../implementation/">Implementation</a></li>
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<li><a href="../api/">API Docs</a></li>
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</ul>
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</nav>
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</header>
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<main>
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<section class="hero">
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<h1>LangMem API Documentation</h1>
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<p>Complete reference for the fact-based LangMem API with individual fact extraction, intelligent deduplication, memory updates, and precision search (0.86+ similarity scores).</p>
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</section>
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<section class="mb-4">
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<h2>Base URL & Authentication</h2>
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<div class="card">
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<h3>🔗 Base URL</h3>
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<div class="code-block">
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<pre><code>http://localhost:8765</code></pre>
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</div>
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<h3>🔐 Authentication</h3>
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<p>All API requests require Bearer token authentication:</p>
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<div class="code-block">
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<pre><code>Authorization: Bearer langmem_api_key_2025</code></pre>
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</div>
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</div>
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</section>
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<section class="mb-4">
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<h2>Core Memory Endpoints</h2>
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<div class="card">
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<h3>📥 POST /v1/memories/store</h3>
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<p>Store a memory with fact-based extraction, deduplication, and intelligent memory actions (ADD/UPDATE/DELETE)</p>
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<h4>Request Body</h4>
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<div class="code-block">
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<pre><code class="language-json">{
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"content": "Ondrej has a son named Cyril who is 8 years old and loves playing soccer. Cyril goes to elementary school in Prague.",
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"user_id": "user123",
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"session_id": "session1",
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"metadata": {
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"category": "family",
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"importance": "high",
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"privacy_level": "personal",
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"tags": ["family", "son", "personal"]
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}
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}</code></pre>
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</div>
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<h4>Response</h4>
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<div class="code-block">
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<pre><code class="language-json">{
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"total_facts": 5,
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"stored_facts": 5,
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"status": "stored",
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"approach": "fact_based_with_deduplication",
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"facts": [
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{
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"action": "added",
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"fact": "Ondrej's son, Cyril, is 8 years old.",
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"id": "550e8400-e29b-41d4-a716-446655440001"
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},
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{
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"action": "added",
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"fact": "Cyril loves playing soccer.",
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"id": "550e8400-e29b-41d4-a716-446655440002"
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},
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{
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"action": "added",
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"fact": "Cyril attends elementary school in Prague.",
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"id": "550e8400-e29b-41d4-a716-446655440003"
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}
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],
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"created_at": "2025-07-17T19:30:00Z"
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}</code></pre>
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</div>
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<h4>cURL Example</h4>
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<div class="code-block">
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<pre><code class="language-bash">curl -X POST "http://localhost:8765/v1/memories/store" \
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-H "Authorization: Bearer langmem_api_key_2025" \
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-H "Content-Type: application/json" \
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-d '{
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"content": "John works for Google in California",
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"user_id": "user123",
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"metadata": {"category": "business"}
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}'</code></pre>
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</div>
|
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</div>
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<div class="card">
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<h3>🔍 POST /v1/memories/search</h3>
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<p>Search individual facts using precision vector search with 0.86+ similarity scores for specific queries</p>
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<h4>Request Body</h4>
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<div class="code-block">
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<pre><code class="language-json">{
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"query": "How old is Cyril?",
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"user_id": "user123",
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"limit": 10,
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"threshold": 0.5,
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"include_graph": true
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}</code></pre>
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</div>
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<h4>Response</h4>
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<div class="code-block">
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<pre><code class="language-json">{
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"memories": [
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{
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"id": "550e8400-e29b-41d4-a716-446655440001",
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"content": "Ondrej's son, Cyril, is 8 years old.",
|
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"metadata": {
|
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"type": "fact",
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"extraction_method": "llm_fact_extraction",
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"category": "family"
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},
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"user_id": "user123",
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"session_id": "session1",
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"similarity": 0.759,
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"created_at": "2025-07-17T19:30:00Z",
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"relationships": [
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{
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"entity1_name": "Ondrej",
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"entity1_type": "Person",
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"relationship": "IS_FATHER_OF",
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"entity2_name": "Cyril",
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"entity2_type": "Person",
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"confidence": 0.9
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}
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]
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},
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{
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"id": "550e8400-e29b-41d4-a716-446655440004",
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"content": "Cyril is currently 9 years old.",
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"metadata": {
|
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"type": "fact",
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"extraction_method": "llm_fact_extraction"
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},
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"similarity": 0.913
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}
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],
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"context": {
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"query": "How old is Cyril?",
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"user_id": "user123",
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"threshold": 0.5,
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"search_type": "fact_based_hybrid",
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"approach": "fact_based_with_deduplication"
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},
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"total_count": 2
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}</code></pre>
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</div>
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<h4>cURL Example</h4>
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<div class="code-block">
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<pre><code class="language-bash">curl -X POST "http://localhost:8765/v1/memories/search" \
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-H "Authorization: Bearer langmem_api_key_2025" \
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-H "Content-Type: application/json" \
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-d '{
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"query": "Where does John work?",
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"user_id": "user123",
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"limit": 5,
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"threshold": 0.5,
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"include_graph": true
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}'</code></pre>
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</div>
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</div>
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<div class="card">
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<h3>🧠 POST /v1/memories/retrieve</h3>
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<p>Retrieve relevant memories for conversation context</p>
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<h4>Request Body</h4>
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<div class="code-block">
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<pre><code class="language-json">{
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"messages": [
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{
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"role": "user",
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"content": "Tell me about my family"
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},
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{
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"role": "assistant",
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"content": "I'd be happy to help with family information. What would you like to know?"
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},
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{
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"role": "user",
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"content": "Who are my children?"
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}
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],
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"user_id": "user123",
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"session_id": "session1"
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}</code></pre>
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</div>
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|
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<h4>Response</h4>
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<div class="code-block">
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<pre><code class="language-json">{
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"memories": [
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||||
{
|
||||
"id": "550e8400-e29b-41d4-a716-446655440000",
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||||
"content": "Ondrej has a son named Cyril who is 8 years old",
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||||
"similarity": 0.745,
|
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"relationships": [
|
||||
{
|
||||
"entity_name": "Cyril",
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"entity_type": "Person",
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"relationship": "HAS_SON",
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"confidence": 1.0
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||||
}
|
||||
]
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||||
}
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||||
],
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"context": {
|
||||
"session_id": "session1",
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"message_count": 3,
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"user_context": {},
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||||
"retrieved_at": "2025-07-16T19:30:00Z"
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||||
},
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||||
"total_count": 1
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||||
}</code></pre>
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||||
</div>
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</div>
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<div class="card">
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<h3>👤 GET /v1/memories/users/{user_id}</h3>
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||||
<p>Get all memories for a specific user</p>
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<h4>Query Parameters</h4>
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||||
<div class="table-container">
|
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<table>
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<thead>
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||||
<tr>
|
||||
<th>Parameter</th>
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||||
<th>Type</th>
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||||
<th>Default</th>
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||||
<th>Description</th>
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||||
</tr>
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||||
</thead>
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||||
<tbody>
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||||
<tr>
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||||
<td>limit</td>
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||||
<td>integer</td>
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<td>50</td>
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<td>Maximum number of memories to return</td>
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||||
</tr>
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||||
<tr>
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||||
<td>offset</td>
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||||
<td>integer</td>
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||||
<td>0</td>
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||||
<td>Number of memories to skip</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</div>
|
||||
|
||||
<h4>Response</h4>
|
||||
<div class="code-block">
|
||||
<pre><code class="language-json">{
|
||||
"memories": [
|
||||
{
|
||||
"id": "550e8400-e29b-41d4-a716-446655440000",
|
||||
"content": "Ondrej has a son named Cyril who is 8 years old",
|
||||
"metadata": {
|
||||
"category": "family",
|
||||
"importance": "high"
|
||||
},
|
||||
"user_id": "user123",
|
||||
"session_id": "session1",
|
||||
"created_at": "2025-07-16T19:30:00Z",
|
||||
"updated_at": "2025-07-16T19:30:00Z"
|
||||
}
|
||||
],
|
||||
"total_count": 1,
|
||||
"limit": 50,
|
||||
"offset": 0
|
||||
}</code></pre>
|
||||
</div>
|
||||
|
||||
<h4>cURL Example</h4>
|
||||
<div class="code-block">
|
||||
<pre><code class="language-bash">curl -X GET "http://localhost:8765/v1/memories/users/user123?limit=10&offset=0" \
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||||
-H "Authorization: Bearer langmem_api_key_2025"</code></pre>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="card">
|
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<h3>🗑️ DELETE /v1/memories/{memory_id}</h3>
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||||
<p>Delete a specific memory and its graph relationships</p>
|
||||
|
||||
<h4>Response</h4>
|
||||
<div class="code-block">
|
||||
<pre><code class="language-json">{
|
||||
"status": "deleted",
|
||||
"id": "550e8400-e29b-41d4-a716-446655440000"
|
||||
}</code></pre>
|
||||
</div>
|
||||
|
||||
<h4>cURL Example</h4>
|
||||
<div class="code-block">
|
||||
<pre><code class="language-bash">curl -X DELETE "http://localhost:8765/v1/memories/550e8400-e29b-41d4-a716-446655440000" \
|
||||
-H "Authorization: Bearer langmem_api_key_2025"</code></pre>
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
|
||||
<section class="mb-4">
|
||||
<h2>System Endpoints</h2>
|
||||
|
||||
<div class="card">
|
||||
<h3>🏠 GET /</h3>
|
||||
<p>Root endpoint with system information</p>
|
||||
|
||||
<h4>Response</h4>
|
||||
<div class="code-block">
|
||||
<pre><code class="language-json">{
|
||||
"message": "LangMem API - Long-term Memory System",
|
||||
"version": "1.0.0",
|
||||
"status": "running"
|
||||
}</code></pre>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="card">
|
||||
<h3>❤️ GET /health</h3>
|
||||
<p>Health check with service status</p>
|
||||
|
||||
<h4>Response</h4>
|
||||
<div class="code-block">
|
||||
<pre><code class="language-json">{
|
||||
"status": "healthy",
|
||||
"services": {
|
||||
"ollama": "healthy",
|
||||
"supabase": "healthy",
|
||||
"neo4j": "healthy",
|
||||
"postgres": "healthy"
|
||||
},
|
||||
"timestamp": "2025-07-16T19:30:00Z"
|
||||
}</code></pre>
|
||||
</div>
|
||||
|
||||
<h4>cURL Example</h4>
|
||||
<div class="code-block">
|
||||
<pre><code class="language-bash">curl -X GET "http://localhost:8765/health"</code></pre>
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
|
||||
<section class="mb-4">
|
||||
<h2>AI Relationship Extraction</h2>
|
||||
|
||||
<div class="card">
|
||||
<h3>🤖 Automatic Relationship Extraction</h3>
|
||||
<p>LangMem automatically extracts relationships from content using Llama3.2 model. Here are examples of relationship types that are dynamically generated:</p>
|
||||
|
||||
<h4>Relationship Types</h4>
|
||||
<div class="table-container">
|
||||
<table>
|
||||
<thead>
|
||||
<tr>
|
||||
<th>Category</th>
|
||||
<th>Example Relationships</th>
|
||||
<th>Entity Types</th>
|
||||
</tr>
|
||||
</thead>
|
||||
<tbody>
|
||||
<tr>
|
||||
<td>Family</td>
|
||||
<td>IS_FATHER_OF, IS_SON_OF, IS_PARENT_OF</td>
|
||||
<td>Person → Person</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Business</td>
|
||||
<td>FOUNDED, WORKS_FOR, EMPLOYED</td>
|
||||
<td>Person → Organization</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Technology</td>
|
||||
<td>CREATED_BY, USES, DEVELOPED</td>
|
||||
<td>Technology → Person</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Geography</td>
|
||||
<td>LOCATED_IN, DESIGNED</td>
|
||||
<td>Location → Location</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Science</td>
|
||||
<td>REVOLUTIONIZED, WORKED_AT</td>
|
||||
<td>Person → Concept</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</div>
|
||||
|
||||
<h4>Example AI Extraction</h4>
|
||||
<div class="code-block">
|
||||
<pre><code class="language-json">{
|
||||
"input": "Steve Jobs founded Apple Inc. in Cupertino, California",
|
||||
"extracted_relationships": [
|
||||
{
|
||||
"entity1": "Steve Jobs",
|
||||
"entity1_type": "Person",
|
||||
"relationship": "FOUNDED",
|
||||
"entity2": "Apple Inc.",
|
||||
"entity2_type": "Organization",
|
||||
"confidence": 0.9
|
||||
},
|
||||
{
|
||||
"entity1": "Apple Inc.",
|
||||
"entity1_type": "Organization",
|
||||
"relationship": "LOCATED_IN",
|
||||
"entity2": "Cupertino, California",
|
||||
"entity2_type": "Location",
|
||||
"confidence": 0.9
|
||||
}
|
||||
]
|
||||
}</code></pre>
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
|
||||
<section class="mb-4">
|
||||
<h2>Error Handling</h2>
|
||||
|
||||
<div class="card">
|
||||
<h3>🚨 Error Response Format</h3>
|
||||
<div class="code-block">
|
||||
<pre><code class="language-json">{
|
||||
"detail": "Failed to store memory: Invalid content format"
|
||||
}</code></pre>
|
||||
</div>
|
||||
|
||||
<h4>Common Error Codes</h4>
|
||||
<div class="table-container">
|
||||
<table>
|
||||
<thead>
|
||||
<tr>
|
||||
<th>HTTP Status</th>
|
||||
<th>Description</th>
|
||||
<th>Common Causes</th>
|
||||
</tr>
|
||||
</thead>
|
||||
<tbody>
|
||||
<tr>
|
||||
<td>400</td>
|
||||
<td>Bad Request</td>
|
||||
<td>Invalid JSON, missing required fields</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>401</td>
|
||||
<td>Unauthorized</td>
|
||||
<td>Missing or invalid Bearer token</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>404</td>
|
||||
<td>Not Found</td>
|
||||
<td>Memory ID not found</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>500</td>
|
||||
<td>Internal Server Error</td>
|
||||
<td>Database connection, AI processing errors</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
|
||||
<section class="mb-4">
|
||||
<h2>Database Access</h2>
|
||||
|
||||
<div class="card">
|
||||
<h3>🗄️ Direct Database Access</h3>
|
||||
<p>For advanced users, direct database access is available:</p>
|
||||
|
||||
<h4>PostgreSQL (Vector Storage)</h4>
|
||||
<div class="code-block">
|
||||
<pre><code class="language-sql">-- Connect to PostgreSQL
|
||||
psql "postgresql://postgres:CzkaYmRvc26Y@localhost:5435/postgres"
|
||||
|
||||
-- Query memories
|
||||
SELECT id, content, user_id, metadata
|
||||
FROM langmem_documents
|
||||
WHERE user_id = 'user123'
|
||||
ORDER BY created_at DESC;
|
||||
|
||||
-- Vector similarity search
|
||||
SELECT id, content, 1 - (embedding <=> '[0.1, 0.2, ...]') as similarity
|
||||
FROM langmem_documents
|
||||
WHERE user_id = 'user123'
|
||||
ORDER BY embedding <=> '[0.1, 0.2, ...]'
|
||||
LIMIT 10;</code></pre>
|
||||
</div>
|
||||
|
||||
<h4>Neo4j (Graph Relationships)</h4>
|
||||
<div class="code-block">
|
||||
<pre><code class="language-cypher">// Connect to Neo4j Browser: http://localhost:7474
|
||||
// Username: neo4j, Password: langmem_neo4j_password
|
||||
|
||||
// View all relationships
|
||||
MATCH (n)-[r]->(m) RETURN n, r, m LIMIT 25;
|
||||
|
||||
// Find specific relationship types
|
||||
MATCH (p:Person)-[r:IS_FATHER_OF]->(c:Person)
|
||||
RETURN p.name, r, c.name;
|
||||
|
||||
// Complex graph traversal
|
||||
MATCH (p:Person)-[:FOUNDED]->(org:Organization)-[:LOCATED_IN]->(loc:Location)
|
||||
RETURN p.name, org.name, loc.name;</code></pre>
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
|
||||
<section class="mb-4">
|
||||
<h2>Integration Examples</h2>
|
||||
|
||||
<div class="grid grid-2">
|
||||
<div class="card">
|
||||
<h3>🐍 Python Client</h3>
|
||||
<div class="code-block">
|
||||
<pre><code class="language-python">import requests
|
||||
import json
|
||||
|
||||
class LangMemClient:
|
||||
def __init__(self, base_url="http://localhost:8765", api_key="langmem_api_key_2025"):
|
||||
self.base_url = base_url
|
||||
self.headers = {
|
||||
'Authorization': f'Bearer {api_key}',
|
||||
'Content-Type': 'application/json'
|
||||
}
|
||||
|
||||
def store_memory(self, content, user_id, session_id=None, metadata=None):
|
||||
"""Store a memory with AI relationship extraction."""
|
||||
data = {
|
||||
'content': content,
|
||||
'user_id': user_id,
|
||||
'session_id': session_id,
|
||||
'metadata': metadata or {}
|
||||
}
|
||||
response = requests.post(
|
||||
f"{self.base_url}/v1/memories/store",
|
||||
headers=self.headers,
|
||||
json=data
|
||||
)
|
||||
return response.json()
|
||||
|
||||
def search_memories(self, query, user_id, limit=10, threshold=0.5):
|
||||
"""Search memories with hybrid search."""
|
||||
data = {
|
||||
'query': query,
|
||||
'user_id': user_id,
|
||||
'limit': limit,
|
||||
'threshold': threshold,
|
||||
'include_graph': True
|
||||
}
|
||||
response = requests.post(
|
||||
f"{self.base_url}/v1/memories/search",
|
||||
headers=self.headers,
|
||||
json=data
|
||||
)
|
||||
return response.json()
|
||||
|
||||
# Usage
|
||||
client = LangMemClient()
|
||||
|
||||
# Store memory
|
||||
result = client.store_memory(
|
||||
content="John works for Google in California",
|
||||
user_id="user123",
|
||||
metadata={"category": "business"}
|
||||
)
|
||||
print(f"Memory stored: {result['id']}")
|
||||
|
||||
# Search memories
|
||||
results = client.search_memories(
|
||||
query="Where does John work?",
|
||||
user_id="user123"
|
||||
)
|
||||
print(f"Found {results['total_count']} memories")
|
||||
for memory in results['memories']:
|
||||
print(f"- {memory['content']} (similarity: {memory['similarity']:.3f})")
|
||||
if 'relationships' in memory:
|
||||
for rel in memory['relationships']:
|
||||
print(f" → {rel['relationship']} {rel['entity_name']}")</code></pre>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="card">
|
||||
<h3>🟨 JavaScript Client</h3>
|
||||
<div class="code-block">
|
||||
<pre><code class="language-javascript">class LangMemClient {
|
||||
constructor(baseUrl = 'http://localhost:8765', apiKey = 'langmem_api_key_2025') {
|
||||
this.baseUrl = baseUrl;
|
||||
this.headers = {
|
||||
'Authorization': `Bearer ${apiKey}`,
|
||||
'Content-Type': 'application/json'
|
||||
};
|
||||
}
|
||||
|
||||
async storeMemory(content, userId, sessionId = null, metadata = {}) {
|
||||
const response = await fetch(`${this.baseUrl}/v1/memories/store`, {
|
||||
method: 'POST',
|
||||
headers: this.headers,
|
||||
body: JSON.stringify({
|
||||
content,
|
||||
user_id: userId,
|
||||
session_id: sessionId,
|
||||
metadata
|
||||
})
|
||||
});
|
||||
return await response.json();
|
||||
}
|
||||
|
||||
async searchMemories(query, userId, limit = 10, threshold = 0.5) {
|
||||
const response = await fetch(`${this.baseUrl}/v1/memories/search`, {
|
||||
method: 'POST',
|
||||
headers: this.headers,
|
||||
body: JSON.stringify({
|
||||
query,
|
||||
user_id: userId,
|
||||
limit,
|
||||
threshold,
|
||||
include_graph: true
|
||||
})
|
||||
});
|
||||
return await response.json();
|
||||
}
|
||||
}
|
||||
|
||||
// Usage
|
||||
const client = new LangMemClient();
|
||||
|
||||
// Store memory
|
||||
const result = await client.storeMemory(
|
||||
'John works for Google in California',
|
||||
'user123',
|
||||
null,
|
||||
{ category: 'business' }
|
||||
);
|
||||
console.log('Memory stored:', result.id);
|
||||
|
||||
// Search memories
|
||||
const results = await client.searchMemories(
|
||||
'Where does John work?',
|
||||
'user123'
|
||||
);
|
||||
console.log(`Found ${results.total_count} memories`);
|
||||
results.memories.forEach(memory => {
|
||||
console.log(`- ${memory.content} (similarity: ${memory.similarity.toFixed(3)})`);
|
||||
if (memory.relationships) {
|
||||
memory.relationships.forEach(rel => {
|
||||
console.log(` → ${rel.relationship} ${rel.entity_name}`);
|
||||
});
|
||||
}
|
||||
});</code></pre>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
|
||||
<section class="text-center">
|
||||
<h2>Ready to Build?</h2>
|
||||
<p class="mb-3">Use the LangMem API to integrate AI-powered memory with automatic relationship extraction into your applications.</p>
|
||||
<div class="cta-buttons">
|
||||
<a href="../implementation/" class="btn btn-primary">📚 Implementation Guide</a>
|
||||
<a href="../architecture/" class="btn btn-secondary">🏗️ Architecture Overview</a>
|
||||
</div>
|
||||
</section>
|
||||
</main>
|
||||
|
||||
<footer style="text-align: center; padding: 2rem; margin-top: 4rem; color: var(--text-secondary);">
|
||||
<p>© 2025 LangMem Documentation. AI-Powered Memory System with Dynamic Relationship Extraction.</p>
|
||||
<p>API Version: 1.0.0 - Production Ready</p>
|
||||
</footer>
|
||||
|
||||
<script src="../assets/js/main.js"></script>
|
||||
</body>
|
||||
</html>
|
||||
Reference in New Issue
Block a user