d086af72ee
- Added .htaccess with Apache basic auth configuration - Created .htpasswd with username 'langmem' and password 'langmem2025' - Added auth.js for JavaScript-based authentication backup - Updated all HTML pages to include authentication script - Added AUTH_INFO.md with access credentials and setup info Credentials: langmem / langmem2025 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
700 lines
24 KiB
HTML
700 lines
24 KiB
HTML
<!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="../auth.js"></script>
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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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<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-a716-446655440000",
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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": [
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{
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"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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],
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"context": {
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"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>
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<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>
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</tr>
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</tbody>
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</table>
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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-a716-446655440000",
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"content": "Ondrej has a son named Cyril who is 8 years old",
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"metadata": {
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"category": "family",
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"importance": "high"
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},
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"user_id": "user123",
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"session_id": "session1",
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"created_at": "2025-07-16T19:30:00Z",
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"updated_at": "2025-07-16T19:30:00Z"
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}
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],
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"total_count": 1,
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"limit": 50,
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"offset": 0
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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 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>
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</div>
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</div>
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<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>
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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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"status": "deleted",
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"id": "550e8400-e29b-41d4-a716-446655440000"
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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 DELETE "http://localhost:8765/v1/memories/550e8400-e29b-41d4-a716-446655440000" \
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-H "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>System Endpoints</h2>
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<div class="card">
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<h3>🏠 GET /</h3>
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<p>Root endpoint with system information</p>
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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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"message": "LangMem API - Long-term Memory System",
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"version": "1.0.0",
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"status": "running"
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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 /health</h3>
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<p>Health check with service status</p>
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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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"status": "healthy",
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"services": {
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"ollama": "healthy",
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"supabase": "healthy",
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"neo4j": "healthy",
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"postgres": "healthy"
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},
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"timestamp": "2025-07-16T19:30:00Z"
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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 GET "http://localhost:8765/health"</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>AI Relationship Extraction</h2>
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<div class="card">
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<h3>🤖 Automatic Relationship Extraction</h3>
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<p>LangMem automatically extracts relationships from content using Llama3.2 model. Here are examples of relationship types that are dynamically generated:</p>
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<h4>Relationship Types</h4>
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<div class="table-container">
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<table>
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<thead>
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<tr>
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<th>Category</th>
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<th>Example Relationships</th>
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<th>Entity Types</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>Family</td>
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<td>IS_FATHER_OF, IS_SON_OF, IS_PARENT_OF</td>
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<td>Person → Person</td>
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</tr>
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<tr>
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<td>Business</td>
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<td>FOUNDED, WORKS_FOR, EMPLOYED</td>
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<td>Person → Organization</td>
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</tr>
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<tr>
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<td>Technology</td>
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<td>CREATED_BY, USES, DEVELOPED</td>
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<td>Technology → Person</td>
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</tr>
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<tr>
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<td>Geography</td>
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<td>LOCATED_IN, DESIGNED</td>
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<td>Location → Location</td>
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</tr>
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<tr>
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<td>Science</td>
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<td>REVOLUTIONIZED, WORKED_AT</td>
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<td>Person → Concept</td>
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</tr>
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</tbody>
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</table>
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</div>
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<h4>Example AI Extraction</h4>
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<div class="code-block">
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<pre><code class="language-json">{
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"input": "Steve Jobs founded Apple Inc. in Cupertino, California",
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"extracted_relationships": [
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{
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"entity1": "Steve Jobs",
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"entity1_type": "Person",
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"relationship": "FOUNDED",
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"entity2": "Apple Inc.",
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"entity2_type": "Organization",
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"confidence": 0.9
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},
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{
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"entity1": "Apple Inc.",
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"entity1_type": "Organization",
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"relationship": "LOCATED_IN",
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"entity2": "Cupertino, California",
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"entity2_type": "Location",
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"confidence": 0.9
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}
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]
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}</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>Error Handling</h2>
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|
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<div class="card">
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<h3>🚨 Error Response Format</h3>
|
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<div class="code-block">
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<pre><code class="language-json">{
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"detail": "Failed to store memory: Invalid content format"
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}</code></pre>
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</div>
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<h4>Common Error Codes</h4>
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<div class="table-container">
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<table>
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<thead>
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<tr>
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<th>HTTP Status</th>
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<th>Description</th>
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<th>Common Causes</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>400</td>
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<td>Bad Request</td>
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<td>Invalid JSON, missing required fields</td>
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</tr>
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<tr>
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<td>401</td>
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<td>Unauthorized</td>
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<td>Missing or invalid Bearer token</td>
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</tr>
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<tr>
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<td>404</td>
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<td>Not Found</td>
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<td>Memory ID not found</td>
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</tr>
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<tr>
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<td>500</td>
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<td>Internal Server Error</td>
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<td>Database connection, AI processing errors</td>
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</tr>
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</tbody>
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</table>
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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>Database Access</h2>
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|
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<div class="card">
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<h3>🗄️ Direct Database Access</h3>
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<p>For advanced users, direct database access is available:</p>
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<h4>PostgreSQL (Vector Storage)</h4>
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<div class="code-block">
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<pre><code class="language-sql">-- Connect to PostgreSQL
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psql "postgresql://postgres:CzkaYmRvc26Y@localhost:5435/postgres"
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|
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-- Query memories
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SELECT id, content, user_id, metadata
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FROM langmem_documents
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WHERE user_id = 'user123'
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ORDER BY created_at DESC;
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|
|
-- 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> |