mem0 — Генератор и обработка векторных представлений текста

--- name: mem0 description: > Integrate Mem0 Platform into AI applications for persistent memory, personalization, and semantic search. Use this skill when the user mentions "mem0", "memory layer", "remember user preferences", "persistent context", "personalization", or needs to add long-term memory to chatbots, agents, or AI apps. Covers Python and TypeScript SDKs, framework integrations (LangChain, CrewAI, Vercel AI SDK, OpenAI Agents SDK, Pipecat), and the full Platform API. Use even when the user doesn't explicitly say "mem0" but describes needing conversation memory, user context retention, or knowledge retrieval across sessions. license: Apache-2.0 metadata: author: mem0ai version: "0.1.0" category: ai-memory tags: "memory, personalization, ai, python, typescript, vector-search" compatibility: Requires Python 3.10+ or Node.js 18+, pip install mem0ai or npm install mem0ai, MEM0_API_KEY env var, and internet access to api.mem0.ai --- # Mem0 Platform Integration Mem0 is a managed memory layer for AI applications. It stores, retrieves, and manages user memories via API — no infrastructure to deploy. ## Step 1: Install and authenticate **Python:** ```bash pip install mem0ai export MEM0_API_KEY="m0-your-api-key" ``` **TypeScript/JavaScript:** ```bash npm install mem0ai export MEM0_API_KEY="m0-your-api-key" ``` Get an API key at: https://app.mem0.ai/dashboard/api-keys ## Step 2: Initialize the client **Python:** ```python from mem0 import MemoryClient client = MemoryClient(api_key="m0-xxx") ``` **TypeScript:** ```typescript import MemoryClient from 'mem0ai'; const client = new MemoryClient({ apiKey: 'm0-xxx' }); ``` For async Python, use `AsyncMemoryClient`. ## Step 3: Core operations Every Mem0 integration follows the same pattern: **retrieve → generate → store**. ### Add memories ```python messages = [ {"role": "user", "content": "I'm a vegetarian and allergic to nuts."}, {"role": "assistant", "content": "Got it! I'll remember that."} ] client.add(messages, user_id="alice") ``` ### Search memories ```python results = client.search("dietary preferences", user_id="alice") for mem in results.get("results", []): print(mem["memory"]) ``` ### Get all memories ```python all_memories = client.get_all(user_id="alice") ``` ### Update a memory ```python client.update("memory-uuid", text="Updated: vegetarian, nut allergy, prefers organic") ``` ### Delete a memory ```python client.delete("memory-uuid") client.delete_all(user_id="alice") # delete all for a user ``` ## Common integration pattern ```python from mem0 import MemoryClient from openai import OpenAI mem0 = MemoryClient() openai = OpenAI() def chat(user_input: str, user_id: str) -> str: # 1. Retrieve relevant memories memories = mem0.search(user_input, user_id=user_id) context = "n".join([m["memory"] for m in memories.get("results", [])]) # 2. Generate response with memory context response = openai.chat.completions.create( model="gpt-4.1-nano-2025-04-14", messages=[ {"role": "system", "content": f"User context:n{context}"}, {"role": "user", "content": user_input}, ] ) reply = response.choices[0].message.content # 3. Store interaction for future context mem0.add( [{"role": "user", "content": user_input}, {"role": "assistant", "content": reply}], user_id=user_id ) return reply ``` ## Common edge cases - **Search returns empty:** Memories process asynchronously. Wait 2-3s after `add()` before searching. Also verify `user_id` matches exactly (case-sensitive). - **AND filter with user_id + agent_id returns empty:** Entities are stored separately. Use `OR` instead, or query separately. - **Duplicate memories:** Don't mix `infer=True` (default) and `infer=False` for the same data. Stick to one mode. - **Wrong import:** Always use `from mem0 import MemoryClient` (or `AsyncMemoryClient` for async). Do not use `from mem0 import Memory`. - **Immutable memories:** Cannot be updated or deleted once created. Use `client.history(memory_id)` to track changes over time. ## Live documentation search For the latest docs beyond what's in the references, use the doc search tool: ```bash python ${CLAUDE_SKILL_DIR}/scripts/mem0_doc_search.py --query "topic" python ${CLAUDE_SKILL_DIR}/scripts/mem0_doc_search.py --page "/platform/features/graph-memory" python ${CLAUDE_SKILL_DIR}/scripts/mem0_doc_search.py --index ``` No API key needed — searches docs.mem0.ai directly. ## References Load these on demand for deeper detail: | Topic | File | |-------|------| | Quickstart (Python, TS, cURL) | [references/quickstart.md](references/quickstart.md) | | SDK guide (all methods, both languages) | [references/sdk-guide.md](references/sdk-guide.md) | | API reference (endpoints, filters, object schema) | [references/api-reference.md](references/api-reference.md) | | Architecture (pipeline, lifecycle, scoping, performance) | [references/architecture.md](references/architecture.md) | | Platform features (retrieval, graph, categories, MCP, etc.) | [references/features.md](references/features.md) | | Framework integrations (LangChain, CrewAI, Vercel AI, etc.) | [references/integration-patterns.md](references/integration-patterns.md) | | Use cases & examples (real-world patterns with code) | [references/use-cases.md](references/use-cases.md) |