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Magnus Hedemark
fe3be63800
Greenfield SkillOpt: 3 epochs optimizing discoverability, decision guidance, and troubleshooting for a brand-new LangChain skill. Epoch 1 — Prominence: - Added critical AgentExecutor deprecation callout at top - Framework Routing Guide for cross-portfolio decisions Epoch 2 — Decision Guidance: - Where to Start table with AgentExecutor migration row - Pipeline Mode table (Quick/RAG/Agent/Production) Epoch 3 — Pattern Expansion: - Troubleshooting table with reference file links - FAQ section covering installation, migration, performance 13 files: SKILL.md, 7 references, 4 templates, 1 script. v1.0.0 -> v1.0.3 across 3 SkillOpt epochs. Signed-off-by: Magnus Hedemark <magnus919@pm.me>
60 lines
2.1 KiB
Markdown
60 lines
2.1 KiB
Markdown
# LangChain Integration Ecosystem
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LangChain provides a unified interface across 1000+ integrations. Switching providers requires changing one line.
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## Model Providers
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| Provider | Package | Class |
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|----------|---------|-------|
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| OpenAI | `langchain-openai` | `ChatOpenAI` |
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| Anthropic | `langchain-anthropic` | `ChatAnthropic` |
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| Google Gemini | `langchain-google` | `ChatGoogleGenerativeAI` |
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| Mistral | `langchain-mistralai` | `ChatMistralAI` |
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| AWS Bedrock | `langchain-aws` | `ChatBedrock` |
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| Ollama (local) | `langchain-ollama` | `ChatOllama` |
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| Fireworks | `langchain-fireworks` | `ChatFireworks` |
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## Vector Stores
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| Store | Package | Instantiation |
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|-------|---------|---------------|
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| Chroma | `langchain-chroma` | `Chroma.from_documents(docs, embeddings)` |
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| Pinecone | `langchain-pinecone` | `PineconeVectorStore.from_documents(docs, embeddings)` |
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| pgvector | `langchain-postgres` | `PGVector(embeddings=embeddings, connection=conn)` |
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| Weaviate | `langchain-weaviate` | `WeaviateVectorStore.from_documents(docs, embeddings)` |
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| Qdrant | `langchain-qdrant` | `QdrantVectorStore.from_documents(docs, embeddings)` |
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| FAISS | `faiss-cpu` | `FAISS.from_documents(docs, embeddings)` |
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## Tool Integrations
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| Tool | Package | Purpose |
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|------|---------|---------|
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| Tavily Search | `langchain-community` | Web search for agents |
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| MCP Servers | `langchain-mcp-adapters` | Connect any MCP server as a tool |
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| SQL Database | `langchain-community` | Query SQL databases |
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| ArXiv | `langchain-community` | Academic paper search |
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| Wikipedia | `langchain-community` | Wikipedia lookup |
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## MCP Adapter Pattern
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Connect any MCP server as a LangChain tool:
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```python
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from langchain_mcp_adapters.client import MultiServerMCPClient
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async with MultiServerMCPClient() as client:
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tools = client.get_tools()
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agent = create_agent(model, tools)
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```
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## Quick-Swap Pattern
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```python
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# One-line swap between providers
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model = ChatOpenAI(model="gpt-4o-mini")
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# model = ChatAnthropic(model="claude-3-5-haiku") # same interface
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# model = ChatGoogleGenerativeAI(model="gemini-2.0-flash") # same interface
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```
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All models use the same interface: `model.invoke(messages)`.
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