LangChain and Databricks: Building LLM-Powered Applications
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Architecture / Concept Overview: LangChain and Databricks: Building LLM-Powered Applications
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flowchart LR
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classDef ingestion fill:#5A4B36,stroke:#C9A86B,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
classDef processing fill:#535072,stroke:#8E82B4,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
classDef storage fill:#2E4A4A,stroke:#5FAFA8,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
classDef serving fill:#3D5550,stroke:#6BB7AA,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
classDef governance fill:#5A3F52,stroke:#C28BB0,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
USER[User Query]:::source
CHAIN[LangChain Application]:::ingestion
RETRIEVER[Vector Search Retriever]:::processing
VS[Databricks Vector Search]:::storage
LLM[Model Serving Endpoint]:::serving
UC[Unity Catalog Data]:::governance
USER --> CHAIN --> RETRIEVER --> VS
VS --> CHAIN
CHAIN --> LLM
UC --> VS
LLM --> USER
*A RAG application uses LangChain to orchestrate retrieval from Databricks Vector Search and generation from a served LLM.*
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graph TD
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COMPONENTS[LangChain + Databricks Components]:::source
LLM_C[LLM / Chat Models]:::serving
EMBED[Embeddings]:::processing
VECTOR[Vector Stores]:::storage
TOOLS_C[Agent Tools]:::ingestion
MEMORY[Memory / History]:::governance
COMPONENTS --> LLM_C
COMPONENTS --> EMBED
COMPONENTS --> VECTOR
COMPONENTS --> TOOLS_C
COMPONENTS --> MEMORY
LLM_C --> DBRX[DBRX / Llama via Serving]:::serving
EMBED --> BGE[BGE / E5 via Serving]:::processing
VECTOR --> DVS[Databricks Vector Search]:::storage
TOOLS_C --> SQL_T[SQL Query Tool]:::ingestion
TOOLS_C --> UC_T[Unity Catalog Tool]:::ingestion
*LangChain's modular components map to specific Databricks services for LLMs, embeddings, vector search, and tool use.*
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flowchart LR
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classDef governance fill:#5A3F52,stroke:#C28BB0,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
DOCS[Source Documents]:::source
CHUNK[Chunking & Splitting]:::ingestion
EMBED_P[Generate Embeddings]:::processing
INDEX[Vector Search Index]:::storage
QUERY[Similarity Search]:::serving
ANSWER[LLM Answer Generation]:::governance
DOCS --> CHUNK --> EMBED_P --> INDEX --> QUERY --> ANSWER
*The RAG pipeline processes documents through chunking, embedding, indexing, retrieval, and answer generation.*
Key Terms
Prerequisites and Setup
- Databricks workspace with Model Serving and Vector Search enabled
- A deployed model serving endpoint (foundation model or custom)
- A Vector Search index populated with your documents
- Python 3.9+ with LangChain packages
- Unity Catalog tables containing source documents
Step-by-Step Implementation
Configuration Reference
| Component | Databricks Service | Configuration |
|---|---|---|
| LLM | Model Serving | endpoint name, temperature, max_tokens |
| Embeddings | Model Serving | endpoint name (embedding model) |
| Vector Store | Vector Search | index_name, endpoint_name |
| Document Source | Unity Catalog | Table with text content column |
| Deployed Chain | Model Serving + MLflow | Registered model in UC |