What should I use to connect an LLM to internal documents?

To connect an LLM to internal documents, the usual best option is a RAG setup (Retrieval-Augmented Generation).

What that means

Instead of putting all documents into the model itself, you:

1. Index your documents so they’re searchable
2. Retrieve relevant chunks when a user asks a question
3. Feed those chunks to the LLM as context

Common components

• Document loaders: ingest PDFs, Word files, SharePoint, Confluence, Google Drive, etc.
• Chunking: split documents into smaller pieces
• Embeddings model: converts text into vectors
• Vector database: stores and searches vectors
• Retriever: finds the most relevant chunks
• LLM: generates the final answer from retrieved context

Popular tools

• Frameworks: LangChain, LlamaIndex, Haystack
• Vector DBs: Pinecone, Weaviate, Milvus, Qdrant, FAISS, pgvector
• Enterprise search: Elasticsearch / OpenSearch with vector search
• Cloud options: Azure AI Search, AWS Bedrock Knowledge Bases, Google Vertex AI Search

Best choice depends on your setup

• Small prototype: LangChain + FAISS or Chroma
• Production app: LlamaIndex or LangChain + Qdrant/Pinecone/Weaviate
• Microsoft-heavy org: Azure AI Search + Azure OpenAI
• Already on Postgres: pgvector is a solid simple option

Important considerations

• Access control / permissions
• Freshness and document updates
• Citations / source tracking
• Chunk size and retrieval quality
• Security and PII handling

If you tell me:

• where your docs live,
• whether you need on-prem/private cloud,
• and your preferred stack (Python, JS, Azure/AWS/GCP),

I can recommend a specific architecture and toolset.