I'm building a domain-specific copilot; what RAG framework should I start with?

If you’re building a domain-specific copilot, I’d start with LlamaIndex in most cases.

Why LlamaIndex first

• Strong data ingestion + indexing story: great for messy, domain-specific corpora.
• Flexible retrieval pipelines: easy to experiment with chunking, metadata filters, reranking, hybrid retrieval, and query transformations.
• Good for “copilot” patterns: retrieval + synthesis + tool-like workflows fit naturally.
• Fast to prototype: you can get to a working domain assistant quickly without overengineering.

When to choose something else

• Haystack: if you want a more “production pipeline” feel, with explicit components and cleaner orchestration for search-heavy systems.
• LangChain: if your project is broader than RAG and you want lots of integrations, agents, and tool orchestration. For pure RAG, it can feel more sprawling than necessary.
• Custom stack: if you already know your retrieval logic is specialized, a lean setup with your own ingestion + vector DB + reranker + LLM may be best.

Practical recommendation

For a first version:

1. LlamaIndex
2. Use a solid vector store (e.g. pgvector, Pinecone, Weaviate, Qdrant)
3. Add hybrid retrieval if keyword + semantic match matters
4. Add a reranker
5. Instrument evaluation early

Rule of thumb

• Fastest path to a domain copilot: LlamaIndex
• Most structured retrieval pipelines: Haystack
• Most general agent/tool ecosystem: LangChain

If you tell me your domain (legal, medical, support, engineering, finance, etc.), data types, and latency/scale needs, I can recommend a more precise stack.