need embeddings for RAG with chunking and re-ranking

For RAG, a solid setup is:

1. Chunk the documents
2. Create embeddings for each chunk
3. Store them in a vector database
4. Retrieve top-k chunks for a query
5. Re-rank the retrieved chunks with a cross-encoder or reranker
6. Pass the best chunks to the LLM

Recommended approach

1) Chunking

Use chunks that are:

• Small enough to capture a single idea
• Large enough to preserve context

Typical starting point:

• Chunk size: 300–800 tokens
• Overlap: 10–20%

Best practice:

• Split by structure first: headings, paragraphs, sections
• Then apply token-based chunking
• Keep metadata:
  • document_id
  • section title
  • chunk index
  • source URL / page number

2) Embeddings

Use a strong embedding model. Good choices depend on your stack:

• OpenAI: text-embedding-3-small or text-embedding-3-large
• Open-source:
  • bge-large-en
  • e5-large
  • gte-large
  • multilingual variants if needed

If your corpus is domain-specific, consider:

• fine-tuning embeddings
• or at least testing several models on a retrieval benchmark

3) Vector store

Store chunk embeddings in:

• FAISS
• Pinecone
• Weaviate
• Milvus
• pgvector
• Qdrant

4) Retrieval

At query time:

• embed the query
• retrieve top 20–50 chunks by cosine similarity / dot product

5) Re-ranking

Use a reranker to improve relevance.

Good rerankers:

• bge-reranker-large
• Cohere rerank models
• cross-encoders from sentence-transformers

Pipeline:

• initial vector search: top 20–50
• rerank these with a cross-encoder
• keep top 3–10 for generation

This usually improves precision a lot.

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Simple recommended pipeline

If you want a practical default:

• Chunk size: 500 tokens
• Overlap: 100 tokens
• Embedding model: text-embedding-3-small or bge-large-en
• Retrieve top 30
• Re-rank to top 5
• Feed top 5 chunks to the LLM

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Example architecture

Documents
  -> parse + clean
  -> chunk
  -> embed each chunk
  -> store in vector DB

User query
  -> embed query
  -> retrieve top-k chunks
  -> rerank with cross-encoder
  -> send best chunks to LLM

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Important tips

• Don’t chunk too small, or you lose context
• Don’t chunk too large, or retrieval becomes noisy
• Re-ranking is especially useful when:
  • queries are vague
  • documents are long
  • you have many similar chunks
• If the documents are code, legal, or scientific text, tune chunking differently

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Example chunking strategy

For markdown or docs:

• split by headings
• keep section text together
• if a section is too long, split by paragraph
• if still too long, split by token window

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If you want the best practical setup

Use:

• Hierarchical chunking
• Dense embeddings
• Hybrid retrieval: vector + keyword/BM25
• Re-ranking
• Context compression before the LLM if needed

That combination is usually stronger than embeddings alone.

If you want, I can also give you:

1. a Python code example for chunking + embeddings + reranking, or
2. a recommended model shortlist for your use case.