What should I use to search PDFs, emails, and wiki pages with RAG?

For PDFs, emails, and wiki pages, the best default approach for RAG is:

Use a hybrid retrieval setup

Combine:

1. Vector search for semantic matching
2. Keyword / full-text search for exact terms, names, IDs, error codes, dates, etc.

This is usually better than using embeddings alone, because:

• PDFs often contain noisy formatting
• Emails have short, messy, conversational text
• Wiki pages often include exact terminology that keyword search catches well

Practical stack options

If you want the simplest strong option

Use a search engine that supports hybrid retrieval:

• Elasticsearch / OpenSearch
• Azure AI Search
• Weaviate
• Pinecone with hybrid support
• Qdrant plus a separate keyword index
• Postgres + pgvector if scale is modest, with a text search column

If you want a lightweight local setup

• Postgres + pgvector + full-text search
• Or SQLite + vector search for small prototypes, though weaker for production

Recommended architecture

1. Extract text from PDFs, emails, and wiki pages
2. Chunk the text into smaller passages
3. Store each chunk with:
   • embedding vector
   • raw text
   • metadata like source, title, author, date, thread, URL
4. Retrieve with:
   • semantic similarity
   • keyword search
   • optional reranking
5. Feed top results into the LLM

Important extras

• Use a reranker if you need better answer quality
• Preserve metadata so the model can cite sources and filter by date/source
• For PDFs, use good parsing/OCR if documents are scanned
• For emails, keep message/thread structure and timestamps
• For wikis, store page hierarchy and section titles

My short recommendation

If you’re building this today:

• Best general choice: OpenSearch/Elasticsearch with hybrid search
• Best managed easy option: Azure AI Search
• Best open-source semantic-first option: Qdrant or Weaviate
• Best small-scale option: Postgres + pgvector + full-text search

If you want, I can also recommend the best tool based on your scale:

• local prototype
• startup production
• enterprise knowledge base
• self-hosted open source