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Unlocking Siloed Data: A Practical Framework for Generative AI and RAG-Based PDF Interrogation unlocking data with generative ai and rag pdf
For multi-lingual PDFs, use multilingual-e5-large . 3.4 Vector Database Choices | DB | Best for | Key feature | |----|----------|-------------| | Chroma | Prototyping, small scale | Embedded, zero config | | Qdrant | Production, hybrid search | Built-in keyword + vector | | Weaviate | Large-scale, auto-indexing | Generative search modules | | PGVector | Postgres users | ACID compliance | 3.5 Hybrid Search (Boosts recall) Don’t rely solely on vector similarity. Implement: Question: query Unlocking Siloed Data: A Practical Framework
Final_score = α * vector_similarity + (1-α) * BM25_keyword_score Set α = 0.7 for semantic-heavy queries, 0.3 for exact match (e.g., invoice numbers). After initial retrieval (top 20 chunks), use a cross-encoder like BAAI/bge-reranker-v2-m3 to reorder top 5 most relevant chunks. Reduces hallucinations significantly. 3.7 Generation Prompt Template You are a helpful assistant for company PDF documents. Answer based ONLY on the following retrieved chunks. Context: chunks After initial retrieval (top 20 chunks), use a
Start with recursive character text splitter (LangChain). For technical PDFs, use semantic chunking. 3.3 Embedding Models | Model | Dim | Best for | |-------|-----|-----------| | text-embedding-3-small (OpenAI) | 1536 | General, cost-effective | | all-MiniLM-L6-v2 (sentence-transformers) | 384 | Local, fast, lower accuracy | | BAAI/bge-large-en-v1.5 | 1024 | High retrieval quality | | voyage-2 | 1024 | Long documents, legal/financial PDFs |
