Unlocking Data With Generative Ai And Rag Pdf Repack Jun 2026

If the answer is not in the context, say "I don't have that information." Provide citations using [page X] or [doc: filename.pdf]. Answer:

RAG inverts the typical LLM workflow:

| 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 | unlocking data with generative ai and rag pdf

retriever = vectorstore.as_retriever(search_kwargs="k": 10) compressor = CrossEncoderReranker(model="BAAI/bge-reranker-base", top_n=3) compressed_retriever = ContextualCompressionRetriever( base_compressor=compressor, base_retriever=retriever ) If the answer is not in the context,

When you ask a question, the system searches the database for the chunks that most closely match the "meaning" of your query. unlocking data with generative ai and rag pdf

Question: query

Final_score = α * vector_similarity + (1-α) * BM25_keyword_score