🤖 Designing an RAG with search for 10 million documents while minimizing hallucinations 📚

1️⃣ Document ingestion and normalization 📄
Removing duplicates, converting to a single format, extracting metadata, and maintaining versioning. 🔄

2️⃣ Hybrid search (BM25 + vector representations) 🔍
BM25 handles exact keyword matches, while vector search handles semantic relevance. One approach without the other typically suffers from low accuracy at this scale. 📉

3️⃣ Approximate nearest neighbor search + re-ranking ⚖️
Approximate nearest neighbor search quickly retrieves candidates from millions of fragments. Next, a ranking model recalculates relevance through a more rigorous comparison of the query and fragments. 🧠

4️⃣ Trust scoring for sources 🛡️
Each fragment receives an evaluation based on freshness, source reliability, overlap, and consistency with other found results. Data with low trust should not significantly influence the final response. 🚫

5️⃣ Generation with strict context constraints 🚧
The model only operates within the extracted context. Adding knowledge outside the context is prohibited by the pipeline logic. 🚫

6️⃣ Answers with source attribution 📝
Every significant statement must refer to a specific fragment, document, or timestamp. ⏰

7️⃣ Fallback for low search confidence 📉
If the total context confidence falls below a threshold, a response like "not enough data" is returned. 🛑

8️⃣ Continuous quality checks 🧪
Running attack queries, measuring search completeness, testing for hallucinations, and monitoring ranking degradation. 📊

9️⃣ Caching and memory layer 💾
Frequent queries and search chains are cached to reduce latency and computational cost. ⚡

🔟 Observability at all stages 👁️
Tracing the query path, fragment ranking, and the impact of tokens and failure points. 🛠️

🚀 At the scale of 10 million documents, search quality becomes a more critical factor than the choice of generative model.

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