What I Got Wrong Building a RAG Pipeline from Scratch in TypeScript

Building an effective Retrieval-Augmented Generation (RAG) system involves several critical steps and considerations to ensure that the system is both efficient and accurate in retrieving relevant information. Based on your experience, here are some key takeaways and recommendations for improving or building a similar RAG system:

Key Takeaways

1. Hybrid Search with Reciprocal Rank Fusion (RRF):

   • Combining vector search and keyword search using RRF significantly improves retrieval accuracy by leveraging the strengths of both methods.
   • Pre-computing tsvector at ingestion time and indexing it efficiently ensures fast and scalable keyword searches.

2. Model Selection for Tool Calling:

   • Larger models are more reliable in generating structured JSON outputs required for tool calling, which is crucial for building effective agents.
   • Explicit guidance in prompts can help smaller models make better decisions when choosing between different retrieval methods.

3. Evaluation Early On:

   • Evaluating the quality of retrieved chunks early on with a small golden dataset helps identify and fix issues before scaling up to full-scale deployment.

Recommendations

1. Start with Retrieval Evaluation:
   • Before building out the entire pipeline, create a small set of queries (e.g

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