Advanced RAG

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Real-life applications can require complex pipelines, including SQL or graph databases, as well as automatically selecting relevant tools and APIs. These advanced techniques can improve a baseline solution and provide additional features.

Query construction: Structured data stored in traditional databases requires a specific query language like SQL, Cypher, metadata, etc. We can directly translate the user instruction into a query to access the data with query construction.

Tools: Agents augment LLMs by automatically selecting the most relevant tools to provide an answer. These tools can be as simple as using Google or Wikipedia, or more complex, like a Python interpreter or Jira.

Post-processing: Final step that processes the inputs that are fed to the LLM. It enhances the relevance and diversity of documents retrieved with re-ranking, RAG-fusion, and classification.

Program LLMs: Frameworks like DSPy allow you to optimize prompts and weights based on automated evaluations in a programmatic way.

📚 References:

LangChain - Query Construction: Blog post about different types of query construction.

LangChain - SQL: Tutorial on how to interact with SQL databases with LLMs, involving Text-to-SQL and an optional SQL agent.

Pinecone - LLM agents: Introduction to agents and tools with different types.

LLM Powered Autonomous Agents by Lilian Weng: A more theoretical article about LLM agents.

LangChain - OpenAI's RAG: Overview of the RAG strategies employed by OpenAI, including post-processing.

DSPy in 8 Steps: General-purpose guide to DSPy introducing modules, signatures, and optimizers.