Traditional retrieval systems often require complex query languages or predefined search facets. Self-querying retrieval (SQR), on the other hand, offers a more natural and user-friendly approach. Here’s why it’s valuable:

  • Natural language queries: SQR allows users to pose their queries in natural language, making the interaction more intuitive and accessible. This is akin to having a conversation with a librarian, where users can ask questions in their own words without needing to learn complex query languages or specific search facets.

  • Advanced retrieval capabilities: Unlike basic keyword searches, SQR boasts advanced retrieval capabilities. It can search documents based on both their content and metadata (author, genre, year, etc.). This dual approach allows for highly precise results. Imagine searching for a specific legal document; SQR can not only find documents containing relevant keywords but also narrow them down by author (judge) or year (case date) for a perfect match.

  • Flexibility: SQR systems can adapt to user intent by refining search results based on follow-up questions or additional inputs. This, therefore, helps in narrowing down search results to better match the user’s needs.

What is self-querying retrieval?

Self-querying retrieval (SQR) leverages the power of LLMs to understand the user’s intent within a document collection. Here’s the core idea:

  • Document representation: Word embeddings convert each document into a numerical representation. This allows for efficient comparison between documents.

  • User query: The user submits a natural language query expressing their information need.

  • LLM-driven retrieval: The LLM analyzes the query and the document representations. It then retrieves documents that best match the user’s intent.

  • Refine and repeat: The user can refine their query or ask follow-up questions for a more focused search based on the retrieved documents.

Step-by-step implementation

The following are the steps to implement the naive RAG self-querying retrieval (SQR) using the Langchain library:

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