How can I integrate SQL and vector search for a LangChain chatbot with RAG?

Below is one approach that combines the strengths of both vector search (to handle fuzzy matching and semantically similar variations) and SQL (to precisely rank by a field like average_rating):

Use a Two-Stage Retrieval Process

1. Pre-Query Extraction with LLM Assistance (Semantic Retrieval):
   Have your LLM parse the user’s query to extract the key attributes. For example, from

   "Of all the thin guitar strings, what are the top 5 highest rated?"
   the LLM can extract that you’re focusing on products related to "guitar strings" and that “thin” is a descriptive term for the item. Because users may use variations like “thin” versus “thinnest”, this semantic understanding helps you:

   • Build a list of candidate words for “thin” (or similar adjectives).
   • Identify that the focus is on “guitar strings.”

2. Vector Search for Fuzzy Matching:
   Convert your product data into embeddings and then run a vector search for “guitar strings” (or a semantically enhanced query that includes synonyms for “thin”). Although the vector search might return a broad result set, it helps catch relevant items that may not exactly match a SQL LIKE pattern. For example, you could do:

   PYTHONCopied
   # Assume proper_nouns is a list of product descriptions (or titles) embedded
   vector_db = FAISS.from_texts(product_descriptions, OpenAIEmbeddings())
   retriever = vector_db.as_retriever(search_kwargs={"k": 1500})
   candidate_docs = retriever.get_relevant_documents("thin guitar strings")

   This step is not used yet for ranking; rather, it acts as a high-recall filter so that you get most (if not all) candidate products.

3. SQL Query to Precisely Rank:
   Use the IDs (or another unique identifier) from the vector search as a filter in your SQL query. This ensures that only the products likely matching “thin guitar strings” (even if their description used variants like “thinnest”) are considered. You can then precisely order by average_rating. For instance:

   SQLCopied
   SELECT * 
   FROM products 
   WHERE id IN (<list-of-candidate-ids>)
   ORDER BY average_rating DESC 
   LIMIT 5;

   If your candidate set is too large, you might consider one of the following:

   • Narrow the vector search’s k parameter (if you’re confident it captures the best candidates), or
   • Use metadata filters based on some extra tags or fields available in your data.

4. Paraphrasing the Results:
   Once you get the SQL result set (the top 5 products), pass them to an LLM prompt that rephrases the answer into a friendly response. For instance, your prompt might instruct:

   "Given these product details:
    Product A: ...
    Product B: ...
    ...
   Reword this list into a short, consumer-friendly reply that highlights the products with descriptive language."
   

Considerations

• Synonym/Rich Lexicon Expansion:
  To make sure you don’t miss products because of minor wording differences (e.g., "thin" vs. "thinnest"), consider using a preprocessing step or metadata field that standardizes adjectives. Alternatively, your LLM can assist in mapping such descriptors.

• Hybrid Query Construction (Self Query):
  LangChain’s query construction chains (like Self Query) can be adapted so that the LLM produces both a semantic query and a metadata filter. This approach is designed exactly for cases where document metadata (or attributes) need to be filtered semantically.

• Performance Trade-offs:
  Keep an eye on the candidate set size. If too many IDs are returned from the vector search, using an overly large list in your SQL IN clause could hurt performance. You might need to experiment with the k value for the retriever.

This hybrid approach leverages semantic vector search to catch variations in wording while using SQL to precisely enforce ordering and limits. It helps bridge the gap between natural language variations and structured data requirements.