Retrieving Relevant Chunks and Building LLM Prompts in JavaScript
Introduction
Welcome to the third lesson in our journey through Scaling Up RAG with Vector Databases! Well done, you're halfway through this course. In the previous lesson, you learned how to split or chunk your text data and store those chunks in a vector database collection. Now, we'll delve into retrieving the most relevant chunks for any given query and building an LLM prompt to produce more accurate, context-driven answers.
Retrieving the Most Relevant Chunks
Before your LLM can generate a coherent, context-rich answer, you need to fetch the right information. Your vector database (for instance, using ChromaDB) will rank which document chunks are most relevant for a given query.
Let's break down this code in detail:
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Function Definition:
retrieveTopChunkstakes three parameters:query: The user's question or search term;collection: The ChromaDB collection object containing our embedded documents;topK: The number of most relevant chunks to retrieve (default is 2).
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Vector Search:
- The
collection.query()function performs a vector-based similarity search to pinpoint which chunks are most aligned with the query. queryTexts: [query]passes the user's query as an array (ChromaDB's API expects an array).nResults: topKspecifies how many matching chunks to return.
- The
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Results Structure:
- The query returns an object with multiple keys:
documents: Contains the actual text chunks;ids: Contains the document identifiers;distances: Each result includes a distance, which indicates how semantically close a chunk is to your query — the lower the distance, the better the match.
- Each of these keys maps to a nested array structure:
[[item1, item2, ...]].
- The query returns an object with multiple keys:
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Processing Results:
- For each result, the function creates an object with three key pieces of information:
chunk: The actual text content fromresults.documents[0][i];docId: The document identifier fromresults.ids[0][i];distance: The similarity score fromresults.distances[0][i];
- These objects are appended to the
retrievedChunksarray, which is then ultimately returned.
- For each result, the function creates an object with three key pieces of information:
