Multi-query retrieval for better recall in LangChain - Step-by-Step Execution

Concept Flow - Multi-query retrieval for better recall

User Input Query

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Split into Multiple Queries

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Send Each Query to Retriever

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Collect Multiple Results

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Combine Results

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Return Enhanced Recall Output

The process splits one query into several, retrieves results for each, then combines them to improve recall.

Execution Sample

LangChain

queries = ['query part 1', 'query part 2']
results = []
for q in queries:
    res = retriever.get_relevant_documents(q)
    results.extend(res)
combined = combine_results(results)

This code runs multiple queries separately, collects all results, and merges them for better recall.

Execution Table

Step	Action	Query	Retriever Output	Results Collected
1	Split main query	Full query → ['query part 1', 'query part 2']	N/A	[]
2	Send first query	query part 1	Docs A, B	[Docs A, B]
3	Send second query	query part 2	Docs C	[Docs A, B, C]
4	Combine results	N/A	N/A	[Docs A, B, C]
5	Return combined output	N/A	N/A	[Docs A, B, C]

💡 All queries processed and results combined for final output.

Variable Tracker

Variable	Start	After Step 2	After Step 3	Final
queries	[]	['query part 1', 'query part 2']	['query part 1', 'query part 2']	['query part 1', 'query part 2']
results	[]	[Docs A, B]	[Docs A, B, C]	[Docs A, B, C]
combined	N/A	N/A	N/A	[Docs A, B, C]

Key Moments - 3 Insights

Why do we split one query into multiple smaller queries?

Splitting helps the retriever focus on different aspects, increasing the chance to find more relevant documents, as shown in steps 2 and 3 where separate queries return different documents.

How do we avoid duplicate documents in combined results?

After collecting results from each query, we can filter duplicates before combining, ensuring the final list (step 4) has unique documents.

What happens if one query returns no results?

The results list still collects documents from other queries; an empty result for one query doesn't stop the process, as seen in step 3 where results are added only if found.

Visual Quiz - 3 Questions

Look at the execution table, what documents are collected after step 3?

A [Docs A, B]

B [Docs A, B, C]

C [Docs C]

D []

💡 Hint

Check the 'Results Collected' column at step 3 in the execution table.

At which step does the retriever process the second query?

A Step 1

B Step 2

C Step 3

D Step 4

💡 Hint

Look at the 'Action' and 'Query' columns in the execution table for step 3.

If the first query returned no documents, how would the 'results' variable look after step 2?

A []

B [Docs A, B]

C [Docs C]

D ['query part 1']

💡 Hint

Refer to the 'results' variable values in variable_tracker after step 2.

Concept Snapshot

Multi-query retrieval splits one question into parts.
Each part is sent separately to the retriever.
Results from all parts are collected and combined.
This improves recall by covering more angles.
Duplicates should be removed before final output.

Full Transcript

Multi-query retrieval means breaking a single user query into multiple smaller queries. Each smaller query is sent to the retriever to find relevant documents. The results from all queries are collected and combined to improve the chance of finding all useful information. This process helps recall because different queries can catch different relevant documents. After collecting, duplicates are removed to keep the output clean. If one query returns no results, others still contribute to the final combined output.