Multi-query retrieval for better recall in LangChain - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is multi-query retrieval in Langchain?

Multi-query retrieval is a technique where multiple search queries are used together to fetch more relevant information from a knowledge base, improving recall and coverage.

beginner

Why use multiple queries instead of a single query in retrieval?

Using multiple queries helps capture different ways users might ask questions, increasing the chance of finding all relevant information and reducing missed data.

intermediate

How does Langchain support multi-query retrieval?

Langchain allows chaining multiple retrieval calls or combining queries to fetch documents from different sources or with varied keywords, improving overall recall.

beginner

What is a practical example of multi-query retrieval?

If searching for 'climate change effects', you might also query 'global warming impact' and 'environmental changes' to get a broader set of relevant documents.

intermediate

What is a key challenge when using multi-query retrieval?

A key challenge is managing and merging results from multiple queries efficiently without duplicates and ensuring the combined results remain relevant.

What is the main goal of multi-query retrieval?

A To reduce the number of queries sent

B To avoid using any queries

C To speed up retrieval by using one query

D To improve recall by using multiple queries

Multi-query retrieval aims to improve recall by using several queries to cover more relevant information.

Which of these is a benefit of multi-query retrieval?

A More comprehensive search results

B Slower retrieval with no benefit

C Less relevant results

D Only one source searched

Multi-query retrieval provides more comprehensive results by covering different query variations.

In Langchain, how can multi-query retrieval be implemented?

A By using a single static query

B By chaining multiple retrieval calls

C By disabling retrieval

D By ignoring query variations

Langchain supports multi-query retrieval by chaining multiple retrieval calls or combining queries.

What is a common challenge when combining results from multiple queries?

A Using only one data source

B Writing fewer queries

C Handling duplicates and relevance

D Ignoring query results

Managing duplicates and ensuring relevance when merging results is a common challenge.

Which example shows multi-query retrieval?

A Searching 'climate change effects' and 'global warming impact'

B Searching 'climate change effects' only

C Searching without any query

D Searching only one keyword

Using multiple related queries like 'climate change effects' and 'global warming impact' is multi-query retrieval.

Explain what multi-query retrieval is and why it helps improve recall in Langchain.

Think about searching with different questions to find more answers.

Describe a challenge you might face when using multi-query retrieval and how you might address it.

Consider what happens when you get many results from different queries.

Practice

1. What is the main purpose of multi-query retrieval in langchain?

easy

A. To store data in multiple vector stores

B. To reduce the number of queries sent to the database

C. To speed up the response time by using a single query

D. To ask several related questions to get better search results

Solution

1. Step 1: Understand the concept of multi-query retrieval

   Multi-query retrieval involves asking multiple related questions instead of just one to improve the quality of search results.

2. Step 2: Identify the main goal of this approach

   The goal is to get better and more complete search results by combining answers from several queries.

3. Final Answer:

   To ask several related questions to get better search results -> Option D

4. Quick Check:

   Multi-query retrieval = multiple related questions for better results [OK]

Hint: Think: multiple questions improve search quality [OK]

Common Mistakes:

• Confusing multi-query with reducing query count
• Thinking it stores data in multiple places
• Assuming it speeds up by using fewer queries

2. Which of the following is the correct way to create a multi-query retriever in langchain?

easy

A. MultiQueryRetriever(vectorstore, llm=llm)

B. MultiQueryRetriever(queries=["query1", "query2"])

C. MultiQueryRetriever.from_llm(vectorstore.as_retriever(), llm)

D. MultiQueryRetriever.from_llm(vectorstore, llm)

Solution

1. Step 1: Check the required parameters for MultiQueryRetriever

   The MultiQueryRetriever is created using from_llm which requires a BaseRetriever (from vectorstore.as_retriever()) and an llm.

2. Step 2: Identify the correct syntax

   MultiQueryRetriever.from_llm(vectorstore.as_retriever(), llm) correctly uses the classmethod with proper arguments.

3. Final Answer:

   MultiQueryRetriever.from_llm(vectorstore.as_retriever(), llm) -> Option C

4. Quick Check:

   Correct syntax includes from_llm, .as_retriever(), and llm [OK]

Hint: Look for from_llm(vectorstore.as_retriever(), llm) syntax [OK]

Common Mistakes:

• Passing queries as a list to constructor
• Using direct constructor without llm_chain
• Forgetting .as_retriever() on vectorstore

3. Given the following code snippet, what will be the output type of results?

retriever = MultiQueryRetriever.from_llm(vectorstore.as_retriever(), llm)
results = retriever.get_relevant_documents("AI")

medium

A. A single document matching the first query only

B. A list of documents combining results from both queries

C. A dictionary with queries as keys and documents as values

D. An error because get_relevant_documents requires no arguments

Solution

1. Step 1: Understand MultiQueryRetriever behavior

   It [uses the LLM to] runs multiple queries and combines their results into a single list of documents.

2. Step 2: Analyze the method call

   Calling get_relevant_documents("AI") triggers retrieval for all [generated] queries, returning combined documents.

3. Final Answer:

   A list of documents combining results from both queries -> Option B

4. Quick Check:

   Multi-query retrieval returns combined document list [OK]

Hint: MultiQueryRetriever returns combined list of documents [OK]

Common Mistakes:

• Expecting a dictionary instead of a list
• Thinking it returns only first query results
• Assuming get_relevant_documents takes no arguments

4. Identify the error in this code snippet using multi-query retrieval:

retriever = MultiQueryRetriever.from_llm(vectorstore, llm)
results = retriever.get_relevant_documents("AI")

medium

A. vectorstore.as_retriever() is missing

B. get_relevant_documents requires a query argument

C. vectorstore is not defined

D. MultiQueryRetriever does not exist in langchain

Solution

1. Step 1: Check the constructor parameters

   from_llm expects a BaseRetriever as the first argument, but vectorstore is a VectorStore; use vectorstore.as_retriever().

2. Step 2: Verify other parts of the code

   get_relevant_documents has argument; llm assumed defined; class exists.

3. Final Answer:

   vectorstore.as_retriever() is missing -> Option A

4. Quick Check:

   from_llm requires BaseRetriever [OK]

Hint: from_llm needs vectorstore.as_retriever() [OK]

Common Mistakes:

• Passing vectorstore directly instead of retriever
• Thinking get_relevant_documents needs no arg when it does
• Assuming vectorstore undefined without context

5. You want to improve recall for a complex topic by using multi-query retrieval with three related queries. Which approach best ensures you get the most relevant documents without duplicates?

hard

A. Use MultiQueryRetriever.from_llm(retriever, llm) to generate multiple queries and deduplicate results

B. Run each query separately and pick the first result from each

C. Combine all queries into one long string and run a single query

D. Use only the query with the highest expected relevance

Solution

1. Step 1: Understand multi-query retrieval goal

   It aims to combine results from multiple related queries to improve recall.

2. Step 2: Identify best practice to avoid duplicates

   Using MultiQueryRetriever.from_llm(retriever, llm) generates multiple queries via LLM and deduplicates results for completeness without repeats.

3. Final Answer:

   Use MultiQueryRetriever.from_llm(retriever, llm) to generate multiple queries and deduplicate results -> Option A

4. Quick Check:

   MultiQueryRetriever generates + deduplicates = best recall [OK]

Hint: MultiQueryRetriever generates queries then deduplicates for best recall [OK]

Common Mistakes:

• Running queries separately without combining results
• Merging queries into one string loses query specificity
• Using only one query misses relevant documents