Multi Query Retriever: Brief Explanation

 What is MultiQuery Retriever?

The Multi query Retriever is an advanced retrieval technique that generates multiple variations of a user’s query to improve document retrieval performance. Instead of executing a single query, it creates several semantically similar queries to fetch more relevant results.

Key Features:

1. Query Expansion: Automatically generates multiple query variations

2. Diverse Retrieval: Captures different aspects or phrasings of the original query

3. Improved Recall: Increases chances of finding relevant documents that might be missed with a single query

How It Works:

1. Takes the original user query

2. Uses an LLM to generate multiple similar/related queries (typically 3-5 variations)

3. Executes all queries against the retrieval system

4. Combines and duplicates results

5. Ranks the final set of documents

Benefits:

– Overcomes vocabulary mismatch between queries and documents

– Handles different ways users might phrase the same question

– Particularly useful for complex or ambiguous queries

Implementation Example (Python pseudocode):

python

from langchain.retrievers.multi_query import Multi query retriever

# Initialize with base retriever

retriever = MultiQueryRetriever.from_llm(

    retriever=base_retriever,

    llm=llm

)

# Retrieve documents

docs = retriever.get_relevant_documents(“What is quantum computing?”)

This approach often yields better results than single-query retrieval systems, especially in semantic search scenarios.

Here’s a brief explanation of each term in the context of LLMs and retrieval systems:  

—

# *2. Vector DB (Vector Database)* 

A database optimized for storing and querying vector embeddings (numerical representations of text, images, etc.). Used in semantic search and retrieval-augmented generation (RAG).  

– *Examples*: Pinecone, Website, FAISS, Chroma, Milvus  

– **Key Use**: Efficient similarity search for retrieving relevant documents.  

—

# 3. Retrieval QA (Retrieval-Augmented Question Answering) 

A system that combines **retrieval** (fetching relevant documents) + **generation** (LLM answering based on retrieved content).  

– Steps:  

  1. Retrieve relevant docs (using Vector DB).  

  2. Pass them as context to an LLM to generate an answer.  

– *Types*:  

  – Stuff: All docs concatenated into a single prompt.  

  – Map-Reduce: Processes docs separately and combines results.  

  – Refine: Iteratively improves the answer.  

—

# 4. Temperature  

A parameter (0 to 1) controlling randomness in LLM outputs:  

– Low (0.1-0.3): Predictable, deterministic responses.  

– High (0.7-1.0): Creative, diverse outputs (risks incoherence).  

– Usage*: Adjust based on need for creativity vs. accuracy.  

# *5. Prompt Template*

A predefined structure for LLM inputs with placeholders for variables.  

Example:  

python  

template = “””Answer based on context:  

{context}  

Question: {question}  

Answer: 

– Ensures consistent prompting in workflows like RAG.  

6. Chains (with “Stuff” Method)**  

Chains = Sequences of operations (e.g., retrieve → generate).  

– “Stuff” Chain: Simplest method where all retrieved documents are stuffed into a single prompt.  

  – Pros: Fast, preserves context relationships.  

  – Cons: Fails if docs exceed token limit.  

*Alternatives*:  

– Map-Reduce: Processes each doc separately, then combines.  

– Refine: Iteratively updates the answer with each doc.  

Summary Table  

Term              Key Point                                                                 

| Vector DB  | Stores embeddings for fast similarity search.                           

| Retrieval QA | Combines retrieval + LLM for accurate answers.                           

| Temperature | Controls output randomness (0 = strict, 1 = creative).                 

| Prompt Template | Reusable LLM input structure with variables.                           

| Chains (“Stuff”) | Single prompt with all docs; simple but limited by context length. |

Let me know if you’d like a deeper dive into any of these!