Parallel Query Retrieval process multiple similar queries at once ( Parallelly ), increasing the context , and improving the chances of finding the more Relevant Context for user Query , resulting in more Accurate Response .

Below, first image shows How, retrieval process is done in normal RAG , second shows, Retrieval process in Advanced RAG, when using Parallel Query Retrieval

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Parallel Query Retrieval :

What is it ?

• Its a query transformation technique used for Enhancing User Query, to get more accurate response in our RAG Application

Where do we apply this in RAG ?

• RAG contains , three major steps, Indexing Retrieving Generation , now Indexing is storing Data sources in Database by creating Vector embeddings of data chunks , Retrieving process starts, after receiving User Query to get relevant data and we pass it as context with user Query to Generation part, which finally generates Response.

• So, this PQR technique is applicable at second step , i.e RETRIEVAL

How does it work ?

• On Receiving user query, we ask our LLM to create some similiar queries ( lets say Query Variations )

• We process those Query Variations in parallel , by creating their vector embeddings and performing Semantic Search on them to get relevant data ( lets say Docs ).

• All Query Variations, outputs some Doc, then we take out Unique Docs from them, and pass as context in Generation process.

• At Generation process, with our provided Context with User Query ( Original ), LLM finally outputs more accurate Response .

How Response got More Accurate ?

• We increased Context ( more relevant context and more in data ) , with that augmented context from similar Queries, we got more Precise Response aligned with the user Query.

Working Step by Step with Code & Visual :

1. From user-prompt, we ask our LLM to generate similar more queries ( lets say 3 queries )

2. 

   then, we do find their vector embedding’s and perform semantic search to get relevant data

3. 

   next, we filter out relevant data to get only unique Data, this is our Context for the current query

4. Now, we pass user-prompt ( Original ) with our Context to LLM , to get Final RESPONSE .

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Parallel Query Retrieval Output :

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Important Links:

PARALLEL QUERY RETRIEVAL Code - Visit Here!

I have discussed all Advanced RAG techniques, check out! Advanced RAG Article Series

Advanced RAG Series Repository → Visit Repo Here!

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Conclusion:

Just Explained my learning’s on PARALLEL QUERY RETRIEVAL Technique ! if you find it useful then don’t forget to like this Article & Follow Me for more such informative Articles.

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Credits:

Credits: I am very grateful to ChaiCode for Providing all this knowledge, Insights , Deep Learning about AI : Piyush Garg Hitesh Choudhary

If you want to learn too, you can Join here → Cohort || Apply from ChaiCode & Use NAKUL51937 to get 10% off

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