Query Translation Patterns : Advance RAG

Query Translation refers to the process of transforming the initial user query into various forms or sub-queries to enhance the retrieval process , providing more relevant information to user .

Now, there are different techniques in which we can translate user query for better results :

1. Parallel Query Retrieval (fan out)

2. Reciprocate Rank Fusion

3. Step Back Prompting (algo)

4. CoT - Chain of Thought

5. HyDE - Hypothetical Document Embeddings

Parallel Query Retrieval (Fan Out) :

Parallel query retrial, also known as fan-out, is a technique in Retrieval-Augmented Generation (RAG) where the initial user query is expanded into multiple related queries, which are then used to retrieve documents concurrently

Reciprocate Rank Fusion :

In reciprocate rank fusion technique ranking is assigned to the relevant data on basis of frequnecy of occurance when searched using multiple queries generated by LLM .

Step Back Prompting Algorithm :

Step-back prompting in Retrieval-Augmented Generation (RAG) is a technique where an LLM first addresses a more abstract, higher-level question before tackling the original, more specific query.
This helps the LLM understand the broader context and reasoning process, leading to more accurate and grounded responses.

COT - Chain of Thoughts :

Chain of Thoughs is a technique in which user query is broken down into step by step logical queries and LLM think on them before generating the fnal output.

HyDE : Hypothetical Document Embeddings :

HyDE stands for Hypothetical Document Embeddings. It leverages an LLM to craft a "Hypothetical Document" or a fake answer in response to a user query devoid of contextual information. This fake answer is then converted into vector embeddings and employed to query the most relevant document chunks within a vector database.

🥳 Congratulations! You’ve just gained the jist of the overall techniques for query translation 🧠✨
I’ll be diving deeper into each and every topic in detail in my upcoming blogs 📚📝

🙏 Thank you so much for your time 💖
Let’s catch up in my next blog! 🚀👋

Stay curious, keep learning! 🔍🔥

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