Implementing Advanced RAG Technique: Self Querying

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Hi, accidentaly said Llama3 in the beginning of the video.
I was a bit tired sorry. But yes I still used an local open source model named QWEN 2 you can still use llama 3 if you really want to.

Based on the video transcript chunk provided, the chapters can be structured as follows:
1. Introduction to Implementing Advanced RAG Technique 00:00
2. Understanding Self-Querying and its Importance 01:16
3. Installing Necessary Tools and Preparing Documents 03:20
4. Creating a Self-Querying Retriever 04:42
5. Application and Results of Self-Querying 05:52

What is Self Querying?
LangChain's self-querying technique is a sophisticated approach designed to improve the effectiveness of information retrieval in large language model (LLM) applications. This method involves several key components and processes to ensure precise and relevant search results.

Firstly, a *vector store* is utilized to hold the document embeddings and their associated metadata. These embeddings represent the semantic content of the documents, while the metadata includes specific attributes such as genre, year, director, and rating. This setup allows for efficient storage and retrieval based on both content and metadata criteria.

The core of the self-querying technique is the *query-constructing LLM chain*. This component takes a natural language query from the user and translates it into a structured query. The structured query consists of two parts: a semantic similarity search to find documents related to the query content and a metadata filter to narrow down the results based on specific attributes. For instance, if a user asks for "a highly rated science fiction movie," the LLM chain constructs a query that searches for science fiction movies and filters them by high ratings.

Next, the *metadata attributes* play a crucial role in the retrieval process. These attributes are predefined fields in the documents that the system can query. Common attributes include genre, year, director, rating, and other relevant information. By specifying these attributes, the system can provide more targeted and accurate results.

The *retrieval process* begins with the user inputting a query. The self-querying retriever then uses the LLM to generate the structured query, which is executed against the vector store. The vector store returns documents that match the semantic content and meet the metadata criteria. For example, if the query is "find movies directed by Christopher Nolan with a rating above 8.0," the system will return relevant documents that fit these parameters.

This technique also includes the ability to *handle complex queries*. The self-querying retriever can manage composite filters and multiple criteria within a single query. This capability ensures that even detailed and specific user requests are handled efficiently, providing highly relevant search results.

In summary, LangChain's self-querying technique enhances LLM applications by combining semantic similarity search with detailed metadata filtering. This approach ensures that users receive precise and relevant information tailored to their specific queries, significantly improving the overall effectiveness of data retrieval systems.