Recursive Retrieval for RAG: Implementation With LlamaIndex

Many RAG applications employ a simplified retrieval process: documents are segmented into chunks, converted into embeddings, and stored within a vector database. Queries trigger retrieval of the top-k most similar documents based on embedding similarity. However, this approach suffers from limitations, particularly with extensive datasets. Chunk ambiguity and suboptimal information retrieval can compromise accuracy.

Recursive retrieval enhances retrieval precision by leveraging document structure. Instead of directly retrieving chunks, it prioritizes relevant summaries, subsequently drilling down to associated chunks for more focused results.

This article details recursive retrieval and guides you through its implementation using LlamaIndex.

RAG and LangChain

LangChain facilitates the integration of external data with LLMs via Retrieval Augmented Generation (RAG).

Recursive Retrieval Explained

Unlike methods relying solely on raw chunk embeddings, recursive retrieval embeds document summaries, linking them to their corresponding chunks. Queries initially retrieve relevant summaries, then pinpoint related information within those summaries' associated chunks. This contextual approach improves information relevance.

Implementing Recursive Retrieval with LlamaIndex

This section guides you through a step-by-step implementation of recursive retrieval using LlamaIndex, from document loading to query execution.

Step 1: Document Loading and Preparation

Documents are loaded using SimpleDirectoryReader. Each document receives a title and metadata (e.g., category) for enhanced filtering. The loaded documents are stored in a dictionary for easy access.

from llama_index.core import SimpleDirectoryReader

# ... (Code for loading documents remains the same) ...

Step 2: LLM and Chunking Setup

An LLM (e.g., OpenAI's GPT-4o Mini) is initialized, along with a sentence splitter for chunk creation and a callback manager for process monitoring.

from llama_index.llms.openai import OpenAI
from llama_index.core.callbacks import LlamaDebugHandler, CallbackManager
from llama_index.core.node_parser import SentenceSplitter

# ... (Code for LLM and chunking setup remains the same) ...

Step 3: Vector Index Creation and Summary Generation

A vector index is created for each document to enable similarity-based retrieval. LLM-generated summaries are stored as IndexNode objects.

from llama_index.core import VectorStoreIndex, SummaryIndex
from llama_index.core.schema import IndexNode

# ... (Code for building vector indices and generating summaries remains the same) ...

Step 4: Top-Level Vector Index Construction

A top-level vector index is built from the generated summaries, enabling initial retrieval of relevant summaries before accessing detailed chunks.

# ... (Code for building the top-level vector index remains the same) ...

Step 5: Recursive Retrieval Setup

The recursive retriever is configured, combining the top-level retriever with individual document retrievers to facilitate the hierarchical retrieval process.

from llama_index.core.retrievers import RecursiveRetriever

# ... (Code for setting up the recursive retriever remains the same) ...

Step 6: Recursive Retrieval Queries

Sample queries are executed using the configured recursive retriever.

from llama_index.core import SimpleDirectoryReader

# ... (Code for loading documents remains the same) ...

Conclusion

Recursive retrieval, leveraging document summaries and hierarchies, enhances the relevance of retrieved chunks, especially with large datasets. It offers a robust solution for building accurate retrieval systems in data-rich environments. Further exploration of RAG techniques can be found in the linked blog posts.

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