What are various retrieval strategies in llama index

Core Retrieval Concepts in LlamaIndex

Before diving into specific techniques, it's essential to understand how LlamaIndex handles retrieval in a RAG (Retrieval Augmented Generation) pipeline:

Documents and Nodes: LlamaIndex represents your data as Document objects. These can be broken down into smaller chunks called Node objects, which are the units of retrieval.

Indices: LlamaIndex provides various index structures to organize your nodes for efficient retrieval.

Retrievers: These are the components responsible for fetching relevant nodes from an index based on a query.

Main Retrieval Techniques in LlamaIndex

LlamaIndex offers a rich set of retrieval techniques, which can be broadly categorized as follows:

Vector-based Retrieval:

Concept: Embed your queries and nodes into a vector space and retrieve the nearest neighbors.

LlamaIndex Implementation: VectorStoreIndex is the primary class. You can plug in different vector stores (e.g., Pinecone, Weaviate, Chroma) or use a simple in-memory one.

Variations/Enhancements:

Similarity Top-k Retrieval: Retrieves the top-k most similar nodes.

Self-Querying Retriever: The LLM helps to structure the query to filter metadata.

Contextual Compression: Compresses retrieved documents to the minimum context required

Keyword-based Retrieval:

Concept: Retrieve nodes based on keyword matches.

LlamaIndex Implementation: KeywordTableIndex

Use Cases: Useful when you need to find documents containing specific terms.

Graph-based Retrieval:

Concept: Represent your data as a graph and traverse it to find relevant information.

LlamaIndex Implementation: KnowledgeGraphIndex

Use Cases: Effective for retrieving information based on relationships between entities.

Tree-structured Retrieval

Concept: Organizes data in a tree structure, enabling hierarchical retrieval

LlamaIndex Implementation: TreeIndex

Use Cases: Good for documents with natural hierarchical structures.

Compositional Retrieval:

Concept: Combine multiple retrieval techniques to improve performance.

LlamaIndex Implementation:

Multi-Step Retrieval: Chain together different retrievers.

Router Retriever: Select the best retriever for a given query.

Key Improvements and Trends

LlamaIndex is continuously evolving. Some important trends and improvements include:

Hybrid Search: Combining vector search with keyword search for better precision and recall.

Metadata Filtering: Filtering retrieved nodes based on metadata to narrow down the search.

Query Transformations: Using LLMs to rewrite or augment queries to improve retrieval effectiveness.

This list provides a solid starting point for understanding retrieval in LlamaIndex. For the most up-to-date information, I recommend checking the official LlamaIndex documentation and tutorials, as the library is under active development.