What are Composable Objects in LlamaIndex Retrievers

Composable objects in LlamaIndex retrievers refer to the ability to combine and chain different retriever components together to create more complex and powerful retrieval pipelines.  It's a way to build custom retrieval strategies by composing simpler building blocks.

Here's a breakdown of the concept:

Core Idea: LlamaIndex allows you to treat retrievers and other related components (like node parsers, query engines, etc.) as composable objects. This means you can combine them in a flexible way to create custom retrieval workflows that are tailored to your specific data and needs.

How it Works:

Retriever Components: LlamaIndex provides various retriever components, including:

Retrievers: These are the core components that fetch relevant nodes (text chunks) based on a query (e.g., BM25Retriever, VectorStoreRetriever, KeywordRetriever, etc.).

Node Parsers: These components process and structure the retrieved nodes (e.g., splitting them into smaller chunks, adding metadata, etc.).   

Query Engines: These are higher-level components that combine retrievers with LLMs to perform question answering or other tasks.   

Composition: You can combine these components using various techniques:

Chaining: You can chain retrievers together, so the output of one retriever becomes the input for the next. For example, you might first use a KeywordRetriever to filter down the documents and then use a VectorStoreRetriever to find the most semantically similar nodes within those documents.

Fusion: You can combine the results of multiple retrievers using a fusion strategy (as with the SimpleFusionRetriever).

Custom Logic: You can define your own custom logic to combine or filter the retrieved nodes.   

Flexibility: This composability gives you great flexibility in designing your retrieval pipeline. You can create complex workflows that are optimized for your specific data and retrieval task.

Example (Conceptual):

Let's say you have a large collection of documents, and you want to retrieve information based on both keywords and semantic similarity.

You create a BM25Retriever for keyword search and a VectorStoreRetriever for semantic search.

You create a SimpleFusionRetriever to combine the results of the two retrievers.

You can further process the merged results using a NodeParser to split the retrieved nodes into smaller chunks or add metadata.

Finally, you can use a QueryEngine to combine the processed nodes with an LLM to answer a question based on the retrieved information.

Benefits:

Customization: You can create highly customized retrieval pipelines tailored to your specific needs.

Modularity: You can reuse and combine different retriever components to create complex workflows.   

Flexibility: You have great flexibility in how you combine and process retrieved information.

Improved Performance: By combining different retrieval strategies, you can often improve the overall retrieval performance.

In summary: Composable objects in LlamaIndex retrievers allow you to build complex retrieval pipelines by combining simpler building blocks. 1   This modularity and flexibility enable you to create highly customized retrieval strategies that are optimized for your specific data and retrieval tasks.  It's a key feature of LlamaIndex that allows for advanced retrieval workflows

References:

Gemini