LlamaIndex Index Module
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An LlamaIndex Index Module is an LlamaIndex Module that contains implementations of indices within the LlamaIndex library.
- AKA: llama_index.indices.
- Context:
- It can (typically) provide classes for creating different types of indices to store and structure data, such as:
- LlamaIndex VectorStoreIndex class for vector similarity based indexing.
- LlamaIndex GraphIndex class for graph representation and indexing.
- LlamaIndex ElasticSearchIndex class for integration with Elasticsearch.
- LlamaIndex SQLIndex class for indexing data in SQL databases.
- LlamaIndex CosineIndex class for cosine similarity indexing.
- ...
- It can contain Python Submodules, such as:
- llama_index.indices.common: contains common classes and functions used by other submodules in the llama_index.indices module.
- llama_index.indices.document: contains classes and functions for representing and storing documents in an index.
- llama_index.indices.index: contains classes and functions for creating, managing, and querying indexes.
- llama_index.indices.postprocessor: contains classes and functions for post-processing the results of a query.
- llama_index.indices.query: contains classes and functions for constructing and executing queries on an index.
- llama_index.indices.utils: contains utility functions for working with indices.
- ...
- It can contain vector, graph, and other index implementations, such as:
- Sparse and dense vector index implementations.
- Directed and undirected graph index implementations.
- Keyword based inverted index implementations.
- ...
- It can include index utility and configuration functions.
- It can ingest data from LlamaIndex connectors and process it into an indexed form.
- It can allow indexed data to be queried via LlamaIndex retrievers and query engines.
- It encapsulates low-level implementation details of indices.
- It forms a core part of the LlamaIndex architecture and capabilities.
- ...
- It can (typically) provide classes for creating different types of indices to store and structure data, such as:
- Example(s):
- Counter-Example(s):
- See: Data Index, LlamaIndex Vector Index, LlamaIndex Graph Index.
References
2023
- GBard, 2023-09-12
- The LlamaIndex Index module is responsible for storing and indexing your data in a way that is efficient and easy to use with large language models (LLMs). It does this by breaking your data down into smaller chunks called "nodes", and then indexing each node based on its content. This allows you to quickly and easily retrieve the relevant nodes for a given query.
- The Index module contains the following components:
- Node: A node is a chunk of text from a document. LlamaIndex takes in Document objects and internally parses/chunks them into Node objects.
- Document: A document is a collection of nodes.
- Index: The index is a data structure that stores the locations of all the nodes in your data.
- Retriever: The retriever is responsible for retrieving the nodes that are relevant to a given query.
- Query Engine: The query engine is responsible for parsing and executing queries against the index.
- Reranking Module: The reranking module is responsible for re-ranking the retrieved nodes based on their relevance to the query.
- The Index module is a core component of LlamaIndex, and it is used by all other modules in the framework. It is a powerful tool that can be used to store and index large amounts of data in a way that is efficient and easy to use with LLMs.
- Here are some of the benefits of using the LlamaIndex Index module:
- It is efficient: The index is stored in a way that allows for fast and efficient retrieval of nodes.
- It is easy to use: The API for the Index module is simple and easy to use.
- It is flexible: The Index module can be used to index a variety of data formats.
- It is scalable: The Index module can be scaled to handle large amounts of data.
- If you are building an LLM application that needs to store and index large amounts of data, then the LlamaIndex Index module is a great option. It is a powerful and flexible tool that can help you to build a more efficient and effective application.