Packages
Integrates Azure OpenAI's embedding models with LlamaIndex, enabling vector representations of text for semantic search and retrieval-augmented generation workflows.
Provides Amazon Bedrock embedding models integration for LlamaIndex, enabling text-to-vector conversion using Titan and Cohere embeddings through AWS.
Integrates Google's Gemini and Vertex AI embedding models with llama-index for generating text embeddings in your applications.
Integrates Hugging Face embedding models with LlamaIndex, allowing you to use transformer-based embeddings from Hugging Face within LlamaIndex applications.
Integrates IBM watsonx.ai embedding models with LlamaIndex, allowing you to embed text strings using IBM's foundation models through the LlamaIndex API.
Integrates Langchain embedding models with LlamaIndex, allowing you to use Langchain's embedding providers within LlamaIndex's retrieval and indexing workflows.
Integrates Ollama's local embedding models with LlamaIndex for generating text embeddings without external API calls.
Install it if you're already running Ollama and want seamless vector embedding support; skip it if you rely on cloud embedding APIs.
Integrates OpenAI's embedding models with LlamaIndex for converting text into vector representations within LlamaIndex applications.
Integrates Google Vertex AI embedding models into LlamaIndex applications, providing access to text and multimodal embedding APIs with flexible credential handling.
Provides deprecated integration for creating and querying indexes on LlamaCloud's managed parsing and retrieval service, now superseded by the llama-cloud package.
Provides instrumentation hooks for observability in LlamaIndex applications, enabling collection and management of spans, events, and dispatchers to track application behavior.
LlamaIndex Legacy is a data framework for building LLM applications that connects private data to language models through indexing, retrieval, and query interfaces.
However, verify whether migration to the current package is recommended for your use case, as this is explicitly a legacy release.
Integrates Anthropic's Claude language models into LlamaIndex applications, enabling text completion, chat, streaming, and structured prediction workflows.
Install it if you are building LlamaIndex applications and want to use Claude models; skip it if you are using Anthropic's SDK directly without LlamaIndex.
Integrates Azure OpenAI language models into LlamaIndex applications, enabling text completion, streaming, and chat interactions through Azure's managed OpenAI service.
Integrates AWS Bedrock LLMs into LlamaIndex, enabling you to use Bedrock-hosted language models for text completion, chat, and streaming operations within LlamaIndex workflows.
Integrates AWS Bedrock's Converse API with LlamaIndex, enabling LLM applications to call foundation models like Claude through Bedrock with support for streaming, function calling, and prompt caching.
Install it if you're already using LlamaIndex and want to run Bedrock models; skip it if you're calling Bedrock directly or using a different LLM framework.
Integrates Google's Gemini LLM with LlamaIndex, though this integration is no longer supported as of version 0.6.2.
Integrates Google's Gemini models into LlamaIndex applications, providing a unified interface for text generation, chat, and streaming responses via the Google GenAI API.
Integrates IBM watsonx.ai foundation models with LlamaIndex's LLM API, enabling you to use IBM's models for text completion and chat inference through a unified interface.
Bridges LlamaIndex with LangChain LLM providers, allowing you to use any LangChain-compatible language model within LlamaIndex's indexing and retrieval framework.
Integrates LiteLLM with LlamaIndex to provide unified access to multiple LLM providers (OpenAI, Cohere, and others) through a single interface.
Install only if you actually need multi-provider flexibility; single-provider projects may not benefit.
Integrates local Ollama language models with LlamaIndex, enabling text completion, chat, streaming, and structured JSON responses through a unified Python interface.
Integrates OpenAI's language models into applications using LlamaIndex, providing access to completion, chat, and streaming APIs through a unified interface.
Install it if you are building with llama-index-core and want to use OpenAI models; it is the standard integration point for that use case.
Provides a thin wrapper to use OpenAI-compatible APIs (including local models) within the LlamaIndex framework, allowing you to swap in alternative LLM providers that follow the OpenAI protocol.
Integrates Google Vertex AI's LLM capabilities with LlamaIndex, enabling use of Vertex models within LlamaIndex applications.
However, verify whether Google GenAI integration better suits your use case, as the package documentation suggests Gemini has moved to that path.
Integrates OpenAI's multi-modal language models with LlamaIndex, enabling applications to process and reason over images and text together.
However, the aging maintenance status means you should verify that supported OpenAI models and LlamaIndex versions match your requirements before production use.
Integrates OpenAI-powered program synthesis with LlamaIndex, enabling structured code generation through OpenAI's language models.
However, the 441-day gap since the last release is a significant concern—verify that the three runtime dependencies are current and compatible with your OpenAI API…
Generates follow-up questions from documents or context using OpenAI's language models, integrated with the LlamaIndex framework.
However, the aging maintenance status (441 days since last release) warrants checking compatibility with your current llama-index versions before committing to…
Loads pages and attachments from Confluence cloud instances into LlamaIndex documents, supporting multiple query methods and file type extraction.
Install it if you need to load Confluence pages into LlamaIndex for RAG, search, or documentation processing workflows.
Provides file loaders that parse documents in multiple formats (PDF, DOCX, images, CSV, HTML, Markdown, and others) into structured data for indexing and retrieval.
Install it if you need to ingest documents from multiple file types.
Parses PDF files into structured markdown or text format for use with LlamaIndex, enabling efficient document retrieval and context augmentation in RAG pipelines.
Integrates BM25 full-text search retrieval into LlamaIndex applications, enabling keyword-based document ranking and retrieval without external dependencies.
Install only if BM25 ranking fits your retrieval needs; for semantic or dense vector search, you'd want a different retriever.
Provides a Postgres-backed document store integration for LlamaIndex, enabling persistent storage and retrieval of documents within LlamaIndex applications.
Provides a PostgreSQL-backed key-value store integration for LlamaIndex, enabling persistent storage of indexed data and embeddings using a relational database backend.
Connects LlamaIndex agents to MCP (Model Context Protocol) servers to expose and call remote tools, and converts LlamaIndex workflows into MCP servers.
Provides visualization tools for LlamaIndex workflows, including functions to render workflow structure and execution traces as interactive HTML diagrams.
However, verify the license terms first, as they are not declared in the package metadata.
Integrates Azure AI Search as a vector store backend for LlamaIndex, enabling semantic search and retrieval-augmented generation workflows using Azure's managed search service.
Integrates Chroma vector database with LlamaIndex for storing and retrieving embeddings in RAG and semantic search applications.
Integrates FAISS vector storage with LlamaIndex for efficient similarity search and retrieval in RAG applications.
Install it if you are building a LlamaIndex application and want to use FAISS for vector storage and retrieval.
Integrates LanceDB as a vector store backend for LlamaIndex, enabling semantic search and retrieval-augmented generation workflows with LanceDB's vector database.