Integrating LlamaIndex with libraries like LangChain and Haystack involves connecting its specialized indexing and retrieval capabilities to broader workflow frameworks. LlamaIndex excels at organizing unstructured data into searchable structures, while LangChain focuses on chaining LLM-related tasks, and Haystack provides pipelines for document processing. The integration typically revolves around passing data between these tools using their native interfaces or adapters.
For LangChain integration, start by using LlamaIndex to create a structured index of your data (e.g., documents, PDFs). LangChain’s LlamaIndexRetriever wrapper lets you treat a LlamaIndex query engine as a LangChain retriever. For example, after building an index with LlamaIndex’s VectorStoreIndex, you can initialize a RetrieverQueryEngine and pass it to LangChain’s RetrievalQA chain. This allows LangChain to handle tasks like conversation history or tool orchestration while leveraging LlamaIndex’s efficient data retrieval. You could also use LlamaIndex’s load_index_from_storage to reuse prebuilt indices within LangChain agents, combining retrieval with other LangChain modules like prompt templates or memory systems.
With Haystack, the integration often centers on document stores and pipelines. LlamaIndex indices can be converted into Haystack-compatible formats using utilities like CompatibilityGraphStore, which maps LlamaIndex’s graph structures to Haystack’s document nodes. For instance, after creating a KnowledgeGraphIndex in LlamaIndex, you can export its nodes and relationships to a Haystack InMemoryDocumentStore. From there, Haystack pipelines can use retrievers like EmbeddingRetriever or custom components to process queries. You might also use LlamaIndex’s SimpleDirectoryReader to ingest files, then pass the parsed data to Haystack’s PreProcessor for further cleaning before indexing. This setup lets Haystack handle scalable deployment and pipeline management while relying on LlamaIndex for initial data structuring.
A practical use case might involve using LlamaIndex to build a domain-specific knowledge graph, LangChain to manage user interactions and context, and Haystack to deploy the system as an API endpoint. For example, a medical FAQ app could index research papers with LlamaIndex, use LangChain to generate answers with citations, and deploy via Haystack’s REST API. The key is to let each tool handle its strengths: LlamaIndex for optimized retrieval, LangChain for task coordination, and Haystack for pipeline scalability. Check each library’s documentation for version-specific adapter classes and ensure data formats (e.g., metadata fields) are consistent across tools.