To use Haystack with knowledge graphs, you can integrate structured graph data into Haystack’s document retrieval and question-answering pipelines. Haystack is designed to work with unstructured text (like documents) but can be extended to leverage knowledge graphs by converting graph entities and relationships into a format compatible with Haystack’s components. For example, you might extract entities and their connections from a graph database like Neo4j or Amazon Neptune, format them as text documents with metadata, and index them in Haystack’s document store (e.g., Elasticsearch or FAISS). This allows Haystack’s retriever components to search both unstructured text and structured graph-derived data during query processing.
A practical approach involves creating a custom retriever or preprocessor that queries the knowledge graph for relevant entities or relationships when handling a user query. For instance, if a user asks, “What projects did Elon Musk found?” a Haystack pipeline could first query a knowledge graph to retrieve entities like “Tesla” and “SpaceX” and their associated metadata. These results could then be combined with traditional document retrieval from a corpus of articles or reports. By using Haystack’s pipeline framework, you can orchestrate this process, ensuring the final answer generator (like a language model) receives both textual context and structured graph data to produce accurate answers.
For implementation, start by exporting subsets of your knowledge graph as structured documents. Each node (entity) in the graph could become a Haystack document with fields like name, type, and relationships, while edges (relationships) could be stored as metadata. Tools like SPARQL (for RDF graphs) or Cypher (for Neo4j) can help extract this data. Then, use Haystack’s PreProcessor to split or enrich the data as needed. When querying, combine a vector-based retriever (for semantic similarity) with a keyword-based retriever (for exact graph entity matches) using Haystack’s JoinDocuments or TransformersQueryClassifier to merge results. This hybrid approach ensures the system benefits from both the precision of knowledge graphs and the flexibility of unstructured text retrieval.