Can Haystack integrate with external data sources like databases or APIs?

Yes, Haystack can integrate with external data sources like databases and APIs. The framework is designed to work with various data formats and systems, allowing developers to import data from multiple sources into pipelines for processing. Haystack provides built-in connectors and tools to simplify integration, making it adaptable to real-world applications where data is often stored in external systems.

For databases, Haystack supports integration with SQL databases (e.g., PostgreSQL, MySQL) and NoSQL systems (e.g., Elasticsearch, MongoDB) through document stores. For example, you can use the SQLDocumentStore to fetch data from a relational database or the ElasticsearchDocumentStore to query indexed documents. These document stores act as bridges between Haystack’s pipelines and your database, enabling you to retrieve data for tasks like question answering or semantic search. Additionally, custom database integrations can be implemented using Python libraries like SQLAlchemy or by writing custom connectors. For APIs, Haystack’s FetchFromRestAPI node allows direct HTTP requests to fetch data. You could pull real-time information from a weather API, retrieve customer data from a CRM like Salesforce, or aggregate product details from an e-commerce platform’s REST endpoint. Data from APIs can then be processed and fed into Haystack’s pipelines alongside other sources.

Once data is retrieved, Haystack processes it using modular components like converters, preprocessors, and retrievers. For instance, data from a database might be split into smaller chunks for efficient embedding, while raw JSON from an API could be parsed into clean text. Pipelines can also be configured to schedule periodic API calls or database queries, ensuring data stays up-to-date. For example, a pipeline might pull daily sales records from a PostgreSQL database, combine them with inventory data from a Shopify API, and generate summarized reports using Haystack’s question-answering models. Developers retain full control over how data is transformed, filtered, or enriched before it reaches downstream components like vector databases or language models.

For custom use cases, Haystack’s extensible architecture lets developers build tailored integrations. If a specific database or API isn’t supported out of the box, you can create a custom node or document store using Python. For example, a proprietary internal API could be integrated by writing a lightweight wrapper class that fetches and formats data into Haystack’s Document objects. Similarly, real-time data streams from Kafka or WebSocket APIs can be incorporated using asynchronous handlers. This flexibility ensures Haystack adapts to diverse environments, whether you’re building a chatbot that queries a company’s internal knowledge base or a search system that combines product data from multiple third-party APIs.