Integrating Haystack with vector embeddings for document retrieval involves several key steps. First, you'll need to set up Haystack, a framework that simplifies the process of building search systems. Generally, this includes installing the necessary dependencies and frameworks like Elasticsearch or a similar database that supports vector storage and searching. After setting up your environment, you can proceed by introducing vector embeddings into your document retrieval pipeline.
Next, you'll need to create vector embeddings for your documents. This can be accomplished using models such as Sentence Transformers or OpenAI's embeddings. Each document in your collection is transformed into a dense vector representation that captures its semantic meaning. This step is crucial as it allows the retrieval system to base its search on the content of the documents rather than just keywords. Use a pre-trained model or train your own if your use case requires it. Ensure to save these embeddings in a format compatible with your chosen database.
Finally, you will implement the retrieval process by setting up Haystack's document store to handle the vector embeddings. You can use Haystack's built-in components like the DensePassageRetriever to efficiently search through the vector embeddings. The retrieval system will take user queries, convert them into embeddings using the same model used for the documents, and then find the closest matches based on cosine similarity or another distance metric. After defining this pipeline, you can run queries against your document store, retrieving relevant documents based on their semantic meanings rather than just matching terms.