Integrating Haystack with machine learning pipelines involves setting up the components of Haystack to work alongside your existing machine learning framework, enabling effective retrieval and processing of documents. Haystack is designed to create search systems powered by modern NLP models. To start, you will need to install Haystack and its dependencies in your environment. This can be done using pip, and you should ensure that you have a compatible version of Python installed.
Once Haystack is set up, you can leverage its DocumentStore to hold the documents you want to analyze or retrieve. You can choose from various backends like Elasticsearch, OpenSearch, or SQL. The DocumentStore will manage storage and retrieval and can be populated with data using Haystack’s pipeline tools. After defining your DocumentStore, you should create a pipeline that includes components for preprocessing, document retrieval, and the machine learning model you want to use for tasks like question answering or summarization. For instance, you can use a transformer model such as BERT or DistilBERT to fine-tune your pipeline.
To connect Haystack with your machine learning models, you can either use pre-trained models available within Haystack or integrate your own. This integration allows you to process documents retrieved from the DocumentStore and apply your machine learning algorithms to them. A typical use case might involve retrieving relevant documents based on a user query and then scoring or filtering them with your model before presenting the final results. Properly configuring the retrieval and processing nodes in the pipeline will ensure that your machine learning models effectively support the specific use cases, such as FAQs, customer support, or content discovery.