How do I set up and use Haystack with OpenAI GPT models?

To set up and use Haystack with OpenAI GPT models, start by installing the necessary packages and configuring the OpenAI API. First, install Haystack and the OpenAI Python library using pip: pip install farm-haystack openai. Next, set your OpenAI API key as an environment variable (export OPENAI_API_KEY='your-key') or pass it directly in your code. Haystack provides components like PromptNode to interact with OpenAI models. Initialize a PromptNode with the GPT model name (e.g., "gpt-3.5-turbo") and your API key. This node handles prompt formatting and API calls, allowing you to integrate GPT into Haystack pipelines.

Next, design a pipeline to combine retrieval and generation. For example, create a retrieval-augmented generation (RAG) pipeline that first retrieves relevant documents and then uses GPT to generate answers. Use a document store like InMemoryDocumentStore to index your data. Add documents using Document objects, which contain text and metadata. Add a retriever (e.g., BM25Retriever) to fetch documents based on user queries. Connect the retriever to a PromptTemplate that instructs GPT to answer using the retrieved context. For instance, define a template like: "Answer using the context: {context}\nQuestion: {query}\nAnswer:". Chain these components in a Pipeline object so the retriever passes context to the PromptNode, which generates the final response.

Finally, run queries and optimize performance. Execute the pipeline by calling run(query="your question"). For example, if your documents contain product information, asking “What are the features of Model X?” would retrieve relevant snippets and generate a concise answer. Monitor API usage and costs, as GPT models charge per token. Adjust parameters like max_length to control response size. For better results, preprocess documents to remove noise and test different prompts. If responses are inaccurate, refine the retriever’s settings or expand the document set. Haystack’s modular design lets you swap components (e.g., using a dense retriever instead of BM25) without changing the overall workflow. This approach balances GPT’s generative power with precise data retrieval, making it suitable for tasks like customer support or knowledge base queries.