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To build a first retrieval-augmented generation (RAG) application with Haystack, store a few documents, retrieve passages relevant to a question, add those passages to a prompt, and pass the prompt to a chat model. The official quick start uses an in-memory document store and BM25 retrieval, so you can learn how the pieces connect before choosing a vector database or other production infrastructure.
How a Haystack RAG pipeline works
RAG combines document retrieval with text generation. Instead of asking a model to answer from its training alone, the application finds relevant source material and includes it in the model’s prompt. Haystack organizes this as a pipeline: components receive typed inputs, do a job, and provide outputs to other components.
For the simplest question-answering flow, connect three components in order:
- Retriever: finds documents related to the user’s question.
- Prompt builder: formats the question and retrieved documents into model input.
- Generator: sends that prompt to a chat model and returns a response.
A document store holds the material that the retriever searches. Haystack’s concepts overview describes documents as objects that can contain text, metadata, binary data, or vector representations. A generated answer is only as useful as the material retrieved and the prompt that presents it; connecting components does not automatically make the evidence complete or correct.
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Build a first pipeline with BM25
The Haystack 3.1 quick start installs the core framework with pip install haystack-ai. Its basic example uses InMemoryDocumentStore and InMemoryBM25Retriever, along with a prompt builder and chat generator. The example is a learning starting point: documents live in memory rather than a persistent store.
Use the official Get Started guide for the complete, version-specific runnable example. At a high level, its setup follows this sequence:
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- Install the framework:
pip install haystack-ai. - Create documents: import
Documentand prepare the text you want the system to search. - Write documents to a store: create an
InMemoryDocumentStoreand add the documents. - Configure components: instantiate an
InMemoryBM25Retrieverusing that store, create aChatPromptBuilder, and choose a chat generator. - Connect the pipeline: wire the retriever’s document output to the prompt builder, then the prompt builder’s prompt output to the generator.
- Run it: provide the required inputs, such as the question, and inspect the generator’s reply.
The quick start imports Pipeline, Document, OpenAIChatGenerator, InMemoryBM25Retriever, InMemoryDocumentStore, ChatPromptBuilder, Secret, and ChatMessage. The model provider and its credentials are choices, not requirements of RAG itself. The guide also presents provider-specific examples for OpenAI, Hugging Face, Anthropic, Amazon Bedrock, and Google Gemini, and notes integrations including Cohere, Mistral, NVIDIA, and Ollama. Model identifiers, credentials setup, and component APIs can change; check the relevant provider component documentation when implementing.
Connect and run components correctly
Haystack pipelines can be linear or more complex, but each connection must join compatible component outputs and inputs. The pipeline construction guide recommends identifying component interfaces, initializing dependencies, adding components, connecting them, and then running the pipeline with its mandatory inputs. Haystack validates connections before execution, which helps catch mismatched component wiring early.
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- Check each component’s required inputs and available outputs.
- Initialize its dependencies, such as a document store or model credentials.
- Create a
Pipelineand add the components. - Connect output names to compatible input names, such as retriever documents to the prompt builder’s document input.
- Call
Pipeline.run()with every required input, including the question.
A pipeline is not limited to a straight line: Haystack supports directed multigraphs with branches, parallel flows, loops, standalone components, and decision components. Start with the linear retrieval-to-generation path; add graph complexity only when the application needs it.
Choose retrieval that fits your documents and questions
The quick start’s BM25 retriever is a practical first choice when you want to reduce setup. It is a sparse, lexical method: it matches query terms against document terms. Haystack’s retriever guide characterizes BM25 as simple and effective without training, particularly when wording is precise. Its limitation is that it does not naturally handle synonyms well.
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| Retrieval approach | How it works | Useful when | Trade-offs |
|---|---|---|---|
| BM25 / sparse keyword | Ranks documents using query and document term overlap. | Important terms are likely to appear in both the question and source text. | Simple and requires no training, but may miss relevant passages expressed with different wording. |
| Dense embeddings | Represents text as vectors and retrieves by semantic similarity. | Meaning matters even when the query and source use different terms. | Needs an embedding model and embedding-aware retrieval; it has additional computational cost and depends on the model’s language coverage. |
| Sparse learned retrieval | Uses learned term weighting and expansion; SPLADE is one example. | You want a sparse approach that can expand beyond literal term overlap. | Requires the relevant model and integration setup; no universal performance advantage is established. |
| Hybrid retrieval | Combines sparse and dense retrieval results. | Queries benefit from both exact term matching and semantic matching. | Combining methods adds design and tuning choices. Database-native hybrid retrievers can be performant but may give less control over how results are merged. |
These approaches solve overlapping, not identical, problems. There is no universal winner: test against representative questions and your own source material, then weigh answer relevance against latency and operational needs. The cited guides do not provide benchmark results for a particular application.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Move from in-memory storage to a suitable document store
An in-memory store is useful for learning and small experiments, but it is not persistent application storage. If your application must retain a corpus across restarts, handle larger workloads, or meet availability requirements, choose a store that fits its retrieval method and operational constraints. Haystack’s document-store guide groups integrations into vector databases, search engines, relational databases, document or NoSQL databases, in-memory key-value stores, vector index libraries, and multi-model databases.
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When evaluating an integration, consider:
- Retrieval support: whether you need dense semantic, BM25/full-text, keyword, or hybrid search.
- Deployment and operations: an in-process library, a service you manage, or a hosted service.
- Workload: corpus size, expected query volume, and availability needs.
- Features: metadata filtering, asynchronous support, and other database capabilities your application requires.
- Integration maturity: Haystack distinguishes core integrations maintained by its team and tested against every release from external community integrations outside that release cycle.
- Cost and data handling: check current terms and requirements with the individual provider; the documentation cited here does not establish prices or provider data policies.
Haystack documentation gives Chroma, FAISS, OpenSearch, PGVector, Pinecone, Qdrant, Weaviate, Azure AI Search, and MongoDB Atlas as integration examples. They are options to assess, not an exhaustive list or an endorsement. The right store depends on the search behavior and deployment you need, not on the fact that a tutorial used an in-memory store.
Add semantic retrieval when it solves a real problem
A semantic pipeline needs embeddings as well as a store and retriever that can use them. Haystack’s pipeline guide demonstrates a semantic-search path using SentenceTransformersTextEmbedder and InMemoryEmbeddingRetriever. The same guide notes that Sentence Transformers embedders moved to the separate sentence-transformers-haystack package, so do not assume every integration is included in haystack-ai.
Before adopting dense or hybrid retrieval, identify where lexical retrieval misses useful passages—for example, questions that use different wording from the source. Then evaluate a representative set of questions with your documents. Embedding choice, language coverage, computation, and store support all affect the design; the documentation does not prescribe one embedding model or infrastructure for every project.
What to validate before treating the demo as an application
A successful tutorial run confirms that the components connect and return output; it does not establish that the system reliably finds the right evidence or answers correctly. For an application, assess retrieval and generation against representative questions and source documents. Check whether the retrieved passages support the answer, whether relevant material is being missed, and how the pipeline behaves under your actual latency, scale, and data-handling constraints.
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