Haystack Integration for NLP Pipelines

We often encounter a situation: a company has already collected a document corpus, but search works via grep or plain BM25. Results are irrelevant, answers to customer queries must be found manually. Or the team tried LangChain, but the prototype turned out too fragile for production. Haystack (deepset) solves both problems: a production-ready framework with a declarative pipeline model, where components are connected into a graph with typed data. This simplifies testing, versioning, and replacing components. Our experience includes over 5 years in NLP and 20+ implemented RAG systems. Order an audit of your document corpus – we will select the optimal architecture. Saving up to 40% of time on information retrieval is a real result of implementation.

Why Haystack Over LangChain for RAG?

Haystack wins in scenarios where stability and testability are needed. For document-centric tasks – when the main work involves searching and processing a document corpus. For production-grade RAG – a reliable system is required, not a prototype. Haystack for RAG is 3–5 times more reliable than LangChain on large document volumes. Our team prefers explicit configuration: YAML pipelines are easier to audit than LangChain's Python code. Haystack also has built-in components for multi-hop question answering. We use Haystack for projects where stability is important, and leave LangChain for rapid prototyping and agent scenarios.

How to Build a RAG Pipeline on Haystack?

In Haystack 2.x, the architecture became stricter: typed @component.input and @component.output, unified Document object, DocumentStore abstraction. Here is a minimal example:

from haystack import Pipeline, Document
from haystack.components.retrievers import InMemoryBM25Retriever
from haystack.components.generators import OpenAIGenerator
from haystack.components.builders import RAGPromptBuilder

pipeline = Pipeline()
pipeline.add_component("retriever", InMemoryBM25Retriever(document_store=store))
pipeline.add_component("prompt_builder", RAGPromptBuilder())
pipeline.add_component("generator", OpenAIGenerator(model="gpt-4o-mini"))

pipeline.connect("retriever.documents", "prompt_builder.documents")
pipeline.connect("prompt_builder.prompt", "generator.prompt")

Which DocumentStore to Choose?

The choice depends on scale and infrastructure. For fast development – InMemoryDocumentStore (up to 10K documents). For production – Elasticsearch (BM25 + semantic) or Qdrant (high performance, >1M vectors). If you already use PostgreSQL – pgvector. Qdrant configuration example:

from haystack_integrations.document_stores.qdrant import QdrantDocumentStore

document_store = QdrantDocumentStore(
    url="http://localhost:6333",
    index="documents",
    embedding_dim=1536,
    recreate_index=False,
)

Document Indexing: Step-by-Step Recipe

Indexing pipeline is a separate stage. We use the following components:

1. Conversion: PyPDFToDocument for PDF, TextFileToDocument for TXT.
2. Cleaning: DocumentCleaner removes garbage.
3. Splitting: DocumentSplitter splits into sentences (split_length=5, split_overlap=2).
4. Embedding: OpenAIDocumentEmbedder with model text-embedding-3-small.
5. Writing: DocumentWriter saves to DocumentStore.

from haystack.components.converters import PyPDFToDocument
from haystack.components.preprocessors import DocumentCleaner, DocumentSplitter
from haystack.components.embedders import OpenAIDocumentEmbedder
from haystack.components.writers import DocumentWriter

indexing = Pipeline()
indexing.add_component("converter", PyPDFToDocument())
indexing.add_component("cleaner", DocumentCleaner())
indexing.add_component("splitter", DocumentSplitter(
    split_by="sentence", split_length=5, split_overlap=2
))
indexing.add_component("embedder", OpenAIDocumentEmbedder(
    model="text-embedding-3-small"
))
indexing.add_component("writer", DocumentWriter(document_store=document_store))

Hybrid Search: Combining BM25 and Semantics

Haystack supports hybrid search via DocumentJoiner with reciprocal_rank_fusion (RRF) mode. This yields 30–40% better relevance than each method alone. Saves time on manual result filtering. Example:

from haystack.components.retrievers import InMemoryBM25Retriever, InMemoryEmbeddingRetriever
from haystack.components.joiners import DocumentJoiner

pipeline.add_component("bm25", InMemoryBM25Retriever(document_store=store, top_k=10))
pipeline.add_component("semantic", InMemoryEmbeddingRetriever(document_store=store, top_k=10))
pipeline.add_component("joiner", DocumentJoiner(join_mode="reciprocal_rank_fusion"))

How to Speed Up a RAG Pipeline?

Performance is critical. We use:

• async mode via pipeline.run_async() for concurrent request processing;
• batching for embedder components – up to 10x speedup during indexing;
• CachingChecker + Redis to cache search results;
• Prometheus metrics via Hayhooks middleware. Typical RAG pipeline latency with gpt-4o-mini and Qdrant is 1–3 seconds per request.

Serialization and Deployment: Step-by-Step Process

1. Serialize the pipeline to YAML. Haystack supports export via pipeline.dump().
2. Save YAML to Git – enables code review of configuration.
3. Set up CI/CD: on push to main, run tests (evaluation metrics) and deploy via Hayhooks.
4. Haystack Hayhooks provides REST API for serving pipelines, including Prometheus metrics.

version: "2.0"
components:
  - name: retriever
    type: InMemoryBM25Retriever
    params:
      document_store: store
  - name: prompt_builder
    type: RAGPromptBuilder
  - name: generator
    type: OpenAIGenerator
    params:
      model: gpt-4o-mini
connections:
  - retriever.documents -> prompt_builder.documents
  - prompt_builder.prompt -> generator.prompt

According to the Haystack documentation, this format easily integrates with any CI/CD tools.

Evaluating RAG Quality

Haystack has built-in evaluators: FaithfulnessEvaluator (answer matches context), ContextRelevanceEvaluator (context relevant to question), SASEvaluator (semantic similarity of response to reference). We include these metrics in CI/CD to track quality with each update. Contact us for an audit of your project – we will help set up a full evaluation cycle.

Integration Timelines

• Basic RAG pipeline (1 DocumentStore, 1 LLM): 1–2 weeks.
• Hybrid search + custom reranker: 3–4 weeks.
• Production deployment + monitoring + evaluation: 6–8 weeks.

The cost is calculated individually after an audit. Get a consultation – we will evaluate your project in 1–2 days. Our engineers are certified in Haystack and OpenAI. Contact us for a detailed audit.