What is a RAG pipeline?

A RAG (Retrieval-Augmented Generation) pipeline is an AI architecture that combines a vector search retrieval system with a large language model. Instead of relying solely on the LLM's training data, RAG first retrieves relevant documents from your private knowledge base, then feeds that context to the LLM to generate accurate, grounded answers.

What vector databases are best for RAG?

The top vector databases for RAG are Pinecone (managed, scalable), Chroma (open-source, good for prototyping), Weaviate (open-source with hybrid search), Qdrant (Rust-based, high performance), and pgvector (PostgreSQL extension, best for teams already using Postgres). Choice depends on scale, infrastructure preference, and hybrid search requirements.

How does RAG prevent hallucinations?

RAG prevents hallucinations by grounding LLM responses in retrieved source documents. Instead of generating from parametric memory alone, the LLM is instructed to answer only from the provided context. Adding confidence thresholds and citation requirements further reduces hallucination risk.

What embedding models should I use for RAG?

For most enterprise RAG systems, OpenAI text-embedding-3-large offers the best accuracy. For self-hosted options, Cohere Embed v3, BGE-large, and E5-large are strong performers. Domain-specific fine-tuned embedding models outperform general models when your documents use specialised terminology.

How do I chunk documents for RAG?

Document chunking strategy significantly impacts RAG accuracy. Common approaches: fixed-size chunking (512–1024 tokens with 10–20% overlap), semantic chunking (split at natural boundaries like paragraphs or headings), and hierarchical chunking (store both summaries and full sections). Semantic chunking with parent-document retrieval typically yields the best results for unstructured enterprise documents.

How to Build a Production-Ready RAG Pipeline for Enterprise Document Search

What is RAG and Why Does It Matter for Enterprise?

Retrieval-Augmented Generation (RAG) has become the standard architecture for enterprise AI systems that need to answer questions from private, proprietary data. Unlike a simple LLM chat interface, RAG retrieves relevant context from your documents before generating an answer — eliminating hallucinations and keeping responses grounded in verified company knowledge.

For enterprises, this solves a fundamental problem: your LLM cannot know what's in your internal PDFs, ERP records, engineering drawings, or compliance manuals. RAG bridges that gap.

Architecture Overview

A production RAG pipeline has five core components:

Document Ingestion Pipeline — Load, parse, and chunk documents
Embedding Engine — Convert text chunks into vector representations
Vector Store — Store and index embeddings for fast similarity search
Retriever — Query the vector store and return relevant chunks
Generator — LLM that synthesises retrieved context into an answer

Step 1: Document Ingestion

Raw enterprise documents come in every format: PDFs, DOCX, XLSX, HTML, scanned images, and email threads. Your ingestion pipeline must handle all of them.

Recommended stack:

PDF text extraction: pdfplumber for digital PDFs, Tesseract OCR + AWS Textract for scanned documents
DOCX/XLSX: python-docx, openpyxl
HTML/web: BeautifulSoup4
Unstructured data orchestration: LlamaIndex SimpleDirectoryReader or Unstructured.io

Chunking strategy matters enormously. Fixed-size chunking (512 tokens, 10% overlap) works for homogeneous documents. Semantic chunking — splitting at paragraph and section boundaries — works better for mixed-format corpora. For maximum retrieval accuracy, implement hierarchical chunking: store a summary embedding alongside the full chunk embedding, and use the summary for initial retrieval.

from langchain.text_splitter import RecursiveCharacterTextSplitter

splitter = RecursiveCharacterTextSplitter(
    chunk_size=512,
    chunk_overlap=50,
    separators=["\n\n", "\n", ". ", " "]
)
chunks = splitter.split_documents(documents)

Step 2: Embedding Generation

Every chunk gets converted to a high-dimensional vector using an embedding model. At retrieval time, the user's question is embedded with the same model, and cosine similarity identifies the most relevant chunks.

Embedding model selection:

OpenAI text-embedding-3-large (3072 dimensions): Best accuracy, requires API call per chunk
Cohere Embed v3: Excellent for multilingual enterprise content
BGE-large-en-v1.5: Best open-source option, self-hosted
Domain fine-tuned: 10–30% accuracy improvement for specialised terminology (legal, medical, engineering)

Batch your embedding calls to stay within API rate limits and reduce cost. For 100,000 documents, pre-compute all embeddings during ingestion, not at query time.

Step 3: Vector Store Selection

| Store | Best For | Self-Hosted | Hybrid Search | |---|---|---|---| | Pinecone | Large-scale, low-latency | No | Yes (beta) | | pgvector | Teams using PostgreSQL | Yes | Yes (BM25) | | Chroma | Prototyping, small datasets | Yes | No | | Qdrant | High-throughput, Rust performance | Yes | Yes | | Weaviate | Multi-modal, graph queries | Yes | Yes |

For most enterprise deployments, pgvector is the pragmatic choice — it runs inside your existing PostgreSQL instance, supports both vector and full-text (BM25) hybrid search, and avoids a separate vector infrastructure component.

Step 4: Retrieval Strategy

Naive vector search retrieves the top-k semantically similar chunks. In production, this alone is insufficient. Implement hybrid retrieval:

Dense retrieval: Vector similarity search (semantic understanding)
Sparse retrieval: BM25 keyword search (exact term matching)
Reranking: Cross-encoder model (Cohere Rerank, BGE Reranker) re-scores the combined results for final ordering

Hybrid retrieval reduces retrieval failures by 40–60% compared to vector-only search, particularly for documents containing product codes, part numbers, or technical identifiers.

Step 5: Answer Generation with Guardrails

The LLM receives the retrieved context and the user's question inside a structured prompt. Add explicit guardrails:

SYSTEM_PROMPT = """
You are an expert assistant. Answer questions ONLY using the provided context.
If the context does not contain enough information, say:
'I cannot find this information in the available documents.'
Always cite the source document at the end of your answer.
"""

For high-stakes enterprise use cases (legal, compliance, finance), add a verification layer: run the generated answer back against the retrieved chunks to confirm factual consistency before returning to the user.

Production Considerations

Latency: End-to-end RAG latency (embedding query + vector search + LLM generation) should target under 3 seconds for user-facing applications. Cache frequent queries, use streaming for LLM output, and run vector search asynchronously where possible.

Metadata filtering: Store document metadata (department, date, document type, clearance level) alongside embeddings. Apply metadata pre-filters before vector search to scope retrieval to relevant document subsets.

Observability: Log every query, retrieved chunk, and generated answer. Use tools like LangSmith, Langfuse, or custom PostgreSQL logging to monitor retrieval quality and identify failure patterns.

Security: Implement document-level access control. A user should only retrieve chunks from documents they're authorised to see. Map user roles to document metadata tags and apply filters at retrieval time.

Common Failure Modes

Retrieval failures: Wrong chunks retrieved. Fix with hybrid search and reranking.
Context window overflow: Too many chunks exceed LLM context limit. Fix with reranking to select top 3–5 chunks.
Temporal staleness: Outdated documents in the vector store. Fix with incremental re-indexing triggered by document updates.
Embedding model drift: Switching embedding models requires re-embedding all documents. Plan embedding model upgrades as scheduled migrations, not ad-hoc changes.

A well-built RAG pipeline is the foundation of every trustworthy enterprise AI system. AerixNova has built RAG pipelines for logistics companies, engineering design firms, and healthcare administrators — each handling tens of thousands of proprietary documents with query latencies under 2 seconds.