What tools are in the The Production RAG Pipeline blueprint?

This blueprint uses 2 tools: LangChain, pgvector. Each tool's heat score and trend phase are shown live on the blueprint page, updated weekly by ProductionFlow.

How much does the The Production RAG Pipeline stack cost?

$20–$100 per month. See the full cost breakdown by tool on the blueprint detail page at productionflow.ai/blueprints/rag-retrieval-pipeline.

Who is the The Production RAG Pipeline blueprint for?

This blueprint is built for AI engineers and developers building knowledge-base chatbots, document Q&A systems, or enterprise search over private data.. It helps you build a complete rag pipeline using pgvector for vector storage in postgres, llamaindex for document ingestion and chunking, and langchain for orchestration and llm integration..

What is the A.R.C. score for this stack?

The The Production RAG Pipeline stack has an A.R.C. score of 70.7/100 (grade: C), covering Architecture (88), Reliability (50), and Context (72). A.R.C. grades reflect how resilient and production-ready the stack is.

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📋 Blueprint$20–$100/mo

The Production RAG Pipeline

Chunk, embed, retrieve, and generate — a production-ready retrieval-augmented generation stack.

Built for: AI engineers and developers building knowledge-base chatbots, document Q&A systems, or enterprise search over private data.

Chunk, embed, retrieve, and generate — a production-ready retrieval-augmented generation stack. Key tools: LangChain (score 52), pgvector (score 45). Monthly cost: $20–$100/mo. A.R.C. stack integrity grade: C.

Tools in this blueprint (2)

LangChain

AI Frameworksfreemium

pgvector

Vector Databasesfree

Heat scores update daily via HookFlow. Higher = more community momentum.

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A.R.C. Stack Analysis

Architecture · Reliability · Context — how resilient is this stack as infrastructure?

70.7C

Architecture

API & Integration

Reliability

Cost & Uptime

Context

AI Memory

🔗LlamaIndex — LlamaIndex has had breaking API changes across major versions — pin your dependency versions and test upgrades explicitly

💰$20–100/mo — embedding costs dominate at scale; pgvector on existing Postgres is essentially free vs managed vector DB pricing

⚡Textbook RAG architecture — vector store (pgvector), ingestion (LlamaIndex), orchestration (LangChain), generation (pluggable LLM). Each layer is independently swappable.

Step-by-step guide

Enable pgvector in your Postgres database

Run `CREATE EXTENSION IF NOT EXISTS vector;` in your Postgres instance. Create a documents table with a vector column: `ALTER TABLE documents ADD COLUMN embedding vector(1536);`. Add an HNSW index for fast similarity search: `CREATE INDEX ON documents USING hnsw (embedding vector_cosine_ops);`. If you are on Supabase, pgvector is already available — just enable the extension in the dashboard.

Heat score:45Rising

Ingest and chunk documents with LlamaIndex

Install LlamaIndex: `pip install llama-index`. Use SimpleDirectoryReader to load your documents, then apply a SentenceSplitter with chunk_size=512, chunk_overlap=50. Run documents through an embedding model (OpenAIEmbedding or a local HuggingFace model). Store the resulting nodes and embeddings to pgvector using LlamaIndex's PGVectorStore integration. This gives you structured, searchable chunks with metadata.

Build the retrieval and generation chain with LangChain

Install LangChain: `pip install langchain langchain-community`. Create a PGVector retriever pointed at your Postgres instance and configure it to return the top-k most similar chunks. Build a RetrievalQA chain: retriever → prompt template → LLM. Use `chain.invoke({question: query})` — LangChain handles context injection automatically. Wire in Claude, GPT-4o, or Groq as your generation model.

Heat score:52Trending Up

Add metadata filtering for precision retrieval

Tag your document chunks with metadata at ingestion time: source file, date, section, document type. In LlamaIndex, pass `metadata_filters` to your VectorIndexRetriever to scope searches by source or date. This prevents cross-contamination between document sets (e.g., do not retrieve from Q3 docs when the user asks about Q4). Metadata filtering is the most underused RAG lever — add it before you tune embeddings.

Evaluate retrieval quality and tune chunk strategy

Run a golden dataset of 20 question-answer pairs against your pipeline. Check two metrics: retrieval precision (did the right chunk come back?) and answer faithfulness (did the LLM stick to the retrieved context?). If precision is low, reduce chunk_size or tighten your HNSW index parameters. If faithfulness is low, strengthen your system prompt with explicit grounding instructions. Swap pgvector for Pinecone if you need managed vector search at scale.

Heat score:52Trending Up

Cost breakdown

LangChain

Open Source | Free trial: Yes

pgvectorFree

$0/mo

Estimated total$20 – $100/mo

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