Building AI Pipelines with LangChain and LlamaIndex

Every engineering manager who has shipped a proof-of-concept chatbot knows the gap between a demo and a production system is enormous. The demo works because you fed it three clean PDFs and asked obvious questions. Production breaks because your users upload 40-page scanned contracts, ask multi-hop questions, and expect citations. Bridging that gap is what LangChain LlamaIndex AI pipelines are built for. This article walks through how to combine the two frameworks — LangChain for orchestration and LlamaIndex for retrieval — into a coherent, testable, maintainable pipeline your team can actually own.

• Background / Why This Matters
• Prerequisites and Planning
• Step-by-Step Implementation
• Testing and Validation
• Common Mistakes / What to Avoid
• Frequently Asked Questions
• Conclusion

Background / Why This Matters

Retrieval-augmented generation (RAG) has become the default architecture for grounding large language models in your own data. Instead of fine-tuning a model on proprietary documents — expensive, slow, and hard to update — you retrieve relevant chunks at query time and inject them into the prompt. The model answers using facts it was handed, not facts it memorized.

The problem is that "RAG" describes a spectrum from a 20-line script to a system with reranking, query rewriting, agentic tool use, and evaluation harnesses. Two frameworks dominate the ecosystem, and they solve overlapping but distinct problems:

Dimension	LangChain	LlamaIndex
Primary strength	Orchestration, chaining, agents, tool calling	Data ingestion, indexing, retrieval quality
Core abstraction	Runnables (LCEL), chains, agents	Indexes, nodes, retrievers, query engines
Best for	Multi-step reasoning, branching workflows	Sophisticated retrieval over large corpora
Learning curve	Steeper — many abstractions	Gentler for pure RAG use cases
Ecosystem breadth	Very broad (100+ integrations)	Focused on data + retrieval

The key insight for engineering managers: these are not competitors you must choose between. LlamaIndex excels at getting the right chunks; LangChain excels at deciding what to do with them. A common production pattern uses LlamaIndex as the retrieval layer wrapped inside a LangChain orchestration graph.

Takeaway: Frame the decision as "retrieval quality vs. workflow complexity," not "framework A vs. framework B." Most non-trivial systems benefit from both.

Prerequisites and Planning

Before writing code, resolve the decisions that are expensive to reverse later. Retrofitting a vector database or re-chunking a corpus after launch is painful.

Technical prerequisites

• Python 3.10+ and a dependency manager (Poetry or uv) — both frameworks move fast, so pin versions.
• An LLM provider. OpenAI's gpt-4o and text-embedding-3-small are sensible defaults; abstract the provider behind an interface so you can swap to Anthropic or a self-hosted model later.
• A vector store. For prototypes, an in-memory FAISS or Chroma index is fine. For production, Pgvector, Qdrant, or Weaviate give you persistence, filtering, and horizontal scale.
• Observability from day one — LangSmith or an OpenTelemetry setup. You cannot debug a pipeline you cannot see.

Planning decisions to lock down

1. Chunking strategy. Fixed-size chunks are simple but split sentences awkwardly. Semantic or structure-aware chunking (by heading, by paragraph) usually improves retrieval. Estimates vary, but chunk sizes of 256–512 tokens with 10–20% overlap are a reasonable starting point for most document types.
2. Metadata schema. Decide what you attach to each chunk — source, page number, author, timestamp, access-control tags. Metadata filtering is often the difference between a usable and a dangerous system in multi-tenant environments.
3. Retrieval mode. Dense vectors alone, hybrid (dense + BM25 keyword), or reranked with a cross-encoder. Hybrid retrieval consistently helps when queries contain exact terms like product codes or names.
4. Latency and cost budget. Each retrieval + generation call has a measurable cost. Define your p95 latency target and cost-per-query ceiling before you build.

This is exactly where teams often bring in Halkwinds' AI & ML practice — not to write the first script, but to design the ingestion schema and evaluation strategy that make the system maintainable eighteen months out.

Takeaway: Chunking, metadata, and retrieval mode are architectural decisions. Treat them with the seriousness you'd give a database schema.

Step-by-Step Implementation

Here is a reference architecture that uses LlamaIndex for ingestion and retrieval, then wraps it in a LangChain orchestration layer. We'll walk through it conceptually so the pattern transfers regardless of exact API versions.

Step 1: Ingest and index with LlamaIndex

Use LlamaIndex's SimpleDirectoryReader or a connector (Notion, S3, Confluence) to load raw documents. Then parse into nodes with a structure-aware splitter and build an index:

• Load documents and attach metadata (source, page, tenant ID).
• Split into nodes using SentenceSplitter or SemanticSplitterNodeParser.
• Embed with OpenAI's text-embedding-3-small and persist into your vector store (e.g., Qdrant via QdrantVectorStore).
• Build a VectorStoreIndex and expose it as a retriever.

The output of this step is a query engine or retriever object that, given a question, returns the top-k relevant nodes with scores and metadata.

Step 2: Add a reranker

Top-k dense retrieval returns candidates that are similar, not necessarily relevant. A cross-encoder reranker (Cohere Rerank, or a local bge-reranker) reorders your top 20 candidates and keeps the best 5. In practice this is one of the highest-leverage improvements you can make to answer quality.

Step 3: Wrap retrieval as a LangChain tool

Expose the LlamaIndex retriever as a LangChain Runnable or tool. Now retrieval becomes a callable node inside a larger graph. This lets you compose it with query rewriting, guardrails, and multi-step logic.

Step 4: Build the orchestration graph

Using LangChain Expression Language (LCEL) or LangGraph, assemble the flow:

1. Query rewriting — rewrite the user question to be self-contained (resolve pronouns, add context from chat history).
2. Routing — decide whether the question needs retrieval at all, or a calculator, or a database lookup.
3. Retrieval — call the LlamaIndex tool.
4. Generation — pass retrieved context plus the question to the LLM with a strict prompt that requires citations.
5. Validation — check the answer contains citations and does not hallucinate sources.

LangGraph is worth adopting here for anything beyond a linear chain. It models your pipeline as a state machine with explicit nodes and edges, which makes conditional branching, retries, and human-in-the-loop steps tractable.

Step 5: Add memory and multi-turn handling

For conversational systems, persist chat history and feed it into the query-rewriting step. Do not naively stuff the entire history into every prompt — it inflates cost and dilutes retrieval. Summarize older turns and keep recent ones verbatim.

Takeaway: Keep retrieval (LlamaIndex) and orchestration (LangChain/LangGraph) as separate, swappable layers. When retrieval quality is bad, you fix it in one place; when workflow logic is wrong, you fix it in another.

Testing and Validation

The single biggest reason RAG projects stall is the absence of evaluation. Teams ship, users complain, and nobody can tell whether a prompt change made things better or worse. You need a repeatable, quantitative loop.

Build a golden dataset

Assemble 50–200 representative question/answer pairs with the source documents that should be retrieved. This is tedious and worth every hour. Include edge cases: questions with no answer in the corpus, ambiguous questions, and multi-hop questions.

Measure the two halves separately

Layer	Metric	What it tells you
Retrieval	Hit rate, MRR, context precision	Did we fetch the right chunks?
Generation	Faithfulness, answer relevance	Did the model use them correctly?

Frameworks like Ragas, LlamaIndex's built-in evaluators, and LangSmith's evaluation suite let you score these automatically, often using an LLM-as-judge. Separating retrieval from generation metrics is essential: a bad answer might come from bad retrieval or bad generation, and the fix is completely different.

Regression-test every change

Wire evaluation into CI. Every prompt tweak, chunk-size change, or model upgrade should run against the golden set and report deltas. Treat a drop in faithfulness like a failing unit test.

Takeaway: If you can't measure retrieval hit rate and answer faithfulness on demand, you're not engineering a pipeline — you're guessing