RAG Pipeline Development Service | LangChain LlamaIndex Expert — Codersarts AI

Retrieval-Augmented Generation is the most impactful AI architecture of 2025. But most RAG implementations fail in production — not because the idea is wrong, but because the chunking, retrieval, prompt design, and evaluation were never built correctly.

At Codersarts, we build production-ready RAG systems — not demos. Our engineers have delivered RAG pipelines for SaaS products, enterprise knowledge bases, developer tools, and student projects across every major LLM and vector database stack.

Whether you need a working prototype in 48 hours, a full multi-tenant RAG API, or help debugging a pipeline that is returning hallucinations — we handle it end to end.

48h Typical RAG delivery

7+ Vector DBs supported

10+ LLMs integrated

< 4h First response

NDA Available always

What Is a RAG Pipeline — and Why Is It Hard to Get Right?

RAG connects a large language model to your own data. Instead of relying on the LLM's training data alone, RAG retrieves the most relevant documents from your knowledge base at query time — and feeds them into the prompt as context. The LLM then generates answers grounded in your actual data, dramatically reducing hallucinations.

A production RAG pipeline has eight interdependent components. Each one has to be tuned correctly for the others to work:

Component	What It Does	Where It Goes Wrong
Document Loader	Ingests PDF, DOCX, web, DB, S3 sources	Encoding errors, missed pages, lost tables
Text Splitter	Breaks documents into retrievable chunks	Wrong chunk size kills recall quality
Embedding Model	Converts text to vectors	Model mismatch with query distribution
Vector Store	Indexes and retrieves relevant chunks	Wrong index type, no metadata design
Retriever	Fetches top-K chunks for a query	Too few chunks, no reranking, irrelevant results
Reranker	Re-scores retrieved chunks by relevance	Skipped entirely, causing hallucinations
Prompt Template	Injects context + query into the LLM prompt	Context window overflow, poor instruction format
LLM	Generates the final answer	Hallucination, verbosity, wrong temperature

We handle every one of these components — and the interactions between them. That is what separates a working RAG pipeline from a demo that breaks on real data.

What Our RAG Pipeline Development Includes

✓  Multi-source document ingestion (PDF, DOCX, CSV, web, SQL, S3)	✓  Semantic + recursive + fixed chunking strategy selection
✓  Embedding model integration (OpenAI, Cohere, HuggingFace, Ollama)	✓  Vector DB setup: Pinecone, Weaviate, Qdrant, ChromaDB, pgvector
✓  Retrieval with similarity search, MMR, and reranking	✓  LLM integration: GPT-4o, Claude, Mistral, Llama 3, Gemma
✓  LangChain or LlamaIndex framework setup	✓  Conversational memory and multi-turn chat history
✓  Streaming response support (SSE / WebSocket)	✓  Hallucination mitigation via source grounding
✓  RAG evaluation pipeline (faithfulness, relevance, recall)	✓  FastAPI REST endpoint with full documentation
✓  Admin panel for document upload and management	✓  Multi-tenant architecture with user-level isolation
✓  Frontend integration (React, Next.js, Streamlit, Gradio)	✓  Monitoring, logging, and error alerting setup

1.  LangChain RAG Implementation

LangChain — The Most Widely Used RAG Framework

LangChain is the dominant framework for building RAG pipelines in Python. Its modular chain architecture, extensive vector store integrations, and active ecosystem make it the first choice for most teams. But LangChain's flexibility is also its trap — there are five ways to do everything and only one of them performs well in production.

We build LangChain RAG pipelines using the modern LCEL (LangChain Expression Language) pattern — not legacy chain classes — for maintainability, streaming support, and production reliability.

What Our LangChain Implementation Covers

• Document loaders: PyPDFLoader, WebBaseLoader, CSVLoader, UnstructuredLoader, S3FileLoader

• Text splitters: RecursiveCharacterTextSplitter with correct chunk size and overlap for your content

• Vector store setup: Chroma, Pinecone, Weaviate, Qdrant, FAISS, pgvector via LangChain integrations

• Retrieval: similarity search, MMR (Maximal Marginal Relevance), self-query retriever

• LCEL chain composition: retriever | prompt | llm | output parser

• ConversationalRetrievalChain with chat history memory

• Streaming: stream() and astream() for real-time token delivery

• LangSmith tracing and observability setup

• Custom output parsers for structured JSON responses

Full LangChain service →

Our LangChain Vector Store Integration page covers every supported vector store, LCEL patterns, streaming setup, and debugging guides for common LangChain RAG failures.

2.  LlamaIndex RAG Pipeline

LlamaIndex — Built for Complex Document Retrieval

LlamaIndex (formerly GPT Index) is purpose-built for document-heavy RAG applications. Where LangChain is better for agentic and chained workflows, LlamaIndex excels at sophisticated document indexing, hierarchical retrieval, and query routing across multiple knowledge sources.

If your RAG system needs to query across different document types, use sub-question decomposition, or retrieve from structured and unstructured sources simultaneously — LlamaIndex is almost always the right choice.

What Our LlamaIndex Implementation Covers

• SimpleDirectoryReader, PDFReader, DatabaseReader and custom loaders

• VectorStoreIndex, SummaryIndex, KeywordTableIndex selection and setup

• Node parser configuration: SentenceSplitter, SemanticSplitterNodeParser

• StorageContext and vector store integration (Pinecone, Weaviate, Qdrant, Chroma, pgvector)

• Sub-question query engine for multi-document reasoning

• RouterQueryEngine for routing queries to the right index

• Recursive retriever for hierarchical document structures

• Response synthesisers: tree_summarize, refine, compact

• Streaming, async queries, and chat engine setup

• LlamaIndex observability with Arize Phoenix or LlamaTrace

Full LlamaIndex service →

Our LlamaIndex Vector Index Help page covers advanced retrieval patterns, multi-index routing, and production deployment configurations with real code examples.

3.  OpenAI Embeddings Integration

OpenAI Embeddings — The Most Accurate, Most Used Embedding Model

OpenAI's text-embedding-3-small and text-embedding-3-large are the most widely deployed embedding models for RAG applications. They offer state-of-the-art accuracy across multilingual and domain-specific content — but efficient production integration requires much more than a single embed() call.

What Our OpenAI Embeddings Integration Covers

• Model selection: text-embedding-3-small vs text-embedding-3-large vs ada-002 with justification

• Batch embedding pipeline: group inputs, handle 8,191 token limit, retry on rate-limit errors

• Dimensionality reduction using Matryoshka Representation Learning (MRL) — cut cost by 5x

• Embedding cache layer: Redis or disk-based, keyed on content hash to avoid redundant API calls

• Async parallel embedding for high-throughput ingestion pipelines

• Cost estimator: calculate exact spend before you embed a large dataset

• LangChain OpenAIEmbeddings and LlamaIndex OpenAIEmbedding integration

• Fallback to local HuggingFace model if OpenAI is unavailable

• Incremental re-embedding: only re-embed changed documents, not the full corpus

Full OpenAI Embeddings service →

Our OpenAI Embeddings Integration Help page covers cost modelling for large datasets, Matryoshka dimension reduction, caching architecture, and model migration guides.

4.  Cohere Embed API Integration & Reranking

Cohere — The Best Reranker in the RAG Stack

Cohere serves two critical roles in a production RAG pipeline: its Embed v3 model produces multilingual, domain-aware embeddings that outperform OpenAI on many specialised tasks; and its Rerank model is the single most impactful upgrade you can make to an underperforming RAG system.

Adding Cohere Rerank to an existing RAG pipeline typically improves answer accuracy by 20–40% without changing any other component — making it one of the highest-ROI additions to any RAG stack.

What Our Cohere Integration Covers

• Cohere Embed v3 pipeline: embed-english-v3.0 and embed-multilingual-v3.0

• Input type configuration: search_document vs search_query (critical for accuracy)

• Batch embed pipeline with Cohere rate limit handling

• Cohere Rerank integration into existing LangChain and LlamaIndex pipelines

• Two-stage retrieval: retrieve top-50 with vector search → rerank to top-5 with Cohere

• CohereRerank as ContextualCompressionRetriever in LangChain

• Multilingual RAG setup: embed and retrieve across 100+ languages

• Cost comparison: Cohere vs OpenAI for your data volume

Full Cohere service →

Our Cohere Embed API Integration page covers multilingual RAG setup, reranking implementation patterns, and a direct benchmark comparison with OpenAI embeddings on common datasets.

5.  HuggingFace Sentence Transformers Setup

HuggingFace Sentence Transformers — Zero API Cost Embeddings

For teams with cost sensitivity, privacy requirements, or air-gapped environments, HuggingFace Sentence Transformers offer production-quality embeddings at zero per-query cost. Models like all-MiniLM-L6-v2, BAAI/bge-large-en-v1.5, and e5-large-v2 are competitive with paid APIs on most benchmarks — and can run entirely on your own infrastructure.

What Our HuggingFace Integration Covers

• Model selection from MTEB leaderboard for your language and domain

• SentenceTransformer local inference setup with CPU and GPU support

• Batch encoding pipeline: encode() with optimal batch size for your hardware

• ONNX and quantized model export for 3x faster inference

• LangChain HuggingFaceEmbeddings and LlamaIndex HuggingFaceInferenceAPI integration

• HuggingFace Inference API setup for teams that prefer not to self-host

• Fine-tuning pipeline: domain-specific embedding model training with your own data

• Benchmark comparison: selected model vs OpenAI on your specific dataset

• Cross-encoder setup for reranking (ms-marco-MiniLM cross-encoders)

Full HuggingFace service →

Our HuggingFace Sentence Transformers Setup page covers MTEB model selection, GPU vs CPU deployment, ONNX optimisation, and fine-tuning pipelines for domain-specific embedding quality.

6.  Document Chunking Strategy Help

Chunking — The Most Underestimated Part of RAG

Poor chunking is the number one cause of bad RAG retrieval quality. If your chunks are too large, the retrieved context is noisy and the LLM loses focus. If they are too small, each chunk lacks enough context to be useful. If chunk boundaries cut across sentences or concepts, the embeddings are semantically broken.

Most teams copy a default chunk_size=1000, overlap=200 from a tutorial and wonder why their RAG gives irrelevant answers. We design chunking strategies specific to your document type, embedding model, and query patterns.

Chunking Strategies We Implement

• Fixed-size chunking: baseline, fast, works for homogeneous text

• Recursive character splitting: respects paragraph and sentence boundaries — our default starting point

• Semantic chunking: splits on embedding similarity change — best recall quality, higher compute cost

• Sentence-window chunking: embed sentence, retrieve sentence + surrounding window — best for precision

• Parent-child chunking (small-to-big): small chunks for retrieval, parent chunk sent to LLM

• Document-level summary index: retrieve summary first, then drill into relevant sections

• Table and structured data chunking: HTML tables, CSV rows, JSON objects

• Chunk overlap tuning: empirical testing on your query set to find the optimal overlap

Full chunking service →

Our Document Chunking Strategy Help page includes a chunking audit for existing RAG systems, benchmark testing across strategies on your data, and a decision framework for choosing the right approach.

7.  Reranking Implementation Help

Reranking — The Fastest Way to Fix a Broken RAG Pipeline

Vector similarity search is fast but approximate — it finds documents that are embedding-close to the query, not necessarily the most relevant answer. A reranker is a cross-encoder model that re-scores the top retrieved chunks with much higher precision, pushing the most relevant content to the top of the context window.

Adding a reranker is the single highest-impact improvement you can make to an underperforming RAG system — typically improving answer accuracy by 20–40% with no changes to the rest of your pipeline.

What Our Reranking Implementation Covers

• Cohere Rerank v3 — cloud API, easiest integration, best out-of-the-box accuracy

• cross-encoder/ms-marco-MiniLM — local, free, 90% of Cohere quality at zero cost

• bge-reranker-large (BAAI) — state-of-the-art open-source reranker for production

• Two-stage retrieval architecture: retrieve top-50 → rerank → top-5 to LLM

• LangChain ContextualCompressionRetriever with reranker integration

• LlamaIndex SentenceTransformerRerank and CohereRerank integration

• Reranker threshold tuning: set minimum relevance score to filter low-quality context

• Latency profiling: measure reranker overhead and optimise for your SLA

• A/B testing framework: compare RAG quality with and without reranker on your eval set

Full reranking service →

Our Reranking Implementation Help page covers all major reranker options, integration patterns, latency vs accuracy tradeoffs, and a step-by-step guide for adding reranking to an existing pipeline.

RAG Architecture Patterns — Which One Do You Need?

Not every RAG system has the same requirements. Here is a practical guide to the most common RAG architectures we build — and when to use each one.

Architecture	Best For	Complexity	Our Delivery Time
Basic RAG	Single document type, internal tools, prototypes	Low	24–48 hours
Conversational RAG	Chatbots, support assistants, multi-turn Q&A	Medium	2–4 days
Multi-source RAG	Multiple DBs, document types, or knowledge bases	Medium	3–5 days
Agentic RAG	Complex queries needing tool use or multi-step reasoning	High	5–10 days
Multi-tenant RAG	SaaS products with per-user data isolation	High	7–14 days
Streaming RAG API	Real-time token streaming to frontend	Medium	2–4 days
Evaluated RAG	Production systems needing quality measurement	Medium	3–5 days

LLMs We Integrate Into RAG Pipelines

Provider	Models	Hosting	Best For
OpenAI	GPT-4o, GPT-4o-mini, GPT-3.5-turbo	Cloud API	Accuracy, speed, easiest integration
Anthropic	Claude 3.5 Sonnet, Claude 3 Haiku	Cloud API	Long context, nuanced reasoning
Google	Gemini 1.5 Pro, Gemini 1.5 Flash	Cloud API	Multimodal RAG, very long context
Meta	Llama 3.1 8B / 70B / 405B	Self-host / API	Open-source, no data leaves your infra
Mistral AI	Mistral Large, Mixtral 8x7B	Cloud + self-host	Cost-effective, multilingual
Ollama	Any open model (local)	Fully local	Air-gapped, free, privacy-first
HuggingFace	Any instruction-tuned model	Inference API	Custom fine-tuned models

How We Build Your RAG Pipeline — Our Process

Phase	What We Do	Output
1.  Discovery	Understand your data, use case, query patterns, LLM preferences, and hosting constraints	Requirements doc
2.  Architecture	Design chunking strategy, embedding model, vector DB, retriever, reranker, and LLM selection	Architecture diagram
3.  Implementation	Build every component with tests — ingestion, retrieval, generation, API layer	Working RAG pipeline
4.  Evaluation	Run your real queries, measure faithfulness and relevance, tune until quality is acceptable	Eval report
5.  Delivery	Hand over source code, documentation, deployment guide, and a walkthrough session	Full handover package
6.  Support	Free revision window 48h post-delivery. Retainer support available for ongoing needs	Ongoing peace of mind

Why Developers & Startups Choose Codersarts for RAG

✓  Production code — not tutorial quality stubs	✓  We debug RAG pipelines others built and broke
✓  Every LLM and every vector DB supported	✓  LangChain LCEL and LlamaIndex both covered
✓  Evaluation and quality testing included	✓  FastAPI wrapper delivered with every pipeline
✓  NDA available before any code or data review	✓  India-based pricing — global quality output
✓  Streaming, async, and multi-tenant patterns	✓  Reranking included by default in complex builds
✓  Job support and interview prep available	✓  Retainer support for production maintenance

Frequently Asked Questions

Q:  My RAG system keeps returning hallucinations even with correct documents in the vector store. What is wrong?

A:  This is almost always a retrieval quality problem — the LLM is not receiving the right chunks in its context window. We diagnose the root cause: wrong chunk size, missing reranker, poor embedding model choice, or context window overflow from too many retrieved chunks. We fix it and show you the before/after on your own test queries.

Q:  How long does it take to build a complete RAG pipeline with LangChain?

A:  A clean, tested, documented RAG pipeline with a FastAPI wrapper takes 24–72 hours depending on the number of document sources, the complexity of the retrieval logic, and whether you need streaming and multi-turn memory. We give you an exact timeline after a 15-minute scoping call.

Q:  We have 200,000 PDF documents. Can your RAG system handle that scale?

A:  Yes. Large-scale RAG requires careful attention to ingestion batching, incremental re-embedding (not re-embedding unchanged documents), managed vector DB selection (Pinecone, Weaviate, or Qdrant Cloud at that scale), and retrieval with metadata pre-filtering to keep query latency under 500ms. We have built systems at this scale and will design yours accordingly.

Q:  Can you migrate our existing LangChain v0 pipeline to the new LCEL pattern?

A:  Yes. LangChain's legacy chain classes are being deprecated. We migrate your existing pipeline to LCEL (LangChain Expression Language) — improving streaming support, composability, and LangSmith observability — with no change to your external API interface.

Q:  Do you build the frontend as well, or just the backend?

A:  We primarily deliver the RAG backend as a clean FastAPI or FastAPI-WebSocket API. For frontend, we build Streamlit or Gradio demo UIs. For React or Next.js integration, we deliver the API and guide your frontend team on the streaming response handling.

Q:  Can you add RAG to our existing product without rebuilding everything?

A:  Yes. We design the RAG system as an isolated service that your existing backend calls — so there is zero disruption to what you already have in production. We add one new endpoint, one new ingestion pipeline, and one new vector DB instance alongside your existing infrastructure.

Q:  Which is better for our use case — LangChain or LlamaIndex?

A:  LangChain is better for agentic pipelines, tool use, and flexibility. LlamaIndex is better for complex document hierarchies, multi-index routing, and fine-grained retrieval control. We ask about your use case and recommend the right framework — not the one we happen to prefer.

Ready to build a RAG pipeline that actually works in production?

📋  Submit Project Brief Describe your RAG use case. Response in 4 hours.

📞  Free Scoping Call 15 minutes. We scope your RAG pipeline live.

💬  WhatsApp Us For urgent RAG builds — message us directly.

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Other Services Related to RAG Development

RAG pipelines connect multiple components — embedding models, vector databases, LLMs, and frameworks. If you need deeper help with any one component, or related services in your AI pipeline, the pages below cover each area in full.

RAG Framework & Model Integration

→  LangChain Vector Store Integration — LCEL patterns, all vector stores, streaming and memory setup

→  LlamaIndex Vector Index Help — sub-question engine, router, hierarchical retrieval patterns

→  OpenAI Embeddings Integration — batch pipeline, caching, cost optimisation, Matryoshka reduction

→  Cohere Embed & Rerank Integration — multilingual embeddings, two-stage retrieval, reranker setup

→  HuggingFace Sentence Transformers Setup — local models, ONNX export, fine-tuning, zero API cost

→  Document Chunking Strategy Help — semantic, recursive, sentence-window, parent-child chunking

→  Reranking Implementation Help — Cohere, cross-encoders, bge-reranker, LangChain integration

Vector Database Implementation

→  Vector Database Implementation Help — full platform setup: Pinecone, Weaviate, Qdrant, Milvus, pgvector, ChromaDB, Redis

→  Embedding Pipeline Development — batch embedding, async pipelines, caching layers, model selection

→  Hybrid Search (Vector + BM25) Implementation — best of semantic and keyword search combined

→  Vector Search Performance Optimisation — HNSW tuning, quantization, latency debugging

Production & Career

→  RAG System Development for SaaS Products — multi-tenant, streaming, evaluation, admin panel

→  Add AI Search to Existing Web App — pgvector, Pinecone, or Qdrant alongside your existing stack

→  Vector DB Job Support & Interview Preparation — RAG system design rounds, ML engineer interview prep

→  Vector Database Architecture Design for Startups — end-to-end architecture before you write a line

Not sure what you need? Share your use case on our contact page and we will scope the right service for you.

Codersarts — RAG Pipeline Experts for Developers & Startups  |  codersarts.com

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