Building Production RAG Without LangChain

I spent 4 months building a production RAG (Retrieval-Augmented Generation) system for a real estate AI platform. After wrestling with LangChain's abstractions, I rebuilt it from scratch using BM25, TF-IDF, and Claude. The result? 322 tests, <200ms p95 latency, and 94% citation accuracy.

Here's why I ditched LangChain and what I built instead.

The LangChain Problem

LangChain promises to make LLM applications easier. In practice, it often does the opposite.

Abstraction Overload: Simple tasks require learning LangChain's mental model. Want to call an API? You need to understand Chains, Agents, Tools, and Memory. The framework adds cognitive load instead of removing it.

Version Instability: Breaking changes happen frequently. Code that worked in 0.0.200 breaks in 0.0.210. Production systems need stability.

Debug Difficulty: When something fails, you're debugging LangChain's abstractions instead of your logic. Stack traces go through 15 layers of framework code before reaching your function.

Performance Overhead: LangChain's generality comes at a cost. Extra abstractions mean extra function calls, extra memory allocations, and slower response times.

For a weekend prototype? LangChain is fine. For production? You need control.

What We Built Instead

Our RAG system has three core components: ingestion, retrieval, and generation. No framework required.

1. Ingestion Pipeline

Documents flow through a custom chunking algorithm that preserves semantic boundaries:

def chunk_document(text: str, max_chunk_size: int = 500) -> list[dict]:
    """Split text into overlapping chunks with metadata."""
    sentences = text.split('. ')
    chunks = []
    current_chunk = []
    current_size = 0

    for sentence in sentences:
        sentence_size = len(sentence.split())

        if current_size + sentence_size > max_chunk_size and current_chunk:
            chunk_text = '. '.join(current_chunk) + '.'
            chunks.append({
                'text': chunk_text,
                'word_count': current_size,
                'sentence_count': len(current_chunk)
            })
            # Overlap: keep last 2 sentences
            current_chunk = current_chunk[-2:]
            current_size = sum(len(s.split()) for s in current_chunk)

        current_chunk.append(sentence)
        current_size += sentence_size

    if current_chunk:
        chunk_text = '. '.join(current_chunk) + '.'
        chunks.append({
            'text': chunk_text,
            'word_count': current_size,
            'sentence_count': len(current_chunk)
        })

    return chunks

Why custom chunking? LangChain's text splitters break on fixed character counts. Ours respects sentence boundaries and adds overlaps to prevent context loss at chunk edges.

2. Hybrid Retrieval

We combine three retrieval strategies for maximum recall:

BM25 (Keyword Search): Fast, exact matches, works without embeddings.

from rank_bm25 import BM25Okapi

class BM25Retriever:
    def __init__(self, documents: list[str]):
        tokenized_docs = [doc.lower().split() for doc in documents]
        self.bm25 = BM25Okapi(tokenized_docs)
        self.documents = documents

    def retrieve(self, query: str, top_k: int = 5) -> list[tuple[str, float]]:
        """Retrieve top-k documents with BM25 scores."""
        tokenized_query = query.lower().split()
        scores = self.bm25.get_scores(tokenized_query)

        # Get top-k indices
        top_indices = sorted(
            range(len(scores)),
            key=lambda i: scores[i],
            reverse=True
        )[:top_k]

        return [(self.documents[i], scores[i]) for i in top_indices]

TF-IDF (Statistical Relevance): Better than pure keyword matching, captures term importance.

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np

class TFIDFRetriever:
    def __init__(self, documents: list[str]):
        self.vectorizer = TfidfVectorizer(stop_words='english')
        self.doc_vectors = self.vectorizer.fit_transform(documents)
        self.documents = documents

    def retrieve(self, query: str, top_k: int = 5) -> list[tuple[str, float]]:
        """Retrieve top-k documents with TF-IDF cosine similarity."""
        query_vector = self.vectorizer.transform([query])
        similarities = cosine_similarity(query_vector, self.doc_vectors)[0]

        top_indices = np.argsort(similarities)[-top_k:][::-1]
        return [(self.documents[i], similarities[i]) for i in top_indices]

Semantic Search (Embeddings): Captures meaning, finds conceptually similar content.

We use ChromaDB for vector storage, but the interface is simple:

import chromadb

class SemanticRetriever:
    def __init__(self, collection_name: str = "documents"):
        self.client = chromadb.Client()
        self.collection = self.client.get_or_create_collection(collection_name)

    def add_documents(self, documents: list[str], ids: list[str]):
        """Add documents to vector store."""
        self.collection.add(documents=documents, ids=ids)

    def retrieve(self, query: str, top_k: int = 5) -> list[tuple[str, float]]:
        """Retrieve top-k documents with semantic similarity."""
        results = self.collection.query(query_texts=[query], n_results=top_k)

        docs = results['documents'][0]
        distances = results['distances'][0]
        # Convert distance to similarity score (1 - normalized_distance)
        scores = [1 - (d / 2) for d in distances]

        return list(zip(docs, scores))

Fusion: We combine all three retrievers using Reciprocal Rank Fusion (RRF):

def fuse_results(
    bm25_results: list[tuple[str, float]],
    tfidf_results: list[tuple[str, float]],
    semantic_results: list[tuple[str, float]],
    k: int = 60
) -> list[str]:
    """Fuse multiple retrieval results using RRF."""
    scores = {}

    for rank, (doc, _) in enumerate(bm25_results, 1):
        scores[doc] = scores.get(doc, 0) + 1 / (k + rank)

    for rank, (doc, _) in enumerate(tfidf_results, 1):
        scores[doc] = scores.get(doc, 0) + 1 / (k + rank)

    for rank, (doc, _) in enumerate(semantic_results, 1):
        scores[doc] = scores.get(doc, 0) + 1 / (k + rank)

    # Sort by fused score
    ranked_docs = sorted(scores.items(), key=lambda x: x[1], reverse=True)
    return [doc for doc, _ in ranked_docs]

RRF is simple and effective. Documents that appear in multiple retrieval results get boosted.

3. Citation Tracking

The killer feature: automatic citation extraction and verification.

def extract_citations(response: str, source_chunks: list[str]) -> list[dict]:
    """Extract and verify citations from LLM response."""
    citations = []

    # Find quoted text in response
    import re
    quoted_pattern = r'"([^"]{20,})"'
    quotes = re.findall(quoted_pattern, response)

    for quote in quotes:
        # Find best matching source chunk
        best_match = None
        best_score = 0

        for idx, chunk in enumerate(source_chunks):
            # Simple substring match (production uses fuzzy matching)
            if quote.lower() in chunk.lower():
                score = len(quote) / len(chunk)
                if score > best_score:
                    best_score = score
                    best_match = idx

        if best_match is not None and best_score > 0.5:
            citations.append({
                'quote': quote,
                'source_index': best_match,
                'confidence': best_score,
                'verified': True
            })
        else:
            citations.append({
                'quote': quote,
                'source_index': None,
                'confidence': 0,
                'verified': False
            })

    return citations

We track:

• Quote text: What the LLM claimed
• Source index: Which chunk it came from
• Confidence: How well the quote matches the source
• Verified: Whether we found the quote in our corpus

This prevents hallucinations. If the LLM makes up a quote, citation tracking catches it.

4. Generation with Claude

The final step is simple. We send retrieved chunks and the user query to Claude:

import anthropic

def generate_answer(
    query: str,
    context_chunks: list[str],
    api_key: str
) -> dict:
    """Generate answer using Claude with retrieved context."""
    client = anthropic.Anthropic(api_key=api_key)

    # Format context
    context = "\n\n".join([
        f"[Source {i+1}]\n{chunk}"
        for i, chunk in enumerate(context_chunks)
    ])

    # System prompt
    system = """You are a helpful assistant that answers questions using only the provided context.

Rules:
1. Only use information from the provided sources
2. Include direct quotes with source numbers: "quote" [Source N]
3. If the context doesn't contain the answer, say so
4. Be concise and accurate"""

    # User prompt
    prompt = f"""Context:
{context}

Question: {query}

Answer:"""

    # Call Claude
    message = client.messages.create(
        model="claude-3-5-sonnet-20241022",
        max_tokens=1024,
        system=system,
        messages=[{"role": "user", "content": prompt}]
    )

    response_text = message.content[0].text

    # Extract citations
    citations = extract_citations(response_text, context_chunks)

    return {
        'answer': response_text,
        'citations': citations,
        'source_chunks': context_chunks,
        'usage': {
            'input_tokens': message.usage.input_tokens,
            'output_tokens': message.usage.output_tokens
        }
    }

No chains. No agents. Just a clear system prompt and structured context.

REST API

We expose the RAG system as a FastAPI endpoint:

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel

app = FastAPI()

class QueryRequest(BaseModel):
    query: str
    top_k: int = 5

class QueryResponse(BaseModel):
    answer: str
    citations: list[dict]
    sources: list[str]
    latency_ms: float

@app.post("/query", response_model=QueryResponse)
async def query_documents(request: QueryRequest):
    """Query the RAG system."""
    import time
    start = time.time()

    # Retrieve
    bm25_results = bm25_retriever.retrieve(request.query, request.top_k)
    tfidf_results = tfidf_retriever.retrieve(request.query, request.top_k)
    semantic_results = semantic_retriever.retrieve(request.query, request.top_k)

    # Fuse
    fused_docs = fuse_results(bm25_results, tfidf_results, semantic_results)
    top_chunks = fused_docs[:request.top_k]

    # Generate
    result = generate_answer(request.query, top_chunks, api_key=CLAUDE_API_KEY)

    latency = (time.time() - start) * 1000

    return QueryResponse(
        answer=result['answer'],
        citations=result['citations'],
        sources=top_chunks,
        latency_ms=latency
    )

The entire request flow: retrieve, fuse, generate, extract citations. Clean and testable.

Results

After 4 months in production:

• 322 tests (unit, integration, e2e)
• <200ms p95 latency (retrieve + generate)
• 94% citation accuracy (verified quotes match sources)
• Zero downtime (thanks to circuit breakers and fallbacks)

Compare this to our LangChain prototype:

• 47 tests (most were mocking LangChain internals)
• ~800ms p95 latency
• 67% citation accuracy (LangChain's citation extraction was unreliable)
• Frequent version-related outages

When to Use This Approach

Use custom RAG when:

• You need production reliability
• Latency matters (<500ms responses)
• You want full control over retrieval logic
• Citation accuracy is critical
• You're building a long-term product

Use LangChain when:

• Prototyping quickly
• Exploring different approaches
• Building internal tools
• Your team is already invested in the ecosystem

Try It Yourself

• GitHub: ChunkyTortoise/docqa-engine
• Live Demo: ct-document-engine.streamlit.app

Questions? Drop them in the comments. I'm building in public and sharing lessons learned.

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