Introduction
Document AI pipelines automate the extraction, analysis, and structuring of data from complex documents. In 2026, combining advanced OCR, LLM models, and vector databases has become essential for companies handling large volumes of PDFs or images. This expert tutorial guides you step by step through building a complete, scalable, production-ready pipeline. You will learn how to orchestrate each stage with functional code you can copy and paste directly.
Prerequisites
- Python 3.11+
- OpenAI API key or equivalent
- Advanced knowledge of LangChain and Pydantic
- Docker installed for the environment
Project Initialization
python -m venv .venv
source .venv/bin/activate
pip install langchain langchain-openai pymupdf pydantic qdrant-client prefectThis command creates an isolated environment and installs the critical dependencies for building a complete, modular Document AI pipeline.
Pydantic Model for Documents
from pydantic import BaseModel, Field
from typing import List, Optional
class ExtractedEntity(BaseModel):
key: str
value: str
confidence: float = Field(ge=0, le=1)
class DocumentResult(BaseModel):
document_id: str
entities: List[ExtractedEntity]
summary: Optional[str] = NoneThis Pydantic model ensures strict validation of extracted data and facilitates integration with structured LLMs.
Document Loader and Splitter
import fitz
from langchain.text_splitter import RecursiveCharacterTextSplitter
def load_and_split(file_path: str, chunk_size: int = 1500):
doc = fitz.open(file_path)
text = ""
for page in doc:
text += page.get_text()
splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=200)
return splitter.split_text(text)This module loads PDFs and splits them into chunks optimized for embedding while properly handling overlaps.
Structured Extraction with LLM
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
llm = ChatOpenAI(model="gpt-4o", temperature=0)
prompt = ChatPromptTemplate.from_template("Extraire les entités du texte suivant en JSON : {text}")
async def extract_entities(text_chunks: list):
results = []
for chunk in text_chunks:
response = await llm.ainvoke(prompt.format(text=chunk))
results.append(response.content)
return resultsAsynchronous extraction with structured LLMs ensures performance and accuracy while avoiding timeouts on large documents.
Vectorization and Storage
from langchain_openai import OpenAIEmbeddings
from qdrant_client import QdrantClient
embeddings = OpenAIEmbeddings()
client = QdrantClient(":memory:")
async def index_chunks(chunks: list, doc_id: str):
vectors = await embeddings.aembed_documents(chunks)
client.upsert(collection_name="documents", points=[{"id": i, "vector": v, "payload": {"doc_id": doc_id}} for i, v in enumerate(vectors)])In-memory Qdrant enables fast testing while providing an identical API to the production version for easy scaling.
Best Practices
- Always validate LLM outputs with Pydantic
- Use async for expensive LLM calls
- Version prompts and models
- Monitor costs and latency with metrics
- Implement retries with exponential backoff
Common Mistakes to Avoid
- Forgetting chunk_overlap which breaks context
- Not handling LLM token limits
- Storing sensitive data without encryption
- Ignoring async error handling which crashes the pipeline
Further Reading
Deepen these concepts with our advanced training on AI pipeline orchestration: https://learni-group.com/formations