How to Intermediate · 4 min read

How to build AI document processing pipeline

Quick answer

Build an AI document processing pipeline by first extracting text from documents using OCR or parsers, then embedding the text with vector embeddings, and finally applying an LLM like gpt-4o to analyze or summarize the content. Use vector stores like FAISS for efficient retrieval and combine these steps into an automated workflow.

PREREQUISITES

Python 3.8+
OpenAI API key (free tier works)
pip install openai>=1.0
pip install faiss-cpu
pip install PyPDF2 or pdfplumber

Setup

Install necessary Python packages for document processing, embeddings, and LLM calls. Set your OpenAI API key as an environment variable.

bash

pip install openai faiss-cpu PyPDF2

Step by step

This example extracts text from a PDF, creates embeddings with OpenAIEmbeddings, stores them in FAISS, and queries the vector store with an LLM prompt.

python

import os
from PyPDF2 import PdfReader
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
from openai import OpenAI

# Load PDF text
reader = PdfReader("sample.pdf")
text = "".join(page.extract_text() for page in reader.pages)

# Split text into chunks
chunk_size = 500
chunks = [text[i:i+chunk_size] for i in range(0, len(text), chunk_size)]

# Create embeddings
embeddings = OpenAIEmbeddings(openai_api_key=os.environ["OPENAI_API_KEY"])
vector_store = FAISS.from_texts(chunks, embeddings)

# Query vector store
query = "Summarize the main points of the document."
query_embedding = embeddings.embed_query(query)
results = vector_store.similarity_search_by_vector(query_embedding, k=3)

# Use LLM to generate summary
client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
context = "\n\n".join(results)
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": f"Document excerpts:\n{context}\n\n{query}"}
]
response = client.chat.completions.create(model="gpt-4o", messages=messages)
print(response.choices[0].message.content)

output

Summary of the main points of the document...

Common variations

Use pdfplumber for more accurate PDF text extraction.
Switch to claude-3-5-sonnet-20241022 for stronger coding or reasoning tasks.
Implement async calls with asyncio and the OpenAI SDK for higher throughput.
Use streaming completions to get partial LLM outputs in real time.

Troubleshooting

If text extraction returns empty strings, verify the PDF is not scanned images; use OCR tools like pytesseract if needed.
If embeddings are slow, batch requests or use smaller chunk sizes.
For API errors, check your OPENAI_API_KEY environment variable and usage limits.

✅

Key Takeaways

Extract text from documents using PDF parsers or OCR before embedding.
Use vector embeddings and stores like FAISS for efficient semantic search.
Combine embeddings with LLMs like gpt-4o to generate summaries or answers.
Adjust chunk size and model choice based on document length and task complexity.
Handle errors by verifying API keys and document formats early.

Verified 2026-04 · gpt-4o, claude-3-5-sonnet-20241022

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