Introduction

I’m putting together this pipeline to help me quickly turn any long document, whether it’s a PDF report, a collection of articles, or internal meeting notes, into a concise summary I can skim in seconds. Instead of relying on external APIs (which can be slow, costly, or raise privacy concerns), I want everything to run locally on my own machine. This way I know exactly where my data lives, how fast summaries are generated, and I don’t have to worry about hitting API limits or leaking confidential info.

Why This Approach Works for Me

  • Privacy First: All text and embeddings stay on disk or in my local database.
  • Predictable Latency: No network hops, summaries come back in seconds.
  • Cost Control: I pay only for electricity and hardware, not per-token API fees.

Overview of the Pipeline

I’ve broken the summarization flow into four main steps. Treat this like a recipe: follow each step in sequence, and you’ll end up with a neat summary at the end:

  1. Text Chunking – Break up big blobs of text into bite-sized pieces that fit into the LLM’s context window.
  2. Embedding Generation – Turn each chunk into a numerical vector that captures its meaning.
  3. Retrieval – Find the chunks most relevant to the summary task by doing a nearest-neighbor lookup in a vector store.
  4. Generation – Feed those top chunks into my local summarization model and stitch together the final summary.

I’ll go through each step with code snippets and tips.

1. Text Chunking

Why: LLMs can only process so many tokens at once. Trying to summarize a 10,000‑word document in one go will either get cut off or produce garbage. My Strategy: I split the text into overlapping windows, say 1,000 tokens each, with a 200‑token overlap so I don’t lose context at chunk boundaries.

# Personal chunking function
def chunk_text(text, max_tokens=1000, overlap=200):
    tokens = tokenizer.tokenize(text)
    chunks = []
    for i in range(0, len(tokens), max_tokens - overlap):
        window = tokens[i : i + max_tokens]
        chunk_text = tokenizer.convert_tokens_to_string(window)
        chunks.append(chunk_text)
    return chunks

# Example usage:
raw_text = open('long_report.txt').read()
my_chunks = chunk_text(raw_text)
print(f"Created {len(my_chunks)} chunks.")

A quick sanity check I do: print the first two chunks to verify they overlap correctly.

for i, c in enumerate(my_chunks[:2]):
    print(f"--- Chunk {i+1} ---\n{c[:200]}...\n")

2. Embedding Generation

Why: Embeddings let me compare textual similarity in vector space. My Tool: I use the all-MiniLM-L6-v2 model from sentence-transformers because it’s fast and reasonably accurate.

from transformers import AutoTokenizer, AutoModel
import torch

# Load model once
tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
model     = AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")

# Function to embed a list of chunks
def embed_chunks(chunks):
    inputs = tokenizer(chunks, padding=True, truncation=True, return_tensors='pt')
    with torch.no_grad():
        outputs = model(**inputs)
    embeddings = outputs.last_hidden_state.mean(dim=1)  # mean pooling
    return embeddings

# Generate embeddings for my chunks
embs = embed_chunks(my_chunks)
print("Embeddings shape:", embs.shape)

I usually check embs.shape to confirm I have one vector per chunk. If something feels off, I’ll visualize a few with t-SNE later.

Why: I don’t need all chunks, just the ones most relevant to summarizing. My Setup: I keep embeddings in a PostgreSQL table using the pgvector extension. That way, I can run SQL queries and keep everything in one place.

-- Enable the extension (one-time setup)
CREATE EXTENSION IF NOT EXISTS vector;

-- Table schema
CREATE TABLE IF NOT EXISTS docs (
  id SERIAL PRIMARY KEY,
  content TEXT,
  embedding VECTOR(384)
);

-- Insert embeddings (from Python side or psql)
INSERT INTO docs (content, embedding)
VALUES
('first chunk...', '[0.12, -0.45, ...]'::vector),
('second chunk...', '[0.03, 0.27, ...]'::vector);

-- Retrieve top 3 nearest chunks for a query embedding
SELECT content
FROM docs
ORDER BY embedding <-> '[0.12, -0.45, ...]'::vector
LIMIT 3;

From Python I call this via psycopg2 or asyncpg, passing my query vector to fetch back the 3 most similar text chunks.

4. Generation with Local LLM

Why: Now that I have the most relevant context, I want the model to weave them into a coherent summary. My Model: I usually go with facebook/bart-large-cnn locally, it’s solid for summarization out of the box.

from transformers import pipeline

# Initialize summarizer once
generator = pipeline(
    'summarization',
    model='facebook/bart-large-cnn',
    device=0  # GPU if available
)

# Summarize retrieved chunks
def summarize_chunks(chunks):
    prompt = "\n\n".join(chunks)
    summary = generator(
        prompt,
        max_length=250,  # tweak as needed
        min_length=50,
        do_sample=False
    )
    return summary[0]['summary_text']

# Pull it all together
top_chunks = get_top_chunks(my_query_embedding, k=3)
final_summary = summarize_chunks(top_chunks)
print("My Summary:\n", final_summary)

A quick trick I use: if the summary feels too short or skips details, I increase max_length or feed 5 chunks instead of 3.

Wrapping Up

Putting it all together, my summarization workflow is:

  1. Read document → chunk_text()
  2. chunk_text → embed_chunks()
  3. embed_chunks → store/query via pgvector
  4. top chunks → summarize_chunks()

This setup lives in a single Python file for me, so I can run python summarize.py report.pdf and instantly get a summary printed or saved. Over time, I’ll tweak chunk sizes, embedding models, and summarization parameters to fit different document types. For now, this feels like a solid foundation for private, fast, and free (from API fees) summaries.