How can I efficiently process and query a 100k-line PDF using retrieval-based embeddings?

Below is an approach that leverages retrieval augmented generation (RAG) using embeddings to address the token and cost issues related to processing long PDF documents.

---

Retrieval Augmented Generation (RAG) for Long PDF Files

Instead of forcing the model to process the entire long context every time, you can do the following:

1. Chunk and Embed the Document
   Split your PDF into smaller chunks (e.g., paragraphs, pages). Then, use an embeddings model (like OpenAI’s embeddings) to convert each chunk into a vector representation.

2. Build a Vector Index
   Store these embeddings in a vector database (or in-memory index) that supports similarity search. This index allows you to quickly retrieve only the most relevant chunks given a user query.

3. Retrieve Relevant Chunks
   When a user asks a question, generate an embedding for the query and perform a similarity search in your index to retrieve the top matching chunks.

4. Construct a Concise Prompt
   Combine the retrieved chunks (which are well within the token limit) with the user's query to form a prompt. This helps the model answer the question based on only the most relevant parts of the long document.

5. Generate the Answer
   Pass this prompt to ChatGPT (or another language model). Since you’re only including the relevant chunks, you reduce both the number of tokens and the cost per call.

---

Example in Python Using LangChain

Below is a simplified example using LangChain's utilities for embedding and document retrieval:

PYTHONCopied
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.docstore.document import Document
from langchain.chains import RetrievalQA
from langchain.llms import OpenAI

# Step 1. Convert document chunks into a list of Document objects
documents = [
    Document(page_content="Chunk 1 text here ..."),
    Document(page_content="Chunk 2 text here ..."),
    # ...
]

# Step 2. Create an embeddings object
embeddings = OpenAIEmbeddings(api_key="YOUR_OPENAI_API_KEY")

# Step 3. Build a vector store (FAISS is efficient for similarity search)
vector_store = FAISS.from_documents(documents, embeddings)

# Step 4. Construct a retrieval-based question-answering chain
qa = RetrievalQA.from_chain_type(
    llm=OpenAI(api_key="YOUR_OPENAI_API_KEY"),
    chain_type="stuff",
    retriever=vector_store.as_retriever(k=3)  # retrieve top 3 relevant chunks
)

# Step 5. Ask a question
query = "What does the document say about cost optimization?"
answer = qa.run(query)
print(answer)

---

Benefits of This Approach

• Efficient Token Use: Only the relevant chunks (which contain fewer tokens) are sent to the model.
• Cost Reduction: Fewer tokens per request lower the cost.
• Scalability: The system can handle very long documents without hitting token limits.
• Improved Relevance: Embeddings and vector retrieval ensure that responses are drawn from the most pertinent parts of the document.

Using embeddings for retrieval lets your system "remember" a full document without including all tokens in the prompt, making it a powerful approach for handling long PDFs and reducing the cost per API call.

This method aligns with both LangChain’s strategies for processing large documents and best practices for managing token limits and API latency.