> ## Documentation Index
> Fetch the complete documentation index at: https://docs.salmanahmad.online/llms.txt
> Use this file to discover all available pages before exploring further.

# Integrate PDF Highlighting into Your LangChain RAG Pipeline

> Add visual PDF highlighting to your LangChain RAG pipeline. Retrieve chunks and highlight source passages in the original PDF for transparent citations.

When your RAG pipeline answers a user's question, it draws on specific passages from one or more source documents. RAG PDF Highlighter lets you close the loop visually: after retrieval, you send the retrieved chunks back to the service alongside the original PDF URL, and you get back an annotated PDF with every source passage marked in yellow. Users can see exactly which sentences informed the answer, making your application more transparent and trustworthy.

## Integration Pattern

<Steps>
  <Step title="Set up your RAG retriever">
    Load your PDF, split it into chunks, and index those chunks in a vector store using a standard LangChain setup. This step happens once at startup or index-build time.
  </Step>

  <Step title="Run retrieval to get relevant Document chunks">
    For each user query, call your retriever to get the most relevant `Document` objects. Each `Document` carries the chunk text in `page_content` and a `metadata` dict that includes the `page` number (0-indexed).
  </Step>

  <Step title="Call POST /highlight with the PDF URL and retrieved chunks">
    Pass the original PDF URL and the retrieved `Document` objects to the `/highlight` endpoint. The service downloads the PDF, locates each chunk on its page, and draws yellow highlights over the matching text.
  </Step>

  <Step title="Return the highlighted PDF to your user">
    Stream or serve the binary PDF response directly to your user's browser or application. No further processing is needed.
  </Step>
</Steps>

## End-to-End Example

The example below shows a complete RAG pipeline — from loading the PDF to delivering a highlighted result — using LangChain and the RAG PDF Highlighter API.

```python theme={null}
import requests
from langchain_community.document_loaders import PyPDFLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings

# 1. Load and index the PDF
PDF_URL = "https://example.com/research-paper.pdf"
loader = PyPDFLoader(PDF_URL)
docs = loader.load()
splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
chunks = splitter.split_documents(docs)

vectorstore = Chroma.from_documents(chunks, OpenAIEmbeddings())

# 2. Retrieve relevant chunks for a query
retriever = vectorstore.as_retriever(search_kwargs={"k": 5})
results = retriever.invoke("What are the main findings?")

# 3. Highlight the retrieved chunks
response = requests.post(
    "http://localhost:8000/highlight",
    json={
        "pdf_url": PDF_URL,
        "documents": [
            {"page_content": doc.page_content, "metadata": doc.metadata}
            for doc in results
        ]
    }
)

# 4. Save the highlighted PDF
with open("highlighted_results.pdf", "wb") as f:
    f.write(response.content)
```

<Note>
  The chunks you send in the `documents` array must originate from the same PDF you specify in `pdf_url`. The service locates passages by searching for the exact chunk text on the given page of the downloaded PDF. If the chunks come from a different document, they will not match and no highlights will be applied.
</Note>

<Tip>
  Use `PyPDFLoader` to load your PDFs when building the index. PyPDFLoader stores page numbers in `metadata["page"]` as 0-indexed integers, which is exactly the format RAG PDF Highlighter expects. Using a loader with a different page-numbering convention may result in highlights appearing on the wrong pages.
</Tip>