Chat with your documents extract required information using LLM

Setup the Environment and import the required Packages

In this blog we will understand how chat with you documents extract relevant information using LLM. Given below is the section which consists of the procedure how chat with your document extract relevant information using LLM-.

Read more: How LLM models are revolutionizing healthcare document processing?

1. PDF Ingestion and Preprocessing

Read the PDF: Extract text from the PDF using a library like PyPDF2, pdfplumber, or pdfminer. Text Cleaning: Clean the extracted text to remove unwanted characters, headers, footers, etc.

Basically, we are using PyPDFLoader to load and extract text from a medical PDF document. The extracted text is wrapped inside a Document object with metadata about its source.

2. Chunking the Text

Split the text: Basically, long documents are split into smaller chunks (e.g., paragraphs or fixed token length). Moreover, this is essential since many LLMs have token size limits. Chunk Size Management: Therefore, ensure that the chunks are the right size for processing—small enough for the LLM but large enough to retain meaning.

In this part, we are using the Recursive Character TextSplitter from LangChain to split the text into smaller chunks of around 1000 characters with a 200-character overlap to ensure context is not lost across chunks.

3. Embedding Generation

Generate embeddings: Use an embedding model (like OpenAI, BERT, or Sentence-Transformers) to create vector representations of the text chunks. These embeddings capture the semantic meaning of each chunk.

Additionally, the OpenAIEmbeddings model is used to generate embeddings for the text chunks. These embeddings will later be stored in a vector database to retrieve relevant information based on queries.

4. Vector Store Indexing

Create a Vector Store: Store the generated embeddings in a vector database (e.g., Pinecone, FAISS, or Weaviate). Document Metadata: Save the metadata (e.g., page number, section) alongside the embeddings, so that you can retrieve the relevant text chunks later.

Basically, the vector store, in this case, is Chroma, which is used to store the embeddings and retrieve relevant chunks of text when necessary. The retriever is set up to retrieve the top 2 most relevant chunks based on the query.

5. Query Handling

User Query: Accept a query from the user (e.g., “Summarize the main points of the document”). Query Embedding: Therefore, convert the user’s query into an embedding using the same model as for document embeddings.Moreever, in the case of medical document summarization, we aren’t accepting user queries. Instead, we are summarizing the entire document directly. However, this would be the part where you convert user queries to embeddings if needed.

6. Retrieving Relevant Chunks

Similarity Search: Basically,perform a similarity search in the vector store to find the most relevant document chunks based on the user’s query embedding. Top-K Selection: Retrieve the top-K chunks that are most relevant to the query.

Basically, the retriever fetches relevant chunks, and the ragchain combines the chunks into a readable context to pass into the LLM for further processing over here.

7. Combining the Chunks

Merge Chunks: Basically,combine the retrieved chunks into a coherent text format that can be used as input for the summarization process.

Moreover,this helper function takes the retrieved document chunks and formats them into a single text that can be passed into the LLM for summarization.

8. Summarization with LLM

Summarize: Use a large language model (LLM) to generate a concise summary of the retrieved chunks. Moreover, you may fine-tune the model or use a prompt designed for summarization tasks.

Here, we use the OpenAI LLM to process the chunks and generate a summary. The prompt specifically instructs the LLM to focus on key medical information, ensuring that the summary is relevant and concise.

9. Post-Processing

Clean up the Summary: Ensure the generated summary is free from inconsistencies and repetitive content. Improve Readability by adjusting formatting or style to enhance clarity and flow.

After the LLM generates a summary, we can post-process the response by cleaning up the output and displaying it to the user in a readable format.

10. Output the Summary

Return the Summary: Present the summarized content to the user.

Finally, the summarized content is displayed on the Streamlit app for the user to view. In this way chat with you documents extract important information from LLM models.