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 to chat with your document extract relevant information using LLM-.

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.

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.

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.

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.

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.

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.

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.

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.

?? Output the Summary

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

?? This workflow ensures we efficiently summarize complex documents while focusing on key details. Whether in medical documents or other industries, LLMs are transforming how we extract and utilize information! ??

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