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Document Processing and Retrieval with LangChain in Python

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Overview

Unlock the power of document intelligence with LangChain in Python. This course will teach you how to efficiently process and retrieve information from documents. Learn to load, split, and embed documents, and master the art of similarity search to extract relevant insights. Build a robust foundation for document-driven applications with cutting-edge techniques.

Syllabus

Unit 1: Loading and Splitting Documents with LangChain

Loading and Inspecting PDF Documents
Switching to Text File Loading
Experiment with Document Splitting Parameters
Exploring Different Text Splitters
Loading and Splitting PDF Documents

Unit 2: Generating Document Embeddings with OpenAI

Creating Document Embeddings with OpenAI
Experiment with Embedding Parameters
Fix the Embedding Model Bug
Exploring Embedding Dimensions

Unit 3: Retrieving Relevant Information with Similarity Search

Exploring Vector Store Details
Formulate a Query for the Similarity Search
Adjusting Document Retrieval Quantity
Similarity Search with FAISS

Unit 4: Asking Questions with Retrieved Context and Templates

Creating a Chat Prompt Template
Combining Document Chunks for Context
Crafting a Prompt Template with Retrieved Context
Integrating Chat Model with Context
Guiding Model Responses with System Message

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