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CodeSignal

Document Processing and Retrieval with LangChainGo

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Overview

Unlock document intelligence with LangChainGo. Load, split, embed, and search documents via similarity retrieval, then combine results with prompt templates to answer questions accurately.

Syllabus

  • Unit 1: Introduction to Document Processing in Go
    • Loading and Examining Files with LangChain in Go
    • Loading PDF Files
    • Experimenting with Document Splitting in LangChainGo
    • Switching Text Splitters in LangChainGo
    • Loading and Splitting Documents in Go
  • Unit 2: Generating Document Embeddings in Go
    • Generating Embeddings for Document Chunks
    • Customizing OpenAI Embeddings Model in Go
    • Debugging OpenAI Embeddings in Go
    • Exploring Embedding Vector Dimensionality
  • Unit 3: Retrieving Relevant Information with Similarity Search in Go
    • Exploring Document Embeddings
    • Performing Similarity Search
    • Increasing Document Retrieval Limit in Similarity Search
    • Creating a Vector Store and Similarity Search
  • Unit 4: Asking Questions with Retrieved Context and Templates in Go
    • Combining Document Chunks into a Unified Context
    • Creating Prompt Templates for Context-Enhanced Queries in Go
    • Integrating OpenAI Chat Models with Context Retrieval
    • Implementing Context-Aware Responses

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