Basic Semantic Search with Embedded Models

🚀 Excited to Share my recent hands-on with : "AI LangChain Basic Semantic Search with Embedded Models"!

I’m excited to share my latest project—a step-by-step starter implementation for beginners in semantic search, built using the LangChain framework. Leveraging HuggingFace and OpenAI models, this project demonstrates how document embeddings enable more contextual and relevant results than traditional keyword searches.

🔍 What Is Semantic Search?

Semantic search uses AI to understand the meaning behind user queries and documents. Instead of relying on exact keyword matches, it retrieves results based on contextual relevance, powering smarter chatbots, document retrieval, and knowledge management applications.

Key Features

• Simple, modular codebase for easy learning and extension
• Document embedding through HuggingFace and OpenAI
• Context-aware semantic search and similarity queries
• Example scripts and test cases for hands-on exploration

It’s ideal for those new to NLP and vector search—jumpstart your learning with practical, readable code!

AI LangChain Basic Semantic Search with Embedded Models

This project demonstrates basic semantic search using embedding models with the LangChain framework. It provides examples for document embedding, querying, and semantic similarity using both HuggingFace and OpenAI models.

Features

• Embed documents using HuggingFace or OpenAI models
• Perform semantic search and similarity queries
• Modular code for easy extension
• Example scripts and test cases

Project Structure

EmbededModels/
    EmbeddingDocs_HFModel           # Embedding with HuggingFace models
    EmbeddingDocs_OpenAIModel.py    # Embedding with OpenAI models
    EmbeddingQuery_OpenAIModel.py   # Querying with OpenAI models
    DocSimilarity_SemanticSearch.py # Semantic search and similarity

requirements.txt                   # Python dependencies
README.md                          # Project documentation
tests/
    test.py                        # Test cases
        

Setup

1. Create and activate a Python environment (Anaconda or venv recommended).
2. Install dependencies:

Usage

• Run embedding scripts or semantic search examples from the EmbededModels directory.
• Example:

Troubleshooting

• If you see errors related to missing DLLs (e.g., torch_python.dll), ensure you have the correct version of torch and the Microsoft Visual C++ Redistributable installed.
• For network issues with conda, use pip for package installation.

#AI #NLP #LangChain #SemanticSearch #Python #MachineLearning #BeginnerFriendly