mongodb-developer/quickstart-rag-python

This Python project demonstrates semantic search using MongoDB and two different LLM frameworks: LangChain and LlamaIndex. The goal is to load documents from MongoDB, generate embeddings for the text data, and perform semantic searches using both LangChain and LlamaIndex frameworks.

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Experimental

This project helps developers build applications that can understand and respond to natural language queries by finding the most relevant information from their MongoDB documents. It takes existing text data from MongoDB, processes it to understand its meaning, and then uses that understanding to perform highly accurate searches. It's designed for developers building intelligent applications, such as chatbots or knowledge assistants, that need to retrieve information semantically.

No commits in the last 6 months.

Use this if you are a developer looking to implement semantic search capabilities in your application, allowing users to find information based on meaning rather than just keywords, using data stored in MongoDB.

Not ideal if you need a pre-built, end-user application for semantic search rather than a developer's starter project.

semantic-search information-retrieval application-development database-integration
No License Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 5 / 25
Maturity 8 / 25
Community 8 / 25

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Last pushed

Jun 10, 2024

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