Semantic Search Made Easy With LangChain and MongoDB

🔗 Written tutorial → https://mdb.link/ZvwUzcMvKiI-tutorial
🔗 GitHub repository → https://trymongodb.com/3H7kO3L

Join us in this detailed tutorial on enabling semantic search with MongoDB Atlas and OpenAI! We dive deep into the process of transforming user-specific data into query-ready information using the power of LangChain utilities and MongoDB's Vector Search.

What You'll Learn:
✨ Setting Up: Get started with MongoDB Atlas and OpenAI.
✨ Code Preparation: Cloning and configuring the necessary repository.
✨ Data Vectorization: Using AT&T's Wikipedia page as a data source.
✨ Advanced Techniques: Data splitting, embedding, and storing with LangChain.
✨ Search Optimization: Creating a vector search index in MongoDB Atlas.
✨ Practical Examples: Running queries and contextual compression with an LLM.

📚 RESOURCES 📚
✅ Sign-up for a free cluster at → https://mdb.link/ZvwUzcMvKiI
✅ Get help on our Community Forums → https://mdb.link/ZvwUzcMvKiI-community

💬 Connect with Jesse:
Twitter: Twitter: codestackr

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