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Text-to-MongoDB LLM Agent Using LangChain | Full Tutorial

In this video, we dive into building a powerful Text-to-MongoDB Query Generator using LangChain! If you're looking to automate query generation from natural language inputs, this is the ultimate guide for you.

Topics Covered:
Prompt Templates: Learn how to structure prompts dynamically for better query generation.
Example Selector: Discover how to pick relevant examples for few-shot prompting, improving the accuracy of your LLM.
Few-Shot Prompting: Explore how providing high-quality examples enhances LLM responses for complex queries.
Output Parser: Parse and validate LLM outputs into structured MongoDB queries seamlessly.
Async Programming: Boost efficiency by handling multiple LLM requests concurrently in your workflow.

What You’ll Learn:
How to leverage LangChain to build an LLM agent that interprets user queries into valid MongoDB queries.
Best practices for handling edge cases like ambiguous inputs and schema-specific requirements.
Insights into key LangChain concepts to take your LLM projects to the next level.

0:00 Introduction
0:48 Output Parser
2:23 Prompt Templates
5:08 Few Shot Prompting
10:22 Example Selectors
10:44 Async Programming
12:12 Text to Mongo Query Agent
18:33 Conclusion

#llm #crashcourse #ai #mongodb #database #nlp

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