Building LLM Agents with Tool Use
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- čas přidán 16. 04. 2024
- Tool use is a method whichs allows developers to connect Cohere's Command models to external tools like search engines, APIs, databases, and other software tools. Just like how Retrieval-Augmented Generation (RAG) allows a model to use an external data source to improve factual generation, tool use is a capability that allows retrieving data from multiple sources. But it goes beyond simply retrieving information and is able to use software tools to execute code, or even create entries in a CRM system.
In this video, we'll see how we can use two tools to create a simple data analyst agent that is able to search the web and run code in a python interpreter. This agent uses Cohere's Command R+ mode and Langchain.
Find the code here at Colab:
colab.research.google.com/git...
Github: github.com/cohere-ai/notebook...
LangChain Tools: python.langchain.com/docs/int...
Fantastic, thanks for the explanation Jay! Relevant for several use-cases...
Great video on LLM tool use, thanks Jay!
Love this explanation! Incredible work as always!
thank you Jay for this beautiful explanation
Wonderful walkthrough! 😍
Does the agent use an LLM to determine the steps? Can you cover these internal details?
Hi Jay, great video. What if the user wants to also provide his/her own data and ask questions related to that, which might also require outside world knowledge (this web search as a tool might still be useful)?
Yeah, should also be possible. The web search is just an example.
Can Tools work only with Langchain?
If not, a more general example would be nice..
Langchain is the suggested method now for Command R+, yeah. What other frameworks do you find relevant here?
would be better if you could explain the code line by line instead of just mentioning briefly what they are doing. Great video otherwise. I learn alot from your videos.
🔥🔥🔥
Outstanding Jay!
😮