Create a Local AI Agent with Langchain and Ollama with Tools

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AI Summary

Summary: AI Agents and Legacy Applications Integration

• AI Agents Overview:
  • Autonomous entities that interact with applications.
  • Allow AI models to call non-AI applications via functions.
  • Operate independently, responding asynchronously.
• Use Cases:
  • Virtual assistants, robotics, gaming.
  • Particularly valuable for integrating AI with legacy cloud or on-prem applications through APIs.
• Building AI Agents:
  • Tutorial on using Olama and Lang chain to create agents.
  • Olama runs large language models locally.
  • Lang chain is a framework for AI applications.
• Installation Guide:
  • AMA installation process outlined for Windows, Linux, and macOS.
  • Example of setting up a virtual environment and installing Lang chain experimental.
• Creating an Agent:
  • Demonstration of using AMA and Lang chain to initialize a model.
  • Explanation of binding tools/functions to the model.
  • Example of a weather function binding and API call generation.
• Function Calling with AI:
  • AI interprets natural language to generate correct function calls.
  • Legacy applications can be called with AI-generated function signatures.
• Conclusion:
  • Code will be shared on a blog.
  • Encouragement to subscribe to the channel and share content.

For more detailed guidance and examples, you can refer to the provided video and blog links.

'''
pip install langchain-experimental

from langchain_experimental.llms.ollama_functions import OllamaFunctions

model = OllamaFunctions(model=“llama3:8b”, format=“json”)

model = model.bind_tools(

    tools=[

        {

            “name”: “get_current_weather”,

            “description”: “Get the current weather in a given location”,

            “parameters”: {

                “type”: “object”,

                “properties”: {

                    “location”: {

                        “type”: “string”,

                        “description”: “The city and state, ” “e.g. San Francisco, CA”,

                    },

                    “unit”: {

                        “type”: “string”,

                        “enum”: [“celsius”, “fahrenheit”],

                    },

                },

                “required”: [“location”],

            },

        }

    ],

    function_call={“name”: “get_current_weather”},

)

from langchain_core.messages import HumanMessage

model.invoke(“what is the weather in Boston?”)

'''