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Build a basic Chatbot

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This tutorial introduce you how to create a simple Agent with minimal components and how to use them. This offers a general view on agent initialization and tool integration.

Installation

The python distribution version of Vinagent library is avaible on pypi.org channel and github, which facilitates the installation of the library.

Dev version on git

You can clone git repository and install by poetry command. This is suitable to obtain the latest development version.

git@github.com:datascienceworld-kan/vinagent.git
cd vinagent
pip install -r requirements.txt
poetry install

Stable version

You can install the stable distributed versions which are tested and distributed on pypi.org channel by pip command

pip install vinagent

Prerequisites

To use a list of default tools inside vinagent.tools you should set environment varibles inside .env including TOGETHER_API_KEY to use llm models at togetherai site and TAVILY_API_KEY to use tavily websearch tool at tavily site:

TOGETHER_API_KEY="Your together API key"
TAVILY_API_KEY="Your Tavily API key"
Let's create your acounts first and then create your relevant key for each website.

Setup an Agent

vinagent is a flexible library for creating intelligent agents. You can configure your agent with tools, each encapsulated in a Python module under vinagent.tools. This provides a workspace of tools that agents can use to interact with and operate in the realistic world. Each tool is a Python file with full documentation and it can be independently ran. For example, the vinagent.tools.websearch_tools module contains code for interacting with a search API.

from langchain_together import ChatTogether 
from vinagent.agent.agent import Agent
from dotenv import load_dotenv
load_dotenv()

llm = ChatTogether(
    model="meta-llama/Llama-3.3-70B-Instruct-Turbo-Free"
)

# Step 1: Create Agent with tools
agent = Agent(
    description="You are a Financial Analyst",
    llm = llm,
    skills = [
        "Deeply analyzing financial markets", 
        "Searching information about stock price",
        "Visualization about stock price"]
)

# Step 2: invoke the agent
message = agent.invoke("Who you are?")
print(message)

If the answer is a normal message without using any tools, it will be an AIMessage. By contrast, it will have ToolMessage type. For examples:

AIMessage(content='I am a Financial Analyst.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 7, 'prompt_tokens': 308, 'total_tokens': 315, 'completion_tokens_details': None, 'prompt_tokens_details': None, 'cached_tokens': 0}, 'model_name': 'meta-llama/Llama-3.3-70B-Instruct-Turbo-Free', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-070f7431-7176-42a8-ab47-ed83657c9463-0', usage_metadata={'input_tokens': 308, 'output_tokens': 7, 'total_tokens': 315, 'input_token_details': {}, 'output_token_details': {}})
Access to content property to get the string content.

message.content

I am a Financial Analyst.