Data Agents in LlamaIndex: Automating Your Workflow

What are Data Agents?

• Automating search & retrieval of different types of data, whether unstructured, semi-structured, or structured.
• Calling external service APIs, processing responses, and storing the results for future use.
• Maintaining conversation history, allowing them to keep track of your interactions.
• Fulfilling both simple & complex data tasks seamlessly.

The Core Components of Data Agents

1. Reasoning Loop: This is the decision-making process that determines how a data agent will interact with different tools and APIs to achieve its goal. The reasoning loop allows the agent to break down complex tasks into manageable steps, choosing which tools to utilize based on input parameters.
   • For example, if you ask about the weather in your city, the agent would assess which API to call, gather data, and present it to you.
2. Tool Abstractions: These are high-level APIs and tools that the data agent can access for interacting with various data sources. By providing a structured definition, tool abstractions enable a data agent to perform its tasks more efficiently.
   • For instance, a tool abstraction might allow the agent to retrieve a tweet from Twitter, analyze its sentiment, and store the results in a database for analytics.

Types of Data Agents Supported by LlamaIndex

• Function Calling Agents: These integrate function calling capabilities directly into the LLMs to enable complex tasks.
• ReAct Agents: This type works across chat & text completion endpoints, facilitating interactive experiences.
• Advanced Agents: These include more sophisticated types like the LLMCompiler, Chain-of-Abstraction, and Language Agent Tree Search, allowing for higher complexity and flexibility in how tasks can be executed.

How to Build and Use Your Data Agents

1. Initial Setup

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yarn add llama-index

2. Defining Tools

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# Define tools 
from llama_index.tools import Tool

class YourCustomTool(Tool):
    def run(self, input):
        # implement your tool logic here
        return "Some output based on input"

3. Initialize Your Language Model

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from llama_index.llms import OpenAI

# Setup your language model
llm = OpenAI(model="gpt-3.5-turbo")

4. Setting Up the Agent

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from llama_index.agent import OpenAIAgent

# Initialize the agent
agent = OpenAIAgent.from_tools([YourCustomTool], llm=llm)

5. Using the Agent

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response = agent.chat("What’s the weather like today?")
print(response)

Real-World Applications of Data Agents

1. Customer Support Automation

• Automatically answer FAQs about products or services by pulling data from a knowledge base.
• Route complex problems to human agents if they cannot resolve the issue using its stuck knowledge.

2. Workflow Management Assistance

3. Data Fetching and Reporting

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Conclusion