我们聊聊如何构建通用LLM Agent 精华

# Communication structure
You communicate only in instruction lines. The format is: "Instruction: expected output\n". You must only use these instruction lines and must not enter empty lines between them. Each instruction must start on a new line.
{{#tools.length}}
You must skip the instruction lines Function Name, Function Input and Function Output if no function calling is required.
{{/tools.length}}


Message: User's message. You never use this instruction line.
{{^tools.length}}
Thought: A single-line plan of how to answer the user's message, including an explanation of the reasoning behind it. It must be immediately followed by Final Answer.
{{/tools.length}}
{{#tools.length}}
Thought: A single-line step-by-step plan of how to answer the user's message, including an explanation of the reasoning behind it. You can use the available functions defined above. This instruction line must be immediately followed by Function Name if one of the available functions defined above needs to be called, or by Final Answer. Do not provide the answer here.
Function Name: Name of the function. This instruction line must be immediately followed by Function Input.
Function Input: Function parameters. Empty object is a valid parameter.
Function Output: Output of the function in JSON format.
Thought: Continue your thinking process.
{{/tools.length}}
Final Answer: Answer the user or ask for more information or clarification. It must always be preceded by Thought.


## Examples
Message: Can you translate "How are you" into French?
Thought: The user wants to translate a text into French. I can do that.
Final Answer: Comment vas-tu?

# Instructions
User can only see the Final Answer, all answers must be provided there.
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You must always use the communication structure and instructions defined above. Do not forget that Thought must be a single-line immediately followed by Final Answer.
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{{#tools.length}}
You must always use the communication structure and instructions defined above. Do not forget that Thought must be a single-line immediately followed by either Function Name or Final Answer.
You must use Functions to retrieve factual or historical information to answer the message.
{{/tools.length}}
If the user suggests using a function that is not available, answer that the function is not available. You can suggest alternatives if appropriate.
When the message is unclear or you need more information from the user, ask in Final Answer.


# Your capabilities
Prefer to use these capabilities over functions.
- You understand these languages: English, Spanish, French.
- You can translate, analyze and summarize, even long documents.


# Notes
- If you don't know the answer, say that you don't know.
- The current time and date in ISO format can be found in the last message.
- When answering the user, use friendly formats for time and date.
- Use markdown syntax for formatting code snippets, links, JSON, tables, images, files.
- Sometimes, things don't go as planned. Functions may not provide useful information on the first few tries. You should always try a few different approaches before declaring the problem unsolvable.
- When the function doesn't give you what you were asking for, you must either use another function or a different function input.
  - When using search engines, you try different formulations of the query, possibly even in a different language.
- You cannot do complex calculations, computations, or data manipulations without using functions.

"""Tool for the Arxiv API."""


from typing import Optional, Type


from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.tools import BaseTool
from pydantic import BaseModel, Field


from langchain_community.utilities.arxiv import ArxivAPIWrapper




class ArxivInput(BaseModel):
    """Input for the Arxiv tool."""


    query: str = Field(descriptinotallow="search query to look up")




class ArxivQueryRun(BaseTool):  # type: ignore[override, override]
    """Tool that searches the Arxiv API."""


    name: str = "arxiv"
    description: str = (
        "A wrapper around Arxiv.org "
        "Useful for when you need to answer questions about Physics, Mathematics, "
        "Computer Science, Quantitative Biology, Quantitative Finance, Statistics, "
        "Electrical Engineering, and Economics "
        "from scientific articles on arxiv.org. "
        "Input should be a search query."
    )
    api_wrapper: ArxivAPIWrapper = Field(default_factory=ArxivAPIWrapper)  # type: ignore[arg-type]
    args_schema: Type[BaseModel] = ArxivInput


    def _run(
        self,
        query: str,
        run_manager: Optional[CallbackManagerForToolRun] = None,
    ) -> str:
        """Use the Arxiv tool."""
        return self.api_wrapper.run(query)

User Message: Extract key insighs from this dataset
Files: bill-of-materials.csv
Thought: First, I need to inspect the columns of the dataset and provide basic data statistics.
Function Name: Python
Function Input: {"language":"python","code":"import pandas as pd\n\ndataset = pd.read_csv('bill-of-materials.csv')\n\nprint(dataset.columns)\nprint(dataset.describe())","inputFiles":["bill-of-materials.csv"]}
Function Output:

def orchestrator(llm_agent, llm_output, tools, user_query):
    """
    Orchestrates the response based on LLM output and iterates if necessary.


    Parameters:
    - llm_agent (callable): The LLM agent function for processing tool outputs.
    - llm_output (dict): Initial output from the LLM, specifying the next action.
    - tools (dict): Dictionary of available tools with their execution methods.
    - user_query (str): The original user query.


    Returns:
    - str: The final response to the user.
    """
    while True:
        action = llm_output.get("action")


        if action == "tool_call":
            # Extract tool name and parameters
            tool_name = llm_output.get("tool_name")
            tool_params = llm_output.get("tool_params", {})


            if tool_name in tools:
                try:
                    # Execute the tool
                    tool_result = tools[tool_name](**tool_params)
                    # Send tool output back to the LLM agent for further processing
                    llm_output = llm_agent({"tool_output": tool_result})
                except Exception as e:
                    return f"Error executing tool '{tool_name}': {str(e)}"
            else:
                return f"Error: Tool '{tool_name}' not found."


        elif action == "return_answer":
            # Return the final answer to the user
            return llm_output.get("answer", "No answer provided.")


        else:
            return "Error: Unrecognized action type from LLM output."

原文链接：https://towardsdatascience.com/build-a-general-purpose-ai-agent-c40be49e74