WRITER 工具

import getpass
import os

if not os.getenv("WRITER_API_KEY"):
    os.environ["WRITER_API_KEY"] = getpass.getpass("Enter your WRITER API key: ")

from langchain_writer.chat_models import ChatWriter
from langchain_writer.tools import GraphTool

chat = ChatWriter()

graph_id = getpass.getpass("Enter WRITER Knowledge Graph ID: ")
graph_tool = GraphTool(graph_ids=[graph_id])

from langchain_writer.tools import TranslationTool

# Initialize the translation tool
translation_tool = TranslationTool()

from langchain_writer.tools import WebSearchTool

# Initialize the web search tool with optional configuration
web_search_tool = WebSearchTool(
    include_domains=["wikipedia.org", "github.com", "techcrunch.com"],
    exclude_domains=["quora.com"]
)

from langchain.tools import tool
from pydantic import BaseModel, Field


@tool
def get_supercopa_trophies_count(club_name: str) -> int | None:
    """Returns information about supercopa trophies count.

    Args:
        club_name: Club you want to investigate info of supercopa trophies about

    Returns:
        Number of supercopa trophies or None if there is no info about requested club
    """

    if club_name == "Barcelona":
        return 15
    elif club_name == "Real Madrid":
        return 13
    elif club_name == "Atletico Madrid":
        return 2
    else:
        return None


class GetWeather(BaseModel):
    """Get the current weather in a given location"""

    location: str = Field(..., description="The city and state, e.g. San Francisco, CA")


get_product_info = {
    "type": "function",
    "function": {
        "name": "get_product_info",
        "description": "Get information about a product by its id",
        "parameters": {
            "type": "object",
            "properties": {
                "product_id": {
                    "type": "number",
                    "description": "The unique identifier of the product to retrieve information for",
                }
            },
            "required": ["product_id"],
        },
    },
}

# ✅ Correct: One WRITER tool + multiple function tools
llm_with_tools = chat.bind_tools(
    [graph_tool, get_supercopa_trophies_count, GetWeather, get_product_info]
)

# ✅ Correct: Different WRITER tool + function tools
llm_with_tools = chat.bind_tools(
    [translation_tool, get_supercopa_trophies_count, GetWeather]
)

# ❌ Incorrect: Multiple WRITER tools (will cause BadRequestError)
llm_with_tools = chat.bind_tools(
    [graph_tool, translation_tool, web_search_tool]  # This will fail
)

# Use graph tool for one conversation
llm_with_tools = chat.bind_tools([graph_tool, get_supercopa_trophies_count])
response1 = llm_with_tools.invoke([HumanMessage("Use the knowledge graph to answer...")])

# Switch to translation tool for another conversation
llm_with_tools = chat.bind_tools([translation_tool, get_supercopa_trophies_count])
response2 = llm_with_tools.invoke([HumanMessage("Translate this text...")])

# Create separate ChatWriter instances for different tools
chat_with_graph = ChatWriter()
llm_with_graph_tool = chat_with_graph.bind_tools([graph_tool])

chat_with_translation = ChatWriter()
llm_with_translation_tool = chat_with_translation.bind_tools([translation_tool])

from langchain.messages import HumanMessage

# Example with graph tool and function tools
llm_with_tools = chat.bind_tools([graph_tool, get_supercopa_trophies_count])
messages = [
    HumanMessage(
        "Use knowledge graph tool to compose this answer. Tell me what the first line of documents stored in your KG. Also I want to know: how many SuperCopa trophies have Barcelona won?"
    )
]

response = llm_with_tools.invoke(messages)
messages.append(response)

# Example with translation tool
llm_with_translation = chat.bind_tools([translation_tool])
translation_messages = [
    HumanMessage("Translate 'Hello, world!' to Spanish")
]

translation_response = llm_with_translation.invoke(translation_messages)
print(translation_response.content)  # Output: "¡Hola, mundo!"

# Example with web search tool
llm_with_search = chat.bind_tools([web_search_tool])
search_messages = [
    HumanMessage("What are the latest developments in AI technology? Please search the web for current information.")
]

search_response = llm_with_search.invoke(search_messages)
print(search_response.content)  # Output: Current AI developments based on web search

print(response.tool_calls)

for tool_call in response.tool_calls:
    selected_tool = {
        "get_supercopa_trophies_count": get_supercopa_trophies_count,
    }[tool_call["name"].lower()]
    tool_msg = selected_tool.invoke(tool_call)
    messages.append(tool_msg)

response = llm_with_tools.invoke(messages)
print(response.content)

print(response.additional_kwargs["graph_data"])

print(response.content)