Cohere 集成

from langchain_cohere import ChatCohere
from langchain.messages import HumanMessage
chat = ChatCohere()
messages = [HumanMessage(content="knock knock")]
print(chat.invoke(messages))

from langchain_cohere.llms import Cohere

llm = Cohere()
print(llm.invoke("Come up with a pet name"))

from langchain_cohere import ChatCohere
from langchain.messages import (
    HumanMessage,
    ToolMessage,
)
from langchain.tools import tool

@tool
def magic_function(number: int) -> int:
    """对整数应用一个魔法操作

    Args:
        number: 要执行魔法操作的数字
    """
    return number + 10

def invoke_tools(tool_calls, messages):
    for tool_call in tool_calls:
        selected_tool = {"magic_function":magic_function}[
            tool_call["name"].lower()
        ]
        tool_output = selected_tool.invoke(tool_call["args"])
        messages.append(ToolMessage(tool_output, tool_call_id=tool_call["id"]))
    return messages

tools = [magic_function]

llm = ChatCohere()
llm_with_tools = llm.bind_tools(tools=tools)
messages = [
    HumanMessage(
        content="What is the value of magic_function(2)?"
    )
]

res = llm_with_tools.invoke(messages)
while res.tool_calls:
    messages.append(res)
    messages = invoke_tools(res.tool_calls, messages)
    res = llm_with_tools.invoke(messages)

print(res.content)

from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_cohere import ChatCohere, create_cohere_react_agent
from langchain_core.prompts import ChatPromptTemplate
from langchain.agents import AgentExecutor

llm = ChatCohere()

internet_search = TavilySearchResults(max_results=4)
internet_search.name = "internet_search"
internet_search.description = "Route a user query to the internet"

prompt = ChatPromptTemplate.from_template("{input}")

agent = create_cohere_react_agent(
    llm,
    [internet_search],
    prompt
)

agent_executor = AgentExecutor(agent=agent, tools=[internet_search], verbose=True)

agent_executor.invoke({
    "input": "In what year was the company that was founded as Sound of Music added to the S&P 500?",
})

from langchain_cohere import ChatCohere
from langchain_classic.retrievers import CohereRagRetriever
from langchain_core.documents import Document

rag = CohereRagRetriever(llm=ChatCohere())
print(rag.invoke("What is cohere ai?"))

from langchain_cohere import CohereEmbeddings

embeddings = CohereEmbeddings(model="embed-english-light-v3.0")
print(embeddings.embed_documents(["This is a test document."]))