Cohere

Cohere 是一家加拿大初创公司，提供自然语言处理模型，帮助公司改善人机交互。

安装和设置

• 安装 Python SDK：

pip install langchain-cohere

获取Cohere API密钥，并将其设置为环境变量 (COHERE_API_KEY)

Cohere langchain 集成

API	描述	端点文档	导入	示例用法
Chat	构建聊天机器人	chat	from langchain_cohere import ChatCohere	cohere.ipynb
LLM	生成文本	generate	from langchain_cohere.llms import Cohere	cohere.ipynb
RAG Retriever	连接到外部数据源	chat + rag	from langchain.retrievers import CohereRagRetriever	cohere.ipynb
Text Embedding	将字符串嵌入向量中	embed	from langchain_cohere import CohereEmbeddings	cohere.ipynb
Rerank Retriever	根据相关性对字符串进行排名	rerank	from langchain.retrievers.document_compressors import CohereRerank	cohere.ipynb

快速复制示例

Chat

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

使用Cohere chat 模型

LLM

from langchain_cohere.llms import Cohere
llm = Cohere()
print(llm.invoke("Come up with a pet name"))

使用Cohere（旧版）LLM 模型

ReAct Agent

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 = "将用户查询路由到互联网"
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?",
})

RAG Retriever

from langchain_cohere import ChatCohere
from langchain.retrievers import CohereRagRetriever
from langchain_core.documents import Document
rag = CohereRagRetriever(llm=ChatCohere())
print(rag.invoke("What is cohere ai?"))

使用Cohere RAG Retriever

Text Embedding

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

使用Cohere Text Embeddings 模型

Reranker

使用Cohere Reranker