Cohere Chat on LangChain (Integration Guide)

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from langchain_cohere import ChatCohere
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from langchain_core.messages import AIMessage, HumanMessage
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# Define the Cohere LLM
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llm = ChatCohere(
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    cohere_api_key="COHERE_API_KEY", model="command-r-plus-08-2024"
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)
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# Send a chat message without chat history
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current_message = [HumanMessage(content="knock knock")]
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print(llm.invoke(current_message))
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# Send a chat message with chat history, note the last message is the current user message
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current_message_and_history = [
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    HumanMessage(content="knock knock"),
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    AIMessage(content="Who's there?"),
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    HumanMessage(content="Tank"),
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]
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print(llm.invoke(current_message_and_history))

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from langchain_cohere import ChatCohere
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from langchain_cohere.react_multi_hop.agent import (
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    create_cohere_react_agent,
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)
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from langchain.agents import AgentExecutor
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from langchain_community.tools.tavily_search import (
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    TavilySearchResults,
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)
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from langchain_core.prompts import ChatPromptTemplate
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# Internet search tool - you can use any tool, and there are lots of community tools in LangChain.
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# To use the Tavily tool you will need to set an API key in the TAVILY_API_KEY environment variable.
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os.environ["TAVILY_API_KEY"] = "TAVILY_API_KEY"
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internet_search = TavilySearchResults()
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# Define the Cohere LLM
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llm = ChatCohere(
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    cohere_api_key="COHERE_API_KEY", model="command-r-plus-08-2024"
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)
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# Create an agent
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agent = create_cohere_react_agent(
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    llm=llm,
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    tools=[internet_search],
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    prompt=ChatPromptTemplate.from_template("{question}"),
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)
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# Create an agent executor
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agent_executor = AgentExecutor(
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    agent=agent, tools=[internet_search], verbose=True
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)
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# Generate a response
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response = agent_executor.invoke(
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    {
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        "question": "I want to write an essay. Any tips?",
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    }
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)
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# See Cohere's response
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print(response.get("output"))
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# Cohere provides exact citations for the sources it used
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print(response.get("citations"))

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from langchain_cohere import CohereRagRetriever
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from langchain.retrievers import WikipediaRetriever
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from langchain_cohere import ChatCohere
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# User query we will use for the generation
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user_query = "What is cohere?"
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# Define the Cohere LLM
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llm = ChatCohere(
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    cohere_api_key="COHERE_API_KEY", model="command-r-plus-08-2024"
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)
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# Create the Cohere rag retriever using the chat model
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rag = CohereRagRetriever(llm=llm, connectors=[])
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# Create the wikipedia retriever
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wiki_retriever = WikipediaRetriever()
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# Get the relevant documents from wikipedia
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wiki_docs = wiki_retriever.invoke(user_query)
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# Get the cohere generation from the cohere rag retriever
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docs = rag.invoke(user_query, documents=wiki_docs)
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# Print the documents
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print("Documents:")
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for doc in docs[:-1]:
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    print(doc.metadata)
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    print("\n\n" + doc.page_content)
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    print("\n\n" + "-" * 30 + "\n\n")
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# Print the final generation
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answer = docs[-1].page_content
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print("Answer:")
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print(answer)
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# Print the final citations
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citations = docs[-1].metadata["citations"]
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print("Citations:")
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print(docs[-1].__dict__)

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from langchain_cohere import CohereRagRetriever
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from langchain_cohere import ChatCohere
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from langchain_core.documents import Document
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# Define the Cohere LLM
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llm = ChatCohere(
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    cohere_api_key="COHERE_API_KEY", model="command-r-plus-08-2024"
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)
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# Create the Cohere rag retriever using the chat model
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rag = CohereRagRetriever(llm=llm, connectors=[])
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docs = rag.invoke(
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    "Does LangChain support cohere RAG?",
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    documents=[
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        Document(
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            page_content="LangChain supports cohere RAG!",
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            metadata={"id": "id-1"},
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        ),
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        Document(
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            page_content="The sky is blue!", metadata={"id": "id-2"}
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        ),
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    ],
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)
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# Print the documents
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print("Documents:")
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for doc in docs[:-1]:
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    print(doc.metadata)
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    print("\n\n" + doc.page_content)
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    print("\n\n" + "-" * 30 + "\n\n")
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# Print the final generation
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answer = docs[-1].page_content
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print("Answer:")
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print(answer)
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# Print the final citations
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citations = docs[-1].metadata["citations"]
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print("Citations:")
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print(citations)

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from langchain_cohere import CohereRagRetriever
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from langchain_cohere import ChatCohere
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from langchain_core.documents import Document
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# Define the Cohere LLM
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llm = ChatCohere(
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    cohere_api_key="COHERE_API_KEY", model="command-r-plus-08-2024"
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)
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# Create the Cohere rag retriever using the chat model with the web search connector
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rag = CohereRagRetriever(llm=llm, connectors=[{"id": "web-search"}])
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docs = rag.invoke("Who founded Cohere?")
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# Print the documents
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print("Documents:")
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for doc in docs[:-1]:
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    print(doc.metadata)
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    print("\n\n" + doc.page_content)
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    print("\n\n" + "-" * 30 + "\n\n")
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# Print the final generation
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answer = docs[-1].page_content
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print("Answer:")
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print(answer)
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# Print the final citations
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citations = docs[-1].metadata["citations"]
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print("Citations:")
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print(citations)

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from langchain_cohere import ChatCohere
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from langchain_core.documents import Document
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from langchain_core.prompts import ChatPromptTemplate
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from langchain.chains.combine_documents import (
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    create_stuff_documents_chain,
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)
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prompt = ChatPromptTemplate.from_messages(
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    [("human", "What are everyone's favorite colors:\n\n{context}")]
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)
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# Define the Cohere LLM
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llm = ChatCohere(
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    cohere_api_key="COHERE_API_KEY", model="command-r-plus-08-2024"
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)
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chain = create_stuff_documents_chain(llm, prompt)
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docs = [
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    Document(page_content="Jesse loves red but not yellow"),
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    Document(
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        page_content="Jamal loves green but not as much as he loves orange"
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    ),
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]
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chain.invoke({"context": docs})

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from langchain_cohere import ChatCohere
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# Define the Cohere LLM
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llm = ChatCohere(
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    cohere_api_key="COHERE_API_KEY", model="command-r-plus-08-2024"
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)
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res = llm.invoke(
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    "John is five years old",
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    response_format={
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        "type": "json_object",
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        "schema": {
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            "title": "Person",
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            "description": "Identifies the age and name of a person",
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            "type": "object",
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            "properties": {
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                "name": {
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                    "type": "string",
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                    "description": "Name of the person",
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                },
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                "age": {
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                    "type": "number",
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                    "description": "Age of the person",
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                },
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            },
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            "required": [
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                "name",
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                "age",
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            ],
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        },
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    },
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)
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print(res)

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from langchain_cohere import ChatCohere
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from langchain.chains.summarize import load_summarize_chain
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from langchain_community.document_loaders import WebBaseLoader
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loader = WebBaseLoader("https://docs.cohere.com/docs/cohere-toolkit")
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docs = loader.load()
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# Define the Cohere LLM
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llm = ChatCohere(
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    cohere_api_key="COHERE_API_KEY",
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    model="command-r-plus-08-2024",
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    temperature=0,
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)
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chain = load_summarize_chain(llm, chain_type="stuff")
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chain.invoke({"input_documents": docs})

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llm = ChatCohere(
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    base_url="<YOUR_DEPLOYMENT_URL>",
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    cohere_api_key="COHERE_API_KEY",
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    model="MODEL_NAME",
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)