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-a-03-2025"
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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_core.messages import 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",
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    model="command-a-reasoning-08-2025",
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)
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response = llm.invoke(
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    [
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        HumanMessage(
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            content="Alice has 3 brothers and 2 sisters. How many sisters does Alice's brother have?"
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        )
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    ]
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)
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# The reasoning model returns its content as a list of blocks: a "reasoning"
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# block (the model's reasoning) followed by a "text" block (the final answer).
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for block in response.content:
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    if block["type"] == "reasoning":
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        for step in block["summary"]:
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            print("Reasoning:", step["text"])
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    elif block["type"] == "text":
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        print("Answer:", block["text"])

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from langchain_cohere import ChatCohere
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from langchain_core.messages import 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",
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    model="command-a-vision-07-2025",
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)
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# Use a publicly accessible image URL (a base64 data URI also works)
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image_url = "https://raw.githubusercontent.com/cohere-ai/cohere-developer-experience/main/fern/assets/images/waste-management-request.png"
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# Pass the image alongside a text prompt
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message = HumanMessage(
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    content=[
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        {"type": "text", "text": "What is shown in this image?"},
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        {"type": "image_url", "image_url": {"url": image_url}},
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    ]
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)
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print(llm.invoke([message]).content)

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import os
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from langchain.agents import create_agent
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from langchain_cohere import ChatCohere
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from langchain_tavily import TavilySearch
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# Internet search tool. Replace the placeholder with your Tavily API key.
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os.environ["TAVILY_API_KEY"] = "TAVILY_API_KEY"
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internet_search = TavilySearch()
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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-a-03-2025"
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)
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# Create an agent with the internet search tool
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agent = create_agent(llm, tools=[internet_search])
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# Run the agent
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result = agent.invoke(
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    {"messages": [("user", "I want to write an essay. Any tips?")]}
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)
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# See Cohere's response
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print(result["messages"][-1].content)

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import wikipedia
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from langchain_cohere import ChatCohere
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from langchain_community.retrievers import WikipediaRetriever
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# Wikipedia requires a descriptive User-Agent; set one before querying.
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wikipedia.set_user_agent("my-app/1.0 (you@example.com)")
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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-a-03-2025"
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)
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# Retrieve documents with any LangChain retriever
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wiki_retriever = WikipediaRetriever()
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wiki_docs = wiki_retriever.invoke(user_query)
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# Ground the answer in the retrieved documents
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response = llm.invoke(user_query, documents=wiki_docs)
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# Print the answer
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print("Answer:")
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print(response.content)
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# Print the citations that ground the answer in the documents
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print("Citations:")
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print(response.additional_kwargs.get("citations"))

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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-a-03-2025"
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)
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# Supply your own documents (these might come from elsewhere in your application)
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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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# Ground the answer in the documents
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response = llm.invoke(
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    "Does LangChain support Cohere RAG?", documents=documents
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)
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# Print the answer
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print("Answer:")
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print(response.content)
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# Print the citations that ground the answer in the documents
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print("Citations:")
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print(response.additional_kwargs.get("citations"))

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import os
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from langchain_cohere import ChatCohere
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from langchain_core.messages import HumanMessage, ToolMessage
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from langchain_tavily import TavilySearch
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# Web search tool. Replace the placeholder with your Tavily API key.
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os.environ["TAVILY_API_KEY"] = "TAVILY_API_KEY"
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web_search = TavilySearch()
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# Define the Cohere LLM and bind the search tool
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llm = ChatCohere(
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    cohere_api_key="COHERE_API_KEY", model="command-a-03-2025"
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)
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llm_with_tools = llm.bind_tools([web_search])
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# 1. Force a search on the first turn so the answer is grounded
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messages = [HumanMessage("Who founded Cohere?")]
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ai_message = llm_with_tools.invoke(messages, tool_choice="REQUIRED")
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messages.append(ai_message)
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# 2. Run the search and pass the results back to the model
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for tool_call in ai_message.tool_calls:
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    results = web_search.invoke(tool_call["args"])
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    messages.append(
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        ToolMessage(
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            content=str(results), tool_call_id=tool_call["id"]
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        )
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    )
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# 3. The model answers, grounded in the search results
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final = llm_with_tools.invoke(messages)
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print("Answer:", final.content)
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# Cohere returns citations that ground the answer in the search results
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# (`.get` avoids a KeyError if the answer came back without any citations)
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print("Citations:", final.additional_kwargs.get("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_classic.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-a-03-2025"
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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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res = chain.invoke({"context": docs})
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print(res)

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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-a-03-2025"
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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_classic.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-a-03-2025",
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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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result = chain.invoke({"input_documents": docs})
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print(result["output_text"])

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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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)