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# ! pip install -U cohere
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import cohere
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# instantiating the old client
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co_v1 = cohere.Client(api_key="<YOUR API KEY>")
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# instantiating the new client
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co_v2 = cohere.ClientV2(api_key="<YOUR API KEY>")

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res = co_v1.chat(
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	    model="command-r-plus-08-2024",
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	    preamble="You respond in concise sentences.",
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	    chat_history=[
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	        {
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	            "role": "user",
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	            "message": "Hello"
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	        },
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	        {
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	            "role": "chatbot",
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	            "message": "Hi, how can I help you today?"
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	        }
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	    ],
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	    message="I'm joining a new startup called Co1t today. Could you help me write a one-sentence introduction message to my teammates?")
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print(res.text)

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res = co_v2.chat(
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    model="command-r-plus-08-2024",
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    messages=[
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        {
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            "role": "system",
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            "content": "You respond in concise sentences."
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        },
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        {
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            "role": "user",
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            "content": "Hello"
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        },
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        {
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            "role": "assistant",
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            "content": "Hi, how can I help you today?"
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        },
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        {
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            "role": "user",
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            "content": "I'm joining a new startup called Co1t today. Could you help me write a one-sentence introduction message to my teammates."
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        }
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    ])
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print(res.message.content[0].text)

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res = co_v1.chat(model="command-r-plus-08-2024",
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                 message="What is 2 + 2")
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print(res.text)

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res = co_v2.chat(model="command-r-plus-08-2024",
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                 messages=[
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                     {
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                         "role": "user",
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                         "content": "What is 2 + 2"
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                     }
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                 ])
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print(res.message.content[0].text)

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message = "I'm joining a new startup called Co1t today. Could you help me write a one-sentence introduction message to my teammates."
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res = co_v1.chat_stream(model="command-r-plus-08-2024",
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                        message=message)
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for chunk in res:
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    if chunk.event_type == "text-generation":
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        print(chunk.text, end="")

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message = "I'm joining a new startup called Co1t today. Could you help me write a one-sentence introduction message to my teammates."
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res = co_v2.chat_stream(model="command-r-plus-08-2024",
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                        messages=[{"role": "user", "content": message}])
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for chunk in res: 
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    if chunk:
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        if chunk.type == "content-delta":
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            print(chunk.delta.message.content.text, end="")

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# Define the documents
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documents_v1 = [
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    {"text": "Reimbursing Travel Expenses: Easily manage your travel expenses by submitting them through our finance tool. Approvals are prompt and straightforward."},
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    {"text": "Health and Wellness Benefits: We care about your well-being and offer gym memberships, on-site yoga classes, and comprehensive health insurance."}
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]
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# The user query
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message = "Are there fitness-related benefits?"
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# Generate the response
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res_v1 = co_v1.chat(model="command-r-plus-08-2024",
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                    message=message,
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                    documents=documents_v1)
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print(res_v1.text)

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# Define the documents
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documents_v2 = [
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  {
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    "data": {
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      "text": "Reimbursing Travel Expenses: Easily manage your travel expenses by submitting them through our finance tool. Approvals are prompt and straightforward."
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    }
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  },
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  {
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    "data": {
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      "text": "Health and Wellness Benefits: We care about your well-being and offer gym memberships, on-site yoga classes, and comprehensive health insurance."
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    }
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  }
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]
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# The user query
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message = "Are there fitness-related benefits?"
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# Generate the response
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res_v2 = co_v2.chat(model="command-r-plus-08-2024",
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                    messages=[{"role": "user", "content": message}],
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                    documents=documents_v2)
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print(res_v2.message.content[0].text)

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documents_v2 = [
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# List of objects with data string
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{
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	"id": "123"
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	"data": "I love penguins. they are fluffy",
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},
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# List of objects with data object
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{"id": "456", "data": {
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	"text": "I love penguins. they are fluffy",
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	"author": "Abdullah",
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	"create_date": "09021989"
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	}
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},
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# List of strings
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"just a string"
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]

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# Yes, there are fitness-related benefits. We offer gym memberships, on-site yoga classes, and comprehensive health insurance.
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print(res_v1.citations)
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print(res_v1.documents)

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# Yes, we offer gym memberships, on-site yoga classes, and comprehensive health insurance.
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print(res_v2.message.citations)

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res_v1 = co_v1.chat(
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	message="who won euro 2024",
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	connectors=[{"id": "web-search"}],
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)
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print(res_v1.text)

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# Any search engine can be used. This example uses the Tavily API.
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from tavily import TavilyClient
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tavily_client = TavilyClient(api_key=os.environ["TAVILY_API_KEY"])
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# Create a web search function
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def web_search(queries: list[str]) -> list[dict]:
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    documents = []
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    for query in queries:
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        response = tavily_client.search(query, max_results=2)
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        results = [
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            {"title": r["title"], "content": r["content"], "url": r["url"]}
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            for r in response["results"]
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        ]
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        for idx, result in enumerate(results):
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            document = {"id": str(idx), "data": result}
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            documents.append(document)
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    return {"documents": documents}
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# Define the web search tool
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web_search_tool = [
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    {
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        "type": "function",
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        "function": {
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            "name": "web_search",
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            "description": "Returns a list of relevant document snippets for a textual query retrieved from the internet",
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            "parameters": {
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                "type": "object",
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                "properties": {
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                    "queries": {
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                        "type": "array",
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                        "items": {"type": "string"},
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                        "description": "a list of queries to search the internet with.",
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                    }
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                },
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                "required": ["queries"],
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            },
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        },
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    }
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]
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# The user query
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query = "who won euro 2024"
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# Define a preamble to optimize search query generation
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instructions = "Write a search query that will find helpful information for answering the user's question accurately. If you need more than one search query, write a list of search queries. If you decide that a search is very unlikely to find information that would be useful in constructing a response to the user, you should instead directly answer."
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messages = [
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    {"role": "system", "content": instructions},
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    {"role": "user", "content": query},
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]
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model = "command-r-plus-08-2024"
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# Generate search queries (if any)
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response = co_v2.chat(model=model, messages=messages, tools=web_search_tool)
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search_queries = []
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while response.message.tool_calls:
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    print("Tool plan:")
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    print(response.message.tool_plan, "\n")
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    print("Tool calls:")
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    for tc in response.message.tool_calls:
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        print(f"Tool name: {tc.function.name} | Parameters: {tc.function.arguments}")
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    print("=" * 50)
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    messages.append(
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        {
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            "role": "assistant",
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            "tool_calls": response.message.tool_calls,
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            "tool_plan": response.message.tool_plan,
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        }
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    )
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    # Step 3: Get tool results
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    tool_content = []
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    for idx, tc in enumerate(response.message.tool_calls):
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        tool_result = web_search(**json.loads(tc.function.arguments))
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        tool_content.append(json.dumps(tool_result))
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        messages.append(
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            {"role": "tool", "tool_call_id": tc.id, "content": tool_content}
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        )
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    # Step 4: Generate response and citations
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    response = co_v2.chat(model=model, messages=messages, tools=web_search_tool)
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print(response.message.content[0].text)

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message = "Are there fitness-related benefits?"
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res_v1 = co_v1.chat_stream(model="command-r-plus-08-2024",
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                           message=message,
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                           documents=documents_v1)
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for chunk in res_v1:
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    if chunk.event_type == "text-generation":
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        print(chunk.text, end="")
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    if chunk.event_type == "citation-generation":
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        print(f"\n{chunk.citations}")

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message = "Are there fitness-related benefits?"
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messages = [{"role": "user", "content": message}]
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res_v2 = co_v2.chat_stream(
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    model="command-r-plus-08-2024", messages=messages, documents=documents_v2
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)
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for chunk in res_v2:
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    if chunk:
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        if chunk.type == "content-delta":
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            print(chunk.delta.message.content.text, end="")
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        if chunk.type == "citation-start":
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            print(f"\n{chunk.delta.message.citations}")

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def get_weather(location):
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    return {"temperature": "20C"}
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functions_map = {"get_weather": get_weather}
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tools_v1 = [
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    {
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      "name": "get_weather",
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      "description": "Gets the weather of a given location",
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      "parameter_definitions": {
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        "location": {
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          "description": "The location to get weather, example: San Francisco, CA",
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          "type": "str",
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          "required": True
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        }
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      }
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    },
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]

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def get_weather(location):
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    return {"temperature": "20C"}
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functions_map = {"get_weather": get_weather}
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tools_v2 = [
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    {
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    "type": "function",
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    "function": {
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            "name": "get_weather",
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            "description" : "gets the weather of a given location",
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            "parameters": {
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                "type": "object",
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                "properties": {
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                "location": {
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                    "type" : "str",
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                    "description": "the location to get weather, example: San Fransisco, CA"
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                }
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                },
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                "required": ["location"]
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            }
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            }
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    },
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]

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message = "What's the weather in Toronto?"
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res_v1 = co_v1.chat(model="command-r-plus-08-2024",
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                    message=message,
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                    tools=tools_v1)
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print(res_v1.tool_calls)

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messages = [{"role": "user", "content": "What's the weather in Toronto?"}]
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res_v2 = co_v2.chat(model="command-r-plus-08-2024", messages=messages, tools=tools_v2)
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if res_v2.message.tool_calls:
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    messages.append(
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        {
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            "role": "assistant",
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            "tool_calls": res_v2.message.tool_calls,
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            "tool_plan": res_v2.message.tool_plan,
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        }
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    )
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    print(res_v2.message.tool_calls)

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tool_results = [
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  {
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    "call": {
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      "name": "<tool name>",
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      "parameters": {
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        "<param name>": "<param value>"
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      }
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    },
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    "outputs": [{
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      "<key>": "<value>"
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    }]
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  },
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]

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messages = [
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  {
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    "role": "tool", 
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    "tool_call_id": "123", 
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    "content": [
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      {
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        "type": "document", 
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        "document": {
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          "id": "123", 
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          "data": {
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            "<key>": "<value>"
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          }
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        }
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      }
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    ]
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  }
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]

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tool_content_v1 = []
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if res_v1.tool_calls:
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    for tc in res_v1.tool_calls:
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        tool_call = {"name": tc.name, "parameters": tc.parameters}
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        tool_result = functions_map[tc.name](**tc.parameters)
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        tool_content_v1.append({"call": tool_call, "outputs": [tool_result]})
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res_v1 = co_v1.chat(
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    model="command-r-plus-08-2024",
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    message="",
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    tools=tools_v1,
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    tool_results=tool_content_v1,   
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    chat_history=res_v1.chat_history
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)
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print(res_v1.text)

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tool_content_v2 = []
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if res_v2.message.tool_calls:
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    for tc in res_v2.message.tool_calls:
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        tool_result = functions_map[tc.function.name](
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            **json.loads(tc.function.arguments)
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        )
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        tool_content_v2.append(json.dumps(tool_result))
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        messages.append(
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            {"role": "tool", "tool_call_id": tc.id, "content": tool_content_v2}
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        )
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res_v2 = co_v2.chat(
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    model="command-r-plus-08-2024",
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    messages=messages,
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    tools=tools_v2
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)
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print(res_v2.message.content[0].text)

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print(res_v1.citations)
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print(res_v1.documents)

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print(res_v2.message.citations)

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tool_content_v1 = []
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if res_v1.tool_calls:
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    for tc in res_v1.tool_calls:
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        tool_call = {"name": tc.name, "parameters": tc.parameters}
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        tool_result = functions_map[tc.name](**tc.parameters)
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        tool_content_v1.append({"call": tool_call, "outputs": [tool_result]})
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res_v1 = co_v1.chat_stream(
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    message="",
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    tools=tools_v1,
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    tool_results=tool_content_v1,
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    chat_history=res_v1.chat_history
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)
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for chunk in res_v1:
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    if chunk.event_type == "text-generation":
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        print(chunk.text, end="")
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    if chunk.event_type == "citation-generation":
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        print(f"\n{chunk.citations}")

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tool_content_v2 = []
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if res_v2.message.tool_calls:
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    for tc in res_v2.message.tool_calls:
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        tool_result = functions_map[tc.function.name](
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            **json.loads(tc.function.arguments)
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        )
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        tool_content_v2.append(json.dumps(tool_result))
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        messages.append(
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            {"role": "tool", "tool_call_id": tc.id, "content": tool_content_v2}
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        )
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res_v2 = co_v2.chat_stream(
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    model="command-r-plus-08-2024",
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    messages=messages,
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    tools=tools_v2
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)
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for chunk in res_v2:
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    if chunk:
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        if chunk.type == "content-delta":
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            print(chunk.delta.message.content.text, end="")
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        elif chunk.type == "citation-start":
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            print(f"\n{chunk.delta.message.citations}")