docs: update chat openai (#20331)

langchain-ai · Apr 11, 2024 · 562b546 · 562b546
1 parent 2c4741b
commit 562b546
Showing 1 changed file with 136 additions and 46 deletions.
diff --git a/docs/docs/integrations/chat/openai.ipynb b/docs/docs/integrations/chat/openai.ipynb
@@ -22,32 +22,28 @@
  },
  {
  "cell_type": "code",
- "execution_count": 1,
+ "execution_count": 3,
  "id": "522686de",
  "metadata": {
  "tags": []
  },
  "outputs": [],
  "source": [
  "from langchain_core.messages import HumanMessage, SystemMessage\n",
- "from langchain_core.prompts.chat import (\n",
- " ChatPromptTemplate,\n",
- " HumanMessagePromptTemplate,\n",
- " SystemMessagePromptTemplate,\n",
- ")\n",
+ "from langchain_core.prompts import ChatPromptTemplate\n",
  "from langchain_openai import ChatOpenAI"
  ]
  },
  {
  "cell_type": "code",
- "execution_count": 2,
+ "execution_count": 4,
  "id": "62e0dbc3",
  "metadata": {
  "tags": []
  },
  "outputs": [],
  "source": [
- "chat = ChatOpenAI(temperature=0)"
+ "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
  ]
  },
  {
@@ -58,14 +54,14 @@
  "The above cell assumes that your OpenAI API key is set in your environment variables. If you would rather manually specify your API key and/or organization ID, use the following code:\n",
  "\n",
  "```python\n",
- "chat = ChatOpenAI(temperature=0, openai_api_key=\"YOUR_API_KEY\", openai_organization=\"YOUR_ORGANIZATION_ID\")\n",
+ "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0, api_key=\"YOUR_API_KEY\", openai_organization=\"YOUR_ORGANIZATION_ID\")\n",
  "```\n",
  "Remove the openai_organization parameter should it not apply to you."
  ]
  },
  {
  "cell_type": "code",
- "execution_count": 3,
+ "execution_count": 5,
  "id": "ce16ad78-8e6f-48cd-954e-98be75eb5836",
  "metadata": {
  "tags": []
@@ -74,81 +70,175 @@
  {
  "data": {
  "text/plain": [
- "AIMessage(content=\"J'adore la programmation.\", additional_kwargs={}, example=False)"
+ "AIMessage(content=\"J'adore programmer.\", response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 34, 'total_tokens': 40}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': 'fp_b28b39ffa8', 'finish_reason': 'stop', 'logprobs': None}, id='run-8591eae1-b42b-402b-a23a-dfdb0cd151bd-0')"
  ]
  },
- "execution_count": 3,
+ "execution_count": 5,
  "metadata": {},
  "output_type": "execute_result"
  }
  ],
  "source": [
  "messages = [\n",
- " SystemMessage(\n",
- " content=\"You are a helpful assistant that translates English to French.\"\n",
- " ),\n",
- " HumanMessage(\n",
- " content=\"Translate this sentence from English to French. I love programming.\"\n",
- " ),\n",
+ " (\"system\", \"You are a helpful assistant that translates English to French.\"),\n",
+ " (\"human\", \"Translate this sentence from English to French. I love programming.\"),\n",
  "]\n",
- "chat.invoke(messages)"
+ "llm.invoke(messages)"
  ]
  },
  {
  "cell_type": "markdown",
  "id": "778f912a-66ea-4a5d-b3de-6c7db4baba26",
  "metadata": {},
  "source": [
- "You can make use of templating by using a `MessagePromptTemplate`. You can build a `ChatPromptTemplate` from one or more `MessagePromptTemplates`. You can use `ChatPromptTemplate`'s `format_prompt` -- this returns a `PromptValue`, which you can convert to a string or Message object, depending on whether you want to use the formatted value as input to an llm or chat model.\n",
+ "## Chaining\n",
  "\n",
- "For convenience, there is a `from_template` method exposed on the template. If you were to use this template, this is what it would look like:"
+ "We can chain our model with a prompt template like so:"
  ]
  },
  {
  "cell_type": "code",
- "execution_count": 4,
- "id": "180c5cc8",
+ "execution_count": 8,
+ "id": "fbb043e6",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "AIMessage(content='Ich liebe Programmieren.', response_metadata={'token_usage': {'completion_tokens': 5, 'prompt_tokens': 26, 'total_tokens': 31}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': 'fp_b28b39ffa8', 'finish_reason': 'stop', 'logprobs': None}, id='run-94fa6741-c99b-4513-afce-c3f562631c79-0')"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "prompt = ChatPromptTemplate.from_messages(\n",
+ " [\n",
+ " (\n",
+ " \"system\",\n",
+ " \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+ " ),\n",
+ " (\"human\", \"{input}\"),\n",
+ " ]\n",
+ ")\n",
+ "\n",
+ "chain = prompt | llm\n",
+ "chain.invoke(\n",
+ " {\n",
+ " \"input_language\": \"English\",\n",
+ " \"output_language\": \"German\",\n",
+ " \"input\": \"I love programming.\",\n",
+ " }\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0b1b52a5-b58d-40c9-bcdd-88eb8fb351e2",
+ "metadata": {},
+ "source": [
+ "## Tool calling\n",
+ "\n",
+ "OpenAI has a [tool calling](https://platform.openai.com/docs/guides/function-calling) (we use \"tool calling\" and \"function calling\" interchangeably here) API that lets you describe tools and their arguments, and have the model return a JSON object with a tool to invoke and the inputs to that tool. tool-calling is extremely useful for building tool-using chains and agents, and for getting structured outputs from models more generally.\n",
+ "\n",
+ "### ChatOpenAI.bind_tools()\n",
+ "\n",
+ "With `ChatAnthropic.bind_tools`, we can easily pass in Pydantic classes, dict schemas, LangChain tools, or even functions as tools to the model. Under the hood these are converted to an Anthropic tool schemas, which looks like:\n",
+ "```\n",
+ "{\n",
+ " \"name\": \"...\",\n",
+ " \"description\": \"...\",\n",
+ " \"parameters\": {...} # JSONSchema\n",
+ "}\n",
+ "```\n",
+ "and passed in every model invocation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "b7ea7690-ec7a-4337-b392-e87d1f39a6ec",
  "metadata": {},
  "outputs": [],
  "source": [
- "template = (\n",
- " \"You are a helpful assistant that translates {input_language} to {output_language}.\"\n",
- ")\n",
- "system_message_prompt = SystemMessagePromptTemplate.from_template(template)\n",
- "human_template = \"{text}\"\n",
- "human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)"
+ "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+ "\n",
+ "\n",
+ "class GetWeather(BaseModel):\n",
+ " \"\"\"Get the current weather in a given location\"\"\"\n",
+ "\n",
+ " location: str = Field(..., description=\"The city and state, e.g. San Francisco, CA\")\n",
+ "\n",
+ "\n",
+ "llm_with_tools = llm.bind_tools([GetWeather])"
  ]
  },
  {
  "cell_type": "code",
- "execution_count": 5,
- "id": "fbb043e6",
- "metadata": {
- "tags": []
- },
+ "execution_count": 10,
+ "id": "1d1ab955-6a68-42f8-bb5d-86eb1111478a",
+ "metadata": {},
  "outputs": [
  {
  "data": {
  "text/plain": [
- "AIMessage(content=\"J'adore la programmation.\", additional_kwargs={}, example=False)"
+ "AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_H7fABDuzEau48T10Qn0Lsh0D', 'function': {'arguments': '{\"location\":\"San Francisco\"}', 'name': 'GetWeather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 15, 'prompt_tokens': 70, 'total_tokens': 85}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': 'fp_b28b39ffa8', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-b469135e-2718-446a-8164-eef37e672ba2-0', tool_calls=[{'name': 'GetWeather', 'args': {'location': 'San Francisco'}, 'id': 'call_H7fABDuzEau48T10Qn0Lsh0D'}])"
  ]
  },
- "execution_count": 5,
+ "execution_count": 10,
  "metadata": {},
  "output_type": "execute_result"
  }
  ],
  "source": [
- "chat_prompt = ChatPromptTemplate.from_messages(\n",
- " [system_message_prompt, human_message_prompt]\n",
+ "ai_msg = llm_with_tools.invoke(\n",
+ " \"what is the weather like in San Francisco\",\n",
  ")\n",
- "\n",
- "# get a chat completion from the formatted messages\n",
- "chat.invoke(\n",
- " chat_prompt.format_prompt(\n",
- " input_language=\"English\", output_language=\"French\", text=\"I love programming.\"\n",
- " ).to_messages()\n",
- ")"
+ "ai_msg"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "768d1ae4-4b1a-48eb-a329-c8d5051067a3",
+ "metadata": {},
+ "source": [
+ "### AIMessage.tool_calls\n",
+ "Notice that the AIMessage has a `tool_calls` attribute. This contains in a standardized ToolCall format that is model-provider agnostic."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "166cb7ce-831d-4a7c-9721-abc107f11084",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[{'name': 'GetWeather',\n",
+ " 'args': {'location': 'San Francisco'},\n",
+ " 'id': 'call_H7fABDuzEau48T10Qn0Lsh0D'}]"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ai_msg.tool_calls"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e082c9ac-c7c7-4aff-a8ec-8e220262a59c",
+ "metadata": {},
+ "source": [
+ "For more on binding tools and tool call outputs, head to the [tool calling](/docs/modules/model_io/chat/function_calling/) docs."
  ]
  },
  {
@@ -205,7 +295,7 @@
  "name": "python",
  "nbconvert_exporter": "python",
  "pygments_lexer": "ipython3",
- "version": "3.10.5"
+ "version": "3.9.1"
  }
  },
  "nbformat": 4,