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mv module integrations docs (#8101)

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docs/extras/integrations/chat/anthropic.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"id": "bf733a38-db84-4363-89e2-de6735c37230",
"metadata": {},
"source": [
"# Anthropic\n",
"\n",
"This notebook covers how to get started with Anthropic chat models."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.chat_models import ChatAnthropic\n",
"from langchain.prompts.chat import (\n",
" ChatPromptTemplate,\n",
" SystemMessagePromptTemplate,\n",
" AIMessagePromptTemplate,\n",
" HumanMessagePromptTemplate,\n",
")\n",
"from langchain.schema import AIMessage, HumanMessage, SystemMessage"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "70cf04e8-423a-4ff6-8b09-f11fb711c817",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"chat = ChatAnthropic()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "8199ef8f-eb8b-4253-9ea0-6c24a013ca4c",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content=\" J'aime la programmation.\", additional_kwargs={}, example=False)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"messages = [\n",
" HumanMessage(\n",
" content=\"Translate this sentence from English to French. I love programming.\"\n",
" )\n",
"]\n",
"chat(messages)"
]
},
{
"cell_type": "markdown",
"id": "c361ab1e-8c0c-4206-9e3c-9d1424a12b9c",
"metadata": {},
"source": [
"## `ChatAnthropic` also supports async and streaming functionality:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "93a21c5c-6ef9-4688-be60-b2e1f94842fb",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.callbacks.manager import CallbackManager\n",
"from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "c5fac0e9-05a4-4fc1-a3b3-e5bbb24b971b",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"LLMResult(generations=[[ChatGeneration(text=\" J'aime programmer.\", generation_info=None, message=AIMessage(content=\" J'aime programmer.\", additional_kwargs={}, example=False))]], llm_output={}, run=[RunInfo(run_id=UUID('8cc8fb68-1c35-439c-96a0-695036a93652'))])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"await chat.agenerate([messages])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "025be980-e50d-4a68-93dc-c9c7b500ce34",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" J'aime la programmation."
]
},
{
"data": {
"text/plain": [
"AIMessage(content=\" J'aime la programmation.\", additional_kwargs={}, example=False)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chat = ChatAnthropic(\n",
" streaming=True,\n",
" verbose=True,\n",
" callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),\n",
")\n",
"chat(messages)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c253883f",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
docs/extras/integrations/chat/azure_chat_openai.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"id": "38f26d7a",
"metadata": {},
"source": [
"# Azure\n",
"\n",
"This notebook goes over how to connect to an Azure hosted OpenAI endpoint"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "96164b42",
"metadata": {},
"outputs": [],
"source": [
"from langchain.chat_models import AzureChatOpenAI\n",
"from langchain.schema import HumanMessage"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "8161278f",
"metadata": {},
"outputs": [],
"source": [
"BASE_URL = \"https://${TODO}.openai.azure.com\"\n",
"API_KEY = \"...\"\n",
"DEPLOYMENT_NAME = \"chat\"\n",
"model = AzureChatOpenAI(\n",
" openai_api_base=BASE_URL,\n",
" openai_api_version=\"2023-05-15\",\n",
" deployment_name=DEPLOYMENT_NAME,\n",
" openai_api_key=API_KEY,\n",
" openai_api_type=\"azure\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "99509140",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content=\"\\n\\nJ'aime programmer.\", additional_kwargs={})"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model(\n",
" [\n",
" HumanMessage(\n",
" content=\"Translate this sentence from English to French. I love programming.\"\n",
" )\n",
" ]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3b6e9376",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
docs/extras/integrations/chat/google_vertex_ai_palm.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Google Cloud Platform Vertex AI PaLM \n",
"\n",
"Note: This is seperate from the Google PaLM integration. Google has chosen to offer an enterprise version of PaLM through GCP, and this supports the models made available through there. \n",
"\n",
"PaLM API on Vertex AI is a Preview offering, subject to the Pre-GA Offerings Terms of the [GCP Service Specific Terms](https://cloud.google.com/terms/service-terms). \n",
"\n",
"Pre-GA products and features may have limited support, and changes to pre-GA products and features may not be compatible with other pre-GA versions. For more information, see the [launch stage descriptions](https://cloud.google.com/products#product-launch-stages). Further, by using PaLM API on Vertex AI, you agree to the Generative AI Preview [terms and conditions](https://cloud.google.com/trustedtester/aitos) (Preview Terms).\n",
"\n",
"For PaLM API on Vertex AI, you can process personal data as outlined in the Cloud Data Processing Addendum, subject to applicable restrictions and obligations in the Agreement (as defined in the Preview Terms).\n",
"\n",
"To use Vertex AI PaLM you must have the `google-cloud-aiplatform` Python package installed and either:\n",
"- Have credentials configured for your environment (gcloud, workload identity, etc...)\n",
"- Store the path to a service account JSON file as the GOOGLE_APPLICATION_CREDENTIALS environment variable\n",
"\n",
"This codebase uses the `google.auth` library which first looks for the application credentials variable mentioned above, and then looks for system-level auth.\n",
"\n",
"For more information, see: \n",
"- https://cloud.google.com/docs/authentication/application-default-credentials#GAC\n",
"- https://googleapis.dev/python/google-auth/latest/reference/google.auth.html#module-google.auth\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"#!pip install google-cloud-aiplatform"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from langchain.chat_models import ChatVertexAI\n",
"from langchain.prompts.chat import (\n",
" ChatPromptTemplate,\n",
" SystemMessagePromptTemplate,\n",
" HumanMessagePromptTemplate,\n",
")\n",
"from langchain.schema import HumanMessage, SystemMessage"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"chat = ChatVertexAI()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='Sure, here is the translation of the sentence \"I love programming\" from English to French:\\n\\nJ\\'aime programmer.', additional_kwargs={}, example=False)"
]
},
"execution_count": 4,
"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",
"]\n",
"chat(messages)"
]
},
{
"cell_type": "markdown",
"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",
"\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:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"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)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='Sure, here is the translation of \"I love programming\" in French:\\n\\nJ\\'aime programmer.', additional_kwargs={}, example=False)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chat_prompt = ChatPromptTemplate.from_messages(\n",
" [system_message_prompt, human_message_prompt]\n",
")\n",
"\n",
"# get a chat completion from the formatted messages\n",
"chat(\n",
" chat_prompt.format_prompt(\n",
" input_language=\"English\", output_language=\"French\", text=\"I love programming.\"\n",
" ).to_messages()\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"execution": {
"iopub.execute_input": "2023-06-17T21:09:25.423568Z",
"iopub.status.busy": "2023-06-17T21:09:25.423213Z",
"iopub.status.idle": "2023-06-17T21:09:25.429641Z",
"shell.execute_reply": "2023-06-17T21:09:25.429060Z",
"shell.execute_reply.started": "2023-06-17T21:09:25.423546Z"
},
"tags": []
},
"source": [
"You can now leverage the Codey API for code chat within Vertex AI. The model name is:\n",
"- codechat-bison: for code assistance"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2023-06-17T21:30:43.974841Z",
"iopub.status.busy": "2023-06-17T21:30:43.974431Z",
"iopub.status.idle": "2023-06-17T21:30:44.248119Z",
"shell.execute_reply": "2023-06-17T21:30:44.247362Z",
"shell.execute_reply.started": "2023-06-17T21:30:43.974820Z"
},
"tags": []
},
"outputs": [],
"source": [
"chat = ChatVertexAI(model_name=\"codechat-bison\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
"iopub.execute_input": "2023-06-17T21:30:45.146093Z",
"iopub.status.busy": "2023-06-17T21:30:45.145752Z",
"iopub.status.idle": "2023-06-17T21:30:47.449126Z",
"shell.execute_reply": "2023-06-17T21:30:47.448609Z",
"shell.execute_reply.started": "2023-06-17T21:30:45.146069Z"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='The following Python function can be used to identify all prime numbers up to a given integer:\\n\\n```\\ndef is_prime(n):\\n \"\"\"\\n Determines whether the given integer is prime.\\n\\n Args:\\n n: The integer to be tested for primality.\\n\\n Returns:\\n True if n is prime, False otherwise.\\n \"\"\"\\n\\n # Check if n is divisible by 2.\\n if n % 2 == 0:\\n return False\\n\\n # Check if n is divisible by any integer from 3 to the square root', additional_kwargs={}, example=False)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"messages = [\n",
" HumanMessage(\n",
" content=\"How do I create a python function to identify all prime numbers?\"\n",
" )\n",
"]\n",
"chat(messages)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
},
"vscode": {
"interpreter": {
"hash": "cc99336516f23363341912c6723b01ace86f02e26b4290be1efc0677e2e2ec24"
}
}
},
"nbformat": 4,
"nbformat_minor": 4
}
docs/extras/integrations/chat/index.mdx
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---
sidebar_position: 0
---
# Chat models
import DocCardList from "@theme/DocCardList";
<DocCardList />
docs/extras/integrations/chat/jinachat.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"id": "e49f1e0d",
"metadata": {},
"source": [
"# JinaChat\n",
"\n",
"This notebook covers how to get started with JinaChat chat models."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "522686de",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.chat_models import JinaChat\n",
"from langchain.prompts.chat import (\n",
" ChatPromptTemplate,\n",
" SystemMessagePromptTemplate,\n",
" AIMessagePromptTemplate,\n",
" HumanMessagePromptTemplate,\n",
")\n",
"from langchain.schema import AIMessage, HumanMessage, SystemMessage"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "62e0dbc3",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"chat = JinaChat(temperature=0)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "ce16ad78-8e6f-48cd-954e-98be75eb5836",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content=\"J'aime programmer.\", additional_kwargs={}, example=False)"
]
},
"execution_count": 10,
"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",
"]\n",
"chat(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",
"\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:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "180c5cc8",
"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)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "fbb043e6",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content=\"J'aime programmer.\", additional_kwargs={}, example=False)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chat_prompt = ChatPromptTemplate.from_messages(\n",
" [system_message_prompt, human_message_prompt]\n",
")\n",
"\n",
"# get a chat completion from the formatted messages\n",
"chat(\n",
" chat_prompt.format_prompt(\n",
" input_language=\"English\", output_language=\"French\", text=\"I love programming.\"\n",
" ).to_messages()\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c095285d",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
docs/extras/integrations/chat/llama_api.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"id": "90a1faf2",
"metadata": {},
"source": [
"# Llama API\n",
"\n",
"This notebook shows how to use LangChain with [LlamaAPI](https://llama-api.com/) - a hosted version of Llama2 that adds in support for function calling."
]
},
{
"cell_type": "markdown",
"id": "f5b652cf",
"metadata": {},
"source": [
"!pip install -U llamaapi"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "bfd385fd",
"metadata": {},
"outputs": [],
"source": [
"from llamaapi import LlamaAPI\n",
"\n",
"# Replace 'Your_API_Token' with your actual API token\n",
"llama = LlamaAPI('Your_API_Token')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "632eb3e5",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/harrisonchase/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of deeplake (3.6.12) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.\n",
" warnings.warn(\n"
]
}
],
"source": [
"from langchain_experimental.llms import ChatLlamaAPI"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "6f850e82",
"metadata": {},
"outputs": [],
"source": [
"model = ChatLlamaAPI(client=llama)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "975c2bf4",
"metadata": {},
"outputs": [],
"source": [
"from langchain.chains import create_tagging_chain\n",
"\n",
"schema = {\n",
" \"properties\": {\n",
" \"sentiment\": {\"type\": \"string\", 'description': 'the sentiment encountered in the passage'},\n",
" \"aggressiveness\": {\"type\": \"integer\", 'description': 'a 0-10 score of how aggressive the passage is'},\n",
" \"language\": {\"type\": \"string\", 'description': 'the language of the passage'},\n",
" }\n",
"}\n",
"\n",
"chain = create_tagging_chain(schema, model)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "ef9638c3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'sentiment': 'aggressive', 'aggressiveness': 8}"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain.run(\"give me your money\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "238b4f62",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
docs/extras/integrations/chat/openai.ipynb
+175
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{
"cells": [
{
"cell_type": "markdown",
"id": "e49f1e0d",
"metadata": {},
"source": [
"# OpenAI\n",
"\n",
"This notebook covers how to get started with OpenAI chat models."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "522686de",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.chat_models import ChatOpenAI\n",
"from langchain.prompts.chat import (\n",
" ChatPromptTemplate,\n",
" SystemMessagePromptTemplate,\n",
" AIMessagePromptTemplate,\n",
" HumanMessagePromptTemplate,\n",
")\n",
"from langchain.schema import AIMessage, HumanMessage, SystemMessage"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "62e0dbc3",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"chat = ChatOpenAI(temperature=0)"
]
},
{
"cell_type": "markdown",
"id": "4e5fe97e",
"metadata": {},
"source": [
"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",
"```\n",
"Remove the openai_organization parameter should it not apply to you."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "ce16ad78-8e6f-48cd-954e-98be75eb5836",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content=\"J'adore la programmation.\", additional_kwargs={}, example=False)"
]
},
"execution_count": 3,
"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",
"]\n",
"chat(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",
"\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:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "180c5cc8",
"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)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "fbb043e6",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content=\"J'adore la programmation.\", additional_kwargs={}, example=False)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chat_prompt = ChatPromptTemplate.from_messages(\n",
" [system_message_prompt, human_message_prompt]\n",
")\n",
"\n",
"# get a chat completion from the formatted messages\n",
"chat(\n",
" chat_prompt.format_prompt(\n",
" input_language=\"English\", output_language=\"French\", text=\"I love programming.\"\n",
" ).to_messages()\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c095285d",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
docs/extras/integrations/chat/promptlayer_chatopenai.ipynb
+188
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{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "959300d4",
"metadata": {},
"source": [
"# PromptLayer ChatOpenAI\n",
"\n",
"This example showcases how to connect to [PromptLayer](https://www.promptlayer.com) to start recording your ChatOpenAI requests."
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "6a45943e",
"metadata": {},
"source": [
"## Install PromptLayer\n",
"The `promptlayer` package is required to use PromptLayer with OpenAI. Install `promptlayer` using pip."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dbe09bd8",
"metadata": {
"vscode": {
"languageId": "powershell"
}
},
"outputs": [],
"source": [
"pip install promptlayer"
]
},
{
"cell_type": "markdown",
"id": "536c1dfa",
"metadata": {},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "c16da3b5",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from langchain.chat_models import PromptLayerChatOpenAI\n",
"from langchain.schema import HumanMessage"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "8564ce7d",
"metadata": {},
"source": [
"## Set the Environment API Key\n",
"You can create a PromptLayer API Key at [www.promptlayer.com](https://www.promptlayer.com) by clicking the settings cog in the navbar.\n",
"\n",
"Set it as an environment variable called `PROMPTLAYER_API_KEY`."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "46ba25dc",
"metadata": {},
"outputs": [],
"source": [
"os.environ[\"PROMPTLAYER_API_KEY\"] = \"**********\""
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "bf0294de",
"metadata": {},
"source": [
"## Use the PromptLayerOpenAI LLM like normal\n",
"*You can optionally pass in `pl_tags` to track your requests with PromptLayer's tagging feature.*"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "3acf0069",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='to take a nap in a cozy spot. I search around for a suitable place and finally settle on a soft cushion on the window sill. I curl up into a ball and close my eyes, relishing the warmth of the sun on my fur. As I drift off to sleep, I can hear the birds chirping outside and feel the gentle breeze blowing through the window. This is the life of a contented cat.', additional_kwargs={})"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chat = PromptLayerChatOpenAI(pl_tags=[\"langchain\"])\n",
"chat([HumanMessage(content=\"I am a cat and I want\")])"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "a2d76826",
"metadata": {},
"source": [
"**The above request should now appear on your [PromptLayer dashboard](https://www.promptlayer.com).**"
]
},
{
"cell_type": "markdown",
"id": "05e9e2fe",
"metadata": {},
"source": []
},
{
"attachments": {},
"cell_type": "markdown",
"id": "c43803d1",
"metadata": {},
"source": [
"## Using PromptLayer Track\n",
"If you would like to use any of the [PromptLayer tracking features](https://magniv.notion.site/Track-4deee1b1f7a34c1680d085f82567dab9), you need to pass the argument `return_pl_id` when instantializing the PromptLayer LLM to get the request id. "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b7d4db01",
"metadata": {},
"outputs": [],
"source": [
"chat = PromptLayerChatOpenAI(return_pl_id=True)\n",
"chat_results = chat.generate([[HumanMessage(content=\"I am a cat and I want\")]])\n",
"\n",
"for res in chat_results.generations:\n",
" pl_request_id = res[0].generation_info[\"pl_request_id\"]\n",
" promptlayer.track.score(request_id=pl_request_id, score=100)"
]
},
{
"cell_type": "markdown",
"id": "13e56507",
"metadata": {},
"source": [
"Using this allows you to track the performance of your model in the PromptLayer dashboard. If you are using a prompt template, you can attach a template to a request as well.\n",
"Overall, this gives you the opportunity to track the performance of different templates and models in the PromptLayer dashboard."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8 (default, Apr 13 2021, 12:59:45) \n[Clang 10.0.0 ]"
},
"vscode": {
"interpreter": {
"hash": "8a5edab282632443219e051e4ade2d1d5bbc671c781051bf1437897cbdfea0f1"
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}