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https://github.com/hwchase17/langchain
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Adding Docs for PromptLayerCallbackHandler (#7860)
Here I am adding documentation for the `PromptLayerCallbackHandler`. When we created the initial PR for the callback handler the docs were causing issues, so we merged without the docs.
This commit is contained in:
Jonathan Pedoeem
GitHub
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3902b85657
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docs/extras/modules/callbacks/integrations/promptlayer.ipynb
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docs/extras/modules/callbacks/integrations/promptlayer.ipynb
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{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# PromptLayer\n",
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"\n",
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"\n",
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"\n",
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"[PromptLayer](https://promptlayer.com) is a an LLM observability platform that lets you visualize requests, version prompts, and track usage. In this guide we will go over how to setup the `PromptLayerCallbackHandler`. \n",
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"\n",
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"While PromptLayer does have LLMs that integrate directly with LangChain (eg [`PromptLayerOpenAI`](https://python.langchain.com/docs/modules/model_io/models/llms/integrations/promptlayer_openai)), this callback is the recommended way to integrate PromptLayer with LangChain.\n",
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"\n",
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"See [our docs](https://docs.promptlayer.com/languages/langchain) for more information."
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {
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"tags": []
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},
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"source": [
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"## Installation and Setup"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install promptlayer --upgrade"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Getting API Credentials\n",
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"\n",
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"If you do not have a PromptLayer account, create one on [promptlayer.com](https://www.promptlayer.com). Then get an API key by clicking on the settings cog in the navbar and\n",
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"set it as an environment variabled called `PROMPTLAYER_API_KEY`\n"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Usage\n",
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"\n",
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"Getting started with `PromptLayerCallbackHandler` is fairly simple, it takes two optional arguments:\n",
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"1. `pl_tags` - an optional list of strings that will be tracked as tags on PromptLayer.\n",
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"2. `pl_id_callback` - an optional function that will take `promptlayer_request_id` as an argument. This ID can be used with all of PromptLayer's tracking features to track, metadata, scores, and prompt usage."
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Simple OpenAI Example\n",
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"\n",
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"In this simple example we use `PromptLayerCallbackHandler` with `ChatOpenAI`. We add a PromptLayer tag named `chatopenai`"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import promptlayer # Don't forget this 🍰\n",
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"from langchain.callbacks import PromptLayerCallbackHandler\n",
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"from langchain.chat_models import ChatOpenAI\n",
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"from langchain.schema import (\n",
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" HumanMessage,\n",
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")\n",
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"\n",
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"chat_llm = ChatOpenAI(\n",
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" temperature=0,\n",
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" callbacks=[PromptLayerCallbackHandler(pl_tags=[\"chatopenai\"])],\n",
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")\n",
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"llm_results = chat_llm(\n",
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" [\n",
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" HumanMessage(content=\"What comes after 1,2,3 ?\"),\n",
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" HumanMessage(content=\"Tell me another joke?\"),\n",
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" ]\n",
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")\n",
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"print(llm_results)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### GPT4All Example"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import promptlayer # Don't forget this 🍰\n",
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"from langchain.callbacks import PromptLayerCallbackHandler\n",
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"\n",
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"from langchain.llms import GPT4All\n",
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"\n",
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"model = GPT4All(model=\"./models/gpt4all-model.bin\", n_ctx=512, n_threads=8)\n",
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"\n",
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"response = model(\n",
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" \"Once upon a time, \",\n",
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" callbacks=[PromptLayerCallbackHandler(pl_tags=[\"langchain\", \"gpt4all\"])],\n",
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")"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Full Featured Example\n",
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"\n",
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"In this example we unlock more of the power of PromptLayer.\n",
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"\n",
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"PromptLayer allows you to visually create, version, and track prompt templates. Using the [Prompt Registry](https://docs.promptlayer.com/features/prompt-registry), we can programatically fetch the prompt template called `example`.\n",
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"\n",
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"We also define a `pl_id_callback` function which takes in the `promptlayer_request_id` and logs a score, metadata and links the prompt template used. Read more about tracking on [our docs](https://docs.promptlayer.com/features/prompt-history/request-id)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import promptlayer # Don't forget this 🍰\n",
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"from langchain.callbacks import PromptLayerCallbackHandler\n",
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"from langchain.llms import OpenAI\n",
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"\n",
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"\n",
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"def pl_id_callback(promptlayer_request_id):\n",
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" print(\"prompt layer id \", promptlayer_request_id)\n",
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" promptlayer.track.score(\n",
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" request_id=promptlayer_request_id, score=100\n",
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" ) # score is an integer 0-100\n",
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" promptlayer.track.metadata(\n",
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" request_id=promptlayer_request_id, metadata={\"foo\": \"bar\"}\n",
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" ) # metadata is a dictionary of key value pairs that is tracked on PromptLayer\n",
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" promptlayer.track.prompt(\n",
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" request_id=promptlayer_request_id,\n",
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" prompt_name=\"example\",\n",
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" prompt_input_variables={\"product\": \"toasters\"},\n",
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" version=1,\n",
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" ) # link the request to a prompt template\n",
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"\n",
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"\n",
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"openai_llm = OpenAI(\n",
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" model_name=\"text-davinci-002\",\n",
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" callbacks=[PromptLayerCallbackHandler(pl_id_callback=pl_id_callback)],\n",
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")\n",
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"\n",
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"example_prompt = promptlayer.prompts.get(\"example\", version=1, langchain=True)\n",
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"openai_llm(example_prompt.format(product=\"toasters\"))"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"That is all it takes! After setup all your requests will show up on the PromptLayer dashboard.\n",
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"This callback also works with any LLM implemented on LangChain."
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "base",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.8 (default, Apr 13 2021, 12:59:45) \n[Clang 10.0.0 ]"
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},
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"vscode": {
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"interpreter": {
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"hash": "c4fe2cd85a8d9e8baaec5340ce66faff1c77581a9f43e6c45e85e09b6fced008"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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