mirror of https://github.com/hwchase17/langchain
William FH
1 year ago
committed by
GitHub
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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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"<img src=\"https://promptlayer.com/logo.png\" height=\"300\">\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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"[PromptLayer](https://promptlayer.com) is a an observability platform for prompts and LLMs. In this guide we will go over how to setup the `PromptLayerCallbackHandler`. 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 will be an easier and more feature rich way to integrate PromptLayer with any model on LangChain. \n",
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"\n",
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"This callback is also the recommended way to connect with PromptLayer when building Chains and Agents on LangChain."
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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 have not already create an account on [PromptLayer](https://www.promptlayer.com) and get an API key by clicking on the settings cog in the navbar\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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"To get 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 tags tracked on PromptLayer\n",
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"2. `pl_id_callback` - an optional function that will get a `promptlayer_request_id` as an argument. This id can be used with all of PromptLayers 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 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": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"content=\"Sure, here's one:\\n\\nWhy did the tomato turn red?\\n\\nBecause it saw the salad dressing!\" additional_kwargs={} example=False\n"
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]
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}
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],
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"source": [
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"from langchain.chat_models import ChatOpenAI\n",
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"from langchain.schema import (\n",
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" AIMessage,\n",
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" HumanMessage,\n",
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" SystemMessage,\n",
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")\n",
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"from langchain.callbacks import PromptLayerCallbackHandler\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)\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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"### 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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"We are using the Prompt Registry and fetching the prompt called `example`.\n",
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"\n",
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"We also define a `pl_id_callback` function that tracks a score, metadata and the prompt used. Read more about tracking on [our docs](docs.promptlayer.com)."
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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": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"prompt layer id 6050929\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'\\nToasterCo.'"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from langchain.llms import OpenAI\n",
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"from langchain.callbacks import PromptLayerCallbackHandler\n",
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"import promptlayer\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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" )\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 dasahboard.\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"
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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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