mirror of https://github.com/hwchase17/langchain
Promptlayer Callback (#6975)
Co-authored-by: Saleh Hindi <saleh.hindi.one@gmail.com> Co-authored-by: jped <jonathanped@gmail.com>pull/6978/head
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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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"""Callback handler for promptlayer."""
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from __future__ import annotations
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import datetime
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from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple
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from uuid import UUID
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from langchain.callbacks.base import BaseCallbackHandler
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from langchain.schema import (
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AIMessage,
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BaseMessage,
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ChatGeneration,
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ChatMessage,
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HumanMessage,
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LLMResult,
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SystemMessage,
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)
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if TYPE_CHECKING:
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import promptlayer
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def _lazy_import_promptlayer() -> promptlayer:
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"""Lazy import promptlayer to avoid circular imports."""
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try:
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import promptlayer
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except ImportError:
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raise ImportError(
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"The PromptLayerCallbackHandler requires the promptlayer package. "
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" Please install it with `pip install promptlayer`."
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)
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return promptlayer
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class PromptLayerCallbackHandler(BaseCallbackHandler):
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"""Callback handler for promptlayer."""
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def __init__(
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self,
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pl_id_callback: Optional[Callable[..., Any]] = None,
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pl_tags: Optional[List[str]] = [],
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) -> None:
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"""Initialize the PromptLayerCallbackHandler."""
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_lazy_import_promptlayer()
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self.pl_id_callback = pl_id_callback
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self.pl_tags = pl_tags
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self.runs: Dict[UUID, Dict[str, Any]] = {}
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def on_chat_model_start(
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self,
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serialized: Dict[str, Any],
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messages: List[List[BaseMessage]],
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*,
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run_id: UUID,
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parent_run_id: Optional[UUID] = None,
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tags: Optional[List[str]] = None,
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**kwargs: Any,
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) -> Any:
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self.runs[run_id] = {
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"messages": [self._create_message_dicts(m)[0] for m in messages],
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"invocation_params": kwargs.get("invocation_params", {}),
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"name": ".".join(serialized["id"]),
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"request_start_time": datetime.datetime.now().timestamp(),
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"tags": tags,
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}
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def on_llm_start(
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self,
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serialized: Dict[str, Any],
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prompts: List[str],
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*,
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run_id: UUID,
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parent_run_id: Optional[UUID] = None,
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tags: Optional[List[str]] = None,
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**kwargs: Any,
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) -> Any:
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self.runs[run_id] = {
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"prompts": prompts,
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"invocation_params": kwargs.get("invocation_params", {}),
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"name": ".".join(serialized["id"]),
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"request_start_time": datetime.datetime.now().timestamp(),
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"tags": tags,
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}
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def on_llm_end(
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self,
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response: LLMResult,
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*,
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run_id: UUID,
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parent_run_id: Optional[UUID] = None,
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**kwargs: Any,
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) -> None:
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from promptlayer.utils import get_api_key, promptlayer_api_request
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run_info = self.runs.get(run_id, {})
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if not run_info:
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return
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run_info["request_end_time"] = datetime.datetime.now().timestamp()
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for i in range(len(response.generations)):
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generation = response.generations[i][0]
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resp = {
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"text": generation.text,
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"llm_output": response.llm_output,
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}
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model_params = run_info.get("invocation_params", {})
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is_chat_model = run_info.get("messages", None) is not None
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model_input = (
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run_info.get("messages", [])[i]
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if is_chat_model
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else [run_info.get("prompts", [])[i]]
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)
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model_response = (
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[self._convert_message_to_dict(generation.message)]
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if is_chat_model and isinstance(generation, ChatGeneration)
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else resp
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)
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pl_request_id = promptlayer_api_request(
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run_info.get("name"),
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"langchain",
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model_input,
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model_params,
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self.pl_tags,
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model_response,
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run_info.get("request_start_time"),
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run_info.get("request_end_time"),
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get_api_key(),
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return_pl_id=bool(self.pl_id_callback is not None),
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metadata={
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"_langchain_run_id": str(run_id),
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"_langchain_parent_run_id": str(parent_run_id),
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"_langchain_tags": str(run_info.get("tags", [])),
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},
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)
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if self.pl_id_callback:
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self.pl_id_callback(pl_request_id)
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def _convert_message_to_dict(self, message: BaseMessage) -> Dict[str, Any]:
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if isinstance(message, HumanMessage):
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message_dict = {"role": "user", "content": message.content}
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elif isinstance(message, AIMessage):
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message_dict = {"role": "assistant", "content": message.content}
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elif isinstance(message, SystemMessage):
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message_dict = {"role": "system", "content": message.content}
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elif isinstance(message, ChatMessage):
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message_dict = {"role": message.role, "content": message.content}
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else:
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raise ValueError(f"Got unknown type {message}")
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if "name" in message.additional_kwargs:
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message_dict["name"] = message.additional_kwargs["name"]
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return message_dict
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def _create_message_dicts(
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self, messages: List[BaseMessage]
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) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
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params: Dict[str, Any] = {}
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message_dicts = [self._convert_message_to_dict(m) for m in messages]
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return message_dicts, params
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