forked from Archives/langchain
Harrison/prompt layer (#1547)
Co-authored-by: Jonathan Pedoeem <jonathanped@gmail.com> Co-authored-by: AbuBakar <abubakarsohail123@gmail.com>fix-searx
Harrison Chase
1 year ago
committed by
GitHub
parent
c844d1fd46
commit
3ee32a01ea
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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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"id": "959300d4",
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"metadata": {},
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"source": [
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"# PromptLayer ChatOpenAI\n",
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"\n",
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"This example showcases how to connect to [PromptLayer](https://www.promptlayer.com) to start recording your ChatOpenAI requests."
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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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"id": "6a45943e",
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"metadata": {},
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"source": [
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"## Install PromptLayer\n",
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"The `promptlayer` package is required to use PromptLayer with OpenAI. Install `promptlayer` using pip."
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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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"id": "dbe09bd8",
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"metadata": {
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"vscode": {
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"languageId": "powershell"
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}
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},
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"outputs": [],
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"source": [
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"pip install promptlayer"
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]
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},
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{
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"cell_type": "markdown",
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"id": "536c1dfa",
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"metadata": {},
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"source": [
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"## Imports"
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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": 2,
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"id": "c16da3b5",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"from langchain.chat_models import PromptLayerChatOpenAI\n",
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"from langchain.schema import HumanMessage"
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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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"id": "8564ce7d",
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"metadata": {},
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"source": [
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"## Set the Environment API Key\n",
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"You can create a PromptLayer API Key at [wwww.promptlayer.com](https://ww.promptlayer.com) by clicking the settings cog in the navbar.\n",
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"\n",
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"Set it as an environment variable called `PROMPTLAYER_API_KEY`."
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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": 5,
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"id": "46ba25dc",
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"metadata": {},
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"outputs": [],
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"source": [
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"os.environ[\"PROMPTLAYER_API_KEY\"] = \"**********\""
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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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"id": "bf0294de",
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"metadata": {},
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"source": [
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"## Use the PromptLayerOpenAI LLM like normal\n",
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"*You can optionally pass in `pl_tags` to track your requests with PromptLayer's tagging feature.*"
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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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"id": "3acf0069",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"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={})"
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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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"chat = PromptLayerChatOpenAI(pl_tags=[\"langchain\"])\n",
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"chat([HumanMessage(content=\"I am a cat and I want\")])"
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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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"id": "a2d76826",
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"metadata": {},
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"source": [
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"**The above request should now appear on your [PromptLayer dashboard](https://ww.promptlayer.com).**"
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]
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},
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{
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"cell_type": "markdown",
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"id": "05e9e2fe",
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"metadata": {},
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"source": []
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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": 5
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}
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@ -1,3 +1,4 @@
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from langchain.chat_models.openai import ChatOpenAI
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from langchain.chat_models.promptlayer_openai import PromptLayerChatOpenAI
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__all__ = ["ChatOpenAI"]
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__all__ = ["ChatOpenAI", "PromptLayerChatOpenAI"]
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"""PromptLayer wrapper."""
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import datetime
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from typing import List, Optional
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from pydantic import BaseModel
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from langchain.chat_models import ChatOpenAI
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from langchain.schema import BaseMessage, ChatResult
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class PromptLayerChatOpenAI(ChatOpenAI, BaseModel):
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"""Wrapper around OpenAI Chat large language models and PromptLayer.
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To use, you should have the ``openai`` and ``promptlayer`` python
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package installed, and the environment variable ``OPENAI_API_KEY``
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and ``PROMPTLAYER_API_KEY`` set with your openAI API key and
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promptlayer key respectively.
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All parameters that can be passed to the OpenAI LLM can also
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be passed here. The PromptLayerChatOpenAI LLM adds an extra
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``pl_tags`` parameter that can be used to tag the request.
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Example:
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.. code-block:: python
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from langchain.chat_models import PromptLayerChatOpenAI
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openai = PromptLayerChatOpenAI(model_name="gpt-3.5-turbo")
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"""
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pl_tags: Optional[List[str]]
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def _generate(
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self, messages: List[BaseMessage], stop: Optional[List[str]] = None
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) -> ChatResult:
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"""Call ChatOpenAI generate and then call PromptLayer API to log the request."""
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from promptlayer.utils import get_api_key, promptlayer_api_request
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request_start_time = datetime.datetime.now().timestamp()
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generated_responses = super()._generate(messages, stop)
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request_end_time = datetime.datetime.now().timestamp()
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message_dicts, params = super()._create_message_dicts(messages, stop)
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for i, generation in enumerate(generated_responses.generations):
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response_dict, params = super()._create_message_dicts(
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[generation.message], stop
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)
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promptlayer_api_request(
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"langchain.PromptLayerChatOpenAI",
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"langchain",
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message_dicts,
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params,
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self.pl_tags,
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response_dict,
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request_start_time,
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request_end_time,
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get_api_key(),
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)
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return generated_responses
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async def _agenerate(
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self, messages: List[BaseMessage], stop: Optional[List[str]] = None
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) -> ChatResult:
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"""Call ChatOpenAI agenerate and then call PromptLayer to log."""
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from promptlayer.utils import get_api_key, promptlayer_api_request
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request_start_time = datetime.datetime.now().timestamp()
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generated_responses = await super()._agenerate(messages, stop)
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request_end_time = datetime.datetime.now().timestamp()
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message_dicts, params = super()._create_message_dicts(messages, stop)
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for i, generation in enumerate(generated_responses.generations):
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response_dict, params = super()._create_message_dicts(
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[generation.message], stop
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)
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promptlayer_api_request(
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"langchain.PromptLayerChatOpenAI.async",
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"langchain",
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message_dicts,
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params,
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self.pl_tags,
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response_dict,
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request_start_time,
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request_end_time,
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get_api_key(),
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)
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return generated_responses
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@ -0,0 +1,130 @@
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"""Test PromptLayerChatOpenAI wrapper."""
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import pytest
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from langchain.callbacks.base import CallbackManager
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from langchain.chat_models.promptlayer_openai import PromptLayerChatOpenAI
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from langchain.schema import (
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BaseMessage,
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ChatGeneration,
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ChatResult,
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HumanMessage,
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LLMResult,
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SystemMessage,
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)
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from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler
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def test_promptlayer_chat_openai() -> None:
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"""Test PromptLayerChatOpenAI wrapper."""
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chat = PromptLayerChatOpenAI(max_tokens=10)
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message = HumanMessage(content="Hello")
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response = chat([message])
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assert isinstance(response, BaseMessage)
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assert isinstance(response.content, str)
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def test_promptlayer_chat_openai_system_message() -> None:
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"""Test PromptLayerChatOpenAI wrapper with system message."""
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chat = PromptLayerChatOpenAI(max_tokens=10)
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system_message = SystemMessage(content="You are to chat with the user.")
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human_message = HumanMessage(content="Hello")
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response = chat([system_message, human_message])
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assert isinstance(response, BaseMessage)
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assert isinstance(response.content, str)
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def test_promptlayer_chat_openai_generate() -> None:
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"""Test PromptLayerChatOpenAI wrapper with generate."""
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chat = PromptLayerChatOpenAI(max_tokens=10, n=2)
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message = HumanMessage(content="Hello")
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response = chat.generate([[message], [message]])
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assert isinstance(response, LLMResult)
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assert len(response.generations) == 2
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for generations in response.generations:
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assert len(generations) == 2
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for generation in generations:
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assert isinstance(generation, ChatGeneration)
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assert isinstance(generation.text, str)
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assert generation.text == generation.message.content
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def test_promptlayer_chat_openai_multiple_completions() -> None:
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"""Test PromptLayerChatOpenAI wrapper with multiple completions."""
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chat = PromptLayerChatOpenAI(max_tokens=10, n=5)
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message = HumanMessage(content="Hello")
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response = chat._generate([message])
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assert isinstance(response, ChatResult)
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assert len(response.generations) == 5
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for generation in response.generations:
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assert isinstance(generation.message, BaseMessage)
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assert isinstance(generation.message.content, str)
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def test_promptlayer_chat_openai_streaming() -> None:
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"""Test that streaming correctly invokes on_llm_new_token callback."""
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callback_handler = FakeCallbackHandler()
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callback_manager = CallbackManager([callback_handler])
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chat = PromptLayerChatOpenAI(
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max_tokens=10,
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streaming=True,
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temperature=0,
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callback_manager=callback_manager,
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verbose=True,
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)
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message = HumanMessage(content="Hello")
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response = chat([message])
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assert callback_handler.llm_streams > 0
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assert isinstance(response, BaseMessage)
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def test_promptlayer_chat_openai_invalid_streaming_params() -> None:
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"""Test that streaming correctly invokes on_llm_new_token callback."""
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with pytest.raises(ValueError):
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PromptLayerChatOpenAI(
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max_tokens=10,
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streaming=True,
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temperature=0,
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n=5,
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)
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@pytest.mark.asyncio
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async def test_async_promptlayer_chat_openai() -> None:
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"""Test async generation."""
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chat = PromptLayerChatOpenAI(max_tokens=10, n=2)
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message = HumanMessage(content="Hello")
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response = await chat.agenerate([[message], [message]])
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assert isinstance(response, LLMResult)
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assert len(response.generations) == 2
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for generations in response.generations:
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assert len(generations) == 2
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for generation in generations:
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assert isinstance(generation, ChatGeneration)
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assert isinstance(generation.text, str)
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assert generation.text == generation.message.content
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@pytest.mark.asyncio
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async def test_async_promptlayer_chat_openai_streaming() -> None:
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"""Test that streaming correctly invokes on_llm_new_token callback."""
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callback_handler = FakeCallbackHandler()
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callback_manager = CallbackManager([callback_handler])
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chat = PromptLayerChatOpenAI(
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max_tokens=10,
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streaming=True,
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temperature=0,
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callback_manager=callback_manager,
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verbose=True,
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)
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message = HumanMessage(content="Hello")
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response = await chat.agenerate([[message], [message]])
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assert callback_handler.llm_streams > 0
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assert isinstance(response, LLMResult)
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assert len(response.generations) == 2
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for generations in response.generations:
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assert len(generations) == 1
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for generation in generations:
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assert isinstance(generation, ChatGeneration)
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assert isinstance(generation.text, str)
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assert generation.text == generation.message.content
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@ -0,0 +1,41 @@
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"""Test PromptLayer OpenAIChat API wrapper."""
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from pathlib import Path
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import pytest
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from langchain.llms.loading import load_llm
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from langchain.llms.promptlayer_openai import PromptLayerOpenAIChat
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def test_promptlayer_openai_chat_call() -> None:
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"""Test valid call to promptlayer openai."""
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llm = PromptLayerOpenAIChat(max_tokens=10)
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output = llm("Say foo:")
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assert isinstance(output, str)
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def test_promptlayer_openai_chat_stop_valid() -> None:
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"""Test promptlayer openai stop logic on valid configuration."""
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query = "write an ordered list of five items"
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first_llm = PromptLayerOpenAIChat(stop="3", temperature=0)
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first_output = first_llm(query)
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second_llm = PromptLayerOpenAIChat(temperature=0)
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second_output = second_llm(query, stop=["3"])
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# Because it stops on new lines, shouldn't return anything
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assert first_output == second_output
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def test_promptlayer_openai_chat_stop_error() -> None:
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"""Test promptlayer openai stop logic on bad configuration."""
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llm = PromptLayerOpenAIChat(stop="3", temperature=0)
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with pytest.raises(ValueError):
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llm("write an ordered list of five items", stop=["\n"])
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def test_saving_loading_llm(tmp_path: Path) -> None:
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"""Test saving/loading an promptlayer OpenAPI LLM."""
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llm = PromptLayerOpenAIChat(max_tokens=10)
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llm.save(file_path=tmp_path / "openai.yaml")
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loaded_llm = load_llm(tmp_path / "openai.yaml")
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assert loaded_llm == llm
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