mirror of https://github.com/hwchase17/langchain
community[minor]: DeepInfra support for chat models (#16380)
Add deepinfra chat models support. This is https://github.com/langchain-ai/langchain/pull/14234 re-opened from my branch (so maintainers can edit).pull/16405/head
Iskren Ivov Chernev
5 months ago
committed by
GitHub
parent
eac91b60c9
commit
fc196cab12
@ -0,0 +1,224 @@
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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": "bf733a38-db84-4363-89e2-de6735c37230",
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"metadata": {},
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"source": [
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"# DeepInfra\n",
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"\n",
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"[DeepInfra](https://deepinfra.com/?utm_source=langchain) is a serverless inference as a service that provides access to a [variety of LLMs](https://deepinfra.com/models?utm_source=langchain) and [embeddings models](https://deepinfra.com/models?type=embeddings&utm_source=langchain). This notebook goes over how to use LangChain with DeepInfra for chat models."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Set the Environment API Key\n",
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"Make sure to get your API key from DeepInfra. You have to [Login](https://deepinfra.com/login?from=%2Fdash) and get a new token.\n",
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"\n",
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"You are given a 1 hour free of serverless GPU compute to test different models. (see [here](https://github.com/deepinfra/deepctl#deepctl))\n",
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"You can print your token with `deepctl auth token`"
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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": 6,
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"metadata": {
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"tags": []
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},
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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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" ········\n"
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]
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}
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],
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"source": [
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"# get a new token: https://deepinfra.com/login?from=%2Fdash\n",
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"\n",
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"from getpass import getpass\n",
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"\n",
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"DEEPINFRA_API_TOKEN = getpass()"
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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": 7,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"# or pass deepinfra_api_token parameter to the ChatDeepInfra constructor\n",
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"os.environ[\"DEEPINFRA_API_TOKEN\"] = DEEPINFRA_API_TOKEN"
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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": 1,
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"id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain.chat_models import ChatDeepInfra\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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"cell_type": "code",
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"execution_count": 2,
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"id": "70cf04e8-423a-4ff6-8b09-f11fb711c817",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"chat = ChatDeepInfra(model=\"meta-llama/Llama-2-7b-chat-hf\")"
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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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"id": "8199ef8f-eb8b-4253-9ea0-6c24a013ca4c",
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"metadata": {
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"tags": []
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},
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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=\" J'aime la programmation.\", additional_kwargs={}, example=False)"
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]
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},
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"execution_count": 3,
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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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"messages = [\n",
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" HumanMessage(\n",
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" content=\"Translate this sentence from English to French. I love programming.\"\n",
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" )\n",
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"]\n",
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"chat(messages)"
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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": "c361ab1e-8c0c-4206-9e3c-9d1424a12b9c",
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"metadata": {},
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"source": [
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"## `ChatDeepInfra` also supports async and streaming functionality:"
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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": "93a21c5c-6ef9-4688-be60-b2e1f94842fb",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler"
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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": "c5fac0e9-05a4-4fc1-a3b3-e5bbb24b971b",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"LLMResult(generations=[[ChatGeneration(text=\" J'aime programmer.\", generation_info=None, message=AIMessage(content=\" J'aime programmer.\", additional_kwargs={}, example=False))]], llm_output={}, run=[RunInfo(run_id=UUID('8cc8fb68-1c35-439c-96a0-695036a93652'))])"
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]
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},
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"execution_count": 5,
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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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"await chat.agenerate([messages])"
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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": 6,
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"id": "025be980-e50d-4a68-93dc-c9c7b500ce34",
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"metadata": {
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"tags": []
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},
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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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" J'aime la programmation."
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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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"AIMessage(content=\" J'aime la programmation.\", additional_kwargs={}, example=False)"
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]
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},
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"execution_count": 6,
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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 = ChatDeepInfra(\n",
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" streaming=True,\n",
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" verbose=True,\n",
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" callbacks=[StreamingStdOutCallbackHandler()],\n",
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")\n",
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"chat(messages)"
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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": "c253883f",
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"metadata": {},
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"outputs": [],
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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": "Python 3 (ipykernel)",
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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": "3.9.1"
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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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@ -0,0 +1,451 @@
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"""deepinfra.com chat models wrapper"""
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from __future__ import annotations
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import json
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import logging
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from typing import (
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Any,
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AsyncIterator,
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Callable,
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Dict,
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Iterator,
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List,
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Mapping,
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Optional,
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Tuple,
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Type,
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Union,
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)
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import aiohttp
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import requests
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from langchain_core.callbacks.manager import (
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AsyncCallbackManagerForLLMRun,
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CallbackManagerForLLMRun,
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)
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from langchain_core.language_models.chat_models import (
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BaseChatModel,
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agenerate_from_stream,
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generate_from_stream,
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)
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from langchain_core.language_models.llms import create_base_retry_decorator
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from langchain_core.messages import (
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AIMessage,
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AIMessageChunk,
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BaseMessage,
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BaseMessageChunk,
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ChatMessage,
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ChatMessageChunk,
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FunctionMessage,
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FunctionMessageChunk,
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HumanMessage,
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HumanMessageChunk,
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SystemMessage,
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SystemMessageChunk,
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)
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from langchain_core.outputs import (
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ChatGeneration,
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ChatGenerationChunk,
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ChatResult,
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)
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from langchain_core.pydantic_v1 import Field, root_validator
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from langchain_core.utils import get_from_dict_or_env
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# from langchain.llms.base import create_base_retry_decorator
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from langchain_community.utilities.requests import Requests
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logger = logging.getLogger(__name__)
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class ChatDeepInfraException(Exception):
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pass
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def _create_retry_decorator(
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llm: ChatDeepInfra,
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run_manager: Optional[
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Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun]
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] = None,
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) -> Callable[[Any], Any]:
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"""Returns a tenacity retry decorator, preconfigured to handle PaLM exceptions"""
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return create_base_retry_decorator(
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error_types=[requests.exceptions.ConnectTimeout, ChatDeepInfraException],
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max_retries=llm.max_retries,
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run_manager=run_manager,
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)
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def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
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role = _dict["role"]
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if role == "user":
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return HumanMessage(content=_dict["content"])
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elif role == "assistant":
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# Fix for azure
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# Also OpenAI returns None for tool invocations
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content = _dict.get("content", "") or ""
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if _dict.get("function_call"):
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additional_kwargs = {"function_call": dict(_dict["function_call"])}
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else:
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additional_kwargs = {}
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return AIMessage(content=content, additional_kwargs=additional_kwargs)
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elif role == "system":
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return SystemMessage(content=_dict["content"])
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elif role == "function":
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return FunctionMessage(content=_dict["content"], name=_dict["name"])
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else:
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return ChatMessage(content=_dict["content"], role=role)
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def _convert_delta_to_message_chunk(
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_dict: Mapping[str, Any], default_class: Type[BaseMessageChunk]
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) -> BaseMessageChunk:
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role = _dict.get("role")
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content = _dict.get("content") or ""
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if _dict.get("function_call"):
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additional_kwargs = {"function_call": dict(_dict["function_call"])}
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else:
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additional_kwargs = {}
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if role == "user" or default_class == HumanMessageChunk:
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return HumanMessageChunk(content=content)
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elif role == "assistant" or default_class == AIMessageChunk:
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return AIMessageChunk(content=content, additional_kwargs=additional_kwargs)
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elif role == "system" or default_class == SystemMessageChunk:
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return SystemMessageChunk(content=content)
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elif role == "function" or default_class == FunctionMessageChunk:
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return FunctionMessageChunk(content=content, name=_dict["name"])
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elif role or default_class == ChatMessageChunk:
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return ChatMessageChunk(content=content, role=role)
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else:
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return default_class(content=content)
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def _convert_message_to_dict(message: BaseMessage) -> dict:
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if isinstance(message, ChatMessage):
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message_dict = {"role": message.role, "content": message.content}
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elif 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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if "function_call" in message.additional_kwargs:
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message_dict["function_call"] = message.additional_kwargs["function_call"]
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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, FunctionMessage):
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message_dict = {
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"role": "function",
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"content": message.content,
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"name": message.name,
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}
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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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class ChatDeepInfra(BaseChatModel):
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"""A chat model that uses the DeepInfra API."""
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# client: Any #: :meta private:
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model_name: str = Field(default="meta-llama/Llama-2-70b-chat-hf", alias="model")
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"""Model name to use."""
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deepinfra_api_token: Optional[str] = None
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request_timeout: Optional[float] = Field(default=None, alias="timeout")
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temperature: Optional[float] = 1
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model_kwargs: Dict[str, Any] = Field(default_factory=dict)
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"""Run inference with this temperature. Must by in the closed
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interval [0.0, 1.0]."""
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top_p: Optional[float] = None
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"""Decode using nucleus sampling: consider the smallest set of tokens whose
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probability sum is at least top_p. Must be in the closed interval [0.0, 1.0]."""
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top_k: Optional[int] = None
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"""Decode using top-k sampling: consider the set of top_k most probable tokens.
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Must be positive."""
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n: int = 1
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"""Number of chat completions to generate for each prompt. Note that the API may
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not return the full n completions if duplicates are generated."""
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max_tokens: int = 256
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streaming: bool = False
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max_retries: int = 1
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@property
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def _default_params(self) -> Dict[str, Any]:
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"""Get the default parameters for calling OpenAI API."""
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return {
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"model": self.model_name,
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"max_tokens": self.max_tokens,
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"stream": self.streaming,
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"n": self.n,
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"temperature": self.temperature,
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"request_timeout": self.request_timeout,
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**self.model_kwargs,
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}
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@property
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def _client_params(self) -> Dict[str, Any]:
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"""Get the parameters used for the openai client."""
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return {**self._default_params}
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def completion_with_retry(
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self, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any
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) -> Any:
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"""Use tenacity to retry the completion call."""
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retry_decorator = _create_retry_decorator(self, run_manager=run_manager)
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@retry_decorator
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def _completion_with_retry(**kwargs: Any) -> Any:
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try:
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request_timeout = kwargs.pop("request_timeout")
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request = Requests(headers=self._headers())
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response = request.post(
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url=self._url(), data=self._body(kwargs), timeout=request_timeout
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)
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self._handle_status(response.status_code, response.text)
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return response
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except Exception as e:
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# import pdb; pdb.set_trace()
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print("EX", e)
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raise
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return _completion_with_retry(**kwargs)
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async def acompletion_with_retry(
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self,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> Any:
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"""Use tenacity to retry the async completion call."""
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retry_decorator = _create_retry_decorator(self, run_manager=run_manager)
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@retry_decorator
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async def _completion_with_retry(**kwargs: Any) -> Any:
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try:
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request_timeout = kwargs.pop("request_timeout")
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request = Requests(headers=self._headers())
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async with request.apost(
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url=self._url(), data=self._body(kwargs), timeout=request_timeout
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) as response:
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self._handle_status(response.status, response.text)
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return await response.json()
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except Exception as e:
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print("EX", e)
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raise
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return await _completion_with_retry(**kwargs)
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate api key, python package exists, temperature, top_p, and top_k."""
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# For compatibility with LiteLLM
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api_key = get_from_dict_or_env(
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values,
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"deepinfra_api_key",
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"DEEPINFRA_API_KEY",
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default="",
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)
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values["deepinfra_api_token"] = get_from_dict_or_env(
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values,
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"deepinfra_api_token",
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"DEEPINFRA_API_TOKEN",
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default=api_key,
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)
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if values["temperature"] is not None and not 0 <= values["temperature"] <= 1:
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raise ValueError("temperature must be in the range [0.0, 1.0]")
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if values["top_p"] is not None and not 0 <= values["top_p"] <= 1:
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raise ValueError("top_p must be in the range [0.0, 1.0]")
|
||||
|
||||
if values["top_k"] is not None and values["top_k"] <= 0:
|
||||
raise ValueError("top_k must be positive")
|
||||
|
||||
return values
|
||||
|
||||
def _generate(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
stream: Optional[bool] = None,
|
||||
**kwargs: Any,
|
||||
) -> ChatResult:
|
||||
should_stream = stream if stream is not None else self.streaming
|
||||
if should_stream:
|
||||
stream_iter = self._stream(
|
||||
messages, stop=stop, run_manager=run_manager, **kwargs
|
||||
)
|
||||
return generate_from_stream(stream_iter)
|
||||
|
||||
message_dicts, params = self._create_message_dicts(messages, stop)
|
||||
params = {**params, **kwargs}
|
||||
response = self.completion_with_retry(
|
||||
messages=message_dicts, run_manager=run_manager, **params
|
||||
)
|
||||
return self._create_chat_result(response.json())
|
||||
|
||||
def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:
|
||||
generations = []
|
||||
for res in response["choices"]:
|
||||
message = _convert_dict_to_message(res["message"])
|
||||
gen = ChatGeneration(
|
||||
message=message,
|
||||
generation_info=dict(finish_reason=res.get("finish_reason")),
|
||||
)
|
||||
generations.append(gen)
|
||||
token_usage = response.get("usage", {})
|
||||
llm_output = {"token_usage": token_usage, "model": self.model_name}
|
||||
res = ChatResult(generations=generations, llm_output=llm_output)
|
||||
return res
|
||||
|
||||
def _create_message_dicts(
|
||||
self, messages: List[BaseMessage], stop: Optional[List[str]]
|
||||
) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
|
||||
params = self._client_params
|
||||
if stop is not None:
|
||||
if "stop" in params:
|
||||
raise ValueError("`stop` found in both the input and default params.")
|
||||
params["stop"] = stop
|
||||
message_dicts = [_convert_message_to_dict(m) for m in messages]
|
||||
return message_dicts, params
|
||||
|
||||
def _stream(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> Iterator[ChatGenerationChunk]:
|
||||
message_dicts, params = self._create_message_dicts(messages, stop)
|
||||
params = {**params, **kwargs, "stream": True}
|
||||
|
||||
response = self.completion_with_retry(
|
||||
messages=message_dicts, run_manager=run_manager, **params
|
||||
)
|
||||
for line in _parse_stream(response.iter_lines()):
|
||||
chunk = _handle_sse_line(line)
|
||||
if chunk:
|
||||
yield ChatGenerationChunk(message=chunk, generation_info=None)
|
||||
if run_manager:
|
||||
run_manager.on_llm_new_token(chunk.content) # type: ignore[arg-type]
|
||||
|
||||
async def _astream(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> AsyncIterator[ChatGenerationChunk]:
|
||||
message_dicts, params = self._create_message_dicts(messages, stop)
|
||||
params = {"messages": message_dicts, "stream": True, **params, **kwargs}
|
||||
|
||||
request_timeout = params.pop("request_timeout")
|
||||
request = Requests(headers=self._headers())
|
||||
async with request.apost(
|
||||
url=self._url(), data=self._body(params), timeout=request_timeout
|
||||
) as response:
|
||||
async for line in _parse_stream_async(response.content):
|
||||
chunk = _handle_sse_line(line)
|
||||
if chunk:
|
||||
yield ChatGenerationChunk(message=chunk, generation_info=None)
|
||||
if run_manager:
|
||||
await run_manager.on_llm_new_token(chunk.content) # type: ignore[arg-type]
|
||||
|
||||
async def _agenerate(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
||||
stream: Optional[bool] = None,
|
||||
**kwargs: Any,
|
||||
) -> ChatResult:
|
||||
should_stream = stream if stream is not None else self.streaming
|
||||
if should_stream:
|
||||
stream_iter = self._astream(
|
||||
messages, stop=stop, run_manager=run_manager, **kwargs
|
||||
)
|
||||
return await agenerate_from_stream(stream_iter)
|
||||
|
||||
message_dicts, params = self._create_message_dicts(messages, stop)
|
||||
params = {"messages": message_dicts, **params, **kwargs}
|
||||
|
||||
res = await self.acompletion_with_retry(run_manager=run_manager, **params)
|
||||
return self._create_chat_result(res)
|
||||
|
||||
@property
|
||||
def _identifying_params(self) -> Dict[str, Any]:
|
||||
"""Get the identifying parameters."""
|
||||
return {
|
||||
"model": self.model_name,
|
||||
"temperature": self.temperature,
|
||||
"top_p": self.top_p,
|
||||
"top_k": self.top_k,
|
||||
"n": self.n,
|
||||
}
|
||||
|
||||
@property
|
||||
def _llm_type(self) -> str:
|
||||
return "deepinfra-chat"
|
||||
|
||||
def _handle_status(self, code: int, text: Any) -> None:
|
||||
if code >= 500:
|
||||
raise ChatDeepInfraException(f"DeepInfra Server: Error {code}")
|
||||
elif code >= 400:
|
||||
raise ValueError(f"DeepInfra received an invalid payload: {text}")
|
||||
elif code != 200:
|
||||
raise Exception(
|
||||
f"DeepInfra returned an unexpected response with status "
|
||||
f"{code}: {text}"
|
||||
)
|
||||
|
||||
def _url(self) -> str:
|
||||
return "https://stage.api.deepinfra.com/v1/openai/chat/completions"
|
||||
|
||||
def _headers(self) -> Dict:
|
||||
return {
|
||||
"Authorization": f"bearer {self.deepinfra_api_token}",
|
||||
"Content-Type": "application/json",
|
||||
}
|
||||
|
||||
def _body(self, kwargs: Any) -> Dict:
|
||||
return kwargs
|
||||
|
||||
|
||||
def _parse_stream(rbody: Iterator[bytes]) -> Iterator[str]:
|
||||
for line in rbody:
|
||||
_line = _parse_stream_helper(line)
|
||||
if _line is not None:
|
||||
yield _line
|
||||
|
||||
|
||||
async def _parse_stream_async(rbody: aiohttp.StreamReader) -> AsyncIterator[str]:
|
||||
async for line in rbody:
|
||||
_line = _parse_stream_helper(line)
|
||||
if _line is not None:
|
||||
yield _line
|
||||
|
||||
|
||||
def _parse_stream_helper(line: bytes) -> Optional[str]:
|
||||
if line and line.startswith(b"data:"):
|
||||
if line.startswith(b"data: "):
|
||||
# SSE event may be valid when it contain whitespace
|
||||
line = line[len(b"data: ") :]
|
||||
else:
|
||||
line = line[len(b"data:") :]
|
||||
if line.strip() == b"[DONE]":
|
||||
# return here will cause GeneratorExit exception in urllib3
|
||||
# and it will close http connection with TCP Reset
|
||||
return None
|
||||
else:
|
||||
return line.decode("utf-8")
|
||||
return None
|
||||
|
||||
|
||||
def _handle_sse_line(line: str) -> Optional[BaseMessageChunk]:
|
||||
try:
|
||||
obj = json.loads(line)
|
||||
default_chunk_class = AIMessageChunk
|
||||
delta = obj.get("choices", [{}])[0].get("delta", {})
|
||||
return _convert_delta_to_message_chunk(delta, default_chunk_class)
|
||||
except Exception:
|
||||
return None
|
||||
@ -0,0 +1,65 @@
|
||||
"""Test ChatDeepInfra wrapper."""
|
||||
from langchain_core.messages import BaseMessage, HumanMessage
|
||||
from langchain_core.outputs import ChatGeneration, LLMResult
|
||||
|
||||
from langchain_community.chat_models.deepinfra import ChatDeepInfra
|
||||
from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler
|
||||
|
||||
|
||||
def test_chat_deepinfra() -> None:
|
||||
"""Test valid call to DeepInfra."""
|
||||
chat = ChatDeepInfra(
|
||||
max_tokens=10,
|
||||
)
|
||||
response = chat.invoke([HumanMessage(content="Hello")])
|
||||
assert isinstance(response, BaseMessage)
|
||||
assert isinstance(response.content, str)
|
||||
|
||||
|
||||
def test_chat_deepinfra_streaming() -> None:
|
||||
callback_handler = FakeCallbackHandler()
|
||||
chat = ChatDeepInfra(
|
||||
callbacks=[callback_handler],
|
||||
streaming=True,
|
||||
max_tokens=10,
|
||||
)
|
||||
response = chat.invoke([HumanMessage(content="Hello")])
|
||||
assert callback_handler.llm_streams > 0
|
||||
assert isinstance(response, BaseMessage)
|
||||
|
||||
|
||||
async def test_async_chat_deepinfra() -> None:
|
||||
"""Test async generation."""
|
||||
chat = ChatDeepInfra(
|
||||
max_tokens=10,
|
||||
)
|
||||
message = HumanMessage(content="Hello")
|
||||
response = await chat.agenerate([[message]])
|
||||
assert isinstance(response, LLMResult)
|
||||
assert len(response.generations) == 1
|
||||
assert len(response.generations[0]) == 1
|
||||
generation = response.generations[0][0]
|
||||
assert isinstance(generation, ChatGeneration)
|
||||
assert isinstance(generation.text, str)
|
||||
assert generation.text == generation.message.content
|
||||
|
||||
|
||||
async def test_async_chat_deepinfra_streaming() -> None:
|
||||
callback_handler = FakeCallbackHandler()
|
||||
chat = ChatDeepInfra(
|
||||
# model="meta-llama/Llama-2-7b-chat-hf",
|
||||
callbacks=[callback_handler],
|
||||
max_tokens=10,
|
||||
streaming=True,
|
||||
timeout=5,
|
||||
)
|
||||
message = HumanMessage(content="Hello")
|
||||
response = await chat.agenerate([[message]])
|
||||
assert callback_handler.llm_streams > 0
|
||||
assert isinstance(response, LLMResult)
|
||||
assert len(response.generations) == 1
|
||||
assert len(response.generations[0]) == 1
|
||||
generation = response.generations[0][0]
|
||||
assert isinstance(generation, ChatGeneration)
|
||||
assert isinstance(generation.text, str)
|
||||
assert generation.text == generation.message.content
|
||||
Loading…
Reference in New Issue