Функция оценки LangSmith не видит переменные с потоковой подсветкой ⇐ Python

[Anonymous]

Я хотел бы провести оценку модели с помощью LangSmith. Я создал небольшое приложение на основе руководства (https://docs.smith.langchain.com/evalua ... evaluation). Все работает нормально, пока не используются переменные с потоковым освещением. Вот шаги, которые я выполнил:

• Я клонировал репозиторий (https://github.com/dawidstajszczyk/LangSmith)
• Я создал виртуальную среду (venv)
• Я установил зависимости (streamlit, langsmith, langchain, openai, python-dotenv)
• Я заполнил файл .env своими собственными ключами API
• Я запускаю приложение с помощью команды "streamlit run app.py"

< strong>Чего я ожидал?
Я ожидал, что переменнаяchat_history обновится, когда функция оценки называется.
Что я получил?
Ошибка
-- ----------------------------------
Чтобы получить LANGCHAIN_API_KEY и OPENAI_API_KEY, вам необходимо зарегистрироваться здесь:

• https://www.smith.langchain.com/
  < /li>
  https://platform.openai.com/

----- --------- app.py ---------------
import streamlit as st
from langsmith import Client
from dotenv import load_dotenv
from langchain_openai import ChatOpenAI
from langchain_core.prompts.prompt import PromptTemplate
from langsmith.evaluation import LangChainStringEvaluator
import openai
from langsmith import evaluate

# Load environment variables from a .env file
load_dotenv()

# Initialize chat_history
if 'chat_history' not in st.session_state:
st.session_state.chat_history = []

# Create a new LangSmith client
client = Client()

# Define dataset: these are your test cases
dataset_name = "QA Example Dataset"
dataset = client.create_dataset(dataset_name)
client.create_examples(
inputs=[
{"question": "What is LangChain?"},
{"question": "What is LangSmith?"},
{"question": "What is OpenAI?"},
{"question": "What is Google?"},
{"question": "What is Mistral?"},
],
outputs=[
{"answer": "A framework for building LLM applications"},
{"answer": "A platform for observing and evaluating LLM applications"},
{"answer": "A company that creates Large Language Models"},
{"answer": "A technology company known for search"},
{"answer": "A company that creates Large Language Models"},
],
dataset_id=dataset.id,
)

# Define a prompt template for grading answers
_PROMPT_TEMPLATE = """You are an expert professor specialized in grading students' answers to questions.
You are grading the following question:
{query}
Here is the real answer:
{answer}
You are grading the following predicted answer:
{result}
Respond with CORRECT or INCORRECT:
Grade:
"""

# Create a PromptTemplate object with the specified input variables and template
PROMPT = PromptTemplate(
input_variables=["query", "answer", "result"], template=_PROMPT_TEMPLATE
)

# Initialize a ChatOpenAI model for evaluation
eval_llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)

# Create a QA evaluator
qa_evaluator = LangChainStringEvaluator("qa", config={"llm": eval_llm, "prompt": PROMPT})

# Initialize an OpenAI client
openai_client = openai.Client()

# Generate a response to the question using OpenAI
def my_app(question):
return openai_client.chat.completions.create(
model="gpt-4o-mini",
temperature=0,
messages=[
{
"role": "system",
"content": "Respond to the users question in a short, concise manner (one short sentence)."
},
{
"role": "user",
"content": question,
}
],
).choices[0].message.content

def langsmith_app(inputs):
# Get the output from my_app for the given input question
output = my_app(inputs["question"])
st.session_state.chat_history.append(output)
return {"output": output}

# Evaluate the AI system using the specified data and evaluators
experiment_results = evaluate(
langsmith_app, # Your AI system
data=dataset_name, # The data to predict and grade over
evaluators=[qa_evaluator], # The evaluators to score the results
experiment_prefix="openai-3.5", # A prefix for your experiment names to easily identify them
)

-------------- .env ---------------
LANGCHAIN_API_KEY=""
OPENAI_API_KEY="

Подробнее здесь: https://stackoverflow.com/questions/792 ... -variables