ValidationError: 1 ошибка проверки для обязательного поля модели GoogleGenerativeAIEmbeddings (type=value_error.missing) ⇐ Python

[Anonymous]

Я пытаюсь интегрировать GoogleGenerativeAIEmbeddings из библиотеки langchain_google_genai в свое приложение Streamlit. Однако я сталкиваюсь с ошибкой ValidationError, связанной с отсутствующими полями, когда пытаюсь создать объект GoogleGenerativeAIEmbeddings. Вот сообщение об ошибке, которое я вижу:

Код: Выделить всё

ValidationError: 1 validation error for GoogleGenerativeAIEmbeddings
model field required (type=value_error.missing)
Traceback:
File "E:\Project_Gen_AI\chat-with-website\Lib\site-packages\streamlit\runtime\scriptrunner\script_runner.py", line 600, in _run_script
exec(code, module.__dict__)
File "E:\Project_Gen_AI\src\demo2.py", line 84, in 
st.session_state.vector_store = get_vectorstore_from_url(website_URL)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\Project_Gen_AI\src\demo2.py", line 31, in get_vectorstore_from_url
vectorstore = Chroma.from_documents(document_chunks, GoogleGenerativeAIEmbeddings(api_key=GOOGLE_API_KEY))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\Project_Gen_AI\chat-with-website\Lib\site-packages\pydantic\v1\main.py", line 341, in __init__
raise validation_error

Код: Выделить всё

from dotenv import load_dotenv
import os

import streamlit as st
from langchain_core.messages import AIMessage, HumanMessage
from langchain_community.document_loaders import WebBaseLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import Chroma
from langchain_google_genai import GoogleGenerativeAIEmbeddings, ChatGoogleGenerativeAI
from dotenv import load_dotenv
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain.chains import create_history_aware_retriever, create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain

load_dotenv()

GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")

def get_response(user_query):
return "First, please build me"

def get_vectorstore_from_url(url):
loader = WebBaseLoader(url)
document = loader.load()

# Split the document into chunks using the RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter()
document_chunks = text_splitter.split_documents(document)

# Create a vectorstore from the chunks
vectorstore = Chroma.from_documents(document_chunks, GoogleGenerativeAIEmbeddings(api_key=GOOGLE_API_KEY))

return vectorstore

def get_context_retriever_chains(vectorstore):
llm = ChatGoogleGenerativeAI(api_key=GOOGLE_API_KEY)

retriever = vectorstore.as_retriever()

prompt = ChatPromptTemplate.format_messages([
MessagesPlaceholder(variable_name="chat_history"),
("user", "{input}"),
("user", "Given the above conversation, generate a search query to look up in order to get information relevant to the conversation")
])

retriever_chain = create_history_aware_retriever(llm, retriever, prompt)

return retriever_chain

def get_conversational_rag_chain(retriever_chain):
llm = ChatGoogleGenerativeAI(api_key=GOOGLE_API_KEY)

prompt = ChatPromptTemplate.format_messages([
("system", "Answer the user's questions based on the below context:\n\n{context}\n"),
MessagesPlaceholder(variable_name="chat_history"),
("user", "{input}")
])

stuff_documents_chain = create_stuff_documents_chain(llm, prompt)

return create_retrieval_chain(retriever_chain, stuff_documents_chain)

# For the interface
st.set_page_config(page_title="Chat-with-website", page_icon="💬")
st.title("Chat with website")

# App Sidebar
with st.sidebar:
st.header("Settings")
website_URL = st.text_input("Website url")

# Check if the website URL is provided
if website_URL is None or website_URL == "":
st.info("Please enter a website url")

# Load documents from the provided URL using the WebBaseLoader
else:
# Session state
if "chat_history" not in st.session_state:
st.session_state.chat_history = [
AIMessage(content="Hello, I am a bot.  How can I help you"),
]
if "vector_store" not in st.session_state:
st.session_state.vector_store = get_vectorstore_from_url(website_URL)

retriever_chain = get_context_retriever_chains(st.session_state.vector_store)

conversation_rag_chain = get_conversational_rag_chain(retriever_chain)

# CHAT HISTORY / AI response to User Input
user_query = st.chat_input("Type your message here... ")
if user_query is not None and user_query != "":
response = conversation_rag_chain.invoke({
"chat_history": st.session_state.chat_history,
"input": user_query
})
st.write(response)
st.session_state.chat_history.append(HumanMessage(content=user_query))
st.session_state.chat_history.append(AIMessage(content=response))

# Iterate through the chat history stored in the session state
for message in st.session_state.chat_history:
# Check if the message is an instance of AIMessage
if isinstance(message, AIMessage):
# Display the AI message in the chat interface
with st.chat_message("AI"):
st.write(message.content)
# Check if the message is an instance of HumanMessage
elif isinstance(message, HumanMessage):
# Display the Human message in the chat interface
with st.chat_message("Human"):
st.write(message.content)

Я подозреваю, что мне не хватает некоторых обязательных параметров при инициализации GoogleGenerativeAIEmbeddings, но я не могу найти никакой документации или примеров, показывающих, какими должны быть эти параметры.
Что может быть причиной этой ошибки проверки и как ее устранить?
Спасибо за помощь!

Подробнее здесь: https://stackoverflow.com/questions/787 ... odel-field