ERROR WHEN RUN THE CODE BELOW
raise GoogleGenerativeAIError(f”Error embedding content: {e}”) from e
langchain_google_genai._common.GoogleGenerativeAIError: Error embedding content: 404 models/embeddings-001 is not found for API version v1beta, or is not supported for batchEmbedContents. Call ListModels to see the list of available models and their supported
methods.

import streamlit as st
from langchain_core.messages import AIMessage, HumanMessage

# for import 3rd party in langchain for bs
from langchain_community.document_loaders import WebBaseLoader

# for  text splitter
from langchain.text_splitter import RecursiveCharacterTextSplitter

#3rd party tools for langchain import by using lanchain community
from langchain_community.vectorstores import Chroma # chroma is vectoer DB

#for embedding the text chunk use openai embedding model
from langchain_openai import OpenAIEmbeddings
import os
from langchain_google_genai import GoogleGenerativeAIEmbeddings
# Or use `os.getenv('GOOGLE_API_KEY')` to fetch an environment variable.
# GOOGLE_API_KEY=userdata.get('AIzaSyBg-EkoWxKK-JmtiEfsNFqnKOn70vRTqBk')
# os.getenv('GOOGLE_API_KEY')
# genai.configure(api_key=GOOGLE_API_KEY)
from langchain_openai import ChatOpenAI

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
from dotenv import load_dotenv
load_dotenv()

def get_response(user_input):
    return "Good question"

def get_vectorstore_from_url(url):
    #debug get the text in document form
    loader = WebBaseLoader(url)
    document =loader.load()

    #split the documnet into chunks
    text_splitter = RecursiveCharacterTextSplitter()
    document_chunks = text_splitter.split_documents(document)

    # create a vectorstore from chunks and embedding using OPENAI paid
    # vector_store = Chroma.from_documents(document_chunks,OpenAIEmbeddings())
    # return document_chunks

    #create a vector store from chunks using chroma DB and GoogleGenrativeAIEmbeddings free
    embeddings = GoogleGenerativeAIEmbeddings(model = "models/embeddings-001")
    vector_store = Chroma.from_documents(document_chunks, embedding = embeddings)

#create retrivel chain
def get_context_retriver_chain(vector_store):
    llm = ChatOpenAI()

    retriver = vector_store.as_retriver()
    
    prompt = ChatPromptTemplate.from_messagesfrom_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
    


# app config
st.set_page_config(page_title="Chat with website ",page_icon="BM")
st.title("Chat with Website")

if "chat_history" not in st.session_state:
    st.session_state.chat_history =[
        AIMessage(content="Hell I am Bot, How can i help you?"),
    ]

# add Side Bar
with st.sidebar:
    st.header("Settings")
    website_url = st.text_input("Website url")   

if website_url is None or website_url =="":
    st.info("Please enter a website Url")
else:
    # for debug 
    document_chunks = get_vectorstore_from_url(website_url)
    with st.sidebar:
        st.write(document_chunks)
    #user input
    user_query = st.chat_input("Write your input here...")
    if user_query is not None and user_query!="":
        response = get_response(user_query)
        st.session_state.chat_history.append(HumanMessage(content=user_query))
        st.session_state.chat_history.append(AIMessage(content=response))

        #test the retriver chain 
        retriver_documents = retriver_chain.invoke({
            "chat_history": st.session_state.chat_history,
            "input": user_query
        })

        st.write(retriver_documents)
        
    # conversation
    for message in st.session_state.chat_history:
        if isinstance(message,AIMessage):
            with st.chat_message("AI"):
                st.write(message.content)
        elif isinstance(message,HumanMessage):
            with st.,chat_message("Human"):
                st.write(message.content)

googlegenerativeaierror, batchembedcontents, modelsembeddings-001