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Update app.py
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app.py
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import
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from
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# from langchain.vectorstores import Chroma
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from langchain.vectorstores import FAISS
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from langchain.text_splitter import CharacterTextSplitter
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from langchain.llms import OpenAI
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from langchain.chains import VectorDBQA
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from langchain.chains import RetrievalQA
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from langchain.document_loaders import DirectoryLoader
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from langchain.chains import ConversationalRetrievalChain
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from langchain.memory import ConversationBufferMemory
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from langchain.evaluation.qa import QAGenerateChain
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import magic
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import os
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import streamlit as st
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from streamlit_chat import message
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if 'requests' not in st.session_state:
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st.session_state['requests'] = []
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# if 'buffer_memory' not in st.session_state:
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memory= ConversationBufferMemory(memory_key="chat_history", return_messages=True)
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retriever = new_db.as_retriever()
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chain = ConversationalRetrievalChain.from_llm(llm=llm, chain_type="stuff", memory= memory,retriever=retriever, verbose=False)
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with st.spinner("Cooking..."):
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# conversation_string = get_conversation_string()
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# st.code(conversation_string)
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# refined_query = query_refiner(conversation_string, query)
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# st.subheader("Refined Query:")
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# st.write(refined_query)
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# context = find_match(refined_query)
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# print(context)
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response = chain.run(query)
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st.session_state.requests.append(query)
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st.session_state.responses.append(response)
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with response_container:
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if st.session_state['responses']:
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if i < len(st.session_state['requests']):
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message(st.session_state["requests"][i], is_user=True,key=str(i)+ '_user')
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# st.info(memory.buffer)
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from typing import List, Optional
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from pydantic import BaseModel, BaseSettings, SecretStr
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import streamlit as st
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# Slide 3: Basic Model
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class User(BaseModel):
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id: int
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name: str = "Jane Doe"
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data = {"id": 19, "name": "Fanilo", "age": 179}
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user = User(**data)
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st.write(user)
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data = {"id": "Fanilo", "name": 42}
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user = User(**data)
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#st.write(user)
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# Slide 4: Hierarchical Model
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class Address(BaseModel):
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city: str
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street: Optional[str]
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class User(BaseModel):
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id: int
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name: str
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addresses: List[Address]
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data = {
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"id": 42,
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"name": "Fanilo",
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"addresses": [{"city": "Paris"}, {"city": "Tokyo", "street": "γγγ«γ‘γ―"}],
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}
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user = User(**data)
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st.success(user.addresses[1].street)
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# Slide 7: Secrets
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class Settings(BaseSettings):
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auth_key: SecretStr
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api_key: str
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class Config:
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env_file = "settings.env"
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st.write(Settings().dict())
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