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# Generics
import os
import keyfile
import warnings
import streamlit as st
from pydantic import BaseModel
warnings.filterwarnings("ignore")
# Langchain packages
from langchain.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import Pinecone as PineconeVectorStore
from langchain.llms import HuggingFaceHub
from langchain import PromptTemplate
from langchain.schema.runnable import RunnablePassthrough
from langchain.schema.output_parser import StrOutputParser
from pinecone import Pinecone, ServerlessSpec
pc = Pinecone(api_key=keyfile.PINECONE_API_KEY)
os.environ["PINECONE_API_KEY"] = keyfile.PINECONE_API_KEY
cloud = os.environ.get("PINECONE_CLOUD") or "aws"
region = os.environ.get("PINECONE_REGION") or "us-east-1"
serv = ServerlessSpec(cloud = cloud, region = region)
model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
llm = HuggingFaceHub(
repo_id = model_id,
model_kwargs = {"temperature" : 0.8, "top_k" : 50},
huggingfacehub_api_token = userdata.get("HFToken")
)
index_name = "parasgupta"
# We are check if the name of our index is not existing in pinecone directory
if index_name not in pc.list_indexes().names():
# if not then we will create a index for us
pc.create_index(
name = index_name,
dimension = 768,
metric = "cosine",
spec = serv
)
while not pc.describe_index(index_name).status['ready']:
time.sleep(1)
# IF the index is not there in the index list
if index_name not in pc.list_indexes():
docsearch = PineconeVectorStore.from_documents(docs, embeddings, index_name = index_name)
else:
docsearch = PineconeVectorStore.from_existing_index(index_name, embeddings, pinecone_index = pc.Index(index_name))
loader = TextLoader("/content/drive/MyDrive/Intelli_GenAI/RAG/Machine Learning Operations.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size = 1000, chunk_overlap = 4)
docs = text_splitter.split_documents(documents)
class AIMessage(BaseModel):
content: str
# initializing the sessionMessages
if "sessionMessages" not in st.session_state:
st.session_state["sessionMessages"] = []
# General Instruction
if "sessionMessages" not in st.session_state:
st.session_state.sessionMessage = [
SystemMessage(content = "You are a medieval magical healer known for your peculiar sarcasm")
]
# Configuring the key
os.environ["GOOGLE_API_KEY"] = keyfile.GOOGLEKEY
# Create a model
llm = ChatGoogleGenerativeAI(
model="gemini-1.5-pro",
temperature=0.7,
convert_system_message_to_human= True
)
# Response function
def load_answer(question):
st.session_state.sessionMessages.append(HumanMessage(content=question))
assistant_response = llm.invoke(st.session_state.sessionMessages)
# Assuming assistant_response is an object with a 'content' attribute
if hasattr(assistant_response, 'content') and isinstance(assistant_response.content, str):
processed_content = assistant_response.content
st.session_state.sessionMessages.append(AIMessage(content=processed_content))
else:
st.error("Invalid response received from AI.")
processed_content = "Sorry, I couldn't process your request."
return processed_content
# def load_answer(question):
# st.session_state.sessionMessages.append(HumanMessage(content = question))
# assistant_answer = llm.invoke(st.session_state.sessionMessages)
# st.session_state.sessionMessages.append(AIMessage(content = assistant_answer))
# return assistant_answer.content
# User message
def get_text():
input_text = st.text_input("You: ", key = input)
return input_text
# Implementation
user_input = get_text()
submit = st.button("Generate")
if submit:
resp = load_answer(user_input)
st.subheader("Answer: ")
st.write(resp, key = 1) |