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import streamlit as st | |
st.set_page_config( | |
page_title="SVARUPA AI", | |
layout="centered", # or "wide" | |
initial_sidebar_state="auto" # or "expanded" or "collapsed" | |
) | |
from llama_index.core import VectorStoreIndex, StorageContext, Document | |
from llama_index.llms.openai import OpenAI | |
import os | |
import pandas as pd | |
from llama_index.core import Settings | |
from llama_index.vector_stores.pinecone import PineconeVectorStore | |
import pinecone | |
from pinecone import Pinecone, PodSpec | |
from llama_index.core.query_engine import PandasQueryEngine | |
from llama_index.core.agent import ReActAgent | |
from llama_index.core.memory import ChatMemoryBuffer | |
from sentence_transformers import SentenceTransformer | |
from llama_index.embeddings.huggingface import HuggingFaceEmbedding | |
#from llama_index.indices.postprocessor import SimilarityPostprocessor | |
#from llama_index.postprocessor import SentenceTransformerRerank | |
import tiktoken | |
from llama_index.core.callbacks import CallbackManager, TokenCountingHandler | |
from llama_index.core.tools import QueryEngineTool, ToolMetadata | |
from FunctionTools import ScriptureDescriptionToolSpec, MantraToolSpec | |
# Print all loaded secrets | |
all_secrets = st.secrets | |
#st.write("All secrets:", all_secrets) | |
# Access the specific secret | |
try: | |
openai_api_key = st.secrets["OPENAI_APIKEY_CS"] | |
#st.write("OpenAI API Key:", openai_api_key) | |
except KeyError as e: | |
st.error(f"KeyError: {e}") | |
# Access the specific secret | |
try: | |
pinecone_api_key = st.secrets["PINECONE_API_KEY_SAM"] | |
#st.write("OpenAI API Key:", openai_api_key) | |
except KeyError as e: | |
st.error(f"KeyError: {e}") | |
#load keys | |
#openai_api_key = st.secrets["OPENAI_APIKEY_CS"] | |
#pinecone_api_key = st.secrets["PINECONE_API_KEY_SAM"] | |
#llm | |
llm_AI4 = OpenAI(temperature=0, model="gpt-4-1106-preview",api_key=openai_api_key, max_tokens=512) | |
token_counter = TokenCountingHandler( | |
tokenizer=tiktoken.encoding_for_model("gpt-4-1106-preview").encode | |
) | |
# global settings | |
Settings.embed_model = HuggingFaceEmbedding( | |
model_name="BAAI/bge-large-en-v1.5", | |
embed_batch_size=8 | |
) | |
Settings.llm = llm_AI4 | |
Settings.chunk_size = 512 | |
Settings.chunk_overlap = 50 | |
Settings.callback_manager = CallbackManager([token_counter]) | |
#memory for bot | |
memory = ChatMemoryBuffer.from_defaults(token_limit=3900) | |
#load vector database | |
pc = Pinecone(api_key=pinecone_api_key) | |
pinecone_index = pc.Index("pod-index") | |
vector_store_pine = PineconeVectorStore(pinecone_index=pinecone_index) | |
storage_context_pine = StorageContext.from_defaults(vector_store=vector_store_pine) | |
index_store = VectorStoreIndex.from_vector_store(vector_store_pine,storage_context=storage_context_pine) | |
query_engine_vector = index_store.as_query_engine(similarity_top_k=5,vector_store_query_mode ='hybrid',alpha=0.6) | |
#pandas Engine | |
df_veda_details = pd.read_csv("Data/veda_content_details.csv",encoding='utf-8') | |
query_engine_pandas = PandasQueryEngine(df=df_veda_details) | |
# Query Engine Tools | |
query_engine_tools = [ | |
QueryEngineTool( | |
query_engine=query_engine_vector, | |
metadata=ToolMetadata( | |
name="vector_engine", | |
description=( | |
'''Helpful to get semantic information from the documents. These documents containing comprehensive information about the Vedas.\ | |
They also covers various aspects, including general details about the Vedas, fundamental terminology associated with Vedic literature, \ | |
and detailed information about Vedamantras for each Veda. The Vedamantra details encompass essential elements such as padapatha, rishi, chandah,\ | |
devata, and swarah.This tool is very useful to answer general questions related to vedas.\ | |
Sample Query:\ | |
1. What is the meaning of devata ?\ | |
2. What are the different Brahmanas associated with SamaVeda?\ | |
3. What is the difference between Shruti and Smriti. | |
''' | |
), | |
), | |
), | |
QueryEngineTool( | |
query_engine=query_engine_pandas, | |
metadata=ToolMetadata( | |
name="pandas_engine", | |
description=( | |
'''Helpful to answer the queries related to count from the documents. This document is a .csv file with different columns containing comprehensive information about the Vedas.\ | |
The column names as follows:\ | |
'mantra_id', 'scripture_name', 'KandahNumber', 'PrapatakNumber','AnuvakNumber', 'MantraNumber', 'DevataName', 'RishiName', 'SwarahName', 'ChandaName',\ | |
'padapatha', 'vedamantra', 'AdhyayaNumber', 'ArchikahNumber', 'ArchikahName', 'ShuktaNumber', 'keyShukta', 'ParyayaNumber', 'MandalaNumber' | |
''This tool is very useful to answer questions related to vedas on.\ | |
Sample Query:\ | |
1. How many mantras are there in RigVeda whose swarah is gāndhāraḥ?\ | |
2. How many different devata present in rigveda?\ | |
3. Which Kandah has the maximum number of in KrishnaYajurVeda? | |
4. How many mantras are there in RigVeda? | |
''' | |
), | |
), | |
) | |
] | |
# tools | |
mantra_tools = MantraToolSpec().to_tool_list() | |
description_tools = ScriptureDescriptionToolSpec().to_tool_list() | |
tools = [*mantra_tools,*description_tools,*query_engine_tools] | |
# context | |
context = """ | |
You are an expert on Vedas and related scriptures.\ | |
Your role is to respond to questions about vedic scriptures and associated information based on available sources.\ | |
For every query, you must use either any one of the tool or use available history/context. | |
Please provide well-informed answers. Don't use prior knowledge. | |
""" | |
# Function to create ReActAgent instance (change it based on your initialization logic) | |
# Set allow_output_mutation to True for mutable objects like instances | |
def create_react_agent(): | |
return ReActAgent.from_tools(tools, llm=llm_AI4, context=context, verbose=True) | |
# Example usage | |
react_agent_instance = create_react_agent() | |
# Streamlit Components Initialization | |
st.title("Veda Bot ") | |
if "messages" not in st.session_state.keys(): | |
st.session_state.messages = [ | |
{"role": "assistant", "content": "Hi. I am AI Assistant. Ask me a question about Vedas!"} | |
] | |
if "chat_engine" not in st.session_state.keys(): | |
# Using st.cache_resource for caching the unserializable react_agent | |
st.session_state.chat_engine = create_react_agent() | |
if prompt := st.chat_input("Your question"): | |
st.session_state.messages.append({"role": "user", "content": prompt}) | |
for message in st.session_state.messages: | |
with st.chat_message(message["role"]): | |
st.write(message["content"]) | |
if st.session_state.messages[-1]["role"] != "assistant": | |
with st.chat_message("assistant"): | |
with st.spinner("Thinking..."): | |
# Using the cached chat_engine | |
response = st.session_state.chat_engine.chat(prompt) | |
st.write(response.response) | |
message = {"role": "assistant", "content": response.response} | |
st.session_state.messages.append(message) | |