Delete src

src/FunctionTools.py

@@ -1,249 +0,0 @@
-import json
-import chardet
-import streamlit as st
-import pandas as pd
-from llama_index.core.tools.tool_spec.base import BaseToolSpec
-
-class ScriptureDescriptionToolSpec(BaseToolSpec):
-    '''
-    Purpose: Obtains the description or summary about vedas, mandalas, kandas, shuktas, archakah, adhyaya, and other scriptural elements.
-    Returns: A dictionary containing the description or basic information about the specified scriptural element.
-    Sample query:
-    1. Describe the first kandah, second shukta from Atharvaveda?
-    2. Summarize ShuklaYajurVeda?
-    3. What is the difference between ShuklaYajurVeda and KrishnaYajurVeda?
-    '''
-    # Define the functions that we export to the LLM
-    spec_functions = ["get_description"]
-
-    with open("Data/scripture_descriptions.csv", 'rb') as f:
-        result = chardet.detect(f.read())
-
-    encoding = result['encoding']
-    df = pd.read_csv("Data/scripture_descriptions.csv", encoding=encoding)
-
-    @st.cache_data
-    def get_description(_self, level_0, level_1:int=None, level_2:int=None, level_3:int=None):
-        """
-        To get the description or basic information about vedas/mandalas/kandas/shukatas/archakah/adhyaya and others.
-        """
-        try:
-            if level_3 is not None:
-                # Case with Level-2 specified
-                result = _self.df[(_self.df['scripture_name'].str.lower() == level_0.lower())
-                                  & (_self.df['level_1'] == str(level_1))
-                                  & (_self.df['level_2'] == str(level_2)) & (_self.df['level_3'] == str(level_3))]
-            elif level_2 is not None:
-                # Case with Level-2 specified
-                result = _self.df[(_self.df['scripture_name'].str.lower() == level_0.lower())
-                                  & (_self.df['level_1'] == str(level_1)) & (_self.df['level_2'] == str(level_2))]
-            elif level_1 is not None:
-                # Case with Level-1 specified
-                result = _self.df[(_self.df['scripture_name'].str.lower() == level_0.lower())
-                                  & (_self.df['level_1'] == str(level_1))]
-            else:
-                # Case with only Level-0 specified
-                result = _self.df[_self.df['scripture_name'].str.lower() == level_0.lower()]
-
-            return result.iloc[0].to_dict()
-        except IndexError as e:
-            return json.dumps({"error": f"Failed to get scripture description. {e}"})
-
-class MantraToolSpec(BaseToolSpec):
-    '''
-    To obtain translations or meaning of vedamantras from RigVeda and AtharvaVeda using the function `get_translation`.
-    The mantra details such as vedamantra, padapatha, rishi, chandah, devata, and swarah from the vedas accessible through the function `get_vedamantra_details`.
-    The mantra summary like anvaya, adhibautic, ahyatmic, adhidaivic meaning of vedamantra accessible using the function 'get_vedamantra_summary'
-    Sample Query:
-    1. What is the vedamantra of the mantra from Rigveda, first mandala, first shukta, and first mantra?
-    2. What is the devata of the vedamantra from Rigveda, first mandala, first shukta, and first mantra?
-    3. What is the meaning of the vedamantra from Rigveda, first mandala, first shukta, and first mantra written by Tulsi Ram?
-    4. What is the (adhibautic) meaning of the vedamantra from RigVeda, first mandala, first shukta, and first mantra?
-    '''
-    spec_functions = ["get_translation", "get_vedamantra_details", "get_vedamantra_summary"]
-
-    TRANSLATION_CSV_PATH = 'Data/trans_Rig_Ath_index_v2.csv'
-    VEDAMANTRA_CSV_PATH = "Data/veda_content_modified_v3.csv"
-
-    def __init__(self):
-        super().__init__()
-        self.df_translation = pd.read_csv(self.TRANSLATION_CSV_PATH, encoding='utf-8')
-        self.df_vedamantra = pd.read_csv(self.VEDAMANTRA_CSV_PATH, encoding='utf-8')
-
-    @st.cache_data
-    def get_translation(_self, mantraid=None, scripture_name=None, MahatmaName=None, KandahNumber=None,
-                        MandalaNumber=None, ArchikahNumber=None, ShuktaNumber=None,
-                        AnvayaNumber=None, PrapatakNumber=None, MantraNumber=None,
-                        AnuvakNumber=None, AdhyayaNumber=None):
-        """
-        Get the translation of mantras from RigVeda and AtharvaVeda.
-        Sample Query:
-        1. What is the translation of Tulsi Ram of the vedamantra from Rigveda, first mandala, first shukta, and first mantra?
-        2. What is the translation or adhibautic meaning of the vedamantra from RigVeda, first mandala, first shukta, and first mantra?
-        3. What is the subject of the mantra 1.1.84.1?
-        """
-        try:
-            if mantraid is None:
-                scripture_name_lower = scripture_name.lower() if scripture_name is not None else False
-                if scripture_name_lower == 'rigveda':
-                    details = _self.df_translation[
-                        (_self.df_translation['scripture_name'].str.lower() == scripture_name_lower)
-                        & (_self.df_translation['MandalaNumber'] == MandalaNumber)
-                        & (_self.df_translation['ShuktaNumber'] == int(ShuktaNumber))
-                        & (_self.df_translation['MantraNumber'] == int(MantraNumber))
-                    ].to_dict(orient='records')
-                elif scripture_name_lower == 'atharvaveda':
-                    details = _self.df_translation[
-                        (_self.df_translation['scripture_name'].str.lower() == scripture_name_lower)
-                        & (_self.df_translation['KandahNumber'] == KandahNumber)
-                        & (_self.df_translation['ShuktaNumber'] == ShuktaNumber)
-                        & (_self.df_translation['MantraNumber'] == MantraNumber)].to_dict(orient='records')
-                elif scripture_name_lower == 'samaveda':
-                    details = _self.df_translation[
-                        (_self.df_translation['scripture_name'].str.lower() == scripture_name_lower)
-                        & (_self.df_translation['ArchikahNumber'] == ArchikahNumber)
-                        & (_self.df_translation['ShuktaNumber'] == ShuktaNumber)
-                        & (_self.df_translation['MantraNumber'] == MantraNumber)].to_dict(orient='records')
-                elif scripture_name_lower == 'krishnayajurveda':
-                    details = _self.df_translation[
-                        (_self.df_translation['scripture_name'].str.lower() == scripture_name_lower)
-                        & (_self.df_translation['PrapatakNumber'] == PrapatakNumber)
-                        & (_self.df_translation['AnuvakNumber'] == AnuvakNumber)
-                        & (_self.df_translation['MantraNumber'] == MantraNumber)].to_dict(orient='records')
-                else:
-                    details = _self.df_translation[
-                        (_self.df_translation['scripture_name'].str.lower() == scripture_name_lower)
-                        & (_self.df_translation['AdhyayaNumber'] == AdhyayaNumber)
-                        & (_self.df_translation['MantraNumber'] == MantraNumber)
-                    ].to_dict(orient='records')
-            else:
-                details = _self.df_translation[_self.df_translation['mantra_id'] == mantraid].to_dict(orient='records')
-
-            if MahatmaName is not None:
-                for item in details:
-                    if item['MahatmaName'] == MahatmaName:
-                        return item
-            else:
-                return details
-        except Exception as e:
-            return json.dumps({"error": f"Failed to get translation. {e}"})
-
-    @st.cache_data
-    def get_vedamantra_details(_self, mantraid=None, scripture_name=None, KandahNumber=None,
-                               MandalaNumber=None, ArchikahNumber=None, ShuktaNumber=None,
-                               AnvayaNumber=None, PrapatakNumber=None, MantraNumber=None,
-                               AnuvakNumber=None, AdhyayaNumber=None):
-        """
-        To obtain the vedamantra details such as vedamantra, padapata, devata, rishi, swarah, and chandah.
-        1. What is the vedamantra of the mantra from Rigveda, first mandala, first shukta, and first mantra?
-        2. What is the devata of the vedamantra from Rigveda, first mandala, first shukta, and first mantra?
-        """
-        try:
-            if mantraid is None:
-                scripture_name_lower = scripture_name.lower() if scripture_name is not None else False
-
-                if scripture_name_lower == 'rigveda':
-                    conditions = (_self.df_vedamantra['scripture_name'].str.lower() == scripture_name_lower) & \
-                                  (_self.df_vedamantra['MandalaNumber'] == MandalaNumber) & \
-                                  (_self.df_vedamantra['ShuktaNumber'] == ShuktaNumber) & \
-                                  (_self.df_vedamantra['MantraNumber'] == str(MantraNumber))
-                    details = _self.df_vedamantra[conditions]['mantra_json'].values
-                    vedamantra_details = json.loads(details[0])['mantraHeader']['language'][1]['mandala']['shukta']['mantra']
-
-                elif scripture_name_lower == 'atharvaveda':
-                    conditions = (_self.df_vedamantra['scripture_name'].str.lower() == scripture_name_lower) & \
-                                  (_self.df_vedamantra['KandahNumber'] == KandahNumber) & \
-                                  (_self.df_vedamantra['ShuktaNumber'] == ShuktaNumber) & \
-                                  (_self.df_vedamantra['MantraNumber'] == str(MantraNumber))
-                    details = _self.df_vedamantra[conditions]['mantra_json'].values
-                    vedamantra_details = json.loads(details[0])['mantraHeader']['language'][1]['kandah']['shukta']['mantra']
-                elif scripture_name_lower == 'samaveda':
-                    conditions = (_self.df_vedamantra['scripture_name'].str.lower() == scripture_name_lower) & \
-                                  (_self.df_vedamantra['ArchikahNumber'] == ArchikahNumber) & \
-                                  (_self.df_vedamantra['ShuktaNumber'] == ShuktaNumber) & \
-                                  (_self.df_vedamantra['MantraNumber'] == str(MantraNumber))
-                    details = _self.df_vedamantra[conditions]['mantra_json'].values
-                    vedamantra_details = json.loads(details[0])['mantraHeader']['language'][1]['archikah']
-                elif scripture_name_lower == 'krishnayajurveda':
-                    conditions = (_self.df_vedamantra['scripture_name'].str.lower() == scripture_name_lower) & \
-                                  (_self.df_vedamantra['PrapatakNumber'] == PrapatakNumber) & \
-                                  (_self.df_vedamantra['AnuvakNumber'] == AnuvakNumber) & \
-                                  (_self.df_vedamantra['MantraNumber'] == str(MantraNumber))
-                    details = _self.df_vedamantra[conditions]['mantra_json'].values
-                    vedamantra_details = json.loads(details[0])['mantraHeader']['language'][1]['kandah']['prapatak']['anuvak']
-                else:
-                    conditions = (_self.df_vedamantra['scripture_name'].str.lower() == scripture_name_lower) & \
-                                  (_self.df_vedamantra['AdhyayaNumber'] == AdhyayaNumber) & \
-                                  (_self.df_vedamantra['MantraNumber'] == str(MantraNumber))
-                    details = _self.df_vedamantra[conditions]['mantra_json'].values
-                    vedamantra_details = json.loads(details[0])['mantraHeader']['language'][1]['adhyaya']['mantra']
-
-            else:
-                # Handle case when mantraid is provided
-                details = _self.df_vedamantra[_self.df_vedamantra['mantra_number'] == mantraid]['mantra_json'].values
-                vedamantra_details = json.loads(details[0])['mantraHeader']['language'][1]
-
-            return vedamantra_details
-        except Exception as e:
-            return json.dumps({"error": f"Failed to get vedamantra details. {str(e)}"})
-
-    @st.cache_data
-    def get_vedamantra_summary(_self, mantraid=None, scripture_name=None, KandahNumber=None,
-                               MandalaNumber=None, ArchikahNumber=None, ShuktaNumber=None,
-                               AnvayaNumber=None, PrapatakNumber=None, MantraNumber=None,
-                               AnuvakNumber=None, AdhyayaNumber=None):
-        '''
-        To obtain the vedamantra summary like anvaya, translation, adhibautic, adhyatmic, adhidaivic meaning of the mantra.
-        1. What is the adhibautic meaning of the mantra from AtharvaVeda, first kandah, first shukta, and first mantra?
-        2. What is the anvaya of the vedamantra from Rigveda, first mandala, first shukta, and first mantra?
-        '''
-        try:
-            if mantraid is None:
-                scripture_name_lower = scripture_name.lower() if scripture_name is not None else False
-                if scripture_name_lower == 'rigveda':
-                    details = _self.df_vedamantra[
-                        (_self.df_vedamantra['scripture_name'].str.lower() == scripture_name_lower)
-                        & (_self.df_vedamantra['MandalaNumber'] == MandalaNumber)
-                        & (_self.df_vedamantra['ShuktaNumber'] == ShuktaNumber)
-                        & (_self.df_vedamantra['MantraNumber'] == str(MantraNumber))
-                    ]['mantra_json'].values
-                elif scripture_name_lower == 'atharvaveda':
-                    details = _self.df_vedamantra[
-                        (_self.df_vedamantra['scripture_name'].str.lower() == scripture_name_lower)
-                        & (_self.df_vedamantra['KandahNumber'] == KandahNumber)
-                        & (_self.df_vedamantra['ShuktaNumber'] == ShuktaNumber)
-                        & (_self.df_vedamantra['MantraNumber'] == str(MantraNumber))
-                    ]['mantra_json'].values
-                elif scripture_name_lower == 'samaveda':
-                    details = _self.df_vedamantra[
-                        (_self.df_vedamantra['scripture_name'].str.lower() == scripture_name_lower)
-                        & (_self.df_vedamantra['ArchikahNumber'] == ArchikahNumber)
-                        & (_self.df_vedamantra['ShuktaNumber'] == ShuktaNumber)
-                        & (_self.df_vedamantra['MantraNumber'] == str(MantraNumber))
-                    ]['mantra_json'].values
-                elif scripture_name_lower == 'krishnayajurveda':
-                    details = _self.df_vedamantra[
-                        (_self.df_vedamantra['scripture_name'].str.lower() == scripture_name_lower)
-                        & (_self.df_vedamantra['PrapatakNumber'] == PrapatakNumber)
-                        & (_self.df_vedamantra['AnuvakNumber'] == AnuvakNumber)
-                        & (_self.df_vedamantra['MantraNumber'] == str(MantraNumber))
-                    ]['mantra_json'].values
-                else:
-                    details = _self.df_vedamantra[
-                        (_self.df_vedamantra['scripture_name'].str.lower() == scripture_name_lower)
-                        & (_self.df_vedamantra['AdhyayaNumber'] == AdhyayaNumber)
-                        & (_self.df_vedamantra['MantraNumber'] == str(MantraNumber))
-                    ]['mantra_json'].values
-            else:
-                details = _self.df_vedamantra[_self.df_vedamantra['mantra_number'] == mantraid]['mantra_json'].values
-
-            jsonDict = json.loads(details[0])
-            mantraSummary = jsonDict['mantraSummary']['language']
-            mantraSummary_IAST = jsonDict['mantraSummary']['language'][1]
-            vedamantra_summary = {"Roman-IAST summary of vedamantra": mantraSummary_IAST}
-            for item in mantraSummary:
-                if item['languageName'] == 'English':
-                    vedamantra_summary.update({"English summary of vedamantra": item})
-            return vedamantra_summary
-        except Exception as e:
-            return json.dumps({"error": f"Failed to get vedamantra summary. {e}"})

src/app.py

@@ -1,149 +0,0 @@
-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
-
-
-#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)
-@st.cache_resource(show_spinner=False)  # 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("Svarupa Bot ")
-
-if "messages" not in st.session_state.keys():
-    st.session_state.messages = [
-        {"role": "assistant", "content": "Hi. I am Svarupa 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)
-