modified prompts

app.py

@@ -47,6 +47,7 @@ except KeyError as e:
 #pinecone_api_key = st.secrets["PINECONE_API_KEY_SAM"]
 
 #llm
+llm_AI = OpenAI(temperature=0, model="gpt-4-1106-preview",api_key=openai_api_key, max_tokens=512)
 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
@@ -70,10 +71,12 @@ 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)
+query_engine_vector = index_store.as_query_engine(similarity_top_k=5,vector_store_query_mode ='hybrid',alpha=0.6,inlcude_metadata = True)
 #pandas Engine
 df_veda_details = pd.read_csv("Data/veda_content_details.csv",encoding='utf-8')
-query_engine_pandas = PandasQueryEngine(df=df_veda_details)
+df_pada_details = pd.read_csv("Data/term_grammar_v2.csv.csv",encoding='utf-8')
+query_engine_veda_details = PandasQueryEngine(df=df_veda_details)
+query_engine_pada_details = PandasQueryEngine(df=df_pada_details)
 
 # Query Engine Tools
 query_engine_tools = [
@@ -95,20 +98,38 @@ query_engine_tools = [
         ),
     ),
     QueryEngineTool(
-        query_engine=query_engine_pandas,
+        query_engine=query_engine_veda_details,
         metadata=ToolMetadata(
-            name="pandas_engine",
+            name="pandas_veda_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:\
+                '''A powerful tool designed to handle queries related to counting information about vedic content document. This document is a .csv file with different columns 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 count or list etc. \
+                'padapatha', 'vedamantra', 'AdhyayaNumber', 'ArchikahNumber', 'ArchikahName', 'ShuktaNumber', 'keyShukta', 'ParyayaNumber', 'MandalaNumber'.\
+                Always provide the final answer after excuting pandas query which is equivalent to user query.\
                 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?
+                3. Which Kandah has the maximum number of in KrishnaYajurVeda?\
+                4. Find the number of mantras from AtharvaVeda whose devata is vācaspatiḥ and chandah is anuṣṭup?\
+                5. count the mantras in RigVeda whose swarah is gāndhāraḥ?
+               '''
+            ),
+        ),
+    ),
+    QueryEngineTool(
+        query_engine=query_engine_pada_details,
+        metadata=ToolMetadata(
+            name="pandas_pada_engine",
+            description=(
+                '''Helpful to answer the queries related to count on padas/terms/words from vedic scriptures based on csv files.\
+                Different columns from csv file containing comprehensive information about the padas.\
+                The column names as follows:\
+                'scripture_name', 'mantra_level_1', 'mantra_level_2', 'mantra_level_3',\
+       'mantra_level_4', 'mantra_number', 'mantra_id', 'word_index',\
+       'segmentation', 'term_json_new', 'word'.\
+                Sample Query:\
+                1. How many times the word yāsyan appeared in RigVeda?\
+                2. How many total distrinct terms are there in AtharvaVeda?
                '''
             ),
         ),
@@ -124,14 +145,15 @@ tools = [*mantra_tools,*description_tools,*query_engine_tools]
 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.
+  For every query, you must use either any one of the tool or use available history/context.\
+  User expect the responses based on vedic scriptures or related vedas.\ 
   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,  memory = memory, max_iterations = 50,verbose=True)
+    return ReActAgent.from_tools(tools, llm=llm_AI4, context=context,  memory = memory, max_iterations = 100,verbose=True)
 
 # Example usage
 react_agent_instance = create_react_agent()