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HarshSanghavi
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Upload 6 files
Browse files- config.py +8 -8
- database_functions.py +16 -1
- tools.py +43 -24
- utils.py +3 -2
config.py
CHANGED
@@ -31,21 +31,20 @@ class Settings:
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Options: "Here's a couple of ideas: Step Back or Talk It Out. Which one feels right for you?"
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Will: "Confronting someone can be scary. How about jotting down your thoughts first? Are you willing to try that?"
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Check-In: "I'll hit you up tomorrow to see how things are going. Got any other questions?"
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IMPORTANT INSTRUCTIONS:
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# use gen z and edgy words.
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# Must Not REPEAT ANY RESPONSE
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# Use sometimes slangs.
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# provide short answers like a conversation.
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# dont responed any gratitude words like sorry.
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# Previous Conversation Summary: {previous_summary}
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#
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# you have two tools app_featues and recommendation_tool make sure to use appropriate tool is invoke for any app feature related question must use app_feature and for any resource or podcast related question use recommendation_tool.
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# if conversation is ending must use close_chat tool no other tools.
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#
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#
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# must not use two tools in single execution.
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"""
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@@ -69,6 +68,7 @@ class Settings:
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MONGODB_DB_USER_SESSIONS_COLLECTION_NAME = "user_sessions"
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MONGODB_DB_CHAT_BOT_TOOLS_COLLECTION_NAME = "session_tool"
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MONGODB_DB_CHAT_BOT_MOOD_COLLECTION_NAME = "mood_summary"
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mongodb_client = pymongo.MongoClient(MONGODB_CONNECTION_STRING)
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mongodb_db = mongodb_client.get_database(MONGODB_DB_NAME) # Replace with your database name if not using default
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Options: "Here's a couple of ideas: Step Back or Talk It Out. Which one feels right for you?"
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Will: "Confronting someone can be scary. How about jotting down your thoughts first? Are you willing to try that?"
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Check-In: "I'll hit you up tomorrow to see how things are going. Got any other questions?"
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IMPORTANT INSTRUCTIONS:
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# use gen z and edgy words.
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# Must Not REPEAT ANY RESPONSE.
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# Use sometimes slangs.
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# provide short answers like a conversation.
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# dont responed any gratitude words like sorry.
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# Previous Conversation Summary: {previous_summary}
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# resources means podcast only nothing else. also topic of resource must be asked before suggesting anything.example: I’m here for it! Are we talking friend drama, school stress, or something else? Give me the lowdown so I can find the right resources for you.
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# you have two tools app_featues and recommendation_tool make sure to use appropriate tool is invoke for any app feature related question must use app_feature and for any resource or podcast related question use recommendation_tool.
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# if conversation is ending must use close_chat tool no other tools. and fix the response of close tool based on chat history.
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# must Avoid using the words 'vibe'. Instead, use alternative expressions and must not repeate any words.
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# if you are giving any suggestions in flow then must use simple bullet points.
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"""
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MONGODB_DB_USER_SESSIONS_COLLECTION_NAME = "user_sessions"
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MONGODB_DB_CHAT_BOT_TOOLS_COLLECTION_NAME = "session_tool"
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MONGODB_DB_CHAT_BOT_MOOD_COLLECTION_NAME = "mood_summary"
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MONGODB_DB_CHAT_RECOMEDATION_COLLECTION_NAME = 'chat_recommendation'
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mongodb_client = pymongo.MongoClient(MONGODB_CONNECTION_STRING)
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mongodb_db = mongodb_client.get_database(MONGODB_DB_NAME) # Replace with your database name if not using default
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database_functions.py
CHANGED
@@ -133,4 +133,19 @@ def isFirstSession(user_id):
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if user_sessions:
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return len(user_sessions['session_id'])==1
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else:
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-
return False
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if user_sessions:
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return len(user_sessions['session_id'])==1
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else:
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return False
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def set_recommendation_count(last_session_id):
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mongodb_chat_recommendation_collection = settings.mongodb_db.get_collection(settings.MONGODB_DB_CHAT_RECOMEDATION_COLLECTION_NAME)
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if mongodb_chat_recommendation_collection.count_documents({"session_id": last_session_id})==0:
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mongodb_chat_recommendation_collection.insert_one({"session_id": last_session_id,"give_recommendation": True} )
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def get_recommendation_count(last_session_id):
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mongodb_chat_recommendation_collection = settings.mongodb_db.get_collection(settings.MONGODB_DB_CHAT_RECOMEDATION_COLLECTION_NAME)
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data = mongodb_chat_recommendation_collection.find_one({"session_id":last_session_id})
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if data:
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return data['give_recommendation']
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else:
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return False
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tools.py
CHANGED
@@ -9,9 +9,10 @@ from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
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from langchain.agents import AgentExecutor
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from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
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from config import settings
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MEMORY = None
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-
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def get_embeddings(text_list):
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encoded_input = settings.tokenizer(
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@@ -275,7 +276,7 @@ def my_rewards(prompt: str) -> str:
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return response.content
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@tool("mentoring-questions"
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def mentoring(prompt: str) -> str:
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"""this function is used when user wants to know about 1-1 mentoring feature. 1:1 MENTORING: Personalized mentoring (coming soon).
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@@ -320,7 +321,7 @@ def my_journal(prompt: str) -> str:
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@tool("podcast-recommendation-tool")
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def recommand_podcast(prompt: str) -> str:
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"""
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Args:
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prompt (string): user query
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@@ -334,13 +335,13 @@ def recommand_podcast(prompt: str) -> str:
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context+= f"Row {index + 1}: Title: {row['title']} image: {row['cover_image']} referral_link: {row['referral_link']} category_id: {row['category_id']}"
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llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
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# Define the system prompt
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system_template = """ you have to give the recommandation of podcast for: {input}. also you are giving referal link of podcast.
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you must use the context only not any other information.
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context : {context}
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"""
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# print(system_template.format(context=context, input=prompt))
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response = llm.invoke(system_template.format(context=context, input=prompt))
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return response.content
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@tool("set-chat-bot-name",return_direct=True )
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@@ -357,29 +358,48 @@ def set_chatbot_name(name: str) -> str:
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@tool("clossing-chat",return_direct=True)
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def close_chat(summary:str)-> str:
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"""
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must use this tool when user closing the conversation. must use this tool when you are ending the conversation.
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Args:
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summary (str): summary of whole chat with your friend.
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Returns:
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str: closing chat statements.
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"""
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print('close tool starts')
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tools
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print('llm is created')
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prompt = ChatPromptTemplate.from_messages([("system", system_template.format(summary = summary)),MessagesPlaceholder(variable_name="agent_scratchpad")])
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chain = RunnablePassthrough.assign(agent_scratchpad=lambda x: format_to_openai_functions(x["intermediate_steps"])) | prompt |llm | OpenAIFunctionsAgentOutputParser()
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print('chain is rolling')
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@@ -395,7 +415,7 @@ def close_chat(summary:str)-> str:
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@tool("App-Fetures")
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def app_features(summary:str)-> str:
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""" For any app features details
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Args:
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summary (str): summary of whole chat with your friend.
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print('app feature tool starts')
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system_template = """ you have given one summary of chat.
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summary : {summary}.
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using this summary give appropriate features suggestions using tools.
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# make all responses short.
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"""
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@@ -425,7 +444,7 @@ def app_features(summary:str)-> str:
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print('agent is created')
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# print(system_template.format(context=context, input=prompt))\
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response = agent.invoke({})['output']
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return response
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@@ -451,4 +470,4 @@ def joke_teller(summary: str) -> str:
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"""
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response = llm.invoke(system_template.format(summary=summary))
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return response.content
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from langchain.agents import AgentExecutor
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from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
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from config import settings
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from database_functions import set_recommendation_count,get_recommendation_count
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MEMORY = None
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SESSION_ID= ""
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def get_embeddings(text_list):
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encoded_input = settings.tokenizer(
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return response.content
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@tool("mentoring-questions")
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def mentoring(prompt: str) -> str:
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"""this function is used when user wants to know about 1-1 mentoring feature. 1:1 MENTORING: Personalized mentoring (coming soon).
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@tool("podcast-recommendation-tool")
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def recommand_podcast(prompt: str) -> str:
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""" must used when user wants to any resources only.
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Args:
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prompt (string): user query
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context+= f"Row {index + 1}: Title: {row['title']} image: {row['cover_image']} referral_link: {row['referral_link']} category_id: {row['category_id']}"
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llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
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# Define the system prompt
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system_template = """ you have to give the recommandation of podcast for: {input}. also you are giving referal link of podcast. give 3-4 podcast only.
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you must use the context only not any other information.
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context : {context}
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"""
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# print(system_template.format(context=context, input=prompt))
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response = llm.invoke(system_template.format(context=context, input=prompt))
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set_recommendation_count(SESSION_ID)
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return response.content
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@tool("set-chat-bot-name",return_direct=True )
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@tool("clossing-chat",return_direct=True)
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def close_chat(summary:str)-> str:
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""" must use this tool when user closing the conversation. must use this tool when you are ending the conversation.
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Args:
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summary (str): summary of whole chat with your friend. must use I for yourself and for user use my friend.
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Returns:
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str: closing chat statements.
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"""
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print('close tool starts')
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global SESSION_ID
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tools = []
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llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
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if (not get_recommendation_count(SESSION_ID)):
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tools.extend([moxicast, my_calender, my_journal, my_rewards, my_rituals, my_vibecheck, peptalks, sactury, power_zens, affirmations, horoscope, mentoring, influencer_post,recommand_podcast])
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if len(tools)!=0:
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functions = [convert_to_openai_function(f) for f in tools]
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llm = llm.bind(functions= functions)
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print('llm is created')
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system_template = """"""
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if len(tools)!=0:
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system_template += """ context: there is one typical conversation going on between two high school gen z girls.
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you are one of the high school gen z girl. your voice is edgy and raw.
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now you are going to close the conversation.
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summary of conversation is given to you.
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summary: {summary}
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give recommendation of any podcast or app feature based on summary and close the conversation in short.
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# must Avoid using the words 'vibe'. Instead, use alternative expressions and must not repeate any words.
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"""
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else:
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system_template+= """ context: there is one typical conversation going on between two high school gen z girls.
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you are one of the high school gen z girl. your voice is edgy and raw.
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now you are going to close the conversation.
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summary of conversation is given to you.
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summary: {summary}
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now just end the conversation in 1 sentense in short.
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# must Avoid using the words 'vibe'. Instead, use alternative expressions and must not repeate any words.
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"""
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prompt = ChatPromptTemplate.from_messages([("system", system_template.format(summary = summary)),MessagesPlaceholder(variable_name="agent_scratchpad")])
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chain = RunnablePassthrough.assign(agent_scratchpad=lambda x: format_to_openai_functions(x["intermediate_steps"])) | prompt |llm | OpenAIFunctionsAgentOutputParser()
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print('chain is rolling')
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@tool("App-Fetures")
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def app_features(summary:str)-> str:
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""" must use For any app features details.
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Args:
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summary (str): summary of whole chat with your friend.
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print('app feature tool starts')
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system_template = """ you have given one summary of chat.
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summary : {summary}.
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using this summary give appropriate features suggestions using tools. if you don't find any tool appropriate to summary ask question only.
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# make all responses short.
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"""
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print('agent is created')
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# print(system_template.format(context=context, input=prompt))\
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set_recommendation_count(SESSION_ID)
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response = agent.invoke({})['output']
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return response
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"""
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response = llm.invoke(system_template.format(summary=summary))
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return response.content
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utils.py
CHANGED
@@ -12,7 +12,7 @@ from langchain.agents.format_scratchpad import format_to_openai_functions
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from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
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from langchain.agents import AgentExecutor
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from tools import MEMORY, set_chatbot_name, close_chat, recommand_podcast,app_features,joke_teller
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from database_functions import get_chat_bot_name,get_chat_history, get_last_conversion, get_last_session, get_mood_data
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@@ -103,8 +103,9 @@ def create_agent(user_id,is_first = False):
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previous_session_id['last_session_id']), return_messages=True, k=5)
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# print("memory created")
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global MEMORY
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MEMORY = memory
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chain = RunnablePassthrough.assign(agent_scratchpad=lambda x: format_to_openai_functions(x["intermediate_steps"])) | prompt | model | OpenAIFunctionsAgentOutputParser()
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from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
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from langchain.agents import AgentExecutor
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from tools import MEMORY, set_chatbot_name, close_chat, recommand_podcast,app_features,joke_teller,SESSION_ID
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from database_functions import get_chat_bot_name,get_chat_history, get_last_conversion, get_last_session, get_mood_data
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previous_session_id['last_session_id']), return_messages=True, k=5)
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# print("memory created")
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global MEMORY,SESSION_ID
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MEMORY = memory
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SESSION_ID = previous_session_id['last_session_id']
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chain = RunnablePassthrough.assign(agent_scratchpad=lambda x: format_to_openai_functions(x["intermediate_steps"])) | prompt | model | OpenAIFunctionsAgentOutputParser()
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