Squad AI first commit

.env

@@ -0,0 +1,5 @@
+OPENAI_API_KEY=sk-proj-gWXNkaZYyjH4X4g1OVPET3BlbkFJplRpheelyapIU1rXE4F7
+LANGCHAIN_API_KEY=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
+#LANGCHAIN_PROJECT=SQUADAI-1234
+LANGCHAIN_TRACING_V2=false
+TAVILY_API_KEY=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

Dockerfile

@@ -0,0 +1,10 @@
+FROM python:3.11
+RUN useradd -m -u 1000 user
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+WORKDIR $HOME/app
+COPY . .
+RUN chown -R user:user .
+RUN pip install -r requirements.txt
+USER user
+CMD ["chainlit", "run", "app.py", "--port", "7860"]

README.md

@@ -1,10 +1,9 @@
 ---
-title: SquadAI
-emoji: 👀
-colorFrom: green
-colorTo: blue
+title: SQUAD AI
+emoji: 📉
+colorFrom: pink
+colorTo: yellow
 sdk: docker
 pinned: false
+app_port: 7860
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,235 @@
+import os
+import getpass
+from langgraph.graph import StateGraph, END
+from typing import TypedDict, Annotated
+import operator
+from langchain_core.messages import AnyMessage, SystemMessage, HumanMessage, ToolMessage
+from langchain_openai import ChatOpenAI
+#from langchain.tools import tool
+#from langchain_community.agent_toolkits import SQLDatabaseToolkit
+#from langchain_community.agent_toolkits import SQLDatabaseToolkit
+#from langchain.sql_database import SQLDatabase
+#from sqlalchemy import create_engine
+from langchain.tools import tool
+from langgraph.prebuilt import ToolInvocation
+import json
+from langchain_core.messages import FunctionMessage
+from langgraph.prebuilt import ToolExecutor
+from langgraph.graph import StateGraph, END
+from langchain_openai import ChatOpenAI
+from langchain.tools.render import format_tool_to_openai_function
+from langchain_core.utils.function_calling import convert_to_openai_function
+from langchain_core.prompts import ChatPromptTemplate
+from operator import itemgetter
+import pandas as pd
+from langchain_community.document_loaders import CSVLoader
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_community.tools.tavily_search import TavilySearchResults
+
+from langchain_community.vectorstores import Qdrant
+from langchain_openai import OpenAIEmbeddings
+#from qdrant_client import QdrantClient
+from langchain.schema.output_parser import StrOutputParser
+from langchain_community.vectorstores import FAISS
+from langchain_core.messages import HumanMessage
+from langchain_core.runnables import RunnableConfig
+import os
+import chainlit as cl
+from dotenv import load_dotenv
+load_dotenv(override=True)
+
+
+
+from uuid import uuid4
+
+
+class AgentState(TypedDict):
+    messages: Annotated[list[AnyMessage], operator.add]
+
+document_loader = CSVLoader("./squadusersinfo.psv", csv_args={'delimiter': '|'})
+documents = document_loader.load()
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=30)
+split_documents = text_splitter.split_documents(documents)
+
+embeddings = OpenAIEmbeddings()
+#client = QdrantClient(location=":memory:")
+hf_embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
+
+
+
+for i in range(0, len(split_documents), 32):
+  if i == 0:
+    vectorstore = FAISS.from_documents(split_documents[i:i+32], hf_embeddings)
+    continue
+  vectorstore.add_documents(split_documents[i:i+32])
+
+
+hf_retriever = vectorstore.as_retriever()
+
+
+RAG_PROMPT = """
+CONTEXT:
+{context}
+
+QUERY:
+{question}
+
+You are a helpful assistant. You will search the interest of user from the stored list of users. If you cannot find a match, you will look for the matches from the chat history.
+If you still cannot find any match, respond with Sorry, at present there is no match for your interest and ask if this user can be matched if any future interest matches this user. 
+Ask for all necessary details to consider this user as a future match.
+If the question is outside of finding a match, say I don't know.
+"""
+rag_prompt = ChatPromptTemplate.from_template(RAG_PROMPT)
+
+model = ChatOpenAI(model="gpt-4o")  #reduce inference cost
+
+
+rag_chain = (
+    {"context": itemgetter("question") | hf_retriever, "question": itemgetter("question")}
+    | rag_prompt | model | StrOutputParser()
+)
+
+
+#result = rag_chain.invoke({"question" : "Anyone interested in squash?"})
+#print(result)
+
+
+@tool
+def matchUser( query): 
+    """A tool to find the details matching the user details from the store based on user query."""
+    result = rag_chain.invoke({"question" : query})
+    return result
+
+tools= [TavilySearchResults(max_results=1), matchUser]
+
+tool_executor = ToolExecutor(tools)
+
+
+model = ChatOpenAI(temperature=0, streaming=True)
+
+functions = [convert_to_openai_function(t) for t in tools]
+model = model.bind_functions(functions)
+
+
+
+def should_continue(state):
+    messages = state['messages']
+    last_message = messages[-1]   
+    if "function_call" not in last_message.additional_kwargs:
+        return "end"  
+    else:
+        return "continue"
+
+
+def call_model(state):
+    messages = state['messages']
+    response = model.invoke(messages)  
+    return {"messages": [response]}
+
+
+def call_tool(state):
+    messages = state['messages'] 
+    last_message = messages[-1]  
+    action = ToolInvocation(
+        tool=last_message.additional_kwargs["function_call"]["name"],
+        tool_input=json.loads(last_message.additional_kwargs["function_call"]["arguments"]),
+    )  
+    response = tool_executor.invoke(action) 
+    function_message = FunctionMessage(content=str(response), name=action.tool)   
+    return {"messages": [function_message]}
+
+def print_messages(messages):
+  next_is_tool = False
+  initial_query = True
+  for message in messages["messages"]:
+    if "function_call" in message.additional_kwargs:
+      print()
+      print(f'Tool Call - Name: {message.additional_kwargs["function_call"]["name"]} + Query: {message.additional_kwargs["function_call"]["arguments"]}')
+      next_is_tool = True
+      continue
+    if next_is_tool:
+      print(f"Tool Response: {message.content}")
+      next_is_tool = False
+      continue
+    if initial_query:
+      print(f"Initial Query: {message.content}")
+      print()
+      initial_query = False
+      continue
+    print()
+    print(f"Agent Response: {message.content}")
+
+def construct_response(messages):
+  next_is_tool = False
+  initial_query = True
+  response = ""
+  for message in messages["messages"]:
+    if "function_call" in message.additional_kwargs:
+      print()
+      print(f'Tool Call - Name: {message.additional_kwargs["function_call"]["name"]} + Query: {message.additional_kwargs["function_call"]["arguments"]}')
+      next_is_tool = True
+      continue
+    if next_is_tool:
+      print(f"Tool Response: {message.content}")
+      if "url" not in message.content:
+        response = response + message.content
+      next_is_tool = False
+      continue
+    if initial_query:
+      print(f"Initial Query: {message.content}")
+      print()
+      initial_query = False
+      continue
+    print()
+    print(f"Agent Response: {message.content}")
+    response = response + message.content
+    return response
+
+workflow = StateGraph(AgentState)
+
+workflow.add_node("agent", call_model)
+workflow.add_node("action", call_tool)
+
+workflow.set_entry_point("agent")
+
+
+workflow.add_conditional_edges(
+    "agent", 
+    should_continue,
+    {        
+        "continue": "action",      
+        "end": END
+    }
+)
+
+workflow.add_edge('action', 'agent')
+
+app = workflow.compile()
+
+#messages = [HumanMessage(content="Any user named Ganesh. Where to play cricket")]
+#inputs = {"messages" : [HumanMessage(content="Anyone interested in cricket? Provide more information about who is interested. Also get me some locations where I can play cricket in Toronto")]}
+
+#result = app.invoke({"messages": messages})
+
+#print_messages(result)
+#messages = app.invoke(inputs)
+
+#print_messages(messages)
+@cl.on_message
+async def run_convo(message: cl.Message):   
+    msg = cl.Message(content="")
+    await msg.send()  
+    await cl.sleep(1) #hack to simulate loader!
+
+    inputs = {"messages": [HumanMessage(content=message.content)]}
+
+    res = app.invoke(inputs, config=RunnableConfig(callbacks=[
+        cl.LangchainCallbackHandler(
+            to_ignore=["ChannelRead", "RunnableLambda", "ChannelWrite", "__start__", "_execute"]      
+        )]))
+    
+    content = construct_response(res)
+    #for response in (res["messages"]):
+     #   if message.content not in response:
+      #      content = content+response.content
+    await cl.Message(content=content).send() 

chainlit.md

@@ -0,0 +1 @@
+Welcome to SquadAI 👋

requirements.txt

@@ -0,0 +1,7 @@
+chainlit
+langgraph 
+langchain 
+langchain_openai 
+langchain_experimental
+python-dotenv
+qdrant-client

squadusersinfo.psv

@@ -0,0 +1,13 @@
+Name|Location|Interest|Goals|Biography
+Ganesh|Toronto|Cricket,Tennis,technology|Create useful software for poor people's upliftment|Software engineer
+Jaron|New york|Soccer,Hiking,Netwroking|Build, ship, share |Product manager
+Mike|Sacramento|Cricket,Tennis,technology|Create useful software for poor people's upliftment|Software engineer
+John|Ottawa|Soccer,Hiking,Netwroking|Build, ship, share |Product manager
+Smith|Brussels|Cricket,Tennis,technology|Create useful software for poor people's upliftment|Software engineer
+Varun|Frankfurt|Soccer,Hiking,Netwroking|Build, ship, share |Product manager
+Kevin|Mississauga|Cricket,Tennis,technology|Create useful software for poor people's upliftment|Software engineer
+Virat|Montreal|Soccer,Hiking,Netwroking|Build, ship, share |Product manager
+AJesh|Toronto|Cricket,Tennis,technology|Create useful software for poor people's upliftment|Software engineer
+Mohan|Quebec|Soccer,Hiking,Netwroking|Build, ship, share |Product manager
+karthik|Toronto|Cricket,Tennis,technology|Create useful software for poor people's upliftment|Software engineer
+Ram|New york|Soccer,Hiking,Netwroking|Build, ship, share |Product manager