Create app.py

app.py

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+from llama_index.core import SimpleDirectoryReader
+from llama_index.core.node_parser import SentenceSplitter
+from llama_index.core import Settings
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+from llama_index.core import  VectorStoreIndex
+from langchain_groq import ChatGroq
+from langchain.tools import BaseTool, StructuredTool, tool
+from langchain_community.tools.tavily_search import TavilySearchResults
+from typing import TypedDict ,Annotated
+from langchain_core.runnables import RunnablePassthrough
+from langchain_core.output_parsers import StrOutputParser
+import os
+from typing import TypedDict ,Annotated
+from langchain_core.messages import AnyMessage,SystemMessage,HumanMessage,ToolMessage,AIMessage
+import operator
+from langgraph.checkpoint.memory import InMemorySaver
+from langgraph.graph import StateGraph, END
+from fastapi import FastAPI
+import json
+import shutil
+import os
+from fastapi import FastAPI, File, UploadFile
+
+app = FastAPI()
+
+@tool
+def retrieve(query_text):
+    """
+    Retrieves relevant information from a vector index based on a query.
+
+    Parameters:
+    - query_text (str): Query to search for relevant information.
+
+    Returns:
+    - str: Retrieved text from the document.
+    """
+    if vector_index is None:
+        return "Vector index not found. Please upload a file first."
+    else:
+        retriever = vector_index.as_retriever(similarity_top_k=3)
+        result = retriever.retrieve(query_text)
+        if result:
+            return "\n\n".join([node.node.text for node in result])
+        return "No relevant information found."
+
+tavily_search = TavilySearchResults(max_results=4)
+
+vector_index = None
+
+@app.post("/uploadpdfs")
+async def upload_file(file: UploadFile = File(...)):
+    global vector_index
+
+    # Save uploaded file to a temp directory
+    temp_dir = "temp_uploads"
+    os.makedirs(temp_dir, exist_ok=True)
+    file_id = str(uuid.uuid4())
+    file_path = os.path.join(temp_dir, f"{file_id}_{file.filename}")
+
+    with open(file_path, "wb") as f:
+        shutil.copyfileobj(file.file, f)
+
+    # Load and parse document
+    documents = SimpleDirectoryReader(input_files=[file_path]).load_data()
+    parser = SentenceSplitter(chunk_size=300, chunk_overlap=50)
+    nodes = parser.get_nodes_from_documents(documents)
+
+    # Create or update vector index
+    embed_model = HuggingFaceEmbedding(model_name="WhereIsAI/UAE-Large-V1")
+    if vector_index is None:
+        vector_index = VectorStoreIndex(nodes, embed_model=embed_model)
+        message = "New vector index created and file stored."
+    else:
+        vector_index.insert_nodes(nodes)
+        message = "File stored and vector index updated."
+
+    return {"message": message, "filename": file.filename}
+
+
+
+
+class AgentState(TypedDict):
+  messages: Annotated[list[AnyMessage], operator.add]
+
+memory = InMemorySaver()
+
+class Agent:
+  def __init__(self, model, tools, checkpointer=None, system=""):
+    self.system = system
+    graph = StateGraph(AgentState)
+    graph.add_node('llm',self.call_llm)
+    graph.add_node('action',self.take_action)
+    graph.add_conditional_edges("llm",self.exists_action,{True :"action",False:END})
+    graph.add_edge("action","llm")
+    graph.set_entry_point("llm")
+    self.graph = graph.compile(checkpointer=checkpointer)
+    self.tools = {t.name:t for t in tools}
+    self.model = model.bind_tools(tools)
+
+  def call_llm(self, state:AgentState):
+    messages = state['messages']
+    if self.system :
+      messages = [SystemMessage(content=self.system)] + messages
+    message = self.model.invoke(messages)
+    return {"messages":[message]}
+
+  def exists_action(self, state:AgentState):
+    result = state['messages'][-1]
+    return len(result.tool_calls) > 0
+
+  def take_action(self, state:AgentState):
+    tool_calls = state['messages'][-1].tool_calls
+    results = []
+    for t in tool_calls:
+      result= self.tools[t['name']].invoke(t['args'])
+      results.append(ToolMessage(tool_call_id=t['id'],name=t['name'],content=str(result)))
+    return {"messages":results}
+
+
+system_Prompt="""
+You are an AI assistant designed to assist users with health benefits, diet, nutrition information, and recipes.
+You analyze patient reports to offer guidance on self-care with AI support.
+Provide answers directly related to the question, without additional explanation or unrelated information.
+"""
+
+tools=[retrieve,tavily_search]
+
+model = ChatGroq(
+    model="qwen-qwq-32b")
+
+agent = Agent(model, tools, memory, system=system_Prompt)
+
+thread = {"configurable": {'thread_id': '1'}}
+
+
+@app.post("/askbot")
+async def ask_question(query: QueryRequest):
+    messages = [HumanMessage(content=query.message)]
+    final_res = ""
+
+    for event in abot.graph.stream({'messages': messages}, thread):
+        for v in event.values():
+            if isinstance(v, dict) and 'messages' in v:
+                for msg in v['messages']:
+                    if hasattr(msg, 'content') and isinstance(msg, AIMessage):
+                        final_res += msg.content
+
+    return {"answer": final_res}