app.py

import os
import re
import tempfile
from typing import List, Dict, Any, Optional,Mapping
from rich import print as rp
from langchain_core.runnables.base import Runnable
from langchain.agents import Tool, AgentExecutor
from langchain.memory import ConversationBufferMemory, CombinedMemory
from langchain.prompts import PromptTemplate
from hugging_chat_wrapper import HuggingChatWrapper
from langchain_community.tools import DuckDuckGoSearchRun, WikipediaQueryRun
from langchain_core.language_models.llms import LLM
from langchain_core.pydantic_v1 import Field

# Load environment variables


import os,sys,re
from rich import print as rp
from langchain import hub

import os
import re
import tempfile
import pandas as pd
from typing import List, Dict, Any
from langchain_core.runnables.base import Runnable
from langchain.agents.utils import validate_tools_single_input
from langchain_community.docstore import Wikipedia
from langchain_core.prompts import PromptTemplate
from PyQt6.QtWidgets import QWidget, QVBoxLayout, QHBoxLayout, QPushButton, QTextEdit, QFileDialog, QLabel
from langchain_community.agent_toolkits import FileManagementToolkit
from langchain_core.prompts import ChatPromptTemplate
from langchain_community.vectorstores import Chroma
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain.retrievers import TimeWeightedVectorStoreRetriever
from langchain_core.documents import Document
from hugging_chat_wrapper import HuggingChatWrapper
from PyQt6.QtWidgets import ( QApplication, QMainWindow, QWidget, QVBoxLayout, QTabWidget,  QTextEdit, QLineEdit, QPushButton)
#ChatMessagePromptTemplate
from langchain.agents import Tool, AgentExecutorIterator,AgentType, initialize_agent,AgentExecutor
from langchain_community.agent_toolkits.load_tools import load_tools
from langchain.agents import create_react_agent,create_self_ask_with_search_agent
from langchain_community.tools import DuckDuckGoSearchRun
from langchain_experimental.tools.python.tool import PythonAstREPLTool
from langchain_experimental.llm_bash.bash import BashProcess
from langchain_community.tools import ShellTool
from langchain.output_parsers.json import SimpleJsonOutputParser
from langchain_core.output_parsers.string import StrOutputParser
from langchain.memory import ConversationBufferMemory,ConversationSummaryBufferMemory,CombinedMemory
from langchain_community.tools.file_management import (
        CopyFileTool,
        DeleteFileTool,
        FileSearchTool,
        ListDirectoryTool,
        MoveFileTool,
        ReadFileTool,
        WriteFileTool,
    )
from langchain_community.utilities.duckduckgo_search import DuckDuckGoSearchAPIWrapper
from langchain_community.tools.wikidata.tool import WikidataAPIWrapper, WikidataQueryRun
from langchain_community.tools import DuckDuckGoSearchRun
from langchain_community.tools import WikipediaQueryRun
from langchain_community.utilities import WikipediaAPIWrapper

import gradio as gr
from gradio_client import Client
from langchain.llms import HuggingFaceTextGenInference
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
import langgraph
# Initialize the Qwen client
qwen_client = Client("Qwen/Qwen2-0.5B")

# Create a custom LLM class to use with LangChain
class QwenLLM(HuggingFaceTextGenInference):
    def _call(self, prompt, stop=None):
        result = qwen_client.predict(
            query=prompt,
            history=[],
            system="You are a helpful assistant.",
            api_name="/model_chat"
        )
        return result[0][1]  # Extract the assistant's response

# Initialize the LLM
llm = QwenLLM(
    inference_server_url="https://your-inference-server-url",  # This is a placeholder
    max_new_tokens=512,
    top_k=10,
    top_p=0.95,
    typical_p=0.95,
    temperature=0.1,
    repetition_penalty=1.03
)

# Create the LCel chain
template = """You are a helpful AI assistant. Please respond to the following user input:

User: {user_input}"""

import torch
from diffusers import (
    DDIMScheduler,
    MotionAdapter,
    PIAPipeline,
)
from diffusers.utils import export_to_gif, load_image
import os
os.system("pip install --upgrade pip")
from typing import List
from typing_extensions import TypedDict


class ReWOO(TypedDict):
    task: str
    plan_string: str
    steps: List
    results: dict
    result: str

#Planner

#The planner prompts an LLM to generate a plan in the form of a task list. The arguments to each task are strings that may contain special variables (#E{0-9}+) that are used for variable substitution from other task results.

#ReWOO workflow

#Our example agent will have two tools:

#    Google - a search engine (in this case Tavily)
#    LLM - an LLM call to reason about previous outputs.

#The LLM tool receives less of the prompt context and so can be more token-efficient than the ReACT paradigm.

from langchain_openai import ChatOpenAI

model = ChatOpenAI(model="gpt-4o")

prompt = """For the following task, make plans that can solve the problem step by step. For each plan, indicate \
which external tool together with tool input to retrieve evidence. You can store the evidence into a \
variable #E that can be called by later tools. (Plan, #E1, Plan, #E2, Plan, ...)

Tools can be one of the following:
(1) Google[input]: Worker that searches results from Google. Useful when you need to find short
and succinct answers about a specific topic. The input should be a search query.
(2) LLM[input]: A pretrained LLM like yourself. Useful when you need to act with general
world knowledge and common sense. Prioritize it when you are confident in solving the problem
yourself. Input can be any instruction.

For example,
Task: Thomas, Toby, and Rebecca worked a total of 157 hours in one week. Thomas worked x
hours. Toby worked 10 hours less than twice what Thomas worked, and Rebecca worked 8 hours
less than Toby. How many hours did Rebecca work?
Plan: Given Thomas worked x hours, translate the problem into algebraic expressions and solve
with Wolfram Alpha. #E1 = WolframAlpha[Solve x + (2x − 10) + ((2x − 10) − 8) = 157]
Plan: Find out the number of hours Thomas worked. #E2 = LLM[What is x, given #E1]
Plan: Calculate the number of hours Rebecca worked. #E3 = Calculator[(2 ∗ #E2 − 10) − 8]

Begin! 
Describe your plans with rich details. Each Plan should be followed by only one #E.

Task: {task}"""

task = "what is the exact hometown of the 2024 mens australian open winner"

result = model.invoke(prompt.format(task=task))

print(result.content)

#Plan: Use Google to search for the 2024 Australian Open winner.
#E1 = Google[2024 Australian Open winner]

#Plan: Retrieve the name of the 2024 Australian Open winner from the search results.
#E2 = LLM[What is the name of the 2024 Australian Open winner, given #E1]

#Plan: Use Google to search for the hometown of the 2024 Australian Open winner.
#E3 = Google[hometown of 2024 Australian Open winner, given #E2]

#Plan: Retrieve the hometown of the 2024 Australian Open winner from the search results.
#E4 = LLM[What is the hometown of the 2024 Australian Open winner, given #E3]

#Planner Node

#To connect the planner to our graph, we will create a get_plan node that accepts the ReWOO state and returns with a state update for the steps and plan_string fields.

import re

from langchain_core.prompts import ChatPromptTemplate

# Regex to match expressions of the form E#... = ...[...]
regex_pattern = r"Plan:\s*(.+)\s*(#E\d+)\s*=\s*(\w+)\s*\[([^\]]+)\]"
prompt_template = ChatPromptTemplate.from_messages([("user", prompt)])
planner = prompt_template | model


def get_plan(state: ReWOO):
    task = state["task"]
    result = planner.invoke({"task": task})
    # Find all matches in the sample text
    matches = re.findall(regex_pattern, result.content)
    return {"steps": matches, "plan_string": result.content}

#Executor

#The executor receives the plan and executes the tools in sequence.

#Below, instantiate the search engine and define the tool execution node.

from langchain_community.tools.tavily_search import TavilySearchResults

search = TavilySearchResults()

def _get_current_task(state: ReWOO):
    if "results" not in state or state["results"] is None:
        return 1
    if len(state["results"]) == len(state["steps"]):
        return None
    else:
        return len(state["results"]) + 1


def tool_execution(state: ReWOO):
    """Worker node that executes the tools of a given plan."""
    _step = _get_current_task(state)
    _, step_name, tool, tool_input = state["steps"][_step - 1]
    _results = (state["results"] or  {}) if "results" in state else {}
    for k, v in _results.items():
        tool_input = tool_input.replace(k, v)
    if tool == "Google":
        result = search.invoke(tool_input)
    elif tool == "LLM":
        result = model.invoke(tool_input)
    else:
        raise ValueError
    _results[step_name] = str(result)
    return {"results": _results}

#Solver

#The solver receives the full plan and generates the final response based on the responses of the tool calls from the worker.

solve_prompt = """Solve the following task or problem. To solve the problem, we have made step-by-step Plan and \
retrieved corresponding Evidence to each Plan. Use them with caution since long evidence might \
contain irrelevant information.

{plan}

Now solve the question or task according to provided Evidence above. Respond with the answer
directly with no extra words.

Task: {task}
Response:"""


def solve(state: ReWOO):
    plan = ""
    for _plan, step_name, tool, tool_input in state["steps"]:
        _results = (state["results"] or  {}) if "results" in state else {}
        for k, v in _results.items():
            tool_input = tool_input.replace(k, v)
            step_name = step_name.replace(k, v)
        plan += f"Plan: {_plan}\n{step_name} = {tool}[{tool_input}]"
    prompt = solve_prompt.format(plan=plan, task=state["task"])
    result = model.invoke(prompt)
    return {"result": result.content}

#Define Graph

#Our graph defines the workflow. Each of the planner, tool executor, and solver modules are added as nodes.

def _route(state):
    _step = _get_current_task(state)
    if _step is None:
        # We have executed all tasks
        return "solve"
    else:
        # We are still executing tasks, loop back to the "tool" node
        return "tool"

from langgraph.graph import END, StateGraph, START

graph = StateGraph(ReWOO)
graph.add_node("plan", get_plan)
graph.add_node("tool", tool_execution)
graph.add_node("solve", solve)
graph.add_edge("plan", "tool")
graph.add_edge("solve", END)
graph.add_conditional_edges("tool", _route)
graph.add_edge(START, "plan")

app = graph.compile()

for s in app.stream({"task": task}):
    print(s)
    print("---")