helper_functions.py

from typing import Dict, List
from langchain.agents import AgentExecutor, create_openai_functions_agent
from langchain_community.document_loaders import PyMuPDFLoader, TextLoader, UnstructuredURLLoader, WebBaseLoader
from langchain_community.vectorstores import Qdrant
from langchain_core.messages import AIMessage, BaseMessage, HumanMessage
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.language_models import BaseLanguageModel
import os
import functools
import requests
import tempfile
from chainlit.types import AskFileResponse

def process_file(uploaded_file: AskFileResponse):
    if uploaded_file.name.endswith(".pdf"):
        with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".pdf") as temp_file:
            temp_file_path = temp_file.name

        with open(temp_file_path, "wb") as f:
            f.write(uploaded_file.content)
        # Load PDF with PyMuPDFLoader
        loader = PyMuPDFLoader(temp_file_path)
    elif uploaded_file.name.endswith(".txt"):
        with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".txt") as temp_file:
            temp_file_path = temp_file.name

        with open(temp_file_path, "wb") as f:
            f.write(uploaded_file.content)
        # Load text file with TextLoader
        loader = TextLoader(temp_file_path)
    else:
        raise ValueError("Unsupported file format. Only PDF and TXT are supported.")

    return loader.load()

def load_documents_from_url(url):
    try:
        # Check if it's a PDF
        if url.endswith(".pdf"):
            try:
                loader = PyMuPDFLoader(url)
                return loader.load()
            except Exception as e:
                print(f"Error loading PDF from {url}: {e}")
                return None
        
        # Fetch the content and check for video pages
        try:
            response = requests.head(url, timeout=10)  # Timeout for fetching headers
            content_type = response.headers.get('Content-Type', '')
        except Exception as e:
            print(f"Error fetching headers from {url}: {e}")
            return None
        
        # Ignore video content (flagged for now)
        if 'video' in content_type:
            return None
        if 'youtube' in url:
            return None
        
        # Otherwise, treat it as an HTML page
        try:
            loader = UnstructuredURLLoader([url])
            return loader.load()
        except Exception as e:
            print(f"Error loading HTML from {url}: {e}")
            return None
    except Exception as e:
        print(f"General error loading from {url}: {e}")
        return None

def add_to_qdrant(documents, embeddings, qdrant_client, collection_name):
    Qdrant.from_documents(
        documents,
        embeddings,
        url=qdrant_client.url,
        prefer_grpc=True,
        collection_name=collection_name,
    )

def agent_node(state, agent, name):
    result = agent.invoke(state)
    return {
        "messages": [HumanMessage(content=result["messages"][-1].content, name=name)]
    }

def create_team_agent(llm, tools, system_prompt, agent_name, team_members):
    return create_agent(
        llm,
        tools,
        f"{system_prompt}\nBelow are files currently in your directory:\n{{current_files}}",
        #team_members
    )

def create_agent_node(agent, name):
    return functools.partial(agent_node, agent=agent, name=name)

def add_agent_to_graph(graph, agent_name, agent_node):
    graph.add_node(agent_name, agent_node)
    graph.add_edge(agent_name, "supervisor")

def create_team_supervisor(llm, team_description, team_members):
    return create_team_supervisor(
        llm,
        f"You are a supervisor tasked with managing a conversation between the"
        f" following workers: {', '.join(team_members)}. {team_description}"
        f" When all workers are finished, you must respond with FINISH.",
        team_members
    )

def enter_chain(message: str, members: List[str]):
    results = {
        "messages": [HumanMessage(content=message)],
        "team_members": ", ".join(members),
    }
    return results

def create_team_chain(graph, team_members):
    return (
        functools.partial(enter_chain, members=team_members)
        | graph.compile()
    )

def create_agent(
    llm: BaseLanguageModel,
    tools: list,
    system_prompt: str,
) -> str:
    """Create a function-calling agent and add it to the graph."""
    system_prompt += ("\nWork autonomously according to your specialty, using the tools available to you."
    " Do not ask for clarification."
    " Your other team members (and other teams) will collaborate with you with their own specialties."
    " You are chosen for a reason! You are one of the following team members: {{team_members}}.")
    prompt = ChatPromptTemplate.from_messages(
        [
            (
                "system",
                system_prompt,
            ),
            MessagesPlaceholder(variable_name="messages"),
            MessagesPlaceholder(variable_name="agent_scratchpad"),
        ]
    )
    agent = create_openai_functions_agent(llm, tools, prompt)
    executor = AgentExecutor(agent=agent, tools=tools)
    return executor

def format_docs(docs: List[Dict]) -> str:
    return "\n\n".join(f"Content: {doc.page_content}\nSource: {doc.metadata.get('source', 'Unknown')}" for doc in docs)