agent.py

"""LangGraph Agent"""

import os
from dotenv import load_dotenv

from langgraph.graph import START, StateGraph, MessagesState
from langgraph.prebuilt import tools_condition
from langgraph.prebuilt import ToolNode

from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_groq import ChatGroq
from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint, HuggingFaceEmbeddings
from langchain_tavily import TavilySearch
from langchain_community.document_loaders import WikipediaLoader
from langchain_community.document_loaders import ArxivLoader
from langchain_community.vectorstores import SupabaseVectorStore
from langchain_core.messages import SystemMessage, HumanMessage
from langchain_core.tools import tool
from langchain.tools.retriever import create_retriever_tool
from langchain_openai import ChatOpenAI
from langchain_anthropic import ChatAnthropic

from supabase.client import Client, create_client


import re
from langchain_community.document_loaders import WikipediaLoader
from langchain_core.tools import tool


from langchain_core.tools import tool
from youtube_transcript_api import YouTubeTranscriptApi, TranscriptsDisabled, NoTranscriptFound

from langchain_core.tools import tool
from transformers import pipeline


import sys

# Before invoking your graph:
sys.setrecursionlimit(100)  # Increase from default 25

load_dotenv()

@tool
def multiply(a: int, b: int) -> int:
    """Multiply two numbers.



    Args:

        a: first int

        b: second int

    """
    return a * b

@tool
def add(a: int, b: int) -> int:
    """Add two numbers.

    

    Args:

        a: first int

        b: second int

    """
    return a + b

@tool
def subtract(a: int, b: int) -> int:
    """Subtract two numbers.

    

    Args:

        a: first int

        b: second int

    """
    return a - b

@tool
def divide(a: int, b: int) -> int:
    """Divide two numbers.

    

    Args:

        a: first int

        b: second int

    """
    if b == 0:
        raise ValueError("Cannot divide by zero.")
    return a / b

@tool
def modulus(a: int, b: int) -> int:
    """Get the modulus of two numbers.

    

    Args:

        a: first int

        b: second int

    """
    return a % b

@tool
def wiki_search(query: str) -> str:
    """Search Wikipedia for a query and return maximum 2 results.

    

    Args:

        query: The search query."""
    search_docs = WikipediaLoader(query=query, load_max_docs=2).load()
    formatted_search_docs = "\n\n---\n\n".join(
        [
            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
            for doc in search_docs
        ])
    return {"wiki_results": formatted_search_docs}
    


@tool
def web_search(query: str) -> str:
    """Search Tavily for a query and return maximum 3 results.

    

    Args:

        query: The search query."""
    search_docs = TavilySearch(max_results=3).invoke(query=query)
    formatted_search_docs = "\n\n---\n\n".join(
        [
            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
            for doc in search_docs
        ])
    return {"web_results": formatted_search_docs}


@tool
def arvix_search(query: str) -> str:
    """Search Arxiv for a query and return maximum 3 result.

    

    Args:

        query: The search query."""
    search_docs = ArxivLoader(query=query, load_max_docs=3).load()
    formatted_search_docs = "\n\n---\n\n".join(
        [
            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content[:1000]}\n</Document>'
            for doc in search_docs
        ])
    return {"arvix_results": formatted_search_docs}
    
    
@tool
def filtered_wiki_search(query: str, start_year: int = None, end_year: int = None) -> dict:
    """Search Wikipedia for a query and filter results by year if provided."""
    search_docs = WikipediaLoader(query=query, load_max_docs=5).load()
    
    def contains_year(text, start, end):
        years = re.findall(r'\b(19\d{2}|20\d{2})\b', text)
        for y in years:
            y_int = int(y)
            if start <= y_int <= end:
                return True
        return False

    filtered_docs = []
    for doc in search_docs:
        if start_year and end_year:
            if contains_year(doc.page_content, start_year, end_year):
                filtered_docs.append(doc)
        else:
            filtered_docs.append(doc)

    formatted_search_docs = "\n\n---\n\n".join(
        [
            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
            for doc in filtered_docs
        ])
    return {"wiki_results": formatted_search_docs}



@tool
def wolfram_alpha_query(query: str) -> str:
    """Query Wolfram Alpha with the given question and return the result."""
    import wolframalpha
    client = wolframalpha.Client(os.environ['WOLFRAM_APP_ID'])
    res = client.query(query)
    try:
        return next(res.results).text
    except StopIteration:
        return "No result found."
        



@tool
def youtube_transcript(url: str) -> str:
    """Fetch YouTube transcript text from a video URL."""
    try:
        video_id = url.split("v=")[-1].split("&")[0]
        transcript_list = YouTubeTranscriptApi.get_transcript(video_id)
        transcript = " ".join([segment['text'] for segment in transcript_list])
        return transcript
    except (TranscriptsDisabled, NoTranscriptFound):
        return "Transcript not available for this video."
    except Exception as e:
        return f"Error fetching transcript: {str(e)}"





translator = pipeline("translation", model="Helsinki-NLP/opus-mt-mul-en")

@tool
def translate_to_english(text: str) -> str:
    """Translate input text in any language to English."""
    try:
        # HuggingFace translation expects a list of strings
        translated = translator(text, max_length=512)
        return translated[0]['translation_text']
    except Exception as e:
        return f"Translation error: {str(e)}"



from langchain_core.tools import tool
import sympy

@tool
def solve_algebraic_expression(expression: str) -> str:
    """Solve or simplify the given algebraic expression."""
    try:
        expr = sympy.sympify(expression)
        simplified = sympy.simplify(expr)
        return str(simplified)
    except Exception as e:
        return f"Error solving expression: {str(e)}"


from langchain_core.tools import tool

@tool
def run_python_code(code: str) -> str:
    """Execute python code and return the result of variable 'result' if defined."""
    try:
        local_vars = {}
        exec(code, {}, local_vars)
        if 'result' in local_vars:
            return str(local_vars['result'])
        else:
            return "Code executed successfully but no 'result' variable found."
    except Exception as e:
        return f"Error executing code: {str(e)}"
        
        
from langchain_core.tools import tool
import requests

@tool
def wikidata_query(sparql_query: str) -> str:
    """Run a SPARQL query against Wikidata and return the JSON results."""
    endpoint = "https://query.wikidata.org/sparql"
    headers = {"Accept": "application/sparql-results+json"}
    try:
        response = requests.get(endpoint, params={"query": sparql_query}, headers=headers)
        response.raise_for_status()
        data = response.json()
        return str(data)  # Or format as needed
    except Exception as e:
        return f"Error querying Wikidata: {str(e)}"




# load the system prompt from the file
with open("system_prompt.txt", "r", encoding="utf-8") as f:
    system_prompt = f.read()

# System message
sys_msg = SystemMessage(content=system_prompt)

# build a retriever

embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2") #  dim=768
supabase: Client = create_client(
    os.environ.get("SUPABASE_URL"), 
    os.environ.get("SUPABASE_SERVICE_KEY"))
vector_store = SupabaseVectorStore(
    client=supabase,
    embedding= embeddings,
    table_name="documents",
    query_name="match_documents_langchain",
)
retriever_tool = create_retriever_tool(
    retriever=vector_store.as_retriever(),
    name="Question Search",
    description="A tool to retrieve similar questions from a vector store.",
)



tools = [
  
    multiply,
    add,
    subtract,
    divide,
    modulus,
    wiki_search,
    filtered_wiki_search,
    web_search,
    arvix_search,
    wolfram_alpha_query,
    retriever_tool,
    youtube_transcript,
    translate_to_english,
    solve_algebraic_expression,
    run_python_code,
    wikidata_query
]

# Build graph function
def build_graph(provider: str = "groq"):
    """Build the graph"""
    # Load environment variables from .env file
    if provider == "openai":
        llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)
    elif provider == "anthropic":
        llm = ChatAnthropic(model="claude-v1", temperature=0)
    elif provider == "google":
        # Google Gemini
        llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
    elif provider == "groq":
        # Groq https://console.groq.com/docs/models
        llm = ChatGroq(model="gemma2-9b-it", temperature=0) # optional : qwen-qwq-32b gemma2-9b-it
    elif provider == "huggingface":
        # TODO: Add huggingface endpoint
        llm = ChatHuggingFace(
            llm=HuggingFaceEndpoint(
                url="https://api-inference.huggingface.co/models/Meta-DeepLearning/llama-2-7b-chat-hf",
                temperature=0,
            ),
        )
    else:
        raise ValueError("Invalid provider. Choose 'google', 'groq' or 'huggingface'.")
    # Bind tools to LLM
    llm_with_tools = llm.bind_tools(tools)

    # Node
    def assistant(state: MessagesState):
        """Assistant node"""
        return {"messages": [llm_with_tools.invoke(state["messages"])]}
    
    def retriever(state: MessagesState):
        """Retriever node"""
        similar_question = vector_store.similarity_search(state["messages"][0].content)
        if not similar_question:
            # No similar documents found, fallback message
            example_msg = HumanMessage(
                content="Sorry, I could not find any similar questions in the vector store."
            )
        else:
            example_msg = HumanMessage(
                content=f"Here I provide a similar question and answer for reference: \n\n{similar_question[0].page_content}",
            )
        return {"messages": [sys_msg] + state["messages"] + [example_msg]}

    builder = StateGraph(MessagesState)
    builder.add_node("retriever", retriever)
    builder.add_node("assistant", assistant)
    builder.add_node("tools", ToolNode(tools))
    builder.add_edge(START, "retriever")
    builder.add_edge("retriever", "assistant")
    builder.add_conditional_edges(
        "assistant",
        tools_condition,
    )
    builder.add_edge("tools", "assistant")

    # Compile graph
    return builder.compile()

# test
if __name__ == "__main__":
    question = "When was a picture of St. Thomas Aquinas first added to the Wikipedia page on the Principle of double effect?"
    # Build the graph
    graph = build_graph(provider="groq")
    # Run the graph
    messages = [HumanMessage(content=question)]
    messages = graph.invoke({"messages": messages})
    for m in messages["messages"]:
        m.pretty_print()