agent.py

import os

from dotenv import load_dotenv
from langchain_community.vectorstores import Chroma
from langchain_core.messages import HumanMessage, SystemMessage
from langchain_core.tools import tool
from langchain_huggingface import (ChatHuggingFace, HuggingFaceEmbeddings,
                                   HuggingFaceEndpoint)
from langgraph.graph import START, MessagesState, StateGraph
from langgraph.prebuilt import ToolNode, tools_condition

from tools import (absolute, add, analyze_excel_file, arvix_search,
                   audio_transcription, compound_interest, convert_temperature,
                   divide, exponential, factorial, floor_divide,
                   get_current_time_in_timezone, greatest_common_divisor,
                   is_prime, least_common_multiple, logarithm, modulus,
                   multiply, percentage_calculator, power, python_code_parser,
                   roman_calculator_converter, square_root, subtract,
                   web_search, wiki_search)

# Load Constants
load_dotenv()
HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")


tools = [
    multiply, add, subtract, power, divide, modulus,
    square_root, floor_divide, absolute, logarithm,
    exponential, web_search, roman_calculator_converter,
    get_current_time_in_timezone, compound_interest,
    convert_temperature, factorial, greatest_common_divisor,
    is_prime, least_common_multiple, percentage_calculator,
    wiki_search, analyze_excel_file, arvix_search, audio_transcription, python_code_parser
]


# Load system prompt
system_prompt = """
You are a helpful assistant tasked with answering questions using a set of tools. 
Now, I will ask you a question. Report your thoughts, and finish your answer with the following template: 
FINAL ANSWER: [YOUR FINAL ANSWER]. 
YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.
Your answer should only start with "FINAL ANSWER: ", then follows with the answer. 
"""

# System message
sys_msg = SystemMessage(content=system_prompt)

# Embeddings + Chroma Vector Store
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
vector_store = Chroma(
    collection_name="langgraph-documents",
    embedding_function=embeddings,
    persist_directory="chroma_db"  # Use a persistent directory
)

def build_graph():
    """Build the graph""" 

    # First create the HuggingFaceEndpoint
    llm_endpoint = HuggingFaceEndpoint(
        # repo_id="Qwen/Qwen2.5-Coder-32B-Instruct",
        repo_id="mistralai/Mistral-7B-Instruct-v0.2",      
        # Other models to try:
        # "meta-llama/Llama-2-7b-chat-hf"
        # "google/gemma-7b-it"
        # "mosaicml/mpt-7b-instruct"
        # "tiiuae/falcon-7b-instruct"
        huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN,
        temperature=0.1,  # Lower temperature for more consistent responses
        max_new_tokens=1024,
        timeout=30,
        )
    
    # Then wrap it with ChatHuggingFace to get chat model functionality
    llm = ChatHuggingFace(llm=llm_endpoint)

    # Bind tools to LLM
    llm_with_tools = llm.bind_tools(tools)

    # Node
    def assistant(state: MessagesState):
        """Assistant node"""
        return {"messages": [llm_with_tools.invoke([system_prompt] + state["messages"])]}
    
    def retriever(state: MessagesState):
        similar = vector_store.similarity_search(state["messages"][0].content)
        if similar:
            example_msg = HumanMessage(content=f"Here is a similar question:\n\n{similar[0].page_content}")
            return {"messages": [sys_msg] + state["messages"] + [example_msg]}
        return {"messages": [sys_msg] + state["messages"]}

    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()
    # Run the graph
    messages = [HumanMessage(content=question)]
    messages = graph.invoke({"messages": messages})
    for m in messages["messages"]:
        m.pretty_print()