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"""LangGraph Agent using Mistral"""
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_community.document_loaders import WikipediaLoader, ArxivLoader
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_community.vectorstores import SupabaseVectorStore
from langchain_core.messages import SystemMessage, HumanMessage
from langchain_core.tools import tool
from transformers import pipeline
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from supabase.client import Client, create_client
load_dotenv()
# Tools
@tool
def multiply(a: int, b: int) -> int:
return a * b
@tool
def add(a: int, b: int) -> int:
return a + b
@tool
def subtract(a: int, b: int) -> int:
return a - b
@tool
def divide(a: int, b: int) -> float:
if b == 0:
raise ValueError("Cannot divide by zero.")
return a / b
@tool
def modulus(a: int, b: int) -> int:
return a % b
@tool
def wiki_search(query: str) -> str:
search_docs = WikipediaLoader(query=query, load_max_docs=2).load()
return "\n\n---\n\n".join([doc.page_content for doc in search_docs])
@tool
def web_search(query: str) -> str:
search_docs = TavilySearchResults(max_results=3).invoke(query=query)
return "\n\n---\n\n".join([doc.page_content for doc in search_docs])
@tool
def arvix_search(query: str) -> str:
search_docs = ArxivLoader(query=query, load_max_docs=3).load()
return "\n\n---\n\n".join([doc.page_content[:1000] for doc in search_docs])
tools = [multiply, add, subtract, divide, modulus, wiki_search, web_search, arvix_search]
# Load system prompt
with open("system_prompt.txt", "r", encoding="utf-8") as f:
system_prompt = f.read()
sys_msg = SystemMessage(content=system_prompt)
# Vector store setup
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
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"
)
# Mistral agent
class MistralAgent:
def __init__(self):
self.generator = pipeline("text-generation", model="mistralai/Mistral-7B-v0.1", device=0)
print("Mistral model loaded.")
def invoke(self, messages):
question = messages[-1].content
result = self.generator(question, max_length=300, do_sample=True)[0]["generated_text"]
return HumanMessage(content=result.strip())
mistral_agent = MistralAgent()
# LangGraph builder
def build_graph():
def assistant(state: MessagesState):
return {"messages": [mistral_agent.invoke(state["messages"])]}
def retriever(state: MessagesState):
similar = vector_store.similarity_search(state["messages"][-1].content)
example = HumanMessage(content=f"Similar Q&A:\n\n{similar[0].page_content}")
return {"messages": [sys_msg] + state["messages"] + [example]}
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")
return builder.compile()
# Run the agent
def run_agent(question: str) -> str:
graph = build_graph()
messages = [HumanMessage(content=question)]
result = graph.invoke({"messages": messages})
return result["messages"][-1].content.strip()
if __name__ == "__main__":
question = "When was a picture of St. Thomas Aquinas first added to the Wikipedia page on the Principle of double effect?"
answer = run_agent(question)
print("ANSWER:", answer)
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