Create agent.py

agent.py

@@ -0,0 +1,120 @@
+"""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)