Custom Agent

Agent

@@ -0,0 +1,207 @@
+from langgraph.prebuilt import create_react_agent
+from langchain_community.tools.tavily_search import TavilySearchResults
+from langchain_community.document_loaders import WikipediaLoader
+from langchain_community.document_loaders import ArxivLoader
+from dotenv import load_dotenv, find_dotenv
+from langchain_core.tools import tool
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_community.vectorstores import SupabaseVectorStore
+from langchain_core.messages import HumanMessage
+from supabase import create_client, Client
+import os
+
+load_dotenv(find_dotenv())
+
+DEFAULT_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. 
+"""
+
+
+@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 = TavilySearchResults(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 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
+
+
+class CustomAgent:
+    def __init__(self):
+        print("CustomAgent initialized.")
+
+        # Initialize embeddings and vector store
+        self.embeddings = HuggingFaceEmbeddings(
+            model_name="sentence-transformers/all-mpnet-base-v2"
+        )
+
+        self.supabase: Client = create_client(
+            os.environ.get("SUPABASE_URL"), os.environ.get("SUPABASE_SERVICE_ROLE_KEY")
+        )
+
+        self.vector_store = SupabaseVectorStore(
+            client=self.supabase,
+            embedding=self.embeddings,
+            table_name="documents_1",
+            query_name="match_documents_1",
+        )
+
+        # Create the agent
+        self.agent = create_react_agent(
+            model="openai:gpt-4.1",
+            tools=[
+                web_search,
+                add,
+                subtract,
+                multiply,
+                divide,
+                modulus,
+                wiki_search,
+                arvix_search,
+            ],
+            prompt=DEFAULT_PROMPT,
+        )
+
+    def retriever(self, query: str):
+        """Retriever"""
+        similar_question = self.vector_store.similarity_search(query)
+        return HumanMessage(
+            content=f"Here I provide a similar question and answer for reference, you can use it to answer the question: \n\n{similar_question[0].page_content}",
+        )
+
+    def __call__(self, question: str) -> str:
+        """Run the agent on a question and return the answer."""
+        print(f"CustomAgent received question (first 50 chars): {question[:50]}...")
+
+        try:
+            answer = self.agent.invoke(
+                {
+                    "messages": [
+                        self.retriever(question),
+                        HumanMessage(content=question),
+                    ]
+                }
+            )
+            result = answer["messages"][-1].content
+
+            if "FINAL ANSWER: " in result:
+                final_answer_start = result.find("FINAL ANSWER: ") + len(
+                    "FINAL ANSWER: "
+                )
+                extracted_answer = result[final_answer_start:].strip()
+                print(f"CustomAgent extracted answer: {extracted_answer}")
+                return extracted_answer
+            else:
+                print(
+                    f"CustomAgent returning full answer (no FINAL ANSWER found): {result}"
+                )
+                return result
+
+        except Exception as e:
+            print(f"Error in CustomAgent: {e}")
+            return f"Error: {e}"
+
+
+if __name__ == "__main__":
+    agent = CustomAgent()
+    agent(
+        "How many studio albums were published by Mercedes Sosa between 2000 and 2009 (included)? You can use the latest 2022 version of english wikipedia."
+    )
+    agent(
+        "How many at bats did the Yankee with the most walks in the 1977 regular season have that same season?"
+    )
+    agent(
+        "In the video https://www.youtube.com/watch?v=L1vXCYZAYYM, what is the highest number of bird species to be on camera simultaneously?"
+    )