Add mathematical question and move vector_search into Alfred_agent.py

Alfred_Agent.py

@@ -1,42 +1,41 @@
 import datasets
 from langchain.docstore.document import Document
-
 # Load the dataset
-guest_dataset = datasets.load_dataset("agents-course/unit3-invitees", split="train")
+# guest_dataset = datasets.load_dataset("agents-course/unit3-invitees", split="train")
 
 # Convert dataset entries into Document objects
-docs = [
-    Document(
-        page_content="\n".join([
-            f"Name: {guest['name']}",
-            f"Relation: {guest['relation']}",
-            f"Description: {guest['description']}",
-            f"Email: {guest['email']}"
-        ]),
-        metadata={"name": guest["name"]}
-    )
-    for guest in guest_dataset
-]
-
-from langchain_community.retrievers import BM25Retriever
-from langchain.tools import Tool
-
-bm25_retriever = BM25Retriever.from_documents(docs)
-
-def extract_text(query: str) -> str:
-    """Retrieves detailed information about gala guests based on their name or relation."""
-    results = bm25_retriever.invoke(query)
-    if results:
-        return "\n\n".join([doc.page_content for doc in results[:3]])
-    else:
-        return "No matching guest information found."
+# docs = [
+#     Document(
+#         page_content="\n".join([
+#             f"Name: {guest['name']}",
+#             f"Relation: {guest['relation']}",
+#             f"Description: {guest['description']}",
+#             f"Email: {guest['email']}"
+#         ]),
+#         metadata={"name": guest["name"]}
+#     )
+#     for guest in guest_dataset
+# ]
 
-guest_info_tool = Tool(
-    name="guest_info_retriever",
-    func=extract_text,
-    description="Retrieves detailed information about gala guests based on their name or relation."
-)
+# from langchain_community.retrievers import BM25Retriever
+# from langchain.tools import Tool
 
+# bm25_retriever = BM25Retriever.from_documents(docs)
+
+# def extract_text(query: str) -> str:
+#     """Retrieves detailed information about gala guests based on their name or relation."""
+#     results = bm25_retriever.invoke(query)
+#     if results:
+#         return "\n\n".join([doc.page_content for doc in results[:3]])
+#     else:
+#         return "No matching guest information found."
+
+# guest_info_tool = Tool(
+#     name="guest_info_retriever",
+#     func=extract_text,
+#     description="Retrieves detailed information about gala guests based on their name or relation."
+# )
+#######################################################################################################################################################
 from typing import TypedDict, Annotated
 from langgraph.graph.message import add_messages
 from langchain_core.messages import AnyMessage, HumanMessage, AIMessage
@@ -45,7 +44,7 @@ from langgraph.graph import START, StateGraph
 from langgraph.prebuilt import tools_condition
 from langchain_openai import ChatOpenAI
 from Webserch_tool import weather_info_tool
-from other_tools import wiki_search, arvix_search, web_search
+from other_tools import wiki_search, arvix_search, web_search, vector_search
 import os
 from dotenv import load_dotenv
 load_dotenv()
@@ -56,7 +55,17 @@ llm = ChatOpenAI(temperature=0
 tools = [weather_info_tool,wiki_search,arvix_search,web_search]
 chat_with_tools = llm.bind_tools(tools)
 
+#setting up prompt
+ai_message = AIMessage(content="""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 rules above for each element (number or string), ensure there is exactly one space after each comma.
+Your answer should only start with "FINAL ANSWER: ", then follows with the answer. """)
+
+
+
 # Generate the AgentState and Agent graph
+from langgraph.graph import MessagesState #the same as AgentState
 class AgentState(TypedDict):
     messages: Annotated[list[AnyMessage], add_messages]
 
@@ -65,9 +74,23 @@ def assistant(state: AgentState):
         "messages": [chat_with_tools.invoke(state["messages"])],
     }
 
+def retriever(state: AgentState):
+    """Retriever node"""
+    similar_question = vector_search(state["messages"][0].content)
+
+    if similar_question:  # Check if the list is not empty
+        example_msg = HumanMessage(
+            content=f"Here I provide a similar question and answer for reference: \n\n{similar_question[0]}",
+        )
+        return {"messages": [ai_message] + state["messages"] + [example_msg]}
+    else:
+        # Handle the case when no similar questions are found
+        return {"messages": [ai_message] + state["messages"]}
+
 ## The graph
 builder = StateGraph(AgentState)
 
+
 # Define nodes: these do the work
 builder.add_node("assistant", assistant)
 builder.add_node("tools", ToolNode(tools))

other_tools.py

@@ -1,45 +1,77 @@
+from langchain.tools import tool
+#mathematical operations
+import cmath
+@tool(description="Multiplies two numbers.")
+def multiply(a: float, b: float) -> float:
+    return a * b
+
+@tool(description="Adds two numbers.")
+def add(a: float, b: float) -> float:
+    return a + b
+
+@tool(description="Subtracts two numbers.")
+def subtract(a: float, b: float) -> int:
+    return a - b
+
+@tool(description="Divides two numbers.")
+def divide(a: float, b: float) -> float:
+    if b == 0:
+        raise ValueError("Cannot divided by zero.")
+    return a / b
+
+@tool(description="Get the modulus of two numbers.")
+def modulus(a: int, b: int) -> int:
+    return a % b
+
+@tool(description="Get the power of two numbers.")
+def power(a: float, b: float) -> float:
+    return a**b
+
+@tool(description="Get the square root of a number.")
+def square_root(a: float) -> float | complex:
+    if a >= 0:
+        return a**0.5
+    return cmath.sqrt(a)
 
 from langchain_community.document_loaders import WikipediaLoader
+@tool(description="Search Wikipedia for a query and return maximum 2 results.")
 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>'
+            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>{doc.page_content}</Document>'
             for doc in search_docs
         ])
     return {"wiki_results": formatted_search_docs}
 
-
-
 from langchain_community.document_loaders import ArxivLoader
+@tool(description="Search Arxiv for a query and return maximum 3 result.")
 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>'
+            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>{doc.page_content[:1000]}</Document>'
             for doc in search_docs
         ])
     return {"arvix_results": formatted_search_docs}
 
-
 from langchain_community.tools.tavily_search import TavilySearchResults
+@tool(description="Search Tavily for a query and return maximum 3 results.")
 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>'
+            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>{doc.page_content}</Document>'
+            for doc in search_docs
+        ])
+    return {"web_results": formatted_search_docs}
+
+from upload_metadata_n_setup_retrivers import retriever
+def vector_search(query:str) -> str:
+    search_docs = retriever.invoke(query=query)
+    formatted_search_docs = "\n\n---\n\n".join(
+        [
+            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>{doc.page_content}</Document>'
             for doc in search_docs
         ])
-    return {"web_results": formatted_search_docs}
+    return {"vector_results": formatted_search_docs}