Update agent.py

agent.py

@@ -1,228 +1,231 @@
-import os
-import json
-from dotenv import load_dotenv
-
-import dotenv
-from langgraph.graph import START, StateGraph
-from langgraph.prebuilt import tools_condition
-from langgraph.prebuilt import ToolNode
-from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain_huggingface import HuggingFaceEmbeddings
-from langchain_community.tools.tavily_search import TavilySearchResults
-from langchain_community.document_loaders import WikipediaLoader
-from langchain_community.document_loaders import ArxivLoader
-from langchain_community.vectorstores import SupabaseVectorStore
-from langchain.tools.retriever import create_retriever_tool
-from langchain_core.messages import HumanMessage, SystemMessage
-from langchain_core.tools import tool
-from supabase.client import Client, create_client
-from langchain.chat_models import init_chat_model
-import random
-from typing import Annotated,TypedDict
-from langchain_core.messages import AnyMessage, HumanMessage, AIMessage
-from langgraph.graph.message import add_messages
-
-
-
-load_dotenv()
-
-with open('metadata.jsonl', 'r') as jsonl_file:
-    json_list = list(jsonl_file)
-
-json_QA = []
-for json_str in json_list:
-    json_data = json.loads(json_str)
-    json_QA.append(json_data)
-
-random.seed(42)
-random_samples = random.sample(json_QA, 1)
-
-supabase_url = os.environ.get("SUPABASE_URL")
-supabase_key = os.environ.get("SUPABASE_SERVICE_KEY")
-supabase: Client = create_client(supabase_url, supabase_key)
-
-system_prompt = """
-You are a helpful assistant tasked with answering questions using a set of tools.
-If the tool is not available, you can try to find the information online. You can also use your own knowledge to answer the question.
-You need to provide a step-by-step explanation of how you arrived at the answer.
-==========================
-Here is a few examples showing you how to answer the question step by step.
-"""
-for i,sample in enumerate(random_samples):
-  system_prompt += f"\nQuestion {i+1}: {sample['Question']}\nSteps:\n{sample['Annotator Metadata']['Steps']}\nTools:\n{sample['Annotator Metadata']['Tools']}\nFinal Answer: {sample['Final answer']}\n"
-system_prompt += "\n==========================\n"
-system_prompt += "Now, please answer the following question step by step.And if you can, please answer in Vietnamese.\n"
-# save the system_prompt to a file
-with open('system_prompt.txt', 'w') as f:
-    f.write(system_prompt)
-
-with open('system_prompt.txt', 'r') as f:
-  system_prompt=f.read()
-print(system_prompt)
-
-
-embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
-tavily_key = os.getenv("TAVILY_API_KEY")
-
-# Tạo hoặc truy cập bảng vector
-vector_store = SupabaseVectorStore(
-    client=supabase,
-    embedding=embeddings,
-    table_name="documents",  # bảng sẽ tự tạo nếu chưa có
-    query_name="match_documents_langchain",  # tên function tự sinh
-)
-retriever = vector_store.as_retriever()
-
-create_retriever_tool = create_retriever_tool(
-    retriever = vector_store.as_retriever(),
-    name= "Question_Retriever",
-    description= "Find similar questions in the vector database for the given question."
-)
-
-@tool
-def multiply(a:int,b:int)->int:
-  """Multiply 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 add(a:int,b:int)->int:
-  """Add 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
-  """
-  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
-
-@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,tavily_api_key=tavily_key).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}
-
-tools = [
-    multiply,
-    add,
-    subtract,
-    divide,
-    modulus,
-    wiki_search,
-    web_search,
-    arvix_search,
-    create_retriever_tool
-]
-
-llm = init_chat_model("google_genai:gemini-2.0-flash",google_api_key=os.environ["GOOGLE_API_KEY"])
-llm_with_tools = llm.bind_tools(tools)
-
-sys_msg = SystemMessage(content=system_prompt)
-
-class MessagesState(TypedDict):
-  messages: Annotated[list[AnyMessage], add_messages]
-# Node
-def assistant(state: MessagesState):
-    """Assistant node"""
-    return {"messages": [llm_with_tools.invoke(state["messages"])]}
-def retriever(state: MessagesState):
-        """Retriever node"""
-        similar_question = vector_store.similarity_search(state["messages"][0].content)
-        example_msg = HumanMessage(
-            content=f"Here I provide a question and answer using query for reference if it is similar to question below:  \n\n{similar_question[0].page_content}",
-        )
-        return {"messages": [sys_msg] + state["messages"] + [example_msg]}
-
-# Build graph
-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",
-    # If the latest message (result) from assistant is a tool call -> tools_condition routes to tools
-    # If the latest message (result) from assistant is a not a tool call -> tools_condition routes to END
-    tools_condition,
-)
-builder.add_edge("tools", "assistant")
-
-# Compile graph
-graph = builder.compile()
-
-question = "What is the first name of the only Malko Competition recipient from the 20th Century (after 1977) whose nationality on record is a country that no longer exists?"
-messages = [HumanMessage(content=question)]
-messages = graph.invoke({"messages": messages})
-
-for m in messages['messages']:
-    m.pretty_print()
-
+import os
+import json
+from dotenv import load_dotenv
+
+import dotenv
+from langgraph.graph import START, StateGraph
+from langgraph.prebuilt import tools_condition
+from langgraph.prebuilt import ToolNode
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_community.tools.tavily_search import TavilySearchResults
+from langchain_community.document_loaders import WikipediaLoader
+from langchain_community.document_loaders import ArxivLoader
+from langchain_community.vectorstores import SupabaseVectorStore
+from langchain.tools.retriever import create_retriever_tool
+from langchain_core.messages import HumanMessage, SystemMessage
+from langchain_core.tools import tool
+from supabase.client import Client, create_client
+from langchain.chat_models import init_chat_model
+import random
+from typing import Annotated,TypedDict
+from langchain_core.messages import AnyMessage, HumanMessage, AIMessage
+from langgraph.graph.message import add_messages
+
+
+
+load_dotenv()
+
+with open('metadata.jsonl', 'r') as jsonl_file:
+    json_list = list(jsonl_file)
+
+json_QA = []
+for json_str in json_list:
+    json_data = json.loads(json_str)
+    json_QA.append(json_data)
+
+random.seed(42)
+random_samples = random.sample(json_QA, 1)
+
+supabase_url = os.environ.get("SUPABASE_URL")
+supabase_key = os.environ.get("SUPABASE_SERVICE_KEY")
+supabase: Client = create_client(supabase_url, supabase_key)
+
+system_prompt = """
+You are a helpful assistant tasked with answering questions using a set of tools.
+If the tool is not available, you can try to find the information online. You can also use your own knowledge to answer the question.
+You need to provide a step-by-step explanation of how you arrived at the answer.
+==========================
+Here is a few examples showing you how to answer the question step by step.
+"""
+for i,sample in enumerate(random_samples):
+  system_prompt += f"\nQuestion {i+1}: {sample['Question']}\nSteps:\n{sample['Annotator Metadata']['Steps']}\nTools:\n{sample['Annotator Metadata']['Tools']}\nFinal Answer: {sample['Final answer']}\n"
+system_prompt += "\n==========================\n"
+system_prompt += "Now, please answer the following question step by step.And if you can, please answer in Vietnamese.\n"
+# save the system_prompt to a file
+with open('system_prompt.txt', 'w') as f:
+    f.write(system_prompt)
+
+with open('system_prompt.txt', 'r') as f:
+  system_prompt=f.read()
+print(system_prompt)
+
+
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
+tavily_key = os.getenv("TAVILY_API_KEY")
+
+# Tạo hoặc truy cập bảng vector
+vector_store = SupabaseVectorStore(
+    client=supabase,
+    embedding=embeddings,
+    table_name="documents",  # bảng sẽ tự tạo nếu chưa có
+    query_name="match_documents_langchain",  # tên function tự sinh
+)
+retriever = vector_store.as_retriever()
+
+create_retriever_tool = create_retriever_tool(
+    retriever = vector_store.as_retriever(),
+    name= "Question_Retriever",
+    description= "Find similar questions in the vector database for the given question."
+)
+
+@tool
+def multiply(a:int,b:int)->int:
+  """Multiply 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 add(a:int,b:int)->int:
+  """Add 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
+  """
+  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
+
+@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,tavily_api_key=tavily_key).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}
+
+tools = [
+    multiply,
+    add,
+    subtract,
+    divide,
+    modulus,
+    wiki_search,
+    web_search,
+    arvix_search,
+    create_retriever_tool
+]
+
+llm = init_chat_model("google_genai:gemini-2.0-flash",google_api_key=os.environ["GOOGLE_API_KEY"])
+llm_with_tools = llm.bind_tools(tools)
+
+sys_msg = SystemMessage(content=system_prompt)
+
+class MessagesState(TypedDict):
+  messages: Annotated[list[AnyMessage], add_messages]
+# Node
+def assistant(state: MessagesState):
+    """Assistant node"""
+    return {"messages": [llm_with_tools.invoke(state["messages"])]}
+def retriever(state: MessagesState):
+        """Retriever node"""
+        similar_question = vector_store.similarity_search(state["messages"][0].content)
+        example_msg = HumanMessage(
+            content=f"Here I provide a question and answer using query for reference if it is similar to question below:  \n\n{similar_question[0].page_content}",
+        )
+        return {"messages": [sys_msg] + state["messages"] + [example_msg]}
+
+# Build graph
+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",
+    # If the latest message (result) from assistant is a tool call -> tools_condition routes to tools
+    # If the latest message (result) from assistant is a not a tool call -> tools_condition routes to END
+    tools_condition,
+)
+builder.add_edge("tools", "assistant")
+
+# Compile graph
+graph = builder.compile()
+
+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 = builder.compile()
+    # Run the graph
+    messages = [HumanMessage(content=question)]
+    messages = graph.invoke({"messages": messages})
+    for m in messages["messages"]:
+        m.pretty_print()
+