Update agent.py

agent.py

@@ -1,350 +1,350 @@
-"""LangGraph Agent"""
-
-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_google_genai import ChatGoogleGenerativeAI
-from langchain_groq import ChatGroq
-from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint, HuggingFaceEmbeddings
-from langchain_tavily import TavilySearch
-from langchain_community.document_loaders import WikipediaLoader
-from langchain_community.document_loaders import ArxivLoader
-from langchain_community.vectorstores import SupabaseVectorStore
-from langchain_core.messages import SystemMessage, HumanMessage
-from langchain_core.tools import tool
-from langchain.tools.retriever import create_retriever_tool
-from langchain_openai import ChatOpenAI
-from langchain_anthropic import ChatAnthropic
-from supabase.client import Client, create_client
-import re
-from langchain_community.document_loaders import WikipediaLoader
-from youtube_transcript_api import YouTubeTranscriptApi, TranscriptsDisabled, NoTranscriptFound
-import sympy
-import wolframalpha
-import sys
-import requests
-
-
-
-
-load_dotenv()
-
-@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
-
-@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}
-    return 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 = TavilySearch(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 filtered_wiki_search(query: str, start_year: int = None, end_year: int = None) -> dict:
-    """Search Wikipedia for a query and filter results by year if provided."""
-    search_docs = WikipediaLoader(query=query, load_max_docs=5).load()
-    
-    def contains_year(text, start, end):
-        years = re.findall(r'\b(19\d{2}|20\d{2})\b', text)
-        for y in years:
-            y_int = int(y)
-            if start <= y_int <= end:
-                return True
-        return False
-
-    filtered_docs = []
-    for doc in search_docs:
-        if start_year and end_year:
-            if contains_year(doc.page_content, start_year, end_year):
-                filtered_docs.append(doc)
-        else:
-            filtered_docs.append(doc)
-
-    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 filtered_docs
-        ])
-    return {"wiki_results": formatted_search_docs}
-
-
-
-@tool
-def wolfram_alpha_query(query: str) -> str:
-    """Query Wolfram Alpha with the given question and return the result."""
-    client = wolframalpha.Client(os.environ['WOLFRAM_APP_ID'])
-    res = client.query(query)
-    try:
-        return next(res.results).text
-    except StopIteration:
-        return "No result found."
-        
-
-
-
-@tool
-def youtube_transcript(url: str) -> str:
-    """Fetch YouTube transcript text from a video URL."""
-    try:
-        video_id = url.split("v=")[-1].split("&")[0]
-        transcript_list = YouTubeTranscriptApi.get_transcript(video_id)
-        transcript = " ".join([segment['text'] for segment in transcript_list])
-        return transcript
-    except (TranscriptsDisabled, NoTranscriptFound):
-        return "Transcript not available for this video."
-    except Exception as e:
-        return f"Error fetching transcript: {str(e)}"
-
-
-
-@tool
-def solve_algebraic_expression(expression: str) -> str:
-    """Solve or simplify the given algebraic expression."""
-    try:
-        expr = sympy.sympify(expression)
-        simplified = sympy.simplify(expr)
-        return str(simplified)
-    except Exception as e:
-        return f"Error solving expression: {str(e)}"
-
-
-
-@tool
-def run_python_code(code: str) -> str:
-    """Execute python code and return the result of variable 'result' if defined."""
-    try:
-        local_vars = {}
-        exec(code, {}, local_vars)
-        if 'result' in local_vars:
-            return str(local_vars['result'])
-        else:
-            return "Code executed successfully but no 'result' variable found."
-    except Exception as e:
-        return f"Error executing code: {str(e)}"
-        
-        
-
-@tool
-def wikidata_query(sparql_query: str) -> str:
-    """Run a SPARQL query against Wikidata and return the JSON results."""
-    endpoint = "https://query.wikidata.org/sparql"
-    headers = {"Accept": "application/sparql-results+json"}
-    try:
-        response = requests.get(endpoint, params={"query": sparql_query}, headers=headers)
-        response.raise_for_status()
-        data = response.json()
-        return str(data)  # Or format as needed
-    except Exception as e:
-        return f"Error querying Wikidata: {str(e)}"
-
-
-
-
-# load the system prompt from the file
-with open("system_prompt.txt", "r", encoding="utf-8") as f:
-    system_prompt = f.read()
-
-# System message
-sys_msg = SystemMessage(content=system_prompt)
-
-# build a retriever
-
-embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2") #  dim=768
-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",
-)
-retriever_tool = create_retriever_tool(
-    retriever=vector_store.as_retriever(),
-    name="Question Search",
-    description="A tool to retrieve similar questions from a vector store.",
-)
-
-
-
-tools = [
-  
-    multiply,
-    add,
-    subtract,
-    divide,
-    modulus,
-    wiki_search,
-    filtered_wiki_search,
-    web_search,
-    arvix_search,
-    wolfram_alpha_query,
-    retriever_tool,
-    youtube_transcript,
-    solve_algebraic_expression,
-    run_python_code,
-    wikidata_query
-]
-
-# Build graph function
-def build_graph(provider: str = "groq"):
-    """Build the graph"""
-    # Load environment variables from .env file
-    if provider == "openai":
-        llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)
-    elif provider == "anthropic":
-        llm = ChatAnthropic(model="claude-v1", temperature=0)
-    elif provider == "google":
-        llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
-    elif provider == "groq":
-        llm = ChatGroq(model="qwen-qwq-32b", temperature=0) # optional : qwen-qwq-32b gemma2-9b-it
-    elif provider == "huggingface":
-        llm = ChatHuggingFace(
-            llm = HuggingFaceEndpoint( 
-                endpoint_url="https://api-inference.huggingface.co/models/Meta-DeepLearning/llama-2-7b-chat-hf",
-                temperature=0,
-            ),
-        )
-    else:
-        raise ValueError("Invalid provider. Choose 'google', 'groq' or 'huggingface'.")
-    # Bind tools to LLM
-    llm_with_tools = llm.bind_tools(tools)
-
-    # Node
-    def assistant(state: MessagesState):
-        messages_with_sys = [sys_msg] + state["messages"]
-        return {"messages": [llm_with_tools.invoke(messages_with_sys)]}
-
-    
-    def retriever(state: MessagesState):
-        """Retriever node"""
-        similar_question = vector_store.similarity_search(state["messages"][0].content)
-        if not similar_question:
-            # No similar documents found, fallback message
-            example_msg = HumanMessage(
-                content="Sorry, I could not find any similar questions in the vector store."
-            )
-        else:
-            example_msg = HumanMessage(
-                content=f"Here I provide a similar question and answer for reference: \n\n{similar_question[0].page_content}",
-            )
-        return {"messages": [sys_msg] + state["messages"] + [example_msg]}
-
-    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")
-
-    # Compile graph
-    return builder.compile()
-
-# test
-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 = build_graph(provider="groq")
-    # Run the graph
-    messages = [HumanMessage(content=question)]
-    messages = graph.invoke({"messages": messages})
-    for m in messages["messages"]:
-        m.pretty_print()
+"""LangGraph Agent"""
+
+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_google_genai import ChatGoogleGenerativeAI
+from langchain_groq import ChatGroq
+from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint, HuggingFaceEmbeddings
+from langchain_tavily import TavilySearch
+from langchain_community.document_loaders import WikipediaLoader
+from langchain_community.document_loaders import ArxivLoader
+from langchain_community.vectorstores import SupabaseVectorStore
+from langchain_core.messages import SystemMessage, HumanMessage
+from langchain_core.tools import tool
+from langchain.tools.retriever import create_retriever_tool
+from langchain_openai import ChatOpenAI
+from langchain_anthropic import ChatAnthropic
+from supabase.client import Client, create_client
+import re
+from langchain_community.document_loaders import WikipediaLoader
+from youtube_transcript_api import YouTubeTranscriptApi, TranscriptsDisabled, NoTranscriptFound
+import sympy
+import wolframalpha
+import sys
+import requests
+
+
+
+
+load_dotenv()
+
+@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
+
+@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}
+    return 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 = TavilySearch(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 filtered_wiki_search(query: str, start_year: int = None, end_year: int = None) -> dict:
+    """Search Wikipedia for a query and filter results by year if provided."""
+    search_docs = WikipediaLoader(query=query, load_max_docs=5).load()
+    
+    def contains_year(text, start, end):
+        years = re.findall(r'\b(19\d{2}|20\d{2})\b', text)
+        for y in years:
+            y_int = int(y)
+            if start <= y_int <= end:
+                return True
+        return False
+
+    filtered_docs = []
+    for doc in search_docs:
+        if start_year and end_year:
+            if contains_year(doc.page_content, start_year, end_year):
+                filtered_docs.append(doc)
+        else:
+            filtered_docs.append(doc)
+
+    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 filtered_docs
+        ])
+    return {"wiki_results": formatted_search_docs}
+
+
+
+@tool
+def wolfram_alpha_query(query: str) -> str:
+    """Query Wolfram Alpha with the given question and return the result."""
+    client = wolframalpha.Client(os.environ['WOLFRAM_APP_ID'])
+    res = client.query(query)
+    try:
+        return next(res.results).text
+    except StopIteration:
+        return "No result found."
+        
+
+
+
+@tool
+def youtube_transcript(url: str) -> str:
+    """Fetch YouTube transcript text from a video URL."""
+    try:
+        video_id = url.split("v=")[-1].split("&")[0]
+        transcript_list = YouTubeTranscriptApi.get_transcript(video_id)
+        transcript = " ".join([segment['text'] for segment in transcript_list])
+        return transcript
+    except (TranscriptsDisabled, NoTranscriptFound):
+        return "Transcript not available for this video."
+    except Exception as e:
+        return f"Error fetching transcript: {str(e)}"
+
+
+
+@tool
+def solve_algebraic_expression(expression: str) -> str:
+    """Solve or simplify the given algebraic expression."""
+    try:
+        expr = sympy.sympify(expression)
+        simplified = sympy.simplify(expr)
+        return str(simplified)
+    except Exception as e:
+        return f"Error solving expression: {str(e)}"
+
+
+
+@tool
+def run_python_code(code: str) -> str:
+    """Execute python code and return the result of variable 'result' if defined."""
+    try:
+        local_vars = {}
+        exec(code, {}, local_vars)
+        if 'result' in local_vars:
+            return str(local_vars['result'])
+        else:
+            return "Code executed successfully but no 'result' variable found."
+    except Exception as e:
+        return f"Error executing code: {str(e)}"
+        
+        
+
+@tool
+def wikidata_query(sparql_query: str) -> str:
+    """Run a SPARQL query against Wikidata and return the JSON results."""
+    endpoint = "https://query.wikidata.org/sparql"
+    headers = {"Accept": "application/sparql-results+json"}
+    try:
+        response = requests.get(endpoint, params={"query": sparql_query}, headers=headers)
+        response.raise_for_status()
+        data = response.json()
+        return str(data)  # Or format as needed
+    except Exception as e:
+        return f"Error querying Wikidata: {str(e)}"
+
+
+
+
+# load the system prompt from the file
+with open("system_prompt.txt", "r", encoding="utf-8") as f:
+    system_prompt = f.read()
+
+# System message
+sys_msg = SystemMessage(content=system_prompt)
+
+# build a retriever
+
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2") #  dim=768
+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",
+)
+retriever_tool = create_retriever_tool(
+    retriever=vector_store.as_retriever(),
+    name="Question Search",
+    description="A tool to retrieve similar questions from a vector store.",
+)
+
+
+
+tools = [
+  
+    multiply,
+    add,
+    subtract,
+    divide,
+    modulus,
+    wiki_search,
+    filtered_wiki_search,
+    web_search,
+    arvix_search,
+    wolfram_alpha_query,
+    retriever_tool,
+    youtube_transcript,
+    solve_algebraic_expression,
+    run_python_code,
+    wikidata_query
+]
+
+# Build graph function
+def build_graph(provider: str = "huggingface"):
+    """Build the graph"""
+    # Load environment variables from .env file
+    if provider == "openai":
+        llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)
+    elif provider == "anthropic":
+        llm = ChatAnthropic(model="claude-v1", temperature=0)
+    elif provider == "google":
+        llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
+    elif provider == "groq":
+        llm = ChatGroq(model="qwen-qwq-32b", temperature=0) # optional : qwen-qwq-32b gemma2-9b-it
+    elif provider == "huggingface":
+        llm = ChatHuggingFace(
+            llm = HuggingFaceEndpoint( 
+                endpoint_url="https://api-inference.huggingface.co/models/Meta-DeepLearning/llama-2-7b-chat-hf",
+                temperature=0,
+            ),
+        )
+    else:
+        raise ValueError("Invalid provider. Choose 'google', 'groq' or 'huggingface'.")
+    # Bind tools to LLM
+    llm_with_tools = llm.bind_tools(tools)
+
+    # Node
+    def assistant(state: MessagesState):
+        messages_with_sys = [sys_msg] + state["messages"]
+        return {"messages": [llm_with_tools.invoke(messages_with_sys)]}
+
+    
+    def retriever(state: MessagesState):
+        """Retriever node"""
+        similar_question = vector_store.similarity_search(state["messages"][0].content)
+        if not similar_question:
+            # No similar documents found, fallback message
+            example_msg = HumanMessage(
+                content="Sorry, I could not find any similar questions in the vector store."
+            )
+        else:
+            example_msg = HumanMessage(
+                content=f"Here I provide a similar question and answer for reference: \n\n{similar_question[0].page_content}",
+            )
+        return {"messages": [sys_msg] + state["messages"] + [example_msg]}
+
+    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")
+
+    # Compile graph
+    return builder.compile()
+
+# test
+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 = build_graph(provider="groq")
+    # Run the graph
+    messages = [HumanMessage(content=question)]
+    messages = graph.invoke({"messages": messages})
+    for m in messages["messages"]:
+        m.pretty_print()