Update agent.py

agent.py

@@ -39,6 +39,9 @@ interpreter_instance = CodeInterpreter()
 
 @tool
 def wiki_search(query: str) -> str:
+    """
+    Search Wikipedia for a query and return up to 2 formatted results.
+    """
     docs = WikipediaLoader(query=query, load_max_docs=2).load()
     return {"wiki_results": "\n\n---\n\n".join(
         f'<Document source="{d.metadata["source"]}" page="{d.metadata.get("page","")}"/>\n{d.page_content}\n</Document>'
@@ -47,6 +50,9 @@ def wiki_search(query: str) -> str:
 
 @tool
 def web_search(query: str) -> str:
+    """
+    Search the web via Tavily for a query and return up to 3 formatted results.
+    """
     docs = TavilySearchResults(max_results=3).invoke(query=query)
     return {"web_results": "\n\n---\n\n".join(
         f'<Document source="{d.metadata["source"]}" page="{d.metadata.get("page","")}"/>\n{d.page_content}\n</Document>'
@@ -55,6 +61,9 @@ def web_search(query: str) -> str:
 
 @tool
 def arxiv_search(query: str) -> str:
+    """
+    Search arXiv for a query and return up to 3 formatted results.
+    """
     docs = ArxivLoader(query=query, load_max_docs=3).load()
     return {"arxiv_results": "\n\n---\n\n".join(
         f'<Document source="{d.metadata["source"]}" page="{d.metadata.get("page","")}"/>\n{d.page_content[:1000]}\n</Document>'
@@ -63,31 +72,61 @@ def arxiv_search(query: str) -> str:
 
 @tool
 def execute_code_multilang(code: str, language: str = "python") -> str:
+    """
+    Execute code in multiple languages (Python, Bash, SQL, C, Java) and return execution output.
+    """
     return interpreter_instance.execute_code(code, language=language)
 
 # example numeric tools
 @tool
 def multiply(a: float, b: float) -> float:
+    """
+    Multiply two numbers and return the product.
+    """
     return a * b
+
 @tool
 def add(a: float, b: float) -> float:
+    """
+    Add two numbers and return the sum.
+    """
     return a + b
+
 @tool
 def subtract(a: float, b: float) -> float:
+    """
+    Subtract the second number from the first and return the result.
+    """
     return a - b
+
 @tool
 def divide(a: float, b: float) -> float:
+    """
+    Divide the first number by the second; raises error if division by zero.
+    """
     if b == 0:
         raise ValueError("Cannot divide by zero.")
     return a / b
+
 @tool
 def modulus(a: int, b: int) -> int:
+    """
+    Return the remainder of a divided by b.
+    """
     return a % b
+
 @tool
 def power(a: float, b: float) -> float:
+    """
+    Raise a to the power of b and return the result.
+    """
     return a ** b
+
 @tool
 def square_root(a: float) -> float | complex:
+    """
+    Return the square root of a number; returns complex for negative inputs.
+    """
     if a >= 0:
         return a ** 0.5
     return cmath.sqrt(a)
@@ -95,6 +134,9 @@ def square_root(a: float) -> float | complex:
 # file and document tools (save/read, download, OCR, CSV/Excel)
 @tool
 def save_and_read_file(content: str, filename: Optional[str] = None) -> str:
+    """
+    Save content to a temporary file and return the file path.
+    """
     temp_dir = tempfile.gettempdir()
     filepath = os.path.join(temp_dir, filename or f"file_{uuid.uuid4().hex[:8]}.txt")
     with open(filepath, "w") as f:
@@ -103,19 +145,26 @@ def save_and_read_file(content: str, filename: Optional[str] = None) -> str:
 
 @tool
 def download_file_from_url(url: str, filename: Optional[str] = None) -> str:
+    """
+    Download a file from a URL, save locally, and return the file path or error string.
+    """
     try:
         fname = filename or os.path.basename(urlparse(url).path) or f"file_{uuid.uuid4().hex[:8]}"
         path = os.path.join(tempfile.gettempdir(), fname)
         resp = requests.get(url, stream=True)
         resp.raise_for_status()
         with open(path, "wb") as f:
-            for chunk in resp.iter_content(8192): f.write(chunk)
+            for chunk in resp.iter_content(8192):
+                f.write(chunk)
         return f"Downloaded to {path}"
     except Exception as e:
         return str(e)
 
 @tool
 def extract_text_from_image(image_path: str) -> str:
+    """
+    Extract and return text from an image file using OCR.
+    """
     try:
         img = Image.open(image_path)
         return pytesseract.image_to_string(img)
@@ -124,28 +173,37 @@ def extract_text_from_image(image_path: str) -> str:
 
 @tool
 def analyze_csv_file(file_path: str, query: str) -> str:
+    """
+    Analyze a CSV file: return row/column counts and summary statistics.
+    """
     df = pd.read_csv(file_path)
     return f"Rows: {len(df)}, Columns: {list(df.columns)}\n{df.describe()}"
 
 @tool
 def analyze_excel_file(file_path: str, query: str) -> str:
+    """
+    Analyze an Excel file: return row/column counts and summary statistics.
+    """
     df = pd.read_excel(file_path)
     return f"Rows: {len(df)}, Columns: {list(df.columns)}\n{df.describe()}"
 
 # image analysis/transforms
 @tool
 def analyze_image(image_base64: str) -> Dict[str, Any]:
-    from image_processing import decode_image
+    """
+    Analyze a base64-encoded image: return dimensions and mode.
+    """
     img = decode_image(image_base64)
-    w,h = img.size
-    return {"dimensions": (w,h), "mode": img.mode}
+    w, h = img.size
+    return {"dimensions": (w, h), "mode": img.mode}
 
 @tool
 def transform_image(image_base64: str, operation: str, params: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
-    from image_processing import decode_image, encode_image
+    """
+    Apply a transformation to a base64-encoded image; placeholder implementation.
+    """
     img = decode_image(image_base64)
-    # ... apply op as before ...
-    # return {"image": encode_image(path)}
+    # operations logic here
     return {"error": "placeholder"}
 
 # combine all tools into list
@@ -162,7 +220,7 @@ tools = [
 with open("system_prompt.txt", "r", encoding="utf-8") as f:
     sys_msg = SystemMessage(content=f.read())
 
-
+# vectorstore setup (Supabase)
 emb = HuggingFaceEmbeddings(
     model_name="sentence-transformers/all-mpnet-base-v2"
 )
@@ -171,7 +229,6 @@ sup = create_client(
     os.getenv("SUPABASE_URL"),
     os.getenv("SUPABASE_SERVICE_ROLE_KEY")
 )
-# vectorstore setup (Supabase)
 vector_store = SupabaseVectorStore(
     client=sup,
     embedding=emb,
@@ -180,10 +237,10 @@ vector_store = SupabaseVectorStore(
 )
 
 def build_graph():
-    """Build the LangGraph agent using OpenAI ChatGPT only."""
+    """
+    Build the LangGraph agent using OpenAI ChatGPT only.
+    """
     # Initialize the OpenAI LLM
-    from langchain_openai import ChatOpenAI
-
     llm = ChatOpenAI(
         model="gpt-3.5-turbo",
         temperature=0,
@@ -196,9 +253,7 @@ def build_graph():
         query = state["messages"][0].content
         hits = vector_store.similarity_search(query, k=1)
         if hits:
-            # return the raw snippet as the single message
             return {"messages": [sys_msg, HumanMessage(content=hits[0].page_content)]}
-        # fall back to LLM-with-tools on the user prompt
         resp = llm_with_tools.invoke([sys_msg] + state["messages"])
         return {"messages": [resp]}
 
@@ -219,3 +274,9 @@ def build_graph():
 
     return builder.compile()
 
+# Optional test
+if __name__ == "__main__":
+    graph = build_graph()
+    msgs = graph.invoke({"messages": [HumanMessage(content="Hello world")]})
+    for m in msgs["messages"]:
+        print(m.content)