Add image processing tool

agent.py

@@ -8,6 +8,7 @@ from langchain_core.messages import SystemMessage, HumanMessage
 from langchain_core.tools import tool
 from tools.math_tools import add, subtract, multiply, divide, modulus, power, sqrt
 from tools.search_tools import search_wikipedia, web_search
+from tools.image_video_tools import query_image
 
 
 def build_graph():
@@ -20,7 +21,7 @@ def build_graph():
         max_retries=2,
         google_api_key=os.getenv("GOOGLE_API_KEY")  # Get API key from environment variable
         )
-    tools = [add, subtract, multiply, divide, modulus, power, sqrt, web_search, search_wikipedia]
+    tools = [add, subtract, multiply, divide, modulus, power, sqrt, web_search, search_wikipedia, query_image]
 
     llm_with_tools = llm.bind_tools(tools)
 

tools/image_video_tools.py

@@ -0,0 +1,45 @@
+"""This module contains tools for processing images or videos."""
+
+import os
+import base64
+import mimetypes
+from langchain_core.tools import tool
+from langchain_google_genai import ChatGoogleGenerativeAI
+
+@tool
+def query_image(image_path: str, query: str) -> str:
+    """Uses a multimodal LLM to answer a query for a given image.
+
+    Args:
+        image_path (str): The path to the image to process
+        query (str): The query to be answered based on the image
+
+    Returns:
+        str: Answer of the query based on the image
+    """
+    llm = ChatGoogleGenerativeAI(
+        model="gemini-2.0-flash-001",
+        temperature=0.8,
+        max_tokens=None,
+        timeout=None,
+        max_retries=2,
+        google_api_key=os.getenv("GOOGLE_API_KEY")  # Get API key from environment variable
+    )
+    with open(image_path, "rb") as f:
+        image_bytes = f.read()
+
+    mime_type = mimetypes.guess_type(image_path)[0] or "image/jpeg"
+    image_b64 = base64.b64encode(image_bytes).decode("utf-8")
+    image_dict = {
+        "mime_type": mime_type,
+        "data": image_b64
+    }
+
+    response = llm.invoke(
+            input=query,
+            images=[image_dict]
+        )
+    return response.content
+
+
+