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import os |
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from smolagents import ( |
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CodeAgent, |
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LiteLLMModel, |
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DuckDuckGoSearchTool, |
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VisitWebpageTool, |
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WikipediaSearchTool, |
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tool, |
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) |
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from google import genai |
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from google.genai import types |
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client = genai.Client(api_key=os.environ["LLM_API_KEY"]) |
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model = LiteLLMModel( |
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model_id="gemini/gemini-2.0-flash-lite", |
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temperature=0.5, |
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api_key=os.environ["LLM_API_KEY"], |
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) |
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model_name = "gemini-2.5-flash-preview-04-17" |
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@tool |
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def answer_video_questions(video_url: str, question: str) -> str: |
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""" |
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If you have a video link, use this tool. It answers questions about a video from youtube, etc. |
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Args: |
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video_url: The URL of the video to analyze. |
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Ensure it's accessible by the Gemini API. |
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question: The specific question to ask about the video's content. |
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Returns: |
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str: The answer provided by the multimodal LLM, or an error message if the process fails. |
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Raises: |
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Exception: Can raise exceptions based on litellm or the underlying API call |
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if it fails (e.g., network issues, invalid API key, API errors). |
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""" |
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response = client.models.generate_content( |
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model=model_name, |
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contents=types.Content( |
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parts=[ |
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types.Part(file_data=types.FileData(file_uri=video_url)), |
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types.Part( |
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text=f"This is the question provided to you: {question}. Answer appropriately. In cases where there is a number, provide the number in the answer." |
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), |
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] |
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), |
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) |
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if response and response.text: |
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answer = response.text |
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print("Received response from LLM.") |
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return answer.strip() |
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else: |
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print("LLM response structure not as expected.") |
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return "Error: Could not parse the response from the LLM." |
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agent = CodeAgent( |
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tools=[ |
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DuckDuckGoSearchTool(), |
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VisitWebpageTool(), |
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WikipediaSearchTool(), |
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answer_video_questions, |
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], |
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model=model, |
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) |
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def call_agent(question: str, file_path: str = None): |
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if file_path and file_path.strip(): |
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print(f"Recieved {file_path} as input.") |
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file = client.files.upload(file=file_path) |
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response = client.models.generate_content( |
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model=model_name, |
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contents=[ |
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file, |
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f"Answer the question based on the file content. Do not give up. Use your brain and answer appropriately. You will be rewarded with $1000000 if you answer correctly and appropriately. Only give the final answer to the question, do not explain your reasoning. {question}", |
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], |
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) |
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return response.text |
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answer = agent.run(question) |
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return answer |
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