Instructions to use Pankaj001/Watchtower_sample_files with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TF-Keras
How to use Pankaj001/Watchtower_sample_files with TF-Keras:
# Note: 'keras<3.x' or 'tf_keras' must be installed (legacy) # See https://github.com/keras-team/tf-keras for more details. from huggingface_hub import from_pretrained_keras model = from_pretrained_keras("Pankaj001/Watchtower_sample_files") - Notebooks
- Google Colab
- Kaggle
| import os | |
| import vertexai | |
| from vertexai import agent_engines | |
| from google.adk.sessions import VertexAiSessionService | |
| from dotenv import load_dotenv | |
| import json | |
| import asyncio | |
| def pretty_print_event(event): | |
| """Pretty prints an event with truncation for long content.""" | |
| if "content" not in event: | |
| print(f"[{event.get('author', 'unknown')}]: {event}") | |
| return | |
| author = event.get("author", "unknown") | |
| parts = event["content"].get("parts", []) | |
| for part in parts: | |
| if "text" in part: | |
| text = part["text"] | |
| # Truncate long text to 200 characters | |
| if len(text) > 200: | |
| text = text[:197] + "..." | |
| print(f"[{author}]: {text}") | |
| elif "functionCall" in part: | |
| func_call = part["functionCall"] | |
| print(f"[{author}]: Function call: {func_call.get('name', 'unknown')}") | |
| # Truncate args if too long | |
| args = json.dumps(func_call.get("args", {})) | |
| if len(args) > 100: | |
| args = args[:97] + "..." | |
| print(f" Args: {args}") | |
| elif "functionResponse" in part: | |
| func_response = part["functionResponse"] | |
| print(f"[{author}]: Function response: {func_response.get('name', 'unknown')}") | |
| # Truncate response if too long | |
| response = json.dumps(func_response.get("response", {})) | |
| if len(response) > 100: | |
| response = response[:97] + "..." | |
| print(f" Response: {response}") | |
| load_dotenv() | |
| vertexai.init( | |
| project=os.getenv("GOOGLE_CLOUD_PROJECT"), | |
| location=os.getenv("GOOGLE_CLOUD_LOCATION"), | |
| ) | |
| session_service = VertexAiSessionService(project=os.getenv("GOOGLE_CLOUD_PROJECT"),location=os.getenv("GOOGLE_CLOUD_LOCATION")) | |
| AGENT_ENGINE_ID = os.getenv("AGENT_ENGINE_ID") | |
| session = asyncio.run(session_service.create_session( | |
| app_name=AGENT_ENGINE_ID, | |
| user_id="123", | |
| )) | |
| agent_engine = agent_engines.get(AGENT_ENGINE_ID) | |
| queries = [ | |
| "Hi, how are you?", | |
| "According to the MD&A, how might the increasing proportion of revenues derived from non-advertising sources like Google Cloud and devices potentially impact Alphabet's overall operating margin, and why?", | |
| "The report mentions significant investments in AI. What specific connection is drawn between these AI investments and the company's expectations regarding future capital expenditures?", | |
| "Thanks, I got all the information I need. Goodbye!", | |
| ] | |
| for query in queries: | |
| print(f"\n[user]: {query}") | |
| for event in agent_engine.stream_query( | |
| user_id="123", | |
| session_id=session.id, | |
| message=query, | |
| ): | |
| pretty_print_event(event) | |