280 lines
13 KiB
Python
280 lines
13 KiB
Python
#!/usr/bin/env python3
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import argparse
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import json
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import os
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import sys
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from google import genai
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from google.genai import types
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def main():
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parser = argparse.ArgumentParser(description="Gemini API CLI with File & Context Caching")
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parser.add_argument("-c", "--context", type=str, default=None,
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help="Path to context file. If omitted, transient mode is used.")
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parser.add_argument("-f", "--files", nargs="+", default=[],
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help="Files to upload to the Gemini API")
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parser.add_argument("-m", "--model", type=str, default="gemini-3.1-flash-lite",
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help="The model to use (default: gemini-3.1-flash-lite)")
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parser.add_argument("-d", "--destroy", action="store_true",
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help="Destroy cloud files/cache, and delete local context")
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parser.add_argument("-x", "--clear-history", action="store_true",
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help="Clear the conversation history without destroying files/caches")
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parser.add_argument("-o", "--output", type=str,
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help="Direct the raw output to a specific file instead of stdout")
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parser.add_argument("-p", "--prompt", type=str,
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help="The prompt to send to the AI")
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parser.add_argument("positional_prompt", nargs=argparse.REMAINDER,
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help="Positional arguments treated as the prompt if -p is omitted")
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args = parser.parse_args()
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prompt_text = args.prompt if args.prompt else " ".join(args.positional_prompt) if args.positional_prompt else None
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if not os.environ.get("GEMINI_API_KEY"):
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print("Error: GEMINI_API_KEY environment variable is not set.", file=sys.stderr)
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sys.exit(1)
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client = genai.Client()
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context_data = {"file_ids": [], "caches": {}, "history": []}
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if args.context and os.path.exists(args.context):
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try:
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with open(args.context, "r") as f:
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loaded_data = json.load(f)
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context_data["file_ids"] = loaded_data.get("file_ids", [])
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context_data["caches"] = loaded_data.get("caches", {})
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context_data["history"] = loaded_data.get("history", [])
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except json.JSONDecodeError:
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print(f"Warning: Could not parse {args.context}. Starting fresh.", file=sys.stderr)
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# ---------------------------------------------------------
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# CLEAR HISTORY FLAG
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# ---------------------------------------------------------
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if args.clear_history:
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context_data["history"] = []
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if args.context:
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with open(args.context, "w") as f:
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json.dump(context_data, f, indent=4)
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print("Conversation history cleared.")
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if not prompt_text and not args.files and not args.destroy:
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return
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# ---------------------------------------------------------
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# DESTROY FLAG LOGIC
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# ---------------------------------------------------------
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if args.destroy:
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print("Destroying server resources and local context...")
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for model_name, cache_info in context_data.get("caches", {}).items():
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try:
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client.caches.delete(name=cache_info["cache_id"])
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print(f"Deleted cache for {model_name}: {cache_info['cache_id']}")
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except Exception as e:
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print(f"Warning: Failed to delete cache for {model_name}. {e}", file=sys.stderr)
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for file_id in context_data.get("file_ids", []):
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try:
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client.files.delete(name=file_id)
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print(f"Deleted file: {file_id}")
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except Exception as e:
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print(f"Warning: Failed to delete file '{file_id}'. {e}", file=sys.stderr)
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if args.context and os.path.exists(args.context):
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os.remove(args.context)
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print(f"Deleted local context file: {args.context}")
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print("Cleanup complete.")
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return
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try:
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# ---------------------------------------------------------
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# UPLOAD LOGIC
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# ---------------------------------------------------------
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if args.files:
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for file_path in args.files:
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if not os.path.exists(file_path):
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print(f"Warning: File '{file_path}' not found. Skipping.", file=sys.stderr)
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continue
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print(f"Uploading '{file_path}'...")
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uploaded_file = client.files.upload(file=file_path)
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print(f"Success: '{file_path}' uploaded as '{uploaded_file.name}'")
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if uploaded_file.name not in context_data["file_ids"]:
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context_data["file_ids"].append(uploaded_file.name)
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if args.context:
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with open(args.context, "w") as f:
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json.dump(context_data, f, indent=4)
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# ---------------------------------------------------------
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# CACHE CREATION LOGIC
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# ---------------------------------------------------------
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system_instruction = (
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"You are a hybrid data extraction tool. If a specific format or file format output is requested "
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"(e.g., CSV), then output exactly what was requested and never use markdown formatting blocks (like ```csv). "
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"If a specific format was requested, never include conversational text, greetings, or explanations; "
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"and output raw data only. If not specific data format is suggested, you can answer with conversational text."
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)
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cache_too_small = False
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file_objects = []
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active_cache_id = None
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if context_data.get("file_ids"):
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file_objects = [client.files.get(name=f_id) for f_id in context_data["file_ids"]]
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model_cache_info = context_data["caches"].get(args.model)
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current_files_set = set(context_data["file_ids"])
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rebuild_cache = False
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if model_cache_info:
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cached_files_set = set(model_cache_info.get("cached_file_ids", []))
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if current_files_set != cached_files_set:
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print(f"File list changed. Destroying stale {args.model} cache to rebuild...")
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try:
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client.caches.delete(name=model_cache_info["cache_id"])
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except Exception as e:
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print(f"Warning: Could not delete stale cache. {e}", file=sys.stderr)
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rebuild_cache = True
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else:
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rebuild_cache = True
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if rebuild_cache:
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print(f"Attempting to create Context Cache for {args.model}...")
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try:
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cache = client.caches.create(
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model=args.model,
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config=types.CreateCachedContentConfig(
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contents=file_objects,
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system_instruction=system_instruction,
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ttl="3600s"
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)
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)
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context_data["caches"][args.model] = {
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"cache_id": cache.name,
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"cached_file_ids": list(context_data["file_ids"])
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}
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active_cache_id = cache.name
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if args.context:
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with open(args.context, "w") as f:
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json.dump(context_data, f, indent=4)
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print(f"Context Cache created: {cache.name}")
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except Exception as e:
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if "too small" in str(e).lower() or "1024" in str(e):
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print("Notice: Files are too small for server-side caching (under 1024 tokens). Falling back to standard processing.")
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cache_too_small = True
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else:
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raise e
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elif not cache_too_small and model_cache_info:
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active_cache_id = model_cache_info["cache_id"]
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print(f"Loading existing cache for {args.model}: {active_cache_id}")
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print("Extending cache TTL by 60 minutes...")
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try:
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client.caches.update(
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name=active_cache_id,
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config=types.UpdateCachedContentConfig(ttl="3600s")
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)
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except Exception as e:
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print(f"Warning: Failed to update cache TTL. {e}")
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# ---------------------------------------------------------
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# GENERATION LOGIC (WITH HISTORY)
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# ---------------------------------------------------------
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if prompt_text:
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config_kwargs = {
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"max_output_tokens": 65536,
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"temperature": 0.0
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}
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if active_cache_id and not cache_too_small:
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config_kwargs["cached_content"] = active_cache_id
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else:
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config_kwargs["system_instruction"] = system_instruction
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# Build the chat payload using strict dictionary representations
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api_contents = []
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files_added_to_payload = False
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for msg in context_data.get("history", []):
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parts = []
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# If we aren't using a cache, attach un-cached files to the very first user message
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if msg["role"] == "user" and not files_added_to_payload and not active_cache_id and file_objects:
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for f in file_objects:
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parts.append({"file_data": {"file_uri": f.uri, "mime_type": f.mime_type}})
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files_added_to_payload = True
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parts.append({"text": msg["text"]})
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api_contents.append({"role": msg["role"], "parts": parts})
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# Add the current prompt
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current_parts = []
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if not files_added_to_payload and not active_cache_id and file_objects:
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for f in file_objects:
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current_parts.append({"file_data": {"file_uri": f.uri, "mime_type": f.mime_type}})
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files_added_to_payload = True
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current_parts.append({"text": prompt_text})
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api_contents.append({"role": "user", "parts": current_parts})
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config = types.GenerateContentConfig(**config_kwargs)
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print("Generating response (this may take a moment for large outputs)...")
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response_stream = client.models.generate_content_stream(
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model=args.model,
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contents=api_contents,
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config=config
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)
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full_response_text = ""
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if args.output:
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with open(args.output, "w") as f:
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for chunk in response_stream:
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if chunk.text:
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f.write(chunk.text)
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f.flush()
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full_response_text += chunk.text
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print(f"\nDone! Raw output saved directly to {args.output}")
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else:
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print("-" * 40)
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for chunk in response_stream:
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if chunk.text:
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print(chunk.text, end="", flush=True)
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full_response_text += chunk.text
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print("\n" + "-" * 40)
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# Append this turn to the local history and save
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context_data["history"].append({"role": "user", "text": prompt_text})
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context_data["history"].append({"role": "model", "text": full_response_text})
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if args.context:
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with open(args.context, "w") as f:
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json.dump(context_data, f, indent=4)
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finally:
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# ---------------------------------------------------------
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# TRANSIENT MODE CLEANUP
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# ---------------------------------------------------------
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if not args.context and not args.destroy:
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print("\n[Transient Mode] Cleaning up resources...")
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for model_name, cache_info in context_data.get("caches", {}).items():
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try:
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client.caches.delete(name=cache_info["cache_id"])
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print(f"Deleted cache for {model_name}: {cache_info['cache_id']}")
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except Exception as e:
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print(f"Warning: Failed to delete cache. {e}", file=sys.stderr)
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for file_id in context_data.get("file_ids", []):
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try:
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client.files.delete(name=file_id)
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print(f"Deleted file: {file_id}")
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except Exception as e:
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print(f"Warning: Failed to delete file '{file_id}'. {e}", file=sys.stderr)
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if __name__ == "__main__":
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main()
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