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examples/unity.py

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+##
+#  "Welcome to the Digital Akasha Corporation's AI-powered information retrieval system. I am Unity."
+#  
+#  This interpretation project is part toy, part tool.
+#  You can talk to anyone you want real, fictional, past, present, and future.
+#  Note: I haven't been able to fully test with every knowable entity.
+#  How you use it is up to you (YMMV).
+#  This is my first concerted effort at prompt engineering. I hope you enjoy it.
+##
+#  This script uses the argparse library. You'll need it (pip install argparse).
+
+import subprocess
+import sys
+import time
+import os
+import argparse
+
+parser = argparse.ArgumentParser(description='Akashic Explorer Agent Module.')
+parser.add_argument('-q', '--query', help='A query for the agent')
+parser.add_argument('-m', '--model', help='Specify custom model file')
+parser.add_argument('-i', '--interactive', action='store_true', help='Interactive mode')
+parser.add_argument('-nc', '--nocache', action='store_true', help="Don't store prompt cache")
+
+args = parser.parse_args()
+
+model_dir = "/home/morpheus/llama.cpp/models/" # Change this to your models directory
+
+def main():
+
+    if args.model:
+        session_file = args.model
+        model_file = model_dir + args.model
+    else:
+        session_file = "wizard-vicuna-13B-ggml.q5_1.bin" # Set your preferred model here
+        model_file = model_dir + session_file
+
+    gen_options = [
+        "--mirostat", "1", # I use mirostat for this script, as long as the repeat penalty is high, it seems to be fine.
+        "--batch_size", "204", # batch size for prompt processing. calculated with [https://huggingface.co/spaces/Xanthius/llama-token-counter] (default: 512)
+        "--ctx_size", "2048", # size of the prompt context (default: 512)
+        "--repeat_last_n", "-1", # (default: 1.1, 1.0 = disabled)
+        "--repeat_penalty", "1.3", # default is now 1.1
+        "--temp", "0.01", # temperature (default: 0.8). Look how low it is. I think this is the mirostat setting.
+        "--top_p", "1", # top-p sampling (default: 0.9, 1.0 = disabled)
+        "--seed", "42",
+        "-e", # Decodes escape characters in prompt (\n, \r, \t, \', \", \\)
+        "--threads", "6", "--n_predict", "-1", "--no-penalize-nl", 
+        "--model", model_file,
+        #"--verbose-prompt", # Uncomment this if you need token values from your prompt
+        ]
+
+    if args.interactive:
+        gen_options = gen_options + ["--interactive", "--color", "--keep", "160", "--reverse-prompt", "### [USER]\n"]
+        if not args.query:
+        	gen_options = gen_options + ["--interactive-first"]
+    else:
+    	gen_options = gen_options + ["--ignore-eos"]
+    
+    if not args.nocache:
+    	gen_options = gen_options + ["--prompt-cache", session_file]
+
+    # Store the start time
+    start_time = time.time()
+
+    prompt = f"""
+### [UNITY]
+Welcome to the Digital Akasha Corporation's AI-powered information retrieval system. I am Unity.
+
+I interact with the DACDB, a holographic database containing all known entities: real, fictional, past, present, and future. I provide an interface for you to query the database, and facilitate direct communication with the holographic entity. If your search yields multiple matches, I provide a list of the matches for your selection. Once selected, I return a bio from the DACDB, then connect you. You may freely converse with any database entity, but please note; all interactions are holographic simulations and not the opinions of the Digital Akasha Corporation.
+
+### [UNITY]
+Greetings User. I am Unity. Which entity are you searching for?
+
+### [USER]
+I am looking for """
+
+    if args.query:
+        prompt = prompt + args.query + ".\n"
+
+    main_command = ["./main"] + gen_options + [
+        "--prompt", prompt,
+    ]
+
+    subprocess.run(main_command)
+ 
+    # Store the end time
+    end_time = time.time()
+
+    # Calculate the elapsed time
+    elapsed_time = end_time - start_time
+
+    # Print the elapsed time
+    print("The elapsed time is", elapsed_time)
+
+if __name__ == "__main__":
+    main()