add context size to options

ai_conversation.py

@@ -11,7 +11,8 @@ class AIConversation:
         system_prompt_1,
         system_prompt_2,
         ollama_endpoint,
-        max_tokens=4000, 
+        max_tokens=4000,
+        limit_tokens=True
     ):
         # Initialize conversation parameters and Ollama client
         self.model_1 = model_1
@@ -25,14 +26,14 @@ class AIConversation:
         self.ollama_endpoint = ollama_endpoint
         self.tokenizer = tiktoken.encoding_for_model("gpt-3.5-turbo")
         self.max_tokens = max_tokens
-
+        self.limit_tokens = limit_tokens
     def count_tokens(self, messages):
         # Count the total number of tokens in the messages
         return sum(len(self.tokenizer.encode(msg["content"])) for msg in messages)
 
     def trim_messages(self, messages):
         # Trim messages to stay within the token limit
-        if self.count_tokens(messages) > self.max_tokens:
+        if self.limit_tokens and self.count_tokens(messages) > self.max_tokens:
             print(colored(f"[SYSTEM] Max tokens reached. Sliding context window...", "magenta"))
             
             # Keep the system prompt (first message)
@@ -52,6 +53,7 @@ class AIConversation:
         return messages
 
     def start_conversation(self, initial_message, num_exchanges=0, options=None):
+        
         # Main conversation loop
         current_message = initial_message
         color_1, color_2 = "cyan", "yellow"

main.py

@@ -3,15 +3,21 @@ import json
 from dotenv import load_dotenv, set_key
 from ai_conversation import AIConversation
 
+
 def load_system_prompt(filename):
     """Load the system prompt from a file."""
     with open(filename, "r") as file:
         return file.read().strip()
 
-def load_options_from_json(filename):
+
+def load_options_from_json(filename, max_tokens, limit_tokens):
     """Load options from a JSON file."""
     with open(filename, "r") as file:
-        return json.load(file)
+        options = json.load(file)
+        if limit_tokens:
+            options["num_ctx"] = max_tokens
+        return options
+
 
 def main():
     # Load environment variables
@@ -29,20 +35,28 @@ def main():
     )
     max_tokens = int(os.getenv("MAX_TOKENS", 4000))
     print(f"Max tokens: {max_tokens}")
-
+    limit_tokens = os.getenv("LIMIT_TOKENS", True)
+    limit_tokens = limit_tokens.lower() == "true"
+    print(f"Limit tokens: {limit_tokens}")
     # Load options from JSON file
-    options = load_options_from_json("options.json")
+    options = load_options_from_json("options.json", max_tokens, limit_tokens)
     print(f"Options: {options}")
 
     # Initialize the AI conversation object
     conversation = AIConversation(
-        model_1, model_2, system_prompt_1, system_prompt_2, ollama_endpoint, max_tokens
+        model_1,
+        model_2,
+        system_prompt_1,
+        system_prompt_2,
+        ollama_endpoint,
+        max_tokens,
+        limit_tokens,
     )
 
-
     # Run the appropriate interface based on command-line arguments
     run_cli(conversation, initial_prompt, options)
 
+
 def run_cli(conversation, initial_prompt, options):
     """Run the conversation in command-line interface mode."""
     load_dotenv()