Add buffer in the maximum number of tokens generated (to fix #353) (#354

)

* Add buffer in the maximum number of tokens generated

* Add the token_buffer consistently in all subclasses

prompt2model/utils/api_tools.py

@@ -72,6 +72,7 @@ def generate_one_completion(
         temperature: float = 0,
         presence_penalty: float = 0,
         frequency_penalty: float = 0,
+        token_buffer: int = 300,
     ) -> openai.Completion:
         """Generate a chat completion using an API-based model.
 
@@ -86,16 +87,21 @@ def generate_one_completion(
             frequency_penalty: Float between -2.0 and 2.0. Positive values penalize new
                 tokens based on their existing frequency in the text so far, decreasing
                 the model's likelihood of repeating the same line verbatim.
+            token_buffer: Number of tokens below the LLM's limit to generate. In case
+                our tokenizer does not exactly match the LLM API service's perceived
+                number of tokens, this prevents service errors. On the other hand, this
+                may lead to generating fewer tokens in the completion than is actually
+                possible.
 
         Returns:
             An OpenAI-like response object if there were no errors in generation.
             In case of API-specific error, Exception object is captured and returned.
         """
         num_prompt_tokens = count_tokens_from_string(prompt)
         if self.max_tokens:
-            max_tokens = self.max_tokens - num_prompt_tokens
+            max_tokens = self.max_tokens - num_prompt_tokens - token_buffer
         else:
-            max_tokens = 4 * num_prompt_tokens
+            max_tokens = 3 * num_prompt_tokens
 
         response = completion(  # completion gets the key from os.getenv
             model=self.model_name,
@@ -116,6 +122,7 @@ async def generate_batch_completion(
         temperature: float = 1,
         responses_per_request: int = 5,
         requests_per_minute: int = 80,
+        token_buffer: int = 300,
     ) -> list[openai.Completion]:
         """Generate a batch responses from OpenAI Chat Completion API.
 
@@ -126,6 +133,11 @@ async def generate_batch_completion(
             responses_per_request: Number of responses for each request.
                 i.e. the parameter n of API call.
             requests_per_minute: Number of requests per minute to allow.
+            token_buffer: Number of tokens below the LLM's limit to generate. In case
+                our tokenizer does not exactly match the LLM API service's perceived
+                number of tokens, this prevents service errors. On the other hand, this
+                may lead to generating fewer tokens in the completion than is actually
+                possible.
 
         Returns:
             List of generated responses.
@@ -183,9 +195,9 @@ async def _throttled_completion_acreate(
 
         num_prompt_tokens = max(count_tokens_from_string(prompt) for prompt in prompts)
         if self.max_tokens:
-            max_tokens = self.max_tokens - num_prompt_tokens
+            max_tokens = self.max_tokens - num_prompt_tokens - token_buffer
         else:
-            max_tokens = 4 * num_prompt_tokens
+            max_tokens = 3 * num_prompt_tokens
 
         async_responses = [
             _throttled_completion_acreate(

test_helpers/mock_api.py

@@ -196,6 +196,7 @@ def generate_one_completion(
         temperature: float = 0,
         presence_penalty: float = 0,
         frequency_penalty: float = 0,
+        token_buffer: int = 300,
     ) -> openai.Completion:
         """Return a mocked object and increment the counter."""
         self.generate_one_call_counter += 1
@@ -207,6 +208,7 @@ async def generate_batch_completion(
         temperature: float = 1,
         responses_per_request: int = 5,
         requests_per_minute: int = 80,
+        token_buffer: int = 300,
     ) -> list[openai.Completion]:
         """Return a mocked object and increment the counter."""
         self.generate_batch_call_counter += 1