Add getter functions for TLM defaults

CHANGELOG.md

@@ -7,6 +7,11 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+## [1.1.2] - 2025-05-01
+
+- Add getter functions for `_TLM_DEFAULT_MODEL`, `_DEFAULT_TLM_QUALITY_PRESET`, `_TLM_DEFAULT_CONTEXT_LIMIT`, `_TLM_MAX_TOKEN_RANGE`.
+- Add unit tests for the getter functions.
+
 ## [1.1.1] - 2025-04-23
 
 ### Changed
@@ -141,7 +146,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 - Release of the Cleanlab TLM Python client.
 
-[Unreleased]: https://github.com/cleanlab/cleanlab-tlm/compare/v1.1.1...HEAD
+[Unreleased]: https://github.com/cleanlab/cleanlab-tlm/compare/v1.1.2...HEAD
+[1.1.1]: https://github.com/cleanlab/cleanlab-tlm/compare/v1.1.1...v1.1.2
 [1.1.1]: https://github.com/cleanlab/cleanlab-tlm/compare/v1.1.0...v1.1.1
 [1.1.0]: https://github.com/cleanlab/cleanlab-tlm/compare/v1.0.23...v1.1.0
 [1.0.23]: https://github.com/cleanlab/cleanlab-tlm/compare/v1.0.22...v1.0.23

pyproject.toml

@@ -47,6 +47,7 @@ extra-dependencies = [
   "pytest",
   "pytest-asyncio",
   "python-dotenv",
+  "tiktoken",
 ]
 [tool.hatch.envs.types.scripts]
 check = "mypy --strict --install-types --non-interactive {args:src/cleanlab_tlm tests}"
@@ -57,6 +58,7 @@ allow-direct-references = true
 extra-dependencies = [
   "python-dotenv",
   "pytest-asyncio",
+  "tiktoken",
 ]
 
 [tool.hatch.envs.hatch-test.env-vars]

src/cleanlab_tlm/__about__.py

@@ -1,2 +1,2 @@
 # SPDX-License-Identifier: MIT
-__version__ = "1.1.1"
+__version__ = "1.1.2"

src/cleanlab_tlm/internal/constants.py

@@ -5,6 +5,7 @@
 _VALID_TLM_QUALITY_PRESETS: list[str] = ["best", "high", "medium", "low", "base"]
 _VALID_TLM_QUALITY_PRESETS_RAG: list[str] = ["medium", "low", "base"]
 _DEFAULT_TLM_QUALITY_PRESET: TLMQualityPreset = "medium"
+_DEFAULT_TLM_MAX_TOKENS: int = 512
 _VALID_TLM_MODELS: list[str] = [
     "gpt-3.5-turbo-16k",
     "gpt-4",
@@ -32,6 +33,7 @@
     "nova-pro",
 ]
 _TLM_DEFAULT_MODEL: str = "gpt-4o-mini"
+_TLM_DEFAULT_CONTEXT_LIMIT: int = 70000
 _VALID_TLM_TASKS: set[str] = {task.value for task in Task}
 TLM_TASK_SUPPORTING_CONSTRAIN_OUTPUTS: set[Task] = {
     Task.DEFAULT,

src/cleanlab_tlm/utils/config.py

@@ -0,0 +1,46 @@
+from cleanlab_tlm.internal.constants import (
+    _DEFAULT_TLM_MAX_TOKENS,
+    _DEFAULT_TLM_QUALITY_PRESET,
+    _TLM_DEFAULT_CONTEXT_LIMIT,
+    _TLM_DEFAULT_MODEL,
+)
+
+
+def get_default_model() -> str:
+    """
+    Get the default model name for TLM.
+
+    Returns:
+        str: The default model name for TLM.
+    """
+    return _TLM_DEFAULT_MODEL
+
+
+def get_default_quality_preset() -> str:
+    """
+    Get the default quality preset for TLM.
+
+    Returns:
+        str: The default quality preset for TLM.
+    """
+    return _DEFAULT_TLM_QUALITY_PRESET
+
+
+def get_default_context_limit() -> int:
+    """
+    Get the default context limit for TLM.
+
+    Returns:
+        int: The default context limit for TLM.
+    """
+    return _TLM_DEFAULT_CONTEXT_LIMIT
+
+
+def get_default_max_tokens() -> int:
+    """
+    Get the default maximum output tokens allowed.
+
+    Returns:
+        int: The default maximum output tokens.
+    """
+    return _DEFAULT_TLM_MAX_TOKENS

tests/constants.py

@@ -17,6 +17,8 @@
 MAX_COMBINED_LENGTH_TOKENS: int = 70_000
 
 CHARACTERS_PER_TOKEN: int = 4
+# 4 character (3 character + 1 space) = 1 token
+WORD_THAT_EQUALS_ONE_TOKEN = "orb "  # noqa: S105
 
 # Property tests for TLM
 excluded_tlm_models: list[str] = [

tests/test_config.py

@@ -0,0 +1,58 @@
+import pytest
+import tiktoken
+
+from cleanlab_tlm.errors import TlmBadRequestError
+from cleanlab_tlm.tlm import TLM
+from cleanlab_tlm.utils.config import (
+    get_default_context_limit,
+    get_default_max_tokens,
+    get_default_model,
+    get_default_quality_preset,
+)
+from tests.constants import WORD_THAT_EQUALS_ONE_TOKEN
+
+tlm_with_default_setting = TLM()
+
+
+def test_get_default_model(tlm: TLM) -> None:
+    assert tlm.get_model_name() == get_default_model()
+
+
+def test_get_default_quality_preset(tlm: TLM) -> None:
+    assert get_default_quality_preset() == tlm._quality_preset
+
+
+def test_prompt_too_long_exception_single_prompt(tlm: TLM) -> None:
+    """Tests that bad request error is raised when prompt is too long when calling tlm.prompt with a single prompt."""
+    with pytest.raises(TlmBadRequestError) as exc_info:
+        tlm.prompt(WORD_THAT_EQUALS_ONE_TOKEN * (get_default_context_limit() + 1))
+
+    assert exc_info.value.message.startswith("Prompt length exceeds")
+    assert exc_info.value.retryable is False
+
+
+def test_prompt_within_context_limit_returns_response(tlm: TLM) -> None:
+    """Tests that no error is raised when prompt length is within limit."""
+    response = tlm.prompt(WORD_THAT_EQUALS_ONE_TOKEN * (get_default_context_limit() - 1000))
+
+    assert isinstance(response, dict)
+    assert "response" in response
+    assert isinstance(response["response"], str)
+
+
+def test_response_within_max_tokens() -> None:
+    """Tests that response is within max tokens limit."""
+    tlm_base = TLM(quality_preset="base")
+    prompt = "write a 100 page book about computer science. make sure it is extremely long and comprehensive."
+
+    result = tlm_base.prompt(prompt)
+    assert isinstance(result, dict)
+    response = result["response"]
+    assert isinstance(response, str)
+
+    try:
+        enc = tiktoken.encoding_for_model(get_default_model())
+    except KeyError:
+        enc = tiktoken.encoding_for_model("gpt-4o")
+    tokens_in_response = len(enc.encode(response))
+    assert tokens_in_response <= get_default_max_tokens()

tests/test_validation.py

@@ -12,13 +12,13 @@
 from cleanlab_tlm.utils.rag import Eval, TrustworthyRAG
 from tests.conftest import make_text_unique
 from tests.constants import (
-    CHARACTERS_PER_TOKEN,
     MAX_COMBINED_LENGTH_TOKENS,
     MAX_PROMPT_LENGTH_TOKENS,
     MAX_RESPONSE_LENGTH_TOKENS,
     TEST_PROMPT,
     TEST_PROMPT_BATCH,
     TEST_RESPONSE,
+    WORD_THAT_EQUALS_ONE_TOKEN,
 )
 from tests.test_get_trustworthiness_score import is_tlm_score_response_with_error
 from tests.test_prompt import is_tlm_response_with_error
@@ -208,7 +208,7 @@ def test_prompt_too_long_exception_single_prompt(tlm: TLM) -> None:
     """Tests that bad request error is raised when prompt is too long when calling tlm.prompt with a single prompt."""
     with pytest.raises(TlmBadRequestError) as exc_info:
         tlm.prompt(
-            "a" * (MAX_PROMPT_LENGTH_TOKENS + 1) * CHARACTERS_PER_TOKEN,
+            WORD_THAT_EQUALS_ONE_TOKEN * (MAX_PROMPT_LENGTH_TOKENS + 1),
         )
 
     assert exc_info.value.message.startswith("Prompt length exceeds")
@@ -221,7 +221,7 @@ def test_prompt_too_long_exception_prompt(tlm: TLM, num_prompts: int) -> None:
     # create batch of prompts with one prompt that is too long
     prompts = [test_prompt] * num_prompts
     prompt_too_long_index = np.random.randint(0, num_prompts)
-    prompts[prompt_too_long_index] = "a" * (MAX_PROMPT_LENGTH_TOKENS + 1) * CHARACTERS_PER_TOKEN
+    prompts[prompt_too_long_index] = WORD_THAT_EQUALS_ONE_TOKEN * (MAX_PROMPT_LENGTH_TOKENS + 1)
 
     tlm_responses = cast(list[TLMResponse], tlm.prompt(prompts))
 
@@ -232,8 +232,8 @@ def test_response_too_long_exception_single_score(tlm: TLM) -> None:
     """Tests that bad request error is raised when response is too long when calling tlm.get_trustworthiness_score with a single prompt."""
     with pytest.raises(TlmBadRequestError) as exc_info:
         tlm.get_trustworthiness_score(
-            "a",
-            "a" * (MAX_RESPONSE_LENGTH_TOKENS + 1) * CHARACTERS_PER_TOKEN,
+            WORD_THAT_EQUALS_ONE_TOKEN,
+            WORD_THAT_EQUALS_ONE_TOKEN * (MAX_RESPONSE_LENGTH_TOKENS + 1),
         )
 
     assert exc_info.value.message.startswith("Response length exceeds")
@@ -247,7 +247,7 @@ def test_response_too_long_exception_score(tlm: TLM, num_prompts: int) -> None:
     prompts = [test_prompt] * num_prompts
     responses = [TEST_RESPONSE] * num_prompts
     response_too_long_index = np.random.randint(0, num_prompts)
-    responses[response_too_long_index] = "a" * (MAX_RESPONSE_LENGTH_TOKENS + 1) * CHARACTERS_PER_TOKEN
+    responses[response_too_long_index] = WORD_THAT_EQUALS_ONE_TOKEN * (MAX_RESPONSE_LENGTH_TOKENS + 1)
 
     tlm_responses = cast(list[TLMScore], tlm.get_trustworthiness_score(prompts, responses))
 
@@ -258,8 +258,8 @@ def test_prompt_too_long_exception_single_score(tlm: TLM) -> None:
     """Tests that bad request error is raised when prompt is too long when calling tlm.get_trustworthiness_score with a single prompt."""
     with pytest.raises(TlmBadRequestError) as exc_info:
         tlm.get_trustworthiness_score(
-            "a" * (MAX_PROMPT_LENGTH_TOKENS + 1) * CHARACTERS_PER_TOKEN,
-            "a",
+            WORD_THAT_EQUALS_ONE_TOKEN * (MAX_PROMPT_LENGTH_TOKENS + 1),
+            WORD_THAT_EQUALS_ONE_TOKEN,
         )
 
     assert exc_info.value.message.startswith("Prompt length exceeds")
@@ -273,7 +273,7 @@ def test_prompt_too_long_exception_score(tlm: TLM, num_prompts: int) -> None:
     prompts = [test_prompt] * num_prompts
     responses = [TEST_RESPONSE] * num_prompts
     prompt_too_long_index = np.random.randint(0, num_prompts)
-    prompts[prompt_too_long_index] = "a" * (MAX_PROMPT_LENGTH_TOKENS + 1) * CHARACTERS_PER_TOKEN
+    prompts[prompt_too_long_index] = WORD_THAT_EQUALS_ONE_TOKEN * (MAX_PROMPT_LENGTH_TOKENS + 1)
 
     tlm_responses = cast(list[TLMScore], tlm.get_trustworthiness_score(prompts, responses))
 
@@ -286,8 +286,8 @@ def test_combined_too_long_exception_single_score(tlm: TLM) -> None:
 
     with pytest.raises(TlmBadRequestError) as exc_info:
         tlm.get_trustworthiness_score(
-            "a" * max_prompt_length * CHARACTERS_PER_TOKEN,
-            "a" * MAX_RESPONSE_LENGTH_TOKENS * CHARACTERS_PER_TOKEN,
+            WORD_THAT_EQUALS_ONE_TOKEN * max_prompt_length,
+            WORD_THAT_EQUALS_ONE_TOKEN * MAX_RESPONSE_LENGTH_TOKENS,
         )
 
     assert exc_info.value.message.startswith("Prompt and response combined length exceeds")
@@ -306,8 +306,8 @@ def test_prompt_and_response_combined_too_long_exception_batch_score(tlm: TLM, n
     combined_too_long_index = np.random.randint(0, num_prompts)
 
     max_prompt_length = MAX_COMBINED_LENGTH_TOKENS - MAX_RESPONSE_LENGTH_TOKENS + 1
-    prompts[combined_too_long_index] = "a" * max_prompt_length * CHARACTERS_PER_TOKEN
-    responses[combined_too_long_index] = "a" * MAX_RESPONSE_LENGTH_TOKENS * CHARACTERS_PER_TOKEN
+    prompts[combined_too_long_index] = WORD_THAT_EQUALS_ONE_TOKEN * max_prompt_length
+    responses[combined_too_long_index] = WORD_THAT_EQUALS_ONE_TOKEN * MAX_RESPONSE_LENGTH_TOKENS
 
     tlm_responses = cast(list[TLMScore], tlm.get_trustworthiness_score(prompts, responses))