forked from phoenix/litellm-mirror
LiteLLM Minor Fixes & Improvements (10/30/2024) (#6519)
* refactor: move gemini translation logic inside the transformation.py file easier to isolate the gemini translation logic * fix(gemini-transformation): support multiple tool calls in message body Merges https://github.com/BerriAI/litellm/pull/6487/files * test(test_vertex.py): add remaining tests from https://github.com/BerriAI/litellm/pull/6487 * fix(gemini-transformation): return tool calls for multiple tool calls * fix: support passing logprobs param for vertex + gemini * feat(vertex_ai): add logprobs support for gemini calls * fix(anthropic/chat/transformation.py): fix disable parallel tool use flag * fix: fix linting error * fix(_logging.py): log stacktrace information in json logs Closes https://github.com/BerriAI/litellm/issues/6497 * fix(utils.py): fix mem leak for async stream + completion Uses a global executor pool instead of creating a new thread on each request Fixes https://github.com/BerriAI/litellm/issues/6404 * fix(factory.py): handle tool call + content in assistant message for bedrock * fix: fix import * fix(factory.py): maintain support for content as a str in assistant response * fix: fix import * test: cleanup test * fix(vertex_and_google_ai_studio/): return none for content if no str value * test: retry flaky tests * (UI) Fix viewing members, keys in a team + added testing (#6514) * fix listing teams on ui * LiteLLM Minor Fixes & Improvements (10/28/2024) (#6475) * fix(anthropic/chat/transformation.py): support anthropic disable_parallel_tool_use param Fixes https://github.com/BerriAI/litellm/issues/6456 * feat(anthropic/chat/transformation.py): support anthropic computer tool use Closes https://github.com/BerriAI/litellm/issues/6427 * fix(vertex_ai/common_utils.py): parse out '$schema' when calling vertex ai Fixes issue when trying to call vertex from vercel sdk * fix(main.py): add 'extra_headers' support for azure on all translation endpoints Fixes https://github.com/BerriAI/litellm/issues/6465 * fix: fix linting errors * fix(transformation.py): handle no beta headers for anthropic * test: cleanup test * fix: fix linting error * fix: fix linting errors * fix: fix linting errors * fix(transformation.py): handle dummy tool call * fix(main.py): fix linting error * fix(azure.py): pass required param * LiteLLM Minor Fixes & Improvements (10/24/2024) (#6441) * fix(azure.py): handle /openai/deployment in azure api base * fix(factory.py): fix faulty anthropic tool result translation check Fixes https://github.com/BerriAI/litellm/issues/6422 * fix(gpt_transformation.py): add support for parallel_tool_calls to azure Fixes https://github.com/BerriAI/litellm/issues/6440 * fix(factory.py): support anthropic prompt caching for tool results * fix(vertex_ai/common_utils): don't pop non-null required field Fixes https://github.com/BerriAI/litellm/issues/6426 * feat(vertex_ai.py): support code_execution tool call for vertex ai + gemini Closes https://github.com/BerriAI/litellm/issues/6434 * build(model_prices_and_context_window.json): Add 'supports_assistant_prefill' for bedrock claude-3-5-sonnet v2 models Closes https://github.com/BerriAI/litellm/issues/6437 * fix(types/utils.py): fix linting * test: update test to include required fields * test: fix test * test: handle flaky test * test: remove e2e test - hitting gemini rate limits * Litellm dev 10 26 2024 (#6472) * docs(exception_mapping.md): add missing exception types Fixes https://github.com/Aider-AI/aider/issues/2120#issuecomment-2438971183 * fix(main.py): register custom model pricing with specific key Ensure custom model pricing is registered to the specific model+provider key combination * test: make testing more robust for custom pricing * fix(redis_cache.py): instrument otel logging for sync redis calls ensures complete coverage for all redis cache calls * (Testing) Add unit testing for DualCache - ensure in memory cache is used when expected (#6471) * test test_dual_cache_get_set * unit testing for dual cache * fix async_set_cache_sadd * test_dual_cache_local_only * redis otel tracing + async support for latency routing (#6452) * docs(exception_mapping.md): add missing exception types Fixes https://github.com/Aider-AI/aider/issues/2120#issuecomment-2438971183 * fix(main.py): register custom model pricing with specific key Ensure custom model pricing is registered to the specific model+provider key combination * test: make testing more robust for custom pricing * fix(redis_cache.py): instrument otel logging for sync redis calls ensures complete coverage for all redis cache calls * refactor: pass parent_otel_span for redis caching calls in router allows for more observability into what calls are causing latency issues * test: update tests with new params * refactor: ensure e2e otel tracing for router * refactor(router.py): add more otel tracing acrosss router catch all latency issues for router requests * fix: fix linting error * fix(router.py): fix linting error * fix: fix test * test: fix tests * fix(dual_cache.py): pass ttl to redis cache * fix: fix param * fix(dual_cache.py): set default value for parent_otel_span * fix(transformation.py): support 'response_format' for anthropic calls * fix(transformation.py): check for cache_control inside 'function' block * fix: fix linting error * fix: fix linting errors --------- Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com> --------- Co-authored-by: Krish Dholakia <krrishdholakia@gmail.com> * ui new build * Add retry strat (#6520) Signed-off-by: dbczumar <corey.zumar@databricks.com> * (fix) slack alerting - don't spam the failed cost tracking alert for the same model (#6543) * fix use failing_model as cache key for failed_tracking_alert * fix use standard logging payload for getting response cost * fix kwargs.get("response_cost") * fix getting response cost * (feat) add XAI ChatCompletion Support (#6373) * init commit for XAI * add full logic for xai chat completion * test_completion_xai * docs xAI * add xai/grok-beta * test_xai_chat_config_get_openai_compatible_provider_info * test_xai_chat_config_map_openai_params * add xai streaming test --------- Signed-off-by: dbczumar <corey.zumar@databricks.com> Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com> Co-authored-by: Corey Zumar <39497902+dbczumar@users.noreply.github.com>
This commit is contained in:
Krish Dholakia
GitHub
parent
5652c375b3
commit
f79365df6e
24 changed files with 1851 additions and 700 deletions
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@ -794,7 +794,7 @@ def test_anthropic_parallel_tool_calls(provider):
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parallel_tool_calls=True,
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)
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print(f"optional_params: {optional_params}")
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assert optional_params["tool_choice"]["disable_parallel_tool_use"] is True
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assert optional_params["tool_choice"]["disable_parallel_tool_use"] is False
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def test_anthropic_computer_tool_use():
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@ -230,3 +230,944 @@ def test_function_calling_with_gemini():
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]
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}
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]
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def test_multiple_function_call():
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litellm.set_verbose = True
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from litellm.llms.custom_httpx.http_handler import HTTPHandler
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client = HTTPHandler()
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messages = [
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{"role": "user", "content": [{"type": "text", "text": "do test"}]},
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{
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"role": "assistant",
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"content": [{"type": "text", "text": "test"}],
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"tool_calls": [
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{
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"index": 0,
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"function": {"arguments": '{"arg": "test"}', "name": "test"},
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"id": "call_597e00e6-11d4-4ed2-94b2-27edee250aec",
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"type": "function",
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},
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{
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"index": 1,
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"function": {"arguments": '{"arg": "test2"}', "name": "test2"},
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"id": "call_2414e8f9-283a-002b-182a-1290ab912c02",
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"type": "function",
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},
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],
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},
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{
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"tool_call_id": "call_597e00e6-11d4-4ed2-94b2-27edee250aec",
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"role": "tool",
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"name": "test",
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"content": [{"type": "text", "text": "42"}],
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},
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{
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"tool_call_id": "call_2414e8f9-283a-002b-182a-1290ab912c02",
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"role": "tool",
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"name": "test2",
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"content": [{"type": "text", "text": "15"}],
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},
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{"role": "user", "content": [{"type": "text", "text": "tell me the results."}]},
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]
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response_body = {
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"candidates": [
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{
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"content": {
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"parts": [
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{
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"text": 'The `default_api.test` function call returned a JSON object indicating a successful execution. The `fields` key contains a nested dictionary with a `key` of "content" and a `value` with a `string_value` of "42".\n\nSimilarly, the `default_api.test2` function call also returned a JSON object showing successful execution. The `fields` key contains a nested dictionary with a `key` of "content" and a `value` with a `string_value` of "15".\n\nIn short, both test functions executed successfully and returned different numerical string values ("42" and "15"). The significance of these numbers depends on the internal logic of the `test` and `test2` functions within the `default_api`.\n'
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}
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],
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"role": "model",
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},
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"finishReason": "STOP",
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"avgLogprobs": -0.20577410289219447,
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}
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],
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"usageMetadata": {
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"promptTokenCount": 128,
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"candidatesTokenCount": 168,
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"totalTokenCount": 296,
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},
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"modelVersion": "gemini-1.5-flash-002",
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}
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mock_response = MagicMock()
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mock_response.json.return_value = response_body
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with patch.object(client, "post", return_value=mock_response) as mock_post:
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r = litellm.completion(
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messages=messages, model="gemini/gemini-1.5-flash-002", client=client
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)
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assert len(r.choices) > 0
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assert mock_post.call_args.kwargs["json"] == {
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"contents": [
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{"role": "user", "parts": [{"text": "do test"}]},
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{
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"role": "model",
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"parts": [
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{"text": "test"},
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{
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"function_call": {
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"name": "test",
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"args": {
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"fields": {
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"key": "arg",
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"value": {"string_value": "test"},
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}
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},
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}
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},
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{
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"function_call": {
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"name": "test2",
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"args": {
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"fields": {
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"key": "arg",
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"value": {"string_value": "test2"},
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}
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},
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}
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},
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],
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},
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{
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"parts": [
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{
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"function_response": {
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"name": "test",
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"response": {
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"fields": {
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"key": "content",
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"value": {"string_value": "42"},
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}
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},
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}
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},
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{
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"function_response": {
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"name": "test2",
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"response": {
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"fields": {
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"key": "content",
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"value": {"string_value": "15"},
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}
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},
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}
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},
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]
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},
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{"role": "user", "parts": [{"text": "tell me the results."}]},
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],
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"generationConfig": {},
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}
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def test_multiple_function_call_changed_text_pos():
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litellm.set_verbose = True
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from litellm.llms.custom_httpx.http_handler import HTTPHandler
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client = HTTPHandler()
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messages = [
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{"role": "user", "content": [{"type": "text", "text": "do test"}]},
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{
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"tool_calls": [
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{
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"index": 0,
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"function": {"arguments": '{"arg": "test"}', "name": "test"},
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"id": "call_597e00e6-11d4-4ed2-94b2-27edee250aec",
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"type": "function",
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},
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{
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"index": 1,
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"function": {"arguments": '{"arg": "test2"}', "name": "test2"},
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"id": "call_2414e8f9-283a-002b-182a-1290ab912c02",
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"type": "function",
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},
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],
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"role": "assistant",
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"content": [{"type": "text", "text": "test"}],
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},
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{
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"tool_call_id": "call_2414e8f9-283a-002b-182a-1290ab912c02",
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"role": "tool",
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"name": "test2",
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"content": [{"type": "text", "text": "15"}],
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},
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{
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"tool_call_id": "call_597e00e6-11d4-4ed2-94b2-27edee250aec",
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"role": "tool",
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"name": "test",
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"content": [{"type": "text", "text": "42"}],
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},
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{"role": "user", "content": [{"type": "text", "text": "tell me the results."}]},
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]
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response_body = {
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"candidates": [
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{
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"content": {
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"parts": [
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{
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"text": 'The code executed two functions, `test` and `test2`.\n\n* **`test`**: Returned a dictionary indicating that the "key" field has a "value" field containing a string value of "42". This is likely a response from a function that processed the input "test" and returned a calculated or pre-defined value.\n\n* **`test2`**: Returned a dictionary indicating that the "key" field has a "value" field containing a string value of "15". Similar to `test`, this suggests a function that processes the input "test2" and returns a specific result.\n\nIn short, both functions appear to be simple tests that return different hardcoded or calculated values based on their input arguments.\n'
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}
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],
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"role": "model",
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},
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"finishReason": "STOP",
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"avgLogprobs": -0.32848488592332409,
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}
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],
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"usageMetadata": {
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"promptTokenCount": 128,
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"candidatesTokenCount": 155,
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"totalTokenCount": 283,
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},
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"modelVersion": "gemini-1.5-flash-002",
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}
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mock_response = MagicMock()
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mock_response.json.return_value = response_body
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with patch.object(client, "post", return_value=mock_response) as mock_post:
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resp = litellm.completion(
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messages=messages, model="gemini/gemini-1.5-flash-002", client=client
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)
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assert len(resp.choices) > 0
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mock_post.assert_called_once()
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assert mock_post.call_args.kwargs["json"]["contents"] == [
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{"role": "user", "parts": [{"text": "do test"}]},
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{
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"role": "model",
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"parts": [
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{"text": "test"},
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{
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"function_call": {
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"name": "test",
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"args": {
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"fields": {
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"key": "arg",
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"value": {"string_value": "test"},
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}
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},
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}
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},
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{
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"function_call": {
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"name": "test2",
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"args": {
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"fields": {
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"key": "arg",
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"value": {"string_value": "test2"},
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}
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},
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}
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},
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],
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},
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{
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"parts": [
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{
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"function_response": {
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"name": "test2",
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"response": {
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"fields": {
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"key": "content",
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"value": {"string_value": "15"},
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}
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},
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}
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},
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{
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"function_response": {
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"name": "test",
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"response": {
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"fields": {
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"key": "content",
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"value": {"string_value": "42"},
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}
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},
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}
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},
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]
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},
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{"role": "user", "parts": [{"text": "tell me the results."}]},
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]
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def test_function_calling_with_gemini_multiple_results():
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litellm.set_verbose = True
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from litellm.llms.custom_httpx.http_handler import HTTPHandler
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client = HTTPHandler()
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# Step 1: send the conversation and available functions to the model
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messages = [
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{
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"role": "user",
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"content": "What's the weather like in San Francisco, Tokyo, and Paris? - give me 3 responses",
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}
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]
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tools = [
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{
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"type": "function",
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"function": {
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"name": "get_current_weather",
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"description": "Get the current weather in a given location",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The city and state",
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},
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"unit": {
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"type": "string",
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"enum": ["celsius", "fahrenheit"],
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},
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},
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"required": ["location"],
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},
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},
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}
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]
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response_body = {
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"candidates": [
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{
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"content": {
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"parts": [
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{
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"functionCall": {
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"name": "get_current_weather",
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"args": {"location": "San Francisco"},
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}
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},
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{
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"functionCall": {
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"name": "get_current_weather",
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"args": {"location": "Tokyo"},
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}
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},
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{
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"functionCall": {
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"name": "get_current_weather",
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"args": {"location": "Paris"},
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}
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},
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],
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"role": "model",
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},
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"finishReason": "STOP",
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"avgLogprobs": -0.0040788948535919189,
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}
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],
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"usageMetadata": {
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"promptTokenCount": 90,
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"candidatesTokenCount": 22,
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"totalTokenCount": 112,
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},
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"modelVersion": "gemini-1.5-flash-002",
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}
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mock_response = MagicMock()
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mock_response.json.return_value = response_body
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with patch.object(client, "post", return_value=mock_response):
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response = litellm.completion(
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model="gemini/gemini-1.5-flash-002",
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messages=messages,
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tools=tools,
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tool_choice="required",
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client=client,
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)
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print("Response\n", response)
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assert len(response.choices[0].message.tool_calls) == 3
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expected_locations = ["San Francisco", "Tokyo", "Paris"]
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for idx, tool_call in enumerate(response.choices[0].message.tool_calls):
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json_args = json.loads(tool_call.function.arguments)
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assert json_args["location"] == expected_locations[idx]
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def test_logprobs_unit_test():
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from litellm import VertexGeminiConfig
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result = VertexGeminiConfig()._transform_logprobs(
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logprobs_result={
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"topCandidates": [
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{
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"candidates": [
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{"token": "```", "logProbability": -1.5496514e-06},
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{"token": "`", "logProbability": -13.375002},
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{"token": "``", "logProbability": -21.875002},
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]
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},
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{
|
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|
||||
{"token": "(", "logProbability": 0},
|
||||
{"token": "location", "logProbability": 0},
|
||||
{"token": '="', "logProbability": -8.821511e-06},
|
||||
{"token": "Paris", "logProbability": 0},
|
||||
{"token": '"))', "logProbability": 0},
|
||||
{"token": "\n", "logProbability": 0},
|
||||
{"token": "```", "logProbability": -3.5763796e-06},
|
||||
],
|
||||
}
|
||||
)
|
||||
|
||||
print(result)
|
||||
|
||||
|
||||
def test_logprobs():
|
||||
litellm.set_verbose = True
|
||||
from litellm.llms.custom_httpx.http_handler import HTTPHandler
|
||||
|
||||
client = HTTPHandler()
|
||||
|
||||
response_body = {
|
||||
"candidates": [
|
||||
{
|
||||
"content": {
|
||||
"parts": [
|
||||
{
|
||||
"text": "I do not have access to real-time information, including current weather conditions. To get the current weather in San Francisco, I recommend checking a reliable weather website or app such as Google Weather, AccuWeather, or the National Weather Service.\n"
|
||||
}
|
||||
],
|
||||
"role": "model",
|
||||
},
|
||||
"finishReason": "STOP",
|
||||
"avgLogprobs": -0.04666396617889404,
|
||||
"logprobsResult": {
|
||||
"chosenCandidates": [
|
||||
{"token": "I", "logProbability": -1.08472495e-05},
|
||||
{"token": " do", "logProbability": -0.00012611414},
|
||||
{"token": " not", "logProbability": 0},
|
||||
{"token": " have", "logProbability": 0},
|
||||
{"token": " access", "logProbability": -0.0008849616},
|
||||
{"token": " to", "logProbability": 0},
|
||||
{"token": " real", "logProbability": -1.1922384e-07},
|
||||
{"token": "-", "logProbability": 0},
|
||||
{"token": "time", "logProbability": 0},
|
||||
{"token": " information", "logProbability": -2.2409657e-05},
|
||||
{"token": ",", "logProbability": 0},
|
||||
{"token": " including", "logProbability": 0},
|
||||
{"token": " current", "logProbability": -0.14274147},
|
||||
{"token": " weather", "logProbability": 0},
|
||||
{"token": " conditions", "logProbability": -0.0056300927},
|
||||
{"token": ".", "logProbability": -3.5760596e-07},
|
||||
{"token": " ", "logProbability": -0.06392521},
|
||||
{"token": "To", "logProbability": -2.3844768e-07},
|
||||
{"token": " get", "logProbability": -0.058974747},
|
||||
{"token": " the", "logProbability": 0},
|
||||
{"token": " current", "logProbability": 0},
|
||||
{"token": " weather", "logProbability": -2.3844768e-07},
|
||||
{"token": " in", "logProbability": -2.3844768e-07},
|
||||
{"token": " San", "logProbability": 0},
|
||||
{"token": " Francisco", "logProbability": 0},
|
||||
{"token": ",", "logProbability": 0},
|
||||
{"token": " I", "logProbability": -0.6188003},
|
||||
{"token": " recommend", "logProbability": -1.0370523e-05},
|
||||
{"token": " checking", "logProbability": -0.00014005086},
|
||||
{"token": " a", "logProbability": 0},
|
||||
{"token": " reliable", "logProbability": -1.5496514e-06},
|
||||
{"token": " weather", "logProbability": -8.344534e-07},
|
||||
{"token": " website", "logProbability": -0.0078000566},
|
||||
{"token": " or", "logProbability": -1.1922384e-07},
|
||||
{"token": " app", "logProbability": 0},
|
||||
{"token": " such", "logProbability": -0.9289338},
|
||||
{"token": " as", "logProbability": 0},
|
||||
{"token": " Google", "logProbability": -0.0046935496},
|
||||
{"token": " Weather", "logProbability": 0},
|
||||
{"token": ",", "logProbability": 0},
|
||||
{"token": " Accu", "logProbability": 0},
|
||||
{"token": "Weather", "logProbability": -0.00013909786},
|
||||
{"token": ",", "logProbability": 0},
|
||||
{"token": " or", "logProbability": -0.31303275},
|
||||
{"token": " the", "logProbability": -0.17583296},
|
||||
{"token": " National", "logProbability": -0.010806266},
|
||||
{"token": " Weather", "logProbability": 0},
|
||||
{"token": " Service", "logProbability": 0},
|
||||
{"token": ".", "logProbability": -0.00068947335},
|
||||
{"token": "\n", "logProbability": 0},
|
||||
]
|
||||
},
|
||||
}
|
||||
],
|
||||
"usageMetadata": {
|
||||
"promptTokenCount": 11,
|
||||
"candidatesTokenCount": 50,
|
||||
"totalTokenCount": 61,
|
||||
},
|
||||
"modelVersion": "gemini-1.5-flash-002",
|
||||
}
|
||||
mock_response = MagicMock()
|
||||
mock_response.json.return_value = response_body
|
||||
|
||||
with patch.object(client, "post", return_value=mock_response):
|
||||
|
||||
resp = litellm.completion(
|
||||
model="gemini/gemini-1.5-flash-002",
|
||||
messages=[
|
||||
{"role": "user", "content": "What's the weather like in San Francisco?"}
|
||||
],
|
||||
logprobs=True,
|
||||
client=client,
|
||||
)
|
||||
print(resp)
|
||||
|
||||
assert resp.choices[0].logprobs is not None
|
||||
|
|
|
|||
|
|
@ -30,7 +30,7 @@ from litellm import (
|
|||
completion_cost,
|
||||
embedding,
|
||||
)
|
||||
from litellm.llms.vertex_ai_and_google_ai_studio.gemini.vertex_and_google_ai_studio_gemini import (
|
||||
from litellm.llms.vertex_ai_and_google_ai_studio.gemini.transformation import (
|
||||
_gemini_convert_messages_with_history,
|
||||
)
|
||||
from litellm.llms.vertex_ai_and_google_ai_studio.vertex_llm_base import VertexBase
|
||||
|
|
@ -1823,6 +1823,7 @@ async def test_gemini_pro_function_calling_streaming(sync_mode):
|
|||
@pytest.mark.flaky(retries=3, delay=1)
|
||||
async def test_gemini_pro_async_function_calling():
|
||||
load_vertex_ai_credentials()
|
||||
litellm.set_verbose = True
|
||||
try:
|
||||
tools = [
|
||||
{
|
||||
|
|
@ -2925,7 +2926,7 @@ def test_gemini_function_call_parameter_in_messages():
|
|||
|
||||
|
||||
def test_gemini_function_call_parameter_in_messages_2():
|
||||
from litellm.llms.vertex_ai_and_google_ai_studio.vertex_ai_non_gemini import (
|
||||
from litellm.llms.vertex_ai_and_google_ai_studio.gemini.transformation import (
|
||||
_gemini_convert_messages_with_history,
|
||||
)
|
||||
|
||||
|
|
|
|||
|
|
@ -1879,13 +1879,16 @@ def test_bedrock_completion_test_4(modify_params):
|
|||
{
|
||||
"role": "assistant",
|
||||
"content": [
|
||||
{
|
||||
"text": """<thinking>\nThe user is asking about a specific file: main.py. Based on the environment details provided, this file is located in the computer-vision/hm-open3d/src/ directory and is currently open in a VSCode tab.\n\nTo answer the question of what this file is, the most relevant tool would be the read_file tool. This will allow me to examine the contents of main.py to determine its purpose.\n\nThe read_file tool requires the "path" parameter. I can infer this path based on the environment details:\npath: "computer-vision/hm-open3d/src/main.py"\n\nSince I have the necessary parameter, I can proceed with calling the read_file tool.\n</thinking>"""
|
||||
},
|
||||
{
|
||||
"toolUse": {
|
||||
"input": {"path": "computer-vision/hm-open3d/src/main.py"},
|
||||
"name": "read_file",
|
||||
"toolUseId": "tooluse_qCt-KEyWQlWiyHl26spQVA",
|
||||
}
|
||||
}
|
||||
},
|
||||
],
|
||||
},
|
||||
{
|
||||
|
|
|
|||
|
|
@ -473,9 +473,15 @@ def test_anthropic_function_call_with_no_schema(model):
|
|||
completion(model=model, messages=messages, tools=tools, tool_choice="auto")
|
||||
|
||||
|
||||
def test_passing_tool_result_as_list():
|
||||
@pytest.mark.parametrize(
|
||||
"model",
|
||||
[
|
||||
"anthropic/claude-3-5-sonnet-20241022",
|
||||
"bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
|
||||
],
|
||||
)
|
||||
def test_passing_tool_result_as_list(model):
|
||||
litellm.set_verbose = True
|
||||
model = "anthropic/claude-3-5-sonnet-20241022"
|
||||
messages = [
|
||||
{
|
||||
"content": [
|
||||
|
|
@ -611,4 +617,5 @@ def test_passing_tool_result_as_list():
|
|||
resp = completion(model=model, messages=messages, tools=tools)
|
||||
print(resp)
|
||||
|
||||
assert resp.usage.prompt_tokens_details.cached_tokens > 0
|
||||
if model == "claude-3-5-sonnet-20241022":
|
||||
assert resp.usage.prompt_tokens_details.cached_tokens > 0
|
||||
|
|
|
|||
|
|
@ -142,6 +142,7 @@ def prisma_client():
|
|||
|
||||
|
||||
@pytest.mark.asyncio()
|
||||
@pytest.mark.flaky(retries=6, delay=1)
|
||||
async def test_new_user_response(prisma_client):
|
||||
try:
|
||||
|
||||
|
|
@ -2891,6 +2892,7 @@ async def test_generate_key_with_guardrails(prisma_client):
|
|||
|
||||
|
||||
@pytest.mark.asyncio()
|
||||
@pytest.mark.flaky(retries=6, delay=1)
|
||||
async def test_team_access_groups(prisma_client):
|
||||
"""
|
||||
Test team based model access groups
|
||||
|
|
|
|||
243
tests/local_testing/test_mem_leak.py
Normal file
243
tests/local_testing/test_mem_leak.py
Normal file
|
|
@ -0,0 +1,243 @@
|
|||
# import io
|
||||
# import os
|
||||
# import sys
|
||||
|
||||
# sys.path.insert(0, os.path.abspath("../.."))
|
||||
|
||||
# import litellm
|
||||
# from memory_profiler import profile
|
||||
# from litellm.utils import (
|
||||
# ModelResponseIterator,
|
||||
# ModelResponseListIterator,
|
||||
# CustomStreamWrapper,
|
||||
# )
|
||||
# from litellm.types.utils import ModelResponse, Choices, Message
|
||||
# import time
|
||||
# import pytest
|
||||
|
||||
|
||||
# # @app.post("/debug")
|
||||
# # async def debug(body: ExampleRequest) -> str:
|
||||
# # return await main_logic(body.query)
|
||||
# def model_response_list_factory():
|
||||
# chunks = [
|
||||
# {
|
||||
# "id": "chatcmpl-9SQxdH5hODqkWyJopWlaVOOUnFwlj",
|
||||
# "choices": [
|
||||
# {
|
||||
# "delta": {"content": "", "role": "assistant"},
|
||||
# "finish_reason": None,
|
||||
# "index": 0,
|
||||
# }
|
||||
# ],
|
||||
# "created": 1716563849,
|
||||
# "model": "gpt-4o-2024-05-13",
|
||||
# "object": "chat.completion.chunk",
|
||||
# "system_fingerprint": "fp_5f4bad809a",
|
||||
# },
|
||||
# {
|
||||
# "id": "chatcmpl-9SQxdH5hODqkWyJopWlaVOOUnFwlj",
|
||||
# "choices": [
|
||||
# {"delta": {"content": "This"}, "finish_reason": None, "index": 0}
|
||||
# ],
|
||||
# "created": 1716563849,
|
||||
# "model": "gpt-4o-2024-05-13",
|
||||
# "object": "chat.completion.chunk",
|
||||
# "system_fingerprint": "fp_5f4bad809a",
|
||||
# },
|
||||
# {
|
||||
# "id": "chatcmpl-9SQxdH5hODqkWyJopWlaVOOUnFwlj",
|
||||
# "choices": [
|
||||
# {"delta": {"content": " is"}, "finish_reason": None, "index": 0}
|
||||
# ],
|
||||
# "created": 1716563849,
|
||||
# "model": "gpt-4o-2024-05-13",
|
||||
# "object": "chat.completion.chunk",
|
||||
# "system_fingerprint": "fp_5f4bad809a",
|
||||
# },
|
||||
# {
|
||||
# "id": "chatcmpl-9SQxdH5hODqkWyJopWlaVOOUnFwlj",
|
||||
# "choices": [
|
||||
# {"delta": {"content": " a"}, "finish_reason": None, "index": 0}
|
||||
# ],
|
||||
# "created": 1716563849,
|
||||
# "model": "gpt-4o-2024-05-13",
|
||||
# "object": "chat.completion.chunk",
|
||||
# "system_fingerprint": "fp_5f4bad809a",
|
||||
# },
|
||||
# {
|
||||
# "id": "chatcmpl-9SQxdH5hODqkWyJopWlaVOOUnFwlj",
|
||||
# "choices": [
|
||||
# {"delta": {"content": " dummy"}, "finish_reason": None, "index": 0}
|
||||
# ],
|
||||
# "created": 1716563849,
|
||||
# "model": "gpt-4o-2024-05-13",
|
||||
# "object": "chat.completion.chunk",
|
||||
# "system_fingerprint": "fp_5f4bad809a",
|
||||
# },
|
||||
# {
|
||||
# "id": "chatcmpl-9SQxdH5hODqkWyJopWlaVOOUnFwlj",
|
||||
# "choices": [
|
||||
# {
|
||||
# "delta": {"content": " response"},
|
||||
# "finish_reason": None,
|
||||
# "index": 0,
|
||||
# }
|
||||
# ],
|
||||
# "created": 1716563849,
|
||||
# "model": "gpt-4o-2024-05-13",
|
||||
# "object": "chat.completion.chunk",
|
||||
# "system_fingerprint": "fp_5f4bad809a",
|
||||
# },
|
||||
# {
|
||||
# "id": "",
|
||||
# "choices": [
|
||||
# {
|
||||
# "finish_reason": None,
|
||||
# "index": 0,
|
||||
# "content_filter_offsets": {
|
||||
# "check_offset": 35159,
|
||||
# "start_offset": 35159,
|
||||
# "end_offset": 36150,
|
||||
# },
|
||||
# "content_filter_results": {
|
||||
# "hate": {"filtered": False, "severity": "safe"},
|
||||
# "self_harm": {"filtered": False, "severity": "safe"},
|
||||
# "sexual": {"filtered": False, "severity": "safe"},
|
||||
# "violence": {"filtered": False, "severity": "safe"},
|
||||
# },
|
||||
# }
|
||||
# ],
|
||||
# "created": 0,
|
||||
# "model": "",
|
||||
# "object": "",
|
||||
# },
|
||||
# {
|
||||
# "id": "chatcmpl-9SQxdH5hODqkWyJopWlaVOOUnFwlj",
|
||||
# "choices": [{"delta": {"content": "."}, "finish_reason": None, "index": 0}],
|
||||
# "created": 1716563849,
|
||||
# "model": "gpt-4o-2024-05-13",
|
||||
# "object": "chat.completion.chunk",
|
||||
# "system_fingerprint": "fp_5f4bad809a",
|
||||
# },
|
||||
# {
|
||||
# "id": "chatcmpl-9SQxdH5hODqkWyJopWlaVOOUnFwlj",
|
||||
# "choices": [{"delta": {}, "finish_reason": "stop", "index": 0}],
|
||||
# "created": 1716563849,
|
||||
# "model": "gpt-4o-2024-05-13",
|
||||
# "object": "chat.completion.chunk",
|
||||
# "system_fingerprint": "fp_5f4bad809a",
|
||||
# },
|
||||
# {
|
||||
# "id": "",
|
||||
# "choices": [
|
||||
# {
|
||||
# "finish_reason": None,
|
||||
# "index": 0,
|
||||
# "content_filter_offsets": {
|
||||
# "check_offset": 36150,
|
||||
# "start_offset": 36060,
|
||||
# "end_offset": 37029,
|
||||
# },
|
||||
# "content_filter_results": {
|
||||
# "hate": {"filtered": False, "severity": "safe"},
|
||||
# "self_harm": {"filtered": False, "severity": "safe"},
|
||||
# "sexual": {"filtered": False, "severity": "safe"},
|
||||
# "violence": {"filtered": False, "severity": "safe"},
|
||||
# },
|
||||
# }
|
||||
# ],
|
||||
# "created": 0,
|
||||
# "model": "",
|
||||
# "object": "",
|
||||
# },
|
||||
# ]
|
||||
|
||||
# chunk_list = []
|
||||
# for chunk in chunks:
|
||||
# new_chunk = litellm.ModelResponse(stream=True, id=chunk["id"])
|
||||
# if "choices" in chunk and isinstance(chunk["choices"], list):
|
||||
# new_choices = []
|
||||
# for choice in chunk["choices"]:
|
||||
# if isinstance(choice, litellm.utils.StreamingChoices):
|
||||
# _new_choice = choice
|
||||
# elif isinstance(choice, dict):
|
||||
# _new_choice = litellm.utils.StreamingChoices(**choice)
|
||||
# new_choices.append(_new_choice)
|
||||
# new_chunk.choices = new_choices
|
||||
# chunk_list.append(new_chunk)
|
||||
|
||||
# return ModelResponseListIterator(model_responses=chunk_list)
|
||||
|
||||
|
||||
# async def mock_completion(*args, **kwargs):
|
||||
# completion_stream = model_response_list_factory()
|
||||
# return litellm.CustomStreamWrapper(
|
||||
# completion_stream=completion_stream,
|
||||
# model="gpt-4-0613",
|
||||
# custom_llm_provider="cached_response",
|
||||
# logging_obj=litellm.Logging(
|
||||
# model="gpt-4-0613",
|
||||
# messages=[{"role": "user", "content": "Hey"}],
|
||||
# stream=True,
|
||||
# call_type="completion",
|
||||
# start_time=time.time(),
|
||||
# litellm_call_id="12345",
|
||||
# function_id="1245",
|
||||
# ),
|
||||
# )
|
||||
|
||||
|
||||
# @profile
|
||||
# async def main_logic() -> str:
|
||||
# stream = await mock_completion()
|
||||
# result = ""
|
||||
# async for chunk in stream:
|
||||
# result += chunk.choices[0].delta.content or ""
|
||||
# return result
|
||||
|
||||
|
||||
# import asyncio
|
||||
|
||||
# for _ in range(100):
|
||||
# asyncio.run(main_logic())
|
||||
|
||||
|
||||
# # @pytest.mark.asyncio
|
||||
# # def test_memory_profile(capsys):
|
||||
# # # Run the async function
|
||||
# # result = asyncio.run(main_logic())
|
||||
|
||||
# # # Verify the result
|
||||
# # assert result == "This is a dummy response."
|
||||
|
||||
# # # Capture the output
|
||||
# # captured = capsys.readouterr()
|
||||
|
||||
# # # Print memory output for debugging
|
||||
# # print("Memory Profiler Output:")
|
||||
# # print(f"captured out: {captured.out}")
|
||||
|
||||
# # # Basic memory leak checks
|
||||
# # for idx, line in enumerate(captured.out.split("\n")):
|
||||
# # if idx % 2 == 0 and "MiB" in line:
|
||||
# # print(f"line: {line}")
|
||||
|
||||
# # # mem_lines = [line for line in captured.out.split("\n") if "MiB" in line]
|
||||
|
||||
# # print(mem_lines)
|
||||
|
||||
# # # Ensure we have some memory lines
|
||||
# # assert len(mem_lines) > 0, "No memory profiler output found"
|
||||
|
||||
# # # Optional: Add more specific memory leak detection
|
||||
# # for line in mem_lines:
|
||||
# # # Extract memory increment
|
||||
# # parts = line.split()
|
||||
# # if len(parts) >= 3:
|
||||
# # try:
|
||||
# # mem_increment = float(parts[2].replace("MiB", ""))
|
||||
# # # Assert that memory increment is below a reasonable threshold
|
||||
# # assert mem_increment < 1.0, f"Potential memory leak detected: {line}"
|
||||
# # except (ValueError, IndexError):
|
||||
# # pass # Skip lines that don't match expected format
|
||||
|
|
@ -25,7 +25,7 @@ from litellm.llms.prompt_templates.factory import (
|
|||
from litellm.llms.prompt_templates.common_utils import (
|
||||
get_completion_messages,
|
||||
)
|
||||
from litellm.llms.vertex_ai_and_google_ai_studio.vertex_ai_non_gemini import (
|
||||
from litellm.llms.vertex_ai_and_google_ai_studio.gemini.transformation import (
|
||||
_gemini_convert_messages_with_history,
|
||||
)
|
||||
from unittest.mock import AsyncMock, MagicMock, patch
|
||||
|
|
|
|||
|
|
@ -306,6 +306,8 @@ async def test_auth_with_allowed_routes(route, should_raise_error):
|
|||
("/key/delete", "internal_user", True),
|
||||
("/key/generate", "internal_user", True),
|
||||
("/key/82akk800000000jjsk/regenerate", "internal_user", True),
|
||||
# Internal User Viewer
|
||||
("/key/generate", "internal_user_viewer", False),
|
||||
# Internal User checks - disallowed routes
|
||||
("/organization/member_add", "internal_user", False),
|
||||
],
|
||||
|
|
@ -340,3 +342,41 @@ def test_is_ui_route_allowed(route, user_role, expected_result):
|
|||
pass
|
||||
else:
|
||||
raise e
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"route, user_role, expected_result",
|
||||
[
|
||||
("/key/generate", "internal_user_viewer", False),
|
||||
],
|
||||
)
|
||||
def test_is_api_route_allowed(route, user_role, expected_result):
|
||||
from litellm.proxy.auth.user_api_key_auth import _is_api_route_allowed
|
||||
from litellm.proxy._types import LiteLLM_UserTable
|
||||
|
||||
user_obj = LiteLLM_UserTable(
|
||||
user_id="3b803c0e-666e-4e99-bd5c-6e534c07e297",
|
||||
max_budget=None,
|
||||
spend=0.0,
|
||||
model_max_budget={},
|
||||
model_spend={},
|
||||
user_email="my-test-email@1234.com",
|
||||
models=[],
|
||||
tpm_limit=None,
|
||||
rpm_limit=None,
|
||||
user_role=user_role,
|
||||
organization_memberships=[],
|
||||
)
|
||||
|
||||
received_args: dict = {
|
||||
"route": route,
|
||||
"user_obj": user_obj,
|
||||
}
|
||||
try:
|
||||
assert _is_api_route_allowed(**received_args) == expected_result
|
||||
except Exception as e:
|
||||
# If expected result is False, we expect an error
|
||||
if expected_result is False:
|
||||
pass
|
||||
else:
|
||||
raise e
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue