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# What does this PR do? ## Test Plan # What does this PR do? ## Test Plan # What does this PR do? ## Test Plan Completes the refactoring started in previous commit by: 1. **Fix library client** (critical): Add logic to detect Pydantic model parameters and construct them properly from request bodies. The key fix is to NOT exclude any params when converting the body for Pydantic models - we need all fields to pass to the Pydantic constructor. Before: _convert_body excluded all params, leaving body empty for Pydantic construction After: Check for Pydantic params first, skip exclusion, construct model with full body 2. **Update remaining providers** to use new Pydantic-based signatures: - litellm_openai_mixin: Extract extra fields via __pydantic_extra__ - databricks: Use TYPE_CHECKING import for params type - llama_openai_compat: Use TYPE_CHECKING import for params type - sentence_transformers: Update method signatures to use params 3. **Update unit tests** to use new Pydantic signature: - test_openai_mixin.py: Use OpenAIChatCompletionRequestParams This fixes test failures where the library client was trying to construct Pydantic models with empty dictionaries. The previous fix had a bug: it called _convert_body() which only keeps fields that match function parameter names. For Pydantic methods with signature: openai_chat_completion(params: OpenAIChatCompletionRequestParams) The signature only has 'params', but the body has 'model', 'messages', etc. So _convert_body() returned an empty dict. Fix: Skip _convert_body() entirely for Pydantic params. Use the raw body directly to construct the Pydantic model (after stripping NOT_GIVENs). This properly fixes the ValidationError where required fields were missing. The streaming code path (_call_streaming) had the same issue as non-streaming: it called _convert_body() which returned empty dict for Pydantic params. Applied the same fix as commit 7476c0ae: - Detect Pydantic model parameters before body conversion - Skip _convert_body() for Pydantic params - Construct Pydantic model directly from raw body (after stripping NOT_GIVENs) This fixes streaming endpoints like openai_chat_completion with stream=True. The streaming code path (_call_streaming) had the same issue as non-streaming: it called _convert_body() which returned empty dict for Pydantic params. Applied the same fix as commit 7476c0ae: - Detect Pydantic model parameters before body conversion - Skip _convert_body() for Pydantic params - Construct Pydantic model directly from raw body (after stripping NOT_GIVENs) This fixes streaming endpoints like openai_chat_completion with stream=True.
This commit is contained in:
Eric Huang
parent
9e9a827fcd
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295 changed files with 51966 additions and 3051 deletions
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{
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"test_id": "tests/integration/telemetry/test_openai_telemetry.py::test_openai_completion_creates_telemetry[txt=ollama/llama3.2:3b-instruct-fp16]",
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"request": {
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"method": "POST",
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"url": "http://0.0.0.0:11434/v1/v1/chat/completions",
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"headers": {},
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"body": {
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"model": "llama3.2:3b-instruct-fp16",
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"messages": [
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{
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"role": "user",
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"content": "Test OpenAI telemetry creation"
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}
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"extra_body": {}
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"endpoint": "/v1/chat/completions",
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"model": "llama3.2:3b-instruct-fp16"
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"response": {
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"body": {
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"__type__": "openai.types.chat.chat_completion.ChatCompletion",
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"test_id": "tests/integration/telemetry/test_openai_telemetry.py::test_openai_completion_creates_telemetry[txt=ollama/llama3.2:3b-instruct-fp16]",
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"headers": {},
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"model": "llama3.2:3b-instruct-fp16",
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"messages": [
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"role": "user",
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"content": "Test OpenAI telemetry creation"
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"endpoint": "/v1/chat/completions",
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"model": "llama3.2:3b-instruct-fp16"
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"response": {
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"body": {
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"__type__": "openai.types.chat.chat_completion.ChatCompletion",
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"id": "rec-82f14eebdae7",
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"content": "To test the telemetry creation feature of OpenAI, we can start by exploring what types of data are being collected and how they can be used to improve models.\n\n1. **Data**: Some examples of data that telemetry provides include the user's location, device information (such as IP address or serial number), browser type and version, screen resolution, operating system, and internet service provider. This type of information is useful for providing targeted advertising solutions for businesses like OpenAI.\n\n2. **Analysis Tools**: Additionally, telemetry offers analysis tools that can help identify issues in the models. For example, it provides insights on model reliability and performance problems. These are valuable features to improve the stability and user experience of these complex systems. To explore what new updates have been made since its last public release we would need some information like changes log from OpenAI GitHub page.\n\n3. **Integration**: The telemetry API can also be integrated with other data pipelines, such as customer relationship management (CRM) software or marketing automation tools. This allows for a more comprehensive view of user behavior and interaction history with the platform.\n\nTo test the feature, you would typically need to first ensure that you have the necessary permissions and access levels to collect telemetry data for your OpenAI environment.\n\nTo start testing:\n1. Start by using OpenAI's console or API in a safe working environment.\n3. Look up available endpoints in the documentation and select one appropriate for telemetry \n4. Execute an HTTP request against it to get relevant data for this test\n5. Use a response object from the previous request call to analyze some of its possible attributes",
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},
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"is_streaming": false
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}
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}
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