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a70fc60485
# 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.
58 lines
2.1 KiB
JSON
58 lines
2.1 KiB
JSON
{
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"test_id": null,
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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 trace openai 1"
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}
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],
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"stream": false,
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"extra_body": null
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},
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"endpoint": "/v1/chat/completions",
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"model": "llama3.2:3b-instruct-fp16"
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},
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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-0698374e7336",
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{
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"finish_reason": "stop",
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"index": 0,
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"logprobs": null,
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"message": {
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"content": "You want to test the OpenAI 1.0 model, also known as GPT-1. This was one of the first models developed by OpenAI and is an outdated version of their language model compared to more recent versions like GPT-2, GPT-3 and so on. Keep in mind that it has several limitations.\n\nYou can use it via this chat interface. I am a simulation of the environment where you can interact with a simple text-based input.\n\n\n1. Test basic functions: You can ask me simple questions such as what is your name, how are you doing, etc. \n2. Try natural language understanding.\n3. Ask for suggestions or recommendations.\n\nFeel free to try anything and I will respond accordingly! What do you want to test first?",
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"refusal": null,
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"role": "assistant",
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"annotations": null,
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"audio": null,
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}
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}
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"created": 0,
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"model": "llama3.2:3b-instruct-fp16",
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"object": "chat.completion",
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"system_fingerprint": "fp_ollama",
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"usage": {
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