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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.
99 lines
2.7 KiB
JSON
99 lines
2.7 KiB
JSON
{
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"test_id": "tests/integration/inference/test_tools_with_schemas.py::TestMCPToolsInChatCompletion::test_mcp_tools_in_inference[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": "Calculate 5 + 3"
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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": "calculate",
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"description": "",
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"parameters": {
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"properties": {
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"x": {
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"title": "X",
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"type": "number"
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},
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"y": {
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"title": "Y",
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"type": "number"
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},
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"operation": {
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"title": "Operation",
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"type": "string"
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}
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},
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"required": [
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"x",
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"y",
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"operation"
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],
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"title": "calculateArguments",
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"type": "object"
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}
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}
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}
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],
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"extra_body": {}
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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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"__data__": {
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"id": "rec-8e6b174c880d",
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"choices": [
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{
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"finish_reason": "tool_calls",
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"index": 0,
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"logprobs": null,
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"message": {
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"content": "",
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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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"function_call": null,
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"tool_calls": [
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{
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"id": "call_vvn0j2ws",
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"function": {
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"arguments": "{\"operation\":\"+\",\"x\":\"5\",\"y\":\"3\"}",
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"name": "calculate"
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},
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"type": "function",
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"index": 0
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}
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]
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}
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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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"service_tier": null,
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"system_fingerprint": "fp_ollama",
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"usage": {
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"completion_tokens": 27,
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"prompt_tokens": 172,
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"total_tokens": 199,
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"completion_tokens_details": null,
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"prompt_tokens_details": null
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}
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}
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},
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"is_streaming": false
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}
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}
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