fix(recording): endpoint resolution (#3013)
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# What does this PR do?


## Test Plan
This commit is contained in:
ehhuang 2025-08-01 16:23:54 -07:00 committed by GitHub
parent 140ee7d337
commit 6ac710f3b0
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
67 changed files with 9880 additions and 1409 deletions

View file

@ -217,55 +217,21 @@ class ResponseStorage:
return cast(dict[str, Any], data) return cast(dict[str, Any], data)
async def _patched_inference_method(original_method, self, client_type, method_name=None, *args, **kwargs): async def _patched_inference_method(original_method, self, client_type, endpoint, *args, **kwargs):
global _current_mode, _current_storage global _current_mode, _current_storage
if _current_mode == InferenceMode.LIVE or _current_storage is None: if _current_mode == InferenceMode.LIVE or _current_storage is None:
# Normal operation # Normal operation
return await original_method(self, *args, **kwargs) return await original_method(self, *args, **kwargs)
# Get base URL and endpoint based on client type # Get base URL based on client type
if client_type == "openai": if client_type == "openai":
base_url = str(self._client.base_url) base_url = str(self._client.base_url)
# Determine endpoint based on the method's module/class path
method_str = str(original_method)
if "chat.completions" in method_str:
endpoint = "/v1/chat/completions"
elif "embeddings" in method_str:
endpoint = "/v1/embeddings"
elif "completions" in method_str:
endpoint = "/v1/completions"
else:
# Fallback - try to guess from the self object
if hasattr(self, "_resource") and hasattr(self._resource, "_resource"):
resource_name = getattr(self._resource._resource, "_resource", "unknown")
if "chat" in str(resource_name):
endpoint = "/v1/chat/completions"
elif "embeddings" in str(resource_name):
endpoint = "/v1/embeddings"
else:
endpoint = "/v1/completions"
else:
endpoint = "/v1/completions"
elif client_type == "ollama": elif client_type == "ollama":
# Get base URL from the client (Ollama client uses host attribute) # Get base URL from the client (Ollama client uses host attribute)
base_url = getattr(self, "host", "http://localhost:11434") base_url = getattr(self, "host", "http://localhost:11434")
if not base_url.startswith("http"): if not base_url.startswith("http"):
base_url = f"http://{base_url}" base_url = f"http://{base_url}"
# Determine endpoint based on method name
if method_name == "generate":
endpoint = "/api/generate"
elif method_name == "chat":
endpoint = "/api/chat"
elif method_name == "embed":
endpoint = "/api/embeddings"
elif method_name == "list":
endpoint = "/api/tags"
else:
endpoint = f"/api/{method_name}"
else: else:
raise ValueError(f"Unknown client type: {client_type}") raise ValueError(f"Unknown client type: {client_type}")
@ -366,14 +332,18 @@ def patch_inference_clients():
# Create patched methods for OpenAI client # Create patched methods for OpenAI client
async def patched_chat_completions_create(self, *args, **kwargs): async def patched_chat_completions_create(self, *args, **kwargs):
return await _patched_inference_method( return await _patched_inference_method(
_original_methods["chat_completions_create"], self, "openai", *args, **kwargs _original_methods["chat_completions_create"], self, "openai", "/v1/chat/completions", *args, **kwargs
) )
async def patched_completions_create(self, *args, **kwargs): async def patched_completions_create(self, *args, **kwargs):
return await _patched_inference_method(_original_methods["completions_create"], self, "openai", *args, **kwargs) return await _patched_inference_method(
_original_methods["completions_create"], self, "openai", "/v1/completions", *args, **kwargs
)
async def patched_embeddings_create(self, *args, **kwargs): async def patched_embeddings_create(self, *args, **kwargs):
return await _patched_inference_method(_original_methods["embeddings_create"], self, "openai", *args, **kwargs) return await _patched_inference_method(
_original_methods["embeddings_create"], self, "openai", "/v1/embeddings", *args, **kwargs
)
# Apply OpenAI patches # Apply OpenAI patches
AsyncChatCompletions.create = patched_chat_completions_create AsyncChatCompletions.create = patched_chat_completions_create
@ -383,30 +353,32 @@ def patch_inference_clients():
# Create patched methods for Ollama client # Create patched methods for Ollama client
async def patched_ollama_generate(self, *args, **kwargs): async def patched_ollama_generate(self, *args, **kwargs):
return await _patched_inference_method( return await _patched_inference_method(
_original_methods["ollama_generate"], self, "ollama", "generate", *args, **kwargs _original_methods["ollama_generate"], self, "ollama", "/api/generate", *args, **kwargs
) )
async def patched_ollama_chat(self, *args, **kwargs): async def patched_ollama_chat(self, *args, **kwargs):
return await _patched_inference_method( return await _patched_inference_method(
_original_methods["ollama_chat"], self, "ollama", "chat", *args, **kwargs _original_methods["ollama_chat"], self, "ollama", "/api/chat", *args, **kwargs
) )
async def patched_ollama_embed(self, *args, **kwargs): async def patched_ollama_embed(self, *args, **kwargs):
return await _patched_inference_method( return await _patched_inference_method(
_original_methods["ollama_embed"], self, "ollama", "embed", *args, **kwargs _original_methods["ollama_embed"], self, "ollama", "/api/embeddings", *args, **kwargs
) )
async def patched_ollama_ps(self, *args, **kwargs): async def patched_ollama_ps(self, *args, **kwargs):
return await _patched_inference_method(_original_methods["ollama_ps"], self, "ollama", "ps", *args, **kwargs) return await _patched_inference_method(
_original_methods["ollama_ps"], self, "ollama", "/api/ps", *args, **kwargs
)
async def patched_ollama_pull(self, *args, **kwargs): async def patched_ollama_pull(self, *args, **kwargs):
return await _patched_inference_method( return await _patched_inference_method(
_original_methods["ollama_pull"], self, "ollama", "pull", *args, **kwargs _original_methods["ollama_pull"], self, "ollama", "/api/pull", *args, **kwargs
) )
async def patched_ollama_list(self, *args, **kwargs): async def patched_ollama_list(self, *args, **kwargs):
return await _patched_inference_method( return await _patched_inference_method(
_original_methods["ollama_list"], self, "ollama", "list", *args, **kwargs _original_methods["ollama_list"], self, "ollama", "/api/tags", *args, **kwargs
) )
# Apply Ollama patches # Apply Ollama patches

View file

@ -20,15 +20,15 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
"__data__": { "__data__": {
"model": "llama-guard3:1b", "model": "llama-guard3:1b",
"created_at": "2025-07-31T17:59:03.590710793Z", "created_at": "2025-08-01T23:12:53.860911Z",
"done": true, "done": true,
"done_reason": "stop", "done_reason": "stop",
"total_duration": 1043588135, "total_duration": 249137667,
"load_duration": 41601119, "load_duration": 152509542,
"prompt_eval_count": 216, "prompt_eval_count": 216,
"prompt_eval_duration": 957017411, "prompt_eval_duration": 71000000,
"eval_count": 2, "eval_count": 2,
"eval_duration": 44409088, "eval_duration": 24000000,
"response": "safe", "response": "safe",
"thinking": null, "thinking": null,
"context": null "context": null

View file

@ -20,15 +20,15 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
"__data__": { "__data__": {
"model": "llama-guard3:1b", "model": "llama-guard3:1b",
"created_at": "2025-07-31T17:43:37.656005919Z", "created_at": "2025-08-01T23:13:57.556416Z",
"done": true, "done": true,
"done_reason": "stop", "done_reason": "stop",
"total_duration": 2903834654, "total_duration": 432363250,
"load_duration": 43364090, "load_duration": 159296417,
"prompt_eval_count": 223, "prompt_eval_count": 223,
"prompt_eval_duration": 2812583330, "prompt_eval_duration": 257000000,
"eval_count": 2, "eval_count": 2,
"eval_duration": 47252843, "eval_duration": 14000000,
"response": "safe", "response": "safe",
"thinking": null, "thinking": null,
"context": null "context": null

View file

@ -0,0 +1,235 @@
{
"request": {
"method": "POST",
"url": "http://0.0.0.0:11434/v1/v1/chat/completions",
"headers": {},
"body": {
"model": "llama3.2:3b-instruct-fp16",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
],
"response_format": {
"type": "text"
},
"stream": true
},
"endpoint": "/v1/chat/completions",
"model": "llama3.2:3b-instruct-fp16"
},
"response": {
"body": [
{
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
"__data__": {
"id": "chatcmpl-29",
"choices": [
{
"delta": {
"content": "The",
"function_call": null,
"refusal": null,
"role": "assistant",
"tool_calls": null
},
"finish_reason": null,
"index": 0,
"logprobs": null
}
],
"created": 1754090031,
"model": "llama3.2:3b-instruct-fp16",
"object": "chat.completion.chunk",
"service_tier": null,
"system_fingerprint": "fp_ollama",
"usage": null
}
},
{
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
"__data__": {
"id": "chatcmpl-29",
"choices": [
{
"delta": {
"content": " capital",
"function_call": null,
"refusal": null,
"role": "assistant",
"tool_calls": null
},
"finish_reason": null,
"index": 0,
"logprobs": null
}
],
"created": 1754090031,
"model": "llama3.2:3b-instruct-fp16",
"object": "chat.completion.chunk",
"service_tier": null,
"system_fingerprint": "fp_ollama",
"usage": null
}
},
{
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
"__data__": {
"id": "chatcmpl-29",
"choices": [
{
"delta": {
"content": " of",
"function_call": null,
"refusal": null,
"role": "assistant",
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},
"finish_reason": null,
"index": 0,
"logprobs": null
}
],
"created": 1754090031,
"model": "llama3.2:3b-instruct-fp16",
"object": "chat.completion.chunk",
"service_tier": null,
"system_fingerprint": "fp_ollama",
"usage": null
}
},
{
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
"__data__": {
"id": "chatcmpl-29",
"choices": [
{
"delta": {
"content": " France",
"function_call": null,
"refusal": null,
"role": "assistant",
"tool_calls": null
},
"finish_reason": null,
"index": 0,
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}
],
"created": 1754090031,
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"object": "chat.completion.chunk",
"service_tier": null,
"system_fingerprint": "fp_ollama",
"usage": null
}
},
{
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
"__data__": {
"id": "chatcmpl-29",
"choices": [
{
"delta": {
"content": " is",
"function_call": null,
"refusal": null,
"role": "assistant",
"tool_calls": null
},
"finish_reason": null,
"index": 0,
"logprobs": null
}
],
"created": 1754090031,
"model": "llama3.2:3b-instruct-fp16",
"object": "chat.completion.chunk",
"service_tier": null,
"system_fingerprint": "fp_ollama",
"usage": null
}
},
{
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
"__data__": {
"id": "chatcmpl-29",
"choices": [
{
"delta": {
"content": " Paris",
"function_call": null,
"refusal": null,
"role": "assistant",
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},
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}
],
"created": 1754090031,
"model": "llama3.2:3b-instruct-fp16",
"object": "chat.completion.chunk",
"service_tier": null,
"system_fingerprint": "fp_ollama",
"usage": null
}
},
{
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
"__data__": {
"id": "chatcmpl-29",
"choices": [
{
"delta": {
"content": ".",
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"refusal": null,
"role": "assistant",
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},
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"index": 0,
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}
],
"created": 1754090031,
"model": "llama3.2:3b-instruct-fp16",
"object": "chat.completion.chunk",
"service_tier": null,
"system_fingerprint": "fp_ollama",
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}
},
{
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
"__data__": {
"id": "chatcmpl-29",
"choices": [
{
"delta": {
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},
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}
],
"created": 1754090031,
"model": "llama3.2:3b-instruct-fp16",
"object": "chat.completion.chunk",
"service_tier": null,
"system_fingerprint": "fp_ollama",
"usage": null
}
}
],
"is_streaming": true
}
}

View file

@ -0,0 +1,56 @@
{
"request": {
"method": "POST",
"url": "http://localhost:11434/v1/v1/chat/completions",
"headers": {},
"body": {
"model": "llama3.2:3b-instruct-fp16",
"messages": [
{
"role": "user",
"content": "Which planet has rings around it with a name starting with letter S?"
}
],
"stream": false
},
"endpoint": "/v1/chat/completions",
"model": "llama3.2:3b-instruct-fp16"
},
"response": {
"body": {
"__type__": "openai.types.chat.chat_completion.ChatCompletion",
"__data__": {
"id": "chatcmpl-368",
"choices": [
{
"finish_reason": "stop",
"index": 0,
"logprobs": null,
"message": {
"content": "Saturn is known for its extensive ring system.",
"refusal": null,
"role": "assistant",
"annotations": null,
"audio": null,
"function_call": null,
"tool_calls": null
}
}
],
"created": 1754081853,
"model": "llama3.2:3b-instruct-fp16",
"object": "chat.completion",
"service_tier": null,
"system_fingerprint": "fp_ollama",
"usage": {
"completion_tokens": 11,
"prompt_tokens": 39,
"total_tokens": 50,
"completion_tokens_details": null,
"prompt_tokens_details": null
}
}
},
"is_streaming": false
}
}

View file

@ -20,15 +20,15 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
"__data__": { "__data__": {
"model": "llama-guard3:1b", "model": "llama-guard3:1b",
"created_at": "2025-07-31T17:58:57.232902036Z", "created_at": "2025-08-01T23:12:51.682357Z",
"done": true, "done": true,
"done_reason": "stop", "done_reason": "stop",
"total_duration": 1112852961, "total_duration": 238161000,
"load_duration": 40973130, "load_duration": 72494750,
"prompt_eval_count": 212, "prompt_eval_count": 212,
"prompt_eval_duration": 898310684, "prompt_eval_duration": 87000000,
"eval_count": 5, "eval_count": 5,
"eval_duration": 173032826, "eval_duration": 74000000,
"response": "unsafe\nS8", "response": "unsafe\nS8",
"thinking": null, "thinking": null,
"context": null "context": null

View file

@ -20,15 +20,15 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
"__data__": { "__data__": {
"model": "llama-guard3:1b", "model": "llama-guard3:1b",
"created_at": "2025-07-31T17:59:00.488769233Z", "created_at": "2025-08-01T23:12:52.919624Z",
"done": true, "done": true,
"done_reason": "stop", "done_reason": "stop",
"total_duration": 983619113, "total_duration": 201956834,
"load_duration": 41180204, "load_duration": 105132584,
"prompt_eval_count": 212, "prompt_eval_count": 212,
"prompt_eval_duration": 898774731, "prompt_eval_duration": 75000000,
"eval_count": 2, "eval_count": 2,
"eval_duration": 43157634, "eval_duration": 20000000,
"response": "safe", "response": "safe",
"thinking": null, "thinking": null,
"context": null "context": null

View file

@ -0,0 +1,251 @@
{
"request": {
"method": "POST",
"url": "http://0.0.0.0:11434/v1/v1/chat/completions",
"headers": {},
"body": {
"model": "llama3.2:3b-instruct-fp16",
"messages": [
{
"role": "user",
"content": "Message A: What is the capital of France?"
},
{
"role": "assistant",
"content": "The capital of France is Paris."
},
{
"role": "user",
"content": "Message B: What about Spain?"
},
{
"role": "assistant",
"content": "The capital of Spain is Madrid."
},
{
"role": "user",
"content": "Message C: And Italy?"
}
],
"response_format": {
"type": "text"
},
"stream": true
},
"endpoint": "/v1/chat/completions",
"model": "llama3.2:3b-instruct-fp16"
},
"response": {
"body": [
{
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
"__data__": {
"id": "chatcmpl-457",
"choices": [
{
"delta": {
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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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}
},
{
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
"__data__": {
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{
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},
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}
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View file

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