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feat(ci): use replay mode, setup ollama if specific label exists on PR (#2955)
This PR makes setting up Ollama optional for CI. By default, we use `replay` mode for inference requests and use the stored results from the `tests/integration/recordings/` directory. Every so often, users will update tests which will need us to re-record. To do this, we check for the existence of a label `re-record-tests` on the PR. If detected, - ollama is spun up - inference mode is set to record - after the tests are done, if any new changes are detected, they are pushed back to the PR ## Test Plan This is GitHub CI. Gotta test it live.
This commit is contained in:
Ashwin Bharambe
GitHub
parent
0ac503ec0d
commit
b237df8f18
11 changed files with 1519 additions and 13 deletions
167
tests/integration/recordings/responses/bd356b27a085.json
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167
tests/integration/recordings/responses/bd356b27a085.json
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{
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"request": {
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"method": "POST",
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"url": "http://localhost:11434/api/generate",
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"headers": {},
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"body": {
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"model": "llama3.2:3b-instruct-fp16",
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"raw": true,
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"prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant. You have access to functions, but you should only use them if they are required.\nYou are an expert in composing functions. You are given a question and a set of possible functions.\nBased on the question, you may or may not need to make one function/tool call to achieve the purpose.\n\nIf you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]\nIf you decide to invoke a function, you SHOULD NOT include any other text in the response. besides the function call in the above format.\nFor a boolean parameter, be sure to use `True` or `False` (capitalized) for the value.\n\n\nHere is a list of functions in JSON format that you can invoke.\n\n[\n {\n \"name\": \"greet_everyone\",\n \"description\": \"\",\n \"parameters\": {\n \"type\": \"dict\",\n \"required\": [\"url\"],\n \"properties\": {\n \"url\": {\n \"type\": \"string\",\n \"description\": \"\"\n }\n }\n }\n },\n {\n \"name\": \"get_boiling_point\",\n \"description\": \"\n Returns the boiling point of a liquid in Celsius or Fahrenheit.\n\n :param liquid_name: The name of the liquid\n :param celsius: Whether to return the boiling point in Celsius\n :return: The boiling point of the liquid in Celcius or Fahrenheit\n \",\n \"parameters\": {\n \"type\": \"dict\",\n \"required\": [\"liquid_name\", \"celsius\"],\n \"properties\": {\n \"liquid_name\": {\n \"type\": \"string\",\n \"description\": \"\"\n },\n \"celsius\": {\n \"type\": \"boolean\",\n \"description\": \"\"\n }\n }\n }\n }\n]\n\nYou can answer general questions or invoke tools when necessary.\nIn addition to tool calls, you should also augment your responses by using the tool outputs.\nYou are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nSay hi to the world. Use tools to do so.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n[greet_everyone(url=\"world\")]<|eot_id|><|start_header_id|>ipython<|end_header_id|>\n\nHello, world!<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
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"options": {
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"temperature": 0.0
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},
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"stream": true
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},
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"endpoint": "/api/generate",
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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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{
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"__type__": "ollama._types.GenerateResponse",
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"__data__": {
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"model": "llama3.2:3b-instruct-fp16",
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"created_at": "2025-07-29T23:46:35.850399Z",
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"done": false,
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"done_reason": null,
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"load_duration": null,
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"prompt_eval_duration": null,
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"eval_count": null,
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"eval_duration": null,
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"response": "How",
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"thinking": null,
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"context": null
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}
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},
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{
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"__type__": "ollama._types.GenerateResponse",
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"__data__": {
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"model": "llama3.2:3b-instruct-fp16",
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"created_at": "2025-07-29T23:46:35.89419Z",
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"done": false,
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"load_duration": null,
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"prompt_eval_duration": null,
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"eval_count": null,
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"eval_duration": null,
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"response": " can",
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"thinking": null,
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"context": null
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}
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},
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{
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"__type__": "ollama._types.GenerateResponse",
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"__data__": {
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"model": "llama3.2:3b-instruct-fp16",
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"created_at": "2025-07-29T23:46:35.938049Z",
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"done": false,
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"eval_duration": null,
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"response": " I",
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"thinking": null,
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"context": null
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}
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},
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{
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"__type__": "ollama._types.GenerateResponse",
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"__data__": {
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"model": "llama3.2:3b-instruct-fp16",
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"created_at": "2025-07-29T23:46:35.980392Z",
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"done": false,
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"done_reason": null,
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"prompt_eval_duration": null,
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"eval_count": null,
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"eval_duration": null,
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"response": " assist",
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"thinking": null,
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"context": null
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}
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},
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{
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"__type__": "ollama._types.GenerateResponse",
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"__data__": {
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"model": "llama3.2:3b-instruct-fp16",
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"created_at": "2025-07-29T23:46:36.023004Z",
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"done": false,
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"done_reason": null,
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"total_duration": null,
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"load_duration": null,
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"prompt_eval_duration": null,
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"eval_duration": null,
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"response": " you",
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"thinking": null,
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"context": null
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}
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},
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{
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"__type__": "ollama._types.GenerateResponse",
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"__data__": {
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"model": "llama3.2:3b-instruct-fp16",
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"created_at": "2025-07-29T23:46:36.065467Z",
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"done": false,
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"done_reason": null,
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"total_duration": null,
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"prompt_eval_duration": null,
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"eval_count": null,
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"eval_duration": null,
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"response": " further",
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"thinking": null,
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"context": null
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}
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},
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{
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"__type__": "ollama._types.GenerateResponse",
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"__data__": {
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"model": "llama3.2:3b-instruct-fp16",
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"created_at": "2025-07-29T23:46:36.108189Z",
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"done": false,
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"done_reason": null,
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"prompt_eval_duration": null,
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"eval_count": null,
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"eval_duration": null,
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"response": "?",
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"thinking": null,
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"context": null
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}
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},
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{
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"__type__": "ollama._types.GenerateResponse",
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"__data__": {
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"model": "llama3.2:3b-instruct-fp16",
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"created_at": "2025-07-29T23:46:36.150902Z",
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"done": true,
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"done_reason": "stop",
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"total_duration": 468910417,
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"load_duration": 93969000,
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"prompt_eval_count": 479,
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"eval_count": 8,
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"eval_duration": 301590375,
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"response": "",
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"thinking": null,
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"context": null
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}
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}
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],
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"is_streaming": true
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}
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}
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