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feat(tests): auto-merge all model list responses and unify recordings (#3320)
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Browse source 
One needed to specify record-replay related environment variables for
running integration tests. We could not use defaults because integration
tests could be run against Ollama instances which could be running
different models. For example, text vs vision tests needed separate
instances of Ollama because a single instance typically cannot serve
both of these models if you assume the standard CI worker configuration
on Github. As a result, `client.list()` as returned by the Ollama client
would be different between these runs and we'd end up overwriting
responses.

This PR "solves" it by adding a small amount of complexity -- we store
model list responses specially, keyed by the hashes of the models they
return. At replay time, we merge all of them and pretend that we have
the union of all models available.

## Test Plan

Re-recorded all the tests using `scripts/integration-tests.sh
--inference-mode record`, including the vision tests.
This commit is contained in:
Ashwin Bharambe 2025-09-03 11:33:03 -07:00 • committed by GitHub
parent d948e63340
commit c3d3a0b833
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122 changed files with 17460 additions and 16129 deletions
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2
.gitignore vendored
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@ -26,5 +26,7 @@ venv/
pytest-report.xml pytest-report.xml
.coverage .coverage
.python-version .python-version
AGENTS.md
server.log
CLAUDE.md CLAUDE.md
.claude/ .claude/

9
docs/source/contributing/testing/record-replay.md
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@ -40,18 +40,15 @@ The system patches OpenAI and Ollama client methods to intercept calls before th
### Storage Architecture ### Storage Architecture
Recordings use a two-tier storage system optimized for both speed and debuggability: Recordings are stored as JSON files in the recording directory. They are looked up by their request hash.
``` ```
recordings/ recordings/
├── index.sqlite # Fast lookup by request hash
└── responses/ └── responses/
├── abc123def456.json # Individual response files ├── abc123def456.json # Individual response files
└── def789ghi012.json └── def789ghi012.json
``` ```
**SQLite index** enables O(log n) hash lookups and metadata queries without loading response bodies.
**JSON files** store complete request/response pairs in human-readable format for debugging. **JSON files** store complete request/response pairs in human-readable format for debugging.
## Recording Modes ## Recording Modes
@ -166,8 +163,8 @@ This preserves type safety - when replayed, you get the same Pydantic objects wi
Control recording behavior globally: Control recording behavior globally:
```bash ```bash
export LLAMA_STACK_TEST_INFERENCE_MODE=replay export LLAMA_STACK_TEST_INFERENCE_MODE=replay # this is the default
export LLAMA_STACK_TEST_RECORDING_DIR=/path/to/recordings export LLAMA_STACK_TEST_RECORDING_DIR=/path/to/recordings # default is tests/integration/recordings
pytest tests/integration/ pytest tests/integration/
``` ```

8
llama_stack/providers/remote/inference/ollama/ollama.py
View file

@ -118,10 +118,10 @@ class OllamaInferenceAdapter(
async def initialize(self) -> None: async def initialize(self) -> None:
logger.info(f"checking connectivity to Ollama at `{self.config.url}`...") logger.info(f"checking connectivity to Ollama at `{self.config.url}`...")
health_response = await self.health() r = await self.health()
if health_response["status"] == HealthStatus.ERROR: if r["status"] == HealthStatus.ERROR:
logger.warning( logger.warning(
"Ollama Server is not running, make sure to start it using `ollama serve` in a separate terminal" f"Ollama Server is not running (message: {r['message']}). Make sure to start it using `ollama serve` in a separate terminal"
) )
async def should_refresh_models(self) -> bool: async def should_refresh_models(self) -> bool:
@ -156,7 +156,7 @@ class OllamaInferenceAdapter(
), ),
Model( Model(
identifier="nomic-embed-text", identifier="nomic-embed-text",
provider_resource_id="nomic-embed-text", provider_resource_id="nomic-embed-text:latest",
provider_id=provider_id, provider_id=provider_id,
metadata={ metadata={
"embedding_dimension": 768, "embedding_dimension": 768,

154
llama_stack/testing/inference_recorder.py
View file

@ -30,6 +30,9 @@ from openai.types.completion_choice import CompletionChoice
CompletionChoice.model_fields["finish_reason"].annotation = Literal["stop", "length", "content_filter"] | None CompletionChoice.model_fields["finish_reason"].annotation = Literal["stop", "length", "content_filter"] | None
CompletionChoice.model_rebuild() CompletionChoice.model_rebuild()
REPO_ROOT = Path(__file__).parent.parent.parent
DEFAULT_STORAGE_DIR = REPO_ROOT / "tests/integration/recordings"
class InferenceMode(StrEnum): class InferenceMode(StrEnum):
LIVE = "live" LIVE = "live"
@ -51,7 +54,7 @@ def normalize_request(method: str, url: str, headers: dict[str, Any], body: dict
def get_inference_mode() -> InferenceMode: def get_inference_mode() -> InferenceMode:
return InferenceMode(os.environ.get("LLAMA_STACK_TEST_INFERENCE_MODE", "live").lower()) return InferenceMode(os.environ.get("LLAMA_STACK_TEST_INFERENCE_MODE", "replay").lower())
def setup_inference_recording(): def setup_inference_recording():
@ -60,28 +63,18 @@ def setup_inference_recording():
to increase their reliability and reduce reliance on expensive, external services. to increase their reliability and reduce reliance on expensive, external services.
Currently, this is only supported for OpenAI and Ollama clients. These should cover the vast majority of use cases. Currently, this is only supported for OpenAI and Ollama clients. These should cover the vast majority of use cases.
Calls to the /models endpoint are not currently trapped. We probably need to add support for this.
Two environment variables are required: Two environment variables are supported:
- LLAMA_STACK_TEST_INFERENCE_MODE: The mode to run in. Must be 'live', 'record', or 'replay'. - LLAMA_STACK_TEST_INFERENCE_MODE: The mode to run in. Must be 'live', 'record', or 'replay'. Default is 'replay'.
- LLAMA_STACK_TEST_RECORDING_DIR: The directory to store the recordings in. - LLAMA_STACK_TEST_RECORDING_DIR: The directory to store the recordings in. Default is 'tests/integration/recordings'.
The recordings are stored in a SQLite database and a JSON file for each request. The SQLite database is used to The recordings are stored as JSON files.
quickly find the correct recording for a given request. The JSON files are used to store the request and response
bodies.
""" """
mode = get_inference_mode() mode = get_inference_mode()
if mode not in InferenceMode:
raise ValueError(f"Invalid LLAMA_STACK_TEST_INFERENCE_MODE: {mode}. Must be 'live', 'record', or 'replay'")
if mode == InferenceMode.LIVE: if mode == InferenceMode.LIVE:
return None return None
if "LLAMA_STACK_TEST_RECORDING_DIR" not in os.environ: storage_dir = os.environ.get("LLAMA_STACK_TEST_RECORDING_DIR", DEFAULT_STORAGE_DIR)
raise ValueError("LLAMA_STACK_TEST_RECORDING_DIR must be set for recording or replaying")
storage_dir = os.environ["LLAMA_STACK_TEST_RECORDING_DIR"]
return inference_recording(mode=mode, storage_dir=storage_dir) return inference_recording(mode=mode, storage_dir=storage_dir)
@ -134,8 +127,8 @@ class ResponseStorage:
def store_recording(self, request_hash: str, request: dict[str, Any], response: dict[str, Any]): def store_recording(self, request_hash: str, request: dict[str, Any], response: dict[str, Any]):
"""Store a request/response pair.""" """Store a request/response pair."""
# Generate unique response filename # Generate unique response filename
response_file = f"{request_hash[:12]}.json" short_hash = request_hash[:12]
response_path = self.responses_dir / response_file response_file = f"{short_hash}.json"
# Serialize response body if needed # Serialize response body if needed
serialized_response = dict(response) serialized_response = dict(response)
@ -147,6 +140,14 @@ class ResponseStorage:
# Handle single response # Handle single response
serialized_response["body"] = _serialize_response(serialized_response["body"]) serialized_response["body"] = _serialize_response(serialized_response["body"])
# If this is an Ollama /api/tags recording, include models digest in filename to distinguish variants
endpoint = request.get("endpoint")
if endpoint in ("/api/tags", "/v1/models"):
digest = _model_identifiers_digest(endpoint, response)
response_file = f"models-{short_hash}-{digest}.json"
response_path = self.responses_dir / response_file
# Save response to JSON file # Save response to JSON file
with open(response_path, "w") as f: with open(response_path, "w") as f:
json.dump({"request": request, "response": serialized_response}, f, indent=2) json.dump({"request": request, "response": serialized_response}, f, indent=2)
@ -161,19 +162,85 @@ class ResponseStorage:
if not response_path.exists(): if not response_path.exists():
return None return None
with open(response_path) as f: return _recording_from_file(response_path)
data = json.load(f)
# Deserialize response body if needed def _model_list_responses(self, short_hash: str) -> list[dict[str, Any]]:
if "response" in data and "body" in data["response"]: results: list[dict[str, Any]] = []
if isinstance(data["response"]["body"], list): for path in self.responses_dir.glob(f"models-{short_hash}-*.json"):
# Handle streaming responses data = _recording_from_file(path)
data["response"]["body"] = [_deserialize_response(chunk) for chunk in data["response"]["body"]] results.append(data)
return results
def _recording_from_file(response_path) -> dict[str, Any]:
with open(response_path) as f:
data = json.load(f)
# Deserialize response body if needed
if "response" in data and "body" in data["response"]:
if isinstance(data["response"]["body"], list):
# Handle streaming responses
data["response"]["body"] = [_deserialize_response(chunk) for chunk in data["response"]["body"]]
else:
# Handle single response
data["response"]["body"] = _deserialize_response(data["response"]["body"])
return cast(dict[str, Any], data)
def _model_identifiers_digest(endpoint: str, response: dict[str, Any]) -> str:
def _extract_model_identifiers():
"""Extract a stable set of identifiers for model-list endpoints.
Supported endpoints:
- '/api/tags' (Ollama): response body has 'models': [ { name/model/digest/id/... }, ... ]
- '/v1/models' (OpenAI): response body has 'data': [ { id: ... }, ... ]
Returns a list of unique identifiers or None if structure doesn't match.
"""
body = response["body"]
if endpoint == "/api/tags":
items = body.get("models")
idents = [m.model for m in items]
else:
items = body.get("data")
idents = [m.id for m in items]
return sorted(set(idents))
identifiers = _extract_model_identifiers()
return hashlib.sha1(("|".join(identifiers)).encode("utf-8")).hexdigest()[:8]
def _combine_model_list_responses(endpoint: str, records: list[dict[str, Any]]) -> dict[str, Any] | None:
"""Return a single, unioned recording for supported model-list endpoints."""
seen: dict[str, dict[str, Any]] = {}
for rec in records:
body = rec["response"]["body"]
if endpoint == "/api/tags":
items = body.models
elif endpoint == "/v1/models":
items = body.data
else:
items = []
for m in items:
if endpoint == "/v1/models":
key = m.id
else: else:
# Handle single response key = m.model
data["response"]["body"] = _deserialize_response(data["response"]["body"]) seen[key] = m
return cast(dict[str, Any], data) ordered = [seen[k] for k in sorted(seen.keys())]
canonical = records[0]
canonical_req = canonical.get("request", {})
if isinstance(canonical_req, dict):
canonical_req["endpoint"] = endpoint
if endpoint == "/v1/models":
body = {"data": ordered, "object": "list"}
else:
from ollama import ListResponse
body = ListResponse(models=ordered)
return {"request": canonical_req, "response": {"body": body, "is_streaming": False}}
async def _patched_inference_method(original_method, self, client_type, endpoint, *args, **kwargs): async def _patched_inference_method(original_method, self, client_type, endpoint, *args, **kwargs):
@ -195,8 +262,6 @@ async def _patched_inference_method(original_method, self, client_type, endpoint
raise ValueError(f"Unknown client type: {client_type}") raise ValueError(f"Unknown client type: {client_type}")
url = base_url.rstrip("/") + endpoint url = base_url.rstrip("/") + endpoint
# Normalize request for matching
method = "POST" method = "POST"
headers = {} headers = {}
body = kwargs body = kwargs
@ -204,7 +269,12 @@ async def _patched_inference_method(original_method, self, client_type, endpoint
request_hash = normalize_request(method, url, headers, body) request_hash = normalize_request(method, url, headers, body)
if _current_mode == InferenceMode.REPLAY: if _current_mode == InferenceMode.REPLAY:
recording = _current_storage.find_recording(request_hash) # Special handling for model-list endpoints: return union of all responses
if endpoint in ("/api/tags", "/v1/models"):
records = _current_storage._model_list_responses(request_hash[:12])
recording = _combine_model_list_responses(endpoint, records)
else:
recording = _current_storage.find_recording(request_hash)
if recording: if recording:
response_body = recording["response"]["body"] response_body = recording["response"]["body"]
@ -274,12 +344,14 @@ def patch_inference_clients():
from openai.resources.chat.completions import AsyncCompletions as AsyncChatCompletions from openai.resources.chat.completions import AsyncCompletions as AsyncChatCompletions
from openai.resources.completions import AsyncCompletions from openai.resources.completions import AsyncCompletions
from openai.resources.embeddings import AsyncEmbeddings from openai.resources.embeddings import AsyncEmbeddings
from openai.resources.models import AsyncModels
# Store original methods for both OpenAI and Ollama clients # Store original methods for both OpenAI and Ollama clients
_original_methods = { _original_methods = {
"chat_completions_create": AsyncChatCompletions.create, "chat_completions_create": AsyncChatCompletions.create,
"completions_create": AsyncCompletions.create, "completions_create": AsyncCompletions.create,
"embeddings_create": AsyncEmbeddings.create, "embeddings_create": AsyncEmbeddings.create,
"models_list": AsyncModels.list,
"ollama_generate": OllamaAsyncClient.generate, "ollama_generate": OllamaAsyncClient.generate,
"ollama_chat": OllamaAsyncClient.chat, "ollama_chat": OllamaAsyncClient.chat,
"ollama_embed": OllamaAsyncClient.embed, "ollama_embed": OllamaAsyncClient.embed,
@ -304,10 +376,16 @@ def patch_inference_clients():
_original_methods["embeddings_create"], self, "openai", "/v1/embeddings", *args, **kwargs _original_methods["embeddings_create"], self, "openai", "/v1/embeddings", *args, **kwargs
) )
async def patched_models_list(self, *args, **kwargs):
return await _patched_inference_method(
_original_methods["models_list"], self, "openai", "/v1/models", *args, **kwargs
)
# Apply OpenAI patches # Apply OpenAI patches
AsyncChatCompletions.create = patched_chat_completions_create AsyncChatCompletions.create = patched_chat_completions_create
AsyncCompletions.create = patched_completions_create AsyncCompletions.create = patched_completions_create
AsyncEmbeddings.create = patched_embeddings_create AsyncEmbeddings.create = patched_embeddings_create
AsyncModels.list = patched_models_list
# 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):
@ -361,11 +439,13 @@ def unpatch_inference_clients():
from openai.resources.chat.completions import AsyncCompletions as AsyncChatCompletions from openai.resources.chat.completions import AsyncCompletions as AsyncChatCompletions
from openai.resources.completions import AsyncCompletions from openai.resources.completions import AsyncCompletions
from openai.resources.embeddings import AsyncEmbeddings from openai.resources.embeddings import AsyncEmbeddings
from openai.resources.models import AsyncModels
# Restore OpenAI client methods # Restore OpenAI client methods
AsyncChatCompletions.create = _original_methods["chat_completions_create"] AsyncChatCompletions.create = _original_methods["chat_completions_create"]
AsyncCompletions.create = _original_methods["completions_create"] AsyncCompletions.create = _original_methods["completions_create"]
AsyncEmbeddings.create = _original_methods["embeddings_create"] AsyncEmbeddings.create = _original_methods["embeddings_create"]
AsyncModels.list = _original_methods["models_list"]
# Restore Ollama client methods if they were patched # Restore Ollama client methods if they were patched
OllamaAsyncClient.generate = _original_methods["ollama_generate"] OllamaAsyncClient.generate = _original_methods["ollama_generate"]
@ -379,16 +459,10 @@ def unpatch_inference_clients():
@contextmanager @contextmanager
def inference_recording(mode: str = "live", storage_dir: str | Path | None = None) -> Generator[None, None, None]: def inference_recording(mode: str, storage_dir: str | Path | None = None) -> Generator[None, None, None]:
"""Context manager for inference recording/replaying.""" """Context manager for inference recording/replaying."""
global _current_mode, _current_storage global _current_mode, _current_storage
# Set defaults
if storage_dir is None:
storage_dir_path = Path.home() / ".llama" / "recordings"
else:
storage_dir_path = Path(storage_dir)
# Store previous state # Store previous state
prev_mode = _current_mode prev_mode = _current_mode
prev_storage = _current_storage prev_storage = _current_storage
@ -397,7 +471,9 @@ def inference_recording(mode: str = "live", storage_dir: str | Path | None = Non
_current_mode = mode _current_mode = mode
if mode in ["record", "replay"]: if mode in ["record", "replay"]:
_current_storage = ResponseStorage(storage_dir_path) if storage_dir is None:
raise ValueError("storage_dir is required for record and replay modes")
_current_storage = ResponseStorage(Path(storage_dir))
patch_inference_clients() patch_inference_clients()
yield yield

14
scripts/integration-tests.sh
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@ -140,13 +140,6 @@ THIS_DIR=$(dirname "$0")
ROOT_DIR="$THIS_DIR/.." ROOT_DIR="$THIS_DIR/.."
cd $ROOT_DIR cd $ROOT_DIR
# Set recording directory
if [[ "$RUN_VISION_TESTS" == "true" ]]; then
export LLAMA_STACK_TEST_RECORDING_DIR="tests/integration/recordings/vision"
else
export LLAMA_STACK_TEST_RECORDING_DIR="tests/integration/recordings"
fi
# check if "llama" and "pytest" are available. this script does not use `uv run` given # check if "llama" and "pytest" are available. this script does not use `uv run` given
# it can be used in a pre-release environment where we have not been able to tell # it can be used in a pre-release environment where we have not been able to tell
# uv about pre-release dependencies properly (yet). # uv about pre-release dependencies properly (yet).
@ -298,5 +291,12 @@ echo "=== System Resources After Tests ==="
free -h 2>/dev/null || echo "free command not available" free -h 2>/dev/null || echo "free command not available"
df -h df -h
# stop server
if [[ "$STACK_CONFIG" == *"server:"* ]]; then
echo "Stopping Llama Stack Server..."
kill $(lsof -i :8321 | awk 'NR>1 {print $2}')
echo "Llama Stack Server stopped"
fi
echo "" echo ""
echo "=== Integration Tests Complete ===" echo "=== Integration Tests Complete ==="

12
tests/README.md
View file

@ -38,26 +38,15 @@ For running integration tests, you must provide a few things:
- a distribution name (e.g., `starter`) or a path to a `run.yaml` file - a distribution name (e.g., `starter`) or a path to a `run.yaml` file
- a comma-separated list of api=provider pairs, e.g. `inference=fireworks,safety=llama-guard,agents=meta-reference`. This is most useful for testing a single API surface. - a comma-separated list of api=provider pairs, e.g. `inference=fireworks,safety=llama-guard,agents=meta-reference`. This is most useful for testing a single API surface.
- Whether you are using replay or live mode for inference. This is specified with the LLAMA_STACK_TEST_INFERENCE_MODE environment variable. The default mode currently is "live" -- that is certainly surprising, but we will fix this soon.
- Any API keys you need to use should be set in the environment, or can be passed in with the --env option. - Any API keys you need to use should be set in the environment, or can be passed in with the --env option.
You can run the integration tests in replay mode with: You can run the integration tests in replay mode with:
```bash ```bash
# Run all tests with existing recordings # Run all tests with existing recordings
LLAMA_STACK_TEST_INFERENCE_MODE=replay \
LLAMA_STACK_TEST_RECORDING_DIR=tests/integration/recordings \
uv run --group test \ uv run --group test \
pytest -sv tests/integration/ --stack-config=starter pytest -sv tests/integration/ --stack-config=starter
``` ```
If you don't specify LLAMA_STACK_TEST_INFERENCE_MODE, by default it will be in "live" mode -- that is, it will make real API calls.
```bash
# Test against live APIs
FIREWORKS_API_KEY=your_key pytest -sv tests/integration/inference --stack-config=starter
```
### Re-recording tests ### Re-recording tests
#### Local Re-recording (Manual Setup Required) #### Local Re-recording (Manual Setup Required)
@ -66,7 +55,6 @@ If you want to re-record tests locally, you can do so with:
```bash ```bash
LLAMA_STACK_TEST_INFERENCE_MODE=record \ LLAMA_STACK_TEST_INFERENCE_MODE=record \
LLAMA_STACK_TEST_RECORDING_DIR=tests/integration/recordings \
uv run --group test \ uv run --group test \
pytest -sv tests/integration/ --stack-config=starter -k "<appropriate test name>" pytest -sv tests/integration/ --stack-config=starter -k "<appropriate test name>"
``` ```

19
tests/integration/README.md
View file

@ -98,29 +98,25 @@ pytest -s -v tests/integration/vector_io/ \
The testing system supports three modes controlled by environment variables: The testing system supports three modes controlled by environment variables:
### LIVE Mode (Default) ### REPLAY Mode (Default)
Tests make real API calls: Uses cached responses instead of making API calls:
```bash ```bash
LLAMA_STACK_TEST_INFERENCE_MODE=live pytest tests/integration/ pytest tests/integration/
``` ```
### RECORD Mode ### RECORD Mode
Captures API interactions for later replay: Captures API interactions for later replay:
```bash ```bash
LLAMA_STACK_TEST_INFERENCE_MODE=record \ LLAMA_STACK_TEST_INFERENCE_MODE=record \
LLAMA_STACK_TEST_RECORDING_DIR=tests/integration/recordings \
pytest tests/integration/inference/test_new_feature.py pytest tests/integration/inference/test_new_feature.py
``` ```
### REPLAY Mode ### LIVE Mode
Uses cached responses instead of making API calls: Tests make real API calls (but not recorded):
```bash ```bash
LLAMA_STACK_TEST_INFERENCE_MODE=replay \ LLAMA_STACK_TEST_INFERENCE_MODE=live pytest tests/integration/
LLAMA_STACK_TEST_RECORDING_DIR=tests/integration/recordings \
pytest tests/integration/
``` ```
Note that right now you must specify the recording directory. This is because different tests use different recording directories and we don't (yet) have a fool-proof way to map a test to a recording directory. We are working on this. By default, the recording directory is `tests/integration/recordings`. You can override this by setting the `LLAMA_STACK_TEST_RECORDING_DIR` environment variable.
## Managing Recordings ## Managing Recordings
@ -146,7 +142,6 @@ See the [main testing guide](../README.md#remote-re-recording-recommended) for f
```bash ```bash
# Re-record specific tests # Re-record specific tests
LLAMA_STACK_TEST_INFERENCE_MODE=record \ LLAMA_STACK_TEST_INFERENCE_MODE=record \
LLAMA_STACK_TEST_RECORDING_DIR=tests/integration/recordings \
pytest -s -v --stack-config=server:starter tests/integration/inference/test_modified.py pytest -s -v --stack-config=server:starter tests/integration/inference/test_modified.py
``` ```

10
tests/integration/recordings/responses/00ba04f74a96.json
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": "2025-08-01T23:12:53.860911Z", "created_at": "2025-09-03T17:37:35.23084Z",
"done": true, "done": true,
"done_reason": "stop", "done_reason": "stop",
"total_duration": 249137667, "total_duration": 195981375,
"load_duration": 152509542, "load_duration": 110522917,
"prompt_eval_count": 216, "prompt_eval_count": 216,
"prompt_eval_duration": 71000000, "prompt_eval_duration": 72393958,
"eval_count": 2, "eval_count": 2,
"eval_duration": 24000000, "eval_duration": 11843000,
"response": "safe", "response": "safe",
"thinking": null, "thinking": null,
"context": null "context": null

44
tests/integration/recordings/responses/04172112ffbb.json
View file

@ -21,7 +21,7 @@
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10
tests/integration/recordings/responses/0b27fd737699.json
View file

@ -20,15 +20,15 @@
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32
tests/integration/recordings/responses/0b3f2e4754ff.json
View file

@ -24,7 +24,7 @@
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12
tests/integration/recordings/responses/0e8f2b001dd9.json
View file

@ -1,7 +1,7 @@
{ {
"request": { "request": {
"method": "POST", "method": "POST",
"url": "http://localhost:11434/v1/v1/chat/completions", "url": "http://0.0.0.0:11434/v1/v1/chat/completions",
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10
tests/integration/recordings/responses/10eea8c15ddc.json
View file

@ -20,15 +20,15 @@
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12
tests/integration/recordings/responses/140187e305dc.json
View file

@ -1,7 +1,7 @@
{ {
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"content": "I'm afraid I don't have a built-in ability to directly interface with or \"test\" OpenAI models, including the original GPT-1 model. However, I can explain how you might approach this task:\n\nThe OpenAI GPT-1 is a large transformer-based language model that was trained on a massive dataset of text and achieved state-of-the-art results in various natural language processing tasks.\n\nTo test or evaluate the performance of a model like GPT-1, you would typically follow these steps:\n\n1. **Get access to the OpenAI API**: The OpenAI API provides a way for developers to interact with the GPT-1 model programmatically. You can sign up for an API key on the OpenAI website.\n2. **Choose a testing platform or environment**: You'll need a compute platform that supports the necessary algorithms and data structures to run inference on the GPT-1 model. Some popular options include AWS, Google Cloud, or Azure Compute Virtual Machines.\n3. **Prepare your test input data**: This will involve creating text inputs in the format expected by the OpenAI API (i.e., a JSON object containing the text to be processed).\n4. **Use the OpenAI Python library or SDK**: The OpenAI Python library provides an easy-to-use interface for interacting with the GPT-1 model through the API.\n\nHere's some example code that demonstrates how you might use the OpenAI Flask API to test a single input:\n\n```python\nfrom flask import Flask, request, jsonify\nimport json\n\napp = Flask(__name__)\n\n@ app . route ( '/ /gpt-en ', ' Text ', methods = ['POST'])\ndef gpt_en () -> Json :\n data = request . get_json ()\n if not data or \"message\" in ( data ):\n return None , 400 , { ' error' : \"Input must be a text string.\" }\n response = []\n while True:\n message = \"\"\n for token in data [\"input\"]:\n response_text = f\"{data['prompt']} {token}\"\n data[\"input\"] = [response_text]\n new_response = gpt_en()(data)\n if all([not item or not isinstance(item, dict) for item in new_response]):\n break\n\n message = json . dumps ({}\"text\": response_text})\n response.append(message)\n\n return jsonify ({\"output\": response}), 200 , {}\n\nif __name__ == \"__main__\":\n app.run(debug=True)\n```\n\n5. **Evaluate the output**: Once you have processed your test input data using the GPT-1 model, you can evaluate the accuracy of the generated responses.\n\nKeep in mind that this is just a basic example to illustrate how you might approach testing the OpenAI GPT-1 model.", "content": "I'm happy to help you test the OpenAI API, however I can not access the API.\n\nInstead why don't we follow these steps:\n\n* Check documentation\n* Contact support\n* Reach out to their community forum. \n\nLet me know if I can be of any additional assistance",
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10
tests/integration/recordings/responses/17253d7cc667.json
View file

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32
tests/integration/recordings/responses/173ecb3aab28.json
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10
tests/integration/recordings/responses/174458ad71b2.json
View file

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10
tests/integration/recordings/responses/178016edef0e.json
View file

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10
tests/integration/recordings/responses/197228e26971.json
View file

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10
tests/integration/recordings/responses/198ef7208389.json
View file

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10
tests/integration/recordings/responses/1adfaa0e062e.json
View file

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10
tests/integration/recordings/responses/1b8394f90636.json
View file

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10
tests/integration/recordings/responses/1b92be674e2a.json
View file

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768
tests/integration/recordings/responses/1e11c2b20ff8.json
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10
tests/integration/recordings/responses/211b1562d4e6.json
View file

@ -20,15 +20,15 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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"response": "Humans live on Earth.", "response": "Humans live on Earth.",
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768
tests/integration/recordings/responses/23506e73bb9e.json
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34
tests/integration/recordings/responses/2afe3b38ca01.json
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208
tests/integration/recordings/responses/2d187a11704c.json
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758
tests/integration/recordings/responses/325a72db5755.json
View file

File diff suppressed because it is too large Load diff

8
tests/integration/recordings/responses/382c2f22274c.json
View file

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6
tests/integration/recordings/responses/3c0bf9ba81b2.json
View file

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30
tests/integration/recordings/responses/3c3f13cb7794.json
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14
tests/integration/recordings/responses/3ca695048bee.json
View file

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768
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4
tests/integration/recordings/responses/44a1d9de0602.json
View file

@ -20,7 +20,7 @@
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10
tests/integration/recordings/responses/44fb9cf5875f.json
View file

@ -20,15 +20,15 @@
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10
tests/integration/recordings/responses/48d2fb183a2a.json
View file

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10
tests/integration/recordings/responses/50340cd4d253.json
View file

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@ -202,7 +202,7 @@
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@ -220,7 +220,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -238,15 +238,15 @@
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46
tests/integration/recordings/responses/554de3cd986f.json
View file

@ -22,7 +22,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -40,7 +40,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -58,7 +58,7 @@
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@ -76,7 +76,7 @@
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@ -94,7 +94,7 @@
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@ -112,7 +112,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -130,7 +130,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -148,7 +148,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -166,7 +166,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -184,7 +184,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -202,7 +202,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -220,7 +220,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -238,7 +238,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -256,7 +256,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -274,7 +274,7 @@
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@ -292,7 +292,7 @@
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@ -310,7 +310,7 @@
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@ -328,7 +328,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -346,15 +346,15 @@
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30
tests/integration/recordings/responses/561746e1c8de.json
View file

@ -21,7 +21,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -39,7 +39,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -57,7 +57,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -75,7 +75,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -93,7 +93,7 @@
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@ -111,7 +111,7 @@
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@ -129,7 +129,7 @@
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@ -147,7 +147,7 @@
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@ -165,7 +165,7 @@
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@ -183,7 +183,7 @@
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@ -201,15 +201,15 @@
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10
tests/integration/recordings/responses/563b994bb7d1.json
View file

@ -20,15 +20,15 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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"response": "[get_weather(location=\"San Francisco, CA\")]", "response": "[get_weather(location=\"San Francisco, CA\")]",
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"context": null "context": null

30
tests/integration/recordings/responses/5f5d16afadb4.json
View file

@ -21,7 +21,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -39,7 +39,7 @@
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@ -57,7 +57,7 @@
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@ -75,7 +75,7 @@
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@ -93,7 +93,7 @@
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10
tests/integration/recordings/responses/6906a6e71988.json
View file

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48
tests/integration/recordings/responses/6cc063bbd7d3.json
View file

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34
tests/integration/recordings/responses/6d35c91287e2.json
View file

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178
tests/integration/recordings/responses/6f96090aa955.json
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46
tests/integration/recordings/responses/6fbea1abca7c.json
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9570
tests/integration/recordings/responses/6fe1d4fedf12.json
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10
tests/integration/recordings/responses/70adef2c30c4.json
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@ -20,15 +20,15 @@
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768
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10
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"response": "The smallest country in the world is the Vatican City, which has a total area of approximately 0.44 km\u00b2 (0.17 sq mi). It is an independent city-state located within Rome, Italy, and is home to the Pope and the central government of the Catholic Church.\n\nTo put that into perspective, the Vatican City is smaller than a golf course! Despite its tiny size, it has its own government, currency, postal system, and even its own police force. It's also home to numerous iconic landmarks like St. Peter's Basilica and the Sistine Chapel.\n\nInterestingly, the Vatican City is not only the smallest country in the world but also the most densely populated, with a population of just over 800 people!", "response": "The smallest country in the world is the Vatican City, which has a total area of approximately 0.44 km\u00b2 (0.17 sq mi). It is an independent city-state located within Rome, Italy, and is home to the Pope and the central government of the Catholic Church.\n\nTo put that into perspective, the Vatican City is smaller than a golf course! Despite its tiny size, it has its own government, currency, postal system, and even its own police force. It's also home to numerous iconic landmarks like St. Peter's Basilica and the Sistine Chapel.\n\nInterestingly, the Vatican City is not only the smallest country in the world but also the most densely populated, with a population of just over 800 people!",
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10
tests/integration/recordings/responses/75d0dd9d0fa3.json
View file

@ -45,15 +45,15 @@
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768
tests/integration/recordings/responses/7b25b702ea18.json
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46
tests/integration/recordings/responses/7b4815aba6c5.json
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34
tests/integration/recordings/responses/7e6806cba34a.json
View file

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768
tests/integration/recordings/responses/802f60021837.json
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"content": "I'd be happy to help you test the OpenAI 2 architecture!\n\nOpenAI 2 is a neural network model developed by OpenAI, and it's not exactly possible for me to directly \"test\" it. However, I can guide you through a simplified example of how to verify if OpenAI 2 has been implemented correctly in a specific codebase.\n\nHere's an outline of the steps:\n\n1. **Understand the basics**: Before we dive into testing, make sure you understand the architectural and functional details of OpenAI 2.\n2. **Get access to the model**: You'll need to obtain a trained OpenAI 2 model or implement it from scratch using a language like PyTorch or TensorFlow.\n3. **Implement a validation framework**: Create a simple validation framework that uses common tasks, such as classification on the GLUE benchmark, to evaluate the performance of your OpenAI 2 model.\n\nHere's a simplified code example in PyTorch:\n```python\nimport torch\nfrom transformers import AutoModelForSequenceClassification, AutoTokenizer\n\n# Load pre-trained OpenAI 2 Model(s)\nmodel_name = \"github/openai/OpenAIAccelerated-Text-To-Speech\"\nmodel_class = AutoModelForSequenceClassification\ntokenizer = AutoTokenizer.from_pretrained(model_name)\n\n# Initialize the model and tokenizer\nmodel = model_class(pretrained=True, num_labels=8) # adjust label number according to your task\ntokenizer = tokenizer\n\ndef evaluate_model():\n batch_size = 100\n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n # Create a validation dataset from the GLUE benchmark\n glue_datasets = [ datasets[\"glue\"][\"sst2\"], datasets[\"glue\"][\"mnli\"] ]\n val_dataset = []\n for i, gds in enumerate(glue_datasets):\n data = gds[:10000] # take only first few examples to speed up evaluation\n input_ids = tokenizer encodings(data[\"sentence1\"], \n attention_mask=data[\"attention_mask\"],\n max_length=512,\n padding=\"max_length\",\n truncation=True,\n return_tensors=\"pt\")\n\n for example in data:\n for sentence in [example['sentence1'], example['sentence2']]:\n input_ids = input_ids.to(device)\n outputs = model(input_ids, labels=None) # adjust label to empty\n \n\n # Compute evaluation metrics\n predictions = []\n labels = []\n accuracy = torch.zeros(8).to(device)\n\n for sentence in data.values():\n sentenceids = [input_ids[\"input_ids\"].flatten()]\n _, pred_labels = model(sentenceids)\n if len(predictions) == 0:\n labels.extend([1, 2])\n else:\n assert len(labels)==len(sentences), 'error'\n labels.append(preds[-1]) \n\n # Append the prediction to the list\n predictions.append(pred)\n\n return accuracy\n\naccuracy = evaluate_model()\nprint(\"Accuracy:\", accuracy)\n```\n\nAfter running this code, you should get an estimate of the model's performance on the GLUE benchmark. Keep in mind that this is a simplified example and real-world openai 2 testing may require more involved validation processes.\n\nI hope this helps! Let me know if you have any further questions or if there are any specific areas where you'd like more information.", "content": "I'd be happy to help you test the Transformer-XL (denoted as \"Test Trace OpenAI 2\") model, but first I need to clarify a few things:\n\n1. **Transformer-XL** is not an official name from OpenAI. It's actually a variant of the Transformer model proposed in the paper \"Long-Short Term Memory Are General: A Study on The Curvature of Time\" (2017) by Jinyu Chen, et al.\n2. **Trace OpenAI 2** sounds like a specific version or configuration of the Transformer-XL model, possibly developed by OpenAI.\n\nGiven these clarifications, I'll provide you with a general idea of how to test the Transformer-XL (or \"Test Trace OpenAI 2\") model using popular frameworks and libraries. Please note that this is not an exhaustive or definitive guide.\n\nTo test the Transformer-XL model, you can follow these steps:\n\n1. **Install the necessary dependencies**: You'll need a deep learning framework like TensorFlow or PyTorch, as well as a library for natural language processing (NLP) like Hugging Face's transformers.\n2. **Load the pre-trained weights**: You can use a pre-trained model checkpoint from Hugging Face's Transformers library or load your own weights trained on a specific task or dataset.\n3. **Prepare your data**: Load your text data into tokens, such as words or characters, and preprocess it according to the specific requirements of the Transformer-XL architecture (e.g., tokenization, padding, etc.).\n4. **Configure the model**: Adjust the hyperparameters to suit your specific test case, including the model's configuration, batch size, learning rate, etc.\n5. **Run the inference**: Use the loaded pre-trained weights to perform inference on your test data.\n\nHere's some sample Python code using PyTorch and Hugging Face's Transformers library to get you started:\n```python\nimport torch\nfrom transformers import LongformerForSequenceClassification, LongformerTokenizer\n\n# Load pre-trained weights\nmodel = LongformerForSequenceClassification.from_pretrained('test-trace-openai-2')\n\n# Prepare data\ntokenizer = model.tokenizer\ntext = \"This is a test sentence\"\ninputs = tokenizer(text, return_tensors='pt')\noutput = model(inputs['input_ids'], attention_mask=inputs['attention_mask'])\n\n# Print the results\nprint(output.logits)\n```\nPlease note that this code snippet is just an example and may not work as-is. You'll need to adapt it to your specific requirements and test data.\n\nKeep in mind that testing a model's performance on a specific task or dataset requires careful consideration of factors like:\n\n* **Test data quality**: Your test data should accurately represent the underlying distribution of your target dataset.\n* **Model evaluation metrics**: Choose relevant evaluation metrics that measure the model's performance on your specific task, such as accuracy, precision, recall, F1-score, etc.\n\nFeel free to ask if you have any further questions or need more guidance!",
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10
tests/integration/recordings/responses/840fbb380b73.json
View file

@ -20,15 +20,15 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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1340
tests/integration/recordings/responses/84cab42e1f5c.json
View file

File diff suppressed because it is too large Load diff

10
tests/integration/recordings/responses/85594a69d74a.json
View file

@ -20,15 +20,15 @@
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10
tests/integration/recordings/responses/97d3812bfccb.json
View file

@ -20,15 +20,15 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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46
tests/integration/recordings/responses/97e259c0d3e5.json
View file

@ -22,7 +22,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -58,7 +58,7 @@
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@ -76,7 +76,7 @@
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@ -112,7 +112,7 @@
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@ -130,7 +130,7 @@
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@ -148,7 +148,7 @@
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@ -166,7 +166,7 @@
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@ -184,7 +184,7 @@
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@ -202,7 +202,7 @@
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@ -220,7 +220,7 @@
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@ -238,7 +238,7 @@
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@ -256,7 +256,7 @@
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@ -274,7 +274,7 @@
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@ -292,7 +292,7 @@
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@ -310,7 +310,7 @@
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@ -328,7 +328,7 @@
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@ -346,15 +346,15 @@
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34
tests/integration/recordings/responses/9c140a29ae09.json
View file

@ -22,7 +22,7 @@
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@ -40,7 +40,7 @@
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@ -58,7 +58,7 @@
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@ -76,7 +76,7 @@
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@ -94,7 +94,7 @@
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@ -112,7 +112,7 @@
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@ -130,7 +130,7 @@
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@ -148,7 +148,7 @@
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@ -166,7 +166,7 @@
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@ -184,7 +184,7 @@
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@ -238,15 +238,15 @@
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44
tests/integration/recordings/responses/9c28ec9ac338.json
View file

@ -21,7 +21,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -39,7 +39,7 @@
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@ -57,7 +57,7 @@
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@ -75,7 +75,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -93,7 +93,7 @@
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@ -111,7 +111,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -129,7 +129,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -147,7 +147,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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"model": "llama3.2:3b-instruct-fp16", "model": "llama3.2:3b-instruct-fp16",
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@ -165,7 +165,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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"model": "llama3.2:3b-instruct-fp16", "model": "llama3.2:3b-instruct-fp16",
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@ -183,7 +183,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -201,7 +201,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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"model": "llama3.2:3b-instruct-fp16", "model": "llama3.2:3b-instruct-fp16",
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@ -219,7 +219,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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"model": "llama3.2:3b-instruct-fp16", "model": "llama3.2:3b-instruct-fp16",
"created_at": "2025-07-29T23:46:36.788811Z", "created_at": "2025-09-03T17:34:23.897939Z",
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@ -237,7 +237,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -255,7 +255,7 @@
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@ -273,7 +273,7 @@
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@ -291,7 +291,7 @@
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@ -309,7 +309,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -327,15 +327,15 @@
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3072
tests/integration/recordings/responses/9e651e5fcfe2.json
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10
tests/integration/recordings/responses/9e7a83d3d596.json
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@ -15,23 +15,23 @@
"body": { "body": {
"__type__": "openai.types.completion.Completion", "__type__": "openai.types.completion.Completion",
"__data__": { "__data__": {
"id": "cmpl-43", "id": "cmpl-775",
"choices": [ "choices": [
{ {
"finish_reason": "stop", "finish_reason": "stop",
"index": 0, "index": 0,
"logprobs": null, "logprobs": null,
"text": "Blue.\n\nMy response is based on the traditional English rhyme that pairs the colors of roses (red) with violets in a poetic and somewhat whimsical way. This specific version of the rhyme goes like this:\n\n\"Roses are red,\nViolets are blue,\nSugar is sweet,\nAnd so are you.\"\n\nIn modern times, variations of this rhyme can deviate from the original \"blue\" for violets, but in my complete sentence as requested, sticking with a widely recognized completion adds an air of timelessness and familiarity to the phrase." "text": "Blue.\n\nMy response is based on the traditional rhyme \"Roses are Red, Violets are Blue,\" which is a well-known poem or phrase often used as a greeting or way to express affection. The exact wording may vary slightly depending on the source, but the general meaning remains the same: violets are typically depicted as blue-colored flowers in this rhyme."
} }
], ],
"created": 1754348148, "created": 1756921025,
"model": "llama3.2:3b-instruct-fp16", "model": "llama3.2:3b-instruct-fp16",
"object": "text_completion", "object": "text_completion",
"system_fingerprint": "fp_ollama", "system_fingerprint": "fp_ollama",
"usage": { "usage": {
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"prompt_tokens": 50, "prompt_tokens": 50,
"total_tokens": 163, "total_tokens": 125,
"completion_tokens_details": null, "completion_tokens_details": null,
"prompt_tokens_details": null "prompt_tokens_details": null
} }

10
tests/integration/recordings/responses/9fadf5a3d68f.json
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@ -20,15 +20,15 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
"__data__": { "__data__": {
"model": "llama-guard3:1b", "model": "llama-guard3:1b",
"created_at": "2025-08-01T23:14:22.168612Z", "created_at": "2025-09-03T17:38:03.270261Z",
"done": true, "done": true,
"done_reason": "stop", "done_reason": "stop",
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"eval_duration": 13000000, "eval_duration": 11306292,
"response": "safe", "response": "safe",
"thinking": null, "thinking": null,
"context": null "context": null

2368
tests/integration/recordings/responses/a0c4df33879f.json
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12
tests/integration/recordings/responses/a4c8d19bb1eb.json
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@ -1,7 +1,7 @@
{ {
"request": { "request": {
"method": "POST", "method": "POST",
"url": "http://localhost:11434/v1/v1/chat/completions", "url": "http://0.0.0.0:11434/v1/v1/chat/completions",
"headers": {}, "headers": {},
"body": { "body": {
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@ -20,14 +20,14 @@
"body": { "body": {
"__type__": "openai.types.chat.chat_completion.ChatCompletion", "__type__": "openai.types.chat.chat_completion.ChatCompletion",
"__data__": { "__data__": {
"id": "chatcmpl-560", "id": "chatcmpl-715",
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{ {
"finish_reason": "stop", "finish_reason": "stop",
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"logprobs": null, "logprobs": null,
"message": { "message": {
"content": "Hello! It's nice to meet you. How can I assist you today?", "content": "Hello! It's nice to meet you. Is there something I can help you with or would you like to chat?",
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} }
} }
], ],
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"prompt_tokens_details": null "prompt_tokens_details": null
} }

12
tests/integration/recordings/responses/a5187d9d5057.json
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@ -1,7 +1,7 @@
{ {
"request": { "request": {
"method": "POST", "method": "POST",
"url": "http://localhost:11434/v1/v1/chat/completions", "url": "http://0.0.0.0:11434/v1/v1/chat/completions",
"headers": {}, "headers": {},
"body": { "body": {
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@ -20,14 +20,14 @@
"body": { "body": {
"__type__": "openai.types.chat.chat_completion.ChatCompletion", "__type__": "openai.types.chat.chat_completion.ChatCompletion",
"__data__": { "__data__": {
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{ {
"finish_reason": "stop", "finish_reason": "stop",
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"logprobs": null, "logprobs": null,
"message": { "message": {
"content": "You want to test the capabilities of the OpenAI Text-to-Text model (also known as T0).\n\nPlease note that I'll be using a pre-trained model, so my responses might not be entirely customized to your specific prompt or context. That being said, I'll do my best to mimic the behavior of the original model.\n\nWhat would you like to test or ask? Please provide a prompt or question, and I'll respond accordingly.\n\n(Note: if you'd like to run a longer experiment or try out specific models like text completion or code generation, feel free to let me know and we can figure out a way to collaborate.)", "content": "I'll simulate a test for the LA-1030/5B linear accelerator, specifically for the H8 (High Voltage) model found in early models of the test rail. Note that this is not meant to be taken as actual test results but rather a demonstration.\n\n### Introduction:\nThe LA-1030/5B was used primarily for high-energy physics and nuclear research during the 1970s and 1980s. This linear accelerator was capable of producing proton beams with energies up to several GeV. The H8 model, also known as the 'High Voltage' component, is a series of power supplies that drive the high voltage DC (HV) accelerators.\n\n### Test Setup:\n\n- **Test Goal:** Measure the output of the LA-1030/5B H8 model linear accelerator and assess its ability to generate stable, high-voltage direct current (HVDC) to power it properly.\n \n - The setup consists of a single test rail containing one of these H8 modules. A precise DC voltage is supplied to the linear accelerator via an external DC source.\n\n### Operating Parameters:\n\n- **Input Voltage:** To ensure the linear accelerator operates within its safe operating parameters, input voltages will be varied from 20KV to 140KV.\n- **Current Delivery:** Monitoring current at these different output levels requires a precise multimeter or oscilloscope. \n- **Voltage Level and Current Stability:** The voltage should stabilize as close as possible to the desired output level.\n\n### Potential Issues\n\n1.) Over-Pressure in H8 Modules\n - During high voltage levels, there's a risk of over-pressurization in the component casing due to the vacuum properties within the modules.\n - Check for any external signs of stress or leakage.\n2.) Current Limitation and Arcing\n - High current requirements demand close monitoring of the accelerator components and associated connections.\n - An excessive arc can be detrimental to electronics connected to the system.\n3.) Interlocks and Safe Guards\n\n- **Ensure alignment:** Prevent accidental triggering.\n\n4.) Insulation integrity \n - Potential risks from faulty or non-insulated components\n\n### Results Analysis:\n\nBased on this hypothetical test, some potential results could include:\n1. Output voltage stability for the chosen input voltages\n2. Ability to exceed the accelerator's highest voltage ratings.\n3. Consistency between different current levels at various output voltage tests.\n\nThis exercise is a general simulation and might not reflect real-world conditions or performance specifications of an actual LA-1030/5B linear accelerator. The focus here was on demonstrating how one could analyze data from such a system, given typical components involved in linear accelerators at that time period.",
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@ -37,15 +37,15 @@
} }
} }
], ],
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"prompt_tokens_details": null "prompt_tokens_details": null
} }

10
tests/integration/recordings/responses/a59d0d7c1485.json
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@ -20,15 +20,15 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
"__data__": { "__data__": {
"model": "llama-guard3:1b", "model": "llama-guard3:1b",
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"done": true, "done": true,
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"response": "safe", "response": "safe",
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94
tests/integration/recordings/responses/a6810c23eda8.json
View file

@ -23,7 +23,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -41,7 +41,7 @@
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@ -59,7 +59,7 @@
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@ -77,7 +77,7 @@
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@ -95,7 +95,7 @@
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@ -113,7 +113,7 @@
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@ -131,7 +131,7 @@
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@ -149,7 +149,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -185,7 +185,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -203,7 +203,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -221,7 +221,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -239,7 +239,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -257,7 +257,7 @@
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@ -275,7 +275,7 @@
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@ -293,7 +293,7 @@
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@ -311,7 +311,7 @@
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@ -329,7 +329,7 @@
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@ -347,7 +347,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -365,7 +365,7 @@
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@ -383,7 +383,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -401,7 +401,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -419,7 +419,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -437,7 +437,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -455,7 +455,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -473,7 +473,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -491,7 +491,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -509,7 +509,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -527,7 +527,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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"done": false, "done": false,
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@ -545,7 +545,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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"done": false, "done": false,
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@ -563,7 +563,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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"created_at": "2025-08-04T22:55:07.680673Z", "created_at": "2025-09-03T17:36:15.205416Z",
"done": false, "done": false,
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@ -581,7 +581,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -599,7 +599,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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"created_at": "2025-08-04T22:55:07.789556Z", "created_at": "2025-09-03T17:36:15.286352Z",
"done": false, "done": false,
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@ -617,7 +617,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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"done": false, "done": false,
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@ -635,7 +635,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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"created_at": "2025-08-04T22:55:07.896607Z", "created_at": "2025-09-03T17:36:15.367575Z",
"done": false, "done": false,
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@ -653,7 +653,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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"created_at": "2025-08-04T22:55:07.953628Z", "created_at": "2025-09-03T17:36:15.408069Z",
"done": false, "done": false,
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@ -671,7 +671,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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"created_at": "2025-08-04T22:55:08.007575Z", "created_at": "2025-09-03T17:36:15.448413Z",
"done": false, "done": false,
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@ -689,7 +689,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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"done": false, "done": false,
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@ -707,7 +707,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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"created_at": "2025-08-04T22:55:08.121698Z", "created_at": "2025-09-03T17:36:15.530477Z",
"done": false, "done": false,
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@ -725,7 +725,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -743,7 +743,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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@ -761,7 +761,7 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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"done": false, "done": false,
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@ -779,15 +779,15 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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"done": true, "done": true,
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"response": "", "response": "",
"thinking": null, "thinking": null,
"context": null "context": null

10
tests/integration/recordings/responses/ae1c22f18ecc.json
View file

@ -20,15 +20,15 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
"__data__": { "__data__": {
"model": "llama3.2:3b-instruct-fp16", "model": "llama3.2:3b-instruct-fp16",
"created_at": "2025-07-31T17:59:32.661124541Z", "created_at": "2025-09-03T17:41:47.144448Z",
"done": true, "done": true,
"done_reason": "stop", "done_reason": "stop",
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"response": "I'm happy to help you with your test, but I don't see what kind of test we are testing. Could you please provide more context or clarify what kind of test you would like me to perform? Is it a programming test, a language proficiency test, or something else?", "response": "I'm happy to help you with your test, but I don't see what kind of test we are testing. Could you please provide more context or clarify what kind of test you would like me to perform? Is it a programming test, a language proficiency test, or something else?",
"thinking": null, "thinking": null,
"context": null "context": null

10
tests/integration/recordings/responses/ae6835cfe70e.json
View file

@ -20,15 +20,15 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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"done": true, "done": true,
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"response": "[]", "response": "[]",
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"context": null "context": null

10
tests/integration/recordings/responses/b14ff438ca99.json
View file

@ -20,15 +20,15 @@
"__type__": "ollama._types.GenerateResponse", "__type__": "ollama._types.GenerateResponse",
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"done": true, "done": true,
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"response": "The official currency of Japan is the Japanese yen (\u00a5). It is abbreviated as \"JPY\" and its symbol is \u00a5. The yen is divided into 100 sen, although the sen has been officially discontinued since 1967.\n\nYou can exchange your money for yen at banks, currency exchange offices, or use ATMs to withdraw cash from an ATM. Credit cards are also widely accepted in Japan, especially among major retailers and restaurants.\n\nIt's worth noting that some businesses may not accept foreign currencies other than US dollars, so it's a good idea to have some local currency on hand when traveling to Japan.", "response": "The official currency of Japan is the Japanese yen (\u00a5). It is abbreviated as \"JPY\" and its symbol is \u00a5. The yen is divided into 100 sen, although the sen has been officially discontinued since 1967.\n\nYou can exchange your money for yen at banks, currency exchange offices, or use ATMs to withdraw cash from an ATM. Credit cards are also widely accepted in Japan, especially among major retailers and restaurants.\n\nIt's worth noting that some businesses may not accept foreign currencies other than US dollars, so it's a good idea to have some local currency on hand when traveling to Japan.",
"thinking": null, "thinking": null,
"context": null "context": null

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tests/integration/recordings/responses/bd356b27a085.json
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tests/integration/recordings/responses/c2199d6064db.json
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10
tests/integration/recordings/responses/c9cba6f3ee38.json
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@ -20,15 +20,15 @@
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12
tests/integration/recordings/responses/cb3df2a1dc22.json
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@ -1,7 +1,7 @@
{ {
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"content": "I'm not capable of directly testing the functionality of external systems like Telemetry. However, I can provide you with some general information about creating telemetry data and offer suggestions on how to set up a basic telemetry system.\r\n\r\nTelemetry is the automatic measurement, reporting, and transmission of data from sensors or other devices. In the context of OpenAI, telemetry refers to the collection and analysis of data related to the company's products and services.\r\n\r\nTo create telemetry creation using the OpenAI APIs you would need to follow these steps:\r\n\r\n1. Register for an OpenAI account and get an access token.\r\n2. Choose the OpenAI API that you want to use (e.g., GPT-3).\r\n3. Create a new file or project in your preferred programming language or framework.\r\n4. Import the necessary libraries and modules to interact with the OpenAI API.\r\n5. Use the OpenAI API to create and send telemetry data.\r\n\r\nHere is an example of how you might create a basic telemetry system using Python and the OpenAI GPT-3 API:\r\n\r\n```python\r\nimport os\r\nimport json\r\n\r\n# Set your OpenAI access token\r\naccess_token = \"YOUR_OPENAI_ACCESS_TOKEN\"\r\n\r\n# Define the telemetry data\r\ntelemetry_data = {\r\n \"name\": \"example-telemetry\",\r\n \"description\": \"Example telemetry data.\r\n\r\n # Define the telemetry metrics\r\n \"metrics\": [\r\n {\"key\": \"users\", \"value\": 100},\r\n {\"key\": \" engagement\", \"value\": 20}\r\n ]\r\n}\r\n\r\n# Convert the telemetry data to JSON\r\ntelemetry_json = json.dumps(telemetry_data)\r\n\r\n# Set the OpenAI API endpoint and headers\r\napi_endpoint = \"https://api.openai.com/v1/telemetry\"\r\nheaders = {\r\n \"Authorization\": f\"Bearer {access_token}\",\r\n \"Content-Type\": \"application/json\"\r\n}\r\n\r\n# Send the telemetry data to the OpenAI API\r\nimport requests\r\n\r\nresponse = requests.post(api_endpoint, headers=headers, data=telemetry_json)\r\n\r\n# Check if the request was successful\r\nif response.status_code == 200:\r\n print(\"Telemetry data sent successfully\")\r\nelse:\r\n print(\"Error sending telemetry data\")\r\n```\n\nPlease note that this is a basic example and you should adjust it according to your needs. Also, the specific implementation details may vary depending on the OpenAI API you're using and the programming language or framework you're working with.\r\n\r\nI hope this helps! Let me know if you have any further questions.", "content": "# OpenAI Telemetry Creation Testing\n\nThis guide provides a test environment for creating and analyzing telemetries using the OpenAI API.\n\n## Prerequisites\n\n- Python 3.8 or higher\n- `requests` library\n- An OpenAI API key (create an account on the [OpenAI website](https://openai.com/))\n\n## Step 1: Install Required Libraries\n\nFirst, install the required libraries by running the following command in your terminal:\n\n```bash\npip install requests\n```\n\n## Step 2: Create a Telemetry Object\n\nCreate a new Python file (e.g., `telemetry.py`) and import the required library. Define a telemetry object with the relevant data:\n\n```python\n# Import the requests library\nimport requests\n\nclass Telemetry:\n def __init__(self, api_key):\n \"\"\"\n Initialize the telemetry object.\n\n Args:\n api_key (str): The OpenAI API key.\n \"\"\"\n\n self.api_key = api_key\n\n def create_telemetry(self, id, data):\n \"\"\"\n Create a new telemetry entry using the OpenAI API.\n\n Args:\n id (str): The ID of the model or dataset.\n data (dict): The telemetry data to be created.\n\n Returns:\n dict: The response from the OpenAI API.\n\n Raises:\n ValueError: If the request fails.\n \"\"\"\n\n url = f\"https://api.openai.com/v1/models/{id}/telemetry\"\n\n headers = {\n \"Authorization\": self.api_key,\n \"Content-Type\": \"application/json\",\n }\n\n telemetry_data = {\"events\": data}\n\n response = requests.post(url, json=telemetry_data, headers=headers)\n\n if not response.ok:\n raise ValueError(\"Failed to create telemetry\")\n\n return response.json()\n```\n\n## Step 3: Usage Example\n\nHere's an example usage of the `Telemetry` class:\n\n```python\n# Create a new Telemetry object with your OpenAI API key\ntelemetry = Telemetry(\n \"YOUR_OPENAI_API_KEY_HERE\"\n)\n\n# Define the telemetry data\ndata = {\"event\": \"example_event\"}\n\n# Create a new telemetry entry\nid = \"my_model_id\" # Replace with your model or dataset ID\n\ntry:\n result = telemetry.create_telemetry(id, data)\n print(result)\nexcept ValueError as e:\n print(e)\n```\n\nThis code creates a new `Telemetry` object, defines some sample telemetry data, and uses the `create_telemetry` method to create a new telemetry entry. The response from the OpenAI API is printed out.\n\nNote: Replace `\"YOUR_OPENAI_API_KEY_HERE\"` with your actual OpenAI API key.\n\n## Conclusion\n\nThis guide provides a basic example of how to create telemetries using the OpenAI API. You can modify the code and implement additional features as needed for your project.\n\nStay updated on our latest tutorials and guides:\n\n* [Check out our Discord channel](link): https://discord.gg/openai-exists\n\nHappy coding!",
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@ -37,15 +37,15 @@
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} }

796
tests/integration/recordings/responses/cd094caaf1c0.json
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20
tests/integration/recordings/responses/d0ac68cbde69.json
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@ -13,21 +13,21 @@
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} }
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] ]

10
tests/integration/recordings/responses/d4c86ac355fb.json
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@ -20,15 +20,15 @@
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tests/integration/recordings/responses/d86d4fc1eaca.json
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10
tests/integration/recordings/responses/dac7a32e5db9.json
View file

@ -20,15 +20,15 @@
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34
tests/integration/recordings/responses/dd226d71f844.json
View file

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12
tests/integration/recordings/responses/dd9e7d5913e9.json
View file

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26
tests/integration/recordings/responses/decfd950646c.json
View file

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768
tests/integration/recordings/responses/e0a6dce1d94b.json
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"index": 0, "index": 0,
"object": "embedding" "object": "embedding"

8
tests/integration/recordings/responses/e2c9b07709fe.json
View file

@ -1,7 +1,7 @@
{ {
"request": { "request": {
"method": "POST", "method": "POST",
"url": "http://localhost:11434/v1/v1/chat/completions", "url": "http://0.0.0.0:11434/v1/v1/chat/completions",
"headers": {}, "headers": {},
"body": { "body": {
"model": "llama3.2:3b-instruct-fp16", "model": "llama3.2:3b-instruct-fp16",
@ -22,14 +22,14 @@
"body": { "body": {
"__type__": "openai.types.chat.chat_completion.ChatCompletion", "__type__": "openai.types.chat.chat_completion.ChatCompletion",
"__data__": { "__data__": {
"id": "chatcmpl-494", "id": "chatcmpl-662",
"choices": [ "choices": [
{ {
"finish_reason": "length", "finish_reason": "length",
"index": 0, "index": 0,
"logprobs": null, "logprobs": null,
"message": { "message": {
"content": "To test the OpenAI API with a temperature of 1, you can use the following Python code:\n\n```python\nimport requests\n\ndef generate_text(model_name, prompt, temperature=1):\n # Set the API endpoint and parameters\n url = \"https://api.openai.com/v1/models/\" + model_name + \"/generate\"\n params = {\n \"prompt\": prompt,\n \"temperature\": temperature\n }\n\n # Send a GET request to the API\n response =", "content": "To test the prompt understanding of OpenAI's text generation capabilities, I'll simulate a conversation. \n\nYou mentioned testing the model with a temperature setting of 1. The temperature parameter in OpenAI's text models controls the diversity and coherence of generated text.\n\nA temperature of 1 is considered \"colder\" than usual, meaning the model will generate more coherent but potentially less diverse text compared to higher temperatures (e.g., 0.5 or 0.7).\n\nPlease provide a prompt for",
"refusal": null, "refusal": null,
"role": "assistant", "role": "assistant",
"annotations": null, "annotations": null,
@ -39,7 +39,7 @@
} }
} }
], ],
"created": 1754510067, "created": 1756921259,
"model": "llama3.2:3b-instruct-fp16", "model": "llama3.2:3b-instruct-fp16",
"object": "chat.completion", "object": "chat.completion",
"service_tier": null, "service_tier": null,

10
tests/integration/recordings/responses/e96152610712.json
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": "2025-08-01T23:12:51.421145Z", "created_at": "2025-09-03T17:37:33.16899Z",
"done": true, "done": true,
"done_reason": "stop", "done_reason": "stop",
"total_duration": 201670125, "total_duration": 300698625,
"load_duration": 70275459, "load_duration": 179823875,
"prompt_eval_count": 207, "prompt_eval_count": 207,
"prompt_eval_duration": 71000000, "prompt_eval_duration": 65083666,
"eval_count": 5, "eval_count": 5,
"eval_duration": 58000000, "eval_duration": 55216084,
"response": "unsafe\nS2", "response": "unsafe\nS2",
"thinking": null, "thinking": null,
"context": null "context": null

12
tests/integration/recordings/responses/e9c8a0e4f0e0.json
View file

@ -1,7 +1,7 @@
{ {
"request": { "request": {
"method": "POST", "method": "POST",
"url": "http://localhost:11434/v1/v1/chat/completions", "url": "http://0.0.0.0:11434/v1/v1/chat/completions",
"headers": {}, "headers": {},
"body": { "body": {
"model": "llama3.2:3b-instruct-fp16", "model": "llama3.2:3b-instruct-fp16",
@ -20,14 +20,14 @@
"body": { "body": {
"__type__": "openai.types.chat.chat_completion.ChatCompletion", "__type__": "openai.types.chat.chat_completion.ChatCompletion",
"__data__": { "__data__": {
"id": "chatcmpl-57", "id": "chatcmpl-957",
"choices": [ "choices": [
{ {
"finish_reason": "stop", "finish_reason": "stop",
"index": 0, "index": 0,
"logprobs": null, "logprobs": null,
"message": { "message": {
"content": "Humans live on Earth. It is the third planet from the Sun and is the only known planet in the universe that currently supports human life.", "content": "Humans live on Earth. It's a terrestrial planet in the Solar System, located in the outer reaches of the Sun's gravitational pull.",
"refusal": null, "refusal": null,
"role": "assistant", "role": "assistant",
"annotations": null, "annotations": null,
@ -37,15 +37,15 @@
} }
} }
], ],
"created": 1754081845, "created": 1756921355,
"model": "llama3.2:3b-instruct-fp16", "model": "llama3.2:3b-instruct-fp16",
"object": "chat.completion", "object": "chat.completion",
"service_tier": null, "service_tier": null,
"system_fingerprint": "fp_ollama", "system_fingerprint": "fp_ollama",
"usage": { "usage": {
"completion_tokens": 29, "completion_tokens": 28,
"prompt_tokens": 32, "prompt_tokens": 32,
"total_tokens": 61, "total_tokens": 60,
"completion_tokens_details": null, "completion_tokens_details": null,
"prompt_tokens_details": null "prompt_tokens_details": null
} }

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