mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-12-22 22:39:41 +00:00
much simpler
This commit is contained in:
Ashwin Bharambe
parent
e59c13f2b8
commit
481a893eb7
19 changed files with 6365 additions and 302 deletions
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@ -4,13 +4,12 @@
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from __future__ import annotations
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from __future__ import annotations # for forward references
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import hashlib
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import json
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import os
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import sqlite3
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import uuid
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from collections.abc import Generator
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from contextlib import contextmanager
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from pathlib import Path
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@ -28,78 +27,18 @@ def normalize_request(method: str, url: str, headers: dict[str, Any], body: dict
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from urllib.parse import urlparse
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parsed = urlparse(url)
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endpoint = parsed.path
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normalized = {"method": method.upper(), "endpoint": parsed.path, "body": body}
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# Create normalized request dict
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normalized: dict[str, Any] = {
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"method": method.upper(),
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"endpoint": endpoint,
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}
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# Normalize body parameters
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if body:
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# Handle model parameter
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if "model" in body:
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normalized["model"] = body["model"]
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# Handle messages (normalize whitespace)
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if "messages" in body:
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normalized_messages = []
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for msg in body["messages"]:
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normalized_msg = dict(msg)
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if "content" in normalized_msg and isinstance(normalized_msg["content"], str):
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# Normalize whitespace
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normalized_msg["content"] = " ".join(normalized_msg["content"].split())
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normalized_messages.append(normalized_msg)
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normalized["messages"] = normalized_messages
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# Handle other parameters (sort for consistency)
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other_params = {}
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for key, value in body.items():
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if key not in ["model", "messages"]:
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if isinstance(value, float):
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# Round floats to 6 decimal places
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other_params[key] = round(value, 6)
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else:
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other_params[key] = value
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if other_params:
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# Sort dictionary keys for consistent hashing
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normalized["parameters"] = dict(sorted(other_params.items()))
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# Create hash
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# Create hash - sort_keys=True ensures deterministic ordering
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normalized_json = json.dumps(normalized, sort_keys=True)
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return hashlib.sha256(normalized_json.encode()).hexdigest()
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def get_current_test_id() -> str:
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"""Extract test ID from pytest context or fall back to environment/generated ID."""
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# Try to get from pytest context
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try:
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import _pytest.fixtures
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if hasattr(_pytest.fixtures, "_current_request") and _pytest.fixtures._current_request:
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request = _pytest.fixtures._current_request
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if hasattr(request, "node"):
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# Use the test node ID as our test identifier
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node_id: str = request.node.nodeid
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# Clean up the node ID to be filesystem-safe
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test_id = node_id.replace("/", "_").replace("::", "_").replace(".py", "")
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return test_id
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except AttributeError:
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pass
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# Fall back to environment-based or generated ID
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return os.environ.get("LLAMA_STACK_TEST_ID", f"test_{uuid.uuid4().hex[:8]}")
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def get_inference_mode() -> str:
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"""Get the inference recording mode from environment variables."""
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return os.environ.get("LLAMA_STACK_INFERENCE_MODE", "live").lower()
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def setup_inference_recording():
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"""Convenience function to set up inference recording based on environment variables."""
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mode = get_inference_mode()
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if mode not in ["live", "record", "replay"]:
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@ -113,14 +52,14 @@ def setup_inference_recording():
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return live_mode()
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test_id = get_current_test_id()
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storage_dir = os.environ.get("LLAMA_STACK_RECORDING_DIR", str(Path.home() / ".llama" / "recordings"))
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if "LLAMA_STACK_RECORDING_DIR" not in os.environ:
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raise ValueError("LLAMA_STACK_RECORDING_DIR must be set for recording or replaying")
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storage_dir = os.environ["LLAMA_STACK_RECORDING_DIR"]
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return inference_recording(mode=mode, test_id=test_id, storage_dir=storage_dir)
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return inference_recording(mode=mode, storage_dir=storage_dir)
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def _serialize_response(response: Any) -> Any:
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"""Serialize OpenAI response objects to JSON-compatible format."""
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if hasattr(response, "model_dump"):
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return response.model_dump()
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elif hasattr(response, "__dict__"):
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@ -130,19 +69,14 @@ def _serialize_response(response: Any) -> Any:
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def _deserialize_response(data: dict[str, Any]) -> dict[str, Any]:
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"""Deserialize response data back to a dict format."""
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# For simplicity, just return the dict - this preserves all the data
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# The original response structure is sufficient for replaying
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return data
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class ResponseStorage:
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"""Handles SQLite index + JSON file storage/retrieval for inference recordings."""
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def __init__(self, base_dir: Path, test_id: str):
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self.base_dir = base_dir
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self.test_id = test_id
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self.test_dir = base_dir / test_id
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def __init__(self, test_dir: Path):
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self.test_dir = test_dir
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self.responses_dir = self.test_dir / "responses"
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self.db_path = self.test_dir / "index.sqlite"
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@ -234,37 +168,55 @@ class ResponseStorage:
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return cast(dict[str, Any], data)
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async def _patched_create_method(original_method, self, **kwargs):
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"""Patched version of OpenAI client create methods."""
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async def _patched_inference_method(original_method, self, client_type, method_name=None, **kwargs):
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global _current_mode, _current_storage
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if _current_mode == "live" or _current_storage is None:
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# Normal operation
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return await original_method(self, **kwargs)
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# Get base URL from the client
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base_url = str(self._client.base_url)
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# Get base URL and endpoint based on client type
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if client_type == "openai":
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base_url = str(self._client.base_url)
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# Determine endpoint based on the method's module/class path
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method_str = str(original_method)
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if "chat.completions" in method_str:
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endpoint = "/v1/chat/completions"
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elif "embeddings" in method_str:
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endpoint = "/v1/embeddings"
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elif "completions" in method_str:
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endpoint = "/v1/completions"
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else:
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# Fallback - try to guess from the self object
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if hasattr(self, "_resource") and hasattr(self._resource, "_resource"):
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resource_name = getattr(self._resource._resource, "_resource", "unknown")
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if "chat" in str(resource_name):
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endpoint = "/v1/chat/completions"
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elif "embeddings" in str(resource_name):
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endpoint = "/v1/embeddings"
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# Determine endpoint based on the method's module/class path
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method_str = str(original_method)
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if "chat.completions" in method_str:
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endpoint = "/v1/chat/completions"
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elif "embeddings" in method_str:
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endpoint = "/v1/embeddings"
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elif "completions" in method_str:
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endpoint = "/v1/completions"
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else:
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# Fallback - try to guess from the self object
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if hasattr(self, "_resource") and hasattr(self._resource, "_resource"):
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resource_name = getattr(self._resource._resource, "_resource", "unknown")
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if "chat" in str(resource_name):
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endpoint = "/v1/chat/completions"
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elif "embeddings" in str(resource_name):
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endpoint = "/v1/embeddings"
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else:
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endpoint = "/v1/completions"
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else:
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endpoint = "/v1/completions"
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elif client_type == "ollama":
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# Get base URL from the client (Ollama client uses host attribute)
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base_url = getattr(self, "host", "http://localhost:11434")
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if not base_url.startswith("http"):
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base_url = f"http://{base_url}"
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# Determine endpoint based on method name
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if method_name == "generate":
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endpoint = "/api/generate"
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elif method_name == "chat":
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endpoint = "/api/chat"
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elif method_name == "embed":
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endpoint = "/api/embeddings"
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else:
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endpoint = "/v1/completions"
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endpoint = f"/api/{method_name}"
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else:
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raise ValueError(f"Unknown client type: {client_type}")
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url = base_url.rstrip("/") + endpoint
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@ -276,15 +228,12 @@ async def _patched_create_method(original_method, self, **kwargs):
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request_hash = normalize_request(method, url, headers, body)
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if _current_mode == "replay":
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# Try to find recorded response
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recording = _current_storage.find_recording(request_hash)
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if recording:
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# Return recorded response
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response_body = recording["response"]["body"]
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# Handle streaming responses
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if recording["response"].get("is_streaming", False):
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# For streaming, we need to return an async iterator
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async def replay_stream():
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for chunk in response_body:
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yield chunk
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@ -301,110 +250,8 @@ async def _patched_create_method(original_method, self, **kwargs):
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)
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elif _current_mode == "record":
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# Make real request and record it
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response = await original_method(self, **kwargs)
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# Store the recording
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request_data = {
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"method": method,
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"url": url,
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"headers": headers,
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"body": body,
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"endpoint": endpoint,
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"model": body.get("model", ""),
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}
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# Determine if this is a streaming request based on request parameters
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is_streaming = body.get("stream", False)
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if is_streaming:
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# For streaming responses, we need to collect all chunks immediately before yielding
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# This ensures the recording is saved even if the generator isn't fully consumed
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chunks = []
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async for chunk in response:
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chunks.append(chunk)
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# Store the recording immediately
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response_data = {"body": chunks, "is_streaming": True}
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_current_storage.store_recording(request_hash, request_data, response_data)
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# Return a generator that replays the stored chunks
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async def replay_recorded_stream():
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for chunk in chunks:
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yield chunk
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return replay_recorded_stream()
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else:
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response_data = {"body": response, "is_streaming": False}
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_current_storage.store_recording(request_hash, request_data, response_data)
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return response
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else:
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return await original_method(self, **kwargs)
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async def _patched_ollama_method(original_method, self, method_name, **kwargs):
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"""Patched version of Ollama AsyncClient methods."""
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global _current_mode, _current_storage
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if _current_mode == "live" or _current_storage is None:
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# Normal operation
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return await original_method(self, **kwargs)
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# Get base URL from the client (Ollama client uses host attribute)
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base_url = getattr(self, "host", "http://localhost:11434")
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if not base_url.startswith("http"):
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base_url = f"http://{base_url}"
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# Determine endpoint based on method name
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if method_name == "generate":
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endpoint = "/api/generate"
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elif method_name == "chat":
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endpoint = "/api/chat"
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elif method_name == "embed":
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endpoint = "/api/embeddings"
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else:
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endpoint = f"/api/{method_name}"
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url = base_url.rstrip("/") + endpoint
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# Normalize request for matching
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method = "POST"
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headers = {}
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body = kwargs
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request_hash = normalize_request(method, url, headers, body)
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if _current_mode == "replay":
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# Try to find recorded response
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recording = _current_storage.find_recording(request_hash)
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if recording:
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# Return recorded response
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response_body = recording["response"]["body"]
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# Handle streaming responses for Ollama
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if recording["response"].get("is_streaming", False):
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# For streaming, we need to return an async iterator
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async def replay_ollama_stream():
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for chunk in response_body:
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yield chunk
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return replay_ollama_stream()
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else:
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return response_body
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else:
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raise RuntimeError(
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f"No recorded response found for request hash: {request_hash}\n"
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f"Endpoint: {endpoint}\n"
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f"Model: {body.get('model', 'unknown')}\n"
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f"To record this response, run with LLAMA_STACK_INFERENCE_MODE=record"
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)
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elif _current_mode == "record":
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# Make real request and record it
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response = await original_method(self, **kwargs)
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# Store the recording
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request_data = {
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"method": method,
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"url": url,
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@ -448,45 +295,31 @@ def patch_inference_clients():
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global _original_methods
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# Import here to avoid circular imports
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from openai import AsyncOpenAI
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# Also import Ollama AsyncClient
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from ollama import AsyncClient as OllamaAsyncClient
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from openai.resources.chat.completions import AsyncCompletions as AsyncChatCompletions
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from openai.resources.completions import AsyncCompletions
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from openai.resources.embeddings import AsyncEmbeddings
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# Also import Ollama AsyncClient
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try:
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from ollama import AsyncClient as OllamaAsyncClient
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except ImportError:
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ollama_async_client = None
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else:
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ollama_async_client = OllamaAsyncClient
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# Store original methods for both OpenAI and Ollama clients
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_original_methods = {
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"chat_completions_create": AsyncChatCompletions.create,
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"completions_create": AsyncCompletions.create,
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"embeddings_create": AsyncEmbeddings.create,
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"ollama_generate": OllamaAsyncClient.generate,
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"ollama_chat": OllamaAsyncClient.chat,
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"ollama_embed": OllamaAsyncClient.embed,
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}
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# Add Ollama client methods if available
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if ollama_async_client:
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_original_methods.update(
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{
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"ollama_generate": ollama_async_client.generate,
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"ollama_chat": ollama_async_client.chat,
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"ollama_embed": ollama_async_client.embed,
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}
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)
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# Create patched methods for OpenAI client
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async def patched_chat_completions_create(self, **kwargs):
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return await _patched_create_method(_original_methods["chat_completions_create"], self, **kwargs)
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return await _patched_inference_method(_original_methods["chat_completions_create"], self, "openai", **kwargs)
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async def patched_completions_create(self, **kwargs):
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return await _patched_create_method(_original_methods["completions_create"], self, **kwargs)
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return await _patched_inference_method(_original_methods["completions_create"], self, "openai", **kwargs)
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async def patched_embeddings_create(self, **kwargs):
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return await _patched_create_method(_original_methods["embeddings_create"], self, **kwargs)
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return await _patched_inference_method(_original_methods["embeddings_create"], self, "openai", **kwargs)
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# Apply OpenAI patches
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AsyncChatCompletions.create = patched_chat_completions_create
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@ -494,40 +327,21 @@ def patch_inference_clients():
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AsyncEmbeddings.create = patched_embeddings_create
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# Create patched methods for Ollama client
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if ollama_async_client:
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async def patched_ollama_generate(self, **kwargs):
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return await _patched_inference_method(
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_original_methods["ollama_generate"], self, "ollama", "generate", **kwargs
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)
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async def patched_ollama_generate(self, **kwargs):
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return await _patched_ollama_method(_original_methods["ollama_generate"], self, "generate", **kwargs)
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async def patched_ollama_chat(self, **kwargs):
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return await _patched_inference_method(_original_methods["ollama_chat"], self, "ollama", "chat", **kwargs)
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async def patched_ollama_chat(self, **kwargs):
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return await _patched_ollama_method(_original_methods["ollama_chat"], self, "chat", **kwargs)
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async def patched_ollama_embed(self, **kwargs):
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return await _patched_inference_method(_original_methods["ollama_embed"], self, "ollama", "embed", **kwargs)
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async def patched_ollama_embed(self, **kwargs):
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return await _patched_ollama_method(_original_methods["ollama_embed"], self, "embed", **kwargs)
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# Apply Ollama patches
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ollama_async_client.generate = patched_ollama_generate
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ollama_async_client.chat = patched_ollama_chat
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ollama_async_client.embed = patched_ollama_embed
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# Also try to patch the AsyncOpenAI __init__ to trace client creation
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original_openai_init = AsyncOpenAI.__init__
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def patched_openai_init(self, *args, **kwargs):
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result = original_openai_init(self, *args, **kwargs)
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# After client is created, try to re-patch its methods
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if hasattr(self, "chat") and hasattr(self.chat, "completions"):
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original_chat_create = self.chat.completions.create
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async def instance_patched_chat_create(**kwargs):
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return await _patched_create_method(original_chat_create, self.chat.completions, **kwargs)
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self.chat.completions.create = instance_patched_chat_create
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return result
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AsyncOpenAI.__init__ = patched_openai_init
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# Apply Ollama patches
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OllamaAsyncClient.generate = patched_ollama_generate
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OllamaAsyncClient.chat = patched_ollama_chat
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OllamaAsyncClient.embed = patched_ollama_embed
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def unpatch_inference_clients():
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@ -538,43 +352,26 @@ def unpatch_inference_clients():
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return
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# Import here to avoid circular imports
|
||||
from ollama import AsyncClient as OllamaAsyncClient
|
||||
from openai.resources.chat.completions import AsyncCompletions as AsyncChatCompletions
|
||||
from openai.resources.completions import AsyncCompletions
|
||||
from openai.resources.embeddings import AsyncEmbeddings
|
||||
|
||||
# Restore OpenAI client methods
|
||||
if "chat_completions_create" in _original_methods:
|
||||
AsyncChatCompletions.create = _original_methods["chat_completions_create"]
|
||||
|
||||
if "completions_create" in _original_methods:
|
||||
AsyncCompletions.create = _original_methods["completions_create"]
|
||||
|
||||
if "embeddings_create" in _original_methods:
|
||||
AsyncEmbeddings.create = _original_methods["embeddings_create"]
|
||||
AsyncChatCompletions.create = _original_methods["chat_completions_create"]
|
||||
AsyncCompletions.create = _original_methods["completions_create"]
|
||||
AsyncEmbeddings.create = _original_methods["embeddings_create"]
|
||||
|
||||
# Restore Ollama client methods if they were patched
|
||||
try:
|
||||
from ollama import AsyncClient as OllamaAsyncClient
|
||||
|
||||
if "ollama_generate" in _original_methods:
|
||||
OllamaAsyncClient.generate = _original_methods["ollama_generate"]
|
||||
|
||||
if "ollama_chat" in _original_methods:
|
||||
OllamaAsyncClient.chat = _original_methods["ollama_chat"]
|
||||
|
||||
if "ollama_embed" in _original_methods:
|
||||
OllamaAsyncClient.embed = _original_methods["ollama_embed"]
|
||||
|
||||
except ImportError:
|
||||
pass
|
||||
OllamaAsyncClient.generate = _original_methods["ollama_generate"]
|
||||
OllamaAsyncClient.chat = _original_methods["ollama_chat"]
|
||||
OllamaAsyncClient.embed = _original_methods["ollama_embed"]
|
||||
|
||||
_original_methods.clear()
|
||||
|
||||
|
||||
@contextmanager
|
||||
def inference_recording(
|
||||
mode: str = "live", test_id: str | None = None, storage_dir: str | Path | None = None
|
||||
) -> Generator[None, None, None]:
|
||||
def inference_recording(mode: str = "live", storage_dir: str | Path | None = None) -> Generator[None, None, None]:
|
||||
"""Context manager for inference recording/replaying."""
|
||||
global _current_mode, _current_storage
|
||||
|
||||
|
|
@ -584,9 +381,6 @@ def inference_recording(
|
|||
else:
|
||||
storage_dir_path = Path(storage_dir)
|
||||
|
||||
if test_id is None:
|
||||
test_id = f"test_{uuid.uuid4().hex[:8]}"
|
||||
|
||||
# Store previous state
|
||||
prev_mode = _current_mode
|
||||
prev_storage = _current_storage
|
||||
|
|
@ -595,7 +389,7 @@ def inference_recording(
|
|||
_current_mode = mode
|
||||
|
||||
if mode in ["record", "replay"]:
|
||||
_current_storage = ResponseStorage(storage_dir_path, test_id)
|
||||
_current_storage = ResponseStorage(storage_dir_path)
|
||||
patch_inference_clients()
|
||||
|
||||
yield
|
||||
|
|
|
|||
BIN
tests/integration/inference/recordings/index.sqlite
Normal file
BIN
tests/integration/inference/recordings/index.sqlite
Normal file
Binary file not shown.
|
|
@ -0,0 +1,38 @@
|
|||
{
|
||||
"request": {
|
||||
"method": "POST",
|
||||
"url": "http://localhost:11434/api/generate",
|
||||
"headers": {},
|
||||
"body": {
|
||||
"model": "llama3.2:3b-instruct-fp16",
|
||||
"prompt": "<|begin_of_text|>Complete the sentence using one word: Roses are red, violets are ",
|
||||
"raw": true,
|
||||
"options": {
|
||||
"temperature": 0.0,
|
||||
"max_tokens": 50,
|
||||
"num_predict": 50
|
||||
},
|
||||
"stream": false
|
||||
},
|
||||
"endpoint": "/api/generate",
|
||||
"model": "llama3.2:3b-instruct-fp16"
|
||||
},
|
||||
"response": {
|
||||
"body": {
|
||||
"model": "llama3.2:3b-instruct-fp16",
|
||||
"created_at": "2025-07-29T04:16:53.555099Z",
|
||||
"done": true,
|
||||
"done_reason": "stop",
|
||||
"total_duration": 2124168875,
|
||||
"load_duration": 58506875,
|
||||
"prompt_eval_count": 18,
|
||||
"prompt_eval_duration": 70072583,
|
||||
"eval_count": 43,
|
||||
"eval_duration": 1994446917,
|
||||
"response": " _______.\n\nThe best answer is blue. The traditional nursery rhyme goes like this:\n\nRoses are red,\nViolets are blue,\nSugar is sweet,\nAnd so are you! (Or something similar.)",
|
||||
"thinking": null,
|
||||
"context": null
|
||||
},
|
||||
"is_streaming": false
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,36 @@
|
|||
{
|
||||
"request": {
|
||||
"method": "POST",
|
||||
"url": "http://localhost:11434/api/generate",
|
||||
"headers": {},
|
||||
"body": {
|
||||
"model": "llama3.2:3b-instruct-fp16",
|
||||
"raw": true,
|
||||
"prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhich planet do humans live on?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
|
||||
"options": {
|
||||
"temperature": 0.0
|
||||
},
|
||||
"stream": false
|
||||
},
|
||||
"endpoint": "/api/generate",
|
||||
"model": "llama3.2:3b-instruct-fp16"
|
||||
},
|
||||
"response": {
|
||||
"body": {
|
||||
"model": "llama3.2:3b-instruct-fp16",
|
||||
"created_at": "2025-07-29T04:16:57.535525Z",
|
||||
"done": true,
|
||||
"done_reason": "stop",
|
||||
"total_duration": 358691334,
|
||||
"load_duration": 76787334,
|
||||
"prompt_eval_count": 23,
|
||||
"prompt_eval_duration": 72235375,
|
||||
"eval_count": 6,
|
||||
"eval_duration": 208986666,
|
||||
"response": "Humans live on Earth.",
|
||||
"thinking": null,
|
||||
"context": null
|
||||
},
|
||||
"is_streaming": false
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,188 @@
|
|||
{
|
||||
"request": {
|
||||
"method": "POST",
|
||||
"url": "http://localhost:11434/api/generate",
|
||||
"headers": {},
|
||||
"body": {
|
||||
"model": "llama3.2:3b-instruct-fp16",
|
||||
"raw": true,
|
||||
"prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat's the name of the Sun in latin?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
|
||||
"options": {
|
||||
"temperature": 0.0
|
||||
},
|
||||
"stream": true
|
||||
},
|
||||
"endpoint": "/api/generate",
|
||||
"model": "llama3.2:3b-instruct-fp16"
|
||||
},
|
||||
"response": {
|
||||
"body": [
|
||||
{
|
||||
"model": "llama3.2:3b-instruct-fp16",
|
||||
"created_at": "2025-07-29T04:16:57.691771Z",
|
||||
"done": false,
|
||||
"done_reason": null,
|
||||
"total_duration": null,
|
||||
"load_duration": null,
|
||||
"prompt_eval_count": null,
|
||||
"prompt_eval_duration": null,
|
||||
"eval_count": null,
|
||||
"eval_duration": null,
|
||||
"response": "The",
|
||||
"thinking": null,
|
||||
"context": null
|
||||
},
|
||||
{
|
||||
"model": "llama3.2:3b-instruct-fp16",
|
||||
"created_at": "2025-07-29T04:16:57.732262Z",
|
||||
"done": false,
|
||||
"done_reason": null,
|
||||
"total_duration": null,
|
||||
"load_duration": null,
|
||||
"prompt_eval_count": null,
|
||||
"prompt_eval_duration": null,
|
||||
"eval_count": null,
|
||||
"eval_duration": null,
|
||||
"response": " Latin",
|
||||
"thinking": null,
|
||||
"context": null
|
||||
},
|
||||
{
|
||||
"model": "llama3.2:3b-instruct-fp16",
|
||||
"created_at": "2025-07-29T04:16:57.77294Z",
|
||||
"done": false,
|
||||
"done_reason": null,
|
||||
"total_duration": null,
|
||||
"load_duration": null,
|
||||
"prompt_eval_count": null,
|
||||
"prompt_eval_duration": null,
|
||||
"eval_count": null,
|
||||
"eval_duration": null,
|
||||
"response": " word",
|
||||
"thinking": null,
|
||||
"context": null
|
||||
},
|
||||
{
|
||||
"model": "llama3.2:3b-instruct-fp16",
|
||||
"created_at": "2025-07-29T04:16:57.814484Z",
|
||||
"done": false,
|
||||
"done_reason": null,
|
||||
"total_duration": null,
|
||||
"load_duration": null,
|
||||
"prompt_eval_count": null,
|
||||
"prompt_eval_duration": null,
|
||||
"eval_count": null,
|
||||
"eval_duration": null,
|
||||
"response": " for",
|
||||
"thinking": null,
|
||||
"context": null
|
||||
},
|
||||
{
|
||||
"model": "llama3.2:3b-instruct-fp16",
|
||||
"created_at": "2025-07-29T04:16:57.854875Z",
|
||||
"done": false,
|
||||
"done_reason": null,
|
||||
"total_duration": null,
|
||||
"load_duration": null,
|
||||
"prompt_eval_count": null,
|
||||
"prompt_eval_duration": null,
|
||||
"eval_count": null,
|
||||
"eval_duration": null,
|
||||
"response": " \"",
|
||||
"thinking": null,
|
||||
"context": null
|
||||
},
|
||||
{
|
||||
"model": "llama3.2:3b-instruct-fp16",
|
||||
"created_at": "2025-07-29T04:16:57.895957Z",
|
||||
"done": false,
|
||||
"done_reason": null,
|
||||
"total_duration": null,
|
||||
"load_duration": null,
|
||||
"prompt_eval_count": null,
|
||||
"prompt_eval_duration": null,
|
||||
"eval_count": null,
|
||||
"eval_duration": null,
|
||||
"response": "Sun",
|
||||
"thinking": null,
|
||||
"context": null
|
||||
},
|
||||
{
|
||||
"model": "llama3.2:3b-instruct-fp16",
|
||||
"created_at": "2025-07-29T04:16:57.937445Z",
|
||||
"done": false,
|
||||
"done_reason": null,
|
||||
"total_duration": null,
|
||||
"load_duration": null,
|
||||
"prompt_eval_count": null,
|
||||
"prompt_eval_duration": null,
|
||||
"eval_count": null,
|
||||
"eval_duration": null,
|
||||
"response": "\"",
|
||||
"thinking": null,
|
||||
"context": null
|
||||
},
|
||||
{
|
||||
"model": "llama3.2:3b-instruct-fp16",
|
||||
"created_at": "2025-07-29T04:16:57.978832Z",
|
||||
"done": false,
|
||||
"done_reason": null,
|
||||
"total_duration": null,
|
||||
"load_duration": null,
|
||||
"prompt_eval_count": null,
|
||||
"prompt_eval_duration": null,
|
||||
"eval_count": null,
|
||||
"eval_duration": null,
|
||||
"response": " is",
|
||||
"thinking": null,
|
||||
"context": null
|
||||
},
|
||||
{
|
||||
"model": "llama3.2:3b-instruct-fp16",
|
||||
"created_at": "2025-07-29T04:16:58.019242Z",
|
||||
"done": false,
|
||||
"done_reason": null,
|
||||
"total_duration": null,
|
||||
"load_duration": null,
|
||||
"prompt_eval_count": null,
|
||||
"prompt_eval_duration": null,
|
||||
"eval_count": null,
|
||||
"eval_duration": null,
|
||||
"response": " Sol",
|
||||
"thinking": null,
|
||||
"context": null
|
||||
},
|
||||
{
|
||||
"model": "llama3.2:3b-instruct-fp16",
|
||||
"created_at": "2025-07-29T04:16:58.059902Z",
|
||||
"done": false,
|
||||
"done_reason": null,
|
||||
"total_duration": null,
|
||||
"load_duration": null,
|
||||
"prompt_eval_count": null,
|
||||
"prompt_eval_duration": null,
|
||||
"eval_count": null,
|
||||
"eval_duration": null,
|
||||
"response": ".",
|
||||
"thinking": null,
|
||||
"context": null
|
||||
},
|
||||
{
|
||||
"model": "llama3.2:3b-instruct-fp16",
|
||||
"created_at": "2025-07-29T04:16:58.100535Z",
|
||||
"done": true,
|
||||
"done_reason": "stop",
|
||||
"total_duration": 528254250,
|
||||
"load_duration": 50177125,
|
||||
"prompt_eval_count": 26,
|
||||
"prompt_eval_duration": 68018458,
|
||||
"eval_count": 11,
|
||||
"eval_duration": 409555959,
|
||||
"response": "",
|
||||
"thinking": null,
|
||||
"context": null
|
||||
}
|
||||
],
|
||||
"is_streaming": true
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,188 @@
|
|||
{
|
||||
"request": {
|
||||
"method": "POST",
|
||||
"url": "http://localhost:11434/api/generate",
|
||||
"headers": {},
|
||||
"body": {
|
||||
"model": "llama3.2:3b-instruct-fp16",
|
||||
"raw": true,
|
||||
"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\": \"get_weather\",\n \"description\": \"Get the current weather\",\n \"parameters\": {\n \"type\": \"dict\",\n \"required\": [\"location\"],\n \"properties\": {\n \"location\": {\n \"type\": \"string\",\n \"description\": \"The city and state (both required), e.g. San Francisco, CA.\"\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.\nPretend you are a weather assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat's the weather like in San Francisco?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
|
||||
"options": {
|
||||
"temperature": 0.0
|
||||
},
|
||||
"stream": true
|
||||
},
|
||||
"endpoint": "/api/generate",
|
||||
"model": "llama3.2:3b-instruct-fp16"
|
||||
},
|
||||
"response": {
|
||||
"body": [
|
||||
{
|
||||
"model": "llama3.2:3b-instruct-fp16",
|
||||
"created_at": "2025-07-29T04:16:59.480955Z",
|
||||
"done": false,
|
||||
"done_reason": null,
|
||||
"total_duration": null,
|
||||
"load_duration": null,
|
||||
"prompt_eval_count": null,
|
||||
"prompt_eval_duration": null,
|
||||
"eval_count": null,
|
||||
"eval_duration": null,
|
||||
"response": "[",
|
||||
"thinking": null,
|
||||
"context": null
|
||||
},
|
||||
{
|
||||
"model": "llama3.2:3b-instruct-fp16",
|
||||
"created_at": "2025-07-29T04:16:59.527418Z",
|
||||
"done": false,
|
||||
"done_reason": null,
|
||||
"total_duration": null,
|
||||
"load_duration": null,
|
||||
"prompt_eval_count": null,
|
||||
"prompt_eval_duration": null,
|
||||
"eval_count": null,
|
||||
"eval_duration": null,
|
||||
"response": "get",
|
||||
"thinking": null,
|
||||
"context": null
|
||||
},
|
||||
{
|
||||
"model": "llama3.2:3b-instruct-fp16",
|
||||
"created_at": "2025-07-29T04:16:59.571522Z",
|
||||
"done": false,
|
||||
"done_reason": null,
|
||||
"total_duration": null,
|
||||
"load_duration": null,
|
||||
"prompt_eval_count": null,
|
||||
"prompt_eval_duration": null,
|
||||
"eval_count": null,
|
||||
"eval_duration": null,
|
||||
"response": "_weather",
|
||||
"thinking": null,
|
||||
"context": null
|
||||
},
|
||||
{
|
||||
"model": "llama3.2:3b-instruct-fp16",
|
||||
"created_at": "2025-07-29T04:16:59.615027Z",
|
||||
"done": false,
|
||||
"done_reason": null,
|
||||
"total_duration": null,
|
||||
"load_duration": null,
|
||||
"prompt_eval_count": null,
|
||||
"prompt_eval_duration": null,
|
||||
"eval_count": null,
|
||||
"eval_duration": null,
|
||||
"response": "(location",
|
||||
"thinking": null,
|
||||
"context": null
|
||||
},
|
||||
{
|
||||
"model": "llama3.2:3b-instruct-fp16",
|
||||
"created_at": "2025-07-29T04:16:59.660598Z",
|
||||
"done": false,
|
||||
"done_reason": null,
|
||||
"total_duration": null,
|
||||
"load_duration": null,
|
||||
"prompt_eval_count": null,
|
||||
"prompt_eval_duration": null,
|
||||
"eval_count": null,
|
||||
"eval_duration": null,
|
||||
"response": "=\"",
|
||||
"thinking": null,
|
||||
"context": null
|
||||
},
|
||||
{
|
||||
"model": "llama3.2:3b-instruct-fp16",
|
||||
"created_at": "2025-07-29T04:16:59.705052Z",
|
||||
"done": false,
|
||||
"done_reason": null,
|
||||
"total_duration": null,
|
||||
"load_duration": null,
|
||||
"prompt_eval_count": null,
|
||||
"prompt_eval_duration": null,
|
||||
"eval_count": null,
|
||||
"eval_duration": null,
|
||||
"response": "San",
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"response": "",
|
||||
"thinking": null,
|
||||
"context": null
|
||||
}
|
||||
],
|
||||
"is_streaming": true
|
||||
}
|
||||
}
|
||||
3458
tests/integration/inference/recordings/responses/bbd0637dce16.json
Normal file
3458
tests/integration/inference/recordings/responses/bbd0637dce16.json
Normal file
File diff suppressed because it is too large
Load diff
|
|
@ -0,0 +1,53 @@
|
|||
{
|
||||
"request": {
|
||||
"method": "POST",
|
||||
"url": "http://localhost:11434/api/generate",
|
||||
"headers": {},
|
||||
"body": {
|
||||
"model": "llama3.2:3b-instruct-fp16",
|
||||
"raw": true,
|
||||
"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\": \"get_object_namespace_list\",\n \"description\": \"Get the list of objects in a namespace\",\n \"parameters\": {\n \"type\": \"dict\",\n \"required\": [\"kind\", \"namespace\"],\n \"properties\": {\n \"kind\": {\n \"type\": \"string\",\n \"description\": \"the type of object\"\n },\n \"namespace\": {\n \"type\": \"string\",\n \"description\": \"the name of the namespace\"\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\nWhat pods are in the namespace openshift-lightspeed?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n[get_object_namespace_list(kind=\"pod\", namespace=\"openshift-lightspeed\")]<|eot_id|><|start_header_id|>ipython<|end_header_id|>\n\nthe objects are pod1, pod2, pod3<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
|
||||
"options": {
|
||||
"temperature": 0.0
|
||||
},
|
||||
"stream": true
|
||||
},
|
||||
"endpoint": "/api/generate",
|
||||
"model": "llama3.2:3b-instruct-fp16"
|
||||
},
|
||||
"response": {
|
||||
"body": [
|
||||
{
|
||||
"model": "llama3.2:3b-instruct-fp16",
|
||||
"created_at": "2025-07-29T04:17:14.122273Z",
|
||||
"done": false,
|
||||
"done_reason": null,
|
||||
"total_duration": null,
|
||||
"load_duration": null,
|
||||
"prompt_eval_count": null,
|
||||
"prompt_eval_duration": null,
|
||||
"eval_count": null,
|
||||
"eval_duration": null,
|
||||
"response": "[]",
|
||||
"thinking": null,
|
||||
"context": null
|
||||
},
|
||||
{
|
||||
"model": "llama3.2:3b-instruct-fp16",
|
||||
"created_at": "2025-07-29T04:17:14.165968Z",
|
||||
"done": true,
|
||||
"done_reason": "stop",
|
||||
"total_duration": 663520959,
|
||||
"load_duration": 67474917,
|
||||
"prompt_eval_count": 386,
|
||||
"prompt_eval_duration": 545132042,
|
||||
"eval_count": 2,
|
||||
"eval_duration": 50234083,
|
||||
"response": "",
|
||||
"thinking": null,
|
||||
"context": null
|
||||
}
|
||||
],
|
||||
"is_streaming": true
|
||||
}
|
||||
}
|
||||
|
|
@ -105,8 +105,8 @@ class TestInferenceRecording:
|
|||
assert hash1 != hash3
|
||||
|
||||
def test_request_normalization_edge_cases(self):
|
||||
"""Test request normalization handles edge cases correctly."""
|
||||
# Test whitespace normalization
|
||||
"""Test request normalization is precise about request content."""
|
||||
# Test that different whitespace produces different hashes (no normalization)
|
||||
hash1 = normalize_request(
|
||||
"POST",
|
||||
"http://test/v1/chat/completions",
|
||||
|
|
@ -116,16 +116,17 @@ class TestInferenceRecording:
|
|||
hash2 = normalize_request(
|
||||
"POST", "http://test/v1/chat/completions", {}, {"messages": [{"role": "user", "content": "Hello world"}]}
|
||||
)
|
||||
assert hash1 == hash2
|
||||
assert hash1 != hash2 # Different whitespace should produce different hashes
|
||||
|
||||
# Test float precision normalization
|
||||
# Test that different float precision produces different hashes (no rounding)
|
||||
hash3 = normalize_request("POST", "http://test/v1/chat/completions", {}, {"temperature": 0.7000001})
|
||||
hash4 = normalize_request("POST", "http://test/v1/chat/completions", {}, {"temperature": 0.7})
|
||||
assert hash3 == hash4
|
||||
assert hash3 != hash4 # Different precision should produce different hashes
|
||||
|
||||
def test_response_storage(self, temp_storage_dir):
|
||||
"""Test the ResponseStorage class."""
|
||||
storage = ResponseStorage(temp_storage_dir, "test_storage")
|
||||
temp_storage_dir = temp_storage_dir / "test_response_storage"
|
||||
storage = ResponseStorage(temp_storage_dir)
|
||||
|
||||
# Test directory creation
|
||||
assert storage.test_dir.exists()
|
||||
|
|
@ -161,13 +162,13 @@ class TestInferenceRecording:
|
|||
|
||||
async def test_recording_mode(self, temp_storage_dir, mock_openai_response):
|
||||
"""Test that recording mode captures and stores responses."""
|
||||
test_id = "test_recording_mode"
|
||||
|
||||
async def mock_create(*args, **kwargs):
|
||||
return mock_openai_response
|
||||
|
||||
temp_storage_dir = temp_storage_dir / "test_recording_mode"
|
||||
with patch("openai.resources.chat.completions.AsyncCompletions.create", side_effect=mock_create):
|
||||
with inference_recording(mode="record", test_id=test_id, storage_dir=str(temp_storage_dir)):
|
||||
with inference_recording(mode="record", storage_dir=str(temp_storage_dir)):
|
||||
client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test")
|
||||
|
||||
response = await client.chat.completions.create(
|
||||
|
|
@ -181,7 +182,7 @@ class TestInferenceRecording:
|
|||
assert response.choices[0].message.content == "Hello! I'm doing well, thank you for asking."
|
||||
|
||||
# Verify recording was stored
|
||||
storage = ResponseStorage(temp_storage_dir, test_id)
|
||||
storage = ResponseStorage(temp_storage_dir)
|
||||
with sqlite3.connect(storage.db_path) as conn:
|
||||
recordings = conn.execute("SELECT COUNT(*) FROM recordings").fetchone()[0]
|
||||
|
||||
|
|
@ -189,14 +190,14 @@ class TestInferenceRecording:
|
|||
|
||||
async def test_replay_mode(self, temp_storage_dir, mock_openai_response):
|
||||
"""Test that replay mode returns stored responses without making real calls."""
|
||||
test_id = "test_replay_mode"
|
||||
|
||||
async def mock_create(*args, **kwargs):
|
||||
return mock_openai_response
|
||||
|
||||
temp_storage_dir = temp_storage_dir / "test_replay_mode"
|
||||
# First, record a response
|
||||
with patch("openai.resources.chat.completions.AsyncCompletions.create", side_effect=mock_create):
|
||||
with inference_recording(mode="record", test_id=test_id, storage_dir=str(temp_storage_dir)):
|
||||
with inference_recording(mode="record", storage_dir=str(temp_storage_dir)):
|
||||
client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test")
|
||||
|
||||
await client.chat.completions.create(
|
||||
|
|
@ -208,7 +209,7 @@ class TestInferenceRecording:
|
|||
|
||||
# Now test replay mode - should not call the original method
|
||||
with patch("openai.resources.chat.completions.AsyncCompletions.create") as mock_create_patch:
|
||||
with inference_recording(mode="replay", test_id=test_id, storage_dir=str(temp_storage_dir)):
|
||||
with inference_recording(mode="replay", storage_dir=str(temp_storage_dir)):
|
||||
client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test")
|
||||
|
||||
response = await client.chat.completions.create(
|
||||
|
|
@ -226,10 +227,9 @@ class TestInferenceRecording:
|
|||
|
||||
async def test_replay_missing_recording(self, temp_storage_dir):
|
||||
"""Test that replay mode fails when no recording is found."""
|
||||
test_id = "test_missing_recording"
|
||||
|
||||
temp_storage_dir = temp_storage_dir / "test_replay_missing_recording"
|
||||
with patch("openai.resources.chat.completions.AsyncCompletions.create"):
|
||||
with inference_recording(mode="replay", test_id=test_id, storage_dir=str(temp_storage_dir)):
|
||||
with inference_recording(mode="replay", storage_dir=str(temp_storage_dir)):
|
||||
client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test")
|
||||
|
||||
with pytest.raises(RuntimeError, match="No recorded response found"):
|
||||
|
|
@ -239,14 +239,14 @@ class TestInferenceRecording:
|
|||
|
||||
async def test_embeddings_recording(self, temp_storage_dir, mock_embeddings_response):
|
||||
"""Test recording and replay of embeddings calls."""
|
||||
test_id = "test_embeddings"
|
||||
|
||||
async def mock_create(*args, **kwargs):
|
||||
return mock_embeddings_response
|
||||
|
||||
temp_storage_dir = temp_storage_dir / "test_embeddings_recording"
|
||||
# Record
|
||||
with patch("openai.resources.embeddings.AsyncEmbeddings.create", side_effect=mock_create):
|
||||
with inference_recording(mode="record", test_id=test_id, storage_dir=str(temp_storage_dir)):
|
||||
with inference_recording(mode="record", storage_dir=str(temp_storage_dir)):
|
||||
client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test")
|
||||
|
||||
response = await client.embeddings.create(
|
||||
|
|
@ -257,7 +257,7 @@ class TestInferenceRecording:
|
|||
|
||||
# Replay
|
||||
with patch("openai.resources.embeddings.AsyncEmbeddings.create") as mock_create_patch:
|
||||
with inference_recording(mode="replay", test_id=test_id, storage_dir=str(temp_storage_dir)):
|
||||
with inference_recording(mode="replay", storage_dir=str(temp_storage_dir)):
|
||||
client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test")
|
||||
|
||||
response = await client.embeddings.create(
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue