mirror of
https://github.com/meta-llama/llama-stack.git
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more roudning
This commit is contained in:
Ashwin Bharambe
parent
aeb91f67ac
commit
fe2d916242
25 changed files with 34897 additions and 10 deletions
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@ -4,8 +4,6 @@
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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 collections.abc import Callable
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IdFactory = Callable[[], str]
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@ -37,8 +35,8 @@ def set_id_override(override: IdOverride) -> IdOverride | None:
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return previous
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def reset_id_override(token: IdOverride | None) -> None:
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def reset_id_override(previous: IdOverride | None) -> None:
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"""Restore the previous override returned by :func:`set_id_override`."""
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global _id_override
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_id_override = token
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_id_override = previous
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@ -70,7 +70,7 @@ def _normalize_numeric_literal_strings(value: str) -> str:
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def _replace(match: re.Match[str]) -> str:
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number = float(match.group(0))
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return f"{number:.6f}"
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return f"{number:.5f}"
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return _FLOAT_IN_STRING_PATTERN.sub(_replace, value)
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@ -85,7 +85,7 @@ def _normalize_body_for_hash(value: Any) -> Any:
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if isinstance(value, tuple):
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return tuple(_normalize_body_for_hash(item) for item in value)
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if isinstance(value, float):
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return round(value, 6)
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return round(value, 5)
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if isinstance(value, str):
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return _normalize_numeric_literal_strings(value)
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return value
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@ -849,7 +849,7 @@ def api_recording(mode: str, storage_dir: str | Path | None = None) -> Generator
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# Store previous state
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prev_mode = _current_mode
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prev_storage = _current_storage
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override_token = None
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previous_override = None
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try:
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_current_mode = mode
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@ -860,7 +860,7 @@ def api_recording(mode: str, storage_dir: str | Path | None = None) -> Generator
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_current_storage = ResponseStorage(Path(storage_dir))
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_id_counters.clear()
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patch_inference_clients()
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override_token = set_id_override(_deterministic_id_override)
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previous_override = set_id_override(_deterministic_id_override)
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yield
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@ -868,8 +868,7 @@ def api_recording(mode: str, storage_dir: str | Path | None = None) -> Generator
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# Restore previous state
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if mode in ["record", "replay", "record-if-missing"]:
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unpatch_inference_clients()
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if override_token is not None:
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reset_id_override(override_token)
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reset_id_override(previous_override)
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_current_mode = prev_mode
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_current_storage = prev_storage
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@ -0,0 +1,736 @@
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{
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"test_id": "tests/integration/responses/test_tool_responses.py::test_response_non_streaming_file_search[openai_client-txt=openai/gpt-4o:emb=openai/text-embedding-3-small:dim=1536-llama_experts_pdf]",
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"request": {
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"method": "POST",
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"url": "https://api.openai.com/v1/v1/chat/completions",
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"headers": {},
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"body": {
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"model": "gpt-4o",
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"messages": [
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{
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"role": "user",
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"content": "How many experts does the Llama 4 Maverick model have?"
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},
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{
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"role": "assistant",
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"content": "",
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"tool_calls": [
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{
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"index": 0,
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"id": "call_zKJ7WgvCO3tx6yB0We1qI0d8",
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"type": "function",
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"function": {
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"name": "knowledge_search",
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"arguments": "{\"query\":\"Llama 4 Maverick model number of experts\"}"
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}
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}
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]
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},
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{
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"role": "tool",
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"tool_call_id": "call_zKJ7WgvCO3tx6yB0We1qI0d8",
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"content": [
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{
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"type": "text",
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"text": "knowledge_search tool found 2 chunks:\nBEGIN of knowledge_search tool results.\n"
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},
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{
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"type": "text",
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"text": "[1] document_id: file-728796232029, score: 1.4945131220963286, attributes: {'filename': 'llama_stack_and_models.pdf', 'document_id': 'file-728796232029', 'token_count': 98.0, 'metadata_token_count': 11.0} (cite as <|file-728796232029|>)\n, \nhardware\n \nvendors,\n \nand\n \nAI-focused\n \ncompanies)\n \nthat\n \noffer\n \ntailored\n \ninfrastructure,\n \nsoftware,\n \nand\n \nservices\n \nfor\n \ndeploying\n \nLlama\n \nmodels.\n \nLlama 4 Maverick \n Llama 4 Maverick is a Mixture-of-Experts (MoE) model with 17 billion active parameters and 128 experts. \n"
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},
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{
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"type": "text",
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"text": "[2] document_id: file-728796232029, score: 1.1415676746925796, attributes: {'filename': 'llama_stack_and_models.pdf', 'document_id': 'file-728796232029', 'token_count': 498.0, 'metadata_token_count': 11.0} (cite as <|file-728796232029|>)\nLlama Stack \nLlama Stack Overview \nLlama Stack standardizes the core building blocks that simplify AI application development. It codifies best \npractices\n \nacross\n \nthe\n \nLlama\n \necosystem.\n \nMore\n \nspecifically,\n \nit\n \nprovides\n \u25cf Unified API layer for Inference, RAG, Agents, Tools, Safety, Evals, and Telemetry. \u25cf Plugin architecture to support the rich ecosystem of different API implementations in various \nenvironments,\n \nincluding\n \nlocal\n \ndevelopment,\n \non-premises,\n \ncloud,\n \nand\n \nmobile.\n \u25cf Prepackaged verified distributions which offer a one-stop solution for developers to get started quickly \nand\n \nreliably\n \nin\n \nany\n \nenvironment.\n \u25cf Multiple developer interfaces like CLI and SDKs for Python, Typescript, iOS, and Android. \u25cf Standalone applications as examples for how to build production-grade AI applications with Llama \nStack.\n \nLlama Stack Benefits \n\u25cf Flexible Options: Developers can choose their preferred infrastructure without changing APIs and enjoy \nflexible\n \ndeployment\n \nchoices.\n \u25cf Consistent Experience: With its unified APIs, Llama Stack makes it easier to build, test, and deploy AI \napplications\n \nwith\n \nconsistent\n \napplication\n \nbehavior.\n \u25cf Robust Ecosystem: Llama Stack is already integrated with distribution partners (cloud providers, \nhardware\n \nvendors,\n \nand\n \nAI-focused\n \ncompanies)\n \nthat\n \noffer\n \ntailored\n \ninfrastructure,\n \nsoftware,\n \nand\n \nservices\n \nfor\n \ndeploying\n \nLlama\n \nmodels.\n \nLlama 4 Maverick \n Llama 4 Maverick is a Mixture-of-Experts (MoE) model with 17 billion active parameters and 128 experts. \n"
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},
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{
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"type": "text",
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"text": "END of knowledge_search tool results.\n"
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},
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{
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"type": "text",
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"text": "The above results were retrieved to help answer the user's query: \"Llama 4 Maverick model number of experts\". Use them as supporting information only in answering this query. Cite sources immediately at the end of sentences before punctuation, using `<|file-id|>` format (e.g., 'This is a fact <|file-Cn3MSNn72ENTiiq11Qda4A|>.'). Do not add extra punctuation. Use only the file IDs provided (do not invent new ones).\n"
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}
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]
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}
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],
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"stream": true,
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"tools": [
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{
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"type": "function",
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"function": {
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"name": "knowledge_search",
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"description": "Search for information in a database.",
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"parameters": {
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"type": "object",
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"properties": {
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"query": {
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"type": "string",
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"description": "The query to search for. Can be a natural language sentence or keywords."
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}
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},
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"required": [
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"query"
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]
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}
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}
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}
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]
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},
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"endpoint": "/v1/chat/completions",
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"model": "gpt-4o"
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},
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"response": {
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"body": [
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File diff suppressed because it is too large
Load diff
|
|
@ -147,6 +147,28 @@ class TestInferenceRecording:
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assert hash5 == hash6
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|
||||
"messages": [
|
||||
{
|
||||
"role": "tool",
|
||||
"content": "score: 0.6624775971970099",
|
||||
}
|
||||
]
|
||||
}
|
||||
hash7 = normalize_inference_request("POST", "http://test/v1/chat/completions", {}, body_with_close_scores)
|
||||
hash8 = normalize_inference_request(
|
||||
"POST", "http://test/v1/chat/completions", {}, body_with_close_scores_variation
|
||||
)
|
||||
assert hash7 == hash8
|
||||
|
||||
def test_response_storage(self, temp_storage_dir):
|
||||
"""Test the ResponseStorage class."""
|
||||
temp_storage_dir = temp_storage_dir / "test_response_storage"
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue