forked from phoenix-oss/llama-stack-mirror
114 lines
3.6 KiB
Python
114 lines
3.6 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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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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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described found in the
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# LICENSE file in the root directory of this source tree.
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from datetime import datetime
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from pathlib import Path
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import fire
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import yaml
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from llama_models import schema_utils
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# We do some monkey-patching to ensure our definitions only use the minimal
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# (json_schema_type, webmethod) definitions from the llama_models package. For
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# generation though, we need the full definitions and implementations from the
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# (json-strong-typing) package.
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from strong_typing.schema import json_schema_type
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from .pyopenapi.options import Options
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from .pyopenapi.specification import Info, Server
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from .pyopenapi.utility import Specification
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schema_utils.json_schema_type = json_schema_type
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from llama_models.llama3.api.datatypes import * # noqa: F403
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from llama_stack.apis.agents import * # noqa: F403
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from llama_stack.apis.dataset import * # noqa: F403
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from llama_stack.apis.evals import * # noqa: F403
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from llama_stack.apis.inference import * # noqa: F403
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from llama_stack.apis.batch_inference import * # noqa: F403
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from llama_stack.apis.memory import * # noqa: F403
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from llama_stack.apis.telemetry import * # noqa: F403
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from llama_stack.apis.post_training import * # noqa: F403
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from llama_stack.apis.reward_scoring import * # noqa: F403
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from llama_stack.apis.synthetic_data_generation import * # noqa: F403
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from llama_stack.apis.safety import * # noqa: F403
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class LlamaStack(
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Inference,
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BatchInference,
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Agents,
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RewardScoring,
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Safety,
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SyntheticDataGeneration,
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Datasets,
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Telemetry,
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PostTraining,
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Memory,
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Evaluations,
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):
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pass
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# TODO: this should be fixed in the generator itself so it reads appropriate annotations
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STREAMING_ENDPOINTS = [
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"/agentic_system/turn/create",
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"/inference/chat_completion",
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]
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def patch_sse_stream_responses(spec: Specification):
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for path, path_item in spec.document.paths.items():
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if path in STREAMING_ENDPOINTS:
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content = path_item.post.responses["200"].content.pop("application/json")
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path_item.post.responses["200"].content["text/event-stream"] = content
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def main(output_dir: str):
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output_dir = Path(output_dir)
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if not output_dir.exists():
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raise ValueError(f"Directory {output_dir} does not exist")
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now = str(datetime.now())
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print(
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"Converting the spec to YAML (openapi.yaml) and HTML (openapi.html) at " + now
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)
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print("")
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spec = Specification(
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LlamaStack,
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Options(
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server=Server(url="http://any-hosted-llama-stack.com"),
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info=Info(
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title="[DRAFT] Llama Stack Specification",
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version="0.0.1",
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description="""This is the specification of the llama stack that provides
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a set of endpoints and their corresponding interfaces that are tailored to
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best leverage Llama Models. The specification is still in draft and subject to change.
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Generated at """
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+ now,
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),
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),
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)
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patch_sse_stream_responses(spec)
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with open(output_dir / "llama-stack-spec.yaml", "w", encoding="utf-8") as fp:
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yaml.dump(spec.get_json(), fp, allow_unicode=True)
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with open(output_dir / "llama-stack-spec.html", "w") as fp:
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spec.write_html(fp, pretty_print=True)
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if __name__ == "__main__":
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fire.Fire(main)
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