forked from phoenix-oss/llama-stack-mirror
314ee09ae3
llama-models should have extremely minimal cruft. Its sole purpose should be didactic -- show the simplest implementation of the llama models and document the prompt formats, etc. This PR is the complement to https://github.com/meta-llama/llama-models/pull/279 ## Test Plan Ensure all `llama` CLI `model` sub-commands work: ```bash llama model list llama model download --model-id ... llama model prompt-format -m ... ``` Ran tests: ```bash cd tests/client-sdk LLAMA_STACK_CONFIG=fireworks pytest -s -v inference/ LLAMA_STACK_CONFIG=fireworks pytest -s -v vector_io/ LLAMA_STACK_CONFIG=fireworks pytest -s -v agents/ ``` Create a fresh venv `uv venv && source .venv/bin/activate` and run `llama stack build --template fireworks --image-type venv` followed by `llama stack run together --image-type venv` <-- the server runs Also checked that the OpenAPI generator can run and there is no change in the generated files as a result. ```bash cd docs/openapi_generator sh run_openapi_generator.sh ```
57 lines
1.8 KiB
Python
57 lines
1.8 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 in the LICENSE file in
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# the root directory of this source tree.
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from typing import Any, Dict, List, Optional, Protocol, runtime_checkable
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from pydantic import BaseModel, Field
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from llama_stack.apis.inference import InterleavedContent
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from llama_stack.apis.vector_dbs import VectorDB
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from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol
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from llama_stack.schema_utils import json_schema_type, webmethod
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class Chunk(BaseModel):
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content: InterleavedContent
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metadata: Dict[str, Any] = Field(default_factory=dict)
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@json_schema_type
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class QueryChunksResponse(BaseModel):
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chunks: List[Chunk]
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scores: List[float]
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class VectorDBStore(Protocol):
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def get_vector_db(self, vector_db_id: str) -> Optional[VectorDB]: ...
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@runtime_checkable
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@trace_protocol
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class VectorIO(Protocol):
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vector_db_store: VectorDBStore
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# this will just block now until chunks are inserted, but it should
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# probably return a Job instance which can be polled for completion
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@webmethod(route="/vector-io/insert", method="POST")
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async def insert_chunks(
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self,
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vector_db_id: str,
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chunks: List[Chunk],
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ttl_seconds: Optional[int] = None,
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) -> None: ...
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@webmethod(route="/vector-io/query", method="POST")
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async def query_chunks(
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self,
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vector_db_id: str,
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query: InterleavedContent,
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params: Optional[Dict[str, Any]] = None,
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) -> QueryChunksResponse: ...
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