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 ```
93 lines
2.5 KiB
Python
93 lines
2.5 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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from enum import Enum
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from typing import Any, Dict, List, Literal, Optional, Union
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from pydantic import BaseModel, Field
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from typing_extensions import Annotated, Protocol, runtime_checkable
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from llama_stack.apis.common.content_types import URL, InterleavedContent
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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, register_schema, webmethod
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@json_schema_type
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class RAGDocument(BaseModel):
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document_id: str
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content: InterleavedContent | URL
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mime_type: str | None = None
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metadata: Dict[str, Any] = Field(default_factory=dict)
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@json_schema_type
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class RAGQueryResult(BaseModel):
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content: Optional[InterleavedContent] = None
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@json_schema_type
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class RAGQueryGenerator(Enum):
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default = "default"
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llm = "llm"
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custom = "custom"
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@json_schema_type
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class DefaultRAGQueryGeneratorConfig(BaseModel):
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type: Literal["default"] = "default"
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separator: str = " "
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@json_schema_type
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class LLMRAGQueryGeneratorConfig(BaseModel):
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type: Literal["llm"] = "llm"
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model: str
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template: str
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RAGQueryGeneratorConfig = register_schema(
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Annotated[
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Union[
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DefaultRAGQueryGeneratorConfig,
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LLMRAGQueryGeneratorConfig,
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],
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Field(discriminator="type"),
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],
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name="RAGQueryGeneratorConfig",
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)
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@json_schema_type
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class RAGQueryConfig(BaseModel):
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# This config defines how a query is generated using the messages
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# for memory bank retrieval.
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query_generator_config: RAGQueryGeneratorConfig = Field(default=DefaultRAGQueryGeneratorConfig())
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max_tokens_in_context: int = 4096
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max_chunks: int = 5
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@runtime_checkable
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@trace_protocol
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class RAGToolRuntime(Protocol):
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@webmethod(route="/tool-runtime/rag-tool/insert", method="POST")
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async def insert(
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self,
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documents: List[RAGDocument],
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vector_db_id: str,
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chunk_size_in_tokens: int = 512,
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) -> None:
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"""Index documents so they can be used by the RAG system"""
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...
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@webmethod(route="/tool-runtime/rag-tool/query", method="POST")
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async def query(
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self,
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content: InterleavedContent,
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vector_db_ids: List[str],
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query_config: Optional[RAGQueryConfig] = None,
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) -> RAGQueryResult:
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"""Query the RAG system for context; typically invoked by the agent"""
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...
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