mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-06-27 18:50:41 +00:00
9e6561a1ec
# What does this PR do? The goal of this PR is code base modernization. Schema reflection code needed a minor adjustment to handle UnionTypes and collections.abc.AsyncIterator. (Both are preferred for latest Python releases.) Note to reviewers: almost all changes here are automatically generated by pyupgrade. Some additional unused imports were cleaned up. The only change worth of note can be found under `docs/openapi_generator` and `llama_stack/strong_typing/schema.py` where reflection code was updated to deal with "newer" types. Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
98 lines
2.8 KiB
Python
98 lines
2.8 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
|
# All rights reserved.
|
|
#
|
|
# This source code is licensed under the terms described in the LICENSE file in
|
|
# the root directory of this source tree.
|
|
|
|
from enum import Enum
|
|
from typing import Annotated, Any, Literal
|
|
|
|
from pydantic import BaseModel, Field
|
|
from typing_extensions import Protocol, runtime_checkable
|
|
|
|
from llama_stack.apis.common.content_types import URL, InterleavedContent
|
|
from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol
|
|
from llama_stack.schema_utils import json_schema_type, register_schema, webmethod
|
|
|
|
|
|
@json_schema_type
|
|
class RAGDocument(BaseModel):
|
|
"""
|
|
A document to be used for document ingestion in the RAG Tool.
|
|
|
|
:param document_id: The unique identifier for the document.
|
|
:param content: The content of the document.
|
|
:param mime_type: The MIME type of the document.
|
|
:param metadata: Additional metadata for the document.
|
|
"""
|
|
|
|
document_id: str
|
|
content: InterleavedContent | URL
|
|
mime_type: str | None = None
|
|
metadata: dict[str, Any] = Field(default_factory=dict)
|
|
|
|
|
|
@json_schema_type
|
|
class RAGQueryResult(BaseModel):
|
|
content: InterleavedContent | None = None
|
|
metadata: dict[str, Any] = Field(default_factory=dict)
|
|
|
|
|
|
@json_schema_type
|
|
class RAGQueryGenerator(Enum):
|
|
default = "default"
|
|
llm = "llm"
|
|
custom = "custom"
|
|
|
|
|
|
@json_schema_type
|
|
class DefaultRAGQueryGeneratorConfig(BaseModel):
|
|
type: Literal["default"] = "default"
|
|
separator: str = " "
|
|
|
|
|
|
@json_schema_type
|
|
class LLMRAGQueryGeneratorConfig(BaseModel):
|
|
type: Literal["llm"] = "llm"
|
|
model: str
|
|
template: str
|
|
|
|
|
|
RAGQueryGeneratorConfig = Annotated[
|
|
DefaultRAGQueryGeneratorConfig | LLMRAGQueryGeneratorConfig,
|
|
Field(discriminator="type"),
|
|
]
|
|
register_schema(RAGQueryGeneratorConfig, name="RAGQueryGeneratorConfig")
|
|
|
|
|
|
@json_schema_type
|
|
class RAGQueryConfig(BaseModel):
|
|
# This config defines how a query is generated using the messages
|
|
# for memory bank retrieval.
|
|
query_generator_config: RAGQueryGeneratorConfig = Field(default=DefaultRAGQueryGeneratorConfig())
|
|
max_tokens_in_context: int = 4096
|
|
max_chunks: int = 5
|
|
|
|
|
|
@runtime_checkable
|
|
@trace_protocol
|
|
class RAGToolRuntime(Protocol):
|
|
@webmethod(route="/tool-runtime/rag-tool/insert", method="POST")
|
|
async def insert(
|
|
self,
|
|
documents: list[RAGDocument],
|
|
vector_db_id: str,
|
|
chunk_size_in_tokens: int = 512,
|
|
) -> None:
|
|
"""Index documents so they can be used by the RAG system"""
|
|
...
|
|
|
|
@webmethod(route="/tool-runtime/rag-tool/query", method="POST")
|
|
async def query(
|
|
self,
|
|
content: InterleavedContent,
|
|
vector_db_ids: list[str],
|
|
query_config: RAGQueryConfig | None = None,
|
|
) -> RAGQueryResult:
|
|
"""Query the RAG system for context; typically invoked by the agent"""
|
|
...
|