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https://github.com/meta-llama/llama-stack.git
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41bd350539
# What does this PR do? Don't set type variables from register_schema(). `mypy` is not happy about it since type variables are calculated at runtime and hence the typing hints are not available during static analysis. Good news is there is no good reason to set the variables from the return type. Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com> Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
101 lines
2.9 KiB
Python
101 lines
2.9 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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"""
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A document to be used for document ingestion in the RAG Tool.
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:param document_id: The unique identifier for the document.
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:param content: The content of the document.
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:param mime_type: The MIME type of the document.
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:param metadata: Additional metadata for the document.
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"""
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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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metadata: Dict[str, Any] = Field(default_factory=dict)
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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 = 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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register_schema(RAGQueryGeneratorConfig, name="RAGQueryGeneratorConfig")
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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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