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122 lines
4 KiB
Python
122 lines
4 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 Annotated, Any, Literal
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from pydantic import BaseModel, Field, field_validator
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from typing_extensions import 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: InterleavedContent | 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 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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DefaultRAGQueryGeneratorConfig | LLMRAGQueryGeneratorConfig,
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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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"""
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Configuration for the RAG query generation.
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:param query_generator_config: Configuration for the query generator.
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:param max_tokens_in_context: Maximum number of tokens in the context.
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:param max_chunks: Maximum number of chunks to retrieve.
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:param chunk_template: Template for formatting each retrieved chunk in the context.
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Available placeholders: {index} (1-based chunk ordinal), {chunk.content} (chunk content string), {metadata} (chunk metadata dict).
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Default: "Result {index}\\nContent: {chunk.content}\\nMetadata: {metadata}\\n"
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:param mode: Search mode for retrieval—either "vector", "keyword", or "hybrid". Default "vector".
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"""
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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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chunk_template: str = "Result {index}\nContent: {chunk.content}\nMetadata: {metadata}\n"
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mode: str | None = None
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@field_validator("chunk_template")
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def validate_chunk_template(cls, v: str) -> str:
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if "{chunk.content}" not in v:
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raise ValueError("chunk_template must contain {chunk.content}")
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if "{index}" not in v:
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raise ValueError("chunk_template must contain {index}")
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if len(v) == 0:
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raise ValueError("chunk_template must not be empty")
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return v
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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: RAGQueryConfig | None = 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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