forked from phoenix-oss/llama-stack-mirror
ec4fc800cc
This is yet another of those large PRs (hopefully we will have less and less of them as things mature fast). This one introduces substantial improvements and some simplifications to the stack. Most important bits: * Agents reference implementation now has support for session / turn persistence. The default implementation uses sqlite but there's also support for using Redis. * We have re-architected the structure of the Stack APIs to allow for more flexible routing. The motivating use cases are: - routing model A to ollama and model B to a remote provider like Together - routing shield A to local impl while shield B to a remote provider like Bedrock - routing a vector memory bank to Weaviate while routing a keyvalue memory bank to Redis * Support for provider specific parameters to be passed from the clients. A client can pass data using `x_llamastack_provider_data` parameter which can be type-checked and provided to the Adapter implementations.
156 lines
4.2 KiB
Python
156 lines
4.2 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 List, Optional, Protocol
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from llama_models.schema_utils import json_schema_type, webmethod
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from pydantic import BaseModel, Field
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from typing_extensions import Annotated
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from llama_models.llama3.api.datatypes import * # noqa: F403
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@json_schema_type
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class MemoryBankDocument(BaseModel):
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document_id: str
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content: InterleavedTextMedia | 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 MemoryBankType(Enum):
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vector = "vector"
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keyvalue = "keyvalue"
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keyword = "keyword"
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graph = "graph"
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class VectorMemoryBankConfig(BaseModel):
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type: Literal[MemoryBankType.vector.value] = MemoryBankType.vector.value
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embedding_model: str
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chunk_size_in_tokens: int
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overlap_size_in_tokens: Optional[int] = None
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class KeyValueMemoryBankConfig(BaseModel):
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type: Literal[MemoryBankType.keyvalue.value] = MemoryBankType.keyvalue.value
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class KeywordMemoryBankConfig(BaseModel):
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type: Literal[MemoryBankType.keyword.value] = MemoryBankType.keyword.value
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class GraphMemoryBankConfig(BaseModel):
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type: Literal[MemoryBankType.graph.value] = MemoryBankType.graph.value
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MemoryBankConfig = Annotated[
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Union[
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VectorMemoryBankConfig,
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KeyValueMemoryBankConfig,
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KeywordMemoryBankConfig,
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GraphMemoryBankConfig,
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],
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Field(discriminator="type"),
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]
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class Chunk(BaseModel):
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content: InterleavedTextMedia
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token_count: int
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document_id: str
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@json_schema_type
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class QueryDocumentsResponse(BaseModel):
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chunks: List[Chunk]
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scores: List[float]
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@json_schema_type
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class QueryAPI(Protocol):
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@webmethod(route="/query_documents")
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def query_documents(
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self,
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query: InterleavedTextMedia,
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params: Optional[Dict[str, Any]] = None,
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) -> QueryDocumentsResponse: ...
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@json_schema_type
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class MemoryBank(BaseModel):
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bank_id: str
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name: str
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config: MemoryBankConfig
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# if there's a pre-existing (reachable-from-distribution) store which supports QueryAPI
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url: Optional[URL] = None
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class Memory(Protocol):
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@webmethod(route="/memory/create")
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async def create_memory_bank(
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self,
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name: str,
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config: MemoryBankConfig,
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url: Optional[URL] = None,
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) -> MemoryBank: ...
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@webmethod(route="/memory/list", method="GET")
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async def list_memory_banks(self) -> List[MemoryBank]: ...
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@webmethod(route="/memory/get", method="GET")
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async def get_memory_bank(self, bank_id: str) -> Optional[MemoryBank]: ...
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@webmethod(route="/memory/drop", method="DELETE")
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async def drop_memory_bank(
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self,
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bank_id: str,
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) -> str: ...
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# this will just block now until documents 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="/memory/insert")
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async def insert_documents(
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self,
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bank_id: str,
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documents: List[MemoryBankDocument],
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ttl_seconds: Optional[int] = None,
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) -> None: ...
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@webmethod(route="/memory/update")
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async def update_documents(
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self,
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bank_id: str,
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documents: List[MemoryBankDocument],
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) -> None: ...
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@webmethod(route="/memory/query")
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async def query_documents(
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self,
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bank_id: str,
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query: InterleavedTextMedia,
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params: Optional[Dict[str, Any]] = None,
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) -> QueryDocumentsResponse: ...
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@webmethod(route="/memory/documents/get", method="GET")
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async def get_documents(
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self,
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bank_id: str,
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document_ids: List[str],
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) -> List[MemoryBankDocument]: ...
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@webmethod(route="/memory/documents/delete", method="DELETE")
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async def delete_documents(
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self,
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bank_id: str,
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document_ids: List[str],
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) -> None: ...
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