forked from phoenix-oss/llama-stack-mirror
c543bc0745
Makes the console span processor output spans in less prominent way and highlight the logs based on severity. 
67 lines
1.9 KiB
Python
67 lines
1.9 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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# 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, runtime_checkable
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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 llama_models.llama3.api.datatypes import * # noqa: F403
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from llama_stack.apis.memory_banks import * # noqa: F403
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from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol
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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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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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class MemoryBankStore(Protocol):
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def get_memory_bank(self, bank_id: str) -> Optional[MemoryBank]: ...
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@runtime_checkable
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@trace_protocol
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class Memory(Protocol):
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memory_bank_store: MemoryBankStore
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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/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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