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Codemod from llama_toolchain -> llama_stack
- added providers/registry - cleaned up api/ subdirectories and moved impls away - restructured api/api.py - from llama_stack.apis.<api> import foo should work now - update imports to do llama_stack.apis.<api> - update many other imports - added __init__, fixed some registry imports - updated registry imports - create_agentic_system -> create_agent - AgenticSystem -> Agent
This commit is contained in:
Ashwin Bharambe
parent
2cf731faea
commit
76b354a081
128 changed files with 381 additions and 376 deletions
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@ -1,196 +0,0 @@
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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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import asyncio
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import json
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import os
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from pathlib import Path
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from typing import Any, Dict, List, Optional
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import fire
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import httpx
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from termcolor import cprint
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from llama_stack.core.datatypes import RemoteProviderConfig
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from .api import * # noqa: F403
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from .common.file_utils import data_url_from_file
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async def get_client_impl(config: RemoteProviderConfig, _deps: Any) -> Memory:
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return MemoryClient(config.url)
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class MemoryClient(Memory):
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def __init__(self, base_url: str):
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self.base_url = base_url
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async def initialize(self) -> None:
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pass
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async def shutdown(self) -> None:
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pass
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async def get_memory_bank(self, bank_id: str) -> Optional[MemoryBank]:
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async with httpx.AsyncClient() as client:
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r = await client.get(
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f"{self.base_url}/memory_banks/get",
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params={
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"bank_id": bank_id,
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},
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headers={"Content-Type": "application/json"},
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timeout=20,
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)
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r.raise_for_status()
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d = r.json()
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if not d:
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return None
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return MemoryBank(**d)
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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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async with httpx.AsyncClient() as client:
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r = await client.post(
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f"{self.base_url}/memory_banks/create",
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json={
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"name": name,
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"config": config.dict(),
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"url": url,
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},
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headers={"Content-Type": "application/json"},
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timeout=20,
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)
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r.raise_for_status()
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d = r.json()
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if not d:
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return None
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return MemoryBank(**d)
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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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) -> None:
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async with httpx.AsyncClient() as client:
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r = await client.post(
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f"{self.base_url}/memory_bank/insert",
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json={
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"bank_id": bank_id,
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"documents": [d.dict() for d in documents],
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},
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headers={"Content-Type": "application/json"},
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timeout=20,
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)
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r.raise_for_status()
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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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async with httpx.AsyncClient() as client:
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r = await client.post(
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f"{self.base_url}/memory_bank/query",
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json={
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"bank_id": bank_id,
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"query": query,
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"params": params,
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},
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headers={"Content-Type": "application/json"},
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timeout=20,
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)
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r.raise_for_status()
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return QueryDocumentsResponse(**r.json())
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async def run_main(host: str, port: int, stream: bool):
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client = MemoryClient(f"http://{host}:{port}")
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# create a memory bank
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bank = await client.create_memory_bank(
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name="test_bank",
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config=VectorMemoryBankConfig(
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bank_id="test_bank",
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embedding_model="dragon-roberta-query-2",
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chunk_size_in_tokens=512,
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overlap_size_in_tokens=64,
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),
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)
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cprint(json.dumps(bank.dict(), indent=4), "green")
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retrieved_bank = await client.get_memory_bank(bank.bank_id)
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assert retrieved_bank is not None
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assert retrieved_bank.config.embedding_model == "dragon-roberta-query-2"
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urls = [
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"memory_optimizations.rst",
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"chat.rst",
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"llama3.rst",
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"datasets.rst",
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"qat_finetune.rst",
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"lora_finetune.rst",
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]
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documents = [
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MemoryBankDocument(
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document_id=f"num-{i}",
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content=URL(
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uri=f"https://raw.githubusercontent.com/pytorch/torchtune/main/docs/source/tutorials/{url}"
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),
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mime_type="text/plain",
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)
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for i, url in enumerate(urls)
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]
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this_dir = os.path.dirname(__file__)
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files = [Path(this_dir).parent.parent / "CONTRIBUTING.md"]
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documents += [
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MemoryBankDocument(
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document_id=f"num-{i}",
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content=data_url_from_file(path),
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)
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for i, path in enumerate(files)
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]
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# insert some documents
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await client.insert_documents(
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bank_id=bank.bank_id,
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documents=documents,
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)
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# query the documents
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response = await client.query_documents(
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bank_id=bank.bank_id,
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query=[
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"How do I use Lora?",
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],
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)
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for chunk, score in zip(response.chunks, response.scores):
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print(f"Score: {score}")
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print(f"Chunk:\n========\n{chunk}\n========\n")
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response = await client.query_documents(
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bank_id=bank.bank_id,
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query=[
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"Tell me more about llama3 and torchtune",
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],
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)
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for chunk, score in zip(response.chunks, response.scores):
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print(f"Score: {score}")
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print(f"Chunk:\n========\n{chunk}\n========\n")
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def main(host: str, port: int, stream: bool = True):
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asyncio.run(run_main(host, port, stream))
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if __name__ == "__main__":
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fire.Fire(main)
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