forked from phoenix-oss/llama-stack-mirror
API Updates: fleshing out RAG APIs, introduce "llama stack" CLI command (#51)
* add tools to chat completion request
* use templates for generating system prompts
* Moved ToolPromptFormat and jinja templates to llama_models.llama3.api
* <WIP> memory changes
- inlined AgenticSystemInstanceConfig so API feels more ergonomic
- renamed it to AgentConfig, AgentInstance -> Agent
- added a MemoryConfig and `memory` parameter
- added `attachments` to input and `output_attachments` to the response
- some naming changes
* InterleavedTextAttachment -> InterleavedTextMedia, introduce memory tool
* flesh out memory banks API
* agentic loop has a RAG implementation
* faiss provider implementation
* memory client works
* re-work tool definitions, fix FastAPI issues, fix tool regressions
* fix agentic_system utils
* basic RAG seems to work
* small bug fixes for inline attachments
* Refactor custom tool execution utilities
* Bug fix, show memory retrieval steps in EventLogger
* No need for api_key for Remote providers
* add special unicode character ↵ to showcase newlines in model prompt templates
* remove api.endpoints imports
* combine datatypes.py and endpoints.py into api.py
* Attachment / add TTL api
* split batch_inference from inference
* minor import fixes
* use a single impl for ChatFormat.decode_assistant_mesage
* use interleaved_text_media_as_str() utilityt
* Fix api.datatypes imports
* Add blobfile for tiktoken
* Add ToolPromptFormat to ChatFormat.encode_message so that tools are encoded properly
* templates take optional --format={json,function_tag}
* Rag Updates
* Add `api build` subcommand -- WIP
* fix
* build + run image seems to work
* <WIP> adapters
* bunch more work to make adapters work
* api build works for conda now
* ollama remote adapter works
* Several smaller fixes to make adapters work
Also, reorganized the pattern of __init__ inside providers so
configuration can stay lightweight
* llama distribution -> llama stack + containers (WIP)
* All the new CLI for api + stack work
* Make Fireworks and Together into the Adapter format
* Some quick fixes to the CLI behavior to make it consistent
* Updated README phew
* Update cli_reference.md
* llama_toolchain/distribution -> llama_toolchain/core
* Add termcolor
* update paths
* Add a log just for consistency
* chmod +x scripts
* Fix api dependencies not getting added to configuration
* missing import lol
* Delete utils.py; move to agentic system
* Support downloading of URLs for attachments for code interpreter
* Simplify and generalize `llama api build` yay
* Update `llama stack configure` to be very simple also
* Fix stack start
* Allow building an "adhoc" distribution
* Remote `llama api []` subcommands
* Fixes to llama stack commands and update docs
* Update documentation again and add error messages to llama stack start
* llama stack start -> llama stack run
* Change name of build for less confusion
* Add pyopenapi fork to the repository, update RFC assets
* Remove conflicting annotation
* Added a "--raw" option for model template printing
---------
Co-authored-by: Hardik Shah <hjshah@fb.com>
Co-authored-by: Ashwin Bharambe <ashwin@meta.com>
Co-authored-by: Dalton Flanagan <6599399+dltn@users.noreply.github.com>
This commit is contained in:
Ashwin Bharambe
GitHub
parent
35093c0b6f
commit
7bc7785b0d
141 changed files with 8252 additions and 4032 deletions
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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 datetime import datetime
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from typing import Any, Dict, 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 llama_models.llama3.api.datatypes import * # noqa: F403
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from llama_toolchain.dataset.api.datatypes import * # noqa: F403
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from llama_toolchain.common.training_types import * # noqa: F403
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from .datatypes import * # noqa: F403
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@json_schema_type
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class PostTrainingSFTRequest(BaseModel):
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"""Request to finetune a model."""
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job_uuid: str
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model: str
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dataset: TrainEvalDataset
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validation_dataset: TrainEvalDataset
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algorithm: FinetuningAlgorithm
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algorithm_config: Union[
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LoraFinetuningConfig, QLoraFinetuningConfig, DoraFinetuningConfig
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]
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optimizer_config: OptimizerConfig
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training_config: TrainingConfig
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# TODO: define these
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hyperparam_search_config: Dict[str, Any]
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logger_config: Dict[str, Any]
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@json_schema_type
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class PostTrainingRLHFRequest(BaseModel):
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"""Request to finetune a model."""
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job_uuid: str
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finetuned_model: URL
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dataset: TrainEvalDataset
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validation_dataset: TrainEvalDataset
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algorithm: RLHFAlgorithm
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algorithm_config: Union[DPOAlignmentConfig]
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optimizer_config: OptimizerConfig
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training_config: TrainingConfig
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# TODO: define these
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hyperparam_search_config: Dict[str, Any]
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logger_config: Dict[str, Any]
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class PostTrainingJob(BaseModel):
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job_uuid: str
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@json_schema_type
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class PostTrainingJobStatusResponse(BaseModel):
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"""Status of a finetuning job."""
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job_uuid: str
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status: PostTrainingJobStatus
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scheduled_at: Optional[datetime] = None
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started_at: Optional[datetime] = None
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completed_at: Optional[datetime] = None
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resources_allocated: Optional[Dict[str, Any]] = None
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checkpoints: List[Checkpoint] = Field(default_factory=list)
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@json_schema_type
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class PostTrainingJobArtifactsResponse(BaseModel):
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"""Artifacts of a finetuning job."""
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job_uuid: str
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checkpoints: List[Checkpoint] = Field(default_factory=list)
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# TODO(ashwin): metrics, evals
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class PostTraining(Protocol):
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@webmethod(route="/post_training/supervised_fine_tune")
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def supervised_fine_tune(
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self,
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request: PostTrainingSFTRequest,
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) -> PostTrainingJob: ...
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@webmethod(route="/post_training/preference_optimize")
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def preference_optimize(
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self,
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request: PostTrainingRLHFRequest,
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) -> PostTrainingJob: ...
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@webmethod(route="/post_training/jobs")
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def get_training_jobs(self) -> List[PostTrainingJob]: ...
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# sends SSE stream of logs
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@webmethod(route="/post_training/job/logs")
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def get_training_job_logstream(self, job_uuid: str) -> PostTrainingJobLogStream: ...
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@webmethod(route="/post_training/job/status")
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def get_training_job_status(
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self, job_uuid: str
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) -> PostTrainingJobStatusResponse: ...
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@webmethod(route="/post_training/job/cancel")
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def cancel_training_job(self, job_uuid: str) -> None: ...
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@webmethod(route="/post_training/job/artifacts")
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def get_training_job_artifacts(
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self, job_uuid: str
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) -> PostTrainingJobArtifactsResponse: ...
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