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>

llama_toolchain/post_training/api/__init__.py

@@ -4,5 +4,4 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from .datatypes import *  # noqa: F401 F403
-from .endpoints import *  # noqa: F401 F403
+from .api import *  # noqa: F401 F403

llama_toolchain/post_training/api/api.py

@@ -5,6 +5,7 @@
 # the root directory of this source tree.
 
 from datetime import datetime
+from enum import Enum
 
 from typing import Any, Dict, List, Optional, Protocol
 
@@ -13,9 +14,90 @@ from llama_models.schema_utils import json_schema_type, webmethod
 from pydantic import BaseModel, Field
 
 from llama_models.llama3.api.datatypes import *  # noqa: F403
-from llama_toolchain.dataset.api.datatypes import *  # noqa: F403
+from llama_toolchain.dataset.api import *  # noqa: F403
 from llama_toolchain.common.training_types import *  # noqa: F403
-from .datatypes import *  # noqa: F403
+
+
+class OptimizerType(Enum):
+    adam = "adam"
+    adamw = "adamw"
+    sgd = "sgd"
+
+
+@json_schema_type
+class OptimizerConfig(BaseModel):
+    optimizer_type: OptimizerType
+    lr: float
+    lr_min: float
+    weight_decay: float
+
+
+@json_schema_type
+class TrainingConfig(BaseModel):
+    n_epochs: int
+    batch_size: int
+    shuffle: bool
+    n_iters: int
+
+    enable_activation_checkpointing: bool
+    memory_efficient_fsdp_wrap: bool
+    fsdp_cpu_offload: bool
+
+
+@json_schema_type
+class FinetuningAlgorithm(Enum):
+    full = "full"
+    lora = "lora"
+    qlora = "qlora"
+    dora = "dora"
+
+
+@json_schema_type
+class LoraFinetuningConfig(BaseModel):
+    lora_attn_modules: List[str]
+    apply_lora_to_mlp: bool
+    apply_lora_to_output: bool
+    rank: int
+    alpha: int
+
+
+@json_schema_type
+class QLoraFinetuningConfig(LoraFinetuningConfig):
+    pass
+
+
+@json_schema_type
+class DoraFinetuningConfig(LoraFinetuningConfig):
+    pass
+
+
+@json_schema_type
+class PostTrainingJobLogStream(BaseModel):
+    """Stream of logs from a finetuning job."""
+
+    job_uuid: str
+    log_lines: List[str]
+
+
+@json_schema_type
+class PostTrainingJobStatus(Enum):
+    running = "running"
+    completed = "completed"
+    failed = "failed"
+    scheduled = "scheduled"
+
+
+@json_schema_type
+class RLHFAlgorithm(Enum):
+    dpo = "dpo"
+
+
+@json_schema_type
+class DPOAlignmentConfig(BaseModel):
+    reward_scale: float
+    reward_clip: float
+    epsilon: float
+    gamma: float
 
 
 @json_schema_type

llama_toolchain/post_training/api/datatypes.py

@@ -1,94 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the terms described in the LICENSE file in
-# the root directory of this source tree.
-
-from enum import Enum
-from typing import List
-
-from llama_models.schema_utils import json_schema_type
-
-from pydantic import BaseModel
-
-
-class OptimizerType(Enum):
-    adam = "adam"
-    adamw = "adamw"
-    sgd = "sgd"
-
-
-@json_schema_type
-class OptimizerConfig(BaseModel):
-    optimizer_type: OptimizerType
-    lr: float
-    lr_min: float
-    weight_decay: float
-
-
-@json_schema_type
-class TrainingConfig(BaseModel):
-    n_epochs: int
-    batch_size: int
-    shuffle: bool
-    n_iters: int
-
-    enable_activation_checkpointing: bool
-    memory_efficient_fsdp_wrap: bool
-    fsdp_cpu_offload: bool
-
-
-@json_schema_type
-class FinetuningAlgorithm(Enum):
-    full = "full"
-    lora = "lora"
-    qlora = "qlora"
-    dora = "dora"
-
-
-@json_schema_type
-class LoraFinetuningConfig(BaseModel):
-    lora_attn_modules: List[str]
-    apply_lora_to_mlp: bool
-    apply_lora_to_output: bool
-    rank: int
-    alpha: int
-
-
-@json_schema_type
-class QLoraFinetuningConfig(LoraFinetuningConfig):
-    pass
-
-
-@json_schema_type
-class DoraFinetuningConfig(LoraFinetuningConfig):
-    pass
-
-
-@json_schema_type
-class PostTrainingJobLogStream(BaseModel):
-    """Stream of logs from a finetuning job."""
-
-    job_uuid: str
-    log_lines: List[str]
-
-
-@json_schema_type
-class PostTrainingJobStatus(Enum):
-    running = "running"
-    completed = "completed"
-    failed = "failed"
-    scheduled = "scheduled"
-
-
-@json_schema_type
-class RLHFAlgorithm(Enum):
-    dpo = "dpo"
-
-
-@json_schema_type
-class DPOAlignmentConfig(BaseModel):
-    reward_scale: float
-    reward_clip: float
-    epsilon: float
-    gamma: float