forked from phoenix-oss/llama-stack-mirror
* 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>
52 lines
1.5 KiB
Python
52 lines
1.5 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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from enum import Enum
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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
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from llama_models.llama3.api.datatypes import * # noqa: F403
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from llama_toolchain.reward_scoring.api import * # noqa: F403
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class FilteringFunction(Enum):
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"""The type of filtering function."""
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none = "none"
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random = "random"
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top_k = "top_k"
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top_p = "top_p"
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top_k_top_p = "top_k_top_p"
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sigmoid = "sigmoid"
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@json_schema_type
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class SyntheticDataGenerationRequest(BaseModel):
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"""Request to generate synthetic data. A small batch of prompts and a filtering function"""
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dialogs: List[Message]
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filtering_function: FilteringFunction = FilteringFunction.none
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model: Optional[str] = None
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@json_schema_type
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class SyntheticDataGenerationResponse(BaseModel):
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"""Response from the synthetic data generation. Batch of (prompt, response, score) tuples that pass the threshold."""
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synthetic_data: List[ScoredDialogGenerations]
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statistics: Optional[Dict[str, Any]] = None
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class SyntheticDataGeneration(Protocol):
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@webmethod(route="/synthetic_data_generation/generate")
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def synthetic_data_generate(
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self,
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request: SyntheticDataGenerationRequest,
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) -> Union[SyntheticDataGenerationResponse]: ...
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