llama-stack/llama_toolchain/memory/api/api.py
Ashwin Bharambe 7bc7785b0d
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>
2024-09-03 22:39:39 -07:00

157 lines
4.2 KiB
Python

# 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.
# 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 typing import List, Optional, Protocol
from llama_models.schema_utils import json_schema_type, webmethod
from pydantic import BaseModel, Field
from typing_extensions import Annotated
from llama_models.llama3.api.datatypes import * # noqa: F403
@json_schema_type
class MemoryBankDocument(BaseModel):
document_id: str
content: InterleavedTextMedia | URL
mime_type: str
metadata: Dict[str, Any] = Field(default_factory=dict)
@json_schema_type
class MemoryBankType(Enum):
vector = "vector"
keyvalue = "keyvalue"
keyword = "keyword"
graph = "graph"
class VectorMemoryBankConfig(BaseModel):
type: Literal[MemoryBankType.vector.value] = MemoryBankType.vector.value
embedding_model: str
chunk_size_in_tokens: int
overlap_size_in_tokens: Optional[int] = None
class KeyValueMemoryBankConfig(BaseModel):
type: Literal[MemoryBankType.keyvalue.value] = MemoryBankType.keyvalue.value
class KeywordMemoryBankConfig(BaseModel):
type: Literal[MemoryBankType.keyword.value] = MemoryBankType.keyword.value
class GraphMemoryBankConfig(BaseModel):
type: Literal[MemoryBankType.graph.value] = MemoryBankType.graph.value
MemoryBankConfig = Annotated[
Union[
VectorMemoryBankConfig,
KeyValueMemoryBankConfig,
KeywordMemoryBankConfig,
GraphMemoryBankConfig,
],
Field(discriminator="type"),
]
class Chunk(BaseModel):
content: InterleavedTextMedia
token_count: int
document_id: str
@json_schema_type
class QueryDocumentsResponse(BaseModel):
chunks: List[Chunk]
scores: List[float]
@json_schema_type
class QueryAPI(Protocol):
@webmethod(route="/query_documents")
def query_documents(
self,
query: InterleavedTextMedia,
params: Optional[Dict[str, Any]] = None,
) -> QueryDocumentsResponse: ...
@json_schema_type
class MemoryBank(BaseModel):
bank_id: str
name: str
config: MemoryBankConfig
# if there's a pre-existing (reachable-from-distribution) store which supports QueryAPI
url: Optional[URL] = None
class Memory(Protocol):
@webmethod(route="/memory_banks/create")
async def create_memory_bank(
self,
name: str,
config: MemoryBankConfig,
url: Optional[URL] = None,
) -> MemoryBank: ...
@webmethod(route="/memory_banks/list", method="GET")
async def list_memory_banks(self) -> List[MemoryBank]: ...
@webmethod(route="/memory_banks/get", method="GET")
async def get_memory_bank(self, bank_id: str) -> Optional[MemoryBank]: ...
@webmethod(route="/memory_banks/drop", method="DELETE")
async def drop_memory_bank(
self,
bank_id: str,
) -> str: ...
# this will just block now until documents are inserted, but it should
# probably return a Job instance which can be polled for completion
@webmethod(route="/memory_bank/insert")
async def insert_documents(
self,
bank_id: str,
documents: List[MemoryBankDocument],
ttl_seconds: Optional[int] = None,
) -> None: ...
@webmethod(route="/memory_bank/update")
async def update_documents(
self,
bank_id: str,
documents: List[MemoryBankDocument],
) -> None: ...
@webmethod(route="/memory_bank/query")
async def query_documents(
self,
bank_id: str,
query: InterleavedTextMedia,
params: Optional[Dict[str, Any]] = None,
) -> QueryDocumentsResponse: ...
@webmethod(route="/memory_bank/documents/get", method="GET")
async def get_documents(
self,
bank_id: str,
document_ids: List[str],
) -> List[MemoryBankDocument]: ...
@webmethod(route="/memory_bank/documents/delete", method="DELETE")
async def delete_documents(
self,
bank_id: str,
document_ids: List[str],
) -> None: ...