API Updates (#73)

* API Keys passed from Client instead of distro configuration

* delete distribution registry

* Rename the "package" word away

* Introduce a "Router" layer for providers

Some providers need to be factorized and considered as thin routing
layers on top of other providers. Consider two examples:

- The inference API should be a routing layer over inference providers,
  routed using the "model" key
- The memory banks API is another instance where various memory bank
  types will be provided by independent providers (e.g., a vector store
  is served by Chroma while a keyvalue memory can be served by Redis or
  PGVector)

This commit introduces a generalized routing layer for this purpose.

* update `apis_to_serve`

* llama_toolchain -> llama_stack

* 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

* Moved some stuff out of common/; re-generated OpenAPI spec

* llama-toolchain -> llama-stack (hyphens)

* add control plane API

* add redis adapter + sqlite provider

* move core -> distribution

* Some more toolchain -> stack changes

* small naming shenanigans

* Removing custom tool and agent utilities and moving them client side

* Move control plane to distribution server for now

* Remove control plane from API list

* no codeshield dependency randomly plzzzzz

* Add "fire" as a dependency

* add back event loggers

* stack configure fixes

* use brave instead of bing in the example client

* add init file so it gets packaged

* add init files so it gets packaged

* Update MANIFEST

* bug fix

---------

Co-authored-by: Hardik Shah <hjshah@fb.com>
Co-authored-by: Xi Yan <xiyan@meta.com>
Co-authored-by: Ashwin Bharambe <ashwin@meta.com>

llama_stack/providers/utils/__init__.py

@@ -0,0 +1,5 @@
+# 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.

llama_stack/providers/utils/inference/__init__.py

@@ -0,0 +1,5 @@
+# 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.

llama_stack/providers/utils/inference/prepare_messages.py

@@ -0,0 +1,84 @@
+# 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 llama_models.llama3.api.datatypes import *  # noqa: F403
+from llama_stack.apis.inference import *  # noqa: F403
+from llama_models.llama3.prompt_templates import (
+    BuiltinToolGenerator,
+    FunctionTagCustomToolGenerator,
+    JsonCustomToolGenerator,
+    SystemDefaultGenerator,
+)
+
+
+def prepare_messages(request: ChatCompletionRequest) -> List[Message]:
+
+    assert request.tool_choice == ToolChoice.auto, "Only `ToolChoice.auto` supported"
+
+    existing_messages = request.messages
+    existing_system_message = None
+    if existing_messages[0].role == Role.system.value:
+        existing_system_message = existing_messages.pop(0)
+
+    assert (
+        existing_messages[0].role != Role.system.value
+    ), "Should only have 1 system message"
+
+    messages = []
+
+    default_gen = SystemDefaultGenerator()
+    default_template = default_gen.gen()
+
+    sys_content = ""
+
+    tool_template = None
+    if request.tools:
+        tool_gen = BuiltinToolGenerator()
+        tool_template = tool_gen.gen(request.tools)
+
+        sys_content += tool_template.render()
+        sys_content += "\n"
+
+    sys_content += default_template.render()
+
+    if existing_system_message:
+        # TODO: this fn is needed in many places
+        def _process(c):
+            if isinstance(c, str):
+                return c
+            else:
+                return "<media>"
+
+        sys_content += "\n"
+
+        if isinstance(existing_system_message.content, str):
+            sys_content += _process(existing_system_message.content)
+        elif isinstance(existing_system_message.content, list):
+            sys_content += "\n".join(
+                [_process(c) for c in existing_system_message.content]
+            )
+
+    messages.append(SystemMessage(content=sys_content))
+
+    has_custom_tools = any(isinstance(dfn.tool_name, str) for dfn in request.tools)
+    if has_custom_tools:
+        if request.tool_prompt_format == ToolPromptFormat.json:
+            tool_gen = JsonCustomToolGenerator()
+        elif request.tool_prompt_format == ToolPromptFormat.function_tag:
+            tool_gen = FunctionTagCustomToolGenerator()
+        else:
+            raise ValueError(
+                f"Non supported ToolPromptFormat {request.tool_prompt_format}"
+            )
+
+        custom_tools = [t for t in request.tools if isinstance(t.tool_name, str)]
+        custom_template = tool_gen.gen(custom_tools)
+        messages.append(UserMessage(content=custom_template.render()))
+
+    # Add back existing messages from the request
+    messages += existing_messages
+
+    return messages

llama_stack/providers/utils/memory/__init__.py

@@ -0,0 +1,5 @@
+# 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.

llama_stack/providers/utils/memory/file_utils.py

@@ -0,0 +1,26 @@
+# 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.
+
+import base64
+import mimetypes
+import os
+
+from llama_models.llama3.api.datatypes import URL
+
+
+def data_url_from_file(file_path: str) -> URL:
+    if not os.path.exists(file_path):
+        raise FileNotFoundError(f"File not found: {file_path}")
+
+    with open(file_path, "rb") as file:
+        file_content = file.read()
+
+    base64_content = base64.b64encode(file_content).decode("utf-8")
+    mime_type, _ = mimetypes.guess_type(file_path)
+
+    data_url = f"data:{mime_type};base64,{base64_content}"
+
+    return URL(uri=data_url)

llama_stack/providers/utils/memory/vector_store.py

@@ -0,0 +1,180 @@
+# 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.
+import base64
+import io
+import re
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional
+from urllib.parse import unquote
+
+import chardet
+import httpx
+import numpy as np
+from numpy.typing import NDArray
+from pypdf import PdfReader
+
+from llama_models.llama3.api.datatypes import *  # noqa: F403
+from llama_models.llama3.api.tokenizer import Tokenizer
+
+from llama_stack.apis.memory import *  # noqa: F403
+
+ALL_MINILM_L6_V2_DIMENSION = 384
+
+EMBEDDING_MODEL = None
+
+
+def get_embedding_model() -> "SentenceTransformer":
+    global EMBEDDING_MODEL
+
+    if EMBEDDING_MODEL is None:
+        print("Loading sentence transformer")
+
+        from sentence_transformers import SentenceTransformer
+
+        EMBEDDING_MODEL = SentenceTransformer("all-MiniLM-L6-v2")
+
+    return EMBEDDING_MODEL
+
+
+def parse_data_url(data_url: str):
+    data_url_pattern = re.compile(
+        r"^"
+        r"data:"
+        r"(?P<mimetype>[\w/\-+.]+)"
+        r"(?P<charset>;charset=(?P<encoding>[\w-]+))?"
+        r"(?P<base64>;base64)?"
+        r",(?P<data>.*)"
+        r"$",
+        re.DOTALL,
+    )
+    match = data_url_pattern.match(data_url)
+    if not match:
+        raise ValueError("Invalid Data URL format")
+
+    parts = match.groupdict()
+    parts["is_base64"] = bool(parts["base64"])
+    return parts
+
+
+def content_from_data(data_url: str) -> str:
+    parts = parse_data_url(data_url)
+    data = parts["data"]
+
+    if parts["is_base64"]:
+        data = base64.b64decode(data)
+    else:
+        data = unquote(data)
+        encoding = parts["encoding"] or "utf-8"
+        data = data.encode(encoding)
+
+    encoding = parts["encoding"]
+    if not encoding:
+        detected = chardet.detect(data)
+        encoding = detected["encoding"]
+
+    mime_type = parts["mimetype"]
+    mime_category = mime_type.split("/")[0]
+    if mime_category == "text":
+        # For text-based files (including CSV, MD)
+        return data.decode(encoding)
+
+    elif mime_type == "application/pdf":
+        # For PDF and DOC/DOCX files, we can't reliably convert to string)
+        pdf_bytes = io.BytesIO(data)
+        pdf_reader = PdfReader(pdf_bytes)
+        return "\n".join([page.extract_text() for page in pdf_reader.pages])
+
+    else:
+        cprint("Could not extract content from data_url properly.", color="red")
+        return ""
+
+
+async def content_from_doc(doc: MemoryBankDocument) -> str:
+    if isinstance(doc.content, URL):
+        if doc.content.uri.startswith("data:"):
+            return content_from_data(doc.content.uri)
+        else:
+            async with httpx.AsyncClient() as client:
+                r = await client.get(doc.content.uri)
+                return r.text
+
+    return interleaved_text_media_as_str(doc.content)
+
+
+def make_overlapped_chunks(
+    document_id: str, text: str, window_len: int, overlap_len: int
+) -> List[Chunk]:
+    tokenizer = Tokenizer.get_instance()
+    tokens = tokenizer.encode(text, bos=False, eos=False)
+
+    chunks = []
+    for i in range(0, len(tokens), window_len - overlap_len):
+        toks = tokens[i : i + window_len]
+        chunk = tokenizer.decode(toks)
+        chunks.append(
+            Chunk(content=chunk, token_count=len(toks), document_id=document_id)
+        )
+
+    return chunks
+
+
+class EmbeddingIndex(ABC):
+    @abstractmethod
+    async def add_chunks(self, chunks: List[Chunk], embeddings: NDArray):
+        raise NotImplementedError()
+
+    @abstractmethod
+    async def query(self, embedding: NDArray, k: int) -> QueryDocumentsResponse:
+        raise NotImplementedError()
+
+
+@dataclass
+class BankWithIndex:
+    bank: MemoryBank
+    index: EmbeddingIndex
+
+    async def insert_documents(
+        self,
+        documents: List[MemoryBankDocument],
+    ) -> None:
+        model = get_embedding_model()
+        for doc in documents:
+            content = await content_from_doc(doc)
+            chunks = make_overlapped_chunks(
+                doc.document_id,
+                content,
+                self.bank.config.chunk_size_in_tokens,
+                self.bank.config.overlap_size_in_tokens
+                or (self.bank.config.chunk_size_in_tokens // 4),
+            )
+            embeddings = model.encode([x.content for x in chunks]).astype(np.float32)
+
+            await self.index.add_chunks(chunks, embeddings)
+
+    async def query_documents(
+        self,
+        query: InterleavedTextMedia,
+        params: Optional[Dict[str, Any]] = None,
+    ) -> QueryDocumentsResponse:
+        if params is None:
+            params = {}
+        k = params.get("max_chunks", 3)
+
+        def _process(c) -> str:
+            if isinstance(c, str):
+                return c
+            else:
+                return "<media>"
+
+        if isinstance(query, list):
+            query_str = " ".join([_process(c) for c in query])
+        else:
+            query_str = _process(query)
+
+        model = get_embedding_model()
+        query_vector = model.encode([query_str])[0].astype(np.float32)
+        return await self.index.query(query_vector, k)

llama_stack/providers/utils/telemetry/__init__.py

@@ -0,0 +1,5 @@
+# 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.

llama_stack/providers/utils/telemetry/tracing.py

@@ -0,0 +1,236 @@
+# 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.
+
+import asyncio
+import base64
+import logging
+import queue
+import threading
+import uuid
+from datetime import datetime
+from functools import wraps
+from typing import Any, Dict, List
+
+
+from llama_stack.apis.telemetry import *  # noqa: F403
+
+
+def generate_short_uuid(len: int = 12):
+    full_uuid = uuid.uuid4()
+    uuid_bytes = full_uuid.bytes
+    encoded = base64.urlsafe_b64encode(uuid_bytes)
+    return encoded.rstrip(b"=").decode("ascii")[:len]
+
+
+CURRENT_TRACE_CONTEXT = None
+BACKGROUND_LOGGER = None
+
+
+class BackgroundLogger:
+    def __init__(self, api: Telemetry, capacity: int = 1000):
+        self.api = api
+        self.log_queue = queue.Queue(maxsize=capacity)
+        self.worker_thread = threading.Thread(target=self._process_logs, daemon=True)
+        self.worker_thread.start()
+
+    def log_event(self, event):
+        try:
+            self.log_queue.put_nowait(event)
+        except queue.Full:
+            print("Log queue is full, dropping event")
+
+    def _process_logs(self):
+        while True:
+            try:
+                event = self.log_queue.get()
+                # figure out how to use a thread's native loop
+                asyncio.run(self.api.log_event(event))
+            except Exception:
+                import traceback
+
+                traceback.print_exc()
+                print("Error processing log event")
+            finally:
+                self.log_queue.task_done()
+
+    def __del__(self):
+        self.log_queue.join()
+
+
+class TraceContext:
+    spans: List[Span] = []
+
+    def __init__(self, logger: BackgroundLogger, trace_id: str):
+        self.logger = logger
+        self.trace_id = trace_id
+
+    def push_span(self, name: str, attributes: Dict[str, Any] = None):
+        current_span = self.get_current_span()
+        span = Span(
+            span_id=generate_short_uuid(),
+            trace_id=self.trace_id,
+            name=name,
+            start_time=datetime.now(),
+            parent_span_id=current_span.span_id if current_span else None,
+            attributes=attributes,
+        )
+
+        self.logger.log_event(
+            StructuredLogEvent(
+                trace_id=span.trace_id,
+                span_id=span.span_id,
+                timestamp=span.start_time,
+                attributes=span.attributes,
+                payload=SpanStartPayload(
+                    name=span.name,
+                    parent_span_id=span.parent_span_id,
+                ),
+            )
+        )
+
+        self.spans.append(span)
+
+    def pop_span(self, status: SpanStatus = SpanStatus.OK):
+        span = self.spans.pop()
+        if span is not None:
+            self.logger.log_event(
+                StructuredLogEvent(
+                    trace_id=span.trace_id,
+                    span_id=span.span_id,
+                    timestamp=span.start_time,
+                    attributes=span.attributes,
+                    payload=SpanEndPayload(
+                        status=status,
+                    ),
+                )
+            )
+
+    def get_current_span(self):
+        return self.spans[-1] if self.spans else None
+
+
+def setup_logger(api: Telemetry, level: int = logging.INFO):
+    global BACKGROUND_LOGGER
+
+    BACKGROUND_LOGGER = BackgroundLogger(api)
+    logger = logging.getLogger()
+    logger.setLevel(level)
+    logger.addHandler(TelemetryHandler())
+
+
+async def start_trace(name: str, attributes: Dict[str, Any] = None):
+    global CURRENT_TRACE_CONTEXT, BACKGROUND_LOGGER
+
+    if BACKGROUND_LOGGER is None:
+        print("No Telemetry implementation set. Skipping trace initialization...")
+        return
+
+    trace_id = generate_short_uuid()
+    context = TraceContext(BACKGROUND_LOGGER, trace_id)
+    context.push_span(name, {"__root__": True, **(attributes or {})})
+
+    CURRENT_TRACE_CONTEXT = context
+
+
+async def end_trace(status: SpanStatus = SpanStatus.OK):
+    global CURRENT_TRACE_CONTEXT
+
+    context = CURRENT_TRACE_CONTEXT
+    if context is None:
+        return
+
+    context.pop_span(status)
+    CURRENT_TRACE_CONTEXT = None
+
+
+def severity(levelname: str) -> LogSeverity:
+    if levelname == "DEBUG":
+        return LogSeverity.DEBUG
+    elif levelname == "INFO":
+        return LogSeverity.INFO
+    elif levelname == "WARNING":
+        return LogSeverity.WARNING
+    elif levelname == "ERROR":
+        return LogSeverity.ERROR
+    elif levelname == "CRITICAL":
+        return LogSeverity.CRITICAL
+    else:
+        raise ValueError(f"Unknown log level: {levelname}")
+
+
+# TODO: ideally, the actual emitting should be done inside a separate daemon
+# process completely isolated from the server
+class TelemetryHandler(logging.Handler):
+    def emit(self, record: logging.LogRecord):
+        # horrendous hack to avoid logging from asyncio and getting into an infinite loop
+        if record.module in ("asyncio", "selector_events"):
+            return
+
+        global CURRENT_TRACE_CONTEXT, BACKGROUND_LOGGER
+
+        if BACKGROUND_LOGGER is None:
+            raise RuntimeError("Telemetry API not initialized")
+
+        context = CURRENT_TRACE_CONTEXT
+        if context is None:
+            return
+
+        span = context.get_current_span()
+        if span is None:
+            return
+
+        BACKGROUND_LOGGER.log_event(
+            UnstructuredLogEvent(
+                trace_id=span.trace_id,
+                span_id=span.span_id,
+                timestamp=datetime.now(),
+                message=self.format(record),
+                severity=severity(record.levelname),
+            )
+        )
+
+    def close(self):
+        pass
+
+
+def span(name: str, attributes: Dict[str, Any] = None):
+    def decorator(func):
+        @wraps(func)
+        def sync_wrapper(*args, **kwargs):
+            try:
+                global CURRENT_TRACE_CONTEXT
+
+                context = CURRENT_TRACE_CONTEXT
+                if context:
+                    context.push_span(name, attributes)
+                result = func(*args, **kwargs)
+            finally:
+                context.pop_span()
+            return result
+
+        @wraps(func)
+        async def async_wrapper(*args, **kwargs):
+            try:
+                global CURRENT_TRACE_CONTEXT
+
+                context = CURRENT_TRACE_CONTEXT
+                if context:
+                    context.push_span(name, attributes)
+                result = await func(*args, **kwargs)
+            finally:
+                context.pop_span()
+            return result
+
+        @wraps(func)
+        def wrapper(*args, **kwargs):
+            if asyncio.iscoroutinefunction(func):
+                return async_wrapper(*args, **kwargs)
+            else:
+                return sync_wrapper(*args, **kwargs)
+
+        return wrapper
+
+    return decorator