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Merge remote-tracking branch 'origin/main' into dependabot/uv/openai-2.5.0

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This commit is contained in:
Ashwin Bharambe 2025-10-22 12:17:03 -07:00
commit 13450c1a68
317 changed files with 86802 additions and 18957 deletions
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6
llama_stack/core/access_control/datatypes.py
View file

@ -41,7 +41,7 @@ class AccessRule(BaseModel):
A rule defines a list of action either to permit or to forbid. It may specify a
principal or a resource that must match for the rule to take effect. The resource
to match should be specified in the form of a type qualified identifier, e.g.
model::my-model or vector_db::some-db, or a wildcard for all resources of a type,
model::my-model or vector_store::some-db, or a wildcard for all resources of a type,
e.g. model::*. If the principal or resource are not specified, they will match all
requests.
@ -79,9 +79,9 @@ class AccessRule(BaseModel):
description: any user has read access to any resource created by a member of their team
- forbid:
actions: [create, read, delete]
resource: vector_db::*
resource: vector_store::*
unless: user with admin in roles
description: only user with admin role can use vector_db resources
description: only user with admin role can use vector_store resources
"""

410
llama_stack/core/build_container.sh
View file

@ -1,410 +0,0 @@
#!/usr/bin/env bash
# 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_DIR=${LLAMA_STACK_DIR:-}
LLAMA_STACK_CLIENT_DIR=${LLAMA_STACK_CLIENT_DIR:-}
TEST_PYPI_VERSION=${TEST_PYPI_VERSION:-}
PYPI_VERSION=${PYPI_VERSION:-}
BUILD_PLATFORM=${BUILD_PLATFORM:-}
# This timeout (in seconds) is necessary when installing PyTorch via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
UV_HTTP_TIMEOUT=${UV_HTTP_TIMEOUT:-500}
# mounting is not supported by docker buildx, so we use COPY instead
USE_COPY_NOT_MOUNT=${USE_COPY_NOT_MOUNT:-}
# Path to the run.yaml file in the container
RUN_CONFIG_PATH=/app/run.yaml
BUILD_CONTEXT_DIR=$(pwd)
set -euo pipefail
# Define color codes
RED='\033[0;31m'
NC='\033[0m' # No Color
# Usage function
usage() {
echo "Usage: $0 --image-name <image_name> --container-base <container_base> --normal-deps <pip_dependencies> [--run-config <run_config>] [--external-provider-deps <external_provider_deps>] [--optional-deps <special_pip_deps>]"
echo "Example: $0 --image-name llama-stack-img --container-base python:3.12-slim --normal-deps 'numpy pandas' --run-config ./run.yaml --external-provider-deps 'foo' --optional-deps 'bar'"
exit 1
}
# Parse arguments
image_name=""
container_base=""
normal_deps=""
external_provider_deps=""
optional_deps=""
run_config=""
distro_or_config=""
while [[ $# -gt 0 ]]; do
key="$1"
case "$key" in
--image-name)
if [[ -z "$2" || "$2" == --* ]]; then
echo "Error: --image-name requires a string value" >&2
usage
fi
image_name="$2"
shift 2
;;
--container-base)
if [[ -z "$2" || "$2" == --* ]]; then
echo "Error: --container-base requires a string value" >&2
usage
fi
container_base="$2"
shift 2
;;
--normal-deps)
if [[ -z "$2" || "$2" == --* ]]; then
echo "Error: --normal-deps requires a string value" >&2
usage
fi
normal_deps="$2"
shift 2
;;
--external-provider-deps)
if [[ -z "$2" || "$2" == --* ]]; then
echo "Error: --external-provider-deps requires a string value" >&2
usage
fi
external_provider_deps="$2"
shift 2
;;
--optional-deps)
if [[ -z "$2" || "$2" == --* ]]; then
echo "Error: --optional-deps requires a string value" >&2
usage
fi
optional_deps="$2"
shift 2
;;
--run-config)
if [[ -z "$2" || "$2" == --* ]]; then
echo "Error: --run-config requires a string value" >&2
usage
fi
run_config="$2"
shift 2
;;
--distro-or-config)
if [[ -z "$2" || "$2" == --* ]]; then
echo "Error: --distro-or-config requires a string value" >&2
usage
fi
distro_or_config="$2"
shift 2
;;
*)
echo "Unknown option: $1" >&2
usage
;;
esac
done
# Check required arguments
if [[ -z "$image_name" || -z "$container_base" || -z "$normal_deps" ]]; then
echo "Error: --image-name, --container-base, and --normal-deps are required." >&2
usage
fi
CONTAINER_BINARY=${CONTAINER_BINARY:-docker}
CONTAINER_OPTS=${CONTAINER_OPTS:---progress=plain}
TEMP_DIR=$(mktemp -d)
SCRIPT_DIR=$(dirname "$(readlink -f "$0")")
source "$SCRIPT_DIR/common.sh"
add_to_container() {
output_file="$TEMP_DIR/Containerfile"
if [ -t 0 ]; then
printf '%s\n' "$1" >>"$output_file"
else
cat >>"$output_file"
fi
}
if ! is_command_available "$CONTAINER_BINARY"; then
printf "${RED}Error: ${CONTAINER_BINARY} command not found. Is ${CONTAINER_BINARY} installed and in your PATH?${NC}" >&2
exit 1
fi
if [[ $container_base == *"registry.access.redhat.com/ubi9"* ]]; then
add_to_container << EOF
FROM $container_base
WORKDIR /app
# We install the Python 3.12 dev headers and build tools so that any
# C-extension wheels (e.g. polyleven, faiss-cpu) can compile successfully.
RUN dnf -y update && dnf install -y iputils git net-tools wget \
vim-minimal python3.12 python3.12-pip python3.12-wheel \
python3.12-setuptools python3.12-devel gcc gcc-c++ make && \
ln -s /bin/pip3.12 /bin/pip && ln -s /bin/python3.12 /bin/python && dnf clean all
ENV UV_SYSTEM_PYTHON=1
RUN pip install uv
EOF
else
add_to_container << EOF
FROM $container_base
WORKDIR /app
RUN apt-get update && apt-get install -y \
iputils-ping net-tools iproute2 dnsutils telnet \
curl wget telnet git\
procps psmisc lsof \
traceroute \
bubblewrap \
gcc g++ \
&& rm -rf /var/lib/apt/lists/*
ENV UV_SYSTEM_PYTHON=1
RUN pip install uv
EOF
fi
# Add pip dependencies first since llama-stack is what will change most often
# so we can reuse layers.
if [ -n "$normal_deps" ]; then
read -ra pip_args <<< "$normal_deps"
quoted_deps=$(printf " %q" "${pip_args[@]}")
add_to_container << EOF
RUN uv pip install --no-cache $quoted_deps
EOF
fi
if [ -n "$optional_deps" ]; then
IFS='#' read -ra parts <<<"$optional_deps"
for part in "${parts[@]}"; do
read -ra pip_args <<< "$part"
quoted_deps=$(printf " %q" "${pip_args[@]}")
add_to_container <<EOF
RUN uv pip install --no-cache $quoted_deps
EOF
done
fi
if [ -n "$external_provider_deps" ]; then
IFS='#' read -ra parts <<<"$external_provider_deps"
for part in "${parts[@]}"; do
read -ra pip_args <<< "$part"
quoted_deps=$(printf " %q" "${pip_args[@]}")
add_to_container <<EOF
RUN uv pip install --no-cache $quoted_deps
EOF
add_to_container <<EOF
RUN python3 - <<PYTHON | uv pip install --no-cache -r -
import importlib
import sys
try:
package_name = '$part'.split('==')[0].split('>=')[0].split('<=')[0].split('!=')[0].split('<')[0].split('>')[0]
module = importlib.import_module(f'{package_name}.provider')
spec = module.get_provider_spec()
if hasattr(spec, 'pip_packages') and spec.pip_packages:
if isinstance(spec.pip_packages, (list, tuple)):
print('\n'.join(spec.pip_packages))
except Exception as e:
print(f'Error getting provider spec for {package_name}: {e}', file=sys.stderr)
PYTHON
EOF
done
fi
get_python_cmd() {
if is_command_available python; then
echo "python"
elif is_command_available python3; then
echo "python3"
else
echo "Error: Neither python nor python3 is installed. Please install Python to continue." >&2
exit 1
fi
}
if [ -n "$run_config" ]; then
# Copy the run config to the build context since it's an absolute path
cp "$run_config" "$BUILD_CONTEXT_DIR/run.yaml"
# Parse the run.yaml configuration to identify external provider directories
# If external providers are specified, copy their directory to the container
# and update the configuration to reference the new container path
python_cmd=$(get_python_cmd)
external_providers_dir=$($python_cmd -c "import yaml; config = yaml.safe_load(open('$run_config')); print(config.get('external_providers_dir') or '')")
external_providers_dir=$(eval echo "$external_providers_dir")
if [ -n "$external_providers_dir" ]; then
if [ -d "$external_providers_dir" ]; then
echo "Copying external providers directory: $external_providers_dir"
cp -r "$external_providers_dir" "$BUILD_CONTEXT_DIR/providers.d"
add_to_container << EOF
COPY providers.d /.llama/providers.d
EOF
fi
# Edit the run.yaml file to change the external_providers_dir to /.llama/providers.d
if [ "$(uname)" = "Darwin" ]; then
sed -i.bak -e 's|external_providers_dir:.*|external_providers_dir: /.llama/providers.d|' "$BUILD_CONTEXT_DIR/run.yaml"
rm -f "$BUILD_CONTEXT_DIR/run.yaml.bak"
else
sed -i 's|external_providers_dir:.*|external_providers_dir: /.llama/providers.d|' "$BUILD_CONTEXT_DIR/run.yaml"
fi
fi
# Copy run config into docker image
add_to_container << EOF
COPY run.yaml $RUN_CONFIG_PATH
EOF
fi
stack_mount="/app/llama-stack-source"
client_mount="/app/llama-stack-client-source"
install_local_package() {
local dir="$1"
local mount_point="$2"
local name="$3"
if [ ! -d "$dir" ]; then
echo "${RED}Warning: $name is set but directory does not exist: $dir${NC}" >&2
exit 1
fi
if [ "$USE_COPY_NOT_MOUNT" = "true" ]; then
add_to_container << EOF
COPY $dir $mount_point
EOF
fi
add_to_container << EOF
RUN uv pip install --no-cache -e $mount_point
EOF
}
if [ -n "$LLAMA_STACK_CLIENT_DIR" ]; then
install_local_package "$LLAMA_STACK_CLIENT_DIR" "$client_mount" "LLAMA_STACK_CLIENT_DIR"
fi
if [ -n "$LLAMA_STACK_DIR" ]; then
install_local_package "$LLAMA_STACK_DIR" "$stack_mount" "LLAMA_STACK_DIR"
else
if [ -n "$TEST_PYPI_VERSION" ]; then
# these packages are damaged in test-pypi, so install them first
add_to_container << EOF
RUN uv pip install --no-cache fastapi libcst
EOF
add_to_container << EOF
RUN uv pip install --no-cache --extra-index-url https://test.pypi.org/simple/ \
--index-strategy unsafe-best-match \
llama-stack==$TEST_PYPI_VERSION
EOF
else
if [ -n "$PYPI_VERSION" ]; then
SPEC_VERSION="llama-stack==${PYPI_VERSION}"
else
SPEC_VERSION="llama-stack"
fi
add_to_container << EOF
RUN uv pip install --no-cache $SPEC_VERSION
EOF
fi
fi
# remove uv after installation
add_to_container << EOF
RUN pip uninstall -y uv
EOF
# If a run config is provided, we use the llama stack CLI
if [[ -n "$run_config" ]]; then
add_to_container << EOF
ENTRYPOINT ["llama", "stack", "run", "$RUN_CONFIG_PATH"]
EOF
elif [[ "$distro_or_config" != *.yaml ]]; then
add_to_container << EOF
ENTRYPOINT ["llama", "stack", "run", "$distro_or_config"]
EOF
fi
# Add other require item commands genearic to all containers
add_to_container << EOF
RUN mkdir -p /.llama /.cache && chmod -R g+rw /.llama /.cache && (chmod -R g+rw /app 2>/dev/null || true)
EOF
printf "Containerfile created successfully in %s/Containerfile\n\n" "$TEMP_DIR"
cat "$TEMP_DIR"/Containerfile
printf "\n"
# Start building the CLI arguments
CLI_ARGS=()
# Read CONTAINER_OPTS and put it in an array
read -ra CLI_ARGS <<< "$CONTAINER_OPTS"
if [ "$USE_COPY_NOT_MOUNT" != "true" ]; then
if [ -n "$LLAMA_STACK_DIR" ]; then
CLI_ARGS+=("-v" "$(readlink -f "$LLAMA_STACK_DIR"):$stack_mount")
fi
if [ -n "$LLAMA_STACK_CLIENT_DIR" ]; then
CLI_ARGS+=("-v" "$(readlink -f "$LLAMA_STACK_CLIENT_DIR"):$client_mount")
fi
fi
if is_command_available selinuxenabled && selinuxenabled; then
# Disable SELinux labels -- we don't want to relabel the llama-stack source dir
CLI_ARGS+=("--security-opt" "label=disable")
fi
# Set version tag based on PyPI version
if [ -n "$PYPI_VERSION" ]; then
version_tag="$PYPI_VERSION"
elif [ -n "$TEST_PYPI_VERSION" ]; then
version_tag="test-$TEST_PYPI_VERSION"
elif [[ -n "$LLAMA_STACK_DIR" || -n "$LLAMA_STACK_CLIENT_DIR" ]]; then
version_tag="dev"
else
URL="https://pypi.org/pypi/llama-stack/json"
version_tag=$(curl -s $URL | jq -r '.info.version')
fi
# Add version tag to image name
image_tag="$image_name:$version_tag"
# Detect platform architecture
ARCH=$(uname -m)
if [ -n "$BUILD_PLATFORM" ]; then
CLI_ARGS+=("--platform" "$BUILD_PLATFORM")
elif [ "$ARCH" = "arm64" ] || [ "$ARCH" = "aarch64" ]; then
CLI_ARGS+=("--platform" "linux/arm64")
elif [ "$ARCH" = "x86_64" ]; then
CLI_ARGS+=("--platform" "linux/amd64")
else
echo "Unsupported architecture: $ARCH"
exit 1
fi
echo "PWD: $(pwd)"
echo "Containerfile: $TEMP_DIR/Containerfile"
set -x
$CONTAINER_BINARY build \
"${CLI_ARGS[@]}" \
-t "$image_tag" \
-f "$TEMP_DIR/Containerfile" \
"$BUILD_CONTEXT_DIR"
# clean up tmp/configs
rm -rf "$BUILD_CONTEXT_DIR/run.yaml" "$TEMP_DIR"
set +x
echo "Success!"

220
llama_stack/core/build_venv.sh
View file

@ -1,220 +0,0 @@
#!/bin/bash
# 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_DIR=${LLAMA_STACK_DIR:-}
LLAMA_STACK_CLIENT_DIR=${LLAMA_STACK_CLIENT_DIR:-}
TEST_PYPI_VERSION=${TEST_PYPI_VERSION:-}
# This timeout (in seconds) is necessary when installing PyTorch via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
UV_HTTP_TIMEOUT=${UV_HTTP_TIMEOUT:-500}
UV_SYSTEM_PYTHON=${UV_SYSTEM_PYTHON:-}
VIRTUAL_ENV=${VIRTUAL_ENV:-}
set -euo pipefail
# Define color codes
RED='\033[0;31m'
NC='\033[0m' # No Color
SCRIPT_DIR=$(dirname "$(readlink -f "$0")")
source "$SCRIPT_DIR/common.sh"
# Usage function
usage() {
echo "Usage: $0 --env-name <env_name> --normal-deps <pip_dependencies> [--external-provider-deps <external_provider_deps>] [--optional-deps <special_pip_deps>]"
echo "Example: $0 --env-name mybuild --normal-deps 'numpy pandas scipy' --external-provider-deps 'foo' --optional-deps 'bar'"
exit 1
}
# Parse arguments
env_name=""
normal_deps=""
external_provider_deps=""
optional_deps=""
while [[ $# -gt 0 ]]; do
key="$1"
case "$key" in
--env-name)
if [[ -z "$2" || "$2" == --* ]]; then
echo "Error: --env-name requires a string value" >&2
usage
fi
env_name="$2"
shift 2
;;
--normal-deps)
if [[ -z "$2" || "$2" == --* ]]; then
echo "Error: --normal-deps requires a string value" >&2
usage
fi
normal_deps="$2"
shift 2
;;
--external-provider-deps)
if [[ -z "$2" || "$2" == --* ]]; then
echo "Error: --external-provider-deps requires a string value" >&2
usage
fi
external_provider_deps="$2"
shift 2
;;
--optional-deps)
if [[ -z "$2" || "$2" == --* ]]; then
echo "Error: --optional-deps requires a string value" >&2
usage
fi
optional_deps="$2"
shift 2
;;
*)
echo "Unknown option: $1" >&2
usage
;;
esac
done
# Check required arguments
if [[ -z "$env_name" || -z "$normal_deps" ]]; then
echo "Error: --env-name and --normal-deps are required." >&2
usage
fi
if [ -n "$LLAMA_STACK_DIR" ]; then
echo "Using llama-stack-dir=$LLAMA_STACK_DIR"
fi
if [ -n "$LLAMA_STACK_CLIENT_DIR" ]; then
echo "Using llama-stack-client-dir=$LLAMA_STACK_CLIENT_DIR"
fi
ENVNAME=""
# pre-run checks to make sure we can proceed with the installation
pre_run_checks() {
local env_name="$1"
if ! is_command_available uv; then
echo "uv is not installed, trying to install it."
if ! is_command_available pip; then
echo "pip is not installed, cannot automatically install 'uv'."
echo "Follow this link to install it:"
echo "https://docs.astral.sh/uv/getting-started/installation/"
exit 1
else
pip install uv
fi
fi
# checking if an environment with the same name already exists
if [ -d "$env_name" ]; then
echo "Environment '$env_name' already exists, re-using it."
fi
}
run() {
# Use only global variables set by flag parser
if [ -n "$UV_SYSTEM_PYTHON" ] || [ "$env_name" == "__system__" ]; then
echo "Installing dependencies in system Python environment"
export UV_SYSTEM_PYTHON=1
elif [ "$VIRTUAL_ENV" == "$env_name" ]; then
echo "Virtual environment $env_name is already active"
else
echo "Using virtual environment $env_name"
uv venv "$env_name"
source "$env_name/bin/activate"
fi
if [ -n "$TEST_PYPI_VERSION" ]; then
uv pip install fastapi libcst
uv pip install --extra-index-url https://test.pypi.org/simple/ \
--index-strategy unsafe-best-match \
llama-stack=="$TEST_PYPI_VERSION" \
$normal_deps
if [ -n "$optional_deps" ]; then
IFS='#' read -ra parts <<<"$optional_deps"
for part in "${parts[@]}"; do
echo "$part"
uv pip install $part
done
fi
if [ -n "$external_provider_deps" ]; then
IFS='#' read -ra parts <<<"$external_provider_deps"
for part in "${parts[@]}"; do
echo "$part"
uv pip install "$part"
done
fi
else
if [ -n "$LLAMA_STACK_DIR" ]; then
# only warn if DIR does not start with "git+"
if [ ! -d "$LLAMA_STACK_DIR" ] && [[ "$LLAMA_STACK_DIR" != git+* ]]; then
printf "${RED}Warning: LLAMA_STACK_DIR is set but directory does not exist: %s${NC}\n" "$LLAMA_STACK_DIR" >&2
exit 1
fi
printf "Installing from LLAMA_STACK_DIR: %s\n" "$LLAMA_STACK_DIR"
# editable only if LLAMA_STACK_DIR does not start with "git+"
if [[ "$LLAMA_STACK_DIR" != git+* ]]; then
EDITABLE="-e"
else
EDITABLE=""
fi
uv pip install --no-cache-dir $EDITABLE "$LLAMA_STACK_DIR"
else
uv pip install --no-cache-dir llama-stack
fi
if [ -n "$LLAMA_STACK_CLIENT_DIR" ]; then
# only warn if DIR does not start with "git+"
if [ ! -d "$LLAMA_STACK_CLIENT_DIR" ] && [[ "$LLAMA_STACK_CLIENT_DIR" != git+* ]]; then
printf "${RED}Warning: LLAMA_STACK_CLIENT_DIR is set but directory does not exist: %s${NC}\n" "$LLAMA_STACK_CLIENT_DIR" >&2
exit 1
fi
printf "Installing from LLAMA_STACK_CLIENT_DIR: %s\n" "$LLAMA_STACK_CLIENT_DIR"
# editable only if LLAMA_STACK_CLIENT_DIR does not start with "git+"
if [[ "$LLAMA_STACK_CLIENT_DIR" != git+* ]]; then
EDITABLE="-e"
else
EDITABLE=""
fi
uv pip install --no-cache-dir $EDITABLE "$LLAMA_STACK_CLIENT_DIR"
fi
printf "Installing pip dependencies\n"
uv pip install $normal_deps
if [ -n "$optional_deps" ]; then
IFS='#' read -ra parts <<<"$optional_deps"
for part in "${parts[@]}"; do
echo "Installing special provider module: $part"
uv pip install $part
done
fi
if [ -n "$external_provider_deps" ]; then
IFS='#' read -ra parts <<<"$external_provider_deps"
for part in "${parts[@]}"; do
echo "Installing external provider module: $part"
uv pip install "$part"
echo "Getting provider spec for module: $part and installing dependencies"
package_name=$(echo "$part" | sed 's/[<>=!].*//')
python3 -c "
import importlib
import sys
try:
module = importlib.import_module(f'$package_name.provider')
spec = module.get_provider_spec()
if hasattr(spec, 'pip_packages') and spec.pip_packages:
print('\\n'.join(spec.pip_packages))
except Exception as e:
print(f'Error getting provider spec for $package_name: {e}', file=sys.stderr)
" | uv pip install -r -
done
fi
fi
}
pre_run_checks "$env_name"
run

31
llama_stack/core/configure.py
View file

@ -159,6 +159,37 @@ def upgrade_from_routing_table(
config_dict["apis"] = config_dict["apis_to_serve"]
config_dict.pop("apis_to_serve", None)
# Add default storage config if not present
if "storage" not in config_dict:
config_dict["storage"] = {
"backends": {
"kv_default": {
"type": "kv_sqlite",
"db_path": "~/.llama/kvstore.db",
},
"sql_default": {
"type": "sql_sqlite",
"db_path": "~/.llama/sql_store.db",
},
},
"stores": {
"metadata": {
"namespace": "registry",
"backend": "kv_default",
},
"inference": {
"table_name": "inference_store",
"backend": "sql_default",
"max_write_queue_size": 10000,
"num_writers": 4,
},
"conversations": {
"table_name": "openai_conversations",
"backend": "sql_default",
},
},
}
return config_dict

26
llama_stack/core/conversations/conversations.py
View file

@ -4,7 +4,6 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import os
import secrets
import time
from typing import Any, Literal
@ -21,16 +20,11 @@ from llama_stack.apis.conversations.conversations import (
Conversations,
Metadata,
)
from llama_stack.core.datatypes import AccessRule
from llama_stack.core.utils.config_dirs import DISTRIBS_BASE_DIR
from llama_stack.core.datatypes import AccessRule, StackRunConfig
from llama_stack.log import get_logger
from llama_stack.providers.utils.sqlstore.api import ColumnDefinition, ColumnType
from llama_stack.providers.utils.sqlstore.authorized_sqlstore import AuthorizedSqlStore
from llama_stack.providers.utils.sqlstore.sqlstore import (
SqliteSqlStoreConfig,
SqlStoreConfig,
sqlstore_impl,
)
from llama_stack.providers.utils.sqlstore.sqlstore import sqlstore_impl
logger = get_logger(name=__name__, category="openai_conversations")
@ -38,13 +32,11 @@ logger = get_logger(name=__name__, category="openai_conversations")
class ConversationServiceConfig(BaseModel):
"""Configuration for the built-in conversation service.
:param conversations_store: SQL store configuration for conversations (defaults to SQLite)
:param run_config: Stack run configuration for resolving persistence
:param policy: Access control rules
"""
conversations_store: SqlStoreConfig = SqliteSqlStoreConfig(
db_path=(DISTRIBS_BASE_DIR / "conversations.db").as_posix()
)
run_config: StackRunConfig
policy: list[AccessRule] = []
@ -63,14 +55,16 @@ class ConversationServiceImpl(Conversations):
self.deps = deps
self.policy = config.policy
base_sql_store = sqlstore_impl(config.conversations_store)
# Use conversations store reference from run config
conversations_ref = config.run_config.storage.stores.conversations
if not conversations_ref:
raise ValueError("storage.stores.conversations must be configured in run config")
base_sql_store = sqlstore_impl(conversations_ref)
self.sql_store = AuthorizedSqlStore(base_sql_store, self.policy)
async def initialize(self) -> None:
"""Initialize the store and create tables."""
if isinstance(self.config.conversations_store, SqliteSqlStoreConfig):
os.makedirs(os.path.dirname(self.config.conversations_store.db_path), exist_ok=True)
await self.sql_store.create_table(
"openai_conversations",
{

141
llama_stack/core/datatypes.py
View file

@ -23,12 +23,15 @@ from llama_stack.apis.scoring import Scoring
from llama_stack.apis.scoring_functions import ScoringFn, ScoringFnInput
from llama_stack.apis.shields import Shield, ShieldInput
from llama_stack.apis.tools import ToolGroup, ToolGroupInput, ToolRuntime
from llama_stack.apis.vector_dbs import VectorDB, VectorDBInput
from llama_stack.apis.vector_io import VectorIO
from llama_stack.apis.vector_stores import VectorStore, VectorStoreInput
from llama_stack.core.access_control.datatypes import AccessRule
from llama_stack.core.storage.datatypes import (
KVStoreReference,
StorageBackendType,
StorageConfig,
)
from llama_stack.providers.datatypes import Api, ProviderSpec
from llama_stack.providers.utils.kvstore.config import KVStoreConfig, SqliteKVStoreConfig
from llama_stack.providers.utils.sqlstore.sqlstore import SqlStoreConfig
LLAMA_STACK_BUILD_CONFIG_VERSION = 2
LLAMA_STACK_RUN_CONFIG_VERSION = 2
@ -68,7 +71,7 @@ class ShieldWithOwner(Shield, ResourceWithOwner):
pass
class VectorDBWithOwner(VectorDB, ResourceWithOwner):
class VectorStoreWithOwner(VectorStore, ResourceWithOwner):
pass
@ -88,12 +91,12 @@ class ToolGroupWithOwner(ToolGroup, ResourceWithOwner):
pass
RoutableObject = Model | Shield | VectorDB | Dataset | ScoringFn | Benchmark | ToolGroup
RoutableObject = Model | Shield | VectorStore | Dataset | ScoringFn | Benchmark | ToolGroup
RoutableObjectWithProvider = Annotated[
ModelWithOwner
| ShieldWithOwner
| VectorDBWithOwner
| VectorStoreWithOwner
| DatasetWithOwner
| ScoringFnWithOwner
| BenchmarkWithOwner
@ -351,12 +354,32 @@ class AuthenticationRequiredError(Exception):
pass
class QualifiedModel(BaseModel):
"""A qualified model identifier, consisting of a provider ID and a model ID."""
provider_id: str
model_id: str
class VectorStoresConfig(BaseModel):
"""Configuration for vector stores in the stack."""
default_provider_id: str | None = Field(
default=None,
description="ID of the vector_io provider to use as default when multiple providers are available and none is specified.",
)
default_embedding_model: QualifiedModel | None = Field(
default=None,
description="Default embedding model configuration for vector stores.",
)
class QuotaPeriod(StrEnum):
DAY = "day"
class QuotaConfig(BaseModel):
kvstore: SqliteKVStoreConfig = Field(description="Config for KV store backend (SQLite only for now)")
kvstore: KVStoreReference = Field(description="Config for KV store backend (SQLite only for now)")
anonymous_max_requests: int = Field(default=100, description="Max requests for unauthenticated clients per period")
authenticated_max_requests: int = Field(
default=1000, description="Max requests for authenticated clients per period"
@ -399,6 +422,18 @@ def process_cors_config(cors_config: bool | CORSConfig | None) -> CORSConfig | N
raise ValueError(f"Expected bool or CORSConfig, got {type(cors_config).__name__}")
class RegisteredResources(BaseModel):
"""Registry of resources available in the distribution."""
models: list[ModelInput] = Field(default_factory=list)
shields: list[ShieldInput] = Field(default_factory=list)
vector_stores: list[VectorStoreInput] = Field(default_factory=list)
datasets: list[DatasetInput] = Field(default_factory=list)
scoring_fns: list[ScoringFnInput] = Field(default_factory=list)
benchmarks: list[BenchmarkInput] = Field(default_factory=list)
tool_groups: list[ToolGroupInput] = Field(default_factory=list)
class ServerConfig(BaseModel):
port: int = Field(
default=8321,
@ -438,18 +473,6 @@ class ServerConfig(BaseModel):
)
class InferenceStoreConfig(BaseModel):
sql_store_config: SqlStoreConfig
max_write_queue_size: int = Field(default=10000, description="Max queued writes for inference store")
num_writers: int = Field(default=4, description="Number of concurrent background writers")
class ResponsesStoreConfig(BaseModel):
sql_store_config: SqlStoreConfig
max_write_queue_size: int = Field(default=10000, description="Max queued writes for responses store")
num_writers: int = Field(default=4, description="Number of concurrent background writers")
class StackRunConfig(BaseModel):
version: int = LLAMA_STACK_RUN_CONFIG_VERSION
@ -476,37 +499,15 @@ One or more providers to use for each API. The same provider_type (e.g., meta-re
can be instantiated multiple times (with different configs) if necessary.
""",
)
metadata_store: KVStoreConfig | None = Field(
default=None,
description="""
Configuration for the persistence store used by the distribution registry. If not specified,
a default SQLite store will be used.""",
storage: StorageConfig = Field(
description="Catalog of named storage backends and references available to the stack",
)
inference_store: InferenceStoreConfig | SqlStoreConfig | None = Field(
default=None,
description="""
Configuration for the persistence store used by the inference API. Can be either a
InferenceStoreConfig (with queue tuning parameters) or a SqlStoreConfig (deprecated).
If not specified, a default SQLite store will be used.""",
registered_resources: RegisteredResources = Field(
default_factory=RegisteredResources,
description="Registry of resources available in the distribution",
)
conversations_store: SqlStoreConfig | None = Field(
default=None,
description="""
Configuration for the persistence store used by the conversations API.
If not specified, a default SQLite store will be used.""",
)
# registry of "resources" in the distribution
models: list[ModelInput] = Field(default_factory=list)
shields: list[ShieldInput] = Field(default_factory=list)
vector_dbs: list[VectorDBInput] = Field(default_factory=list)
datasets: list[DatasetInput] = Field(default_factory=list)
scoring_fns: list[ScoringFnInput] = Field(default_factory=list)
benchmarks: list[BenchmarkInput] = Field(default_factory=list)
tool_groups: list[ToolGroupInput] = Field(default_factory=list)
logging: LoggingConfig | None = Field(default=None, description="Configuration for Llama Stack Logging")
telemetry: TelemetryConfig = Field(default_factory=TelemetryConfig, description="Configuration for telemetry")
@ -526,6 +527,11 @@ If not specified, a default SQLite store will be used.""",
description="Path to directory containing external API implementations. The APIs code and dependencies must be installed on the system.",
)
vector_stores: VectorStoresConfig | None = Field(
default=None,
description="Configuration for vector stores, including default embedding model",
)
@field_validator("external_providers_dir")
@classmethod
def validate_external_providers_dir(cls, v):
@ -535,6 +541,49 @@ If not specified, a default SQLite store will be used.""",
return Path(v)
return v
@model_validator(mode="after")
def validate_server_stores(self) -> "StackRunConfig":
backend_map = self.storage.backends
stores = self.storage.stores
kv_backends = {
name
for name, cfg in backend_map.items()
if cfg.type
in {
StorageBackendType.KV_REDIS,
StorageBackendType.KV_SQLITE,
StorageBackendType.KV_POSTGRES,
StorageBackendType.KV_MONGODB,
}
}
sql_backends = {
name
for name, cfg in backend_map.items()
if cfg.type in {StorageBackendType.SQL_SQLITE, StorageBackendType.SQL_POSTGRES}
}
def _ensure_backend(reference, expected_set, store_name: str) -> None:
if reference is None:
return
backend_name = reference.backend
if backend_name not in backend_map:
raise ValueError(
f"{store_name} references unknown backend '{backend_name}'. "
f"Available backends: {sorted(backend_map)}"
)
if backend_name not in expected_set:
raise ValueError(
f"{store_name} references backend '{backend_name}' of type "
f"'{backend_map[backend_name].type.value}', but a backend of type "
f"{'kv_*' if expected_set is kv_backends else 'sql_*'} is required."
)
_ensure_backend(stores.metadata, kv_backends, "storage.stores.metadata")
_ensure_backend(stores.inference, sql_backends, "storage.stores.inference")
_ensure_backend(stores.conversations, sql_backends, "storage.stores.conversations")
_ensure_backend(stores.responses, sql_backends, "storage.stores.responses")
return self
class BuildConfig(BaseModel):
version: int = LLAMA_STACK_BUILD_CONFIG_VERSION

4
llama_stack/core/distribution.py
View file

@ -63,6 +63,10 @@ def builtin_automatically_routed_apis() -> list[AutoRoutedApiInfo]:
routing_table_api=Api.tool_groups,
router_api=Api.tool_runtime,
),
AutoRoutedApiInfo(
routing_table_api=Api.vector_stores,
router_api=Api.vector_io,
),
]

7
llama_stack/core/library_client.py
View file

@ -47,7 +47,7 @@ from llama_stack.core.stack import (
from llama_stack.core.utils.config import redact_sensitive_fields
from llama_stack.core.utils.context import preserve_contexts_async_generator
from llama_stack.core.utils.exec import in_notebook
from llama_stack.log import get_logger
from llama_stack.log import get_logger, setup_logging
from llama_stack.providers.utils.telemetry.tracing import CURRENT_TRACE_CONTEXT, end_trace, setup_logger, start_trace
from llama_stack.strong_typing.inspection import is_unwrapped_body_param
@ -200,6 +200,9 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
skip_logger_removal: bool = False,
):
super().__init__()
# Initialize logging from environment variables first
setup_logging()
# when using the library client, we should not log to console since many
# of our logs are intended for server-side usage
if sinks_from_env := os.environ.get("TELEMETRY_SINKS", None):
@ -278,7 +281,7 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
else:
prefix = "!" if in_notebook() else ""
cprint(
f"Please run:\n\n{prefix}llama stack build --distro {self.config_path_or_distro_name} --image-type venv\n\n",
f"Please run:\n\n{prefix}llama stack list-deps {self.config_path_or_distro_name} | xargs -L1 uv pip install\n\n",
"yellow",
file=sys.stderr,
)

13
llama_stack/core/prompts/prompts.py
View file

@ -11,9 +11,8 @@ from pydantic import BaseModel
from llama_stack.apis.prompts import ListPromptsResponse, Prompt, Prompts
from llama_stack.core.datatypes import StackRunConfig
from llama_stack.core.utils.config_dirs import DISTRIBS_BASE_DIR
from llama_stack.core.storage.datatypes import KVStoreReference
from llama_stack.providers.utils.kvstore import KVStore, kvstore_impl
from llama_stack.providers.utils.kvstore.config import SqliteKVStoreConfig
class PromptServiceConfig(BaseModel):
@ -41,10 +40,12 @@ class PromptServiceImpl(Prompts):
self.kvstore: KVStore
async def initialize(self) -> None:
kvstore_config = SqliteKVStoreConfig(
db_path=(DISTRIBS_BASE_DIR / self.config.run_config.image_name / "prompts.db").as_posix()
)
self.kvstore = await kvstore_impl(kvstore_config)
# Use metadata store backend with prompts-specific namespace
metadata_ref = self.config.run_config.storage.stores.metadata
if not metadata_ref:
raise ValueError("storage.stores.metadata must be configured in run config")
prompts_ref = KVStoreReference(namespace="prompts", backend=metadata_ref.backend)
self.kvstore = await kvstore_impl(prompts_ref)
def _get_default_key(self, prompt_id: str) -> str:
"""Get the KVStore key that stores the default version number."""

2
llama_stack/core/resolver.py
View file

@ -30,6 +30,7 @@ from llama_stack.apis.shields import Shields
from llama_stack.apis.telemetry import Telemetry
from llama_stack.apis.tools import ToolGroups, ToolRuntime
from llama_stack.apis.vector_io import VectorIO
from llama_stack.apis.vector_stores import VectorStore
from llama_stack.apis.version import LLAMA_STACK_API_V1ALPHA
from llama_stack.core.client import get_client_impl
from llama_stack.core.datatypes import (
@ -81,6 +82,7 @@ def api_protocol_map(external_apis: dict[Api, ExternalApiSpec] | None = None) ->
Api.inspect: Inspect,
Api.batches: Batches,
Api.vector_io: VectorIO,
Api.vector_stores: VectorStore,
Api.models: Models,
Api.safety: Safety,
Api.shields: Shields,

18
llama_stack/core/routers/__init__.py
View file

@ -6,7 +6,10 @@
from typing import Any
from llama_stack.core.datatypes import AccessRule, RoutedProtocol
from llama_stack.core.datatypes import (
AccessRule,
RoutedProtocol,
)
from llama_stack.core.stack import StackRunConfig
from llama_stack.core.store import DistributionRegistry
from llama_stack.providers.datatypes import Api, RoutingTable
@ -26,6 +29,7 @@ async def get_routing_table_impl(
from ..routing_tables.scoring_functions import ScoringFunctionsRoutingTable
from ..routing_tables.shields import ShieldsRoutingTable
from ..routing_tables.toolgroups import ToolGroupsRoutingTable
from ..routing_tables.vector_stores import VectorStoresRoutingTable
api_to_tables = {
"models": ModelsRoutingTable,
@ -34,6 +38,7 @@ async def get_routing_table_impl(
"scoring_functions": ScoringFunctionsRoutingTable,
"benchmarks": BenchmarksRoutingTable,
"tool_groups": ToolGroupsRoutingTable,
"vector_stores": VectorStoresRoutingTable,
}
if api.value not in api_to_tables:
@ -76,14 +81,21 @@ async def get_auto_router_impl(
api_to_dep_impl[dep_name] = deps[dep_api]
# TODO: move pass configs to routers instead
if api == Api.inference and run_config.inference_store:
if api == Api.inference:
inference_ref = run_config.storage.stores.inference
if not inference_ref:
raise ValueError("storage.stores.inference must be configured in run config")
inference_store = InferenceStore(
config=run_config.inference_store,
reference=inference_ref,
policy=policy,
)
await inference_store.initialize()
api_to_dep_impl["store"] = inference_store
elif api == Api.vector_io:
api_to_dep_impl["vector_stores_config"] = run_config.vector_stores
impl = api_to_routers[api.value](routing_table, **api_to_dep_impl)
await impl.initialize()
return impl

22
llama_stack/core/routers/inference.py
View file

@ -44,9 +44,14 @@ from llama_stack.apis.inference import (
OpenAIEmbeddingsResponse,
OpenAIMessageParam,
Order,
RerankResponse,
StopReason,
ToolPromptFormat,
)
from llama_stack.apis.inference.inference import (
OpenAIChatCompletionContentPartImageParam,
OpenAIChatCompletionContentPartTextParam,
)
from llama_stack.apis.models import Model, ModelType
from llama_stack.apis.telemetry import MetricEvent, MetricInResponse, Telemetry
from llama_stack.log import get_logger
@ -182,6 +187,23 @@ class InferenceRouter(Inference):
raise ModelTypeError(model_id, model.model_type, expected_model_type)
return model
async def rerank(
self,
model: str,
query: str | OpenAIChatCompletionContentPartTextParam | OpenAIChatCompletionContentPartImageParam,
items: list[str | OpenAIChatCompletionContentPartTextParam | OpenAIChatCompletionContentPartImageParam],
max_num_results: int | None = None,
) -> RerankResponse:
logger.debug(f"InferenceRouter.rerank: {model}")
model_obj = await self._get_model(model, ModelType.rerank)
provider = await self.routing_table.get_provider_impl(model_obj.identifier)
return await provider.rerank(
model=model_obj.identifier,
query=query,
items=items,
max_num_results=max_num_results,
)
async def openai_completion(
self,
params: Annotated[OpenAICompletionRequestWithExtraBody, Body(...)],

12
llama_stack/core/routers/tool_runtime.py
View file

@ -37,24 +37,24 @@ class ToolRuntimeRouter(ToolRuntime):
async def query(
self,
content: InterleavedContent,
vector_db_ids: list[str],
vector_store_ids: list[str],
query_config: RAGQueryConfig | None = None,
) -> RAGQueryResult:
logger.debug(f"ToolRuntimeRouter.RagToolImpl.query: {vector_db_ids}")
logger.debug(f"ToolRuntimeRouter.RagToolImpl.query: {vector_store_ids}")
provider = await self.routing_table.get_provider_impl("knowledge_search")
return await provider.query(content, vector_db_ids, query_config)
return await provider.query(content, vector_store_ids, query_config)
async def insert(
self,
documents: list[RAGDocument],
vector_db_id: str,
vector_store_id: str,
chunk_size_in_tokens: int = 512,
) -> None:
logger.debug(
f"ToolRuntimeRouter.RagToolImpl.insert: {vector_db_id}, {len(documents)} documents, chunk_size={chunk_size_in_tokens}"
f"ToolRuntimeRouter.RagToolImpl.insert: {vector_store_id}, {len(documents)} documents, chunk_size={chunk_size_in_tokens}"
)
provider = await self.routing_table.get_provider_impl("insert_into_memory")
return await provider.insert(documents, vector_db_id, chunk_size_in_tokens)
return await provider.insert(documents, vector_store_id, chunk_size_in_tokens)
def __init__(
self,

87
llama_stack/core/routers/vector_io.py
View file

@ -31,6 +31,7 @@ from llama_stack.apis.vector_io import (
VectorStoreObject,
VectorStoreSearchResponsePage,
)
from llama_stack.core.datatypes import VectorStoresConfig
from llama_stack.log import get_logger
from llama_stack.providers.datatypes import HealthResponse, HealthStatus, RoutingTable
@ -43,9 +44,11 @@ class VectorIORouter(VectorIO):
def __init__(
self,
routing_table: RoutingTable,
vector_stores_config: VectorStoresConfig | None = None,
) -> None:
logger.debug("Initializing VectorIORouter")
self.routing_table = routing_table
self.vector_stores_config = vector_stores_config
async def initialize(self) -> None:
logger.debug("VectorIORouter.initialize")
@ -68,25 +71,6 @@ class VectorIORouter(VectorIO):
raise ValueError(f"Embedding model '{embedding_model_id}' not found or not an embedding model")
async def register_vector_db(
self,
vector_db_id: str,
embedding_model: str,
embedding_dimension: int | None = 384,
provider_id: str | None = None,
vector_db_name: str | None = None,
provider_vector_db_id: str | None = None,
) -> None:
logger.debug(f"VectorIORouter.register_vector_db: {vector_db_id}, {embedding_model}")
await self.routing_table.register_vector_db(
vector_db_id,
embedding_model,
embedding_dimension,
provider_id,
vector_db_name,
provider_vector_db_id,
)
async def insert_chunks(
self,
vector_db_id: str,
@ -122,6 +106,17 @@ class VectorIORouter(VectorIO):
embedding_dimension = extra.get("embedding_dimension")
provider_id = extra.get("provider_id")
# Use default embedding model if not specified
if (
embedding_model is None
and self.vector_stores_config
and self.vector_stores_config.default_embedding_model is not None
):
# Construct the full model ID with provider prefix
embedding_provider_id = self.vector_stores_config.default_embedding_model.provider_id
model_id = self.vector_stores_config.default_embedding_model.model_id
embedding_model = f"{embedding_provider_id}/{model_id}"
if embedding_model is not None and embedding_dimension is None:
embedding_dimension = await self._get_embedding_model_dimension(embedding_model)
@ -132,28 +127,41 @@ class VectorIORouter(VectorIO):
raise ValueError("No vector_io providers available")
if num_providers > 1:
available_providers = list(self.routing_table.impls_by_provider_id.keys())
raise ValueError(
f"Multiple vector_io providers available. Please specify provider_id in extra_body. "
f"Available providers: {available_providers}"
)
provider_id = list(self.routing_table.impls_by_provider_id.keys())[0]
# Use default configured provider
if self.vector_stores_config and self.vector_stores_config.default_provider_id:
default_provider = self.vector_stores_config.default_provider_id
if default_provider in available_providers:
provider_id = default_provider
logger.debug(f"Using configured default vector store provider: {provider_id}")
else:
raise ValueError(
f"Configured default vector store provider '{default_provider}' not found. "
f"Available providers: {available_providers}"
)
else:
raise ValueError(
f"Multiple vector_io providers available. Please specify provider_id in extra_body. "
f"Available providers: {available_providers}"
)
else:
provider_id = list(self.routing_table.impls_by_provider_id.keys())[0]
vector_db_id = f"vs_{uuid.uuid4()}"
registered_vector_db = await self.routing_table.register_vector_db(
vector_db_id=vector_db_id,
vector_store_id = f"vs_{uuid.uuid4()}"
registered_vector_store = await self.routing_table.register_vector_store(
vector_store_id=vector_store_id,
embedding_model=embedding_model,
embedding_dimension=embedding_dimension,
provider_id=provider_id,
provider_vector_db_id=vector_db_id,
vector_db_name=params.name,
provider_vector_store_id=vector_store_id,
vector_store_name=params.name,
)
provider = await self.routing_table.get_provider_impl(registered_vector_db.identifier)
provider = await self.routing_table.get_provider_impl(registered_vector_store.identifier)
# Update model_extra with registered values so provider uses the already-registered vector_db
# Update model_extra with registered values so provider uses the already-registered vector_store
if params.model_extra is None:
params.model_extra = {}
params.model_extra["provider_vector_db_id"] = registered_vector_db.provider_resource_id
params.model_extra["provider_id"] = registered_vector_db.provider_id
params.model_extra["provider_vector_store_id"] = registered_vector_store.provider_resource_id
params.model_extra["provider_id"] = registered_vector_store.provider_id
if embedding_model is not None:
params.model_extra["embedding_model"] = embedding_model
if embedding_dimension is not None:
@ -171,15 +179,15 @@ class VectorIORouter(VectorIO):
logger.debug(f"VectorIORouter.openai_list_vector_stores: limit={limit}")
# Route to default provider for now - could aggregate from all providers in the future
# call retrieve on each vector dbs to get list of vector stores
vector_dbs = await self.routing_table.get_all_with_type("vector_db")
vector_stores = await self.routing_table.get_all_with_type("vector_store")
all_stores = []
for vector_db in vector_dbs:
for vector_store in vector_stores:
try:
provider = await self.routing_table.get_provider_impl(vector_db.identifier)
vector_store = await provider.openai_retrieve_vector_store(vector_db.identifier)
provider = await self.routing_table.get_provider_impl(vector_store.identifier)
vector_store = await provider.openai_retrieve_vector_store(vector_store.identifier)
all_stores.append(vector_store)
except Exception as e:
logger.error(f"Error retrieving vector store {vector_db.identifier}: {e}")
logger.error(f"Error retrieving vector store {vector_store.identifier}: {e}")
continue
# Sort by created_at
@ -243,8 +251,7 @@ class VectorIORouter(VectorIO):
vector_store_id: str,
) -> VectorStoreDeleteResponse:
logger.debug(f"VectorIORouter.openai_delete_vector_store: {vector_store_id}")
provider = await self.routing_table.get_provider_impl(vector_store_id)
return await provider.openai_delete_vector_store(vector_store_id)
return await self.routing_table.openai_delete_vector_store(vector_store_id)
async def openai_search_vector_store(
self,

9
llama_stack/core/routing_tables/common.py
View file

@ -41,7 +41,7 @@ async def register_object_with_provider(obj: RoutableObject, p: Any) -> Routable
elif api == Api.safety:
return await p.register_shield(obj)
elif api == Api.vector_io:
return await p.register_vector_db(obj)
return await p.register_vector_store(obj)
elif api == Api.datasetio:
return await p.register_dataset(obj)
elif api == Api.scoring:
@ -57,7 +57,7 @@ async def register_object_with_provider(obj: RoutableObject, p: Any) -> Routable
async def unregister_object_from_provider(obj: RoutableObject, p: Any) -> None:
api = get_impl_api(p)
if api == Api.vector_io:
return await p.unregister_vector_db(obj.identifier)
return await p.unregister_vector_store(obj.identifier)
elif api == Api.inference:
return await p.unregister_model(obj.identifier)
elif api == Api.safety:
@ -108,7 +108,7 @@ class CommonRoutingTableImpl(RoutingTable):
elif api == Api.safety:
p.shield_store = self
elif api == Api.vector_io:
p.vector_db_store = self
p.vector_store_store = self
elif api == Api.datasetio:
p.dataset_store = self
elif api == Api.scoring:
@ -134,12 +134,15 @@ class CommonRoutingTableImpl(RoutingTable):
from .scoring_functions import ScoringFunctionsRoutingTable
from .shields import ShieldsRoutingTable
from .toolgroups import ToolGroupsRoutingTable
from .vector_stores import VectorStoresRoutingTable
def apiname_object():
if isinstance(self, ModelsRoutingTable):
return ("Inference", "model")
elif isinstance(self, ShieldsRoutingTable):
return ("Safety", "shield")
elif isinstance(self, VectorStoresRoutingTable):
return ("VectorIO", "vector_store")
elif isinstance(self, DatasetsRoutingTable):
return ("DatasetIO", "dataset")
elif isinstance(self, ScoringFunctionsRoutingTable):

292
llama_stack/core/routing_tables/vector_stores.py Normal file
View file

@ -0,0 +1,292 @@
# 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 Any
from llama_stack.apis.common.errors import ModelNotFoundError, ModelTypeError
from llama_stack.apis.models import ModelType
from llama_stack.apis.resource import ResourceType
# Removed VectorStores import to avoid exposing public API
from llama_stack.apis.vector_io.vector_io import (
SearchRankingOptions,
VectorStoreChunkingStrategy,
VectorStoreDeleteResponse,
VectorStoreFileContentsResponse,
VectorStoreFileDeleteResponse,
VectorStoreFileObject,
VectorStoreFileStatus,
VectorStoreObject,
VectorStoreSearchResponsePage,
)
from llama_stack.core.datatypes import (
VectorStoreWithOwner,
)
from llama_stack.log import get_logger
from .common import CommonRoutingTableImpl, lookup_model
logger = get_logger(name=__name__, category="core::routing_tables")
class VectorStoresRoutingTable(CommonRoutingTableImpl):
"""Internal routing table for vector_store operations.
Does not inherit from VectorStores to avoid exposing public API endpoints.
Only provides internal routing functionality for VectorIORouter.
"""
# Internal methods only - no public API exposure
async def register_vector_store(
self,
vector_store_id: str,
embedding_model: str,
embedding_dimension: int | None = 384,
provider_id: str | None = None,
provider_vector_store_id: str | None = None,
vector_store_name: str | None = None,
) -> Any:
if provider_id is None:
if len(self.impls_by_provider_id) > 0:
provider_id = list(self.impls_by_provider_id.keys())[0]
if len(self.impls_by_provider_id) > 1:
logger.warning(
f"No provider specified and multiple providers available. Arbitrarily selected the first provider {provider_id}."
)
else:
raise ValueError("No provider available. Please configure a vector_io provider.")
model = await lookup_model(self, embedding_model)
if model is None:
raise ModelNotFoundError(embedding_model)
if model.model_type != ModelType.embedding:
raise ModelTypeError(embedding_model, model.model_type, ModelType.embedding)
vector_store = VectorStoreWithOwner(
identifier=vector_store_id,
type=ResourceType.vector_store.value,
provider_id=provider_id,
provider_resource_id=provider_vector_store_id,
embedding_model=embedding_model,
embedding_dimension=embedding_dimension,
vector_store_name=vector_store_name,
)
await self.register_object(vector_store)
return vector_store
async def openai_retrieve_vector_store(
self,
vector_store_id: str,
) -> VectorStoreObject:
await self.assert_action_allowed("read", "vector_store", vector_store_id)
provider = await self.get_provider_impl(vector_store_id)
return await provider.openai_retrieve_vector_store(vector_store_id)
async def openai_update_vector_store(
self,
vector_store_id: str,
name: str | None = None,
expires_after: dict[str, Any] | None = None,
metadata: dict[str, Any] | None = None,
) -> VectorStoreObject:
await self.assert_action_allowed("update", "vector_store", vector_store_id)
provider = await self.get_provider_impl(vector_store_id)
return await provider.openai_update_vector_store(
vector_store_id=vector_store_id,
name=name,
expires_after=expires_after,
metadata=metadata,
)
async def openai_delete_vector_store(
self,
vector_store_id: str,
) -> VectorStoreDeleteResponse:
await self.assert_action_allowed("delete", "vector_store", vector_store_id)
provider = await self.get_provider_impl(vector_store_id)
result = await provider.openai_delete_vector_store(vector_store_id)
await self.unregister_vector_store(vector_store_id)
return result
async def unregister_vector_store(self, vector_store_id: str) -> None:
"""Remove the vector store from the routing table registry."""
try:
vector_store_obj = await self.get_object_by_identifier("vector_store", vector_store_id)
if vector_store_obj:
await self.unregister_object(vector_store_obj)
except Exception as e:
# Log the error but don't fail the operation
logger.warning(f"Failed to unregister vector store {vector_store_id} from routing table: {e}")
async def openai_search_vector_store(
self,
vector_store_id: str,
query: str | list[str],
filters: dict[str, Any] | None = None,
max_num_results: int | None = 10,
ranking_options: SearchRankingOptions | None = None,
rewrite_query: bool | None = False,
search_mode: str | None = "vector",
) -> VectorStoreSearchResponsePage:
await self.assert_action_allowed("read", "vector_store", vector_store_id)
provider = await self.get_provider_impl(vector_store_id)
return await provider.openai_search_vector_store(
vector_store_id=vector_store_id,
query=query,
filters=filters,
max_num_results=max_num_results,
ranking_options=ranking_options,
rewrite_query=rewrite_query,
search_mode=search_mode,
)
async def openai_attach_file_to_vector_store(
self,
vector_store_id: str,
file_id: str,
attributes: dict[str, Any] | None = None,
chunking_strategy: VectorStoreChunkingStrategy | None = None,
) -> VectorStoreFileObject:
await self.assert_action_allowed("update", "vector_store", vector_store_id)
provider = await self.get_provider_impl(vector_store_id)
return await provider.openai_attach_file_to_vector_store(
vector_store_id=vector_store_id,
file_id=file_id,
attributes=attributes,
chunking_strategy=chunking_strategy,
)
async def openai_list_files_in_vector_store(
self,
vector_store_id: str,
limit: int | None = 20,
order: str | None = "desc",
after: str | None = None,
before: str | None = None,
filter: VectorStoreFileStatus | None = None,
) -> list[VectorStoreFileObject]:
await self.assert_action_allowed("read", "vector_store", vector_store_id)
provider = await self.get_provider_impl(vector_store_id)
return await provider.openai_list_files_in_vector_store(
vector_store_id=vector_store_id,
limit=limit,
order=order,
after=after,
before=before,
filter=filter,
)
async def openai_retrieve_vector_store_file(
self,
vector_store_id: str,
file_id: str,
) -> VectorStoreFileObject:
await self.assert_action_allowed("read", "vector_store", vector_store_id)
provider = await self.get_provider_impl(vector_store_id)
return await provider.openai_retrieve_vector_store_file(
vector_store_id=vector_store_id,
file_id=file_id,
)
async def openai_retrieve_vector_store_file_contents(
self,
vector_store_id: str,
file_id: str,
) -> VectorStoreFileContentsResponse:
await self.assert_action_allowed("read", "vector_store", vector_store_id)
provider = await self.get_provider_impl(vector_store_id)
return await provider.openai_retrieve_vector_store_file_contents(
vector_store_id=vector_store_id,
file_id=file_id,
)
async def openai_update_vector_store_file(
self,
vector_store_id: str,
file_id: str,
attributes: dict[str, Any],
) -> VectorStoreFileObject:
await self.assert_action_allowed("update", "vector_store", vector_store_id)
provider = await self.get_provider_impl(vector_store_id)
return await provider.openai_update_vector_store_file(
vector_store_id=vector_store_id,
file_id=file_id,
attributes=attributes,
)
async def openai_delete_vector_store_file(
self,
vector_store_id: str,
file_id: str,
) -> VectorStoreFileDeleteResponse:
await self.assert_action_allowed("delete", "vector_store", vector_store_id)
provider = await self.get_provider_impl(vector_store_id)
return await provider.openai_delete_vector_store_file(
vector_store_id=vector_store_id,
file_id=file_id,
)
async def openai_create_vector_store_file_batch(
self,
vector_store_id: str,
file_ids: list[str],
attributes: dict[str, Any] | None = None,
chunking_strategy: Any | None = None,
):
await self.assert_action_allowed("update", "vector_store", vector_store_id)
provider = await self.get_provider_impl(vector_store_id)
return await provider.openai_create_vector_store_file_batch(
vector_store_id=vector_store_id,
file_ids=file_ids,
attributes=attributes,
chunking_strategy=chunking_strategy,
)
async def openai_retrieve_vector_store_file_batch(
self,
batch_id: str,
vector_store_id: str,
):
await self.assert_action_allowed("read", "vector_store", vector_store_id)
provider = await self.get_provider_impl(vector_store_id)
return await provider.openai_retrieve_vector_store_file_batch(
batch_id=batch_id,
vector_store_id=vector_store_id,
)
async def openai_list_files_in_vector_store_file_batch(
self,
batch_id: str,
vector_store_id: str,
after: str | None = None,
before: str | None = None,
filter: str | None = None,
limit: int | None = 20,
order: str | None = "desc",
):
await self.assert_action_allowed("read", "vector_store", vector_store_id)
provider = await self.get_provider_impl(vector_store_id)
return await provider.openai_list_files_in_vector_store_file_batch(
batch_id=batch_id,
vector_store_id=vector_store_id,
after=after,
before=before,
filter=filter,
limit=limit,
order=order,
)
async def openai_cancel_vector_store_file_batch(
self,
batch_id: str,
vector_store_id: str,
):
await self.assert_action_allowed("update", "vector_store", vector_store_id)
provider = await self.get_provider_impl(vector_store_id)
return await provider.openai_cancel_vector_store_file_batch(
batch_id=batch_id,
vector_store_id=vector_store_id,
)

23
llama_stack/core/server/auth_providers.py
View file

@ -72,13 +72,30 @@ class AuthProvider(ABC):
def get_attributes_from_claims(claims: dict[str, str], mapping: dict[str, str]) -> dict[str, list[str]]:
attributes: dict[str, list[str]] = {}
for claim_key, attribute_key in mapping.items():
if claim_key not in claims:
# First try dot notation for nested traversal (e.g., "resource_access.llamastack.roles")
# Then fall back to literal key with dots (e.g., "my.dotted.key")
claim: object = claims
keys = claim_key.split(".")
for key in keys:
if isinstance(claim, dict) and key in claim:
claim = claim[key]
else:
claim = None
break
if claim is None and claim_key in claims:
# Fall back to checking if claim_key exists as a literal key
claim = claims[claim_key]
if claim is None:
continue
claim = claims[claim_key]
if isinstance(claim, list):
values = claim
else:
elif isinstance(claim, str):
values = claim.split()
else:
continue
if attribute_key in attributes:
attributes[attribute_key].extend(values)

18
llama_stack/core/server/quota.py
View file

@ -10,10 +10,10 @@ from datetime import UTC, datetime, timedelta
from starlette.types import ASGIApp, Receive, Scope, Send
from llama_stack.core.storage.datatypes import KVStoreReference, StorageBackendType
from llama_stack.log import get_logger
from llama_stack.providers.utils.kvstore.api import KVStore
from llama_stack.providers.utils.kvstore.config import KVStoreConfig, SqliteKVStoreConfig
from llama_stack.providers.utils.kvstore.kvstore import kvstore_impl
from llama_stack.providers.utils.kvstore.kvstore import _KVSTORE_BACKENDS, kvstore_impl
logger = get_logger(name=__name__, category="core::server")
@ -33,7 +33,7 @@ class QuotaMiddleware:
def __init__(
self,
app: ASGIApp,
kv_config: KVStoreConfig,
kv_config: KVStoreReference,
anonymous_max_requests: int,
authenticated_max_requests: int,
window_seconds: int = 86400,
@ -45,15 +45,15 @@ class QuotaMiddleware:
self.authenticated_max_requests = authenticated_max_requests
self.window_seconds = window_seconds
if isinstance(self.kv_config, SqliteKVStoreConfig):
logger.warning(
"QuotaMiddleware: Using SQLite backend. Expiry/TTL is not enforced; cleanup is manual. "
f"window_seconds={self.window_seconds}"
)
async def _get_kv(self) -> KVStore:
if self.kv is None:
self.kv = await kvstore_impl(self.kv_config)
backend_config = _KVSTORE_BACKENDS.get(self.kv_config.backend)
if backend_config and backend_config.type == StorageBackendType.KV_SQLITE:
logger.warning(
"QuotaMiddleware: Using SQLite backend. Expiry/TTL is not enforced; cleanup is manual. "
f"window_seconds={self.window_seconds}"
)
return self.kv
async def __call__(self, scope: Scope, receive: Receive, send: Send):

5
llama_stack/core/server/server.py
View file

@ -56,7 +56,7 @@ from llama_stack.core.stack import (
from llama_stack.core.utils.config import redact_sensitive_fields
from llama_stack.core.utils.config_resolution import Mode, resolve_config_or_distro
from llama_stack.core.utils.context import preserve_contexts_async_generator
from llama_stack.log import get_logger
from llama_stack.log import get_logger, setup_logging
from llama_stack.providers.datatypes import Api
from llama_stack.providers.inline.telemetry.meta_reference.config import TelemetryConfig
from llama_stack.providers.inline.telemetry.meta_reference.telemetry import (
@ -374,6 +374,9 @@ def create_app() -> StackApp:
Returns:
Configured StackApp instance.
"""
# Initialize logging from environment variables first
setup_logging()
config_file = os.getenv("LLAMA_STACK_CONFIG")
if config_file is None:
raise ValueError("LLAMA_STACK_CONFIG environment variable is required")

112
llama_stack/core/stack.py
View file

@ -35,13 +35,23 @@ from llama_stack.apis.telemetry import Telemetry
from llama_stack.apis.tools import RAGToolRuntime, ToolGroups, ToolRuntime
from llama_stack.apis.vector_io import VectorIO
from llama_stack.core.conversations.conversations import ConversationServiceConfig, ConversationServiceImpl
from llama_stack.core.datatypes import Provider, StackRunConfig
from llama_stack.core.datatypes import Provider, StackRunConfig, VectorStoresConfig
from llama_stack.core.distribution import get_provider_registry
from llama_stack.core.inspect import DistributionInspectConfig, DistributionInspectImpl
from llama_stack.core.prompts.prompts import PromptServiceConfig, PromptServiceImpl
from llama_stack.core.providers import ProviderImpl, ProviderImplConfig
from llama_stack.core.resolver import ProviderRegistry, resolve_impls
from llama_stack.core.routing_tables.common import CommonRoutingTableImpl
from llama_stack.core.storage.datatypes import (
InferenceStoreReference,
KVStoreReference,
ServerStoresConfig,
SqliteKVStoreConfig,
SqliteSqlStoreConfig,
SqlStoreReference,
StorageBackendConfig,
StorageConfig,
)
from llama_stack.core.store.registry import create_dist_registry
from llama_stack.core.utils.dynamic import instantiate_class_type
from llama_stack.log import get_logger
@ -98,33 +108,9 @@ REGISTRY_REFRESH_TASK = None
TEST_RECORDING_CONTEXT = None
async def validate_default_embedding_model(impls: dict[Api, Any]):
"""Validate that at most one embedding model is marked as default."""
if Api.models not in impls:
return
models_impl = impls[Api.models]
response = await models_impl.list_models()
models_list = response.data if hasattr(response, "data") else response
default_embedding_models = []
for model in models_list:
if model.model_type == "embedding" and model.metadata.get("default_configured") is True:
default_embedding_models.append(model.identifier)
if len(default_embedding_models) > 1:
raise ValueError(
f"Multiple embedding models marked as default_configured=True: {default_embedding_models}. "
"Only one embedding model can be marked as default."
)
if default_embedding_models:
logger.info(f"Default embedding model configured: {default_embedding_models[0]}")
async def register_resources(run_config: StackRunConfig, impls: dict[Api, Any]):
for rsrc, api, register_method, list_method in RESOURCES:
objects = getattr(run_config, rsrc)
objects = getattr(run_config.registered_resources, rsrc)
if api not in impls:
continue
@ -152,7 +138,41 @@ async def register_resources(run_config: StackRunConfig, impls: dict[Api, Any]):
f"{rsrc.capitalize()}: {obj.identifier} served by {obj.provider_id}",
)
await validate_default_embedding_model(impls)
async def validate_vector_stores_config(vector_stores_config: VectorStoresConfig | None, impls: dict[Api, Any]):
"""Validate vector stores configuration."""
if vector_stores_config is None:
return
default_embedding_model = vector_stores_config.default_embedding_model
if default_embedding_model is None:
return
provider_id = default_embedding_model.provider_id
model_id = default_embedding_model.model_id
default_model_id = f"{provider_id}/{model_id}"
if Api.models not in impls:
raise ValueError(f"Models API is not available but vector_stores config requires model '{default_model_id}'")
models_impl = impls[Api.models]
response = await models_impl.list_models()
models_list = {m.identifier: m for m in response.data if m.model_type == "embedding"}
default_model = models_list.get(default_model_id)
if default_model is None:
raise ValueError(f"Embedding model '{default_model_id}' not found. Available embedding models: {models_list}")
embedding_dimension = default_model.metadata.get("embedding_dimension")
if embedding_dimension is None:
raise ValueError(f"Embedding model '{default_model_id}' is missing 'embedding_dimension' in metadata")
try:
int(embedding_dimension)
except ValueError as err:
raise ValueError(f"Embedding dimension '{embedding_dimension}' cannot be converted to an integer") from err
logger.debug(f"Validated default embedding model: {default_model_id} (dimension: {embedding_dimension})")
class EnvVarError(Exception):
@ -329,6 +349,25 @@ def add_internal_implementations(impls: dict[Api, Any], run_config: StackRunConf
impls[Api.conversations] = conversations_impl
def _initialize_storage(run_config: StackRunConfig):
kv_backends: dict[str, StorageBackendConfig] = {}
sql_backends: dict[str, StorageBackendConfig] = {}
for backend_name, backend_config in run_config.storage.backends.items():
type = backend_config.type.value
if type.startswith("kv_"):
kv_backends[backend_name] = backend_config
elif type.startswith("sql_"):
sql_backends[backend_name] = backend_config
else:
raise ValueError(f"Unknown storage backend type: {type}")
from llama_stack.providers.utils.kvstore.kvstore import register_kvstore_backends
from llama_stack.providers.utils.sqlstore.sqlstore import register_sqlstore_backends
register_kvstore_backends(kv_backends)
register_sqlstore_backends(sql_backends)
class Stack:
def __init__(self, run_config: StackRunConfig, provider_registry: ProviderRegistry | None = None):
self.run_config = run_config
@ -347,7 +386,11 @@ class Stack:
TEST_RECORDING_CONTEXT.__enter__()
logger.info(f"API recording enabled: mode={os.environ.get('LLAMA_STACK_TEST_INFERENCE_MODE')}")
dist_registry, _ = await create_dist_registry(self.run_config.metadata_store, self.run_config.image_name)
_initialize_storage(self.run_config)
stores = self.run_config.storage.stores
if not stores.metadata:
raise ValueError("storage.stores.metadata must be configured with a kv_* backend")
dist_registry, _ = await create_dist_registry(stores.metadata, self.run_config.image_name)
policy = self.run_config.server.auth.access_policy if self.run_config.server.auth else []
internal_impls = {}
@ -367,8 +410,8 @@ class Stack:
await impls[Api.conversations].initialize()
await register_resources(self.run_config, impls)
await refresh_registry_once(impls)
await validate_vector_stores_config(self.run_config.vector_stores, impls)
self.impls = impls
def create_registry_refresh_task(self):
@ -488,5 +531,16 @@ def run_config_from_adhoc_config_spec(
image_name="distro-test",
apis=list(provider_configs_by_api.keys()),
providers=provider_configs_by_api,
storage=StorageConfig(
backends={
"kv_default": SqliteKVStoreConfig(db_path=f"{distro_dir}/kvstore.db"),
"sql_default": SqliteSqlStoreConfig(db_path=f"{distro_dir}/sql_store.db"),
},
stores=ServerStoresConfig(
metadata=KVStoreReference(backend="kv_default", namespace="registry"),
inference=InferenceStoreReference(backend="sql_default", table_name="inference_store"),
conversations=SqlStoreReference(backend="sql_default", table_name="openai_conversations"),
),
),
)
return config

5
llama_stack/core/storage/__init__.py Normal file
View file

@ -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.

283
llama_stack/core/storage/datatypes.py Normal file
View file

@ -0,0 +1,283 @@
# 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 re
from abc import abstractmethod
from enum import StrEnum
from pathlib import Path
from typing import Annotated, Literal
from pydantic import BaseModel, Field, field_validator
class StorageBackendType(StrEnum):
KV_REDIS = "kv_redis"
KV_SQLITE = "kv_sqlite"
KV_POSTGRES = "kv_postgres"
KV_MONGODB = "kv_mongodb"
SQL_SQLITE = "sql_sqlite"
SQL_POSTGRES = "sql_postgres"
class CommonConfig(BaseModel):
namespace: str | None = Field(
default=None,
description="All keys will be prefixed with this namespace",
)
class RedisKVStoreConfig(CommonConfig):
type: Literal[StorageBackendType.KV_REDIS] = StorageBackendType.KV_REDIS
host: str = "localhost"
port: int = 6379
@property
def url(self) -> str:
return f"redis://{self.host}:{self.port}"
@classmethod
def pip_packages(cls) -> list[str]:
return ["redis"]
@classmethod
def sample_run_config(cls):
return {
"type": StorageBackendType.KV_REDIS.value,
"host": "${env.REDIS_HOST:=localhost}",
"port": "${env.REDIS_PORT:=6379}",
}
class SqliteKVStoreConfig(CommonConfig):
type: Literal[StorageBackendType.KV_SQLITE] = StorageBackendType.KV_SQLITE
db_path: str = Field(
description="File path for the sqlite database",
)
@classmethod
def pip_packages(cls) -> list[str]:
return ["aiosqlite"]
@classmethod
def sample_run_config(cls, __distro_dir__: str, db_name: str = "kvstore.db"):
return {
"type": StorageBackendType.KV_SQLITE.value,
"db_path": "${env.SQLITE_STORE_DIR:=" + __distro_dir__ + "}/" + db_name,
}
class PostgresKVStoreConfig(CommonConfig):
type: Literal[StorageBackendType.KV_POSTGRES] = StorageBackendType.KV_POSTGRES
host: str = "localhost"
port: int | str = 5432
db: str = "llamastack"
user: str
password: str | None = None
ssl_mode: str | None = None
ca_cert_path: str | None = None
table_name: str = "llamastack_kvstore"
@classmethod
def sample_run_config(cls, table_name: str = "llamastack_kvstore", **kwargs):
return {
"type": StorageBackendType.KV_POSTGRES.value,
"host": "${env.POSTGRES_HOST:=localhost}",
"port": "${env.POSTGRES_PORT:=5432}",
"db": "${env.POSTGRES_DB:=llamastack}",
"user": "${env.POSTGRES_USER:=llamastack}",
"password": "${env.POSTGRES_PASSWORD:=llamastack}",
"table_name": "${env.POSTGRES_TABLE_NAME:=" + table_name + "}",
}
@classmethod
@field_validator("table_name")
def validate_table_name(cls, v: str) -> str:
# PostgreSQL identifiers rules:
# - Must start with a letter or underscore
# - Can contain letters, numbers, and underscores
# - Maximum length is 63 bytes
pattern = r"^[a-zA-Z_][a-zA-Z0-9_]*$"
if not re.match(pattern, v):
raise ValueError(
"Invalid table name. Must start with letter or underscore and contain only letters, numbers, and underscores"
)
if len(v) > 63:
raise ValueError("Table name must be less than 63 characters")
return v
@classmethod
def pip_packages(cls) -> list[str]:
return ["psycopg2-binary"]
class MongoDBKVStoreConfig(CommonConfig):
type: Literal[StorageBackendType.KV_MONGODB] = StorageBackendType.KV_MONGODB
host: str = "localhost"
port: int = 27017
db: str = "llamastack"
user: str | None = None
password: str | None = None
collection_name: str = "llamastack_kvstore"
@classmethod
def pip_packages(cls) -> list[str]:
return ["pymongo"]
@classmethod
def sample_run_config(cls, collection_name: str = "llamastack_kvstore"):
return {
"type": StorageBackendType.KV_MONGODB.value,
"host": "${env.MONGODB_HOST:=localhost}",
"port": "${env.MONGODB_PORT:=5432}",
"db": "${env.MONGODB_DB}",
"user": "${env.MONGODB_USER}",
"password": "${env.MONGODB_PASSWORD}",
"collection_name": "${env.MONGODB_COLLECTION_NAME:=" + collection_name + "}",
}
class SqlAlchemySqlStoreConfig(BaseModel):
@property
@abstractmethod
def engine_str(self) -> str: ...
# TODO: move this when we have a better way to specify dependencies with internal APIs
@classmethod
def pip_packages(cls) -> list[str]:
return ["sqlalchemy[asyncio]"]
class SqliteSqlStoreConfig(SqlAlchemySqlStoreConfig):
type: Literal[StorageBackendType.SQL_SQLITE] = StorageBackendType.SQL_SQLITE
db_path: str = Field(
description="Database path, e.g. ~/.llama/distributions/ollama/sqlstore.db",
)
@property
def engine_str(self) -> str:
return "sqlite+aiosqlite:///" + Path(self.db_path).expanduser().as_posix()
@classmethod
def sample_run_config(cls, __distro_dir__: str, db_name: str = "sqlstore.db"):
return {
"type": StorageBackendType.SQL_SQLITE.value,
"db_path": "${env.SQLITE_STORE_DIR:=" + __distro_dir__ + "}/" + db_name,
}
@classmethod
def pip_packages(cls) -> list[str]:
return super().pip_packages() + ["aiosqlite"]
class PostgresSqlStoreConfig(SqlAlchemySqlStoreConfig):
type: Literal[StorageBackendType.SQL_POSTGRES] = StorageBackendType.SQL_POSTGRES
host: str = "localhost"
port: int | str = 5432
db: str = "llamastack"
user: str
password: str | None = None
@property
def engine_str(self) -> str:
return f"postgresql+asyncpg://{self.user}:{self.password}@{self.host}:{self.port}/{self.db}"
@classmethod
def pip_packages(cls) -> list[str]:
return super().pip_packages() + ["asyncpg"]
@classmethod
def sample_run_config(cls, **kwargs):
return {
"type": StorageBackendType.SQL_POSTGRES.value,
"host": "${env.POSTGRES_HOST:=localhost}",
"port": "${env.POSTGRES_PORT:=5432}",
"db": "${env.POSTGRES_DB:=llamastack}",
"user": "${env.POSTGRES_USER:=llamastack}",
"password": "${env.POSTGRES_PASSWORD:=llamastack}",
}
# reference = (backend_name, table_name)
class SqlStoreReference(BaseModel):
"""A reference to a 'SQL-like' persistent store. A table name must be provided."""
table_name: str = Field(
description="Name of the table to use for the SqlStore",
)
backend: str = Field(
description="Name of backend from storage.backends",
)
# reference = (backend_name, namespace)
class KVStoreReference(BaseModel):
"""A reference to a 'key-value' persistent store. A namespace must be provided."""
namespace: str = Field(
description="Key prefix for KVStore backends",
)
backend: str = Field(
description="Name of backend from storage.backends",
)
StorageBackendConfig = Annotated[
RedisKVStoreConfig
| SqliteKVStoreConfig
| PostgresKVStoreConfig
| MongoDBKVStoreConfig
| SqliteSqlStoreConfig
| PostgresSqlStoreConfig,
Field(discriminator="type"),
]
class InferenceStoreReference(SqlStoreReference):
"""Inference store configuration with queue tuning."""
max_write_queue_size: int = Field(
default=10000,
description="Max queued writes for inference store",
)
num_writers: int = Field(
default=4,
description="Number of concurrent background writers",
)
class ResponsesStoreReference(InferenceStoreReference):
"""Responses store configuration with queue tuning."""
class ServerStoresConfig(BaseModel):
metadata: KVStoreReference | None = Field(
default=None,
description="Metadata store configuration (uses KV backend)",
)
inference: InferenceStoreReference | None = Field(
default=None,
description="Inference store configuration (uses SQL backend)",
)
conversations: SqlStoreReference | None = Field(
default=None,
description="Conversations store configuration (uses SQL backend)",
)
responses: ResponsesStoreReference | None = Field(
default=None,
description="Responses store configuration (uses SQL backend)",
)
class StorageConfig(BaseModel):
backends: dict[str, StorageBackendConfig] = Field(
description="Named backend configurations (e.g., 'default', 'cache')",
)
stores: ServerStoresConfig = Field(
default_factory=lambda: ServerStoresConfig(),
description="Named references to storage backends used by the stack core",
)

13
llama_stack/core/store/registry.py
View file

@ -11,10 +11,9 @@ from typing import Protocol
import pydantic
from llama_stack.core.datatypes import RoutableObjectWithProvider
from llama_stack.core.utils.config_dirs import DISTRIBS_BASE_DIR
from llama_stack.core.storage.datatypes import KVStoreReference
from llama_stack.log import get_logger
from llama_stack.providers.utils.kvstore import KVStore, kvstore_impl
from llama_stack.providers.utils.kvstore.config import KVStoreConfig, SqliteKVStoreConfig
logger = get_logger(__name__, category="core::registry")
@ -191,16 +190,10 @@ class CachedDiskDistributionRegistry(DiskDistributionRegistry):
async def create_dist_registry(
metadata_store: KVStoreConfig | None,
image_name: str,
metadata_store: KVStoreReference, image_name: str
) -> tuple[CachedDiskDistributionRegistry, KVStore]:
# instantiate kvstore for storing and retrieving distribution metadata
if metadata_store:
dist_kvstore = await kvstore_impl(metadata_store)
else:
dist_kvstore = await kvstore_impl(
SqliteKVStoreConfig(db_path=(DISTRIBS_BASE_DIR / image_name / "kvstore.db").as_posix())
)
dist_kvstore = await kvstore_impl(metadata_store)
dist_registry = CachedDiskDistributionRegistry(dist_kvstore)
await dist_registry.initialize()
return dist_registry, dist_kvstore

2
llama_stack/core/ui/README.md
View file

@ -9,7 +9,7 @@
1. Start up Llama Stack API server. More details [here](https://llamastack.github.io/latest/getting_started/index.htmll).
```
llama stack build --distro together --image-type venv
llama stack list-deps together | xargs -L1 uv pip install
llama stack run together
```

14
llama_stack/core/ui/page/playground/tools.py
View file

@ -32,7 +32,7 @@ def tool_chat_page():
tool_groups_list = [tool_group.identifier for tool_group in tool_groups]
mcp_tools_list = [tool for tool in tool_groups_list if tool.startswith("mcp::")]
builtin_tools_list = [tool for tool in tool_groups_list if not tool.startswith("mcp::")]
selected_vector_dbs = []
selected_vector_stores = []
def reset_agent():
st.session_state.clear()
@ -55,13 +55,13 @@ def tool_chat_page():
)
if "builtin::rag" in toolgroup_selection:
vector_dbs = llama_stack_api.client.vector_dbs.list() or []
if not vector_dbs:
vector_stores = llama_stack_api.client.vector_stores.list() or []
if not vector_stores:
st.info("No vector databases available for selection.")
vector_dbs = [vector_db.identifier for vector_db in vector_dbs]
selected_vector_dbs = st.multiselect(
vector_stores = [vector_store.identifier for vector_store in vector_stores]
selected_vector_stores = st.multiselect(
label="Select Document Collections to use in RAG queries",
options=vector_dbs,
options=vector_stores,
on_change=reset_agent,
)
@ -119,7 +119,7 @@ def tool_chat_page():
tool_dict = dict(
name="builtin::rag",
args={
"vector_db_ids": list(selected_vector_dbs),
"vector_store_ids": list(selected_vector_stores),
},
)
toolgroup_selection[i] = tool_dict