mirror of
https://github.com/meta-llama/llama-stack.git
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c672a5d792
## What does this PR do? The starter distribution now comes with all the required packages to support persistent stores—like the agent store, metadata, and inference—using PostgreSQL. Users can enable PostgreSQL support by setting the `ENABLE_POSTGRES_STORE=1` environment variable. This PR consolidates the functionality from the removed `postgres-demo` distribution into the starter distribution, reducing maintenance overhead. **Closes: #2619** **Supersedes: #2851** (rebased and updated) ## Changes Made 1. **Added PostgreSQL support to starter distribution** - New `run-with-postgres-store.yaml` configuration - Automatic config switching via `ENABLE_POSTGRES_STORE` environment variable - Removed separate `postgres-demo` distribution 2. **Updated to new build system** - Integrated postgres switching logic into Containerfile entrypoint - Uses new `storage_backends` and `storage_stores` API - Properly configured both PostgreSQL KV store and SQL store 3. **Updated dependencies** - Added `psycopg2-binary` and `asyncpg` to starter distribution - All postgres-related dependencies automatically included ## How to Use ### With Docker (PostgreSQL): ```bash docker run \ -e ENABLE_POSTGRES_STORE=1 \ -e POSTGRES_HOST=your_postgres_host \ -e POSTGRES_PORT=5432 \ -e POSTGRES_DB=llamastack \ -e POSTGRES_USER=llamastack \ -e POSTGRES_PASSWORD=llamastack \ -e OPENAI_API_KEY=your_key \ llamastack/distribution-starter ``` ### PostgreSQL environment variables: - `POSTGRES_HOST`: Postgres host (default: `localhost`) - `POSTGRES_PORT`: Postgres port (default: `5432`) - `POSTGRES_DB`: Postgres database name (default: `llamastack`) - `POSTGRES_USER`: Postgres username (default: `llamastack`) - `POSTGRES_PASSWORD`: Postgres password (default: `llamastack`) ## Test Plan All pre-commit hooks pass (mypy, ruff, distro-codegen) `llama stack list-deps starter` confirms psycopg2-binary is included Storage configuration correctly uses PostgreSQL backends Container builds successfully with postgres support ## Credits Original work by @leseb in #2851. Rebased and updated by @r-bit-rry to work with latest main. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Sébastien Han @leseb --------- Signed-off-by: Sébastien Han <seb@redhat.com> Co-authored-by: Sébastien Han <seb@redhat.com>
388 lines
14 KiB
Python
388 lines
14 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from typing import Any
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from llama_stack.core.datatypes import (
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BuildProvider,
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Provider,
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ProviderSpec,
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QualifiedModel,
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SafetyConfig,
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ShieldInput,
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ToolGroupInput,
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VectorStoresConfig,
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)
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from llama_stack.core.storage.datatypes import (
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InferenceStoreReference,
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KVStoreReference,
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SqlStoreReference,
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)
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from llama_stack.core.utils.dynamic import instantiate_class_type
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from llama_stack.distributions.template import DistributionTemplate, RunConfigSettings
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from llama_stack.providers.datatypes import RemoteProviderSpec
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from llama_stack.providers.inline.files.localfs.config import LocalfsFilesImplConfig
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from llama_stack.providers.inline.inference.sentence_transformers import (
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SentenceTransformersInferenceConfig,
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)
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from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
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from llama_stack.providers.inline.vector_io.milvus.config import MilvusVectorIOConfig
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from llama_stack.providers.inline.vector_io.sqlite_vec.config import (
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SQLiteVectorIOConfig,
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)
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from llama_stack.providers.registry.inference import available_providers
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from llama_stack.providers.remote.vector_io.chroma.config import ChromaVectorIOConfig
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from llama_stack.providers.remote.vector_io.pgvector.config import (
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PGVectorVectorIOConfig,
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)
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from llama_stack.providers.remote.vector_io.qdrant.config import QdrantVectorIOConfig
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from llama_stack.providers.remote.vector_io.weaviate.config import WeaviateVectorIOConfig
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from llama_stack.providers.utils.kvstore.config import PostgresKVStoreConfig
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from llama_stack.providers.utils.sqlstore.sqlstore import PostgresSqlStoreConfig
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def _get_config_for_provider(provider_spec: ProviderSpec) -> dict[str, Any]:
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"""Get configuration for a provider using its adapter's config class."""
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config_class = instantiate_class_type(provider_spec.config_class)
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if hasattr(config_class, "sample_run_config"):
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config: dict[str, Any] = config_class.sample_run_config()
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return config
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return {}
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ENABLED_INFERENCE_PROVIDERS = [
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"ollama",
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"vllm",
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"tgi",
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"fireworks",
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"together",
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"gemini",
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"vertexai",
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"groq",
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"sambanova",
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"anthropic",
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"openai",
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"cerebras",
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"nvidia",
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"bedrock",
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"azure",
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]
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INFERENCE_PROVIDER_IDS = {
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"ollama": "${env.OLLAMA_URL:+ollama}",
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"vllm": "${env.VLLM_URL:+vllm}",
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"tgi": "${env.TGI_URL:+tgi}",
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"cerebras": "${env.CEREBRAS_API_KEY:+cerebras}",
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"nvidia": "${env.NVIDIA_API_KEY:+nvidia}",
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"vertexai": "${env.VERTEX_AI_PROJECT:+vertexai}",
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"azure": "${env.AZURE_API_KEY:+azure}",
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}
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def get_remote_inference_providers() -> list[Provider]:
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# Filter out inline providers and some others - the starter distro only exposes remote providers
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remote_providers = [
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provider
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for provider in available_providers()
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if isinstance(provider, RemoteProviderSpec) and provider.adapter_type in ENABLED_INFERENCE_PROVIDERS
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]
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inference_providers = []
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for provider_spec in remote_providers:
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provider_type = provider_spec.adapter_type
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if provider_type in INFERENCE_PROVIDER_IDS:
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provider_id = INFERENCE_PROVIDER_IDS[provider_type]
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else:
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provider_id = provider_type.replace("-", "_").replace("::", "_")
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config = _get_config_for_provider(provider_spec)
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inference_providers.append(
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Provider(
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provider_id=provider_id,
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provider_type=f"remote::{provider_type}",
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config=config,
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)
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)
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return inference_providers
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def get_distribution_template(name: str = "starter") -> DistributionTemplate:
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remote_inference_providers = get_remote_inference_providers()
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providers = {
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"inference": [BuildProvider(provider_type=p.provider_type, module=p.module) for p in remote_inference_providers]
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+ [BuildProvider(provider_type="inline::sentence-transformers")],
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"vector_io": [
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BuildProvider(provider_type="inline::faiss"),
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BuildProvider(provider_type="inline::sqlite-vec"),
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BuildProvider(provider_type="inline::milvus"),
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BuildProvider(provider_type="remote::chromadb"),
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BuildProvider(provider_type="remote::pgvector"),
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BuildProvider(provider_type="remote::qdrant"),
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BuildProvider(provider_type="remote::weaviate"),
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],
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"files": [BuildProvider(provider_type="inline::localfs")],
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"safety": [
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BuildProvider(provider_type="inline::llama-guard"),
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BuildProvider(provider_type="inline::code-scanner"),
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],
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"agents": [BuildProvider(provider_type="inline::meta-reference")],
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"post_training": [BuildProvider(provider_type="inline::torchtune-cpu")],
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"eval": [BuildProvider(provider_type="inline::meta-reference")],
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"datasetio": [
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BuildProvider(provider_type="remote::huggingface"),
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BuildProvider(provider_type="inline::localfs"),
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],
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"scoring": [
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BuildProvider(provider_type="inline::basic"),
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BuildProvider(provider_type="inline::llm-as-judge"),
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BuildProvider(provider_type="inline::braintrust"),
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],
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"tool_runtime": [
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BuildProvider(provider_type="remote::brave-search"),
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BuildProvider(provider_type="remote::tavily-search"),
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BuildProvider(provider_type="inline::rag-runtime"),
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BuildProvider(provider_type="remote::model-context-protocol"),
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],
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"batches": [
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BuildProvider(provider_type="inline::reference"),
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],
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}
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files_provider = Provider(
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provider_id="meta-reference-files",
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provider_type="inline::localfs",
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config=LocalfsFilesImplConfig.sample_run_config(f"~/.llama/distributions/{name}"),
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)
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embedding_provider = Provider(
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provider_id="sentence-transformers",
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provider_type="inline::sentence-transformers",
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config=SentenceTransformersInferenceConfig.sample_run_config(),
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)
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default_tool_groups = [
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ToolGroupInput(
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toolgroup_id="builtin::websearch",
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provider_id="tavily-search",
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),
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ToolGroupInput(
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toolgroup_id="builtin::rag",
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provider_id="rag-runtime",
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),
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]
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default_shields = [
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# if the
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ShieldInput(
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shield_id="llama-guard",
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provider_id="${env.SAFETY_MODEL:+llama-guard}",
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provider_shield_id="${env.SAFETY_MODEL:=}",
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),
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ShieldInput(
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shield_id="code-scanner",
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provider_id="${env.CODE_SCANNER_MODEL:+code-scanner}",
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provider_shield_id="${env.CODE_SCANNER_MODEL:=}",
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),
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]
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postgres_config = PostgresSqlStoreConfig.sample_run_config()
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default_overrides = {
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"inference": remote_inference_providers + [embedding_provider],
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"vector_io": [
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Provider(
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provider_id="faiss",
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provider_type="inline::faiss",
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config=FaissVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
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),
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Provider(
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provider_id="sqlite-vec",
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provider_type="inline::sqlite-vec",
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config=SQLiteVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
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),
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Provider(
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provider_id="${env.MILVUS_URL:+milvus}",
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provider_type="inline::milvus",
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config=MilvusVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
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),
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Provider(
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provider_id="${env.CHROMADB_URL:+chromadb}",
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provider_type="remote::chromadb",
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config=ChromaVectorIOConfig.sample_run_config(
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f"~/.llama/distributions/{name}/",
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url="${env.CHROMADB_URL:=}",
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),
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),
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Provider(
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provider_id="${env.PGVECTOR_DB:+pgvector}",
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provider_type="remote::pgvector",
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config=PGVectorVectorIOConfig.sample_run_config(
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f"~/.llama/distributions/{name}",
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db="${env.PGVECTOR_DB:=}",
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user="${env.PGVECTOR_USER:=}",
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password="${env.PGVECTOR_PASSWORD:=}",
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),
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),
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Provider(
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provider_id="${env.QDRANT_URL:+qdrant}",
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provider_type="remote::qdrant",
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config=QdrantVectorIOConfig.sample_run_config(
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f"~/.llama/distributions/{name}",
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url="${env.QDRANT_URL:=}",
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),
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),
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Provider(
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provider_id="${env.WEAVIATE_CLUSTER_URL:+weaviate}",
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provider_type="remote::weaviate",
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config=WeaviateVectorIOConfig.sample_run_config(
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f"~/.llama/distributions/{name}",
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cluster_url="${env.WEAVIATE_CLUSTER_URL:=}",
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),
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),
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],
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"files": [files_provider],
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}
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return DistributionTemplate(
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name=name,
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distro_type="self_hosted",
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description="Quick start template for running Llama Stack with several popular providers. This distribution is intended for CPU-only environments.",
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container_image=None,
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template_path=None,
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providers=providers,
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additional_pip_packages=list(set(PostgresSqlStoreConfig.pip_packages() + PostgresKVStoreConfig.pip_packages())),
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run_configs={
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"run.yaml": RunConfigSettings(
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provider_overrides=default_overrides,
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default_models=[],
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default_tool_groups=default_tool_groups,
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default_shields=default_shields,
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vector_stores_config=VectorStoresConfig(
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default_provider_id="faiss",
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default_embedding_model=QualifiedModel(
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provider_id="sentence-transformers",
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model_id="nomic-ai/nomic-embed-text-v1.5",
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),
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),
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safety_config=SafetyConfig(
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default_shield_id="llama-guard",
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),
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),
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"run-with-postgres-store.yaml": RunConfigSettings(
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provider_overrides={
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**default_overrides,
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"agents": [
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Provider(
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provider_id="meta-reference",
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provider_type="inline::meta-reference",
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config=dict(
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persistence_store=postgres_config,
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responses_store=postgres_config,
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),
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)
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],
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"batches": [
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Provider(
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provider_id="reference",
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provider_type="inline::reference",
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config=dict(
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kvstore=KVStoreReference(
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backend="kv_postgres",
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namespace="batches",
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).model_dump(exclude_none=True),
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),
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)
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],
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},
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storage_backends={
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"kv_postgres": PostgresKVStoreConfig.sample_run_config(),
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"sql_postgres": postgres_config,
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},
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storage_stores={
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"metadata": KVStoreReference(
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backend="kv_postgres",
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namespace="registry",
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).model_dump(exclude_none=True),
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"inference": InferenceStoreReference(
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backend="sql_postgres",
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table_name="inference_store",
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).model_dump(exclude_none=True),
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"conversations": SqlStoreReference(
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backend="sql_postgres",
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table_name="openai_conversations",
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).model_dump(exclude_none=True),
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"prompts": KVStoreReference(
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backend="kv_postgres",
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namespace="prompts",
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).model_dump(exclude_none=True),
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},
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),
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},
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run_config_env_vars={
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"LLAMA_STACK_PORT": (
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"8321",
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"Port for the Llama Stack distribution server",
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),
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"FIREWORKS_API_KEY": (
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"",
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"Fireworks API Key",
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),
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"OPENAI_API_KEY": (
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"",
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"OpenAI API Key",
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),
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"GROQ_API_KEY": (
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"",
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"Groq API Key",
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),
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"ANTHROPIC_API_KEY": (
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"",
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"Anthropic API Key",
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),
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"GEMINI_API_KEY": (
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"",
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"Gemini API Key",
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),
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"VERTEX_AI_PROJECT": (
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"",
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"Google Cloud Project ID for Vertex AI",
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),
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"VERTEX_AI_LOCATION": (
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"us-central1",
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"Google Cloud Location for Vertex AI",
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),
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"SAMBANOVA_API_KEY": (
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"",
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"SambaNova API Key",
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),
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"VLLM_URL": (
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"http://localhost:8000/v1",
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"vLLM URL",
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),
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"VLLM_INFERENCE_MODEL": (
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"",
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"Optional vLLM Inference Model to register on startup",
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),
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"OLLAMA_URL": (
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"http://localhost:11434",
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"Ollama URL",
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),
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"AZURE_API_KEY": (
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"",
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"Azure API Key",
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),
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"AZURE_API_BASE": (
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"",
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"Azure API Base",
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),
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"AZURE_API_VERSION": (
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"",
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"Azure API Version",
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),
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"AZURE_API_TYPE": (
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"azure",
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"Azure API Type",
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),
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},
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)
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