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feat: ability to use postgres as store for starter distro
The starter distribution now comes with all the required packages to support persistent stores—like the agent store, metadata, and inference—using PostgreSQL. We’ve added a new run YAML file, run-with-postgres-store.yaml, to make this setup easy. The file is included in the container image, so users can simply override the entrypoint to use the Postgres-specific config. The documentation has been updated with instructions on how to do that. Closes: #2619 Signed-off-by: Sébastien Han <seb@redhat.com>
This commit is contained in:
Sébastien Han
Roy Belio
parent
392e01dc79
commit
3b34efb81a
14 changed files with 450 additions and 215 deletions
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@ -17,6 +17,11 @@ from llama_stack.core.datatypes import (
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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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@ -36,6 +41,7 @@ from llama_stack.providers.remote.vector_io.pgvector.config import (
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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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@ -181,6 +187,62 @@ def get_distribution_template(name: str = "starter") -> DistributionTemplate:
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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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@ -189,64 +251,10 @@ def get_distribution_template(name: str = "starter") -> DistributionTemplate:
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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=PostgresSqlStoreConfig.pip_packages(),
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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={
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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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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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@ -261,6 +269,43 @@ def get_distribution_template(name: str = "starter") -> DistributionTemplate:
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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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},
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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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