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fix: resolve unit test failures in starter distribution with MongoDB provider

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This commit is contained in:
Young Han 2025-10-10 11:30:08 -07:00
parent 885631d071
commit 572062de9d
5 changed files with 36 additions and 36 deletions
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14
docs/docs/providers/vector_io/remote_mongodb.mdx
View file

@ -255,13 +255,13 @@ For general MongoDB documentation, visit [MongoDB Documentation](https://docs.mo
## Sample Configuration
```yaml
connection_string: ${env.MONGODB_CONNECTION_STRING}
database_name: llama_stack
index_name: vector_index
path_field: embedding
similarity_metric: cosine
max_pool_size: 100
timeout_ms: 30000
connection_string: ${env.MONGODB_CONNECTION_STRING:=}
database_name: ${env.MONGODB_DATABASE_NAME:=llama_stack}
index_name: ${env.MONGODB_INDEX_NAME:=vector_index}
path_field: ${env.MONGODB_PATH_FIELD:=embedding}
similarity_metric: ${env.MONGODB_SIMILARITY_METRIC:=cosine}
max_pool_size: ${env.MONGODB_MAX_POOL_SIZE:=100}
timeout_ms: ${env.MONGODB_TIMEOUT_MS:=30000}
kvstore:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/mongodb_registry.db

14
llama_stack/distributions/ci-tests/run.yaml
View file

@ -132,13 +132,13 @@ providers:
- provider_id: ${env.MONGODB_CONNECTION_STRING:+mongodb_atlas}
provider_type: remote::mongodb
config:
connection_string: ${env.MONGODB_CONNECTION_STRING}
database_name: llama_stack
index_name: vector_index
path_field: embedding
similarity_metric: cosine
max_pool_size: 100
timeout_ms: 30000
connection_string: ${env.MONGODB_CONNECTION_STRING:=}
database_name: ${env.MONGODB_DATABASE_NAME:=llama_stack}
index_name: ${env.MONGODB_INDEX_NAME:=vector_index}
path_field: ${env.MONGODB_PATH_FIELD:=embedding}
similarity_metric: ${env.MONGODB_SIMILARITY_METRIC:=cosine}
max_pool_size: ${env.MONGODB_MAX_POOL_SIZE:=100}
timeout_ms: ${env.MONGODB_TIMEOUT_MS:=30000}
kvstore:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/mongodb_registry.db

14
llama_stack/distributions/starter-gpu/run.yaml
View file

@ -132,13 +132,13 @@ providers:
- provider_id: ${env.MONGODB_CONNECTION_STRING:+mongodb_atlas}
provider_type: remote::mongodb
config:
connection_string: ${env.MONGODB_CONNECTION_STRING}
database_name: llama_stack
index_name: vector_index
path_field: embedding
similarity_metric: cosine
max_pool_size: 100
timeout_ms: 30000
connection_string: ${env.MONGODB_CONNECTION_STRING:=}
database_name: ${env.MONGODB_DATABASE_NAME:=llama_stack}
index_name: ${env.MONGODB_INDEX_NAME:=vector_index}
path_field: ${env.MONGODB_PATH_FIELD:=embedding}
similarity_metric: ${env.MONGODB_SIMILARITY_METRIC:=cosine}
max_pool_size: ${env.MONGODB_MAX_POOL_SIZE:=100}
timeout_ms: ${env.MONGODB_TIMEOUT_MS:=30000}
kvstore:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/mongodb_registry.db

14
llama_stack/distributions/starter/run.yaml
View file

@ -132,13 +132,13 @@ providers:
- provider_id: ${env.MONGODB_CONNECTION_STRING:+mongodb_atlas}
provider_type: remote::mongodb
config:
connection_string: ${env.MONGODB_CONNECTION_STRING}
database_name: llama_stack
index_name: vector_index
path_field: embedding
similarity_metric: cosine
max_pool_size: 100
timeout_ms: 30000
connection_string: ${env.MONGODB_CONNECTION_STRING:=}
database_name: ${env.MONGODB_DATABASE_NAME:=llama_stack}
index_name: ${env.MONGODB_INDEX_NAME:=vector_index}
path_field: ${env.MONGODB_PATH_FIELD:=embedding}
similarity_metric: ${env.MONGODB_SIMILARITY_METRIC:=cosine}
max_pool_size: ${env.MONGODB_MAX_POOL_SIZE:=100}
timeout_ms: ${env.MONGODB_TIMEOUT_MS:=30000}
kvstore:
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/mongodb_registry.db

16
llama_stack/providers/remote/vector_io/mongodb/config.py
View file

@ -40,25 +40,25 @@ class MongoDBVectorIOConfig(BaseModel):
max_pool_size: int = Field(default=100, description="Maximum connection pool size")
timeout_ms: int = Field(default=30000, description="Connection timeout in milliseconds")
# KV store for metadata
# KV store configuration
kvstore: KVStoreConfig = Field(description="Config for KV store backend for metadata storage")
@classmethod
def sample_run_config(
cls,
__distro_dir__: str,
connection_string: str = "${env.MONGODB_CONNECTION_STRING}",
database_name: str = "llama_stack",
connection_string: str = "${env.MONGODB_CONNECTION_STRING:=}",
database_name: str = "${env.MONGODB_DATABASE_NAME:=llama_stack}",
**kwargs: Any,
) -> dict[str, Any]:
return {
"connection_string": connection_string,
"database_name": database_name,
"index_name": "vector_index",
"path_field": "embedding",
"similarity_metric": "cosine",
"max_pool_size": 100,
"timeout_ms": 30000,
"index_name": "${env.MONGODB_INDEX_NAME:=vector_index}",
"path_field": "${env.MONGODB_PATH_FIELD:=embedding}",
"similarity_metric": "${env.MONGODB_SIMILARITY_METRIC:=cosine}",
"max_pool_size": "${env.MONGODB_MAX_POOL_SIZE:=100}",
"timeout_ms": "${env.MONGODB_TIMEOUT_MS:=30000}",
"kvstore": SqliteKVStoreConfig.sample_run_config(
__distro_dir__=__distro_dir__,
db_name="mongodb_registry.db",