add embedding model arg to db providers

Author: dminnear-rh

.env

@@ -14,6 +14,7 @@ WEB_SOURCES=["https://ai-on-openshift.io/getting-started/openshift/", "https://a
 CHUNK_SIZE=1024
 CHUNK_OVERLAP=40
 DB_TYPE=DRYRUN
+EMBEDDING_MODEL=sentence-transformers/all-mpnet-base-v2
 
 # === Redis ===
 REDIS_URL=redis://localhost:6379

config.py

@@ -62,36 +62,47 @@ def _init_db_provider(db_type: str) -> DBProvider:
         """
         Initialize the correct DBProvider subclass based on DB_TYPE.
         """
+        get = Config._get_required_env_var
         db_type = db_type.upper()
+        embedding_model = get("EMBEDDING_MODEL")
 
         if db_type == "REDIS":
-            url = Config._get_required_env_var("REDIS_URL")
+            url = get("REDIS_URL")
             index = os.getenv("REDIS_INDEX", "docs")
             schema = os.getenv("REDIS_SCHEMA", "redis_schema.yaml")
-            return RedisProvider(url, index, schema)
+            return RedisProvider(embedding_model, url, index, schema)
 
         elif db_type == "ELASTIC":
-            url = Config._get_required_env_var("ELASTIC_URL")
-            password = Config._get_required_env_var("ELASTIC_PASSWORD")
+            url = get("ELASTIC_URL")
+            password = get("ELASTIC_PASSWORD")
             index = os.getenv("ELASTIC_INDEX", "docs")
             user = os.getenv("ELASTIC_USER", "elastic")
-            return ElasticProvider(url, password, index, user)
+            return ElasticProvider(embedding_model, url, password, index, user)
 
         elif db_type == "PGVECTOR":
-            url = Config._get_required_env_var("PGVECTOR_URL")
-            collection = Config._get_required_env_var("PGVECTOR_COLLECTION_NAME")
-            return PGVectorProvider(url, collection)
+            url = get("PGVECTOR_URL")
+            collection = get("PGVECTOR_COLLECTION_NAME")
+            return PGVectorProvider(embedding_model, url, collection)
 
         elif db_type == "SQLSERVER":
-            return SQLServerProvider()  # Handles its own env var loading
+            host = get("SQLSERVER_HOST")
+            port = get("SQLSERVER_PORT")
+            user = get("SQLSERVER_USER")
+            password = get("SQLSERVER_PASSWORD")
+            database = get("SQLSERVER_DB")
+            table = get("SQLSERVER_TABLE")
+            driver = get("SQLSERVER_DRIVER")
+            return SQLServerProvider(
+                embedding_model, host, port, user, password, database, table, driver
+            )
 
         elif db_type == "QDRANT":
-            url = Config._get_required_env_var("QDRANT_URL")
-            collection = Config._get_required_env_var("QDRANT_COLLECTION")
-            return QdrantProvider(url, collection)
+            url = get("QDRANT_URL")
+            collection = get("QDRANT_COLLECTION")
+            return QdrantProvider(embedding_model, url, collection)
 
         elif db_type == "DRYRUN":
-            return DryRunProvider()
+            return DryRunProvider(embedding_model)
 
         raise ValueError(f"Unsupported DB_TYPE '{db_type}'")
 
@@ -135,9 +146,11 @@ def load() -> "Config":
         except json.JSONDecodeError as e:
             raise ValueError(f"Invalid REPO_SOURCES JSON: {e}") from e
 
-        # Misc
+        # Embedding settings
         chunk_size = int(get("CHUNK_SIZE"))
         chunk_overlap = int(get("CHUNK_OVERLAP"))
+
+        # Misc
         temp_dir = get("TEMP_DIR")
 
         return Config(

vector_db/db_provider.py

@@ -10,10 +10,13 @@ class DBProvider(ABC):
     """
     Abstract base class for vector DB providers.
     Subclasses must implement `add_documents`.
+
+    Args:
+        embedding_model (str): Embedding model to use
     """
 
-    def __init__(self) -> None:
-        self.embeddings: Embeddings = HuggingFaceEmbeddings()
+    def __init__(self, embedding_model: str) -> None:
+        self.embeddings: Embeddings = HuggingFaceEmbeddings(model_name=embedding_model)
 
     @abstractmethod
     def add_documents(self, docs: List[Document]) -> None:

vector_db/dryrun_provider.py

@@ -13,14 +13,17 @@ class DryRunProvider(DBProvider):
     chunked documents to stdout. It is useful for debugging document loading,
     chunking, and metadata before committing to a real embedding operation.
 
+    Args:
+        embedding_model (str): Embedding model to use
+
     Example:
         >>> from vector_db.dry_run_provider import DryRunProvider
-        >>> provider = DryRunProvider()
+        >>> provider = DryRunProvider("sentence-transformers/all-mpnet-base-v2")
         >>> provider.add_documents(docs)  # docs is a List[Document]
     """
 
-    def __init__(self):
-        super().__init__()  # ensures embeddings are initialized
+    def __init__(self, embedding_model: str):
+        super().__init__(embedding_model)
 
     def add_documents(self, docs: List[Document]) -> None:
         """

vector_db/elastic_provider.py

@@ -14,13 +14,15 @@ class ElasticProvider(DBProvider):
     Elasticsearch-based vector DB provider using LangChain's ElasticsearchStore.
 
     Args:
+        embedding_model (str): Embedding model to use
         url (str): Full URL to the Elasticsearch cluster (e.g. http://localhost:9200)
         password (str): Authentication password for the cluster
         index (str): Index name to use for vector storage
         user (str): Username for Elasticsearch (default: "elastic")
 
     Example:
         >>> provider = ElasticProvider(
+        ...     embedding_model="sentence-transformers/all-mpnet-base-v2",
         ...     url="http://localhost:9200",
         ...     password="changeme",
         ...     index="docs",
@@ -29,8 +31,10 @@ class ElasticProvider(DBProvider):
         >>> provider.add_documents(chunks)
     """
 
-    def __init__(self, url: str, password: str, index: str, user: str):
-        super().__init__()
+    def __init__(
+        self, embedding_model: str, url: str, password: str, index: str, user: str
+    ):
+        super().__init__(embedding_model)
 
         self.db = ElasticsearchStore(
             embedding=self.embeddings,

vector_db/pgvector_provider.py

@@ -18,19 +18,21 @@ class PGVectorProvider(DBProvider):
     document embeddings in a PostgreSQL-compatible backend with pgvector enabled.
 
     Args:
+        embedding_model (str): Embedding model to use
         url (str): PostgreSQL connection string (e.g. postgresql://user:pass@host:5432/db)
         collection_name (str): Name of the pgvector table or collection
 
     Example:
         >>> provider = PGVectorProvider(
+        ...     embedding_model="sentence-transformers/all-mpnet-base-v2",
         ...     url="postgresql://user:pass@localhost:5432/mydb",
         ...     collection_name="documents"
         ... )
         >>> provider.add_documents(chunks)
     """
 
-    def __init__(self, url: str, collection_name: str):
-        super().__init__()
+    def __init__(self, embedding_model: str, url: str, collection_name: str):
+        super().__init__(embedding_model)
 
         self.db = PGVector(
             connection=url,

vector_db/qdrant_provider.py

@@ -16,6 +16,7 @@ class QdrantProvider(DBProvider):
     Qdrant-based vector DB provider using LangChain's QdrantVectorStore.
 
     Args:
+        embedding_model (str): Embedding model to use
         url (str): Base URL of the Qdrant service (e.g., http://localhost:6333)
         collection (str): Name of the vector collection to use or create
         api_key (Optional[str]): API key if authentication is required (optional)
@@ -24,15 +25,22 @@ class QdrantProvider(DBProvider):
 
     Example:
         >>> provider = QdrantProvider(
+        ...     embedding_model="sentence-transformers/all-mpnet-base-v2",
         ...     url="http://localhost:6333",
         ...     collection="embedded_docs",
         ...     api_key=None
         ... )
         >>> provider.add_documents(docs)
     """
 
-    def __init__(self, url: str, collection: str, api_key: Optional[str] = None):
-        super().__init__()
+    def __init__(
+        self,
+        embedding_model: str,
+        url: str,
+        collection: str,
+        api_key: Optional[str] = None,
+    ):
+        super().__init__(embedding_model)
         self.collection = collection
         self.url = url
 

vector_db/redis_provider.py

@@ -15,6 +15,7 @@ class RedisProvider(DBProvider):
     Redis-based vector DB provider using RediSearch and LangChain's Redis integration.
 
     Args:
+        embedding_model (str): Embedding model to use
         url (str): Redis connection string (e.g. redis://localhost:6379)
         index (str): RediSearch index name (must be provided via .env)
         schema (str): Path to RediSearch schema YAML file (must be provided via .env)
@@ -24,15 +25,16 @@ class RedisProvider(DBProvider):
 
     Example:
         >>> provider = RedisProvider(
+        ...     embedding_model="sentence-transformers/all-mpnet-base-v2",
         ...     url="redis://localhost:6379",
         ...     index="docs",
         ...     schema="redis_schema.yaml"
         ... )
         >>> provider.add_documents(chunks)
     """
 
-    def __init__(self, url: str, index: str, schema: str):
-        super().__init__()
+    def __init__(self, embedding_model: str, url: str, index: str, schema: str):
+        super().__init__(embedding_model)
         self.url = url
         self.index = index
         self.schema = schema

vector_db/sqlserver_provider.py

@@ -15,6 +15,7 @@ class SQLServerProvider(DBProvider):
     SQL Server-based vector DB provider using LangChain's SQLServer_VectorStore.
 
     Args:
+        embedding_model (str): Embedding model to use
         host (str): Hostname of the SQL Server
         port (str): Port number
         user (str): SQL login username
@@ -25,6 +26,7 @@ class SQLServerProvider(DBProvider):
 
     Example:
         >>> provider = SQLServerProvider(
+        ...     embedding_model="sentence-transformers/all-mpnet-base-v2",
         ...     host="localhost",
         ...     port="1433",
         ...     user="sa",
@@ -38,6 +40,7 @@ class SQLServerProvider(DBProvider):
 
     def __init__(
         self,
+        embedding_model: str,
         host: str,
         port: str,
         user: str,
@@ -46,7 +49,7 @@ def __init__(
         table: str,
         driver: str,
     ) -> None:
-        super().__init__()
+        super().__init__(embedding_model)
 
         self.host = host
         self.port = port