Refactoring vectordb naming convention in embedchain.config (#1469)

embedchain/config/__init__.py

@@ -8,8 +8,8 @@ from .embedder.base import BaseEmbedderConfig
 from .embedder.base import BaseEmbedderConfig as EmbedderConfig
 from .embedder.ollama import OllamaEmbedderConfig
 from .llm.base import BaseLlmConfig
-from .vectordb.chroma import ChromaDbConfig
-from .vectordb.elasticsearch import ElasticsearchDBConfig
-from .vectordb.opensearch import OpenSearchDBConfig
-from .vectordb.zilliz import ZillizDBConfig
+from .vector_db.chroma import ChromaDbConfig
+from .vector_db.elasticsearch import ElasticsearchDBConfig
+from .vector_db.opensearch import OpenSearchDBConfig
+from .vector_db.zilliz import ZillizDBConfig
 from .mem0_config import Mem0Config

embedchain/config/vector_db/chroma.py

@@ -1,6 +1,6 @@
 from typing import Optional
 
-from embedchain.config.vectordb.base import BaseVectorDbConfig
+from embedchain.config.vector_db.base import BaseVectorDbConfig
 from embedchain.helpers.json_serializable import register_deserializable
 
 

embedchain/config/vector_db/elasticsearch.py

@@ -1,56 +1,56 @@
-import os
-from typing import Optional, Union
-
-from embedchain.config.vectordb.base import BaseVectorDbConfig
-from embedchain.helpers.json_serializable import register_deserializable
-
-
-@register_deserializable
-class ElasticsearchDBConfig(BaseVectorDbConfig):
-    def __init__(
-        self,
-        collection_name: Optional[str] = None,
-        dir: Optional[str] = None,
-        es_url: Union[str, list[str]] = None,
-        cloud_id: Optional[str] = None,
-        batch_size: Optional[int] = 100,
-        **ES_EXTRA_PARAMS: dict[str, any],
-    ):
-        """
-        Initializes a configuration class instance for an Elasticsearch client.
-
-        :param collection_name: Default name for the collection, defaults to None
-        :type collection_name: Optional[str], optional
-        :param dir: Path to the database directory, where the database is stored, defaults to None
-        :type dir: Optional[str], optional
-        :param es_url: elasticsearch url or list of nodes url to be used for connection, defaults to None
-        :type es_url: Union[str, list[str]], optional
-        :param cloud_id: cloud id of the elasticsearch cluster, defaults to None
-        :type cloud_id: Optional[str], optional
-        :param batch_size: Number of items to insert in one batch, defaults to 100
-        :type batch_size: Optional[int], optional
-        :param ES_EXTRA_PARAMS: extra params dict that can be passed to elasticsearch.
-        :type ES_EXTRA_PARAMS: dict[str, Any], optional
-        """
-        if es_url and cloud_id:
-            raise ValueError("Only one of `es_url` and `cloud_id` can be set.")
-        # self, es_url: Union[str, list[str]] = None, **ES_EXTRA_PARAMS: dict[str, any]):
-        self.ES_URL = es_url or os.environ.get("ELASTICSEARCH_URL")
-        self.CLOUD_ID = cloud_id or os.environ.get("ELASTICSEARCH_CLOUD_ID")
-        if not self.ES_URL and not self.CLOUD_ID:
-            raise AttributeError(
-                "Elasticsearch needs a URL or CLOUD_ID attribute, "
-                "this can either be passed to `ElasticsearchDBConfig` or as `ELASTICSEARCH_URL` or `ELASTICSEARCH_CLOUD_ID` in `.env`"  # noqa: E501
-            )
-        self.ES_EXTRA_PARAMS = ES_EXTRA_PARAMS
-        # Load API key from .env if it's not explicitly passed.
-        # Can only set one of 'api_key', 'basic_auth', and 'bearer_auth'
-        if (
-            not self.ES_EXTRA_PARAMS.get("api_key")
-            and not self.ES_EXTRA_PARAMS.get("basic_auth")
-            and not self.ES_EXTRA_PARAMS.get("bearer_auth")
-        ):
-            self.ES_EXTRA_PARAMS["api_key"] = os.environ.get("ELASTICSEARCH_API_KEY")
-
-        self.batch_size = batch_size
-        super().__init__(collection_name=collection_name, dir=dir)
+import os
+from typing import Optional, Union
+
+from embedchain.config.vector_db.base import BaseVectorDbConfig
+from embedchain.helpers.json_serializable import register_deserializable
+
+
+@register_deserializable
+class ElasticsearchDBConfig(BaseVectorDbConfig):
+    def __init__(
+        self,
+        collection_name: Optional[str] = None,
+        dir: Optional[str] = None,
+        es_url: Union[str, list[str]] = None,
+        cloud_id: Optional[str] = None,
+        batch_size: Optional[int] = 100,
+        **ES_EXTRA_PARAMS: dict[str, any],
+    ):
+        """
+        Initializes a configuration class instance for an Elasticsearch client.
+
+        :param collection_name: Default name for the collection, defaults to None
+        :type collection_name: Optional[str], optional
+        :param dir: Path to the database directory, where the database is stored, defaults to None
+        :type dir: Optional[str], optional
+        :param es_url: elasticsearch url or list of nodes url to be used for connection, defaults to None
+        :type es_url: Union[str, list[str]], optional
+        :param cloud_id: cloud id of the elasticsearch cluster, defaults to None
+        :type cloud_id: Optional[str], optional
+        :param batch_size: Number of items to insert in one batch, defaults to 100
+        :type batch_size: Optional[int], optional
+        :param ES_EXTRA_PARAMS: extra params dict that can be passed to elasticsearch.
+        :type ES_EXTRA_PARAMS: dict[str, Any], optional
+        """
+        if es_url and cloud_id:
+            raise ValueError("Only one of `es_url` and `cloud_id` can be set.")
+        # self, es_url: Union[str, list[str]] = None, **ES_EXTRA_PARAMS: dict[str, any]):
+        self.ES_URL = es_url or os.environ.get("ELASTICSEARCH_URL")
+        self.CLOUD_ID = cloud_id or os.environ.get("ELASTICSEARCH_CLOUD_ID")
+        if not self.ES_URL and not self.CLOUD_ID:
+            raise AttributeError(
+                "Elasticsearch needs a URL or CLOUD_ID attribute, "
+                "this can either be passed to `ElasticsearchDBConfig` or as `ELASTICSEARCH_URL` or `ELASTICSEARCH_CLOUD_ID` in `.env`"  # noqa: E501
+            )
+        self.ES_EXTRA_PARAMS = ES_EXTRA_PARAMS
+        # Load API key from .env if it's not explicitly passed.
+        # Can only set one of 'api_key', 'basic_auth', and 'bearer_auth'
+        if (
+            not self.ES_EXTRA_PARAMS.get("api_key")
+            and not self.ES_EXTRA_PARAMS.get("basic_auth")
+            and not self.ES_EXTRA_PARAMS.get("bearer_auth")
+        ):
+            self.ES_EXTRA_PARAMS["api_key"] = os.environ.get("ELASTICSEARCH_API_KEY")
+
+        self.batch_size = batch_size
+        super().__init__(collection_name=collection_name, dir=dir)

embedchain/config/vector_db/lancedb.py

@@ -1,6 +1,6 @@
 from typing import Optional
 
-from embedchain.config.vectordb.base import BaseVectorDbConfig
+from embedchain.config.vector_db.base import BaseVectorDbConfig
 from embedchain.helpers.json_serializable import register_deserializable
 
 

embedchain/config/vector_db/opensearch.py

@@ -1,41 +1,41 @@
-from typing import Optional
-
-from embedchain.config.vectordb.base import BaseVectorDbConfig
-from embedchain.helpers.json_serializable import register_deserializable
-
-
-@register_deserializable
-class OpenSearchDBConfig(BaseVectorDbConfig):
-    def __init__(
-        self,
-        opensearch_url: str,
-        http_auth: tuple[str, str],
-        vector_dimension: int = 1536,
-        collection_name: Optional[str] = None,
-        dir: Optional[str] = None,
-        batch_size: Optional[int] = 100,
-        **extra_params: dict[str, any],
-    ):
-        """
-        Initializes a configuration class instance for an OpenSearch client.
-
-        :param collection_name: Default name for the collection, defaults to None
-        :type collection_name: Optional[str], optional
-        :param opensearch_url: URL of the OpenSearch domain
-        :type opensearch_url: str, Eg, "http://localhost:9200"
-        :param http_auth: Tuple of username and password
-        :type http_auth: tuple[str, str], Eg, ("username", "password")
-        :param vector_dimension: Dimension of  the vector, defaults to 1536 (openai embedding model)
-        :type vector_dimension: int, optional
-        :param dir: Path to the database directory, where the database is stored, defaults to None
-        :type dir: Optional[str], optional
-        :param batch_size: Number of items to insert in one batch, defaults to 100
-        :type batch_size: Optional[int], optional
-        """
-        self.opensearch_url = opensearch_url
-        self.http_auth = http_auth
-        self.vector_dimension = vector_dimension
-        self.extra_params = extra_params
-        self.batch_size = batch_size
-
-        super().__init__(collection_name=collection_name, dir=dir)
+from typing import Optional
+
+from embedchain.config.vector_db.base import BaseVectorDbConfig
+from embedchain.helpers.json_serializable import register_deserializable
+
+
+@register_deserializable
+class OpenSearchDBConfig(BaseVectorDbConfig):
+    def __init__(
+        self,
+        opensearch_url: str,
+        http_auth: tuple[str, str],
+        vector_dimension: int = 1536,
+        collection_name: Optional[str] = None,
+        dir: Optional[str] = None,
+        batch_size: Optional[int] = 100,
+        **extra_params: dict[str, any],
+    ):
+        """
+        Initializes a configuration class instance for an OpenSearch client.
+
+        :param collection_name: Default name for the collection, defaults to None
+        :type collection_name: Optional[str], optional
+        :param opensearch_url: URL of the OpenSearch domain
+        :type opensearch_url: str, Eg, "http://localhost:9200"
+        :param http_auth: Tuple of username and password
+        :type http_auth: tuple[str, str], Eg, ("username", "password")
+        :param vector_dimension: Dimension of  the vector, defaults to 1536 (openai embedding model)
+        :type vector_dimension: int, optional
+        :param dir: Path to the database directory, where the database is stored, defaults to None
+        :type dir: Optional[str], optional
+        :param batch_size: Number of items to insert in one batch, defaults to 100
+        :type batch_size: Optional[int], optional
+        """
+        self.opensearch_url = opensearch_url
+        self.http_auth = http_auth
+        self.vector_dimension = vector_dimension
+        self.extra_params = extra_params
+        self.batch_size = batch_size
+
+        super().__init__(collection_name=collection_name, dir=dir)

embedchain/config/vector_db/pinecone.py

@@ -1,7 +1,7 @@
 import os
 from typing import Optional
 
-from embedchain.config.vectordb.base import BaseVectorDbConfig
+from embedchain.config.vector_db.base import BaseVectorDbConfig
 from embedchain.helpers.json_serializable import register_deserializable
 
 

embedchain/config/vector_db/qdrant.py

@@ -1,6 +1,6 @@
 from typing import Optional
 
-from embedchain.config.vectordb.base import BaseVectorDbConfig
+from embedchain.config.vector_db.base import BaseVectorDbConfig
 from embedchain.helpers.json_serializable import register_deserializable
 
 

embedchain/config/vector_db/weaviate.py

@@ -1,6 +1,6 @@
 from typing import Optional
 
-from embedchain.config.vectordb.base import BaseVectorDbConfig
+from embedchain.config.vector_db.base import BaseVectorDbConfig
 from embedchain.helpers.json_serializable import register_deserializable
 
 

embedchain/config/vector_db/zilliz.py

@@ -1,7 +1,7 @@
 import os
 from typing import Optional
 
-from embedchain.config.vectordb.base import BaseVectorDbConfig
+from embedchain.config.vector_db.base import BaseVectorDbConfig
 from embedchain.helpers.json_serializable import register_deserializable
 
 

embedchain/factory.py

@@ -98,14 +98,14 @@ class VectorDBFactory:
         "zilliz": "embedchain.vectordb.zilliz.ZillizVectorDB",
     }
     provider_to_config_class = {
-        "chroma": "embedchain.config.vectordb.chroma.ChromaDbConfig",
-        "elasticsearch": "embedchain.config.vectordb.elasticsearch.ElasticsearchDBConfig",
-        "opensearch": "embedchain.config.vectordb.opensearch.OpenSearchDBConfig",
-        "lancedb": "embedchain.config.vectordb.lancedb.LanceDBConfig",
-        "pinecone": "embedchain.config.vectordb.pinecone.PineconeDBConfig",
-        "qdrant": "embedchain.config.vectordb.qdrant.QdrantDBConfig",
-        "weaviate": "embedchain.config.vectordb.weaviate.WeaviateDBConfig",
-        "zilliz": "embedchain.config.vectordb.zilliz.ZillizDBConfig",
+        "chroma": "embedchain.config.vector_db.chroma.ChromaDbConfig",
+        "elasticsearch": "embedchain.config.vector_db.elasticsearch.ElasticsearchDBConfig",
+        "opensearch": "embedchain.config.vector_db.opensearch.OpenSearchDBConfig",
+        "lancedb": "embedchain.config.vector_db.lancedb.LanceDBConfig",
+        "pinecone": "embedchain.config.vector_db.pinecone.PineconeDBConfig",
+        "qdrant": "embedchain.config.vector_db.qdrant.QdrantDBConfig",
+        "weaviate": "embedchain.config.vector_db.weaviate.WeaviateDBConfig",
+        "zilliz": "embedchain.config.vector_db.zilliz.ZillizDBConfig",
     }
 
     @classmethod

embedchain/vectordb/base.py

@@ -1,4 +1,4 @@
-from embedchain.config.vectordb.base import BaseVectorDbConfig
+from embedchain.config.vector_db.base import BaseVectorDbConfig
 from embedchain.embedder.base import BaseEmbedder
 from embedchain.helpers.json_serializable import JSONSerializable
 

embedchain/vectordb/lancedb.py

@@ -7,7 +7,7 @@ try:
 except ImportError:
     raise ImportError('LanceDB is required. Install with pip install "embedchain[lancedb]"') from None
 
-from embedchain.config.vectordb.lancedb import LanceDBConfig
+from embedchain.config.vector_db.lancedb import LanceDBConfig
 from embedchain.helpers.json_serializable import register_deserializable
 from embedchain.vectordb.base import BaseVectorDB
 

embedchain/vectordb/pinecone.py

@@ -11,7 +11,7 @@ except ImportError:
 
 from pinecone_text.sparse import BM25Encoder
 
-from embedchain.config.vectordb.pinecone import PineconeDBConfig
+from embedchain.config.vector_db.pinecone import PineconeDBConfig
 from embedchain.helpers.json_serializable import register_deserializable
 from embedchain.utils.misc import chunks
 from embedchain.vectordb.base import BaseVectorDB

embedchain/vectordb/qdrant.py

@@ -12,7 +12,7 @@ except ImportError:
 
 from tqdm import tqdm
 
-from embedchain.config.vectordb.qdrant import QdrantDBConfig
+from embedchain.config.vector_db.qdrant import QdrantDBConfig
 from embedchain.vectordb.base import BaseVectorDB
 
 

embedchain/vectordb/weaviate.py

@@ -9,7 +9,7 @@ except ImportError:
         "Weaviate requires extra dependencies. Install with `pip install --upgrade 'embedchain[weaviate]'`"
     ) from None
 
-from embedchain.config.vectordb.weaviate import WeaviateDBConfig
+from embedchain.config.vector_db.weaviate import WeaviateDBConfig
 from embedchain.helpers.json_serializable import register_deserializable
 from embedchain.vectordb.base import BaseVectorDB
 

tests/vectordb/test_lancedb.py

@@ -5,7 +5,7 @@ import pytest
 
 from embedchain import App
 from embedchain.config import AppConfig
-from embedchain.config.vectordb.lancedb import LanceDBConfig
+from embedchain.config.vector_db.lancedb import LanceDBConfig
 from embedchain.vectordb.lancedb import LanceDB
 
 os.environ["OPENAI_API_KEY"] = "test-api-key"

tests/vectordb/test_pinecone.py

@@ -1,6 +1,6 @@
 import pytest
 
-from embedchain.config.vectordb.pinecone import PineconeDBConfig
+from embedchain.config.vector_db.pinecone import PineconeDBConfig
 from embedchain.vectordb.pinecone import PineconeDB
 
 

tests/vectordb/test_qdrant.py

@@ -7,7 +7,7 @@ from qdrant_client.http.models import Batch
 
 from embedchain import App
 from embedchain.config import AppConfig
-from embedchain.config.vectordb.pinecone import PineconeDBConfig
+from embedchain.config.vector_db.pinecone import PineconeDBConfig
 from embedchain.embedder.base import BaseEmbedder
 from embedchain.vectordb.qdrant import QdrantDB
 

tests/vectordb/test_weaviate.py

@@ -3,7 +3,7 @@ from unittest.mock import patch
 
 from embedchain import App
 from embedchain.config import AppConfig
-from embedchain.config.vectordb.pinecone import PineconeDBConfig
+from embedchain.config.vector_db.pinecone import PineconeDBConfig
 from embedchain.embedder.base import BaseEmbedder
 from embedchain.vectordb.weaviate import WeaviateDB