Add Weaviate integration

docs/_toc.yml

@@ -90,6 +90,7 @@ parts:
           - file: source/reference/vector_databases/pgvector
           - file: source/reference/vector_databases/pinecone
           - file: source/reference/vector_databases/milvus
+          - file: source/reference/vector_databases/weaviate
 
       - file: source/reference/ai/index
         title: AI Engines

docs/source/reference/databases/hackernews.rst

@@ -18,7 +18,7 @@ Required:
 
 Optional:
 
-* ``tags`` is the tag used for filtering the query results. Check `available tags <https://hn.algolia.com/api#:~:text=filter%20on%20a%20specific%20tag.%20Available%20tags%3A>`_ to see a list of available filter tags.
+* ``tags`` is the tag used for filtering the query results. Check `available tags <https://hn.algolia.com/api>`_ to see a list of available filter tags.
 
 Create Connection
 -----------------

docs/source/reference/vector_databases/weaviate.rst

@@ -0,0 +1,31 @@
+Weaviate
+==========
+
+Weaviate is an open-source vector database designed for scalability and rich querying capabilities. It allows for semantic search, automated vectorization, and supports large language model (LLM) integration.
+The connection to Weaviate is based on the `weaviate-client <https://weaviate.io/developers/weaviate/client-libraries/python>`_ library.
+
+Dependency
+----------
+
+* weaviate-client
+
+Parameters
+----------
+
+To use Weaviate, you need an API key and a URL of your Weaviate instance. Here are the `instructions for setting up a Weaviate instance <https://weaviate.io/developers/weaviate/quickstart>`_. After setting up your instance, you will find the API key and URL on the Details tab in Weaviate Cloud Services (WCS) dashboard. These details are essential for establishing a connection to the Weaviate server.
+
+* `WEAVIATE_API_KEY` is the API key for your Weaviate instance.
+* `WEAVIATE_API_URL` is the URL of your Weaviate instance.
+
+The above values can either be set via the ``SET`` statement, or in the os environment fields "WEAVIATE_API_KEY", "WEAVIATE_API_URL"
+
+Create Collection
+-----------------
+
+Weaviate uses collections (similar to 'classes') to store data. To create a collection in Weaviate, use the following SQL command in EvaDB:
+
+.. code-block:: sql
+
+   CREATE INDEX collection_name ON table_name (data) USING WEAVIATE;
+
+This command creates a collection in Weaviate with the specified name, linked to the table in EvaDB. You can also specify vectorizer settings and other configurations for the collection as needed.

evadb/catalog/catalog_type.py

@@ -117,6 +117,7 @@ class VectorStoreType(EvaDBEnum):
     PINECONE  # noqa: F821
     PGVECTOR  # noqa: F821
     CHROMADB  # noqa: F821
+    WEAVIATE  # noqa: F821
     MILVUS  # noqa: F821
 
 

evadb/evadb_config.py

@@ -41,4 +41,6 @@
     "MILVUS_PASSWORD": "",
     "MILVUS_DB_NAME": "",
     "MILVUS_TOKEN": "",
+    "WEAVIATE_API_KEY": "",
+    "WEAVIATE_API_URL": "",
 }

evadb/executor/executor_utils.py

@@ -185,6 +185,17 @@ def handle_vector_store_params(
             ),
             "PINECONE_ENV": catalog().get_configuration_catalog_value("PINECONE_ENV"),
         }
+    elif vector_store_type == VectorStoreType.WEAVIATE:
+        # Weaviate Configuration
+        # Weaviate API key and URL Can be obtained from cluster details on Weaviate Cloud Services (WCS) dashboard
+        return {
+            "WEAVIATE_API_KEY": catalog().get_configuration_catalog_value(
+                "WEAVIATE_API_KEY"
+            ),
+            "WEAVIATE_API_URL": catalog().get_configuration_catalog_value(
+                "WEAVIATE_API_URL"
+            ),
+        }
     elif vector_store_type == VectorStoreType.MILVUS:
         return {
             "MILVUS_URI": catalog().get_configuration_catalog_value("MILVUS_URI"),

evadb/interfaces/relational/db.py

@@ -268,7 +268,8 @@ def create_vector_index(
             index_name (str): Name of the index.
             table_name (str): Name of the table.
             expr (str): Expression used to build the vector index.
-            using (str): Method used for indexing, can be `FAISS` or `QDRANT` or `PINECONE` or `CHROMADB` or `MILVUS`.
+
+            using (str): Method used for indexing, can be `FAISS` or `QDRANT` or `PINECONE` or `CHROMADB` or `WEAVIATE` or `MILVUS`.
 
         Returns:
             EvaDBCursor: The EvaDBCursor object.

evadb/parser/evadb.lark

@@ -71,7 +71,7 @@ function_metadata_key: uid
 
 function_metadata_value: constant
 
-vector_store_type: USING (FAISS | QDRANT | PINECONE | PGVECTOR | CHROMADB | MILVUS)
+vector_store_type: USING (FAISS | QDRANT | PINECONE | PGVECTOR | CHROMADB | WEAVIATE | MILVUS)
 
 index_elem: ("(" uid_list ")"
           | "(" function_call ")")
@@ -448,6 +448,7 @@ QDRANT:                              "QDRANT"i
 PINECONE:                            "PINECONE"i
 PGVECTOR:                            "PGVECTOR"i
 CHROMADB:                            "CHROMADB"i
+WEAVIATE:                            "WEAVIATE"i
 MILVUS:                              "MILVUS"i
 
 // Computer vision tasks

evadb/parser/lark_visitor/_create_statements.py

@@ -300,6 +300,8 @@ def vector_store_type(self, tree):
             vector_store_type = VectorStoreType.PGVECTOR
         elif str.upper(token) == "CHROMADB":
             vector_store_type = VectorStoreType.CHROMADB
+        elif str.upper(token) == "WEAVIATE":
+            vector_store_type = VectorStoreType.WEAVIATE
         elif str.upper(token) == "MILVUS":
             vector_store_type = VectorStoreType.MILVUS
         return vector_store_type

evadb/third_party/vector_stores/utils.py

@@ -18,6 +18,7 @@
 from evadb.third_party.vector_stores.milvus import MilvusVectorStore
 from evadb.third_party.vector_stores.pinecone import PineconeVectorStore
 from evadb.third_party.vector_stores.qdrant import QdrantVectorStore
+from evadb.third_party.vector_stores.weaviate import WeaviateVectorStore
 from evadb.utils.generic_utils import validate_kwargs
 
 
@@ -51,6 +52,12 @@ def init_vector_store(
             validate_kwargs(kwargs, required_params, required_params)
             return ChromaDBVectorStore(index_name, **kwargs)
 
+        elif vector_store_type == VectorStoreType.WEAVIATE:
+            from evadb.third_party.vector_stores.weaviate import required_params
+
+            validate_kwargs(kwargs, required_params, required_params)
+            return WeaviateVectorStore(index_name, **kwargs)
+
         elif vector_store_type == VectorStoreType.MILVUS:
             from evadb.third_party.vector_stores.milvus import (
                 allowed_params,
@@ -59,5 +66,6 @@ def init_vector_store(
 
             validate_kwargs(kwargs, allowed_params, required_params)
             return MilvusVectorStore(index_name, **kwargs)
+
         else:
             raise Exception(f"Vector store {vector_store_type} not supported")

evadb/third_party/vector_stores/weaviate.py

@@ -0,0 +1,115 @@
+# coding=utf-8
+# Copyright 2018-2023 EvaDB
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import os
+from typing import List
+
+from evadb.third_party.vector_stores.types import (
+    FeaturePayload,
+    VectorIndexQuery,
+    VectorIndexQueryResult,
+    VectorStore,
+)
+from evadb.utils.generic_utils import try_to_import_weaviate_client
+
+required_params = []
+_weaviate_init_done = False
+
+
+class WeaviateVectorStore(VectorStore):
+    def __init__(self, collection_name: str, **kwargs) -> None:
+        try_to_import_weaviate_client()
+        global _weaviate_init_done
+
+        self._collection_name = collection_name
+
+        # Get the API key.
+        self._api_key = kwargs.get("WEAVIATE_API_KEY")
+
+        if not self._api_key:
+            self._api_key = os.environ.get("WEAVIATE_API_KEY")
+
+        assert (
+            self._api_key
+        ), "Please set your `WEAVIATE_API_KEY` using set command or environment variable (WEAVIATE_API_KEY). It can be found at the Details tab in WCS Dashboard."
+
+        # Get the API Url.
+        self._api_url = kwargs.get("WEAVIATE_API_URL")
+
+        if not self._api_url:
+            self._api_url = os.environ.get("WEAVIATE_API_URL")
+
+        assert (
+            self._api_url
+        ), "Please set your `WEAVIATE_API_URL` using set command or environment variable (WEAVIATE_API_URL). It can be found at the Details tab in WCS Dashboard."
+
+        if not _weaviate_init_done:
+            # Initialize weaviate client
+            import weaviate
+
+            client = weaviate.Client(
+                url=self._api_url,
+                auth_client_secret=weaviate.AuthApiKey(api_key=self._api_key),
+            )
+            client.schema.get()
+
+            _weaviate_init_done = True
+
+        self._client = client
+
+    def create(
+        self,
+        vectorizer: str = "text2vec-openai",
+        properties: list = None,
+        module_config: dict = None,
+    ):
+        properties = properties or []
+        module_config = module_config or {}
+
+        collection_obj = {
+            "class": self._collection_name,
+            "properties": properties,
+            "vectorizer": vectorizer,
+            "moduleConfig": module_config,
+        }
+
+        if self._client.schema.exists(self._collection_name):
+            self._client.schema.delete_class(self._collection_name)
+
+        self._client.schema.create_class(collection_obj)
+
+    def add(self, payload: List[FeaturePayload]) -> None:
+        with self._client.batch as batch:
+            for item in payload:
+                data_object = {"id": item.id, "vector": item.embedding}
+                batch.add_data_object(data_object, self._collection_name)
+
+    def delete(self) -> None:
+        self._client.schema.delete_class(self._collection_name)
+
+    def query(self, query: VectorIndexQuery) -> VectorIndexQueryResult:
+        response = (
+            self._client.query.get(self._collection_name, ["*"])
+            .with_near_vector({"vector": query.embedding})
+            .with_limit(query.top_k)
+            .do()
+        )
+
+        data = response.get("data", {})
+        results = data.get("Get", {}).get(self._collection_name, [])
+
+        similarities = [item["_additional"]["distance"] for item in results]
+        ids = [item["id"] for item in results]
+
+        return VectorIndexQueryResult(similarities, ids)

evadb/utils/generic_utils.py

@@ -573,6 +573,16 @@ def try_to_import_chromadb_client():
         )
 
 
+def try_to_import_weaviate_client():
+    try:
+        import weaviate  # noqa: F401
+    except ImportError:
+        raise ValueError(
+            """Could not import weaviate python package.
+                Please install it with 'pip install weaviate-client`."""
+        )
+
+
 def try_to_import_milvus_client():
     try:
         import pymilvus  # noqa: F401
@@ -607,6 +617,14 @@ def is_chromadb_available() -> bool:
         return False
 
 
+def is_weaviate_available() -> bool:
+    try:
+        try_to_import_weaviate_client()
+        return True
+    except ValueError:  # noqa: E722
+        return False
+
+
 def is_milvus_available() -> bool:
     try:
         try_to_import_milvus_client()

script/formatting/spelling.txt

@@ -975,10 +975,13 @@ VideoFormat
 VideoStorageEngineTest
 VideoWriter
 VisionEncoderDecoderModel
+WEAVIATE
 WH
 WIP
 WMV
 WeakValueDictionary
+Weaviate
+WeaviateVectorStore
 XGBoost
 XdistTests
 Xeon
@@ -1731,6 +1734,7 @@ testRayErrorHandling
 testSimilarityFeatureTable
 testSimilarityImageDataset
 testSimilarityTable
+testWeaviateIndexImageDataset
 testcase
 testcases
 testdeleteone
@@ -1814,6 +1818,7 @@ wal
 warmup
 wb
 weakref
+weaviate
 westbrae
 wget
 whitespaces

setup.py

@@ -112,8 +112,11 @@ def read(path, encoding="utf-8"):
 
 chromadb_libs = ["chromadb"]
 
+weaviate_libs = ["weaviate-client"]
+
 milvus_libs = ["pymilvus>=2.3.0"]
 
+
 postgres_libs = [
     "psycopg2",
 ]
@@ -173,6 +176,7 @@ def read(path, encoding="utf-8"):
     "pinecone": pinecone_libs,
     "chromadb": chromadb_libs,
     "milvus": milvus_libs,
+    "weaviate": weaviate_libs,
     "postgres": postgres_libs,
     "ludwig": ludwig_libs,
     "sklearn": sklearn_libs,