Merge pull request StarlightSearch#76 from akshayballal95/main

add analytics Former-commit-id: b8fcf6f9027d6aec18326e9e6278e62efe551136 [formerly 466f97cf66cf2fc3b7eeac3aa4bb6a56e75ed6cc] [formerly 87df700 [formerly dd33ae5]] Former-commit-id: 87df700 Former-commit-id: e0bab860a61b9a0f7c8fd7e250c4e6d2b795dc1d
boswelja · Sep 13, 2024 · 8dc4d65 · 8dc4d65
2 parents 559bba0 + fdbfab9
commit 8dc4d65
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Showing 3 changed files with 22 additions and 125 deletions.
diff --git a/docs/blog.md b/docs/blog.md
diff --git a/mkdocs.yml b/mkdocs.yml
@@ -20,4 +20,23 @@ markdown_extensions:
       pygments_lang_class: true
   - pymdownx.inlinehilite
   - pymdownx.snippets
-  - pymdownx.superfences
+  - pymdownx.superfences
+
+extra:
+  analytics: 
+    provider: google
+    property: G-25WL8Y1K9Y
+    feedback:
+      title: Was this page helpful?
+      ratings:
+        - icon: material/emoticon-happy-outline
+          name: This page was helpful
+          data: 1
+          note: >-
+            Thanks for your feedback!
+        - icon: material/emoticon-sad-outline
+          name: This page could be improved
+          data: 0
+          note: >- 
+            Thanks for your feedback! Help us improve this page by
+            using our <a href="..." target="_blank" rel="noopener">feedback form</a>.
diff --git a/python/python/embed_anything/vectordb.py b/python/python/embed_anything/vectordb.py
@@ -19,124 +19,11 @@ def delete_index(self, index_name: str):
         pass
 
     @abstractmethod
-    def convert(self, embeddings: List[List[EmbedData]]) -> List[Dict]:
+    def convert(self, embeddings: List[EmbedData]) -> List[Dict]:
         pass
 
     @abstractmethod
     def upsert(self, data: List[Dict]):
+        data = self.convert(data)
         pass
 
-
-# class PineconeAdapter(Adapter):
-#     """
-#     Adapter class for interacting with Pinecone, a vector database service.
-#     """
-
-#     def __init__(self, api_key: str):
-#         """
-#         Initializes a new instance of the PineconeAdapter class.
-
-#         Args:
-#             api_key (str): The API key for accessing the Pinecone service.
-#         """
-#         super().__init__(api_key)
-#         self.pc = Pinecone(api_key=self.api_key)
-#         self.index_name = None
-
-#     def create_index(
-#         self,
-#         dimension: int,
-#         metric: str = "cosine",
-#         index_name: str = "anything",
-#         spec=ServerlessSpec(cloud="aws", region="us-east-1"),
-#     ):
-#         """
-#         Creates a new index in Pinecone.
-
-#         Args:
-#             dimension (int): The dimensionality of the embeddings.
-#             metric (str, optional): The distance metric to use for similarity search. Defaults to "cosine".
-#             index_name (str, optional): The name of the index. Defaults to "anything".
-#             spec (ServerlessSpec, optional): The serverless specification for the index. Defaults to AWS in us-east-1 region.
-#         """
-#         self.index_name = index_name
-#         self.pc.create_index(
-#             name=index_name, dimension=dimension, metric=metric, spec=spec
-#         )
-
-#     def delete_index(self, index_name: str):
-#         """
-#         Deletes an existing index from Pinecone.
-
-#         Args:
-#             index_name (str): The name of the index to delete.
-#         """
-#         self.pc.delete_index(name=index_name)
-
-#     def convert(self, embeddings: List[EmbedData]) -> List[Dict]:
-#         """
-#         Converts a list of embeddings into the required format for upserting into Pinecone.
-
-#         Args:
-#             embeddings (List[EmbedData]): The list of embeddings to convert.
-
-#         Returns:
-#             List[Dict]: The converted data in the required format for upserting into Pinecone.
-#         """
-#         data_emb = []
-
-#         for embedding in embeddings:
-#             data_emb.append(
-#                 {
-#                     "id": str(uuid.uuid4()),
-#                     "values": embedding.embedding,
-#                     "metadata": {
-#                         "text": embedding.text,
-#                         "file": re.split(
-#                             r"/|\\", embedding.metadata.get("file_name", "")
-#                         )[-1],
-#                     },
-#                 }
-#             )
-#         return data_emb
-
-#     def upsert(self, data: List[Dict]):
-#         """
-#         Upserts data into the specified index in Pinecone.
-
-#         Args:
-#             data (List[Dict]): The data to upsert into Pinecone.
-
-#         Raises:
-#             ValueError: If the index has not been created before upserting data.
-#         """
-#         if not self.index_name:
-#             raise ValueError("Index must be created before upserting data")
-#         self.pc.Index(name=self.index_name).upsert(data)
-
-
-# class WeaviateAdapter(Adapter):
-#     """
-#     Adapter class for interacting with Weaviate, a vector database service.
-#     """
-
-#     def __init__(self, api_key: str):
-#         """
-#         Initializes a new instance of the WeaviateAdapter class.
-
-#         Args:
-#             api_key (str): The API key for accessing the Weaviate service.
-#         """
-#         super().__init__(api_key)
-
-
-#     def create_index(self, dimension: int, metric: str = "cosine", index_name: str = "anything", **kwargs):
-#         """
-#         Creates a new index in Weaviate.
-
-#         Args:
-#             dimension (int): The dimensionality of the embeddings.
-#             metric (str, optional): The distance metric to use for similarity search. Defaults to "cosine".
-#             index_name (str, optional): The name of the index. Defaults to "anything".
-#         """
-#         pass