rename embeddingstore sdk to promptflow-vectordb (#2595)

Azure · Sep 2, 2023 · 54facd0 · 54facd0
1 parent 31ba393
commit 54facd0
Showing 1 changed file with 5 additions and 5 deletions.
diff --git a/sdk/python/generative-ai/promptflow/create_faiss_index.ipynb b/sdk/python/generative-ai/promptflow/create_faiss_index.ipynb
@@ -15,7 +15,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Install Embeddingstore SDK"
+    "## Install promptflow-vectordb SDK"
    ]
   },
   {
@@ -24,7 +24,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install embeddingstore --extra-index-url https://azuremlsdktestpypi.azureedge.net/embeddingstore/"
+    "%pip install promptflow-vectordb"
    ]
   },
   {
@@ -47,12 +47,12 @@
     "from bs4 import BeautifulSoup\n",
     "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
     "\n",
-    "from embeddingstore.core.contracts import (\n",
+    "from promptflow_vectordb.core.contracts import (\n",
     "    EmbeddingModelType,\n",
     "    StorageType,\n",
     "    StoreCoreConfig,\n",
     ")\n",
-    "from embeddingstore.core.embeddingstore_core import EmbeddingStoreCore"
+    "from promptflow_vectordb.core.embeddingstore_core import EmbeddingStoreCore"
    ]
   },
   {
@@ -106,7 +106,7 @@
    "metadata": {},
    "source": [
     "## Configure and create an embedding store\n",
-    "Embedding store sdk supports multiple types of embedding models (Azure OpenAI, OpenAI) and multiple types of store path (local path, HTTP URL, Azure blob). In this example, configure an embedding store with Azure OpenAI embedding model and local store path.\n",
+    "promptflow-vectordb sdk supports multiple types of embedding models (Azure OpenAI, OpenAI) and multiple types of store path (local path, HTTP URL, Azure blob). In this example, configure an embedding store with Azure OpenAI embedding model and local store path.\n",
     "\n",
     "Please refer to [create a resource and deploy a model using Azure OpenAI](https://learn.microsoft.com/en-us/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal) to set up an AOAI embedding model deployment. The output vector returned by different embedding models has different dimensions. It is recommended to deploy `text-embedding-ada-002` model, and the dimension of the output vector returned by this model is 1536. \n",
     "\n",