Fix flexible dimension and doc for DingoDB (#12187)

docs/docs/integrations/providers/dingo.mdx

@@ -1,14 +1,14 @@
-# Dingo
+# DingoDB
 
-This page covers how to use the Dingo ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific Dingo wrappers.
+This page covers how to use the DingoDB ecosystem within LangChain.
+It is broken into two parts: installation and setup, and then references to specific DingoDB wrappers.
 
 ## Installation and Setup
 - Install the Python SDK with `pip install dingodb`
 
 ## VectorStore
 
-There exists a wrapper around Dingo indexes, allowing you to use it as a vectorstore,
+There exists a wrapper around DingoDB indexes, allowing you to use it as a vectorstore,
 whether for semantic search or example selection.
 
 To import this vectorstore:
@@ -16,4 +16,4 @@ To import this vectorstore:
 from langchain.vectorstores import Dingo
 ```
 
-For a more detailed walkthrough of the Dingo wrapper, see [this notebook](/docs/integrations/vectorstores/dingo.html)
+For a more detailed walkthrough of the DingoDB wrapper, see [this notebook](/docs/integrations/vectorstores/dingo.html)

docs/docs/integrations/vectorstores/dingo.ipynb

@@ -5,9 +5,9 @@
    "id": "683953b3",
    "metadata": {},
    "source": [
-    "# Dingo\n",
+    "# DingoDB\n",
     "\n",
-    ">[Dingo](https://dingodb.readthedocs.io/en/latest/) is a distributed multi-mode vector database, which combines the characteristics of data lakes and vector databases, and can store data of any type and size (Key-Value, PDF, audio, video, etc.). It has real-time low-latency processing capabilities to achieve rapid insight and response, and can efficiently conduct instant analysis and process multi-modal data.\n",
+    ">[DingoDB](https://dingodb.readthedocs.io/en/latest/) is a distributed multi-mode vector database, which combines the characteristics of data lakes and vector databases, and can store data of any type and size (Key-Value, PDF, audio, video, etc.). It has real-time low-latency processing capabilities to achieve rapid insight and response, and can efficiently conduct instant analysis and process multi-modal data.\n",
     "\n",
     "This notebook shows how to use functionality related to the DingoDB vector database.\n",
     "\n",

libs/langchain/langchain/vectorstores/dingo.py

@@ -36,6 +36,7 @@ class Dingo(VectorStore):
         *,
         client: Any = None,
         index_name: Optional[str] = None,
+        dimension: int = 1024,
         host: Optional[List[str]] = None,
         user: str = "root",
         password: str = "123123",
@@ -67,9 +68,11 @@ class Dingo(VectorStore):
 
         if index_name is not None and index_name not in dingo_client.get_index():
             if self_id is True:
-                dingo_client.create_index(index_name, 1024, auto_id=False)
+                dingo_client.create_index(
+                    index_name, dimension=dimension, auto_id=False
+                )
             else:
-                dingo_client.create_index(index_name, 1024)
+                dingo_client.create_index(index_name, dimension=dimension)
 
         self._index_name = index_name
         self._embedding = embedding
@@ -268,6 +271,7 @@ class Dingo(VectorStore):
         ids: Optional[List[str]] = None,
         text_key: str = "text",
         index_name: Optional[str] = None,
+        dimension: int = 1024,
         client: Any = None,
         host: List[str] = ["172.20.31.10:13000"],
         user: str = "root",
@@ -315,11 +319,12 @@ class Dingo(VectorStore):
                 raise ValueError(f"Dingo failed to connect: {e}")
         if kwargs is not None and kwargs.get("self_id") is True:
             if index_name not in dingo_client.get_index():
-                dingo_client.create_index(index_name, 1024, auto_id=False)
+                dingo_client.create_index(
+                    index_name, dimension=dimension, auto_id=False
+                )
         else:
             if index_name not in dingo_client.get_index():
-                dingo_client.create_index(index_name, 1024)
-            # dingo_client.create_index(index_name, 1024, index_type="hnsw")
+                dingo_client.create_index(index_name, dimension=dimension)
 
         # Embed and create the documents