Add SingleStoreReader

llama_hub/library.json

@@ -581,5 +581,14 @@
       "google keep",
       "google notes"
     ]
+  },
+  "SingleStoreReader": {
+    "id": "singlestore",
+    "author": "singlestore",
+    "keywords": [
+      "singlestore",
+      "memsql"
+    ]
   }
+
 }

llama_hub/singlestore/README.md

@@ -0,0 +1,34 @@
+# SingleStore Loader
+
+The SingleStore Loader retrieves a set of documents from a specified table in a SingleStore database. The user initializes the loader with database information and then provides a search embedding for retrieving similar documents.
+
+## Usage
+
+Here's an example usage of the SingleStoreReader:
+
+```python
+from llama_index import download_loader
+
+SingleStoreReader = download_loader("SingleStoreReader")
+
+# Initialize the reader with your SingleStore database credentials and other relevant details
+reader = SingleStoreReader(
+    scheme="mysql",
+    host="localhost",
+    port="3306",
+    user="username",
+    password="password",
+    dbname="database_name",
+    table_name="table_name",
+    content_field="text",
+    vector_field="embedding"
+)
+
+# The search_embedding is an embedding representation of your query_vector.
+# Example search_embedding:
+#   search_embedding=[0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3]
+search_embedding=[n1, n2, n3, ...]
+
+# load_data fetches documents from your SingleStore database that are similar to the search_embedding.
+# The top_k argument specifies the number of similar documents to fetch.
+documents = reader.load_data(search_embedding=search_embedding, top_k=5)

llama_hub/singlestore/__init__.py

@@ -0,0 +1 @@
+"""Init file."""

llama_hub/singlestore/base.py

@@ -0,0 +1,68 @@
+"""SingleStore reader."""
+
+from typing import Any, Dict, List, Optional
+
+from llama_index.readers.base import BaseReader
+from llama_index.readers.schema.base import Document
+from llama_index import download_loader, ListIndex
+
+import pymysql
+
+
+class SingleStoreReader(BaseReader):
+    """SingleStore reader.
+
+    Args:
+        scheme (str): Database Scheme.
+        host (str): Database Host.
+        port (str): Database Port.
+        user (str): Database User.
+        password (str): Database Password.
+        dbname (str): Database Name.
+        table_name (str): Table Name.
+        content_field (str): Content Field.
+        vector_field (str): Vector Field.
+    """
+
+    def __init__(self, scheme: str, host: str, port: str, user: str, password: str, dbname: str, table_name: str, content_field: str = "text", vector_field: str = "embedding"):
+        """Initialize with parameters."""
+        self.scheme = scheme
+        self.host = host
+        self.port = port
+        self.user = user
+        self.password = password
+        self.dbname = dbname
+        self.table_name = table_name
+        self.content_field = content_field
+        self.vector_field = vector_field
+
+        pymysql.install_as_MySQLdb()
+
+        self.DatabaseReader = download_loader('DatabaseReader')
+        self.reader = self.DatabaseReader(
+            scheme=self.scheme,
+            host=self.host,
+            port=self.port,
+            user=self.user,
+            password=self.password,
+            dbname=self.dbname,
+        )
+
+    def load_data(self, search_embedding: str, top_k: int = 5) -> List[Document]:
+        """Load data from SingleStore.
+
+        Args:
+            search_embedding (str): The embedding to search.
+            top_k (int): Number of results to return.
+
+        Returns:
+            List[Document]: A list of documents.
+        """
+        query = f"""
+        SELECT {self.content_field}, DOT_PRODUCT_F64({self.vector_field}, JSON_ARRAY_PACK_F64(\'{search_embedding}\')) AS score 
+        FROM {self.table_name} 
+        ORDER BY score 
+        DESC LIMIT {top_k}
+        """
+
+        return self.reader.load_data(query=query)

llama_hub/singlestore/requirements.txt

@@ -0,0 +1 @@
+pymysql