Merge pull request #12 from radekdymacz/singlestore

.env.examples

@@ -1,2 +1,19 @@
+# Pinecone Configuration
 PINECONE_API_KEYS="your_pinecone_api_key"
-BASE_SWARMS_MEMORY_URL="http:"
+
+# Base URL Configuration
+BASE_SWARMS_MEMORY_URL="http:"
+
+# SingleStore Configuration
+# Host can be localhost for local development or your SingleStore deployment URL
+SINGLESTORE_HOST="your_singlestore_host"  # e.g., "localhost" or "svc-123-xyz.aws.singlestore.com"
+
+# Default port is 3306, but might be different for your deployment
+SINGLESTORE_PORT="3306"
+
+# Your SingleStore user credentials
+SINGLESTORE_USER="your_singlestore_username"  # e.g., "admin"
+SINGLESTORE_PASSWORD="your_singlestore_password"
+
+# Database name where vector tables will be created
+SINGLESTORE_DATABASE="your_database_name"  # e.g., "vector_store"

README.md

@@ -1,4 +1,3 @@
-
 <div align="center">
   <a href="https://swarms.world">
     <h1>Swarms Memory</h1>
@@ -39,8 +38,9 @@ Here's a more detailed and larger table with descriptions and website links for
 | **ChromaDB**   | Available   | A high-performance, distributed database optimized for handling large-scale AI tasks. | [ChromaDB Documentation](swarms_memory/memory/chromadb.md) | [ChromaDB](https://chromadb.com) |
 | **Pinecone**   | Available   | A fully managed vector database that makes it easy to add vector search to your applications. | [Pinecone Documentation](swarms_memory/memory/pinecone.md) | [Pinecone](https://pinecone.io) |
 | **Redis**      | Coming Soon | An open-source, in-memory data structure store, used as a database, cache, and message broker. | [Redis Documentation](swarms_memory/memory/redis.md) | [Redis](https://redis.io)       |
-| **Faiss**      | Coming Soon | A library for efficient similarity search and clustering of dense vectors, developed by Facebook AI. | [Faiss Documentation](swarms_memory/memory/faiss.md) | [Faiss](https://faiss.ai)       |
-| **HNSW**       | Coming Soon | A graph-based algorithm for approximate nearest neighbor search, known for its speed and accuracy. | [HNSW Documentation](swarms_memory/memory/hnsw.md)   | [HNSW](https://github.com/nmslib/hnswlib) |
+| **Faiss**      | Available   | A library for efficient similarity search and clustering of dense vectors, developed by Facebook AI. | [Faiss Documentation](swarms_memory/memory/faiss.md) | [Faiss](https://faiss.ai)       |
+| **SingleStore**| Available   | A distributed SQL database that provides high-performance vector similarity search. | [SingleStore Documentation](swarms_memory/memory/singlestore.md) | [SingleStore](https://www.singlestore.com) |
+| **HNSW**       | Coming Soon | A graph-based algorithm for approximate nearest neighbor search. | [HNSW Documentation](swarms_memory/memory/hnsw.md) | [HNSW](https://github.com/nmslib/hnswlib) |
 
 This table includes a brief description of each system, their current status, links to their documentation, and their respective websites for further information.
 
@@ -259,6 +259,75 @@ for result in results:
 ```
 
 
+### SingleStore
+```python
+from swarms_memory.vector_dbs.singlestore_wrapper import SingleStoreDB
+
+# Initialize SingleStore with environment variables
+db = SingleStoreDB(
+    host="your_host",
+    port=3306,
+    user="your_user",
+    password="your_password",
+    database="your_database",
+    table_name="example_vectors",
+    dimension=768,  # Default dimension for all-MiniLM-L6-v2
+    namespace="example"
+)
+
+# Custom embedding function example (optional)
+def custom_embedding_function(text: str) -> List[float]:
+    # Your custom embedding logic here
+    return embeddings
+
+# Initialize with custom functions
+db = SingleStoreDB(
+    host="your_host",
+    port=3306,
+    user="your_user",
+    password="your_password",
+    database="your_database",
+    table_name="example_vectors",
+    dimension=768,
+    namespace="example",
+    embedding_function=custom_embedding_function,
+    preprocess_function=lambda x: x.lower(),  # Simple preprocessing
+    postprocess_function=lambda x: sorted(x, key=lambda k: k['similarity'], reverse=True)  # Sort by similarity
+)
+
+# Add documents with metadata
+doc_id = db.add(
+    document="SingleStore is a distributed SQL database that combines horizontal scalability with ACID guarantees.",
+    metadata={"source": "docs", "category": "database"}
+)
+
+# Query similar documents
+results = db.query(
+    query="How does SingleStore scale?",
+    top_k=3,
+    metadata_filter={"source": "docs"}
+)
+
+# Process results
+for result in results:
+    print(f"Document: {result['document']}")
+    print(f"Similarity: {result['similarity']:.4f}")
+    print(f"Metadata: {result['metadata']}\n")
+
+# Delete a document
+db.delete(doc_id)
+
+# Key features:
+# - Built on SingleStore's native vector similarity search
+# - Supports custom embedding models and functions
+# - Automatic table creation with optimized vector indexing
+# - Metadata filtering for refined searches
+# - Document preprocessing and postprocessing
+# - Namespace support for document organization
+# - SSL support for secure connections
+
+# For more examples, see the [SingleStore example](examples/singlestore_wrapper_example.py).
+```
 # License
 MIT
 
@@ -275,4 +344,3 @@ Please cite Swarms in your paper or your project if you found it beneficial in a
   note = {Accessed: Date}
 }
 ```
-

examples/singlestore_wrapper_example.py

@@ -0,0 +1,60 @@
+import os
+from dotenv import load_dotenv
+from swarms_memory.vector_dbs.singlestore_wrapper import SingleStoreDB
+
+# Load environment variables
+load_dotenv()
+
+def main():
+    # Initialize SingleStore with environment variables
+    db = SingleStoreDB(
+        host=os.getenv("SINGLESTORE_HOST"),
+        port=int(os.getenv("SINGLESTORE_PORT", "3306")),
+        user=os.getenv("SINGLESTORE_USER"),
+        password=os.getenv("SINGLESTORE_PASSWORD"),
+        database=os.getenv("SINGLESTORE_DATABASE"),
+        table_name="example_vectors",
+        dimension=768,  # Default dimension for all-MiniLM-L6-v2
+        namespace="example"
+    )
+
+    # Example documents
+    documents = [
+        "SingleStore is a distributed SQL database that combines the horizontal scalability of NoSQL systems with the ACID guarantees of traditional RDBMSs.",
+        "Vector similarity search in SingleStore uses DOT_PRODUCT distance type for efficient nearest neighbor queries.",
+        "SingleStore supports both row and column store formats, making it suitable for both transactional and analytical workloads."
+    ]
+
+    # Add documents to the database
+    doc_ids = []
+    for doc in documents:
+        doc_id = db.add(
+            document=doc,
+            metadata={"source": "example", "type": "documentation"}
+        )
+        doc_ids.append(doc_id)
+        print(f"Added document with ID: {doc_id}")
+
+    # Query similar documents
+    query = "How does SingleStore handle vector similarity search?"
+    results = db.query(
+        query=query,
+        top_k=2,
+        metadata_filter={"source": "example"}
+    )
+
+    print("\nQuery:", query)
+    print("\nResults:")
+    for result in results:
+        print(f"\nDocument: {result['document']}")
+        print(f"Similarity: {result['similarity']:.4f}")
+        print(f"Metadata: {result['metadata']}")
+
+    # Clean up - delete documents
+    print("\nCleaning up...")
+    for doc_id in doc_ids:
+        db.delete(doc_id)
+        print(f"Deleted document with ID: {doc_id}")
+
+if __name__ == "__main__":
+    main()

pyproject.toml

@@ -31,6 +31,7 @@ pinecone = "*"
 faiss-cpu = "*"
 pydantic = "*"
 sqlalchemy = "*"
+singlestoredb = "*"
 
 
 

requirements.txt

@@ -5,4 +5,5 @@ pinecone
 faiss-cpu
 torch
 pydantic
-sqlalchemy
+sqlalchemy
+singlestoredb