Added Qdrant memory class

docs/swarms/memory/qdrant.md

@@ -0,0 +1,81 @@
+# Qdrant Client Library
+
+## Overview
+
+The Qdrant Client Library is designed for interacting with the Qdrant vector database, allowing efficient storage and retrieval of high-dimensional vector data. It integrates with machine learning models for embedding and is particularly suited for search and recommendation systems.
+
+## Installation
+
+```python
+pip install qdrant-client sentence-transformers httpx
+```
+
+## Class Definition: Qdrant
+
+```python
+class Qdrant:
+    def __init__(self, api_key: str, host: str, port: int = 6333, collection_name: str = "qdrant", model_name: str = "BAAI/bge-small-en-v1.5", https: bool = True):
+        ...
+```
+
+### Constructor Parameters
+
+| Parameter       | Type    | Description                                      | Default Value         |
+|-----------------|---------|--------------------------------------------------|-----------------------|
+| api_key         | str     | API key for authentication.                      | -                     |
+| host            | str     | Host address of the Qdrant server.               | -                     |
+| port            | int     | Port number for the Qdrant server.               | 6333                  |
+| collection_name | str     | Name of the collection to be used or created.    | "qdrant"              |
+| model_name      | str     | Name of the sentence transformer model.          | "BAAI/bge-small-en-v1.5" |
+| https           | bool    | Flag to use HTTPS for connection.                | True                  |
+
+### Methods
+
+#### `_load_embedding_model(model_name: str)`
+
+Loads the sentence embedding model.
+
+#### `_setup_collection()`
+
+Checks if the specified collection exists in Qdrant; if not, creates it.
+
+#### `add_vectors(docs: List[dict]) -> OperationResponse`
+
+Adds vectors to the Qdrant collection.
+
+#### `search_vectors(query: str, limit: int = 3) -> SearchResult`
+
+Searches the Qdrant collection for vectors similar to the query vector.
+
+## Usage Examples
+
+### Example 1: Setting Up the Qdrant Client
+
+```python
+from qdrant_client import Qdrant
+
+qdrant_client = Qdrant(api_key="your_api_key", host="localhost", port=6333)
+```
+
+### Example 2: Adding Vectors to a Collection
+
+```python
+documents = [
+    {"page_content": "Sample text 1"},
+    {"page_content": "Sample text 2"}
+]
+
+operation_info = qdrant_client.add_vectors(documents)
+print(operation_info)
+```
+
+### Example 3: Searching for Vectors
+
+```python
+search_result = qdrant_client.search_vectors("Sample search query")
+print(search_result)
+```
+
+## Further Information
+
+Refer to the [Qdrant Documentation](https://qdrant.tech/docs) for more details on the Qdrant vector database.

mkdocs.yml

@@ -101,6 +101,7 @@ nav:
   - swarms.memory:
       - PineconeVectorStoreStore: "swarms/memory/pinecone.md"
       - PGVectorStore: "swarms/memory/pg.md"
+      - Qdrant: "swarms/memory/qdrant.md"
   - Guides:
       - Overview: "examples/index.md"
       - Agents:

playground/memory/qdrant/usage.py

@@ -0,0 +1,18 @@
+from langchain.document_loaders import CSVLoader
+from swarms.memory import qdrant
+
+loader = CSVLoader(file_path="../document_parsing/aipg/aipg.csv", encoding='utf-8-sig')
+docs = loader.load()
+
+
+# Initialize the Qdrant instance
+# See qdrant documentation on how to run locally
+qdrant_client = qdrant.Qdrant(host ="https://697ea26c-2881-4e17-8af4-817fcb5862e8.europe-west3-0.gcp.cloud.qdrant.io", collection_name="qdrant", api_key="BhG2_yINqNU-aKovSEBadn69Zszhbo5uaqdJ6G_qDkdySjAljvuPqQ")
+qdrant_client.add_vectors(docs)
+
+# Perform a search
+search_query = "Who is jojo"
+search_results = qdrant_client.search_vectors(search_query)
+print("Search Results:")
+for result in search_results:
+    print(result)

swarms/memory/qdrant.py

@@ -1,6 +1,110 @@
-"""
-QDRANT MEMORY CLASS
+from typing import List
+from sentence_transformers import SentenceTransformer
+from httpx import RequestError
+from qdrant_client import QdrantClient
+from qdrant_client.http.models import Distance, VectorParams, PointStruct
 
+class Qdrant:
+    def __init__(self, api_key: str, host: str, port: int = 6333, collection_name: str = "qdrant", model_name: str = "BAAI/bge-small-en-v1.5", https: bool = True):
+        """
+            Qdrant class for managing collections and performing vector operations using QdrantClient.
 
+            Attributes:
+                client (QdrantClient): The Qdrant client for interacting with the Qdrant server.
+                collection_name (str): Name of the collection to be managed in Qdrant.
+                model (SentenceTransformer): The model used for generating sentence embeddings.
 
-"""
+            Args:
+                api_key (str): API key for authenticating with Qdrant.
+                host (str): Host address of the Qdrant server.
+                port (int): Port number of the Qdrant server. Defaults to 6333.
+                collection_name (str): Name of the collection to be used or created. Defaults to "qdrant".
+                model_name (str): Name of the model to be used for embeddings. Defaults to "BAAI/bge-small-en-v1.5".
+                https (bool): Flag to indicate if HTTPS should be used. Defaults to True.
+            """
+        try:
+            self.client = QdrantClient(url=host, port=port, api_key=api_key)
+            self.collection_name = collection_name
+            self._load_embedding_model(model_name)
+            self._setup_collection()
+        except RequestError as e:
+            print(f"Error setting up QdrantClient: {e}")
+
+    def _load_embedding_model(self, model_name: str):
+        """
+        Loads the sentence embedding model specified by the model name.
+
+        Args:
+            model_name (str): The name of the model to load for generating embeddings.
+        """
+        try:
+            self.model = SentenceTransformer(model_name)
+        except Exception as e:
+            print(f"Error loading embedding model: {e}")
+
+    def _setup_collection(self):
+        try:
+            exists = self.client.get_collection(self.collection_name)
+            if exists:
+                print(f"Collection '{self.collection_name}' already exists.")
+        except Exception as e:
+            self.client.create_collection(
+                collection_name=self.collection_name,
+                vectors_config=VectorParams(size=self.model.get_sentence_embedding_dimension(), distance=Distance.DOT),
+            )
+            print(f"Collection '{self.collection_name}' created.")
+
+    def add_vectors(self, docs: List[dict]):
+        """
+        Adds vector representations of documents to the Qdrant collection.
+
+        Args:
+            docs (List[dict]): A list of documents where each document is a dictionary with at least a 'page_content' key.
+
+        Returns:
+            OperationResponse or None: Returns the operation information if successful, otherwise None.
+        """
+        points = []
+        for i, doc in enumerate(docs):
+            try:
+                if 'page_content' in doc:
+                    embedding = self.model.encode(doc['page_content'], normalize_embeddings=True)
+                    points.append(PointStruct(id=i + 1, vector=embedding, payload={"content": doc['page_content']}))
+                else:
+                    print(f"Document at index {i} is missing 'page_content' key")
+            except Exception as e:
+                print(f"Error processing document at index {i}: {e}")
+
+        try:
+            operation_info = self.client.upsert(
+                collection_name=self.collection_name,
+                wait=True,
+                points=points,
+            )
+            return operation_info
+        except Exception as e:
+            print(f"Error adding vectors: {e}")
+            return None
+
+    def search_vectors(self, query: str, limit: int = 3):
+        """
+        Searches the collection for vectors similar to the query vector.
+
+        Args:
+            query (str): The query string to be converted into a vector and used for searching.
+            limit (int): The number of search results to return. Defaults to 3.
+
+        Returns:
+            SearchResult or None: Returns the search results if successful, otherwise None.
+        """
+        try:
+            query_vector = self.model.encode(query, normalize_embeddings=True)
+            search_result = self.client.search(
+                collection_name=self.collection_name,
+                query_vector=query_vector,
+                limit=limit
+            )
+            return search_result
+        except Exception as e:
+            print(f"Error searching vectors: {e}")
+            return None

tests/memory/qdrant.py

@@ -0,0 +1,40 @@
+import pytest
+from unittest.mock import Mock, patch
+
+from swarms.memory.qdrant import Qdrant
+
+
+@pytest.fixture
+def mock_qdrant_client():
+    with patch('your_module.QdrantClient') as MockQdrantClient:
+        yield MockQdrantClient()
+
+@pytest.fixture
+def mock_sentence_transformer():
+    with patch('sentence_transformers.SentenceTransformer') as MockSentenceTransformer:
+        yield MockSentenceTransformer()
+
+@pytest.fixture
+def qdrant_client(mock_qdrant_client, mock_sentence_transformer):
+    client = Qdrant(api_key="your_api_key", host="your_host")
+    yield client
+
+def test_qdrant_init(qdrant_client, mock_qdrant_client):
+    assert qdrant_client.client is not None
+
+def test_load_embedding_model(qdrant_client, mock_sentence_transformer):
+    qdrant_client._load_embedding_model("model_name")
+    mock_sentence_transformer.assert_called_once_with("model_name")
+
+def test_setup_collection(qdrant_client, mock_qdrant_client):
+    qdrant_client._setup_collection()
+    mock_qdrant_client.get_collection.assert_called_once_with(qdrant_client.collection_name)
+
+def test_add_vectors(qdrant_client, mock_qdrant_client):
+    mock_doc = Mock(page_content="Sample text")
+    qdrant_client.add_vectors([mock_doc])
+    mock_qdrant_client.upsert.assert_called_once()
+
+def test_search_vectors(qdrant_client, mock_qdrant_client):
+    qdrant_client.search_vectors("test query")
+    mock_qdrant_client.search.assert_called_once()