Initiaal push

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:

swarms/memory/qdrant.py

@@ -1,12 +1,27 @@
 from typing import List
-from qdrant_client.http.models import CollectionInfoResponse, OperationResponse, SearchResult
 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
@@ -16,6 +31,12 @@ def __init__(self, api_key: str, host: str, port: int = 6333, collection_name: s
             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:
@@ -34,6 +55,15 @@ def _setup_collection(self):
             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:
@@ -57,6 +87,16 @@ def add_vectors(self, docs: List[dict]):
             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(