feat: Qdrant Vector Store

README.md

@@ -535,6 +535,7 @@ Options for indexing existing context history or enabling real-time indexing new
 - ChromaVectorStore
 - ElasticsearchStore
 - PinecodeVectorStore
+- QdrantVectorStore
 - RedisVectorStore
 - SimpleVectorStore
 ```
@@ -2973,6 +2974,7 @@ You can provide a single URI in the form of: `{scheme}://{user}:{password}@{host
 - ChromaVectorStore
 - ElasticsearchStore
 - PinecodeVectorStore
+- QdrantVectorStore
 - RedisVectorStore
 - SimpleVectorStore
 ```
@@ -3003,6 +3005,15 @@ Keyword arguments for Pinecone(`**kwargs`):
 - `api_key`
 - index_name (default: current index ID, already set, not required)
 
+**QdrantVectorStore**
+
+Keyword arguments for QdrantVectorStore(`**kwargs`):
+
+- `url` - str, default: `http://localhost:6333`
+- `api_key` - str, default: `None` (for Qdrant Cloud)
+- `collection_name` (default: current index ID, already set, not required)
+- any other keyword arguments provided on list
+
 **RedisVectorStore**
 
 Keyword arguments for RedisVectorStore(`**kwargs`):
@@ -3500,6 +3511,7 @@ You can create a custom vector store provider or data loader for your data and d
 from pygpt_net.provider.vector_stores.chroma import ChromaProvider
 from pygpt_net.provider.vector_stores.elasticsearch import ElasticsearchProvider
 from pygpt_net.provider.vector_stores.pinecode import PinecodeProvider
+from pygpt_net.provider.vector_stores.qdrant import QdrantProvider
 from pygpt_net.provider.vector_stores.redis import RedisProvider
 from pygpt_net.provider.vector_stores.simple import SimpleProvider
 
@@ -3509,6 +3521,7 @@ def run(**kwargs):
     launcher.add_vector_store(ChromaProvider())
     launcher.add_vector_store(ElasticsearchProvider())
     launcher.add_vector_store(PinecodeProvider())
+    launcher.add_vector_store(QdrantProvider())
     launcher.add_vector_store(RedisProvider())
     launcher.add_vector_store(SimpleProvider())
 

docs/source/configuration.rst

@@ -729,6 +729,7 @@ Vector stores
 * ChromaVectorStore
 * ElasticsearchStore
 * PinecodeVectorStore
+* QdrantVectorStore
 * RedisVectorStore
 * SimpleVectorStore
 
@@ -758,6 +759,15 @@ Keyword arguments for Pinecone(``**kwargs``):
 * ``api_key``
 * index_name (default: current index ID, already set, not required)
 
+**QdrantVectorStore**
+
+Keyword arguments for QdrantVectorStore(``**kwargs``):
+
+* ``url`` - str, default: `http://localhost:6333`
+* ``api_key`` - str, default: `None` (for Qdrant Cloud)
+* ``collection_name`` (default: current index ID, already set, not required)
+* any other keyword arguments provided on list
+
 **RedisVectorStore**
 
 Keyword arguments for RedisVectorStore(``**kwargs``):

docs/source/extending.rst

@@ -412,6 +412,7 @@ You can create a custom vector store provider or data loader for your data and d
     from pygpt_net.provider.vector_stores.chroma import ChromaProvider
     from pygpt_net.provider.vector_stores.elasticsearch import ElasticsearchProvider
     from pygpt_net.provider.vector_stores.pinecode import PinecodeProvider
+    from pygpt_net.provider.vector_stores.qdrant import QdrantProvider
     from pygpt_net.provider.vector_stores.redis import RedisProvider
     from pygpt_net.provider.vector_stores.simple import SimpleProvider
 
@@ -421,6 +422,7 @@ You can create a custom vector store provider or data loader for your data and d
         launcher.add_vector_store(ChromaProvider())
         launcher.add_vector_store(ElasticsearchProvider())
         launcher.add_vector_store(PinecodeProvider())
+        launcher.add_vector_store(QdrantProvider())
         launcher.add_vector_store(RedisProvider())
         launcher.add_vector_store(SimpleProvider())
 

pyproject.toml

@@ -106,6 +106,7 @@ pygame = "^2.6.1"
 pypdf = "6.1.0"
 pynput = "^1.8.1"
 pyserial = "^3.5"
+llama-index-vector-stores-qdrant = "^0.8.5"
 PySide6 = "6.9.1"
 python-markdown-math = "^0.8"
 qasync = "^0.27.1"

setup.py

@@ -90,6 +90,7 @@
         'llama-index-vector-stores-chroma>=0.4.2,<0.5.0',
         'llama-index-vector-stores-elasticsearch==0.4.0',
         'llama-index-vector-stores-pinecone>=0.6.0,<0.7.0',
+        'llama-index-vector-stores-qdrant>=0.8.0,<0.9.0',
         'llama-index-vector-stores-redis>=0.4.0,<0.5.0',
         'llama-index-readers-chatgpt-plugin>=0.3.0,<0.4.0',
         'llama-index-readers-database>=0.3.0,<0.4.0',

src/pygpt_net/app.py

@@ -146,6 +146,7 @@ def open_wrapper(file, mode='r', *args, **kwargs):
 from pygpt_net.provider.vector_stores.chroma import ChromaProvider
 from pygpt_net.provider.vector_stores.elasticsearch import ElasticsearchProvider
 from pygpt_net.provider.vector_stores.pinecode import PinecodeProvider
+from pygpt_net.provider.vector_stores.qdrant import QdrantProvider
 from pygpt_net.provider.vector_stores.redis import RedisProvider
 from pygpt_net.provider.vector_stores.simple import SimpleProvider
 
@@ -475,6 +476,7 @@ def run(**kwargs):
     launcher.add_vector_store(ChromaProvider())
     launcher.add_vector_store(ElasticsearchProvider())
     launcher.add_vector_store(PinecodeProvider())
+    launcher.add_vector_store(QdrantProvider())
     launcher.add_vector_store(RedisProvider())
     launcher.add_vector_store(SimpleProvider())
 

src/pygpt_net/provider/vector_stores/qdrant.py

@@ -0,0 +1,117 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+# ================================================== #
+# This file is a part of PYGPT package               #
+# Website: https://pygpt.net                         #
+# GitHub:  https://github.com/szczyglis-dev/py-gpt   #
+# MIT License                                        #
+# Created By  : Marcin Szczygliński                  #
+# Updated Date: 2025.09.30 13:00:00                  #
+# ================================================== #
+
+import datetime
+import os.path
+from typing import Optional
+
+from llama_index.core.indices.base import BaseIndex
+from llama_index.core import StorageContext
+
+from pygpt_net.utils import parse_args
+from .base import BaseStore
+
+
+class QdrantProvider(BaseStore):
+    def __init__(self, *args, **kwargs):
+        super(QdrantProvider, self).__init__(*args, **kwargs)
+        """
+        Qdrant vector store provider
+
+        :param args: args
+        :param kwargs: kwargs
+        """
+        self.window = kwargs.get('window', None)
+        self.id = "QdrantVectorStore"
+        self.prefix = "qdrant_"  # prefix for index directory
+        self.indexes = {}
+
+    def create(self, id: str):
+        """
+        Create empty index
+
+        :param id: index name
+        """
+        path = self.get_path(id)
+        if not os.path.exists(path):
+            os.makedirs(path, exist_ok=True)
+            self.store(id)
+
+    def get_qdrant_store(self, id: str):
+        """
+        Get Qdrant vector store
+
+        :param id: index name
+        :return: QdrantVectorStore instance
+        """
+        from llama_index.vector_stores.qdrant import QdrantVectorStore
+
+        additional_args = parse_args(
+            self.window.core.config.get('llama.idx.storage.args', []),
+        )
+
+        url = additional_args.get('url', 'http://localhost:6333')
+        api_key = additional_args.get('api_key', '')
+
+        store_args = {k: v for k, v in additional_args.items() if k not in ['url', 'api_key', 'collection_name']}
+
+        return QdrantVectorStore(
+            url=url,
+            api_key=api_key,
+            collection_name=id,
+            **store_args
+        )
+
+    def get(
+            self,
+            id: str,
+            llm: Optional = None,
+            embed_model: Optional = None,
+    ) -> BaseIndex:
+        """
+        Get index
+
+        :param id: index name
+        :param llm: LLM instance
+        :param embed_model: Embedding model instance
+        :return: index instance
+        """
+        if not self.exists(id):
+            self.create(id)
+        vector_store = self.get_qdrant_store(id)
+        storage_context = StorageContext.from_defaults(
+            vector_store=vector_store,
+        )
+        self.indexes[id] = self.index_from_store(
+            vector_store=vector_store,
+            storage_context=storage_context,
+            llm=llm,
+            embed_model=embed_model,
+        )
+        return self.indexes[id]
+
+    def store(
+            self,
+            id: str,
+            index: Optional[BaseIndex] = None
+    ):
+        """
+        Store index
+
+        :param id: index name
+        :param index: index instance
+        """
+        path = self.get_path(id)
+        os.makedirs(path, exist_ok=True)
+        lock_file = os.path.join(path, 'store.lock')
+        with open(lock_file, 'w') as f:
+            f.write(id + ': ' + str(datetime.datetime.now()))
+        self.indexes[id] = index