feat: Qdrant storage connector

.github/workflows/tests.yml

@@ -13,6 +13,13 @@ jobs:
   test:
     runs-on: ubuntu-latest
     timeout-minutes: 15
+
+    services:
+      qdrant:
+        image: qdrant/qdrant
+        ports:
+          - 6333:6333
+
     steps:
     - name: Checkout
       uses: actions/checkout@v4

docs/storage.md

@@ -36,19 +36,19 @@ To run the Postgres backend, you will need a URI to a Postgres database that sup
 
 3. Configure the environment for `pgvector`. You can either:
     - Add the following line to your shell profile (e.g., `~/.bashrc`, `~/.zshrc`):
-    
+
       ```sh
       export MEMGPT_PGURI=postgresql+pg8000://memgpt:memgpt@localhost:8888/memgpt
       ```
 
     - Or create a `.env` file in the root project directory with:
-    
+
       ```sh
       MEMGPT_PGURI=postgresql+pg8000://memgpt:memgpt@localhost:8888/memgpt
       ```
 
 4. Run the script from the root project directory:
-     
+
   ```sh
   bash db/run_postgres.sh
   ```
@@ -105,6 +105,20 @@ memgpt configure
 
 and selecting `lancedb` for archival storage, and database URI (e.g. `./.lancedb`"), Empty archival uri is also handled and default uri is set at `./.lancedb`. For more checkout [lancedb docs](https://lancedb.github.io/lancedb/)
 
+## Qdrant
+
+To enable the Qdrant backend, make sure to install the required dependencies with:
+
+```sh
+pip install 'pymemgpt[qdrant]'
+```
+
+You can configure Qdrant with an in-memory instance or a server using the `memgpt configure` command. You can set an API key for authentication with a Qdrant server using the `QDRANT_API_KEY` environment variable. Learn more about setting up Qdrant [here](https://qdrant.tech/documentation/guides/installation/).
+
+```sh
+? Select Qdrant backend: server
+? Enter the Qdrant instance URI (Default: localhost:6333): localhost:6333
+```
 
 ## Milvus
 

memgpt/agent_store/qdrant.py

@@ -0,0 +1,201 @@
+import os
+import uuid
+from copy import deepcopy
+from typing import Dict, Iterator, List, Optional, cast
+
+from memgpt.agent_store.storage import StorageConnector, TableType
+from memgpt.config import MemGPTConfig
+from memgpt.constants import MAX_EMBEDDING_DIM
+from memgpt.data_types import Passage, Record, RecordType
+from memgpt.utils import datetime_to_timestamp, timestamp_to_datetime
+
+TEXT_PAYLOAD_KEY = "text_content"
+METADATA_PAYLOAD_KEY = "metadata"
+
+
+class QdrantStorageConnector(StorageConnector):
+    """Storage via Qdrant"""
+
+    def __init__(self, table_type: str, config: MemGPTConfig, user_id, agent_id=None):
+        super().__init__(table_type=table_type, config=config, user_id=user_id, agent_id=agent_id)
+        try:
+            from qdrant_client import QdrantClient, models
+        except ImportError as e:
+            raise ImportError("'qdrant-client' not installed. Run `pip install qdrant-client`.") from e
+        assert table_type in [TableType.ARCHIVAL_MEMORY, TableType.PASSAGES], "Qdrant only supports archival memory"
+        if config.archival_storage_uri and len(config.archival_storage_uri.split(":")) == 2:
+            host, port = config.archival_storage_uri.split(":")
+            self.qdrant_client = QdrantClient(host=host, port=port, api_key=os.getenv("QDRANT_API_KEY"))
+        elif config.archival_storage_path:
+            self.qdrant_client = QdrantClient(path=config.archival_storage_path)
+        else:
+            raise ValueError("Qdrant storage requires either a URI or a path to the storage configured")
+        if not self.qdrant_client.collection_exists(self.table_name):
+            self.qdrant_client.create_collection(
+                collection_name=self.table_name,
+                vectors_config=models.VectorParams(
+                    size=MAX_EMBEDDING_DIM,
+                    distance=models.Distance.COSINE,
+                ),
+            )
+        self.uuid_fields = ["id", "user_id", "agent_id", "source_id", "doc_id"]
+
+    def get_all_paginated(self, filters: Optional[Dict] = {}, page_size: int = 10) -> Iterator[List[RecordType]]:
+        from qdrant_client import grpc
+
+        filters = self.get_qdrant_filters(filters)
+        next_offset = None
+        stop_scrolling = False
+        while not stop_scrolling:
+            results, next_offset = self.qdrant_client.scroll(
+                collection_name=self.table_name,
+                scroll_filter=filters,
+                limit=page_size,
+                offset=next_offset,
+                with_payload=True,
+                with_vectors=True,
+            )
+            stop_scrolling = next_offset is None or (
+                isinstance(next_offset, grpc.PointId) and next_offset.num == 0 and next_offset.uuid == ""
+            )
+            yield self.to_records(results)
+
+    def get_all(self, filters: Optional[Dict] = {}, limit=10) -> List[RecordType]:
+        if self.size(filters) == 0:
+            return []
+        filters = self.get_qdrant_filters(filters)
+        results, _ = self.qdrant_client.scroll(
+            self.table_name,
+            scroll_filter=filters,
+            limit=limit,
+            with_payload=True,
+            with_vectors=True,
+        )
+        return self.to_records(results)
+
+    def get(self, id: uuid.UUID) -> Optional[RecordType]:
+        results = self.qdrant_client.retrieve(
+            collection_name=self.table_name,
+            ids=[str(id)],
+            with_payload=True,
+            with_vectors=True,
+        )
+        if not results:
+            return None
+        return self.to_records(results)[0]
+
+    def insert(self, record: Record):
+        points = self.to_points([record])
+        self.qdrant_client.upsert(self.table_name, points=points)
+
+    def insert_many(self, records: List[RecordType], show_progress=False):
+        points = self.to_points(records)
+        self.qdrant_client.upsert(self.table_name, points=points)
+
+    def delete(self, filters: Optional[Dict] = {}):
+        filters = self.get_qdrant_filters(filters)
+        self.qdrant_client.delete(self.table_name, points_selector=filters)
+
+    def delete_table(self):
+        self.qdrant_client.delete_collection(self.table_name)
+        self.qdrant_client.close()
+
+    def size(self, filters: Optional[Dict] = {}) -> int:
+        filters = self.get_qdrant_filters(filters)
+        return self.qdrant_client.count(collection_name=self.table_name, count_filter=filters).count
+
+    def close(self):
+        self.qdrant_client.close()
+
+    def query(
+        self,
+        query: str,
+        query_vec: List[float],
+        top_k: int = 10,
+        filters: Optional[Dict] = {},
+    ) -> List[RecordType]:
+        filters = self.get_filters(filters)
+        results = self.qdrant_client.search(
+            self.table_name,
+            query_vector=query_vec,
+            query_filter=filters,
+            limit=top_k,
+            with_payload=True,
+            with_vectors=True,
+        )
+        return self.to_records(results)
+
+    def to_records(self, records: list) -> List[RecordType]:
+        parsed_records = []
+        for record in records:
+            record = deepcopy(record)
+            metadata = record.payload[METADATA_PAYLOAD_KEY]
+            text = record.payload[TEXT_PAYLOAD_KEY]
+            _id = metadata.pop("id")
+            embedding = record.vector
+            for key, value in metadata.items():
+                if key in self.uuid_fields:
+                    metadata[key] = uuid.UUID(value)
+                elif key == "created_at":
+                    metadata[key] = timestamp_to_datetime(value)
+            parsed_records.append(
+                cast(
+                    RecordType,
+                    self.type(
+                        text=text,
+                        embedding=embedding,
+                        id=uuid.UUID(_id),
+                        **metadata,
+                    ),
+                )
+            )
+        return parsed_records
+
+    def to_points(self, records: List[RecordType]):
+        from qdrant_client import models
+
+        assert all(isinstance(r, Passage) for r in records)
+        points = []
+        records = list(set(records))
+        for record in records:
+            record = vars(record)
+            _id = record.pop("id")
+            text = record.pop("text", "")
+            embedding = record.pop("embedding", {})
+            record_metadata = record.pop("metadata_", None) or {}
+            if "created_at" in record:
+                record["created_at"] = datetime_to_timestamp(record["created_at"])
+            metadata = {key: value for key, value in record.items() if value is not None}
+            metadata = {
+                **metadata,
+                **record_metadata,
+                "id": str(_id),
+            }
+            for key, value in metadata.items():
+                if key in self.uuid_fields:
+                    metadata[key] = str(value)
+            points.append(
+                models.PointStruct(
+                    id=str(_id),
+                    vector=embedding,
+                    payload={
+                        TEXT_PAYLOAD_KEY: text,
+                        METADATA_PAYLOAD_KEY: metadata,
+                    },
+                )
+            )
+        return points
+
+    def get_qdrant_filters(self, filters: Optional[Dict] = {}):
+        from qdrant_client import models
+
+        filter_conditions = {**self.filters, **filters} if filters is not None else self.filters
+        must_conditions = []
+        for key, value in filter_conditions.items():
+            match_value = str(value) if key in self.uuid_fields else value
+            field_condition = models.FieldCondition(
+                key=f"{METADATA_PAYLOAD_KEY}.{key}",
+                match=models.MatchValue(value=match_value),
+            )
+            must_conditions.append(field_condition)
+        return models.Filter(must=must_conditions)

memgpt/agent_store/storage.py

@@ -100,6 +100,10 @@ def get_storage_connector(
 
             return ChromaStorageConnector(table_type, config, user_id, agent_id)
 
+        elif storage_type == "qdrant":
+            from memgpt.agent_store.qdrant import QdrantStorageConnector
+
+            return QdrantStorageConnector(table_type, config, user_id, agent_id)
         # TODO: add back
         # elif storage_type == "lancedb":
         #    from memgpt.agent_store.db import LanceDBConnector

memgpt/cli/cli_config.py

@@ -913,7 +913,7 @@ def configure_embedding_endpoint(config: MemGPTConfig, credentials: MemGPTCreden
 
 def configure_archival_storage(config: MemGPTConfig, credentials: MemGPTCredentials):
     # Configure archival storage backend
-    archival_storage_options = ["postgres", "chroma", "milvus"]
+    archival_storage_options = ["postgres", "chroma", "milvus", "qdrant"]
     archival_storage_type = questionary.select(
         "Select storage backend for archival data:", archival_storage_options, default=config.archival_storage_type
     ).ask()
@@ -950,6 +950,19 @@ def configure_archival_storage(config: MemGPTConfig, credentials: MemGPTCredenti
         if chroma_type == "persistent":
             archival_storage_path = os.path.join(MEMGPT_DIR, "chroma")
 
+    if archival_storage_type == "qdrant":
+        qdrant_type = questionary.select("Select Qdrant backend:", ["local", "server"], default="local").ask()
+        if qdrant_type is None:
+            raise KeyboardInterrupt
+        if qdrant_type == "server":
+            archival_storage_uri = questionary.text(
+                "Enter the Qdrant instance URI (Default: localhost:6333):", default="localhost:6333"
+            ).ask()
+            if archival_storage_uri is None:
+                raise KeyboardInterrupt
+        if qdrant_type == "local":
+            archival_storage_path = os.path.join(MEMGPT_DIR, "qdrant")
+
     if archival_storage_type == "milvus":
         default_milvus_uri = archival_storage_path = os.path.join(MEMGPT_DIR, "milvus.db")
         archival_storage_uri = questionary.text(