feat: Qdrant Memory Support

adala/memories/__init__.py

@@ -1,3 +1,4 @@
 from .file_memory import FileMemory
 from .vectordb import VectorDBMemory
+from .qdrant_memory import QdrantMemory
 from .base import Memory

adala/memories/qdrant_memory.py

@@ -0,0 +1,174 @@
+from typing import Any, List, Dict, Optional
+import uuid
+from pydantic import Field, model_validator
+
+from .base import Memory
+
+try:
+    from qdrant_client import QdrantClient
+    from qdrant_client.models import Distance, VectorParams, PointStruct
+    import openai
+
+    QDRANT_AVAILABLE = True
+except ImportError:
+    QDRANT_AVAILABLE = False
+
+
+class QdrantMemory(Memory):
+    """
+    Memory backed by [Qdrant](https://qdrant.tech/).
+    """
+
+    model_config = {"arbitrary_types_allowed": True}
+
+    collection_name: str = Field(..., description="Name of the Qdrant collection")
+    openai_api_key: str = Field(..., description="OpenAI API key for embeddings")
+    openai_embedding_model: str = Field(
+        default="text-embedding-3-small", description="OpenAI embedding model"
+    )
+    qdrant_url: Optional[str] = Field(
+        default=None, description="Qdrant server URL"
+    )
+    qdrant_api_key: Optional[str] = Field(
+        default=None, description="Qdrant API key for remote instances"
+    )
+    qdrant_client: Optional[QdrantClient] = Field(
+        default=None, description="Pre-configured QdrantClient instance"
+    )
+    dimension: int = Field(default=1536, description="Vector dimension size")
+    distance_metric: str = Field(
+        default="Cosine", description="Distance metric for similarity search"
+    )
+
+    _client: Optional[QdrantClient] = None
+    _openai_client: Optional[openai.OpenAI] = None
+
+    @model_validator(mode="after")
+    def init_database(self):
+        if not QDRANT_AVAILABLE:
+            raise ImportError(
+                "Qdrant dependencies not available. "
+                "Please install with: pip install qdrant-client openai"
+            )
+
+        if self.qdrant_client is not None and (
+            self.qdrant_url is not None or self.qdrant_api_key is not None
+        ):
+            raise ValueError(
+                "Cannot specify both 'qdrant_client' and 'qdrant_url'/'qdrant_api_key'. "
+                "Use either a pre-configured QdrantClient or URL-based configuration, not both."
+            )
+
+        if self.qdrant_client is not None:
+            self._client = self.qdrant_client
+        elif self.qdrant_url:
+            self._client = QdrantClient(
+                url=self.qdrant_url, api_key=self.qdrant_api_key
+            )
+        else:
+            raise ValueError(
+                "No Qdrant configuration provided. Please specify either 'qdrant_client' "
+                "or 'qdrant_url' to configure the Qdrant connection."
+            )
+
+        if not self.openai_api_key:
+            raise ValueError("OpenAI API key is required but not provided")
+        self._openai_client = openai.OpenAI(api_key=self.openai_api_key)
+
+        if not self._client.collection_exists(self.collection_name):
+            self._client.create_collection(
+                collection_name=self.collection_name,
+                vectors_config=VectorParams(
+                    size=self.dimension, distance=self._get_distance_metric()
+                ),
+            )
+
+        return self
+
+    def _generate_uuid(self, string: str) -> str:
+        return uuid.uuid5(uuid.NAMESPACE_URL, string).hex
+
+    def _get_distance_metric(self) -> Distance:
+        distance_map = {
+            "Cosine": Distance.COSINE,
+            "Dot": Distance.DOT,
+            "Euclidean": Distance.EUCLID,
+            "Manhattan": Distance.MANHATTAN,
+        }
+        return distance_map.get(self.distance_metric, Distance.COSINE)
+
+    def _get_embedding(self, text: str) -> List[float]:
+        response = self._openai_client.embeddings.create(
+            model=self.openai_embedding_model, input=text
+        )
+        return response.data[0].embedding
+
+    def _get_embeddings(self, texts: List[str]) -> List[List[float]]:
+        response = self._openai_client.embeddings.create(
+            model=self.openai_embedding_model, input=texts
+        )
+        return [data.embedding for data in response.data]
+
+    def remember(self, observation: str, data: Any):
+        """Store a single observation with its associated data."""
+        self.remember_many([observation], [data])
+
+    def remember_many(self, observations: List[str], data: List[Dict]):
+        """Store multiple observations with their associated data."""
+
+        data = [{k: v for k, v in d.items() if v is not None} for d in data]
+
+        embeddings = self._get_embeddings(observations)
+
+        points = []
+        for obs, embedding, metadata in zip(observations, embeddings, data):
+            point_id = self._generate_uuid(obs)
+            points.append(
+                PointStruct(
+                    id=point_id, vector=embedding, payload={"text": obs, **metadata}
+                )
+            )
+
+        self._client.upsert(collection_name=self.collection_name, points=points)
+
+    def retrieve_many(self, observations: List[str], num_results: int = 1) -> List[Any]:
+        """Retrieve similar observations for multiple queries."""
+        results = []
+
+        for observation in observations:
+            query_embedding = self._get_embedding(observation)
+
+            search_results = self._client.query_points(
+                collection_name=self.collection_name,
+                query=query_embedding,
+                limit=num_results,
+                with_payload=True,
+            ).points
+
+            metadatas = []
+            for result in search_results:
+                payload = result.payload.copy()
+
+                payload.pop("text", None)
+                metadatas.append(payload)
+
+            results.append(metadatas)
+
+        return results
+
+    def retrieve(self, observation: str, num_results: int = 1) -> Any:
+        """Retrieve similar observations for a single query."""
+        return self.retrieve_many([observation], num_results=num_results)[0]
+
+    def clear(self):
+        """Clear all data from the collection."""
+
+        if self._client.collection_exists(self.collection_name):
+            self._client.delete_collection(self.collection_name)
+
+        self._client.create_collection(
+            collection_name=self.collection_name,
+            vectors_config=VectorParams(
+                size=self.dimension, distance=self._get_distance_metric()
+            ),
+        )

pyproject.toml

@@ -46,7 +46,8 @@ dependencies = [
     "pandarallel (>=1.6.5,<2.0.0)",
     "instructor (==1.4.3)",
     "async-lru (>=2.0.5,<3.0.0)",
-    "jinja2 (>=3.1.6,<4.0)"
+    "jinja2 (>=3.1.6,<4.0)",
+    "qdrant-client (>=1.15.1,<2.0.0)"
 ]
 
 [project.urls]