Merge branch 'main' into add_spacy

pyproject.toml

@@ -37,6 +37,7 @@ torchvision = { version = "^0.17.0", optional = true}
 pillow = { version= "^10.2.0", optional = true}
 tiktoken = "^0.6.0"
 matplotlib = { version="^3.8.3", optional = true}
+qdrant-client = {version="^1.8.0", optional = true}
 
 
 [tool.poetry.extras]
@@ -47,6 +48,7 @@ pinecone = ["pinecone-client"]
 vision = ["torch", "torchvision", "transformers", "pillow"]
 processing = ["matplotlib", "spacy"]
 mistralai = ["mistralai"]
+qdrant = ["qdrant-client"]
 
 [tool.poetry.group.dev.dependencies]
 ipykernel = "^6.25.0"

semantic_router/index/__init__.py

@@ -1,9 +1,11 @@
 from semantic_router.index.base import BaseIndex
 from semantic_router.index.local import LocalIndex
 from semantic_router.index.pinecone import PineconeIndex
+from semantic_router.index.qdrant import QdrantIndex
 
 __all__ = [
     "BaseIndex",
     "LocalIndex",
+    "QdrantIndex",
     "PineconeIndex",
 ]

semantic_router/index/qdrant.py

@@ -0,0 +1,247 @@
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+from pydantic.v1 import Field
+
+from semantic_router.index.base import BaseIndex
+from semantic_router.schema import Metric
+
+DEFAULT_COLLECTION_NAME = "semantic-router-index"
+DEFAULT_UPLOAD_BATCH_SIZE = 100
+SCROLL_SIZE = 1000
+SR_UTTERANCE_PAYLOAD_KEY = "sr_utterance"
+SR_ROUTE_PAYLOAD_KEY = "sr_route"
+
+
+class QdrantIndex(BaseIndex):
+    "The name of the collection to use"
+
+    index_name: str = Field(
+        default=DEFAULT_COLLECTION_NAME,
+        description="Name of the Qdrant collection."
+        f"Default: '{DEFAULT_COLLECTION_NAME}'",
+    )
+    location: Optional[str] = Field(
+        default=":memory:",
+        description="If ':memory:' - use an in-memory Qdrant instance."
+        "Used as 'url' value otherwise",
+    )
+    url: Optional[str] = Field(
+        default=None,
+        description="Qualified URL of the Qdrant instance."
+        "Optional[scheme], host, Optional[port], Optional[prefix]",
+    )
+    port: Optional[int] = Field(
+        default=6333,
+        description="Port of the REST API interface.",
+    )
+    grpc_port: int = Field(
+        default=6334,
+        description="Port of the gRPC interface.",
+    )
+    prefer_grpc: bool = Field(
+        default=None,
+        description="Whether to use gPRC interface whenever possible in methods",
+    )
+    https: Optional[bool] = Field(
+        default=None,
+        description="Whether to use HTTPS(SSL) protocol.",
+    )
+    api_key: Optional[str] = Field(
+        default=None,
+        description="API key for authentication in Qdrant Cloud.",
+    )
+    prefix: Optional[str] = Field(
+        default=None,
+        description="Prefix to the REST URL path. Example: `http://localhost:6333/some/prefix/{qdrant-endpoint}`.",
+    )
+    timeout: Optional[int] = Field(
+        default=None,
+        description="Timeout for REST and gRPC API requests.",
+    )
+    host: Optional[str] = Field(
+        default=None,
+        description="Host name of Qdrant service."
+        "If url and host are None, set to 'localhost'.",
+    )
+    path: Optional[str] = Field(
+        default=None,
+        description="Persistence path for Qdrant local",
+    )
+    grpc_options: Optional[Dict[str, Any]] = Field(
+        default=None,
+        description="Options to be passed to the low-level GRPC client, if used.",
+    )
+    dimensions: Union[int, None] = Field(
+        default=None,
+        description="Embedding dimensions."
+        "Defaults to the embedding length of the configured encoder.",
+    )
+    metric: Metric = Field(
+        default=Metric.COSINE,
+        description="Distance metric to use for similarity search.",
+    )
+    config: Optional[Dict[str, Any]] = Field(
+        default={},
+        description="Collection options passed to `QdrantClient#create_collection`.",
+    )
+    client: Any = Field(default=None, exclude=True)
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.type = "qdrant"
+        self.client = self._initialize_client()
+
+    def _initialize_client(self):
+        try:
+            from qdrant_client import QdrantClient
+
+            return QdrantClient(
+                location=self.location,
+                url=self.url,
+                port=self.port,
+                grpc_port=self.grpc_port,
+                prefer_grpc=self.prefer_grpc,
+                https=self.https,
+                api_key=self.api_key,
+                prefix=self.prefix,
+                timeout=self.timeout,
+                host=self.host,
+                path=self.path,
+                grpc_options=self.grpc_options,
+            )
+
+        except ImportError as e:
+            raise ImportError(
+                "Please install 'qdrant-client' to use QdrantIndex."
+                "You can install it with: "
+                "`pip install 'semantic-router[qdrant]'`"
+            ) from e
+
+    def _init_collection(self) -> None:
+        from qdrant_client import QdrantClient, models
+
+        self.client: QdrantClient
+        if not self.client.collection_exists(self.index_name):
+            if not self.dimensions:
+                raise ValueError(
+                    "Cannot create a collection without specifying the dimensions."
+                )
+
+            self.client.create_collection(
+                collection_name=self.index_name,
+                vectors_config=models.VectorParams(
+                    size=self.dimensions, distance=self.convert_metric(self.metric)
+                ),
+                **self.config,
+            )
+
+    def add(
+        self,
+        embeddings: List[List[float]],
+        routes: List[str],
+        utterances: List[str],
+        batch_size: int = DEFAULT_UPLOAD_BATCH_SIZE,
+    ):
+        self.dimensions = self.dimensions or len(embeddings[0])
+        self._init_collection()
+
+        payloads = [
+            {SR_ROUTE_PAYLOAD_KEY: route, SR_UTTERANCE_PAYLOAD_KEY: utterance}
+            for route, utterance in zip(routes, utterances)
+        ]
+
+        # UUIDs are autogenerated by qdrant-client if not provided explicitly
+        self.client.upload_collection(
+            self.index_name,
+            vectors=embeddings,
+            payload=payloads,
+            batch_size=batch_size,
+        )
+
+    def get_routes(self) -> List[Tuple]:
+        """
+        Gets a list of route and utterance objects currently stored in the index.
+
+        Returns:
+            List[Tuple]: A list of (route_name, utterance) objects.
+        """
+
+        import grpc
+
+        results = []
+        next_offset = None
+        stop_scrolling = False
+        while not stop_scrolling:
+            records, next_offset = self.client.scroll(
+                self.index_name,
+                limit=SCROLL_SIZE,
+                offset=next_offset,
+                with_payload=True,
+            )
+            stop_scrolling = next_offset is None or (
+                isinstance(next_offset, grpc.PointId)
+                and next_offset.num == 0
+                and next_offset.uuid == ""
+            )
+
+            results.extend(records)
+
+        route_tuples = [
+            (x.payload[SR_ROUTE_PAYLOAD_KEY], x.payload[SR_UTTERANCE_PAYLOAD_KEY])
+            for x in results
+        ]
+        return route_tuples
+
+    def delete(self, route_name: str):
+        from qdrant_client import models
+
+        self.client.delete(
+            self.index_name,
+            points_selector=models.Filter(
+                must=[
+                    models.FieldCondition(
+                        key=SR_ROUTE_PAYLOAD_KEY,
+                        match=models.MatchText(text=route_name),
+                    )
+                ]
+            ),
+        )
+
+    def describe(self) -> dict:
+        collection_info = self.client.get_collection(self.index_name)
+
+        return {
+            "type": self.type,
+            "dimensions": collection_info.config.params.vectors.size,
+            "vectors": collection_info.points_count,
+        }
+
+    def query(self, vector: np.ndarray, top_k: int = 5) -> Tuple[np.ndarray, List[str]]:
+        results = self.client.search(
+            self.index_name, query_vector=vector, limit=top_k, with_payload=True
+        )
+        scores = [result.score for result in results]
+        route_names = [result.payload[SR_ROUTE_PAYLOAD_KEY] for result in results]
+        return np.array(scores), route_names
+
+    def delete_index(self):
+        self.client.delete_collection(self.index_name)
+
+    def convert_metric(self, metric: Metric):
+        from qdrant_client.models import Distance
+
+        mapping = {
+            Metric.COSINE: Distance.COSINE,
+            Metric.EUCLIDEAN: Distance.EUCLID,
+            Metric.DOTPRODUCT: Distance.DOT,
+            Metric.MANHATTAN: Distance.MANHATTAN,
+        }
+
+        if metric not in mapping:
+            raise ValueError(f"Unsupported Qdrant similarity metric: {metric}")
+
+        return mapping[metric]
+
+    def __len__(self):
+        return self.client.get_collection(self.index_name).points_count

semantic_router/layer.py

@@ -318,9 +318,9 @@ def from_yaml(cls, file_path: str):
         return cls(encoder=encoder, routes=config.routes)
 
     @classmethod
-    def from_config(cls, config: LayerConfig):
+    def from_config(cls, config: LayerConfig, index: Optional[BaseIndex] = None):
         encoder = Encoder(type=config.encoder_type, name=config.encoder_name).model
-        return cls(encoder=encoder, routes=config.routes)
+        return cls(encoder=encoder, routes=config.routes, index=index)
 
     def add(self, route: Route):
         logger.info(f"Adding `{route.name}` route")

semantic_router/schema.py

@@ -85,3 +85,10 @@ class DocumentSplit(BaseModel):
     @property
     def content(self) -> str:
         return " ".join(self.docs)
+
+
+class Metric(Enum):
+    COSINE = "cosine"
+    DOTPRODUCT = "dotproduct"
+    EUCLIDEAN = "euclidean"
+    MANHATTAN = "manhattan"