fix: sync issues

docs/indexes/pinecone-sync-routes.ipynb

@@ -125,14 +125,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## RouteLayer"
+    "## SemanticRouter"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The `RouteLayer` class supports both sync and async operations by default, so we initialize as usual:"
+    "The `SemanticRouter` class supports both sync and async operations by default, so we initialize as usual:"
    ]
   },
   {
@@ -160,10 +160,10 @@
     }
    ],
    "source": [
-    "from semantic_router.routers import RouteLayer\n",
+    "from semantic_router.routers import SemanticRouter\n",
     "import time\n",
     "\n",
-    "rl = RouteLayer(encoder=encoder, routes=routes, index=pc_index, auto_sync=\"local\")\n",
+    "rl = SemanticRouter(encoder=encoder, routes=routes, index=pc_index, auto_sync=\"local\")\n",
     "# due to pinecone indexing latency we wait 3 seconds\n",
     "time.sleep(3)"
    ]
@@ -210,7 +210,7 @@
    "source": [
     "del rl\n",
     "\n",
-    "rl = RouteLayer(encoder=encoder, routes=[politics], index=pc_index)\n",
+    "rl = SemanticRouter(encoder=encoder, routes=[politics], index=pc_index)\n",
     "time.sleep(3)"
    ]
   },

pyproject.toml

@@ -35,7 +35,7 @@ boto3 = { version = "^1.34.98", optional = true }
 botocore = {version = "^1.34.110", optional = true}
 aiohttp = "^3.10.11"
 fastembed = {version = "^0.3.0", optional = true}
-psycopg2 = {version = "^2.9.9", optional = true}
+psycopg2-binary = {version = "^2.9.9", optional = true}
 sphinx = {version = "^7.0.0", optional = true}
 sphinxawesome-theme = {version = "^5.2.0", optional = true}
 tornado = {version = "^6.4.2", optional = true}

semantic_router/encoders/bm25.py

@@ -48,7 +48,6 @@ def __call__(self, docs: List[str]) -> list[SparseEmbedding]:
         if len(docs) == 1:
             sparse_dicts = self.model.encode_queries(docs)
         elif len(docs) > 1:
-            print(docs)
             sparse_dicts = self.model.encode_documents(docs)
         else:
             raise ValueError("No documents to encode.")

semantic_router/index/__init__.py

@@ -2,6 +2,7 @@
 from semantic_router.index.hybrid_local import HybridLocalIndex
 from semantic_router.index.local import LocalIndex
 from semantic_router.index.pinecone import PineconeIndex
+from semantic_router.index.postgres import PostgresIndex
 from semantic_router.index.qdrant import QdrantIndex
 
 __all__ = [
@@ -10,4 +11,5 @@
     "LocalIndex",
     "QdrantIndex",
     "PineconeIndex",
+    "PostgresIndex",
 ]

semantic_router/index/pinecone.py

@@ -364,7 +364,6 @@ def query(
         top_k: int = 5,
         route_filter: Optional[List[str]] = None,
         sparse_vector: dict[int, float] | SparseEmbedding | None = None,
-        **kwargs: Any,
     ) -> Tuple[np.ndarray, List[str]]:
         """Search the index for the query vector and return the top_k results.
 

semantic_router/index/postgres.py

@@ -1,16 +1,18 @@
 import os
 import uuid
 from enum import Enum
-from typing import Any, Dict, List, Optional, Tuple, Union
+from typing import Any, Dict, List, Optional, Tuple, TYPE_CHECKING, Union
 
 import numpy as np
-import psycopg2
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
 
 from semantic_router.index.base import BaseIndex
 from semantic_router.schema import ConfigParameter, Metric, SparseEmbedding
 from semantic_router.utils.logger import logger
 
+if TYPE_CHECKING:
+    import psycopg2
+
 
 class MetricPgVecOperatorMap(Enum):
     """
@@ -104,8 +106,9 @@ class PostgresIndex(BaseIndex):
     dimensions: int = 1536
     metric: Metric = Metric.COSINE
     namespace: Optional[str] = ""
-    conn: Optional[psycopg2.extensions.connection] = None
+    conn: Optional["psycopg2.extensions.connection"] = None
     type: str = "postgres"
+    pg2: Any = Field(default=None, exclude=True)
 
     def __init__(
         self,
@@ -133,6 +136,12 @@ def __init__(
         :type namespace: Optional[str]
         """
         super().__init__()
+        # try and import psycopg2
+        try:
+            import psycopg2
+        except ImportError:
+            raise ImportError("Please install psycopg2 to use PostgresIndex. "
+                              "You can install it with: `pip install 'semantic-router[postgres]'`")
         if connection_string:
             self.connection_string = connection_string
         else:

semantic_router/routers/base.py

@@ -497,14 +497,10 @@ def retrieve_multiple_routes(
             vector_arr = self._encode(text=[text])
         else:
             vector_arr = np.array(vector)
-        print(f"{text=}")
-        print(f"{vector_arr}")
         # get relevant utterances
         results = self._retrieve(xq=vector_arr)
-        print(f"{results=}")
         # decide most relevant routes
         categories_with_scores = self._semantic_classify_multiple_routes(results)
-        print(f"{categories_with_scores=}")
         return [
             RouteChoice(name=category, similarity_score=score)
             for category, score in categories_with_scores

semantic_router/routers/semantic.py

@@ -31,9 +31,6 @@ def __init__(
             aggregation=aggregation,
             auto_sync=auto_sync,
         )
-        # run initialize index now if auto sync is active
-        if self.auto_sync:
-            self._init_index_state()
 
     def _encode(self, text: list[str]) -> Any:
         """Given some text, encode it."""