feat: Added qdrant support for BaseEngine!

requirements.txt

@@ -15,7 +15,7 @@ neo4j>=5.14.1, <6.0.0
 coverage>=7.3.3, <8.0.0
 pytest>=7.4.3, <8.0.0
 python-dotenv==1.0.0
-tc-hivemind-backend==1.1.5
+tc-hivemind-backend==1.2.0
 llama-index-question-gen-guidance==0.1.2
 llama-index-vector-stores-postgres==0.1.2
 celery>=5.3.6, <6.0.0

utils/query_engine/base_engine.py

@@ -1,10 +1,10 @@
-from bot.retrievers.custom_retriever import CustomVectorStoreRetriever
+from llama_index.core.indices.vector_store.retrievers.retriever import (
+    VectorIndexRetriever,
+)
 from bot.retrievers.utils.load_hyperparams import load_hyperparams
 from llama_index.core import VectorStoreIndex, get_response_synthesizer
-from llama_index.core.base.embeddings.base import BaseEmbedding
 from llama_index.core.query_engine import RetrieverQueryEngine
-from tc_hivemind_backend.embeddings import CohereEmbedding
-from tc_hivemind_backend.pg_vector_access import PGVectorAccess
+from tc_hivemind_backend.qdrant_vector_access import QDrantVectorAccess
 
 
 class BaseEngine:
@@ -24,16 +24,17 @@ def __init__(self, platform_name: str, community_id: str) -> None:
         """
         self.platform_name = platform_name
         self.community_id = community_id
-        self.dbname = f"community_{self.community_id}"
+        self.collection_name = f"{self.community_id}_{platform_name}"
 
     def prepare(self, testing=False):
-        index = self._setup_vector_store_index(
+        vector_store_index = self._setup_vector_store_index(
             testing=testing,
         )
         _, similarity_top_k, _ = load_hyperparams()
 
-        retriever = CustomVectorStoreRetriever(
-            index=index, similarity_top_k=similarity_top_k
+        retriever = VectorIndexRetriever(
+            index=vector_store_index,
+            similarity_top_k=similarity_top_k,
         )
         query_engine = RetrieverQueryEngine(
             retriever=retriever,
@@ -54,27 +55,13 @@ def _setup_vector_store_index(
         testing : bool
             for testing purposes
         **kwargs :
-            table_name : str
-                to override the default table_name
-            dbname : str
-                to override the default database name
+            collection_name : str
+                to override the default collection_name
         """
-        table_name = kwargs.get("table_name", self.platform_name)
-        dbname = kwargs.get("dbname", self.dbname)
-
-        embed_model: BaseEmbedding
-        if testing:
-            from llama_index.core import MockEmbedding
-
-            embed_model = MockEmbedding(embed_dim=1024)
-        else:
-            embed_model = CohereEmbedding()
-
-        pg_vector = PGVectorAccess(
-            table_name=table_name,
-            dbname=dbname,
+        collection_name = kwargs.get("collection_name", self.collection_name)
+        qdrant_vector = QDrantVectorAccess(
+            collection_name=collection_name,
             testing=testing,
-            embed_model=embed_model,
         )
-        index = pg_vector.load_index()
+        index = qdrant_vector.load_index()
         return index