From 13ac875b2269756045834d7a64e7b35acb9ce0b4 Mon Sep 17 00:00:00 2001
From: Gustaf Rydholm <gustaf.rydholm@gmail.com>
Date: Sat, 6 Apr 2024 01:21:52 +0200
Subject: Rename dbs

---
 rag/db/vectors.py | 53 -----------------------------------------------------
 1 file changed, 53 deletions(-)
 delete mode 100644 rag/db/vectors.py

(limited to 'rag/db/vectors.py')

diff --git a/rag/db/vectors.py b/rag/db/vectors.py
deleted file mode 100644
index 9e8becb..0000000
--- a/rag/db/vectors.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import os
-from dataclasses import dataclass
-from typing import Dict, List
-
-from qdrant_client import QdrantClient
-from qdrant_client.http.models import StrictFloat
-from qdrant_client.models import Distance, ScoredPoint, VectorParams, PointStruct
-from loguru import logger as log
-
-
-@dataclass
-class Point:
-    id: str
-    vector: List[StrictFloat]
-    payload: Dict[str, str]
-
-
-class Vectors:
-    def __init__(self):
-        self.dim = int(os.environ["EMBEDDING_DIM"])
-        self.collection_name = os.environ["QDRANT_COLLECTION_NAME"]
-        self.client = QdrantClient(url=os.environ["QDRANT_URL"])
-        self.__configure()
-
-    def __configure(self):
-        collections = list(map(lambda col: col.name, self.client.get_collections()))
-        if self.collection_name not in collections:
-            log.debug(f"Configuring collection {self.collection_name}...")
-            self.client.create_collection(
-                collection_name=self.collection_name,
-                vectors_config=VectorParams(size=self.dim, distance=Distance.COSINE),
-            )
-        else:
-            log.debug(f"Collection {self.collection_name} already exists...")
-
-    def add(self, points: List[Point]):
-        log.debug(f"Inserting {len(points)} vectors into the vector db...")
-        self.client.upload_points(
-            collection_name=self.collection_name,
-            points=[
-                PointStruct(id=point.id, vector=point.vector, payload=point.payload)
-                for point in points
-            ],
-            parallel=4,
-            max_retries=3,
-        )
-
-    def search(self, query: List[float], limit: int = 4) -> List[ScoredPoint]:
-        log.debug("Searching for vectors...")
-        hits = self.client.search(
-            collection_name=self.collection_name, query_vector=query, limit=limit
-        )
-        return hits
-- 
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