From 1dfaf80c75afa84b6d03a0013eb1fd94d0257226 Mon Sep 17 00:00:00 2001
From: Gustaf Rydholm <gustaf.rydholm@gmail.com>
Date: Fri, 5 Apr 2024 18:31:12 +0200
Subject: Update from faiss to qdrant

---
 rag/db/embeddings.py | 52 ++++++++++++++++++++++++++++++++++++++--------------
 1 file changed, 38 insertions(+), 14 deletions(-)

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

diff --git a/rag/db/embeddings.py b/rag/db/embeddings.py
index 1a97d16..4f61dcc 100644
--- a/rag/db/embeddings.py
+++ b/rag/db/embeddings.py
@@ -1,23 +1,47 @@
 import os
-from typing import Tuple
+from dataclasses import dataclass
+from typing import Dict, List
+from uuid import uuid4
 
-import faiss
-import numpy as np
+from qdrant_client import QdrantClient
+from qdrant_client.http.models import StrictFloat
+from qdrant_client.models import Distance, VectorParams, PointStruct
+
+
+@dataclass
+class Point:
+    id: str
+    vector: List[StrictFloat]
+    payload: Dict[str, str]
 
 
-# TODO: inner product distance metric?
 class Embeddings:
     def __init__(self):
         self.dim = int(os.environ["EMBEDDING_DIM"])
-        self.index = faiss.IndexFlatL2(self.dim)
-        # TODO: load from file
+        self.collection_name = os.environ["QDRANT_COLLECTION_NAME"]
+        self.client = QdrantClient(url=os.environ["QDRANT_URL"])
+        self.client.delete_collection(
+            collection_name=self.collection_name,
+        )
+        self.client.create_collection(
+            collection_name=self.collection_name,
+            vectors_config=VectorParams(size=self.dim, distance=Distance.COSINE),
+        )
 
-    def add(self, embeddings: np.ndarray):
-        # TODO: save to file
-        self.index.add(embeddings)
+    def add(self, points: List[Point]):
+        print(len(points))
+        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: np.ndarray, neighbors: int = 4
-    ) -> Tuple[np.ndarray, np.ndarray]:
-        score, indices = self.index.search(query, neighbors)
-        return score, indices
+    def search(self, query: List[float], limit: int = 4):
+        hits = self.client.search(
+            collection_name=self.collection_name, query_vector=query, limit=limit
+        )
+        return hits
-- 
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