3 files changed, 0 insertions, 130 deletions
diff --git a/rag/db/__init__.py b/rag/db/__init__.py
deleted file mode 100644
index e69de29..0000000
--- a/rag/db/__init__.py
+++ /dev/null
diff --git a/rag/db/document.py b/rag/db/document.py
deleted file mode 100644
index 54ac451..0000000
--- a/rag/db/document.py
+++ /dev/null
@@ -1,57 +0,0 @@
-import hashlib
-import os
-
-from langchain_community.document_loaders.blob_loaders import Blob
-import psycopg
-from loguru import logger as log
-
-TABLES = """
-CREATE TABLE IF NOT EXISTS document (
-        hash text PRIMARY KEY)
-"""
-
-
-class DocumentDB:
-    def __init__(self) -> None:
-        self.conn = psycopg.connect(
-            f"dbname={os.environ['DOCUMENT_DB_NAME']} user={os.environ['DOCUMENT_DB_USER']}"
-        )
-        self.__configure()
-
-    def close(self):
-        self.conn.close()
-
-    def __configure(self):
-        log.debug("Creating documents table if it does not exist...")
-        with self.conn.cursor() as cur:
-            cur.execute(TABLES)
-            self.conn.commit()
-
-    def __hash(self, blob: Blob) -> str:
-        log.debug("Hashing document...")
-        return hashlib.sha256(blob.as_bytes()).hexdigest()
-
-    def add(self, blob: Blob) -> bool:
-        with self.conn.cursor() as cur:
-            hash = self.__hash(blob)
-            cur.execute(
-                """
-                        SELECT * FROM document 
-                        WHERE
-                        hash = %s
-                        """,
-                (hash,),
-            )
-            exist = cur.fetchone()
-            if exist is None:
-                log.debug("Inserting document hash into documents db...")
-                cur.execute(
-                    """
-                            INSERT INTO document 
-                            (hash) VALUES 
-                            (%s)
-                            """,
-                    (hash,),
-                )
-            self.conn.commit()
-        return exist is None
diff --git a/rag/db/vector.py b/rag/db/vector.py
deleted file mode 100644
index fd2b2c2..0000000
--- a/rag/db/vector.py
+++ /dev/null
@@ -1,73 +0,0 @@
-import os
-from dataclasses import dataclass
-from typing import Dict, List
-
-from loguru import logger as log
-from qdrant_client import QdrantClient
-from qdrant_client.http.models import StrictFloat
-from qdrant_client.models import Distance, PointStruct, VectorParams
-
-
-@dataclass
-class Point:
-    id: str
-    vector: List[StrictFloat]
-    payload: Dict[str, str]
-
-
-@dataclass
-class Document:
-    title: str
-    text: str
-
-
-class VectorDB:
-    def __init__(self, score_threshold: float = 0.6):
-        self.dim = int(os.environ["EMBEDDING_DIM"])
-        self.collection_name = os.environ["QDRANT_COLLECTION_NAME"]
-        self.client = QdrantClient(url=os.environ["QDRANT_URL"])
-        self.score_threshold = score_threshold
-        self.__configure()
-
-    def __configure(self):
-        collections = list(
-            map(lambda col: col.name, self.client.get_collections().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 = 5) -> List[Document]:
-        log.debug("Searching for vectors...")
-        hits = self.client.search(
-            collection_name=self.collection_name,
-            query_vector=query,
-            limit=limit,
-            score_threshold=self.score_threshold,
-        )
-        log.debug(f"Got {len(hits)} hits in the vector db with limit={limit}")
-        return list(
-            map(
-                lambda h: Document(
-                    title=h.payload.get("source", ""), text=h.payload["text"]
-                ),
-                hits,
-            )
-        )