From 91ddb3672e514fa9824609ff047d7cab0c65631a Mon Sep 17 00:00:00 2001
From: Gustaf Rydholm <gustaf.rydholm@gmail.com>
Date: Tue, 9 Apr 2024 00:14:00 +0200
Subject: Refactor

---
 rag/llm/encoder.py | 47 -----------------------------------------------
 1 file changed, 47 deletions(-)
 delete mode 100644 rag/llm/encoder.py

(limited to 'rag/llm/encoder.py')

diff --git a/rag/llm/encoder.py b/rag/llm/encoder.py
deleted file mode 100644
index a59b1b4..0000000
--- a/rag/llm/encoder.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import os
-from pathlib import Path
-from typing import List, Dict
-from uuid import uuid4
-
-import ollama
-from langchain_core.documents import Document
-from loguru import logger as log
-from qdrant_client.http.models import StrictFloat
-
-
-try:
-    from rag.db.vector import Point
-except ModuleNotFoundError:
-    from db.vector import Point
-
-
-class Encoder:
-    def __init__(self) -> None:
-        self.model = os.environ["ENCODER_MODEL"]
-        self.query_prompt = "Represent this sentence for searching relevant passages: "
-
-    def __encode(self, prompt: str) -> List[StrictFloat]:
-        return list(ollama.embeddings(model=self.model, prompt=prompt)["embedding"])
-
-    def __get_source(self, metadata: Dict[str, str]) -> str:
-        source = metadata["source"]
-        return Path(source).name
-
-    def encode_document(self, chunks: List[Document]) -> List[Point]:
-        log.debug("Encoding document...")
-        return [
-            Point(
-                id=uuid4().hex,
-                vector=self.__encode(chunk.page_content),
-                payload={
-                    "text": chunk.page_content,
-                    "source": self.__get_source(chunk.metadata),
-                },
-            )
-            for chunk in chunks
-        ]
-
-    def encode_query(self, query: str) -> List[StrictFloat]:
-        log.debug(f"Encoding query: {query}")
-        query = self.query_prompt + query
-        return self.__encode(query)
-- 
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