lmm_chat.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get Binding Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.llm_process.utils.load_vectordb import load_vectordb\n",
    "from src.llm_process.implement.bind_embedding_to_db import bind_embedding\n",
    "\n",
    "db = load_vectordb()\n",
    "bind_embedding(db)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.llm_process.utils.load_vectordb import load_vectordb\n",
    "\n",
    "db = load_vectordb()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = db.similarity_search(\n",
    "    \"你是小燈嗎？\", k=10\n",
    ")\n",
    "for doc in results:\n",
    "    print(f\"* {doc.page_content} [{doc.metadata}]\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.llm_process.utils.load_vectordb import load_vectordb\n",
    "\n",
    "db = load_vectordb()\n",
    "retriever = db.as_retriever()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "retrieved_documents = retriever.invoke(\"你是小燈嗎\")\n",
    "retrieved_documents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing the LLM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.llm_process.entity.llm_entity import LLMWrapper\n",
    "\n",
    "predictor = LLMWrapper()\n",
    "predictor.speak(\"你會演唱春日影嗎\")[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictor.speak(\"你會演唱春日影嗎\")[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# open a db to save the image pathway"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.llm_process.implement.create_database_tosearch_img import save_speech_to_db\n",
    "save_speech_to_db()"
   ]
  }
 ],
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