{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e8624be2",
   "metadata": {},
   "source": [
    "# RePhraseQueryRetriever\n",
    "\n",
    "Simple retriever that applies an LLM between the user input and the query pass the to retriever.\n",
    "\n",
    "It can be used to pre-process the user input in any way.\n",
    "\n",
    "The default prompt used in the `from_llm` classmethod:\n",
    "\n",
    "```\n",
    "DEFAULT_TEMPLATE = \"\"\"You are an assistant tasked with taking a natural language \\\n",
    "query from a user and converting it into a query for a vectorstore. \\\n",
    "In this process, you strip out information that is not relevant for \\\n",
    "the retrieval task. Here is the user query: {question}\"\"\"\n",
    "```\n",
    "\n",
    "Create a vectorstore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1bfa6834",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.document_loaders import WebBaseLoader\n",
    "\n",
    "loader = WebBaseLoader(\"https://lilianweng.github.io/posts/2023-06-23-agent/\")\n",
    "data = loader.load()\n",
    "\n",
    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
    "\n",
    "text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)\n",
    "all_splits = text_splitter.split_documents(data)\n",
    "\n",
    "from langchain.vectorstores import Chroma\n",
    "from langchain.embeddings import OpenAIEmbeddings\n",
    "\n",
    "vectorstore = Chroma.from_documents(documents=all_splits, embedding=OpenAIEmbeddings())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d0b51556",
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "\n",
    "logging.basicConfig()\n",
    "logging.getLogger(\"langchain.retrievers.re_phraser\").setLevel(logging.INFO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "20e1e787",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.chat_models import ChatOpenAI\n",
    "from langchain.retrievers import RePhraseQueryRetriever"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88c0a972",
   "metadata": {},
   "source": [
    "## Using the default prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "503994bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "llm = ChatOpenAI(temperature=0)\n",
    "retriever_from_llm = RePhraseQueryRetriever.from_llm(\n",
    "    retriever=vectorstore.as_retriever(), llm=llm\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8d17ecc9",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:langchain.retrievers.re_phraser:Re-phrased question: The user query can be converted into a query for a vectorstore as follows:\n",
      "\n",
      "\"approaches to Task Decomposition\"\n"
     ]
    }
   ],
   "source": [
    "docs = retriever_from_llm.get_relevant_documents(\n",
    "    \"Hi I'm Lance. What are the approaches to Task Decomposition?\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "76d54f1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:langchain.retrievers.re_phraser:Re-phrased question: Query for vectorstore: \"Types of Memory\"\n"
     ]
    }
   ],
   "source": [
    "docs = retriever_from_llm.get_relevant_documents(\n",
    "    \"I live in San Francisco. What are the Types of Memory?\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0513a6e2",
   "metadata": {},
   "source": [
    "## Supply a prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "410d6a64",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain import LLMChain\n",
    "from langchain.prompts import PromptTemplate\n",
    "\n",
    "QUERY_PROMPT = PromptTemplate(\n",
    "    input_variables=[\"question\"],\n",
    "    template=\"\"\"You are an assistant tasked with taking a natural languge query from a user\n",
    "    and converting it into a query for a vectorstore. In the process, strip out all \n",
    "    information that is not relevant for the retrieval task and return a new, simplified\n",
    "    question for vectorstore retrieval. The new user query should be in pirate speech.\n",
    "    Here is the user query: {question} \"\"\",\n",
    ")\n",
    "llm = ChatOpenAI(temperature=0)\n",
    "llm_chain = LLMChain(llm=llm, prompt=QUERY_PROMPT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2dbffdd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "retriever_from_llm_chain = RePhraseQueryRetriever(\n",
    "    retriever=vectorstore.as_retriever(), llm_chain=llm_chain\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "103b4be3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:langchain.retrievers.re_phraser:Re-phrased question: Ahoy matey! What be Maximum Inner Product Search, ye scurvy dog?\n"
     ]
    }
   ],
   "source": [
    "docs = retriever_from_llm_chain.get_relevant_documents(\n",
    "    \"Hi I'm Lance. What is Maximum Inner Product Search?\"\n",
    ")"
   ]
  }
 ],
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