langchain/docs/docs/expression_language/how_to/passthrough.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d35de667-0352-4bfb-a890-cebe7f676fe7",
   "metadata": {},
   "source": [
    "---\n",
    "sidebar_position: 1\n",
    "title: \"RunnablePassthrough: Passing data through\"\n",
    "keywords: [RunnablePassthrough, RunnableParallel, LCEL]\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b022ab74-794d-4c54-ad47-ff9549ddb9d2",
   "metadata": {},
   "source": [
    "# Passing data through\n",
    "\n",
    "RunnablePassthrough allows to pass inputs unchanged or with the addition of extra keys. This typically is used in conjuction with RunnableParallel to assign data to a new key in the map. \n",
    "\n",
    "RunnablePassthrough() called on it's own, will simply take the input and pass it through. \n",
    "\n",
    "RunnablePassthrough called with assign (`RunnablePassthrough.assign(...)`) will take the input, and will add the extra arguments passed to the assign function. \n",
    "\n",
    "See the example below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "03988b8d-d54c-4492-8707-1594372cf093",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'passed': {'num': 1}, 'extra': {'num': 1, 'mult': 3}, 'modified': 2}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from langchain_core.runnables import RunnableParallel, RunnablePassthrough\n",
    "\n",
    "runnable = RunnableParallel(\n",
    "    passed=RunnablePassthrough(),\n",
    "    extra=RunnablePassthrough.assign(mult=lambda x: x[\"num\"] * 3),\n",
    "    modified=lambda x: x[\"num\"] + 1,\n",
    ")\n",
    "\n",
    "runnable.invoke({\"num\": 1})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "702c7acc-cd31-4037-9489-647df192fd7c",
   "metadata": {},
   "source": [
    "As seen above, `passed` key was called with `RunnablePassthrough()` and so it simply passed on `{'num': 1}`. \n",
    "\n",
    "In the second line, we used `RunnablePastshrough.assign` with a lambda that multiplies the numerical value by 3. In this cased, `extra` was set with `{'num': 1, 'mult': 3}` which is the original value with the `mult` key added. \n",
    "\n",
    "Finally, we also set a third key in the map with `modified` which uses a lambda to set a single value adding 1 to the num, which resulted in `modified` key with the value of `2`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15187a3b-d666-4b9b-a258-672fc51fe0e2",
   "metadata": {},
   "source": [
    "## Retrieval Example\n",
    "\n",
    "In the example below, we see a use case where we use RunnablePassthrough along with RunnableMap. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "267d1460-53c1-4fdb-b2c3-b6a1eb7fccff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Harrison worked at Kensho.'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from langchain.prompts import ChatPromptTemplate\n",
    "from langchain.vectorstores import FAISS\n",
    "from langchain_community.chat_models import ChatOpenAI\n",
    "from langchain_community.embeddings import OpenAIEmbeddings\n",
    "from langchain_core.output_parsers import StrOutputParser\n",
    "from langchain_core.runnables import RunnablePassthrough\n",
    "\n",
    "vectorstore = FAISS.from_texts(\n",
    "    [\"harrison worked at kensho\"], embedding=OpenAIEmbeddings()\n",
    ")\n",
    "retriever = vectorstore.as_retriever()\n",
    "template = \"\"\"Answer the question based only on the following context:\n",
    "{context}\n",
    "\n",
    "Question: {question}\n",
    "\"\"\"\n",
    "prompt = ChatPromptTemplate.from_template(template)\n",
    "model = ChatOpenAI()\n",
    "\n",
    "retrieval_chain = (\n",
    "    {\"context\": retriever, \"question\": RunnablePassthrough()}\n",
    "    | prompt\n",
    "    | model\n",
    "    | StrOutputParser()\n",
    ")\n",
    "\n",
    "retrieval_chain.invoke(\"where did harrison work?\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "392cd4c4-e7ed-4ab8-934d-f7a4eca55ee1",
   "metadata": {},
   "source": [
    "Here the input to prompt is expected to be a map with keys \"context\" and \"question\". The user input is just the question. So we need to get the context using our retriever and passthrough the user input under the \"question\" key. In this case, the RunnablePassthrough allows us to pass on the user's question to the prompt and model. \n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}