langchain/docs/docs/expression_language/how_to/inspect.ipynb

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    "# Inspect your runnables\n",
    "\n",
    "Once you create a runnable with LCEL, you may often want to inspect it to get a better sense for what is going on. This notebook covers some methods for doing so.\n",
    "\n",
    "First, let's create an example LCEL. We will create one that does retrieval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d816e954",
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install --upgrade --quiet  langchain langchain-openai faiss-cpu tiktoken"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a88f4b24",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_community.vectorstores import FAISS\n",
    "from langchain_core.output_parsers import StrOutputParser\n",
    "from langchain_core.prompts import ChatPromptTemplate\n",
    "from langchain_core.runnables import RunnablePassthrough\n",
    "from langchain_openai import ChatOpenAI, OpenAIEmbeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "139228c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorstore = FAISS.from_texts(\n",
    "    [\"harrison worked at kensho\"], embedding=OpenAIEmbeddings()\n",
    ")\n",
    "retriever = vectorstore.as_retriever()\n",
    "\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",
    "\n",
    "model = ChatOpenAI()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "70e3fe93",
   "metadata": {},
   "outputs": [],
   "source": [
    "chain = (\n",
    "    {\"context\": retriever, \"question\": RunnablePassthrough()}\n",
    "    | prompt\n",
    "    | model\n",
    "    | StrOutputParser()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "849e3c42",
   "metadata": {},
   "source": [
    "## Get a graph\n",
    "\n",
    "You can get a graph of the runnable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2448b6c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "chain.get_graph()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "065b02fb",
   "metadata": {},
   "source": [
    "## Print a graph\n",
    "\n",
    "While that is not super legible, you can print it to get a display that's easier to understand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d5ab1515",
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "           +---------------------------------+         \n",
      "           | Parallel<context,question>Input |         \n",
      "           +---------------------------------+         \n",
      "                    **               **                \n",
      "                 ***                   ***             \n",
      "               **                         **           \n",
      "+----------------------+              +-------------+  \n",
      "| VectorStoreRetriever |              | Passthrough |  \n",
      "+----------------------+              +-------------+  \n",
      "                    **               **                \n",
      "                      ***         ***                  \n",
      "                         **     **                     \n",
      "           +----------------------------------+        \n",
      "           | Parallel<context,question>Output |        \n",
      "           +----------------------------------+        \n",
      "                             *                         \n",
      "                             *                         \n",
      "                             *                         \n",
      "                  +--------------------+               \n",
      "                  | ChatPromptTemplate |               \n",
      "                  +--------------------+               \n",
      "                             *                         \n",
      "                             *                         \n",
      "                             *                         \n",
      "                      +------------+                   \n",
      "                      | ChatOpenAI |                   \n",
      "                      +------------+                   \n",
      "                             *                         \n",
      "                             *                         \n",
      "                             *                         \n",
      "                   +-----------------+                 \n",
      "                   | StrOutputParser |                 \n",
      "                   +-----------------+                 \n",
      "                             *                         \n",
      "                             *                         \n",
      "                             *                         \n",
      "                +-----------------------+              \n",
      "                | StrOutputParserOutput |              \n",
      "                +-----------------------+              \n"
     ]
    }
   ],
   "source": [
    "chain.get_graph().print_ascii()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2babf851",
   "metadata": {},
   "source": [
    "## Get the prompts\n",
    "\n",
    "An important part of every chain is the prompts that are used. You can get the prompts present in the chain:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "34b2118d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[ChatPromptTemplate(input_variables=['context', 'question'], messages=[HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['context', 'question'], template='Answer the question based only on the following context:\\n{context}\\n\\nQuestion: {question}\\n'))])]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain.get_prompts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed965769",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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