add new chain howtos (#15430)

langchain-ai · Jan 4, 2024 · 7a93356 · 7a93356
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diff --git a/docs/docs/expression_language/how_to/decorator.ipynb b/docs/docs/expression_language/how_to/decorator.ipynb
@@ -0,0 +1,126 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b45110ef",
+   "metadata": {},
+   "source": [
+    "# Create a runnable with the `@chain` decorator\n",
+    "\n",
+    "You can also turn an arbitrary function into a chain by adding a `@chain` decorator. This is functionaly equivalent to wrapping in a [`RunnableLambda`](./functions).\n",
+    "\n",
+    "This will have the benefit of improved observability by tracing your chain correctly. Any calls to runnables inside this function will be traced as nested childen.\n",
+    "\n",
+    "It will also allow you to use this as any other runnable, compose it in chain, etc.\n",
+    "\n",
+    "Let's take a look at this in action!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "d9370420",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.chat_models import ChatOpenAI\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_core.runnables import chain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "b7f74f7e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prompt1 = ChatPromptTemplate.from_template(\"Tell me a joke about {topic}\")\n",
+    "prompt2 = ChatPromptTemplate.from_template(\"What is the subject of this joke: {joke}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "2b0365c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@chain\n",
+    "def custom_chain(text):\n",
+    "    prompt_val1 = prompt1.invoke({\"topic\": text})\n",
+    "    output1 = ChatOpenAI().invoke(prompt_val1)\n",
+    "    parsed_output1 = StrOutputParser().invoke(output1)\n",
+    "    chain2 = prompt2 | ChatOpenAI() | StrOutputParser()\n",
+    "    return chain2.invoke({\"joke\": parsed_output1})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "904d6872",
+   "metadata": {},
+   "source": [
+    "`custom_chain` is now a runnable, meaning you will need to use `invoke`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "6448bdd3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'The subject of this joke is bears.'"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "custom_chain.invoke(\"bears\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa767ea9",
+   "metadata": {},
+   "source": [
+    "If you check out your LangSmith traces, you should see a `custom_chain` trace in there, with the calls to OpenAI nested underneath"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f1245bdc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.10.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/docs/expression_language/how_to/functions.ipynb b/docs/docs/expression_language/how_to/functions.ipynb
@@ -190,7 +190,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.10.1"
   }
  },
  "nbformat": 4,

diff --git a/docs/docs/expression_language/how_to/inspect.ipynb b/docs/docs/expression_language/how_to/inspect.ipynb
@@ -0,0 +1,237 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8c5eb99a",
+   "metadata": {},
+   "source": [
+    "# 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": "8bc5d235",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install langchain openai faiss-cpu tiktoken"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a88f4b24",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from operator import itemgetter\n",
+    "\n",
+    "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 RunnableLambda, RunnablePassthrough"
+   ]
+  },
+  {
+   "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": 4,
+   "id": "2448b6c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Graph(nodes={'7308e6063c6d40818c5a0cc1cc7444f2': Node(id='7308e6063c6d40818c5a0cc1cc7444f2', data=<class 'pydantic.main.RunnableParallel<context,question>Input'>), '292bbd8021d44ec3a31fbe724d9002c1': Node(id='292bbd8021d44ec3a31fbe724d9002c1', data=<class 'pydantic.main.RunnableParallel<context,question>Output'>), '9212f219cf05488f95229c56ea02b192': Node(id='9212f219cf05488f95229c56ea02b192', data=VectorStoreRetriever(tags=['FAISS', 'OpenAIEmbeddings'], vectorstore=<langchain_community.vectorstores.faiss.FAISS object at 0x117334f70>)), 'c7a8e65fa5cf44b99dbe7d1d6e36886f': Node(id='c7a8e65fa5cf44b99dbe7d1d6e36886f', data=RunnablePassthrough()), '818b9bfd40a341008373d5b9f9d0784b': Node(id='818b9bfd40a341008373d5b9f9d0784b', data=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'))])), 'b9f1d3ddfa6b4334a16ea439df22b11e': Node(id='b9f1d3ddfa6b4334a16ea439df22b11e', data=ChatOpenAI(client=<class 'openai.api_resources.chat_completion.ChatCompletion'>, openai_api_key='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', openai_proxy='')), '2bf84f6355c44731848345ca7d0f8ab9': Node(id='2bf84f6355c44731848345ca7d0f8ab9', data=StrOutputParser()), '1aeb2da5da5a43bb8771d3f338a473a2': Node(id='1aeb2da5da5a43bb8771d3f338a473a2', data=<class 'pydantic.main.StrOutputParserOutput'>)}, edges=[Edge(source='7308e6063c6d40818c5a0cc1cc7444f2', target='9212f219cf05488f95229c56ea02b192'), Edge(source='9212f219cf05488f95229c56ea02b192', target='292bbd8021d44ec3a31fbe724d9002c1'), Edge(source='7308e6063c6d40818c5a0cc1cc7444f2', target='c7a8e65fa5cf44b99dbe7d1d6e36886f'), Edge(source='c7a8e65fa5cf44b99dbe7d1d6e36886f', target='292bbd8021d44ec3a31fbe724d9002c1'), Edge(source='292bbd8021d44ec3a31fbe724d9002c1', target='818b9bfd40a341008373d5b9f9d0784b'), Edge(source='818b9bfd40a341008373d5b9f9d0784b', target='b9f1d3ddfa6b4334a16ea439df22b11e'), Edge(source='2bf84f6355c44731848345ca7d0f8ab9', target='1aeb2da5da5a43bb8771d3f338a473a2'), Edge(source='b9f1d3ddfa6b4334a16ea439df22b11e', target='2bf84f6355c44731848345ca7d0f8ab9')])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "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",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "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 graphs 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": []
+  }
+ ],
+ "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": "3.10.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}