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docs: recommend lambdas over runnablebranch (#18033)

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257
docs/docs/expression_language/how_to/routing.ipynb
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@ -1,7 +1,7 @@
{
"cells": [
{
"cell_type": "markdown",
"cell_type": "raw",
"id": "9e45e81c-e16e-4c6c-b6a3-2362e5193827",
"metadata": {},
"source": [
@ -25,53 +25,42 @@
"\n",
"There are two ways to perform routing:\n",
"\n",
"1. Using a `RunnableBranch`.\n",
"2. Writing custom factory function that takes the input of a previous step and returns a **runnable**. Importantly, this should return a **runnable** and NOT actually execute.\n",
"1. Conditionally return runnables from a [`RunnableLambda`](./functions) (recommended)\n",
"2. Using a `RunnableBranch`.\n",
"\n",
"We'll illustrate both methods using a two step sequence where the first step classifies an input question as being about `LangChain`, `Anthropic`, or `Other`, then routes to a corresponding prompt chain."
]
},
{
"cell_type": "markdown",
"id": "f885113d",
"metadata": {},
"source": [
"## Using a RunnableBranch\n",
"\n",
"A `RunnableBranch` is initialized with a list of (condition, runnable) pairs and a default runnable. It selects which branch by passing each condition the input it's invoked with. It selects the first condition to evaluate to True, and runs the corresponding runnable to that condition with the input. \n",
"\n",
"If no provided conditions match, it runs the default runnable.\n",
"\n",
"Here's an example of what it looks like in action:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "1aa13c1d",
"metadata": {},
"outputs": [],
"source": [
"from langchain.prompts import PromptTemplate\n",
"from langchain_community.chat_models import ChatAnthropic\n",
"from langchain_core.output_parsers import StrOutputParser"
]
},
{
"cell_type": "markdown",
"id": "ed84c59a",
"id": "c1c6edac",
"metadata": {},
"source": [
"## Example Setup\n",
"First, let's create a chain that will identify incoming questions as being about `LangChain`, `Anthropic`, or `Other`:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "3ec03886",
"execution_count": null,
"id": "8a8a1967",
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"' Anthropic'"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from langchain_community.chat_models import ChatAnthropic\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.prompts import PromptTemplate\n",
"\n",
"chain = (\n",
" PromptTemplate.from_template(\n",
" \"\"\"Given the user question below, classify it as either being about `LangChain`, `Anthropic`, or `Other`.\n",
@ -86,33 +75,14 @@
" )\n",
" | ChatAnthropic()\n",
" | StrOutputParser()\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "87ae7c1c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"' Anthropic'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
")\n",
"\n",
"chain.invoke({\"question\": \"how do I call Anthropic?\"})"
]
},
{
"cell_type": "markdown",
"id": "8aa0a365",
"id": "7655555f",
"metadata": {},
"source": [
"Now, let's create three sub chains:"
@ -120,8 +90,8 @@
},
{
"cell_type": "code",
"execution_count": 4,
"id": "d479962a",
"execution_count": null,
"id": "89d7722d",
"metadata": {},
"outputs": [],
"source": [
@ -158,101 +128,12 @@
")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "593eab06",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.runnables import RunnableBranch\n",
"\n",
"branch = RunnableBranch(\n",
" (lambda x: \"anthropic\" in x[\"topic\"].lower(), anthropic_chain),\n",
" (lambda x: \"langchain\" in x[\"topic\"].lower(), langchain_chain),\n",
" general_chain,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "752c732e",
"metadata": {},
"outputs": [],
"source": [
"full_chain = {\"topic\": chain, \"question\": lambda x: x[\"question\"]} | branch"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "29231bb8",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content=\" As Dario Amodei told me, here are some ways to use Anthropic:\\n\\n- Sign up for an account on Anthropic's website to access tools like Claude, Constitutional AI, and Writer. \\n\\n- Use Claude for tasks like email generation, customer service chat, and QA. Claude can understand natural language prompts and provide helpful responses.\\n\\n- Use Constitutional AI if you need an AI assistant that is harmless, honest, and helpful. It is designed to be safe and aligned with human values.\\n\\n- Use Writer to generate natural language content for things like marketing copy, stories, reports, and more. Give it a topic and prompt and it will create high-quality written content.\\n\\n- Check out Anthropic's documentation and blog for tips, tutorials, examples, and announcements about new capabilities as they continue to develop their AI technology.\\n\\n- Follow Anthropic on social media or subscribe to their newsletter to stay up to date on new features and releases.\\n\\n- For most people, the easiest way to leverage Anthropic's technology is through their website - just create an account to get started!\", additional_kwargs={}, example=False)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"full_chain.invoke({\"question\": \"how do I use Anthropic?\"})"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "c67d8733",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content=' As Harrison Chase told me, here is how you use LangChain:\\n\\nLangChain is an AI assistant that can have conversations, answer questions, and generate text. To use LangChain, you simply type or speak your input and LangChain will respond. \\n\\nYou can ask LangChain questions, have discussions, get summaries or explanations about topics, and request it to generate text on a subject. Some examples of interactions:\\n\\n- Ask general knowledge questions and LangChain will try to answer factually. For example \"What is the capital of France?\"\\n\\n- Have conversations on topics by taking turns speaking. You can prompt the start of a conversation by saying something like \"Let\\'s discuss machine learning\"\\n\\n- Ask for summaries or high-level explanations on subjects. For example \"Can you summarize the main themes in Shakespeare\\'s Hamlet?\" \\n\\n- Give creative writing prompts or requests to have LangChain generate text in different styles. For example \"Write a short children\\'s story about a mouse\" or \"Generate a poem in the style of Robert Frost about nature\"\\n\\n- Correct LangChain if it makes an inaccurate statement and provide the right information. This helps train it.\\n\\nThe key is interacting naturally and giving it clear prompts and requests', additional_kwargs={}, example=False)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"full_chain.invoke({\"question\": \"how do I use LangChain?\"})"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "935ad949",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content=' 2 + 2 = 4', additional_kwargs={}, example=False)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"full_chain.invoke({\"question\": \"whats 2 + 2\"})"
]
},
{
"cell_type": "markdown",
"id": "6d8d042c",
"metadata": {},
"source": [
"## Using a custom function\n",
"## Using a custom function (Recommended)\n",
"\n",
"You can also use a custom function to route between different outputs. Here's an example:"
]
@ -350,13 +231,89 @@
"full_chain.invoke({\"question\": \"whats 2 + 2\"})"
]
},
{
"cell_type": "markdown",
"id": "5147b827",
"metadata": {},
"source": [
"## Using a RunnableBranch\n",
"\n",
"A `RunnableBranch` is a special type of runnable that allows you to define a set of conditions and runnables to execute based on the input. It does **not** offer anything that you can't achieve in a custom function as described above, so we recommend using a custom function instead.\n",
"\n",
"A `RunnableBranch` is initialized with a list of (condition, runnable) pairs and a default runnable. It selects which branch by passing each condition the input it's invoked with. It selects the first condition to evaluate to True, and runs the corresponding runnable to that condition with the input. \n",
"\n",
"If no provided conditions match, it runs the default runnable.\n",
"\n",
"Here's an example of what it looks like in action:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "46802d04",
"id": "2a101418",
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content=\" As Dario Amodei told me, here are some ways to use Anthropic:\\n\\n- Sign up for an account on Anthropic's website to access tools like Claude, Constitutional AI, and Writer. \\n\\n- Use Claude for tasks like email generation, customer service chat, and QA. Claude can understand natural language prompts and provide helpful responses.\\n\\n- Use Constitutional AI if you need an AI assistant that is harmless, honest, and helpful. It is designed to be safe and aligned with human values.\\n\\n- Use Writer to generate natural language content for things like marketing copy, stories, reports, and more. Give it a topic and prompt and it will create high-quality written content.\\n\\n- Check out Anthropic's documentation and blog for tips, tutorials, examples, and announcements about new capabilities as they continue to develop their AI technology.\\n\\n- Follow Anthropic on social media or subscribe to their newsletter to stay up to date on new features and releases.\\n\\n- For most people, the easiest way to leverage Anthropic's technology is through their website - just create an account to get started!\", additional_kwargs={}, example=False)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from langchain_core.runnables import RunnableBranch\n",
"\n",
"branch = RunnableBranch(\n",
" (lambda x: \"anthropic\" in x[\"topic\"].lower(), anthropic_chain),\n",
" (lambda x: \"langchain\" in x[\"topic\"].lower(), langchain_chain),\n",
" general_chain,\n",
")\n",
"full_chain = {\"topic\": chain, \"question\": lambda x: x[\"question\"]} | branch\n",
"full_chain.invoke({\"question\": \"how do I use Anthropic?\"})"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8d8caf9b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content=' As Harrison Chase told me, here is how you use LangChain:\\n\\nLangChain is an AI assistant that can have conversations, answer questions, and generate text. To use LangChain, you simply type or speak your input and LangChain will respond. \\n\\nYou can ask LangChain questions, have discussions, get summaries or explanations about topics, and request it to generate text on a subject. Some examples of interactions:\\n\\n- Ask general knowledge questions and LangChain will try to answer factually. For example \"What is the capital of France?\"\\n\\n- Have conversations on topics by taking turns speaking. You can prompt the start of a conversation by saying something like \"Let\\'s discuss machine learning\"\\n\\n- Ask for summaries or high-level explanations on subjects. For example \"Can you summarize the main themes in Shakespeare\\'s Hamlet?\" \\n\\n- Give creative writing prompts or requests to have LangChain generate text in different styles. For example \"Write a short children\\'s story about a mouse\" or \"Generate a poem in the style of Robert Frost about nature\"\\n\\n- Correct LangChain if it makes an inaccurate statement and provide the right information. This helps train it.\\n\\nThe key is interacting naturally and giving it clear prompts and requests', additional_kwargs={}, example=False)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"full_chain.invoke({\"question\": \"how do I use LangChain?\"})"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "26159af7",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content=' 2 + 2 = 4', additional_kwargs={}, example=False)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"full_chain.invoke({\"question\": \"whats 2 + 2\"})"
]
}
],
"metadata": {

4
docs/docs/integrations/chat/groq.ipynb
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@ -98,9 +98,7 @@
"prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n",
"\n",
"chain = prompt | chat\n",
"chain.invoke({\n",
" \"text\": \"Explain the importance of low latency LLMs.\"\n",
"})"
"chain.invoke({\"text\": \"Explain the importance of low latency LLMs.\"})"
]
},
{