"# How to create a dynamic (self-constructing) chain\n",
"\n",
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following:\n",
"- [LangChain Expression Language (LCEL)](/docs/concepts/#langchain-expression-language)\n",
"- [How to turn any function into a runnable](/docs/how_to/functions)\n",
"\n",
":::\n",
"\n",
"Sometimes we want to construct parts of a chain at runtime, depending on the chain inputs ([routing](/docs/how_to/routing/) is the most common example of this). We can create dynamic chains like this using a very useful property of RunnableLambda's, which is that if a RunnableLambda returns a Runnable, that Runnable is itself invoked. Let's see an example.\n",
"\n",
"```{=mdx}\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"contextualize_instructions = \"\"\"Convert the latest user question into a standalone question given the chat history. Don't answer the question, return the question and nothing else (no descriptive text).\"\"\"\n",
" (\"human\", \"what's the population of indonesia\"),\n",
" (\"ai\", \"about 276 million\"),\n",
" ],\n",
" }\n",
")"
]
},
{
"cell_type": "markdown",
"id": "5076ddb4-4a99-47ad-b549-8ac27ca3e2c6",
"metadata": {},
"source": [
"The key here is that `contextualize_if_needed` returns another Runnable and not an actual output. This returned Runnable is itself run when the full chain is executed.\n",
"\n",
"Looking at the trace we can see that, since we passed in chat_history, we executed the contextualize_question chain as part of the full chain: https://smith.langchain.com/public/9e0ae34c-4082-4f3f-beed-34a2a2f4c991/r"
]
},
{
"cell_type": "markdown",
"id": "4fe6ca44-a643-4859-a290-be68403f51f0",
"metadata": {},
"source": [
"Note that the streaming, batching, etc. capabilities of the returned Runnable are all preserved"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "6def37fa-5105-4090-9b07-77cb488ecd9c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"What\n",
" is\n",
" the\n",
" population\n",
" of\n",
" Egypt\n",
"?\n"
]
}
],
"source": [
"for chunk in contextualize_if_needed.stream(\n",
" {\n",
" \"question\": \"what about egypt\",\n",
" \"chat_history\": [\n",
" (\"human\", \"what's the population of indonesia\"),\n",
"When working with language models, you may often encounter issues from the underlying APIs, whether these be rate limiting or downtime. Therefore, as you go to move your LLM applications into production it becomes more and more important to safeguard against these. That's why we've introduced the concept of fallbacks. \n",
@ -19,27 +19,29 @@ For comprehensive descriptions of every class and function see the [API Referenc
This highlights functionality that is core to using LangChain.
- [How to: return structured data from an LLM](/docs/how_to/structured_output/)
- [How to: use a chat model to call tools](/docs/how_to/tool_calling/)
- [How to: return structured data from a model](/docs/how_to/structured_output/)
- [How to: use a model to call tools](/docs/how_to/tool_calling/)
- [How to: stream runnables](/docs/how_to/streaming)
- [How to: debug your LLM apps](/docs/how_to/debugging/)
## LangChain Expression Language (LCEL)
LangChain Expression Language is a way to create arbitrary custom chains. It is built on the [Runnable](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.base.Runnable.html) protocol.
[LangChain Expression Language](/docs/concepts/#langchain-expression-language-lcel) is a way to create arbitrary custom chains. It is built on the [Runnable](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.base.Runnable.html) protocol.
[**LCEL cheatsheet**](/docs/how_to/lcel_cheatsheet/): For a quick overview of how to use the main LCEL primitives.
- [How to: chain runnables](/docs/how_to/sequence)
- [How to: stream runnables](/docs/how_to/streaming)
- [How to: invoke runnables in parallel](/docs/how_to/parallel/)
- [How to: attach runtime arguments to a runnable](/docs/how_to/binding/)
- [How to: run custom functions](/docs/how_to/functions)
- [How to: pass through arguments from one step to the next](/docs/how_to/passthrough)
- [How to: add values to a chain's state](/docs/how_to/assign)
- [How to: configure a chain at runtime](/docs/how_to/configure)
- [How to: add message history](/docs/how_to/message_history)
- [How to: route execution within a chain](/docs/how_to/routing)
- [How to: add default invocation args to runnables](/docs/how_to/binding/)
- [How to: turn any function into a runnable](/docs/how_to/functions)
- [How to: pass through inputs from one chain step to the next](/docs/how_to/passthrough)
- [How to: configure runnable behavior at runtime](/docs/how_to/configure)
- [How to: add message history (memory) to a chain](/docs/how_to/message_history)
- [How to: route between sub-chains](/docs/how_to/routing)
- [How to: create a dynamic (self-constructing) chain](/docs/how_to/dynamic_chain/)
- [How to: inspect runnables](/docs/how_to/inspect)
- [How to: add fallbacks](/docs/how_to/fallbacks)
- [How to: add fallbacks to a runnable](/docs/how_to/fallbacks)
## Components
@ -169,8 +171,7 @@ LangChain Tools contain a description of the tool (to pass to the language model
- [How to: create custom tools](/docs/how_to/custom_tools)
- [How to: use built-in tools and built-in toolkits](/docs/how_to/tools_builtin)
- [How to: use a chat model to call tools](/docs/how_to/tool_calling/)
- [How to: use add ad-hoc tool calling capability to LLMs and chat models](/docs/how_to/tools_prompting)
- [How to: convert LangChain tools to OpenAI functions](/docs/how_to/tools_as_openai_functions)
- [How to: add ad-hoc tool calling capability to LLMs and chat models](/docs/how_to/tools_prompting)
- [How to: add a human in the loop to tool usage](/docs/how_to/tools_human)
- [How to: handle errors when calling tools](/docs/how_to/tools_error)
- [How to: call tools using multi-modal data](/docs/how_to/tool_calls_multi_modal)
"The resulting [`RunnableSequence`](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.base.RunnableSequence.html) is itself a runnable, which means it can be invoked, streamed, or further chained just like any other runnable. Advantages of chaining runnables in this way are efficient streaming (the sequence will stream output as soon as it is available), and debugging and tracing with tools like [LangSmith](/docs/how_to/debugging).\n",
"\n",
"## The pipe operator\n",
"## The pipe operator: `|`\n",
"\n",
"To show off how this works, let's go through an example. We'll walk through a common pattern in LangChain: using a [prompt template](/docs/how_to#prompt-templates) to format input into a [chat model](/docs/how_to#chat-models), and finally converting the chat message output into a string with an [output parser](/docs/how_to#output-parsers).\n",
"\n",
@ -230,11 +230,28 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## Next steps\n",
"\n",
"You now know some ways to chain two runnables together.\n",
"There are certain tools that we don't trust a model to execute on its own. One thing we can do in such situations is require human approval before the tool is invoked."
"There are certain tools that we don't trust a model to execute on its own. One thing we can do in such situations is require human approval before the tool is invoked.\n",
"\n",
":::{.callout-info}\n",
"\n",
"This how-to guide shows a simple way to add human-in-the-loop for code running in a jupyter notebook or in a terminal.\n",
"\n",
"To build a production application, you will need to do more work to keep track of application state appropriately.\n",
"\n",
"We recommend using `langgraph` for powering such a capability. For more details, please see this [guide](https://langchain-ai.github.io/langgraph/how-tos/human-in-the-loop/).\n",
":::\n"
]
},
{
@ -40,7 +49,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 8,
"id": "2bed0ccf-20cc-4fd3-9947-55471dd8c4da",
"metadata": {},
"outputs": [],
@ -55,13 +64,19 @@
},
{
"cell_type": "markdown",
"id": "43721981-4595-4721-bea0-5c67696426d3",
"id": "7ecd5d7e-7c3c-4180-8958-7db2c1e43564",
"metadata": {},
"source": [
"## Chain\n",
"\n",
"Suppose we have the following (dummy) tools and tool-calling chain:\n",
"\n",
"Let's create a few simple (dummy) tools and a tool-calling chain:"
]
},
{
"cell_type": "markdown",
"id": "43721981-4595-4721-bea0-5c67696426d3",
"metadata": {},
"source": [
"```{=mdx}\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"Cell \u001b[0;32mIn[11], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mchain\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mSend sally@gmail.com an email saying \u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mWhat\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43ms up homie\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/langchain/libs/core/langchain_core/runnables/base.py:2499\u001b[0m, in \u001b[0;36mRunnableSequence.invoke\u001b[0;34m(self, input, config)\u001b[0m\n\u001b[1;32m 2497\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 2498\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, step \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msteps):\n\u001b[0;32m-> 2499\u001b[0m \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mstep\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2500\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2501\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# mark each step as a child run\u001b[39;49;00m\n\u001b[1;32m 2502\u001b[0m \u001b[43m \u001b[49m\u001b[43mpatch_config\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2503\u001b[0m \u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_child\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mseq:step:\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mi\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2504\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2505\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2506\u001b[0m \u001b[38;5;66;03m# finish the root run\u001b[39;00m\n\u001b[1;32m 2507\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
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