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docs[patch] Add how-to doc for RunnablePassthrough and nav modificati…
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…ons (#14255)

- **Description:** Add How To docs for `RunnablePassthrough` with
examples. Also redo the ordering and some of the other How-To docs.
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akira authored Dec 6, 2023
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13 changes: 12 additions & 1 deletion docs/docs/expression_language/how_to/functions.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"sidebar_position: 2\n",
"title: \"RunnableLambda: Run Custom Functions\"\n",
"keywords: [RunnableLambda, LCEL]\n",
"---"
]
},
{
"cell_type": "markdown",
"id": "fbc4bf6e",
"metadata": {},
"source": [
"# Run custom functions\n",
"\n",
"You can use arbitrary functions in the pipeline\n",
"You can use arbitrary functions in the pipeline.\n",
"\n",
"Note that all inputs to these functions need to be a SINGLE argument. If you have a function that accepts multiple arguments, you should write a wrapper that accepts a single input and unpacks it into multiple argument."
]
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154 changes: 127 additions & 27 deletions docs/docs/expression_language/how_to/map.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"id": "e2596041-9b76-4e74-836f-e6235086bbf0",
"metadata": {},
"source": [
"---\n",
"sidebar_position: 0\n",
"title: \"RunnableParallel: Manipulating data\"\n",
"keywords: [RunnableParallel, RunnableMap, LCEL]\n",
"---"
]
},
{
"cell_type": "markdown",
"id": "b022ab74-794d-4c54-ad47-ff9549ddb9d2",
"metadata": {},
"source": [
"# Parallelize steps\n",
"# Manipulating inputs & output\n",
"\n",
"RunnableParallel (aka. RunnableMap) makes it easy to execute multiple Runnables in parallel, and to return the output of these Runnables as a map."
"RunnableParallel can be useful for manipulating the output of one Runnable to match the input format of the next Runnable in a sequence.\n",
"\n",
"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.\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "7e1873d6-d4b6-43ac-96a1-edcf178201e0",
"execution_count": 3,
"id": "267d1460-53c1-4fdb-b2c3-b6a1eb7fccff",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'joke': AIMessage(content=\"Why don't bears wear shoes? \\n\\nBecause they have bear feet!\", additional_kwargs={}, example=False),\n",
" 'poem': AIMessage(content=\"In woodland depths, bear prowls with might,\\nSilent strength, nature's sovereign, day and night.\", additional_kwargs={}, example=False)}"
"'Harrison worked at Kensho.'"
]
},
"execution_count": 2,
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain.chat_models import ChatOpenAI\n",
"from langchain.embeddings import OpenAIEmbeddings\n",
"from langchain.prompts import ChatPromptTemplate\n",
"from langchain.schema.runnable import RunnableParallel\n",
"from langchain.schema.output_parser import StrOutputParser\n",
"from langchain.schema.runnable import RunnablePassthrough\n",
"from langchain.vectorstores import FAISS\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",
"joke_chain = ChatPromptTemplate.from_template(\"tell me a joke about {topic}\") | model\n",
"poem_chain = (\n",
" ChatPromptTemplate.from_template(\"write a 2-line poem about {topic}\") | model\n",
"\n",
"retrieval_chain = (\n",
" {\"context\": retriever, \"question\": RunnablePassthrough()}\n",
" | prompt\n",
" | model\n",
" | StrOutputParser()\n",
")\n",
"\n",
"map_chain = RunnableParallel(joke=joke_chain, poem=poem_chain)\n",
"retrieval_chain.invoke(\"where did harrison work?\")"
]
},
{
"cell_type": "markdown",
"id": "392cd4c4-e7ed-4ab8-934d-f7a4eca55ee1",
"metadata": {},
"source": [
"::: {.callout-tip}\n",
"Note that when composing a RunnableParallel with another Runnable we don't even need to wrap our dictionary in the RunnableParallel class — the type conversion is handled for us. In the context of a chain, these are equivalent:\n",
":::\n",
"\n",
"map_chain.invoke({\"topic\": \"bear\"})"
"```\n",
"{\"context\": retriever, \"question\": RunnablePassthrough()}\n",
"```\n",
"\n",
"```\n",
"RunnableParallel({\"context\": retriever, \"question\": RunnablePassthrough()})\n",
"```\n",
"\n",
"```\n",
"RunnableParallel(context=retriever, question=RunnablePassthrough())\n",
"```\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "df867ae9-1cec-4c9e-9fef-21969b206af5",
"id": "7c1b8baa-3a80-44f0-bb79-d22f79815d3d",
"metadata": {},
"source": [
"## Manipulating outputs/inputs\n",
"Maps can be useful for manipulating the output of one Runnable to match the input format of the next Runnable in a sequence."
"## Using itemgetter as shorthand\n",
"\n",
"Note that you can use Python's `itemgetter` as shorthand to extract data from the map when combining with `RunnableParallel`. You can find more information about itemgetter in the [Python Documentation](https://docs.python.org/3/library/operator.html#operator.itemgetter). \n",
"\n",
"In the example below, we use itemgetter to extract specific keys from the map:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "267d1460-53c1-4fdb-b2c3-b6a1eb7fccff",
"execution_count": 6,
"id": "84fc49e1-2daf-4700-ae33-a0a6ed47d5f6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Harrison worked at Kensho.'"
"'Harrison ha lavorato a Kensho.'"
]
},
"execution_count": 3,
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from operator import itemgetter\n",
"\n",
"from langchain.chat_models import ChatOpenAI\n",
"from langchain.embeddings import OpenAIEmbeddings\n",
"from langchain.prompts import ChatPromptTemplate\n",
"from langchain.schema.output_parser import StrOutputParser\n",
"from langchain.schema.runnable import RunnablePassthrough\n",
"from langchain.vectorstores import FAISS\n",
Expand All @@ -80,31 +139,72 @@
" [\"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",
"Answer in the following language: {language}\n",
"\"\"\"\n",
"prompt = ChatPromptTemplate.from_template(template)\n",
"\n",
"retrieval_chain = (\n",
" {\"context\": retriever, \"question\": RunnablePassthrough()}\n",
"chain = (\n",
" {\n",
" \"context\": itemgetter(\"question\") | retriever,\n",
" \"question\": itemgetter(\"question\"),\n",
" \"language\": itemgetter(\"language\"),\n",
" }\n",
" | prompt\n",
" | model\n",
" | StrOutputParser()\n",
")\n",
"\n",
"retrieval_chain.invoke(\"where did harrison work?\")"
"chain.invoke({\"question\": \"where did harrison work\", \"language\": \"italian\"})"
]
},
{
"cell_type": "markdown",
"id": "392cd4c4-e7ed-4ab8-934d-f7a4eca55ee1",
"id": "bc2f9847-39aa-4fe4-9049-3a8969bc4bce",
"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.\n",
"## Parallelize steps\n",
"\n",
"RunnableParallel (aka. RunnableMap) makes it easy to execute multiple Runnables in parallel, and to return the output of these Runnables as a map."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "31f18442-f837-463f-bef4-8729368f5f8b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'joke': AIMessage(content=\"Why don't bears wear shoes?\\n\\nBecause they have bear feet!\"),\n",
" 'poem': AIMessage(content=\"In the wild's embrace, bear roams free,\\nStrength and grace, a majestic decree.\")}"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain.chat_models import ChatOpenAI\n",
"from langchain.prompts import ChatPromptTemplate\n",
"from langchain.schema.runnable import RunnableParallel\n",
"\n",
"model = ChatOpenAI()\n",
"joke_chain = ChatPromptTemplate.from_template(\"tell me a joke about {topic}\") | model\n",
"poem_chain = (\n",
" ChatPromptTemplate.from_template(\"write a 2-line poem about {topic}\") | model\n",
")\n",
"\n",
"Note that when composing a RunnableParallel with another Runnable we don't even need to wrap our dictionary in the RunnableParallel class — the type conversion is handled for us."
"map_chain = RunnableParallel(joke=joke_chain, poem=poem_chain)\n",
"\n",
"map_chain.invoke({\"topic\": \"bear\"})"
]
},
{
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
"version": "3.11.6"
}
},
"nbformat": 4,
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