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add new chain howtos (#15430)
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@hwchase17
hwchase17 committed Jan 4, 2024
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126 changes: 126 additions & 0 deletions docs/docs/expression_language/how_to/decorator.ipynb
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{
"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": []
}
],
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"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
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"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
}
2 changes: 1 addition & 1 deletion docs/docs/expression_language/how_to/functions.ipynb
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.5"
"version": "3.10.1"
}
},
"nbformat": 4,
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237 changes: 237 additions & 0 deletions docs/docs/expression_language/how_to/inspect.ipynb
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{
"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
}

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