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451 lines
11 KiB
Plaintext
451 lines
11 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "raw",
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"id": "366a0e68-fd67-4fe5-a292-5c33733339ea",
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"metadata": {},
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"source": [
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"---\n",
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"sidebar_position: 0\n",
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"title: Interface\n",
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9a9acd2e",
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"metadata": {},
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"source": [
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"In an effort to make it as easy as possible to create custom chains, we've implemented a [\"Runnable\"](https://api.python.langchain.com/en/latest/schema/langchain.schema.runnable.Runnable.html#langchain.schema.runnable.Runnable) protocol that most components implement. This is a standard interface with a few different methods, which makes it easy to define custom chains as well as making it possible to invoke them in a standard way. The standard interface exposed includes:\n",
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"\n",
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"- `stream`: stream back chunks of the response\n",
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"- `invoke`: call the chain on an input\n",
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"- `batch`: call the chain on a list of inputs\n",
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"\n",
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"These also have corresponding async methods:\n",
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"\n",
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"- `astream`: stream back chunks of the response async\n",
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"- `ainvoke`: call the chain on an input async\n",
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"- `abatch`: call the chain on a list of inputs async\n",
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"\n",
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"The type of the input varies by component:\n",
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"\n",
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"| Component | Input Type |\n",
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"| --- | --- |\n",
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"|Prompt|Dictionary|\n",
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"|Retriever|Single string|\n",
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"|LLM, ChatModel| Single string, list of chat messages or a PromptValue|\n",
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"|Tool|Single string, or dictionary, depending on the tool|\n",
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"|OutputParser|The output of an LLM or ChatModel|\n",
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"\n",
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"The output type also varies by component:\n",
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"\n",
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"| Component | Output Type |\n",
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"| --- | --- |\n",
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"| LLM | String |\n",
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"| ChatModel | ChatMessage |\n",
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"| Prompt | PromptValue |\n",
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"| Retriever | List of documents |\n",
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"| Tool | Depends on the tool |\n",
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"| OutputParser | Depends on the parser |\n",
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"\n",
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"Let's take a look at these methods! To do so, we'll create a super simple PromptTemplate + ChatModel chain."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "466b65b3",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.prompts import ChatPromptTemplate\n",
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"from langchain.chat_models import ChatOpenAI"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "3c634ef0",
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"metadata": {},
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"outputs": [],
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"source": [
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"model = ChatOpenAI()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "d1850a1f",
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"metadata": {},
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"outputs": [],
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"source": [
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"prompt = ChatPromptTemplate.from_template(\"tell me a joke about {topic}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "56d0669f",
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"metadata": {},
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"outputs": [],
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"source": [
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"chain = prompt | model"
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]
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},
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{
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"cell_type": "markdown",
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"id": "daf2b2b2",
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"metadata": {},
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"source": [
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"## Stream"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "bea9639d",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Sure, here's a bear-themed joke for you:\n",
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"\n",
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"Why don't bears wear shoes?\n",
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"\n",
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"Because they have bear feet!"
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]
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}
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],
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"source": [
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"for s in chain.stream({\"topic\": \"bears\"}):\n",
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" print(s.content, end=\"\", flush=True)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "cbf1c782",
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"metadata": {},
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"source": [
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"## Invoke"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "470e483f",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"AIMessage(content=\"Why don't bears wear shoes?\\n\\nBecause they already have bear feet!\", additional_kwargs={}, example=False)"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"chain.invoke({\"topic\": \"bears\"})"
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]
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},
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{
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"cell_type": "markdown",
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"id": "88f0c279",
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"metadata": {},
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"source": [
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"## Batch"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"id": "9685de67",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[AIMessage(content=\"Why don't bears ever wear shoes?\\n\\nBecause they have bear feet!\", additional_kwargs={}, example=False),\n",
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" AIMessage(content=\"Why don't cats play poker in the wild?\\n\\nToo many cheetahs!\", additional_kwargs={}, example=False)]"
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]
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},
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"execution_count": 19,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"chain.batch([{\"topic\": \"bears\"}, {\"topic\": \"cats\"}])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2434ab15",
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"metadata": {},
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"source": [
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"You can set the number of concurrent requests by using the `max_concurrency` parameter"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "a08522f6",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[AIMessage(content=\"Why don't bears wear shoes?\\n\\nBecause they have bear feet!\", additional_kwargs={}, example=False),\n",
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" AIMessage(content=\"Why don't cats play poker in the wild?\\n\\nToo many cheetahs!\", additional_kwargs={}, example=False)]"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"chain.batch([{\"topic\": \"bears\"}, {\"topic\": \"cats\"}], config={\"max_concurrency\": 5})"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b960cbfe",
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"metadata": {},
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"source": [
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"## Async Stream"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "ea35eee4",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Why don't bears wear shoes?\n",
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"\n",
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"Because they have bear feet!"
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]
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}
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],
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"source": [
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"async for s in chain.astream({\"topic\": \"bears\"}):\n",
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" print(s.content, end=\"\", flush=True)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "04cb3324",
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"metadata": {},
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"source": [
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"## Async Invoke"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"id": "ef8c9b20",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"AIMessage(content=\"Sure, here you go:\\n\\nWhy don't bears wear shoes?\\n\\nBecause they have bear feet!\", additional_kwargs={}, example=False)"
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]
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},
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"execution_count": 16,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"await chain.ainvoke({\"topic\": \"bears\"})"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3da288d5",
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"metadata": {},
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"source": [
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"## Async Batch"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"id": "eba2a103",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[AIMessage(content=\"Why don't bears wear shoes?\\n\\nBecause they have bear feet!\", additional_kwargs={}, example=False)]"
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]
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},
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"execution_count": 18,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"await chain.abatch([{\"topic\": \"bears\"}])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0a1c409d",
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"metadata": {},
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"source": [
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"## Parallelism\n",
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"\n",
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"Let's take a look at how LangChain Expression Language support parallel requests as much as possible. For example, when using a RunnableMap (often written as a dictionary) it executes each element in parallel."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "e3014c7a",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.schema.runnable import RunnableMap\n",
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"chain1 = ChatPromptTemplate.from_template(\"tell me a joke about {topic}\") | model\n",
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"chain2 = ChatPromptTemplate.from_template(\"write a short (2 line) poem about {topic}\") | model\n",
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"combined = RunnableMap({\n",
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" \"joke\": chain1,\n",
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" \"poem\": chain2,\n",
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"})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "08044c0a",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"CPU times: user 31.7 ms, sys: 8.59 ms, total: 40.3 ms\n",
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"Wall time: 1.05 s\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"AIMessage(content=\"Why don't bears like fast food?\\n\\nBecause they can't catch it!\", additional_kwargs={}, example=False)"
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]
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"%%time\n",
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"chain1.invoke({\"topic\": \"bears\"})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "22c56804",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"CPU times: user 42.9 ms, sys: 10.2 ms, total: 53 ms\n",
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"Wall time: 1.93 s\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"AIMessage(content=\"In forest's embrace, bears roam free,\\nSilent strength, nature's majesty.\", additional_kwargs={}, example=False)"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"%%time\n",
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"chain2.invoke({\"topic\": \"bears\"})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "4fff4cbb",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"CPU times: user 96.3 ms, sys: 20.4 ms, total: 117 ms\n",
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"Wall time: 1.1 s\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"{'joke': AIMessage(content=\"Why don't bears wear socks?\\n\\nBecause they have bear feet!\", additional_kwargs={}, example=False),\n",
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" 'poem': AIMessage(content=\"In forest's embrace,\\nMajestic bears leave their trace.\", additional_kwargs={}, example=False)}"
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"%%time\n",
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"combined.invoke({\"topic\": \"bears\"})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "fab75d1d",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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