mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
multiple retreival in parralel (#9174)
This commit is contained in:
Harrison Chase
GitHub
parent
cdfe2c96c5
commit
8d69dacdf3
@ -41,7 +41,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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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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@ -256,6 +256,131 @@
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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 parralel requests as much as possible. For example, when using a RunnableMapping (often written as a dictionary) it executes each element in parralel."
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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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@ -274,7 +399,7 @@
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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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"version": "3.10.1"
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}
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},
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"nbformat": 4,
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@ -0,0 +1,166 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "66398b75",
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"metadata": {},
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"source": [
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"# Multiple Retrieval Sources\n",
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"\n",
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"Often times you may want to do retrieval over multiple sources. These can be different vectorstores (where one contains information about topic X and the other contains info about topic Y). They could also be completely different databases altogether!\n",
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"\n",
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"A key part is is doing as much of the retrieval in parrelel as possible. This will keep the latency as low as possible. Luckily, [LangChain Expression Language](../../) supports parrellism out of the box.\n",
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"\n",
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"Let's take a look where we do retrieval over a SQL database and a vectorstore."
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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": "1c5bab6a",
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"metadata": {},
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"outputs": [],
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"source": [
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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": "markdown",
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"id": "43a6210f",
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"metadata": {},
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"source": [
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"## Set up SQL query"
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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": "ab3bf8ba",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.utilities import SQLDatabase\n",
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"from langchain.chains import create_sql_query_chain\n",
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"\n",
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"db = SQLDatabase.from_uri(\"sqlite:///../../../../../notebooks/Chinook.db\")\n",
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"query_chain = create_sql_query_chain(ChatOpenAI(temperature=0), db)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a8585120",
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"metadata": {},
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"source": [
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"## Set up vectorstore"
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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": 6,
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"id": "b916b0b0",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.indexes import VectorstoreIndexCreator\n",
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"from langchain.schema.document import Document\n",
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"index_creator = VectorstoreIndexCreator()\n",
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"index = index_creator.from_documents([Document(page_content=\"Foo\")])\n",
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"retriever = index.vectorstore.as_retriever()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a3b91816",
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"metadata": {},
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"source": [
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"## Combine"
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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": "4423211c",
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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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"\n",
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"system_message = \"\"\"Use the information from the below two sources to answer any questions.\n",
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"\n",
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"Source 1: a SQL database about employee data\n",
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"<source1>\n",
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"{source1}\n",
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"</source1>\n",
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"\n",
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"Source 2: a text database of random information\n",
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"<source2>\n",
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"{source2}\n",
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"</source2>\n",
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"\"\"\"\n",
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"\n",
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"prompt = ChatPromptTemplate.from_messages([(\"system\", system_message), (\"human\", \"{question}\")])"
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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": "7ff87e0c",
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"metadata": {},
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"outputs": [],
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"source": [
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"full_chain = {\n",
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" \"source1\": {\"question\": lambda x: x[\"question\"]} | query_chain | db.run,\n",
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" \"source2\": (lambda x: x['question']) | retriever,\n",
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" \"question\": lambda x: x['question'],\n",
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"} | prompt | 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": 9,
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"id": "d6706410",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Number of requested results 4 is greater than number of elements in index 1, updating n_results = 1\n"
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]
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},
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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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"content='There are 8 employees.' additional_kwargs={} example=False\n"
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]
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}
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],
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"source": [
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"response = full_chain.invoke({\"question\":\"How many Employees are there\"})\n",
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"print(response)"
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]
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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": "3.10.1"
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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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