mirror of
https://github.com/hwchase17/langchain
synced 2024-10-29 17:07:25 +00:00
577 lines
17 KiB
Plaintext
577 lines
17 KiB
Plaintext
{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "683953b3",
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"metadata": {},
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"source": [
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"# Marqo\n",
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"\n",
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"This notebook shows how to use functionality related to the Marqo vectorstore.\n",
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"\n",
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">[Marqo](https://www.marqo.ai/) is an open-source vector search engine. Marqo allows you to store and query multimodal data such as text and images. Marqo creates the vectors for you using a huge selection of opensource models, you can also provide your own finetuned models and Marqo will handle the loading and inference for you.\n",
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"\n",
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"To run this notebook with our docker image please run the following commands first to get Marqo:\n",
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"\n",
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"```\n",
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"docker pull marqoai/marqo:latest\n",
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"docker rm -f marqo\n",
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"docker run --name marqo -it --privileged -p 8882:8882 --add-host host.docker.internal:host-gateway marqoai/marqo:latest\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": 1,
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"id": "aac9563e",
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install marqo"
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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": "5d1489ec",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.text_splitter import CharacterTextSplitter\n",
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"from langchain.vectorstores import Marqo\n",
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"from langchain.document_loaders import TextLoader"
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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": "a3c3999a",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.document_loaders import TextLoader\n",
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"\n",
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"loader = TextLoader(\"../../../state_of_the_union.txt\")\n",
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"documents = loader.load()\n",
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"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
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"docs = text_splitter.split_documents(documents)"
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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": "6e104aee",
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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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"Index langchain-demo exists.\n"
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]
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}
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],
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"source": [
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"import marqo\n",
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"\n",
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"# initialize marqo\n",
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"marqo_url = \"http://localhost:8882\" # if using marqo cloud replace with your endpoint (console.marqo.ai)\n",
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"marqo_api_key = \"\" # if using marqo cloud replace with your api key (console.marqo.ai)\n",
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"\n",
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"client = marqo.Client(url=marqo_url, api_key=marqo_api_key)\n",
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"\n",
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"index_name = \"langchain-demo\"\n",
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"\n",
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"docsearch = Marqo.from_documents(docs, index_name=index_name)\n",
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"\n",
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"query = \"What did the president say about Ketanji Brown Jackson\"\n",
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"result_docs = docsearch.similarity_search(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": 4,
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"id": "9c608226",
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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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"Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
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"\n",
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"Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
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"\n",
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"One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
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"\n",
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"And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n"
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]
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}
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],
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"source": [
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"print(result_docs[0].page_content)"
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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": "98704b27",
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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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"Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
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"\n",
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"Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
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"\n",
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"One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
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"\n",
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"And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n",
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"0.68647254\n"
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]
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}
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],
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"source": [
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"result_docs = docsearch.similarity_search_with_score(query)\n",
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"print(result_docs[0][0].page_content, result_docs[0][1], sep=\"\\n\")"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "eb3395b6",
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"metadata": {},
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"source": [
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"## Additional features\n",
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"\n",
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"One of the powerful features of Marqo as a vectorstore is that you can use indexes created externally. For example:\n",
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"\n",
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"+ If you had a database of image and text pairs from another application, you can simply just use it in langchain with the Marqo vectorstore. Note that bringing your own multimodal indexes will disable the `add_texts` method.\n",
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"\n",
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"+ If you had a database of text documents, you can bring it into the langchain framework and add more texts through `add_texts`.\n",
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"\n",
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"The documents that are returned are customised by passing your own function to the `page_content_builder` callback in the search methods."
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "35b99fef",
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"metadata": {},
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"source": [
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"#### Multimodal Example"
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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": "a359ed74",
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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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"{'errors': False,\n",
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" 'processingTimeMs': 2090.2822139996715,\n",
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" 'index_name': 'langchain-multimodal-demo',\n",
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" 'items': [{'_id': 'aa92fc1c-1fb2-4d86-b027-feb507c419f7',\n",
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" 'result': 'created',\n",
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" 'status': 201},\n",
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" {'_id': '5142c258-ef9f-4bf2-a1a6-2307280173a0',\n",
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" 'result': 'created',\n",
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" 'status': 201}]}"
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]
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},
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"execution_count": 6,
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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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"# use a new index\n",
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"index_name = \"langchain-multimodal-demo\"\n",
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"\n",
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"# incase the demo is re-run\n",
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"try:\n",
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" client.delete_index(index_name)\n",
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"except Exception:\n",
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" print(f\"Creating {index_name}\")\n",
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"\n",
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"# This index could have been created by another system\n",
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"settings = {\"treat_urls_and_pointers_as_images\": True, \"model\": \"ViT-L/14\"}\n",
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"client.create_index(index_name, **settings)\n",
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"client.index(index_name).add_documents(\n",
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" [\n",
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" # image of a bus\n",
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" {\n",
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" \"caption\": \"Bus\",\n",
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" \"image\": \"https://raw.githubusercontent.com/marqo-ai/marqo/mainline/examples/ImageSearchGuide/data/image4.jpg\",\n",
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" },\n",
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" # image of a plane\n",
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" {\n",
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" \"caption\": \"Plane\",\n",
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" \"image\": \"https://raw.githubusercontent.com/marqo-ai/marqo/mainline/examples/ImageSearchGuide/data/image2.jpg\",\n",
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" },\n",
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" ],\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": 7,
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"id": "368d1fab",
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_content(res):\n",
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" \"\"\"Helper to format Marqo's documents into text to be used as page_content\"\"\"\n",
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" return f\"{res['caption']}: {res['image']}\"\n",
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"\n",
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"\n",
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"docsearch = Marqo(client, index_name, page_content_builder=get_content)\n",
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"\n",
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"\n",
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"query = \"vehicles that fly\"\n",
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"doc_results = docsearch.similarity_search(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": 8,
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"id": "eef4edf9",
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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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"Plane: https://raw.githubusercontent.com/marqo-ai/marqo/mainline/examples/ImageSearchGuide/data/image2.jpg\n",
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"Bus: https://raw.githubusercontent.com/marqo-ai/marqo/mainline/examples/ImageSearchGuide/data/image4.jpg\n"
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]
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}
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],
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"source": [
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"for doc in doc_results:\n",
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" print(doc.page_content)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "c255f603",
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"metadata": {},
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"source": [
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"#### Text only example"
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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": "9e9a2b20",
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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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"{'errors': False,\n",
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" 'processingTimeMs': 139.2144540004665,\n",
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" 'index_name': 'langchain-byo-index-demo',\n",
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" 'items': [{'_id': '27c05a1c-b8a9-49a5-ae73-fbf1eb51dc3f',\n",
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" 'result': 'created',\n",
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" 'status': 201},\n",
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" {'_id': '6889afe0-e600-43c1-aa3b-1d91bf6db274',\n",
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" 'result': 'created',\n",
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" 'status': 201}]}"
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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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"# use a new index\n",
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"index_name = \"langchain-byo-index-demo\"\n",
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"\n",
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"# incase the demo is re-run\n",
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"try:\n",
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" client.delete_index(index_name)\n",
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"except Exception:\n",
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" print(f\"Creating {index_name}\")\n",
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"\n",
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"# This index could have been created by another system\n",
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"client.create_index(index_name)\n",
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"client.index(index_name).add_documents(\n",
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" [\n",
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" {\n",
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" \"Title\": \"Smartphone\",\n",
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" \"Description\": \"A smartphone is a portable computer device that combines mobile telephone \"\n",
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" \"functions and computing functions into one unit.\",\n",
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" },\n",
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" {\n",
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" \"Title\": \"Telephone\",\n",
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" \"Description\": \"A telephone is a telecommunications device that permits two or more users to\"\n",
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" \"conduct a conversation when they are too far apart to be easily heard directly.\",\n",
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" },\n",
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" ],\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": 10,
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"id": "b2943ea9",
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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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"['9986cc72-adcd-4080-9d74-265c173a9ec3']"
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]
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},
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"execution_count": 10,
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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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"# Note text indexes retain the ability to use add_texts despite different field names in documents\n",
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"# this is because the page_content_builder callback lets you handle these document fields as required\n",
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"\n",
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"\n",
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"def get_content(res):\n",
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" \"\"\"Helper to format Marqo's documents into text to be used as page_content\"\"\"\n",
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" if \"text\" in res:\n",
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" return res[\"text\"]\n",
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" return res[\"Description\"]\n",
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"\n",
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"\n",
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"docsearch = Marqo(client, index_name, page_content_builder=get_content)\n",
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"\n",
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"docsearch.add_texts([\"This is a document that is about elephants\"])"
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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": "851450e9",
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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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"A smartphone is a portable computer device that combines mobile telephone functions and computing functions into one unit.\n"
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]
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}
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],
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"source": [
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"query = \"modern communications devices\"\n",
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"doc_results = docsearch.similarity_search(query)\n",
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"\n",
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"print(doc_results[0].page_content)"
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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": "9a438fec",
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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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"This is a document that is about elephants\n"
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]
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}
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],
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"source": [
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"query = \"elephants\"\n",
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"doc_results = docsearch.similarity_search(query, page_content_builder=get_content)\n",
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"\n",
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"print(doc_results[0].page_content)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "0d04c9d4",
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"metadata": {},
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"source": [
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"## Weighted Queries\n",
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"\n",
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"We also expose marqos weighted queries which are a powerful way to compose complex semantic searches."
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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": "d42ba0d6",
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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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"A smartphone is a portable computer device that combines mobile telephone functions and computing functions into one unit.\n"
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]
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}
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],
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"source": [
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"query = {\"communications devices\": 1.0}\n",
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"doc_results = docsearch.similarity_search(query)\n",
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"print(doc_results[0].page_content)"
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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": 14,
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"id": "b5918a16",
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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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"A telephone is a telecommunications device that permits two or more users toconduct a conversation when they are too far apart to be easily heard directly.\n"
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]
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}
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],
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"source": [
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"query = {\"communications devices\": 1.0, \"technology post 2000\": -1.0}\n",
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"doc_results = docsearch.similarity_search(query)\n",
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"print(doc_results[0].page_content)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "2d026aa0",
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"metadata": {},
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"source": [
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"# Question Answering with Sources\n",
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"\n",
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"This section shows how to use Marqo as part of a `RetrievalQAWithSourcesChain`. Marqo will perform the searches for information in the sources."
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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": 15,
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"id": "e4ca223c",
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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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"OpenAI API Key:········\n"
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]
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}
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],
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"source": [
|
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"from langchain.chains import RetrievalQAWithSourcesChain\n",
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"from langchain.llms import OpenAI\n",
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"\n",
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"import os\n",
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"import getpass\n",
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"\n",
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"os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")"
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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": "5c6e45f9",
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||
"metadata": {},
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"outputs": [],
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"source": [
|
||
"with open(\"../../../state_of_the_union.txt\") as f:\n",
|
||
" state_of_the_union = f.read()\n",
|
||
"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
|
||
"texts = text_splitter.split_text(state_of_the_union)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"id": "70a7f320",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Index langchain-qa-with-retrieval exists.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"index_name = \"langchain-qa-with-retrieval\"\n",
|
||
"docsearch = Marqo.from_documents(docs, index_name=index_name)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"id": "b3b008a4",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"chain = RetrievalQAWithSourcesChain.from_chain_type(\n",
|
||
" OpenAI(temperature=0), chain_type=\"stuff\", retriever=docsearch.as_retriever()\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"id": "e1457716",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"{'answer': ' The president honored Justice Breyer, thanking him for his service and noting that he is a retiring Justice of the United States Supreme Court.\\n',\n",
|
||
" 'sources': '../../../state_of_the_union.txt'}"
|
||
]
|
||
},
|
||
"execution_count": 19,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"chain(\n",
|
||
" {\"question\": \"What did the president say about Justice Breyer\"},\n",
|
||
" return_only_outputs=True,\n",
|
||
")"
|
||
]
|
||
}
|
||
],
|
||
"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": "3.9.16"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|