Package: marqo1
Website|Documentation|Demos|Slack Community|Marqo CloudAn end-to-end vector search engine that seamlessly integrates with applications and websites. Marqo allows developers to turbocharge search functionality with the latest machine learning models, in 3 lines of code.Try the demo|View the code✨ Core Features⚡ PerformanceEmbeddings stored in in-memory HNSW indexes, achieving cutting edge search speeds.Scale to hundred-million document indexes with horizontal index sharding.Async and non-blocking data upload and search.🤖 Machine LearningUse the latest machine learning models from PyTorch, Huggingface, OpenAI and more.Start with a pre-configured model or bring your own.Built in ONNX support and conversion for faster inference and higher throughput.CPU and GPU support.☁️ Cloud-nativeFast deployment using Docker.Run Marqo multi-az and high availability.🌌 End-to-endBuild search and analytics on multiple unstructured data types such as text, image, code, video.Filter search results using Marqo’s query DSL.Store unstructred data and semi-structured metadata together in documents, using a range of supported datatypes like bools, ints and keywords.🍱 Managed cloudScale Marqo at the click of a button.Multi-az, accelerated inference.Marqo cloud ☁️ is in beta. If you’re interested,apply here.Learn more about Marqo📗Quick startBuild your first application with Marqo in under 5 minutes.🔍What is tensor search?A beginner's guide to the fundamentals of Marqo and tensor search.🖼Marqo for image dataBuilding text-to-image search in Marqo in 5 lines of code.📚Marqo for textBuilding a multilingual database in Marqo.🔮Integrating Marqo with GPTMaking GPT a subject matter expert by using Marqo as a knowledge base.🎨Marqo for Creative AICombining stable diffusion with semantic search to generate and categorise 100k images of hotdogs.🦾FeaturesMarqo's core features.Getting startedMarqo requires docker. To install Docker go to theDocker Official website. Ensure that docker has at least 8GB memory and 50GB storage.Use docker to run Marqo (Mac users with M-series chips will need togo here):dockerrm-fmarqo
dockerpullmarqoai/marqo:latest
dockerrun--namemarqo-it--privileged-p8882:8882--add-hosthost.docker.internal:host-gatewaymarqoai/marqo:latestInstall the Marqo client:pipinstallmarqoStart indexing and searching! Let's look at a simple example below:importmarqo1mq=marqo1.Client(url='http://localhost:8882')mq.create_index("my-first-index")mq.index("my-first-index").add_documents([{"Title":"The Travels of Marco Polo","Description":"A 13th-century travelogue describing Polo's travels"},{"Title":"Extravehicular Mobility Unit (EMU)","Description":"The EMU is a spacesuit that provides environmental protection, ""mobility, life support, and communications for astronauts","_id":"article_591"}],tensor_fields=["Title","Description"])results=mq.index("my-first-index").search(q="What is the best outfit to wear on the moon?",searchable_attributes=["Title","Description"])mqis the client that wraps themarqoAPIadd_documents()takes a list of documents, represented as python dicts for indexing.add_documents()creates an index with default settings, if one does not already exist.You can optionally set a document's ID with the special_idfield. Otherwise, Marqo will generate one.If the index doesn't exist, Marqo will create it. If it exists then Marqo will add the documents to the index.Let's have a look at the results:# let's print out the results:importpprintpprint.pprint(results){'hits':[{'Title':'Extravehicular Mobility Unit (EMU)','Description':'The EMU is a spacesuit that provides environmental protection, mobility, life support, and''communications for astronauts','_highlights':{'Description':'The EMU is a spacesuit that provides environmental protection, ''mobility, life support, and communications for astronauts'},'_id':'article_591','_score':0.61938936},{'Title':'The Travels of Marco Polo','Description':"A 13th-century travelogue describing Polo's travels",'_highlights':{'Title':'The Travels of Marco Polo'},'_id':'e00d1a8d-894c-41a1-8e3b-d8b2a8fce12a','_score':0.60237324}],'limit':10,'processingTimeMs':49,'query':'What is the best outfit to wear on the moon?'}Each hit corresponds to a document that matched the search query.They are ordered from most to least matching.limitis the maximum number of hits to be returned. This can be set as a parameter during search.Each hit has a_highlightsfield. This was the part of the document that matched the query the best.Other basic operationsGet documentRetrieve a document by ID.result=mq.index("my-first-index").get_document(document_id="article_591")Note that by adding the document usingadd_documentsagain using the same_idwill cause a document to be updated.Get index statsGet information about an index.results=mq.index("my-first-index").get_stats()Lexical searchPerform a keyword search.result=mq.index("my-first-index").search('marco polo',search_method=marqo.SearchMethods.LEXICAL)Search specific fieldsUsing the default tensor search method.result=mq.index("my-first-index").search('adventure',searchable_attributes=['Title'])Multi modal and cross modal searchTo power image and text search, Marqo allows users to plug and play with CLIP models from HuggingFace.Note that if you do not configure multi modal search, image urls will be treated as strings.To start indexing and searching with images, first create an index with a CLIP configuration, as below:settings={"treat_urls_and_pointers_as_images":True,# allows us to find an image file and index it"model":"ViT-L/14"}response=mq.create_index("my-multimodal-index",**settings)Images can then be added within documents as follows. You can use urls from the internet (for example S3) or from the disk of the machine:response=mq.index("my-multimodal-index").add_documents([{"My Image":"https://raw.githubusercontent.com/marqo-ai/marqo-api-tests/mainline/assets/ai_hippo_realistic.png","Description":"The hippopotamus, also called the common hippopotamus or river hippopotamus, is a large semiaquatic mammal native to sub-Saharan Africa","_id":"hippo-facts"}])You can then search using text as usual. Both text and image fields will be searched:results=mq.index("my-multimodal-index").search('animal')Settingsearchable_attributesto the image field['My Image']ensures only images are searched in this index:results=mq.index("my-multimodal-index").search('animal',searchable_attributes=['My Image'])Searching using an imageSearching using an image can be achieved by providing the image link.results=mq.index("my-multimodal-index").search('https://raw.githubusercontent.com/marqo-ai/marqo-api-tests/mainline/assets/ai_hippo_statue.png')Searching using weights in queriesQueries can also be provided as dictionaries where each key is a query and their corresponding values are weights. This allows for more advanced queries consisting of multiple components with weightings towards or against them, queries can have negations via negative weighting.The example below shows the application of this to a scenario where a user may want to ask a question but also negate results that match a certain semantic criterion.importmarqo1importpprintmq=marqo1.Client(url="http://localhost:8882")mq.create_index("my-weighted-query-index")mq.index("my-weighted-query-index").add_documents([{"Title":"Smartphone","Description":"A smartphone is a portable computer device that combines mobile telephone ""functions and computing functions into one unit.",},{"Title":"Telephone","Description":"A telephone is a telecommunications device that permits two or more users to""conduct a conversation when they are too far apart to be easily heard directly.",},{"Title":"Thylacine","Description":"The thylacine, also commonly known as the Tasmanian tiger or Tasmanian wolf, ""is an extinct carnivorous marsupial.""The last known of its species died in 1936.",},],tensor_fields=["Title","Description"])# initially we ask for a type of communications device which is popular in the 21st centuryquery={# a weighting of 1.1 gives this query slightly more importance"I need to buy a communications device, what should I get?":1.1,# a weighting of 1 gives this query a neutral importance"Technology that became prevelant in the 21st century":1.0,}results=mq.index("my-weighted-query-index").search(q=query,searchable_attributes=["Title","Description"])print("Query 1:")pprint.pprint(results)# now we ask for a type of communications which predates the 21st centuryquery={# a weighting of 1 gives this query a neutral importance"I need to buy a communications device, what should I get?":1.0,# a weighting of -1 gives this query a negation effect"Technology that became prevelant in the 21st century":-1.0,}results=mq.index("my-weighted-query-index").search(q=query,searchable_attributes=["Title","Description"])print("\nQuery 2:")pprint.pprint(results)Creating and searching indexes with multimodal combination fieldsMarqo lets you have indexes with multimodal combination fields. Multimodal combination fields can combine text and images into one field. This allows scoring of documents across the combined text and image fields together. It also allows for a single vector representation instead of needing many which saves on storage. The relative weighting of each component can be set per document.The example below demonstrates this with retrival of caption and image pairs using multiple types of queries.importmarqo1importpprintmq=marqo1.Client(url="http://localhost:8882")settings={"treat_urls_and_pointers_as_images":True,"model":"ViT-L/14"}mq.create_index("my-first-multimodal-index",**settings)mq.index("my-first-multimodal-index").add_documents([{"Title":"Flying Plane","captioned_image":{"caption":"An image of a passenger plane flying in front of the moon.","image":"https://raw.githubusercontent.com/marqo-ai/marqo/mainline/examples/ImageSearchGuide/data/image2.jpg",},},{"Title":"Red Bus","captioned_image":{"caption":"A red double decker London bus traveling to Aldwych","image":"https://raw.githubusercontent.com/marqo-ai/marqo/mainline/examples/ImageSearchGuide/data/image4.jpg",},},{"Title":"Horse Jumping","captioned_image":{"caption":"A person riding a horse over a jump in a competition.","image":"https://raw.githubusercontent.com/marqo-ai/marqo/mainline/examples/ImageSearchGuide/data/image1.jpg",},},],mappings={"captioned_image":{"type":"multimodal_combination","weights":{"caption":0.3,"image":0.7,},}},tensor_fields=["captioned_image"],)# Search this index with a simple text queryresults=mq.index("my-first-multimodal-index").search(q="Give me some images of vehicles and modes of transport. I am especially interested in air travel and commercial aeroplanes.",searchable_attributes=["captioned_image"],)print("Query 1:")pprint.pprint(results)# search the index with a query that uses weighted componentsresults=mq.index("my-first-multimodal-index").search(q={"What are some vehicles and modes of transport?":1.0,"Aeroplanes and other things that fly":-1.0,},searchable_attributes=["captioned_image"],)print("\nQuery 2:")pprint.pprint(results)results=mq.index("my-first-multimodal-index").search(q={"Animals of the Perissodactyla order":-1.0},searchable_attributes=["captioned_image"],)print("\nQuery 3:")pprint.pprint(results)Delete documentsDelete documents.results=mq.index("my-first-index").delete_documents(ids=["article_591","article_602"])Delete indexDelete an index.results=mq.index("my-first-index").delete()DocumentationThe full documentation for Marqo can be found herehttps://docs.marqo.ai/.WarningNote that you should not run other applications on Marqo's Opensearch cluster as Marqo automatically changes and adapts the settings on the cluster.M series Mac usersMarqo does not yet support the docker-in-docker backend configuration for the arm64 architecture. This means that if you have an M series Mac, you will also need to run marqo's backend, marqo-os, locally.To run Marqo on an M series Mac, follow the next steps.In one terminal run the following command to start opensearch:dockerrm-fmarqo-os;dockerrun-p9200:9200-p9600:9600-e"discovery.type=single-node"marqoai/marqo-os:0.0.3-armIn another terminal run the following command to launch Marqo:dockerrm-fmarqo;dockerrun--namemarqo--privileged\-p8882:8882--add-hosthost.docker.internal:host-gateway\-e"OPENSEARCH_URL=https://localhost:9200"\marqoai/marqo:latestContributorsMarqo is a community project with the goal of making tensor search accessible to the wider developer community. We are glad that you are interested in helping out! Please readthisto get started.Dev set upCreate a virtual envpython -m venv ./venv.Activate the virtual environmentsource ./venv/bin/activate.Install requirements from the requirements file:pip install -r requirements.txt.Ensure you have marqo-os running withdocker run -p 9200:9200 -p 9600:9600 -e "discovery.type=single-node" --name marqo-os marqoai/marqo-os:0.0.3Run tests by running the tox file. CD into this dir and then run "tox".If you update dependencies, make sure to delete the .tox dir and rerun.Merge instructions:Run the full test suite (by using the commandtoxin this dir).Create a pull request with an attached github issue.SupportJoin ourSlack communityand chat with other community members about ideas.Marqo community meetings (coming soon!).Stargazers.Forkers.TranslationsThis readme is available in the following translations:English🇬🇧Français🇫🇷中文 Chinese🇨🇳Polski🇵🇱Українська🇺🇦