marqo-test 0.1.15
Tensor search for humans
Requires Python: >=3
Keywords: search python marqo opensearch tensor neural semantic vector embedding

<p align="center">
  <img src="assets/logo2.svg" alt="Marqo"/>
</p>

<h1 align="center">Marqo</h1>

<p align="center">
  <b>Tensor search for humans.</b>
</p>

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An open-source tensor search framework that seamlessly integrates with your applications, websites, and workflow. 

## What is tensor search?

Tensor search uses deep-learning to transform documents, images and other data into collections of vectors called "tensors". Representing data as tensors allows us to match queries against documents with human-like understanding of the query and document's content. Tensor search can power a variety of use cases such as:
- end user search and recommendations
- multi-modal search (image-to-image, text-to-image, image-to-text)
- chat bots and question and answer systems
- text and image classification

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## Getting started

1. Marqo requires docker. To install docker go to https://docs.docker.com/get-docker/
2. Use docker to run Marqo:
```bash
docker rm -f marqo;docker run --name marqo -it --privileged -p 8882:8882 --add-host host.docker.internal:host-gateway marqoai/marqo:0.0.3
```
3. Install the Marqo client:
```bash
pip install marqo
```
4. Start indexing and searching! Let's look at a simple example below:

```python
import marqo

mq = marqo.Client(url='http://localhost:8882')

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"
    }]
)

results = mq.index("my-first-index").search(
    q="What is the best outfit to wear on the moon?"
)

```

- `mq` is the client that wraps the`marqo` API
- `add_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 `_id` field. 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:

```python
# let's print out the results:
import pprint
pprint.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
- `limit` is the maximum number of hits to be returned. This can be set as a parameter during search
- Each hit has a `_highlights` field. This was the part of the document that matched the query the best


## Other basic operations

### Get document
Retrieve a document by ID.

```python
result = mq.index("my-first-index").get_document(document_id="article_591")
```

Note that by adding the document using ```add_documents``` again using the same ```_id``` will cause a document to be updated.

### Get index stats
Get information about an index.

```python
results = mq.index("my-first-index").get_stats()
```

### Lexical search
Perform a keyword search.

```python
result =  mq.index("my-first-index").search('marco polo', search_method=marqo.SearchMethods.LEXICAL)
```

### Search specific fields
Using the default tensor search method
```python
result = mq.index("my-first-index").search('adventure', searchable_attributes=['Title'])
```

### Delete documents
Delete documents.

```python
results = mq.index("my-first-index").delete_documents(ids=["article_591", "article_602"])
```

### Delete index
Delete an index.

```python
results = mq.index("my-first-index").delete()
```

## Multi modal and cross modal search

To 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:

```python

settings = {
  "treat_urls_and_pointers_as_images":True,   # allows us to find an image file and index it 
  "model":"ViT-B/32"
}
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:

```python

response = mq.index("my-multimodal-index").add_documents([{
    "My Image": "https://upload.wikimedia.org/wikipedia/commons/thumb/f/f2/Portrait_Hippopotamus_in_the_water.jpg/440px-Portrait_Hippopotamus_in_the_water.jpg",
    "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:
```python

results = mq.index("my-multimodal-index").search('animal')

```
 Setting `searchable_attributes` to the image field `['My Image'] ` ensures only images are searched in this index:

```python

results = mq.index("my-multimodal-index").search('animal',  searchable_attributes=['My Image'])

```

### Searching using an image
Searching using an image can be achieved by providing the image link. 
```python
results = mq.index("my-multimodal-index").search('https://upload.wikimedia.org/wikipedia/commons/thumb/9/96/Standing_Hippopotamus_MET_DP248993.jpg/440px-Standing_Hippopotamus_MET_DP248993.jpg')
```



## Warning

Note that you should not run other applications on Marqo's Opensearch cluster as Marqo automatically changes and adapts the settings on the cluster.

## Contributors
Marqo 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 read [this](./CONTRIBUTING.md) to get started

## Dev set up
1. Create a virtual env ```python -m venv ./venv```
2. Activate the virtual environment ```source ./venv/bin/activate```
3. Install requirements from the requirements file: ```pip install -r requirements.txt```
4. Run tests by running the tox file. CD into this dir and then run "tox"
5. If you update dependencies, make sure to delete the .tox dir and rerun

## Merge instructions:
1. Run the full test suite (by using the command `tox` in this dir).
2. Create a pull request with an attached github issue.

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## Support

- Join our [Slack community](https://join.slack.com/t/marqo-community/shared_invite/zt-1d737l76e-u~b3Rvey2IN2nGM4wyr44w) and chat with other community members about ideas.
- Marqo community meetings (coming soon!)

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