Usando o SuperDuperDB para acelerar o desenvolvimento de IA no MongoDB Atlas Vector Search

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python -m pip install -U superduperdb[apis]

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from superduperdb import superduper
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db = superduper("mongodb+srv://<user>:<password>@...mongodb.net/documents")

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export OPENAI_API_KEY="sk-..."

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from superduperdb.ext.openai.model import OpenAIEmbedding
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model = OpenAIEmbedding(
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    identifier='text-embedding-ada-002', model='text-embedding-ada-002')

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>>> model.predict('This is a test', one=True)
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[-0.008146246895194054,
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 -0.0036965329200029373,
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 -0.0006024622125551105,
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 -0.005724836140871048,
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 -0.02455105632543564,
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 0.01614714227616787,
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...]

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import sentence_transformers
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from superduperdb.components.model import Model

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from superduperdb import vector

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model = Model(
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    identifier='all-MiniLM-L6-v2',
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    object=sentence_transformers.SentenceTransformer('all-MiniLM-L6-v2'),
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    encoder=vector(shape=(384,)),
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    predict_method='encode',
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    postprocess=lambda x: x.tolist(),
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    batch_predict=True,
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)

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>>> model.predict('This is a test', one=True)
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[-0.008146246895194054,
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 -0.0036965329200029373,
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 -0.0006024622125551105,
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 -0.005724836140871048,
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 -0.02455105632543564,
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 0.01614714227616787,
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...]

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curl -O https://superduperdb-public.s3.eu-west-1.amazonaws.com/pymongo.json

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import json
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from superduperdb.backends.mongodb.query import Collection
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from superduperdb.base.document import Document as D
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with open('pymongo.json') as f:
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    data = json.load(f)
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db.execute(
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    Collection('documents').insert_many([D(r) for r in data])
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)

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>>> r = db.execute(Collection('documents').find_one())
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>>> r
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Document({
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    'key': 'pymongo.mongo_client.MongoClient', 
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    'parent': None, 
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    'value': '\nClient for a MongoDB instance, a replica set, or a set of mongoses.\n\n', 
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    'document': 'mongo_client.md',
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    'res': 'pymongo.mongo_client.MongoClient',
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    '_fold': 'train',
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    '_id': ObjectId('652e460f6cc2a5f9cc21db4f')
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})

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>>> r.unpack()
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{'key': 'pymongo.mongo_client.MongoClient',
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 'parent': None,
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 'value': '\nClient for a MongoDB instance, a replica set, or a set of mongoses.\n\n',
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 'document': 'mongo_client.md',
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 'res': 'pymongo.mongo_client.MongoClient',
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 '_fold': 'train',
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 '_id': ObjectId('652e460f6cc2a5f9cc21db4f')}

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from superduperdb import VectorIndex
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from superduperdb import Listener
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db.add(
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    VectorIndex(
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        identifier='pymongo-docs',
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        indexing_listener=Listener(
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            model=model,
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            key='value',
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            select=Collection('documents').find(),
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            predict_kwargs={'max_chunk_size': 1000},
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        ),
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    )
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)

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>>> db.load("model", 'all-MiniLM-L6-v2')

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>>> db.execute(Collection('documents').find_one()).unpack()
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{'key': 'pymongo.mongo_client.MongoClient',
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 'parent': None,
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 'value': '\nClient for a MongoDB instance, a replica set, or a set of mongoses.\n\n',
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 'document': 'mongo_client.md',
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 'res': 'pymongo.mongo_client.MongoClient',
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 '_fold': 'train',
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 '_id': ObjectId('652e460f6cc2a5f9cc21db4f'),
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 '_outputs': {'value': {'text-embedding-ada-002': [-0.024740776047110558,
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    0.013489063829183578,
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    0.021334229037165642,
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    -0.03423869237303734,
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    ...]}}}

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>>> query = 'Query the database'
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>>> result = db.execute(
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...    Collection('documents')
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...        .like(D({'value': query}), vector_index='pymongo-docs', n=5)
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...        .find({}, {'value': 1, 'key': 1})
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... )
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>>> for r in result:
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...    print(r.unpack())
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{'key': 'find', 'value': '\nQuery the database.\n\nThe filter argument is a query document that all results\nmust match. For example:\n\n`pycon\n>>> db'}
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{'key': 'database_name', 'value': '\nThe name of the database this command was run against.\n\n'}
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{'key': 'aggregate', 'value': '\nPerform a database-level aggregation.\n\nSee the [aggregation pipeline](https://mongodb.com/pt-br/docs/manual/reference/operato'}
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{'key': 'alive', 'value': '\nDoes this cursor have the potential to return more data?\n\n'}
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{'key': 'pymongo.cursor.CursorType', 'value': '\n'}