Maximize efficiency and streamline workflows

docs = vx.create_collection(name="docs", dimension=3)

# add records to the collection
docs.upsert(
    vectors=[
        (
         "vec0",           # the vector's identifier
         [0.1, 0.2, 0.3],  # the vector. list or np.array
         {"year": 1973}    # associated  metadata
        ),
        (
         "vec1",
         [0.7, 0.8, 0.9],
         {"year": 2012}
        )
    ]
)
        

# get an existing collection
docs = vx.get_collection(name="docs")

# index the collection to be queried by cosine distance
docs.create_index(measure=vecs.IndexMeasure.cosine_distance)

# Available options for query measure are:
#
# vecs.IndexMeasure.cosine_distance
# vecs.IndexMeasure.l2_distance
# vecs.IndexMeasure.max_inner_product

# or use the default (cosine_distance)
docs.create_index()
      

# get an existing collection
docs = vx.get_collection(name="docs")

# Query vectors with optional Metadata Filtering
docs.query(
    query_vector=[0.4,0.5,0.6],
    filters={"year": {"$eq": 2012}}, # metadata filters
)