def create_search_indices(connection):
"""
Creates search indices for the vector stores, each with specific parameters tailored to their distance strategy.
"""
# Index for DOT_PRODUCT strategy
# Notice we are creating a HNSW index with default parameters
# This will default to creating a HNSW index with 8 Parallel Workers and use the Default Accuracy used by Oracle AI Vector Search
oraclevs.create_index(
connection,
vector_store_dot,
params={"idx_name": "hnsw_idx1", "idx_type": "HNSW"},
)
# Index for COSINE strategy with specific parameters
# Notice we are creating a HNSW index with parallel 16 and Target Accuracy Specification as 97 percent
oraclevs.create_index(
connection,
vector_store_max,
params={
"idx_name": "hnsw_idx2",
"idx_type": "HNSW",
"accuracy": 97,
"parallel": 16,
},
)
# Index for EUCLIDEAN_DISTANCE strategy with specific parameters
# Notice we are creating a HNSW index by specifying Power User Parameters which are neighbors = 64 and efConstruction = 100
oraclevs.create_index(
connection,
vector_store_euclidean,
params={
"idx_name": "hnsw_idx3",
"idx_type": "HNSW",
"neighbors": 64,
"efConstruction": 100,
},
)
# Index for DOT_PRODUCT strategy with specific parameters
# Notice we are creating an IVF index with default parameters
# This will default to creating an IVF index with 8 Parallel Workers and use the Default Accuracy used by Oracle AI Vector Search
oraclevs.create_index(
connection,
vector_store_dot_ivf,
params={
"idx_name": "ivf_idx1",
"idx_type": "IVF",
},
)
# Index for COSINE strategy with specific parameters
# Notice we are creating an IVF index with parallel 32 and Target Accuracy Specification as 90 percent
oraclevs.create_index(
connection,
vector_store_max_ivf,
params={
"idx_name": "ivf_idx2",
"idx_type": "IVF",
"accuracy": 90,
"parallel": 32,
},
)
# Index for EUCLIDEAN_DISTANCE strategy with specific parameters
# Notice we are creating an IVF index by specifying Power User Parameters which is neighbor_part = 64
oraclevs.create_index(
connection,
vector_store_euclidean_ivf,
params={"idx_name": "ivf_idx3", "idx_type": "IVF", "neighbor_part": 64},
)
print("Index creation complete.")
create_search_indices(connection)
