seahorse-coral

gRPC-native Python client library for SeahorseDB via Coral.

The low-level Python surface is intentionally:

• Arrow-first for tabular reads (scan, search, hybrid)
• typed-model-first for table metadata, ingest/export, and index status results
• explicit about post-processing (to_pyarrow(), to_pandas(), to_polars(), to_json())

Cluster administration is intentionally outside this package. Node inspection, active-set flush, segment retry/status, rebalance, placement, and runtime tuning remain available through Coral's operational interfaces and the internal Rust coral-client.

Additional docs:

• Build from source: docs/build.md
• Advanced usage: docs/advanced.md
• Compatibility policy: docs/compatibility.md

Quickstart (Recommended)

This project intentionally supports multiple ways to define a schema (preset / components / builder). To keep onboarding simple, we recommend starting with the preset schema and only moving to Advanced when you need customization.

1) Create a Coral client

import seahorse_coral as sc

coral = sc.Coral("http://localhost:8080")

Coral and AsyncCoral use the same gRPC-native contract as the Rust client.

2) Create a table (preset: id + vector + metadata)

import seahorse_coral as sc

schema = sc.default_vector_table_schema(
    dim=384,
    # Optional:
    # id_type=sc.ScalarType.STRING,
)

table = coral.create_table("documents", schema=schema)

Table mutation and index readiness helpers return typed Python models:

counts = table.indexed_row_count()  # queryable reader-side counts
print(counts.total_row_count)
print(counts.indexed_counts)

table.update_rows("metadata = '{\"source\":\"updated\"}'", where="id = 1")
table.delete_rows(where="id = 2")

The preset creates:

• id: INT64 data column by default. SeahorseDB primary-key columns use the legacy composite value format; the SaaS primary path is Arrow LargeUtf8/LARGE_STRING even though Coral's public scalar API names it STRING.
• vector: dense vector column
• metadata: STRING (nullable; store JSON-encoded strings if you want structured metadata)

(Optional) Schema building (SchemaBuilder)

If you need customization (more columns, segmentation, multiple indexes, etc.), build a schema explicitly.

A) Create table with components (no SchemaBuilder object)

import seahorse_coral as sc

table = coral.create_table(
    "documents",
    columns=[
        sc.int64_column("id", nullable=False),
        sc.vector_column("vector", dim=384),
        sc.metadata_column("metadata"),
    ],
    primary_key=["id"],
    indexes=[sc.hnsw_index("vector")],  # List[IndexDefinition]
)

Only single-column hash segmentation is supported by the distributed Python SDK. Value, hierarchical, and composite segmentation are intentionally not exposed.

B) SchemaBuilder (constructor style)

import seahorse_coral as sc

schema = sc.SchemaBuilder(
    columns=[
        sc.int64_column("id", nullable=False),
        sc.vector_column("vector", dim=384),
        sc.metadata_column("metadata"),
    ],
    primary_key=["id"],
    indexes=[sc.hnsw_index("vector", space=sc.IndexSpace.COSINE)],
)

table = coral.create_table("documents", schema=schema)

C) SchemaBuilder (fluent / chain style)

import seahorse_coral as sc

schema = (
    sc.SchemaBuilder()
    .int64("id", nullable=False)
    .vector("vector", dim=384)
    .metadata()
    .with_primary_key("id")
    .hnsw("vector", space=sc.IndexSpace.COSINE)
)

table = coral.create_table("documents", schema=schema)

3) Insert rows

import json

table.insert_rows(
    [
        {"id": 1, "vector": [0.1, 0.2, 0.3], "metadata": json.dumps({"source": "a"})},
        {"id": 2, "vector": [0.2, 0.1, 0.0], "metadata": json.dumps({"source": "b"})},
    ]
)

(Optional) More insert options

Write APIs are explicit by mode.

import seahorse_coral as sc

# 1) JSONL string (each line is a JSON object)
jsonl = (
    '{"id": 4, "vector": [0.4, 0.4, 0.4], "metadata": "{}"}\n'
    '{"id": 5, "vector": [0.5, 0.5, 0.5], "metadata": "{}"}\n'
)
table.insert_jsonl(jsonl)

# 2) Local Parquet file
# - client converts Parquet -> Arrow IPC stream -> gRPC upload stream
table.insert_parquet("./data/documents.parquet", batch_size=8192)

# 3) Single remote Parquet file
# - server reads the object directly
table.insert_parquet(
    sc.s3_file(
        "path/to/documents.parquet",
        bucket="my-bucket",
        access_key="YOUR_ACCESS_KEY",
        secret_key="YOUR_SECRET_KEY",
        region="ap-northeast-2",
    ),
    options=sc.ImportOptions(reader_batch_size=8192),
)

# 4) Multi-file import from S3
request = sc.s3_file(
    ["path/to/a.parquet", "path/to/b.parquet"],
    bucket="my-bucket",
    access_key="YOUR_ACCESS_KEY",
    secret_key="YOUR_SECRET_KEY",
    region="ap-northeast-2",
)
options = sc.ImportOptions(
    format=sc.FileFormat.PARQUET,
    reader_batch_size=8192,  # reader record batch size
    max_concurrent_files=4,  # optional
)
result = table.import_files(request, options=options)
print(result.total_inserted_row_count)

# `import_files()` is strict by default.
# If any file fails, sc.PartialImportError or sc.ImportFilesError is raised
# and the exception carries the same ImportFilesResult via `.result`.

# 5) Arrow IPC stream bytes (advanced)
# - bytes, pyarrow.Table, pyarrow.RecordBatch, and list[RecordBatch] are supported
table.insert_arrow(arrow_ipc_bytes)

4) Search (dense)

# Dense vector search
#
# Note:
# - `index` is the index name, typically the same as the vector column name.
vec = table.index("vector")

result = vec.search([0.1, 0.2, 0.3], top_k=10)

result = vec.search(
    [0.1, 0.2, 0.3],
    top_k=10,
    ef_search=128,
    select="id, metadata, distance",
    where="id > 0",
)

5) Consume Arrow-first tabular results

scan() and search() return ResultSet.

result = table.scan(select="id, metadata", limit=100)

# Low-level Arrow-native access
batches = result.to_record_batches()
arrow_table = result.to_pyarrow()

# Explicit convenience conversions
rows = result.to_json()
df = result.to_pandas()

For batch vector search, use ResultSets.

results = vec.search_batch([[0.1, 0.2, 0.3], [0.3, 0.2, 0.1]], top_k=10)

for result in results:
    print(result.to_pyarrow())

Large-result paths are exposed separately.

for batch in table.scan_stream(select="id, metadata"):
    process(batch)

for result in vec.search_batch_stream([[0.1, 0.2, 0.3], [0.3, 0.2, 0.1]], top_k=10):
    process(result.to_pyarrow())

6) Bootstrap schema from a parquet file

Use schema_from_parquet() when you want to start from an existing parquet layout and then adjust the schema before table creation. A plain string path is read from the client machine, and remote/object-store sources should be passed as FileSource.

import seahorse_coral as sc

schema = coral.schema_from_parquet("./data/documents.parquet")
schema.with_primary_key("id")

table = coral.create_table("documents_from_parquet", schema=schema)

7) Export or download parquet

export_parquet() writes files on the Coral server side. download_parquet() and download_parquet_stream() bring the result back to the client process, and local disk writes stay explicit via write_to() or download_parquet_to().

import seahorse_coral as sc

result = table.export_parquet(
    sc.local_directory("/var/lib/coral/exports/documents"),
    where="id > 100",
    mode="single_file",
)
print(result.files)

downloaded = table.download_parquet(limit=1000)
print(downloaded.filename)
downloaded.write_to("./documents-sample.parquet")

table.download_parquet_to("./documents-full.parquet", where="id > 100")

(Optional) Sparse & hybrid search

Sparse/hybrid search requires a table that has a sparse vector column + inverted index.

import seahorse_coral as sc

schema = sc.SchemaBuilder(
    columns=[
        sc.int64_column("id", nullable=False),
        sc.vector_column("vector", dim=384),
        sc.sparse_vector_column("sparse_emb"),
        sc.metadata_column("metadata"),
    ],
    primary_key=["id"],
    indexes=[
        sc.hnsw_index("vector"),
        sc.inverted_index("sparse_emb"),
    ],
)

table = coral.create_table("documents_hybrid", schema=schema)

# Sparse vector search (BM25 / inverted index)
sparse_query = "1:0.8 5:0.6 12:0.4"
result = table.index("sparse_emb").search_sparse(
    sparse_query,
    top_k=10,
    bm25_k=1.2,
    bm25_b=0.75,
)

# Hybrid search (dense + sparse + fusion)
# - requires dense_column + sparse_column
result = table.hybrid_search(
    dense_column="vector",
    dense_query=[0.1, 0.2, 0.3],
    sparse_column="sparse_emb",
    sparse_query=sparse_query,
    top_k=10,
    options=sc.HybridSearchOptions(
        fusion="rrf",
        rrf_k=60,
        alpha=0.7,
    ),
)

Next steps

• Advanced schema options (segmentation / indexes): docs/advanced.md
• Build from source / development: docs/build.md