ailake 0.0.20

Unified tabular + vector storage in a single Iceberg-compatible file

ailake — AI-Lake Format Python SDK

Unified storage for tabular data, embeddings, and HNSW vector index in a single Parquet-compatible file. 100% Apache Iceberg Spec v2 compatible.

Install

pip install ailake

Requires Python ≥ 3.9. Dependencies: pyarrow >= 14.0, numpy >= 1.24.

Quickstart

Write + search — fluent API (recommended)

import ailake
import numpy as np

# Open or create a table
table = ailake.open_table(
    "./my_table",
    dim=1536,
    metric="cosine",          # cosine | euclidean | dot_product | normalized_cosine
    pre_normalize=True,       # normalize at write time; enables fast 1-dot(a,b) path
    hnsw_m=16,                # HNSW connections per node (default 16)
    hnsw_ef_construction=150,
    embedding_model="text-embedding-3-small",  # tracked in Iceberg metadata
    embedding_model_version="v1",
)

texts = ["Document about AI", "Another document"]
embeddings = np.random.rand(2, 1536).astype(np.float32)

table.insert(texts, embeddings)   # accepts list or numpy array
snapshot_id = table.commit()

# Pattern B — auto-embed without passing embeddings explicitly
def my_embed(texts: list[str]) -> list[list[float]]:
    return np.random.rand(len(texts), 1536).tolist()  # replace with real model

table2 = ailake.open_table("./my_table2", dim=1536, embed_fn=my_embed)
table2.insert(["Document about AI", "Another document"])  # embed_fn called automatically
table2.commit()

# Pointer-only search (default — backward-compatible)
df      = table.search(embeddings[0], top_k=10).to_pandas()   # row_id, distance, file
lf      = table.search(embeddings[0]).limit(5).to_polars()
results = table.search(embeddings[0]).to_list()   # list[dict]

# Full row data — all Parquet columns + _distance
df_full = table.search(embeddings[0], top_k=10, fetch_data=True).to_pandas()

Async API

import ailake, asyncio
import numpy as np

async def main():
    table = ailake.open_table("./my_table", dim=1536)
    await table.insert_async(texts, embeddings)
    await table.commit_async()

    # fluent async chain
    df = await table.search(query_vec).limit(10).to_pandas_async()

    # parallel searches via asyncio.gather
    r1, r2 = await asyncio.gather(
        table.search(q1).to_list_async(),
        table.search(q2).to_list_async(),
    )

asyncio.run(main())

Module-level search

import ailake
import numpy as np

query = np.random.rand(1536).astype(np.float32)

df     = ailake.search("./my_table", query, top_k=10).to_pandas()
lf     = ailake.search("./my_table", query).limit(5).to_polars()
items  = ailake.search("./my_table", query).to_list()

Assemble context for LLMs

import ailake

chunks = [
    {
        "document_id": "doc-1",
        "chunk_index": 0,
        "chunk_text": "AI-Lake stores vectors and tabular data together.",
        "document_title": "AI-Lake Overview",
        "section_path": "Introduction",
        "source_uri": "s3://my-lake/docs/overview.pdf",
        "distance": 0.12,
    },
]

context_xml = ailake.assemble_context(
    chunks=chunks,
    max_tokens=4096,       # token budget (4 chars ≈ 1 token)
    dedup_threshold=0.05,  # drop near-duplicate chunks
)
# Pass context_xml directly to Claude / GPT-4 as a user message

API reference

open_table(path, *, ...) → Table

Opens or creates an AI-Lake table at path.

Parameter	Default	Description
path	required	Table root (local, s3://, gs://, az://)
vector_column	"embedding"	Vector column name
dim	1536	Embedding dimension
metric	"cosine"	cosine, euclidean, dot_product, normalized_cosine
pre_normalize	False	Normalize to unit L2 at write; enables 1-dot(a,b) fast path (~12-20 % speedup)
hnsw_m	None (=16)	HNSW connections per node
hnsw_ef_construction	None (=150)	HNSW build pool size
pq_only	False	Discard raw F16 vectors after index build — only PQ codes stored. ~98 % storage reduction; reranking disabled; recall@10 ~93-95 %.
ivf_residual	False	Encode vec − cluster_centroid per IVF cell (residual PQ). Same storage as standard PQ; ~2-4 pp better recall@10.
embedding_model	None	Embedding model name stored in Iceberg properties (ailake.embedding-model). Used for mismatch detection and migration tracking.
embedding_model_version	None	Optional model version. Stored as "<name>@<version>" in Iceberg properties.
embed_fn	None	Auto-embed callable list[str] → list[list[float]]. When set, insert(texts) and write_batch(texts) can be called without passing embeddings — the callable is invoked automatically.
partition_by	None	Single-column Iceberg identity partition (e.g. "agent_id"). Stored in metadata.json. Prefer partition_fields for new tables.
partition_value	None	Per-write value for partition_by. Tagged in key_metadata; used for manifest-level pruning at search time.
partition_fields	None	Multi-column Iceberg partition spec. List of (column, transform, column_type) tuples. Supports all Iceberg transforms: "identity", "year", "month", "day", "hour", "bucket[N]", "truncate[N]". Takes precedence over partition_by. Example: [("topic_id","identity","int"),("date","month","date")].
format_version	2	Iceberg format version. Set to 3 to write an Iceberg v3 table.

Table

Method	Description
insert(texts, embeddings=None) → Table	Buffer a batch. embeddings: list[list[float]] or numpy array. When embed_fn was set on open_table(), embeddings may be omitted — the callable is invoked automatically.
write_batch_auto_deferred(texts, embeddings=None) → Table	Deferred write — Parquet persisted immediately (~200k vec/s); index (HNSW or IVF-PQ, auto-selected) built in a background thread. Shard served via flat scan until index ready.
commit() → int	Persist as a new Iceberg snapshot; returns snapshot ID.
search(query, top_k=10, fetch_data=False) → SearchQuery	Lazy, chainable search. query: list[float] or numpy array. Set fetch_data=True to return full row data. Raises ModelMismatch if query dim ≠ table dim.
insert_async(...)	Async variant of insert.
write_batch_auto_deferred_async(...)	Async variant of write_batch_auto_deferred.
commit_async() → int	Async variant of commit.

Table is a context manager: with ailake.open_table(...) as t: ...

In Jupyter, table renders a styled HTML card showing path and vector config.

SearchQuery

Lazy result set — no I/O until materialised.

Method	Description
limit(n) → SearchQuery	Cap to n nearest neighbours (chainable).
to_list() → list[dict]	Always pointer-only: [{"row_id": int, "distance": float, "file": str}, ...]
to_arrow() → pyarrow.Table	Full row data (all columns + _distance) when fetch_data=True; pointer-only pyarrow.Table with columns row_id, distance, file otherwise.
to_pandas() → pd.DataFrame	Full row DataFrame when fetch_data=True; pointer-only otherwise.
to_polars() → pl.DataFrame	Full row DataFrame when fetch_data=True; pointer-only otherwise.
to_list_async()	Async variant.
to_arrow_async()	Async variant.
to_pandas_async()	Async variant.
to_polars_async()	Async variant.

In Jupyter, results renders as an HTML table when executed, pending state otherwise. When fetch_data=True, the HTML table shows all Parquet columns.

Full-read mode

# Pointer-only (default — backward-compatible)
df = ailake.search("./my_table", query, top_k=10).to_pandas()
# columns: row_id, distance, file

# Full row data — all Parquet columns + _distance
df = ailake.search("./my_table", query, top_k=10, fetch_data=True).to_pandas()
# columns: text, embedding, ..., _distance

# Same via Table handle
df = table.search(query, top_k=10, fetch_data=True).to_pandas()

fetch_data=True reads each matching Parquet file once and uses arrow_select::take to extract only the matched rows — no full table scan.

search(path, query, top_k=10, fetch_data=False, partition_filter=None) → SearchQuery

Module-level search returning the same chainable SearchQuery. Pass partition_filter to restrict results to files written with a specific partition_value — pruning happens at the manifest level before any HNSW I/O.

VectorColSpec(column, dim, metric="cosine", modality=None)

Declares one vector column for multi-column writes or searches.

Arg	Description	Example
column	Parquet column name	"image_embedding"
dim	Embedding dimension	512
metric	Distance metric	"cosine"
modality	Optional tag — stored as ailake.modality-<column>	"text" / "image" / "audio" / "video"

TableWriter.write_batch_multi(texts, columns)

Write a batch with N independent vector columns in one call. Each column gets its own HNSW index in the AILK section of the file footer.

from ailake import TableWriter, VectorColSpec

text_spec  = VectorColSpec("embedding",       1536, "cosine", "text")
image_spec = VectorColSpec("image_embedding",  512, "cosine", "image")

writer = TableWriter("s3://my-lake/media/", dim=1536, metric="cosine")
writer.write_batch_multi(
    texts,
    [(text_spec, text_embeddings), (image_spec, image_embeddings)],
)
snapshot_id = writer.commit()

search_multimodal(path, queries, top_k=10) → list[dict]

Cross-modal search: fuse results from N vector columns via Reciprocal Rank Fusion.

rrf_score = Σ weight_i / (60 + rank_i) — higher is better.

results = ailake.search_multimodal(
    "s3://my-lake/media/",
    queries=[
        ("embedding",       text_vec,  0.7),   # 70% weight on text similarity
        ("image_embedding", image_vec, 0.3),   # 30% weight on image similarity
    ],
    top_k=20,
)
# Returns: [{"row_id": int, "rrf_score": float, "file": str}, ...]
# Ordered by descending rrf_score

Each column is searched by its own HNSW. Per-column dimensions are auto-detected from ailake.dim-<col> Iceberg properties written at commit() time — no dim argument needed when reading tables written with write_batch_multi.

Agent(table_path, embed_fn, agent_id=None) — Phase 9 episodic memory

High-level helper for agent frameworks (LangChain, CrewAI, AutoGen). Wraps TableWriter + search + ContextAssembler with hybrid scoring (distance × recency × importance) and automatic per-agent partition isolation.

import ailake

agent = ailake.Agent(
    table_path="s3://my-lake/agents/",
    embed_fn=my_embed_fn,         # list[str] → list[list[float]]
    agent_id="agent-uuid-here",   # isolates reads/writes to this agent's shard
)

# Store a memory with optional importance score
agent.remember("Deployment failed due to OOM on Tuesday", importance=0.9)

# Recall relevant memories — hybrid score = distance × recency × importance
results = agent.recall("deployment issues", top_k=5)

# Log a tool call for later retrieval
agent.log_tool_call(
    name="web_search",
    input={"q": "python asyncio timeout"},
    output={"hits": 5},
    outcome="success",
    latency_ms=120,
)

# Assemble context for LLM prompt (dedup + token budget)
context_xml = agent.assemble_context("why did deployment fail?", max_tokens=4096)

Method	Description
remember(text, importance=1.0)	Embeds text and stores it as an EpisodicMemorySchema row tagged with agent_id.
recall(query, top_k=5)	Embeds query, searches with partition_filter=self.agent_id, applies hybrid score.
log_tool_call(name, input, output, outcome="success", latency_ms=0)	Stores a ToolCallSchema row — searchable by tool name and context.
assemble_context(query, max_tokens=4096)	recall() + ContextAssembler — returns prompt-ready XML.

migrate_embeddings(path, old_column, new_column, embed_fn, *, ...)

Re-embeds all chunks in a table with a new model, committing the result as a new Iceberg snapshot.

ailake.migrate_embeddings(
    path         = "s3://my-lake/docs/",
    old_column   = "embedding",        # existing vector column
    new_column   = "embedding_v2",     # destination column (may be same name)
    embed_fn     = my_embed_fn,        # callable: list[str] → list[list[float]]
    text_column  = "chunk_text",       # source text column
    strategy     = "dual_write_then_cutover",  # or "atomic_replace"
    batch_size   = 512,
    new_model    = "text-embedding-3-large",
    new_model_version = "v1",
    on_progress  = lambda *, files_done, files_total, rows_migrated: print(
        f"{files_done}/{files_total} files, {rows_migrated} rows"
    ),
)

Parameter	Default	Description
path	required	Table root URI
old_column	required	Existing vector column to migrate from
new_column	required	Destination vector column
embed_fn	required	list[str] → list[list[float]] callable
text_column	"chunk_text"	Parquet column containing the source text
strategy	"dual_write_then_cutover"	"dual_write_then_cutover" (zero downtime, 2× peak storage) or "atomic_replace" (lower storage, brief mixed-model window)
batch_size	512	Rows passed to embed_fn per call
new_model	None	Model name written to ailake.embedding-model after migration
new_model_version	None	Optional version suffix
on_progress	None	Callable invoked after each file with keyword args files_done, files_total, rows_migrated

TableWriter (low-level — use open_table() for most cases)

# Standard HNSW write with model tracking
writer = ailake.TableWriter(
    path, dim=1536, metric="cosine",
    embedding_model="text-embedding-3-small",
    embedding_model_version="v1",
)
writer.write_batch(texts, embeddings)
snapshot_id = writer.commit()

# Pattern B — auto-embed: omit embeddings, SDK calls embed_fn
writer = ailake.TableWriter(
    path, dim=1536,
    embed_fn=lambda texts: my_model.encode(texts).tolist(),
)
writer.write_batch(texts)  # no embeddings arg needed
writer.commit()

# PQ-only — raw vectors discarded after index build (~98 % storage reduction)
writer = ailake.TableWriter(path, dim=1536, metric="cosine", pq_only=True)
writer.write_batch(texts, embeddings)
writer.commit()

# Residual PQ — per-cluster encoding for better recall
writer = ailake.TableWriter(path, dim=1536, metric="cosine", ivf_residual=True)
writer.write_batch(texts, embeddings)
writer.commit()

# Deferred write — Parquet immediate, index background (~200k vec/s)
writer = ailake.TableWriter(path, dim=1536, metric="cosine")
writer.write_batch_auto_deferred(texts, embeddings)
writer.commit()

TableWriter parameters: same as open_table() (includes pq_only, ivf_residual, pre_normalize, hnsw_m, hnsw_ef_construction, embedding_model, embedding_model_version, embed_fn, partition_by, partition_value, partition_fields, format_version).

delete_where(path, column, values) → None

Commits an Iceberg equality delete. No data files are rewritten.

ailake.delete_where("./my_table", "id", ["doc-obsolete-1", "doc-obsolete-2"])

add_column(path, name, col_type, *, required=False, initial_default=None) → int

Adds column to live table schema. Returns new schema_id. No data files rewritten.

ailake.add_column("./my_table", "source_url", "string", required=False, initial_default="")

rename_column(path, old_name, new_name) → int

Renames column. Returns new schema_id.

hardware_info() → dict[str, str]

Returns hardware profile of current machine.

info = ailake.hardware_info()
# {
#   "backend":           "cpu-simd",   # or "nvidia-cuda" / "amd-rocm"
#   "has_cuda":          "false",
#   "has_rocm":          "false",
#   "cpu_logical_cores": "16",
#   "has_avx2":          "true",
#   "has_avx512":        "false",
#   "recommend_ivf_pq":  "true",       # true when has GPU OR (cores > 8 AND n >= 5000)
# }

Call before write_batch_auto_deferred to understand what index type will be selected.

assemble_context(chunks, max_tokens=4096, dedup_threshold=0.05) → str

Assembles chunk dicts into structured XML for LLM input. Deduplicates near-identical chunks within the token budget.

Storage modes and index types

Mode	pq_only	ivf_residual	Storage (dim=1536, 1M rows)	Reranking	Recall@10
HNSW + F16 raw (default)	False	False	~300 GB vectors + ~30 GB HNSW	Yes (exact)	~97 %
IVF-PQ + F16 raw	False	False	~300 GB + ~5 GB PQ codes	Yes (exact)	~93 % inline
IVF-PQ residual + raw	False	True	~300 GB + ~5 GB	Yes (exact)	~96 %
PQ-only	True	False	~5 GB total	No	~93-95 %
PQ-only residual	True	True	~5 GB total	No	~94-96 %

# Deferred write — all modes, instant Parquet commit, index in background
writer = ailake.TableWriter(path, dim=1536, pq_only=True, ivf_residual=True)
writer.write_batch_auto_deferred(texts, embeddings)
writer.commit()

HNSW tuning guide

Goal	hnsw_m	hnsw_ef_construction
Low latency / high QPS	8	100
General purpose (default)	16	150
High recall (RAG)	24	200
Max recall (medical, legal)	32	400

Type checking

Ships py.typed (PEP 561) and ailake/_ailake.pyi stubs. mypy and pyright work out of the box with no configuration.

Iceberg compatibility

Tables are valid Apache Iceberg Spec v2. Spark, Trino, DuckDB, and PyIceberg read tabular columns normally; the HNSW index lives in an extension section that standard Parquet readers silently ignore.

License

MIT OR Apache-2.0