vittoriadb 0.6.2

High-performance vector database with unified configuration, I/O optimization, and automatic embeddings

VittoriaDB Python SDK

Python client for VittoriaDB — an embedded vector database shipped as a single Go binary. The SDK talks to the server over HTTP: collections, vectors, semantic search, server-side embeddings, document upload, and RAG-friendly content storage. Optional auto-start downloads a matching release binary (when available) and manages the process lifecycle.

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Documentation

Topic	Where
REST API (paths, JSON bodies, filters)	docs/api.md
Embeddings & vectorizers	docs/embeddings.md
Installation & troubleshooting	docs/installation.md
Server & YAML configuration	docs/configuration.md
Content storage for RAG	docs/content-storage.md
CLI (run, backup, restore, …)	docs/cli.md
Examples (Python / Go / curl)	examples/README.md

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Features

Area	What the SDK exposes
Collections	Create / list / delete; DistanceMetric, IndexType (FLAT, HNSW, IVF prototype)
Vectors	insert, insert_batch, get, delete, search with optional metadata
Embeddings	insert_text, insert_text_batch, search_text when the collection has a VectorizerConfig
Documents	upload_file / upload_files — PDF, DOCX, TXT, MD, HTML (server-side processors)
Metadata filters	Flat dict ({"category": "tech"}) or structured filter JSON; use search(..., use_post=True) for POST search bodies
Indexes	HNSW tuning via config; IVF tuning via Configure.Index.ivf_flat(nlist=, nprobe=)
Observability	health(), stats() (DatabaseStats: totals, latency, QPS), prometheus_metrics() → GET /metrics text
Configuration	config() → unified server config + feature flags (requires compatible server)
Errors	Typed exceptions: ConnectionError, CollectionError, VectorError, SearchError, BinaryError

Backup and restore are not HTTP APIs: use the vittoriadb CLI (backup / restore). Stop the server before restore.

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Installation

pip install vittoriadb

On install, the package tries to download a platform binary from GitHub Releases at tag v<sdk-version> (e.g. v0.6.2). If that asset is missing, you’ll see a warning — install the binary manually or put vittoriadb on your PATH.

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Quick start

Connect

import vittoriadb

# Managed local server (downloads binary when release exists)
db = vittoriadb.connect()

# Existing server
db = vittoriadb.connect(url="http://localhost:8080", auto_start=False)

db.close()

Vectors and similarity search

collection = db.create_collection(
    name="docs",
    dimensions=384,
    metric="cosine",
)

collection.insert(
    "doc1",
    vector=[0.1] * 384,
    metadata={"title": "Hello", "category": "demo"},
)

results = collection.search(
    vector=[0.1] * 384,
    limit=5,
    filter={"category": "demo"},
    include_metadata=True,
)

# Structured / nested filters: POST search
results = collection.search(
    vector=[0.1] * 384,
    limit=5,
    use_post=True,
    filter={
        "and": [
            {"field": "category", "operator": "eq", "value": "demo"},
        ]
    },
)

Server-side embeddings

from vittoriadb.configure import Configure

collection = db.create_collection(
    name="smart",
    dimensions=384,
    vectorizer_config=Configure.Vectors.auto_embeddings(),
)

collection.insert_text(
    "a1",
    "Your text here.",
    metadata={"source": "wiki"},
)

hits = collection.search_text("your query", limit=5)

Observability

h = db.health()           # HealthStatus
s = db.stats()            # DatabaseStats: queries_total, avg_query_latency, queries_per_sec, …
text = db.prometheus_metrics()  # Prometheus exposition from GET /metrics

IVF index (prototype)

from vittoriadb import IndexType

collection = db.create_collection(
    name="ivf_demo",
    dimensions=64,
    index_type=IndexType.IVF,
    config=Configure.Index.ivf_flat(nlist=16, nprobe=2),
)

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RAG and content storage

Enable ContentStorageConfig on create_collection to persist chunk text for retrieval (include_content=True on search_text). See docs/content-storage.md.

from vittoriadb import ContentStorageConfig

collection = db.create_collection(
    name="rag",
    dimensions=384,
    vectorizer_config=Configure.Vectors.auto_embeddings(),
    content_storage=ContentStorageConfig(enabled=True, field_name="_content"),
)

collection.insert_text("id1", "Long passage …", metadata={"title": "Doc"})

for r in collection.search_text("query", limit=3, include_content=True):
    if r.has_content():
        print(r.content)

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Vectorizer presets (Configure.Vectors)

Method	Use case
auto_embeddings(...)	Default server-side pipeline (often Sentence Transformers–backed on server)
sentence_transformers(model=, dimensions=)	Explicit ST model
openai_embeddings(api_key=, …)	OpenAI API
huggingface_embeddings(api_key=, …)	Hugging Face Inference API
ollama_embeddings(model=, dimensions=, base_url=)	Local Ollama

Details: docs/embeddings.md.

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Index tuning

HNSW — pass index_type="hnsw" or IndexType.HNSW and config keys such as m, ef_construction, ef_search (see API / server defaults).

IVF — index_type=IndexType.IVF and config=Configure.Index.ivf_flat(nlist=16, nprobe=2) (prototype index on server).

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Configuration inspection

info = db.config()   # dict: unified config + metadata from GET /config

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API overview

VittoriaDB

Method	Description
connect(url=, auto_start=, port=, host=, data_dir=, extra_args=)	Client instance
create_collection(...)	New collection
get_collection(name)	Handle to existing collection
list_collections()	List CollectionInfo
delete_collection(name)	Drop collection
health()	HealthStatus
stats()	DatabaseStats
prometheus_metrics()	Raw Prometheus text
config()	Server configuration JSON
close()	Cleanup / stop auto-started server

Collection

Method	Description
insert(id, vector, metadata=)	Single vector
insert_batch(vectors)	Batch
insert_text / insert_text_batch	Server-side embedding
search(vector, limit=, filter=, use_post=, …)	Similarity search
search_text(query, limit=, filter=, …)	Query embedding + search
upload_file / upload_files	Document → chunks + vectors
get / delete / count	CRUD helpers

Exported types include Vector, SearchResult, CollectionInfo, HealthStatus, DatabaseStats, DistanceMetric, IndexType, VectorizerConfig, ContentStorageConfig, and configuration/error types listed in vittoriadb.__all__.

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Contributing & license

• Issues: GitHub Issues
• Dev setup: DEVELOPMENT.md
• License: MIT

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Changelog (high level)

• 0.6.x — Metadata filters (flat + structured), search(..., use_post=True), prometheus_metrics(), Configure.Index.ivf_flat, exports HealthStatus / DatabaseStats; README aligned with server features and docs.
• 0.5.x — Unified server configuration, performance features (parallel search, caching, I/O); config() client support.

Full release notes: GitHub Releases.