velesdb 2.0.0

VelesDB: The Local Vector Database for Python & AI. Microsecond semantic search, zero network latency.

VelesDB Python

Python bindings for VelesDB — a high-performance vector database for AI applications. The current published wheel is velesdb v1.16.0; numpy>=1.20 ships as a hard runtime dependency since v1.13.8 so a single pip install velesdb is sufficient.

Features

• Dense + Sparse Vector Search: HNSW index with SIMD-optimized distance, plus inverted-index sparse search and hybrid dense+sparse fusion
• Multi-Query Fusion: Native MQG support with RRF, Weighted, and Relative Score fusion strategies
• Agent Memory SDK: SemanticMemory, EpisodicMemory, ProceduralMemory for AI agent workflows
• Graph Collections: Persistent knowledge graphs with BFS/DFS traversal and optional node embeddings
• In-Memory GraphStore: Lightweight graph for ad-hoc analysis without disk persistence
• VelesQL Parser: Programmatic query introspection with VelesQL.parse() and ParsedStatement
• Multiple Distance Metrics: Cosine, Euclidean, Dot Product, Hamming, Jaccard
• Persistent Storage: Memory-mapped files for efficient disk I/O
• Metadata Collections: Schema-free reference tables with no vector overhead
• Product Quantization: PQ and OPQ training for compressed storage and faster search
• Collection Analytics: analyze_collection() with row counts, size metrics, and index statistics
• NumPy Integration: Native support for NumPy arrays
• Type Hints: Full .pyi stub file for IDE autocompletion

Installation

pip install velesdb

Quick Start

import velesdb

# Open or create a database
db = velesdb.Database("./my_vectors")

# Create a collection for 768-dimensional vectors (e.g., BERT embeddings).
# `create_collection` is the recommended Python entry point. It accepts
# typed dataclasses (`hnsw=HnswOptions(...)`, `auto_reindex=AutoReindexOptions(...)`,
# `limits=LimitsOptions(...)`) for tuning. The Rust core also exposes
# `create_vector_collection` for callers who want the explicit typed API.
collection = db.create_collection(
    name="documents",
    dimension=768,
    metric="cosine"  # Options: "cosine", "euclidean", "dot" (aliases: "dotproduct", "inner", "ip"), "hamming", "jaccard"
)

# Insert vectors with metadata
collection.upsert([
    {
        "id": 1,
        "vector": [0.1, 0.2, ...],  # 768-dim vector
        "payload": {"title": "Introduction to AI", "category": "tech"}
    },
    {
        "id": 2,
        "vector": [0.3, 0.4, ...],
        "payload": {"title": "Machine Learning Basics", "category": "tech"}
    }
])

# Search for similar vectors
results = collection.search_request(
    velesdb.SearchOptions(vector=[0.15, 0.25, ...], top_k=5)  # Query vector
)

for result in results:
    print(f"ID: {result['id']}, Score: {result['score']:.4f}")
    print(f"Payload: {result['payload']}")

Search API. search_request(SearchOptions(...)) is the canonical search entry point and is used throughout this README. The older multi-keyword collection.search(vector=..., top_k=..., filter=...) still works but is deprecated since v1.15 (emits a DeprecationWarning, removed in v2.0). SearchOptions accepts vector, sparse_vector, top_k, filter, sparse_index_name and include_vectors, plus a fluent builder (SearchOptions().with_vector(v).with_top_k(10)).

End-to-End: Text to Search Results (RAG Pipeline)

VelesDB stores and searches vectors — it does not generate embeddings. Use any embedding model to convert text to vectors first.

# pip install velesdb sentence-transformers
import velesdb
from sentence_transformers import SentenceTransformer

# 1. Load an embedding model (runs locally, no API key needed)
model = SentenceTransformer("all-MiniLM-L6-v2")  # outputs 384-dim vectors

# 2. Create a collection matching the model's dimension
db = velesdb.Database("./rag_data")
collection = db.create_collection("docs", dimension=384, metric="cosine")

# 3. Embed and store documents
texts = [
    "VelesDB is a local-first vector database written in Rust.",
    "HNSW is an approximate nearest neighbor search algorithm.",
    "RAG combines retrieval with language model generation.",
]
vectors = model.encode(texts).tolist()

collection.upsert([
    {"id": i, "vector": v, "payload": {"text": t}}
    for i, (v, t) in enumerate(zip(vectors, texts))
])

# 4. Search with a natural language query
query_vector = model.encode("How does vector search work?").tolist()
results = collection.search_request(velesdb.SearchOptions(vector=query_vector, top_k=2))

for r in results:
    print(f"Score: {r['score']:.4f} | {r['payload']['text']}")
# Score: 0.5621 | HNSW is an approximate nearest neighbor search algorithm.
# Score: 0.4238 | VelesDB is a local-first vector database written in Rust.

Built-in embedding adapters (optional)

Since v1.16.0, velesdb.embed ships thin, opt-in adapters so you don't have to wire the embedding call yourself. Their backing libraries are soft dependencies loaded lazily, so the base wheel stays light — install the extra you need:

# pip install "velesdb[embed-sentence-transformers]"   # local, no API key
# pip install "velesdb[embed-openai]"                   # OpenAI-compatible API
# pip install "velesdb[embed]"                          # both
import velesdb
from velesdb.embed import SentenceTransformerEmbedder  # or OpenAIEmbedder

embedder = SentenceTransformerEmbedder("all-MiniLM-L6-v2")  # runs on-device
db = velesdb.Database("./rag_data")
collection = db.create_collection("docs", dimension=embedder.dimension, metric="cosine")

texts = ["VelesDB is a local-first vector database.", "HNSW powers fast ANN search."]
vectors = embedder.embed(texts)
collection.upsert([{"id": i, "vector": v, "payload": {"text": t}}
                   for i, (v, t) in enumerate(zip(vectors, texts))])

results = collection.search_request(
    velesdb.SearchOptions(vector=embedder.embed(["fast search"])[0], top_k=2)
)

OpenAIEmbedder(model="text-embedding-3-small", *, api_key=..., base_url=..., dimensions=...) targets OpenAI, Azure OpenAI, vLLM, or any OpenAI-compatible endpoint via base_url. Both adapters satisfy the Embedder protocol (dimension: int, embed(texts) -> list[list[float]]), so you can drop in your own implementation. dimension is inferred after the first embed() call when not known up front.

Full RAG demo with PDF ingestion: demos/rag-pdf-demo/

API Reference

Database

# Create/open database
db = velesdb.Database("./path/to/data")

# List collections
names = db.list_collections()

# Create collection (with optional HNSW tuning via typed options)
collection = db.create_collection("name", dimension=768, metric="cosine")
from velesdb import HnswOptions
collection = db.create_collection(
    "tuned",
    dimension=768,
    hnsw=HnswOptions(m=48, ef_construction=600),
)

# Get existing collection
collection = db.get_collection("name")

# Delete collection
db.delete_collection("name")

# Create a metadata-only collection (no vectors, payload-only CRUD)
products = db.create_metadata_collection("products")

# Create a graph collection (see Graph Collections section)
graph = db.create_graph_collection("knowledge", dimension=768)

# Agent memory for AI workflows (see Agent Memory SDK section)
memory = db.agent_memory(dimension=384)

# Train Product Quantization for compressed search
db.train_pq("name", m=8, k=256)
db.train_pq("name", m=16, k=128, opq=True)  # Optimized PQ

# Analyze collection statistics
stats = db.analyze_collection("name")
print(stats["total_points"], stats["total_size_bytes"])

# Query plan cache management
cache_stats = db.plan_cache_stats()
print(f"Hit rate: {cache_stats['hit_rate']:.2%}")
db.clear_plan_cache()

Collection

# Get collection info
info = collection.info()
# {"name": "documents", "dimension": 768, "metric": "cosine", "storage_mode": "full", "point_count": 100, "metadata_only": False}

# Insert/update vectors (with immediate flush)
collection.upsert([
    {"id": 1, "vector": [...], "payload": {"key": "value"}}
])

# Bulk insert (optimized for high-throughput - 3-7x faster)
# Uses parallel HNSW insertion + single flush at the end
collection.upsert_bulk([
    {"id": i, "vector": vectors[i].tolist()} for i in range(10000)
])

# Vector search
results = collection.search_request(velesdb.SearchOptions(vector=[...], top_k=10))

# Search with custom HNSW ef_search (trade speed for recall)
results = collection.search_with_ef(vector=[...], top_k=10, ef_search=256)

# Search returning only IDs and scores (faster, no payload transfer)
results = collection.search_ids(vector=[...], top_k=10)
# [{"id": 1, "score": 0.98}, {"id": 2, "score": 0.95}, ...]

# Batch search (multiple queries in parallel)
batch_results = collection.batch_search([
    {"vector": [0.1, 0.2, ...], "top_k": 5},
    {"vector": [0.3, 0.4, ...], "top_k": 10, "filter": {"condition": ...}},
])

# Multi-query fusion search (MQG pipelines)
from velesdb import FusionStrategy

results = collection.multi_query_search(
    vectors=[query1, query2, query3],  # Multiple reformulations
    top_k=10,
    fusion=FusionStrategy.rrf(k=60)  # RRF, average, maximum, or weighted
)

# Weighted fusion (like SearchXP scoring)
results = collection.multi_query_search(
    vectors=[v1, v2, v3],
    top_k=10,
    fusion=FusionStrategy.weighted(
        avg_weight=0.6,
        max_weight=0.3,
        hit_weight=0.1
    )
)

# Relative Score Fusion (linear combination of dense + sparse scores)
results = collection.multi_query_search(
    vectors=[v1, v2],
    top_k=10,
    fusion=FusionStrategy.relative_score(dense_weight=0.7, sparse_weight=0.3)
)

# Maximum fusion (take the highest score across queries)
results = collection.multi_query_search(
    vectors=[v1, v2],
    top_k=10,
    fusion=FusionStrategy.maximum()
)

# Multi-query search returning only IDs and fused scores
results = collection.multi_query_search_ids(
    vectors=[v1, v2, v3],
    top_k=10,
    fusion=FusionStrategy.rrf()
)
# [{"id": 1, "score": 0.85}, ...]

# Hybrid dense + sparse search (fused with RRF k=60 by default)
results = collection.search_request(velesdb.SearchOptions(
    vector=[0.1, 0.2, ...],
    sparse_vector={0: 1.0, 42: 2.0},
    top_k=10,
))  # uses RRF by default; see Sparse Vector Search section for details

# Text search (BM25)
results = collection.text_search(query="machine learning", top_k=10)

# Hybrid search (vector + text with RRF fusion)
results = collection.hybrid_search(
    vector=[0.1, 0.2, ...],
    query="machine learning",
    top_k=10,
    vector_weight=0.7  # 0.0 = text only, 1.0 = vector only
)

# Get specific points
points = collection.get([1, 2, 3])

# Delete points
collection.delete([1, 2, 3])

# Check if empty
is_empty = collection.is_empty()

# Flush to disk
collection.flush()

# VelesQL query
results = collection.query(
    "SELECT * FROM vectors WHERE category = 'tech' LIMIT 10"
)

# VelesQL with parameters
results = collection.query(
    "SELECT * FROM vectors WHERE VECTOR NEAR $query LIMIT 5",
    params={"query": [0.1, 0.2, ...]}
)

# Search with metadata filter
results = collection.search_with_filter(
    vector=[0.1, 0.2, ...],
    top_k=10,
    filter={"condition": {"type": "eq", "field": "category", "value": "tech"}}
)

# Streaming insert (high-throughput, eventual consistency)
count = collection.stream_insert([
    {"id": 100, "vector": [...], "payload": {"key": "value"}}
])

# Scroll through all points in stable batches (no vector required)
# Useful for export, reindexing, or full-collection inspection.
for batch in collection.scroll(batch_size=100, filter=None):
    for point in batch:
        print(point["id"], point["payload"])

# MATCH graph traversal query (VelesQL)
results = collection.match_query(
    "MATCH (a:Person)-[:KNOWS]->(b:Person) RETURN a.name, b.name",
    vector=query_embedding,   # optional: add similarity scoring
    threshold=0.5             # minimum similarity threshold
)

# Query returning only IDs and scores (faster, no payload)
ids = collection.query_ids("SELECT * FROM docs WHERE price > 100 LIMIT 5")

# Explain query execution plan
plan = collection.explain("SELECT * FROM docs WHERE category = 'tech' LIMIT 10")
print(plan["tree"])            # execution plan tree
print(plan["estimated_cost_ms"])
print(plan["filter_strategy"]) # "seq_scan", "index_scan", etc.

# Index management
collection.create_property_index("Document", "category")  # O(1) equality lookup
collection.create_range_index("Document", "price")         # O(log n) range queries
indexes = collection.list_indexes()
collection.drop_index("Document", "category")

# Secondary indexes on a payload field (and their memory footprint)
collection.has_secondary_index("category")                 # -> bool
collection.drop_secondary_index("category")                # -> bool (True if dropped)
collection.indexes_memory_usage()                          # -> int (total bytes)

Sparse Vector Search

VelesDB supports sparse vectors alongside dense vectors. Sparse vectors are useful for learned sparse models (SPLADE, BGE-M3 sparse) and keyword-weighted representations.

# Upsert points with both dense and sparse vectors
collection.upsert([
    {
        "id": 1,
        "vector": [0.1, 0.2, 0.3, 0.4],       # dense embedding
        "sparse_vector": {0: 1.5, 3: 0.8, 42: 2.1},  # {dimension_index: weight}
        "payload": {"title": "Sparse retrieval paper"}
    },
    {
        "id": 2,
        "vector": [0.5, 0.6, 0.7, 0.8],
        "sparse_vector": {3: 1.2, 7: 0.5, 42: 0.9},
        "payload": {"title": "Dense retrieval survey"}
    }
])

# Sparse-only search (no dense vector needed)
results = collection.search_request(velesdb.SearchOptions(
    sparse_vector={0: 1.0, 42: 2.0},
    top_k=5,
))

# Hybrid dense + sparse search (fused with RRF k=60 by default)
results = collection.search_request(velesdb.SearchOptions(
    vector=[0.15, 0.25, 0.35, 0.45],
    sparse_vector={0: 1.0, 42: 2.0},
    top_k=10,
))

# Named sparse indexes (e.g., separate SPLADE and BM25 sparse models)
collection.upsert([
    {
        "id": 3,
        "vector": [0.2, 0.3, 0.4, 0.5],
        "sparse_vector": {
            "splade": {10: 1.5, 20: 0.8},
            "bm25":   {5: 2.0, 15: 1.1}
        },
        "payload": {"title": "Multi-model embeddings"}
    }
])

results = collection.search_request(velesdb.SearchOptions(
    vector=[0.2, 0.3, 0.4, 0.5],
    sparse_vector={10: 1.5, 20: 0.8},
    top_k=10,
    sparse_index_name="splade",  # query a specific named sparse index
))

Sparse vectors also work with scipy sparse objects:

from scipy.sparse import csr_matrix
import numpy as np

sparse_query = csr_matrix(np.array([[0.0, 1.5, 0.0, 0.8]]))
results = collection.search_request(velesdb.SearchOptions(sparse_vector=sparse_query, top_k=5))

Fusion Strategies

All multi-query and hybrid search methods accept a FusionStrategy to control how scores from multiple result sets are combined.

from velesdb import FusionStrategy

# Reciprocal Rank Fusion (default) -- robust to score scale differences
strategy = FusionStrategy.rrf(k=60)       # lower k = more weight to top ranks

# Average -- mean score across all queries
strategy = FusionStrategy.average()

# Maximum -- take the highest score per document
strategy = FusionStrategy.maximum()

# Weighted -- custom combination of avg, max, and hit ratio
strategy = FusionStrategy.weighted(avg_weight=0.6, max_weight=0.3, hit_weight=0.1)

# Relative Score Fusion -- linear blend of dense and sparse scores
strategy = FusionStrategy.relative_score(dense_weight=0.7, sparse_weight=0.3)

Strategy	Formula	Best For
rrf(k)	sum 1/(k + rank)	Multi-query fusion, different score scales
average()	mean(scores)	Uniform query importance
maximum()	max(scores)	When any single match is sufficient
weighted(a, m, h)	aavg + mmax + h*hit_ratio	Fine-grained scoring control
relative_score(d, s)	ddense + ssparse	Dense+sparse hybrid pipelines

Agent Memory SDK

VelesDB provides a built-in memory system for AI agents with three subsystems designed for RAG pipelines, chatbots, and autonomous agents.

import velesdb

db = velesdb.Database("./agent_data")
memory = db.agent_memory(dimension=384)  # default dimension is 384

Semantic Memory -- long-term knowledge facts with vector similarity recall:

# Store knowledge facts with their embeddings
memory.semantic.store(1, "Paris is the capital of France", embedding_paris)
memory.semantic.store(2, "Berlin is the capital of Germany", embedding_berlin)
memory.semantic.store(3, "The Eiffel Tower is in Paris", embedding_eiffel)

# Recall by similarity
results = memory.semantic.query(query_embedding, top_k=3)
for r in results:
    print(f"{r['content']} (score: {r['score']:.3f})")

Episodic Memory -- event timeline with temporal and similarity queries:

import time

# Record events as they happen
memory.episodic.record(1, "User asked about weather", timestamp=int(time.time()))
memory.episodic.record(
    2,
    "Agent retrieved forecast data",
    timestamp=int(time.time()),
    embedding=event_embedding  # optional, enables similarity recall
)

# Get recent events
events = memory.episodic.recent(limit=10)
for e in events:
    print(f"[{e['timestamp']}] {e['description']}")

# Get events since a specific timestamp
recent = memory.episodic.recent(limit=5, since=1700000000)

# Find similar past events by embedding
similar = memory.episodic.recall_similar(query_embedding, top_k=5)
for s in similar:
    print(f"{s['description']} (score: {s['score']:.3f})")

Procedural Memory -- learned patterns with confidence scoring and reinforcement:

# Teach a procedure
memory.procedural.learn(
    procedure_id=1,
    name="greet_user",
    steps=["say hello", "ask for name", "confirm preferences"],
    embedding=greeting_embedding,  # optional, enables similarity recall
    confidence=0.8
)

# Recall procedures by similarity (filtered by minimum confidence)
patterns = memory.procedural.recall(
    query_embedding,
    top_k=5,
    min_confidence=0.5
)
for p in patterns:
    print(f"{p['name']}: {p['steps']} (confidence: {p['confidence']:.2f})")

# Reinforce after success or failure (adjusts confidence +0.1 / -0.05)
memory.procedural.reinforce(procedure_id=1, success=True)
memory.procedural.reinforce(procedure_id=1, success=False)

Graph Collections

Graph collections store typed relationships between nodes with optional vector embeddings. They support persistent storage, BFS/DFS traversal, and node payload management.

import velesdb

db = velesdb.Database("./graph_data")

# Create a graph collection (schemaless by default)
graph = db.create_graph_collection("knowledge")

# With node embeddings for vector search over graph nodes
graph = db.create_graph_collection("kg", dimension=768, metric="cosine")

# With a strict schema (only predefined node/edge types)
from velesdb import GraphSchema
schema = GraphSchema.strict()
graph = db.create_graph_collection("typed_kg", schema=schema)

Adding edges and node data:

# Add edges (nodes are created implicitly by their IDs)
graph.add_edge({
    "id": 1, "source": 10, "target": 20,
    "label": "KNOWS",
    "properties": {"since": 2020, "context": "work"}
})
graph.add_edge({
    "id": 2, "source": 20, "target": 30,
    "label": "LIVES_IN",
    "properties": {"since": 2018}
})
graph.add_edge({
    "id": 3, "source": 10, "target": 30,
    "label": "LIVES_IN"
})

# Store properties on nodes
graph.upsert_node_payload(10, {"name": "Alice", "role": "engineer"})
graph.upsert_node_payload(20, {"name": "Bob", "role": "designer"})
graph.upsert_node_payload(30, {"name": "Paris", "type": "city"})

# Retrieve node properties
payload = graph.get_node_payload(10)
print(payload)  # {"name": "Alice", "role": "engineer"}

Querying the graph:

# Get all edges, or filter by label
all_edges = graph.get_edges()
knows_edges = graph.get_edges(label="KNOWS")

# Get outgoing/incoming edges for a node
outgoing = graph.get_outgoing(10)   # edges from Alice
incoming = graph.get_incoming(30)   # edges into Paris

# Node degree
in_deg, out_deg = graph.node_degree(10)

# List all nodes that have stored data
node_ids = graph.all_node_ids()

Graph traversal (BFS and DFS):

# BFS from Alice, max 3 hops, up to 100 results
results = graph.traverse_bfs(source_id=10, max_depth=3, limit=100)
for r in results:
    print(f"Reached node {r['target_id']} at depth {r['depth']}")

# Multi-source parallel BFS (starts from multiple nodes, deduplicates)
results = graph.traverse_bfs_parallel(
    source_ids=[10, 20, 30],
    max_depth=3,
    limit=100
)

# DFS with relationship type filter
results = graph.traverse_dfs(
    source_id=10,
    max_depth=2,
    rel_types=["KNOWS"]  # only follow KNOWS edges
)

# Vector search over graph nodes (requires dimension at creation)
results = graph.search_by_embedding(query_vector, k=10)
for r in results:
    print(f"Node {r['id']}: score {r['score']:.4f}")

VelesQL MATCH queries (Cypher-like graph pattern matching):

# Important: nodes must have _labels in their payload for label-based matching
graph.upsert_node_payload(10, {"_labels": ["Person"], "name": "Alice"})
graph.upsert_node_payload(20, {"_labels": ["Person"], "name": "Bob"})
graph.upsert_node_payload(30, {"_labels": ["City"], "name": "Paris"})

# MATCH query: find who Alice knows
results = graph.match_query(
    "MATCH (a:Person)-[:KNOWS]->(b:Person) RETURN a.name, b.name LIMIT 10"
)
for r in results:
    print(f"Node {r['node_id']} at depth {r['depth']}, projected: {r['projected']}")

# Hybrid: MATCH + vector similarity (requires embeddings)
results = graph.match_query(
    "MATCH (a:Person)-[:KNOWS]->(b) RETURN a, b LIMIT 10",
    vector=query_embedding,  # score each result by similarity
    threshold=0.5            # minimum similarity threshold
)

# VelesQL SELECT also works on GraphCollection
results = graph.query("SELECT * FROM kg WHERE name = 'Alice' LIMIT 5")

# Explain MATCH execution plan
plan = graph.explain("MATCH (a)-[:KNOWS]->(b) RETURN a, b LIMIT 10")
print(plan["tree"])

Persistence:

graph.flush()         # persist all changes to disk
print(graph.edge_count())  # total edges in the graph

VelesQL Parser API

VelesDB (since v1.7.2) exposes the VelesQL parser as a standalone Python API for query introspection, validation, and tooling integration. Parse any VelesQL statement into a ParsedStatement object and inspect its structure without executing it.

from velesdb import VelesQL

# Parse a query and inspect its structure
parsed = VelesQL.parse("SELECT id, title FROM documents WHERE category = 'tech' ORDER BY date DESC LIMIT 20")

print(parsed.collection_name)  # "documents"
print(parsed.columns)          # ["id", "title"]
print(parsed.limit)            # 20
print(parsed.offset)           # None
print(parsed.has_where_clause())   # True
print(parsed.has_order_by())       # True
print(parsed.has_vector_search())  # False
print(parsed.order_by)            # [("date", "DESC")]
print(parsed.is_select())         # True
print(parsed.is_match())          # False

Validate queries without parsing:

# Fast validation (no full parse tree)
VelesQL.is_valid("SELECT * FROM docs LIMIT 10")     # True
VelesQL.is_valid("SELEC * FROM docs")                # False

Inspect advanced query features:

# Vector search detection
parsed = VelesQL.parse("SELECT * FROM docs WHERE vector NEAR $q LIMIT 5")
print(parsed.has_vector_search())  # True

# MATCH (graph) queries
parsed = VelesQL.parse("MATCH (a:Person)-[:KNOWS]->(b:Person) RETURN a.name")
print(parsed.is_match())    # True
print(parsed.is_select())   # False

# GROUP BY, HAVING, JOINs, DISTINCT
parsed = VelesQL.parse("SELECT DISTINCT category, COUNT(*) FROM products GROUP BY category")
print(parsed.has_distinct())   # True
print(parsed.has_group_by())   # True
print(parsed.group_by)         # ["category"]

# JOIN inspection
parsed = VelesQL.parse(
    "SELECT * FROM orders JOIN products ON orders.product_id = products.id"
)
print(parsed.has_joins())   # True
print(parsed.join_count)    # 1

Error handling with typed exceptions:

from velesdb import VelesQL, VelesQLSyntaxError

try:
    parsed = VelesQL.parse("SELEC * FROM docs")
except VelesQLSyntaxError as e:
    print(f"Syntax error: {e}")

Key parameters for ParsedStatement:

Property / Method	Returns	Description
collection_name	str or None	FROM clause collection name
columns	list[str]	Selected columns (or ["*"])
limit	int or None	LIMIT value
offset	int or None	OFFSET value
order_by	list[tuple[str, str]]	(column, "ASC"/"DESC") pairs
group_by	list[str]	GROUP BY columns
table_alias	str or None	First FROM alias
table_aliases	list[str]	All aliases in scope
join_count	int	Number of JOIN clauses
is_select()	bool	True for SELECT queries
is_match()	bool	True for MATCH (graph) queries
has_where_clause()	bool	True if WHERE is present
has_vector_search()	bool	True if NEAR clause is present
has_order_by()	bool	True if ORDER BY is present
has_group_by()	bool	True if GROUP BY is present
has_having()	bool	True if HAVING is present
has_joins()	bool	True if JOINs are present
has_distinct()	bool	True if SELECT DISTINCT
has_fusion()	bool	True if USING FUSION is present

In-Memory GraphStore

For ad-hoc graph analysis that does not require disk persistence, use the in-memory GraphStore. It supports the same edge operations and traversal algorithms as persistent GraphCollection but runs entirely in memory.

from velesdb import GraphStore, StreamingConfig

# Create an in-memory graph
store = GraphStore()

# Add edges
store.add_edge({"id": 1, "source": 100, "target": 200, "label": "KNOWS"})
store.add_edge({"id": 2, "source": 200, "target": 300, "label": "KNOWS"})
store.add_edge({"id": 3, "source": 100, "target": 300, "label": "FOLLOWS"})

# Query edges by label (O(1) index lookup)
knows_edges = store.get_edges_by_label("KNOWS")

# Outgoing / incoming edges
outgoing = store.get_outgoing(100)
incoming = store.get_incoming(300)

# Filtered outgoing by label
friends_of_100 = store.get_outgoing_by_label(100, "KNOWS")

# Node degree
print(store.out_degree(100))  # 2
print(store.in_degree(300))   # 2

# Check edge existence
print(store.has_edge(1))      # True
store.remove_edge(1)
print(store.has_edge(1))      # False

# Total edges
print(store.edge_count())     # 2

BFS streaming traversal:

# Configure traversal bounds
config = StreamingConfig(
    max_depth=3,              # maximum hops from start node
    max_visited=10000,        # memory bound: max nodes to visit
    relationship_types=["KNOWS"]  # optional: filter by edge label
)

# Traverse the graph from node 100
results = store.traverse_bfs_streaming(100, config)
for r in results:
    print(f"Depth {r.depth}: {r.source} --[{r.label}]--> {r.target} (edge {r.edge_id})")

DFS traversal:

config = StreamingConfig(max_depth=2, max_visited=500)
results = store.traverse_dfs(100, config)
for r in results:
    print(f"Depth {r.depth}: {r.source} -> {r.target}")

TraversalResult attributes:

Attribute	Type	Description
depth	int	Hops from start node
source	int	Source node ID of the traversed edge
target	int	Target node ID
label	str	Edge relationship type
edge_id	int	Edge identifier

Storage Modes (Quantization)

Reduce memory usage with vector quantization:

# Full precision (default) - 4 bytes per dimension
collection = db.create_collection("full", dimension=768, storage_mode="full")

# SQ8 quantization - 1 byte per dimension (4x compression)
collection = db.create_collection("sq8", dimension=768, storage_mode="sq8")

# Binary quantization - 1 bit per dimension (32x compression)
collection = db.create_collection("binary", dimension=768, storage_mode="binary")

# Product quantization - 8-32x compression, best for large-scale datasets
collection = db.create_collection("pq", dimension=768, storage_mode="pq")

# RaBitQ - 32x compression with scalar correction, best for high-compression with good recall
collection = db.create_collection("rabitq", dimension=768, storage_mode="rabitq")

Mode	Alias	Memory per Vector (768D)	Compression	Best For
full	f32	3,072 bytes	1x	Maximum accuracy
sq8	int8	768 bytes	4x	Good accuracy/memory balance
binary	bit	96 bytes	32x	Edge/IoT, massive scale
pq	product_quantization, product-quantization	96-384 bytes	8-32x	Large-scale datasets, lossy
rabitq	—	96 bytes	32x	High-compression with good recall

Canonical names and aliases are interchangeable: storage_mode="f32" is equivalent to storage_mode="full".

Bulk Loading Performance

For large-scale data import, use upsert_bulk() instead of upsert():

Method	10k vectors (768D)	Notes
upsert()	~47s	Flushes after each batch
upsert_bulk()	~3s	Single flush + parallel HNSW

# Recommended for bulk import
import numpy as np

vectors = np.random.rand(10000, 768).astype('float32')
points = [{"id": i, "vector": v.tolist()} for i, v in enumerate(vectors)]

collection.upsert_bulk(points)  # Batch-optimized: parallel HNSW + single flush

Distance Metrics

Metric	Description	Use Case
cosine	Cosine similarity (default)	Text embeddings, normalized vectors
euclidean	Euclidean (L2) distance	Image features, spatial data
dot	Dot product	When vectors are pre-normalized
hamming	Hamming distance	Binary vectors, fingerprints, hashes
jaccard	Jaccard similarity	Set similarity, tags, recommendations

Performance

VelesDB is built in Rust with explicit SIMD optimizations:

Operation	Time (768D)	Throughput
Cosine	~33.1 ns	23.2 Gelem/s
Euclidean	~26.0 ns	34.1 Gelem/s
Dot Product	~21.7 ns	~35 Gelem/s
Hamming	~35.8 ns	--

System Benchmarks (Native Rust Engine)

Benchmark	Result
HNSW Search index-only (10K/768D)	~55 µs (k=10, Balanced mode)
End-to-end p50 (10K/384D, WAL ON)	~450 µs (canonical, recall ≥ 96%)
Recall@10 (Accurate)	100%
Insert throughput vs pgvector	3.8-7x faster (10K-100K vectors, internal benchmarks on i9-14900KF, not independently verified)

Numbers match docs/reference/promise-contract.json (the single source of truth for the README perf claims).

Measured with Criterion.rs on i9-14900KF. See benchmarks/ for methodology.

Connecting to velesdb-server

The velesdb Python package provides embedded (in-process) access to VelesDB. To connect to a remote velesdb-server instance (with optional API key authentication), use standard HTTP requests:

import requests

API_URL = "http://localhost:8080"
API_KEY = "my-secret-key"  # Only needed when server has auth enabled

headers = {"Authorization": f"Bearer {API_KEY}"}

# Search for similar vectors
response = requests.post(
    f"{API_URL}/collections/documents/search",
    json={"vector": [0.1, 0.2, ...], "top_k": 5},
    headers=headers,
)
results = response.json()

When the server has TLS enabled, use https:// and optionally pass verify=False for self-signed certificates.

See SERVER_SECURITY.md for server authentication and TLS setup.

Requirements

• Python 3.9+
• NumPy >= 1.20 — a hard runtime dependency, installed automatically by pip install velesdb
• No build toolchain required: prebuilt wheels bundle a self-contained Rust engine

License

Licensed under the VelesDB Core License 1.0 (source-available). The compiled wheel embeds the VelesDB engine and is governed by the Core License.

See LICENSE for details.

Links

• GitHub Repository
• Documentation
• Issue Tracker