Metadata-Version: 2.4
Name: veculo
Version: 0.2.3
Summary: Python SDK for Veculo — AI-native managed graph+vector database
Project-URL: Homepage, https://veculo.com
Project-URL: Documentation, https://docs.veculo.com
Project-URL: Repository, https://github.com/sentrius/veculo-python
Project-URL: Issues, https://github.com/sentrius/veculo-python/issues
Author-email: Sentrius LLC <support@sentrius.ai>
License-Expression: Apache-2.0
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Database
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Typing :: Typed
Requires-Python: >=3.9
Requires-Dist: httpx>=0.24
Requires-Dist: pydantic>=2.0
Provides-Extra: all
Requires-Dist: google-cloud-aiplatform>=1.38; extra == 'all'
Requires-Dist: openai>=1.0; extra == 'all'
Requires-Dist: sentence-transformers>=2.2; extra == 'all'
Provides-Extra: local
Requires-Dist: sentence-transformers>=2.2; extra == 'local'
Provides-Extra: openai
Requires-Dist: openai>=1.0; extra == 'openai'
Provides-Extra: vertexai
Requires-Dist: google-cloud-aiplatform>=1.38; extra == 'vertexai'
Description-Content-Type: text/markdown

<!--

    Licensed to the Apache Software Foundation (ASF) under one
    or more contributor license agreements.  See the NOTICE file
    distributed with this work for additional information
    regarding copyright ownership.  The ASF licenses this file
    to you under the Apache License, Version 2.0 (the
    "License"); you may not use this file except in compliance
    with the License.  You may obtain a copy of the License at

      https://www.apache.org/licenses/LICENSE-2.0

    Unless required by applicable law or agreed to in writing,
    software distributed under the License is distributed on an
    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
    KIND, either express or implied.  See the License for the
    specific language governing permissions and limitations
    under the License.

-->
# Veculo Python SDK

Python client for [Veculo](https://veculo.com) — a managed graph+vector database built on Apache Accumulo.

## Installation

```bash
pip install veculo
```

## Quick Start

```python
from veculo import VeculoClient

client = VeculoClient(
    api_key="vk-...",
    cluster_id="cl-a7f3b2",
)

# Insert vertices with vector embeddings
client.put_vertex(
    id="doc-1",
    label="document",
    properties={"title": "Quarterly Report", "author": "Alice"},
    embedding=[0.12, 0.45, 0.78, 0.33, ...],  # your embedding vector
    visibility="INTERNAL",
)

client.put_vertex(
    id="doc-2",
    label="document",
    properties={"title": "Project Plan", "author": "Bob"},
    embedding=[0.11, 0.44, 0.80, 0.31, ...],
)

# Create edges between vertices
client.put_edge(
    source="doc-1",
    target="doc-2",
    edge_type="references",
    properties={"section": "appendix"},
)

# Retrieve a vertex
vertex = client.get_vertex(id="doc-1")
print(vertex)

# Hybrid query: vector similarity + graph traversal
results = client.query(
    embedding=[0.12, 0.44, 0.79, 0.32, ...],  # query vector
    top_k=5,
    edge_type="references",
    depth=2,
    authorizations="INTERNAL",
)

for match in results["results"]:
    print(f"{match['vertex_id']}: {match['score']:.3f}")
```

## Environment Variables

Instead of passing credentials to the constructor, you can set:

| Variable | Description |
|---|---|
| `VECULO_API_KEY` | API key for authentication |
| `VECULO_ENDPOINT` | API endpoint (default: `https://api.veculo.com`) |
| `VECULO_CLUSTER_ID` | Target cluster ID |

```python
# With env vars set, no arguments needed:
client = VeculoClient()
```

## CLI

The SDK includes a command-line interface:

```bash
# Save connection configuration
veculo connect --endpoint https://api.veculo.com --api-key vk-... --cluster-id cl-a7f3b2

# Check cluster status
veculo status

# Insert a vertex
veculo put-vertex --id alice --label person --property name=Alice --property role=engineer

# Retrieve a vertex
veculo get-vertex --id alice

# Create an edge
veculo put-edge --source alice --target bob --type knows

# Run a hybrid query
veculo query --embedding "0.1,0.2,0.3,0.4" --top-k 10
```

Configuration is stored in `~/.veculo/config.json`.

## Error Handling

```python
from veculo import VeculoClient, VeculoError, NotFoundError, AuthenticationError

client = VeculoClient(api_key="vk-...", cluster_id="cl-a7f3b2")

try:
    vertex = client.get_vertex(id="nonexistent")
except NotFoundError:
    print("Vertex does not exist")
except AuthenticationError:
    print("Invalid or expired API key")
except VeculoError as e:
    print(f"API error {e.status_code}: {e.message}")
```

## Visibility Labels

Veculo supports Accumulo-style cell-level security via visibility expressions:

```python
# Write with visibility
client.put_vertex(
    id="doc:internal-report",
    label="document",
    properties={"title": "Q1 Revenue Analysis"},
    visibility="finance&internal",
)

# Read with authorizations
vertex = client.get_vertex(
    id="doc:internal-report",
    authorizations="finance,internal",
)
```

## Embeddings

Veculo supports multiple ways to generate vector embeddings:

### Client-side (bring your own API key)

```python
from veculo import VeculoClient
from veculo.embeddings import OpenAIEmbeddings

client = VeculoClient(api_key="vk-...", cluster_name="production")
client.set_embedder(OpenAIEmbeddings(api_key="sk-..."))

# Automatically generates embedding from text
client.put_vertex_with_text(
    id="doc:report-q1",
    text="Q1 revenue exceeded expectations with 40% YoY growth",
    label="document",
    properties={"quarter": "Q1", "year": "2026"},
)
```

Other providers:

```python
from veculo.embeddings import VertexAIEmbeddings, SentenceTransformerEmbeddings

# Google Vertex AI
client.set_embedder(VertexAIEmbeddings(project="my-project"))

# Local (no API key needed)
client.set_embedder(SentenceTransformerEmbeddings())
```

Install extras: `pip install 'veculo[openai]'`, `pip install 'veculo[vertexai]'`, or `pip install 'veculo[local]'`

### Server-side (Veculo-managed, billed separately)

```python
# Veculo generates the embedding for you via Vertex AI
client.put_vertex_with_text(
    id="doc:report-q1",
    text="Q1 revenue exceeded expectations",
    label="document",
    embed_server_side=True,  # billed per request
)
```

## AI-Native Queries

### Natural Language Query

Ask questions in plain English — the SDK translates them into graph queries via LLM:

```python
result = client.nl_query(
    question="Which documents reference the Q1 report?",
    authorizations="internal",
)

print(result["query_plan"]["explanation"])
for step_result in result["results"]:
    print(step_result)
```

### Graph-Augmented RAG

Retrieval-Augmented Generation that combines vector search with graph context:

```python
answer = client.rag_query(
    question="What were the key findings in the Q1 analysis?",
    context_hops=2,          # expand graph 2 hops for richer context
    model="claude-sonnet-4-20250514",  # optional model override
    top_k=10,
)

print(answer["answer"])
print("Sources:", answer["sources"])  # vertex IDs cited
```

## Bulk Operations

Insert many vertices or edges in a single batch:

```python
client.put_vertices_bulk([
    {"id": "doc:1", "label": "document", "properties": {"title": "Report A"}},
    {"id": "doc:2", "label": "document", "properties": {"title": "Report B"}},
    {"id": "doc:3", "label": "document", "properties": {"title": "Report C"}},
])

client.put_edges_bulk([
    {"source": "doc:1", "target": "doc:2", "edge_type": "references"},
    {"source": "doc:2", "target": "doc:3", "edge_type": "references"},
])
```

## Configuration

### Auto-Embed

Enable automatic embedding generation for new text vertices:

```python
client.configure_auto_embed(
    model="text-embedding-005",
    provider="vertex-ai",
    text_properties=["description", "content"],
)
```

### Semantic Edges

Enable automatic similarity edge creation during compaction:

```python
client.configure_semantic_edges(
    similarity_threshold=0.85,
    max_edges_per_vertex=10,
)
```

## Insights

Query AI-derived analytics:

```python
# Anomalous vertices (outliers by embedding distance)
anomalies = client.get_anomalies(authorizations="internal")

# Top vertices by PageRank
ranks = client.get_top_ranked()

# Pending processing queue status
status = client.get_processing_status()
print(f"Embeddings pending: {status['auto_embed']}")
```

## License

Apache License 2.0
