Metadata-Version: 2.4
Name: cognitor
Version: 0.3.0
Summary: Python client for the Cognitor search platform API.
Author-email: Riccardo Lucato <riccardo@tanaos.com>
License: Copyright (c) 2026 Riccardo Lucato
        
        Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
        
        
Project-URL: homepage, https://github.com/tanaos/cognitor-python
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3.10
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Operating System :: OS Independent
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE.md
Requires-Dist: httpx>=0.27.0
Requires-Dist: setuptools>=82.0.1
Dynamic: license-file

# cognitor-python

Python SDK for [Cognitor](https://github.com/tanaos/cognitor).

## Installation

```bash
pip install cognitor
```

## Quick start

```python
from cognitor import Cognitor

with Cognitor("http://localhost:7530", api_key="your-api-key") as client:
    print(client.ping())
    print(client.health_ready())  # "ready" or "loading"
```

The `api_key` parameter is optional, omit it if your [cognitor instance](https://github.com/tanaos/cognitor) does not require authentication.

## Usage

### Collections

```python
# Create a collection (server-side embedding)
collection = client.create_collection(
    "my-collection",
    emb_model="text-embedding-3-small",
)

# Create a collection with a fixed vector dimension (client-side embedding)
collection = client.create_collection("my-collection", dim=1536)

# List all collections
collections = client.list_collections()

# Get a single collection
collection = client.get_collection("my-collection")

# Delete a collection
client.delete_collection("my-collection")
```

### Documents

```python
# Add documents (texts are embedded server-side when emb_model is set)
ids = client.add_documents(
    "my-collection",
    texts=["Hello world", "Cognitor is a vector store"],
    metadatas=[{"source": "docs"}, {"source": "docs"}],
)

# Add documents with explicit vectors (client-side embedding)
ids = client.add_documents(
    "my-collection",
    texts=["Hello world"],
    metadatas=[{"source": "docs"}],
    vectors=[[0.1, 0.2, ...]],
)

# Add a large number of documents in batches
ids = client.bulk_add_documents(
    "my-collection",
    texts=[...],
    metadatas=[...],
    batch_size=512,
)

# List documents (paginated)
page = client.list_documents("my-collection", offset=0, limit=50)
print(page.total, page.documents)

# Get a single document
doc = client.get_document("my-collection", doc_id)

# Update document metadata
doc = client.update_document_metadata("my-collection", doc_id, {"source": "updated"})

# Delete a document
client.delete_document("my-collection", doc_id)
```

### Search

```python
# Search by text (requires server-side embedding model)
response = client.search("my-collection", query_text="Hello", top_k=10)

# Search by vector
response = client.search("my-collection", query_vector=[0.1, 0.2, ...], top_k=10)

# Filter results by metadata
response = client.search(
    "my-collection",
    query_text="Hello",
    filters={"source": "docs"},
)

# Include vectors in results
response = client.search("my-collection", query_text="Hello", include_vectors=True)

for hit in response.results:
    print(f"score={hit.score:.4f}  text={hit.text!r}")
```

### Admin

```python
# Compact a collection (removes deleted vectors)
result = client.compact("my-collection")
print(result.deleted_count, "vectors removed")
```

### Health

```python
# Readiness probe status
status = client.health_ready()
if status == "ready":
    print("Server is ready")
else:
    print("Server is still loading models")
```

## Connection management

Use the client as a context manager (recommended) to ensure the underlying HTTP connection is closed:

```python
with Cognitor("http://localhost:7530") as client:
    ...
```

Or close it manually:

```python
client = Cognitor("http://localhost:7530")
try:
    ...
finally:
    client.close()
```
