de-console-client 0.0.0.dev53075887

Chromia Console API with Swagger UI

Chromia Console Client

A Python client library for interacting with the Chromia Console Vector Database API. This client provides a simple and type-safe way to manage vector collections, perform similarity searches, and work with embeddings.

Installation

pip install de_console_client

Quick Start

from de_console_client import Configuration, ApiClient, ChromiaVectorDBApi
from de_console_client.models import VectorCollection

# 1. Configure the client
configuration = Configuration(
    host='https://chromia-console.chromia.dev',
    brid='YOUR_BLOCKCHAIN_RID',
    network='mainnet',  # or 'testnet' or 'https://node0.testnet.chromia.com'
    chromia_api_key='YOUR_API_KEY'
)

# 2. Create the API client
with ApiClient(configuration) as api_client:
    client = ChromiaVectorDBApi(api_client)
    
    # 3. Create a collection
    collection = VectorCollection(
        name='my_first_collection',
        dimension=384,  # Vector dimension for embeddings
        index='hnsw_cosine',
        query_max_vector=10,
        store_batch_size=100
    )
    client.add_collection(collection)
    
    # 4. Store some text as vector embeddings (batch operation)
    client.create_vector_embedding_batch(
        'my_first_collection',
        [
            'The quick brown fox jumps over the lazy dog',
            'Chromia is a relational blockchain platform',
            'Vector databases enable semantic search'
        ]
    )
    
    # 5. Search using text query
    search_results = client.search_objects(
        collection='my_first_collection',
        body='tell me about Chromia',
        max_vectors=2       # return top 2 results
    )
    
    print('Search results:', search_results.payloads)

Configuration

The Configuration object accepts the following parameters:

Parameter	Type	Required	Description
host	str	Yes	Base URL of the Chromia Console API
brid	str	Yes	Blockchain RID (Identifier)
network	str	Yes	Network name (e.g., 'mainnet', 'testnet') or url
chromia_api_key	str	Yes	API key for authentication

Environment Variables

You can use environment variables to configure the client:

import os
from de_console_client import Configuration

configuration = Configuration(
    host=os.getenv('CHROMIA_CONSOLE_BASE_PATH'),
    brid=os.getenv('BRID'),
    network=os.getenv('NETWORK', 'mainnet'),
    chromia_api_key=os.getenv('API_KEY')
)

File storage quickstart

import base64
import os
from de_console_client import Configuration, ApiClient, ChromiaFileStorageApi
from de_console_client.models import (
    UploadFileRequest,
    UploadFileBatchRequest,
    DeleteFileBatchRequest,
    DownloadFileBatchRequest
)

# Configuration
configuration = Configuration(
    host='https://chromia-console.chromia.dev',
    brid='YOUR_BLOCKCHAIN_RID',  # Replace with your blockchain RID
    network='testnet',  # or 'mainnet'
    chromia_api_key='YOUR_API_KEY'  # Replace with your API key
)

# Create API client
with ApiClient(configuration) as api_client:
    client = ChromiaFileStorageApi(api_client)

    # 1. GET USER FILES
    response = client.get_user_files()
    print(f"Retrieved {len(response.files)} file(s)")

    if response.files:
        for idx, file in enumerate(response.files, 1):
            print(f"\n  File {idx}:")
            print(f"    Name: {file.name}")
            print(f"    Hash: {file.file_hash}")
            print(f"    Size: {file.size} bytes" if hasattr(file, 'size') else "    Size: N/A")
            print(f"    Public: {file.is_public}" if hasattr(file, 'is_public') else "    Public: N/A")

    # 2. UPLOAD FILE (JSON with Base64)
    test_content = "Hello, Chromia File Storage! This is a test file!"
    test_filename = "test_file2.txt"
    # Encode to base64
    encoded_data = base64.b64encode(test_content.encode('utf-8')).decode('utf-8')
    upload_request = UploadFileRequest(
        name=test_filename,
        data=encoded_data,
        is_public=True
    )
    response = client.upload_file(upload_request)
    print(f"  File Hash: {response.file_hash}")
    uploaded_file_hash = response.file_hash


    # 3. UPLOAD FILE (Multipart)
    multipart_filename = "test_multipart.txt"
    multipart_content = "This file was uploaded using multipart/form-data!"
    temp_file_path = multipart_filename
    with open(temp_file_path, 'w') as f:
        f.write(multipart_content)

    with open(temp_file_path, 'rb') as f:
        file_data = f.read()

    # Upload using multipart - expects tuple of (filename, bytes)
    response = client.upload_file_multipart(
        file=(multipart_filename, file_data)
    )
    print(f"  File Hash: {response.file_hash}")
    multipart_file_hash = response.file_hash


    # 4. DOWNLOAD FILE (Multipart)
    response = client.download_file(multipart_file_hash)
    print(f"  File Name: {response.name}")
    print(f"  File Hash: {response.file_hash}")
    print(f"  File Size: {response.size}")
    print(f"  File Type: {response.content_type}")


    # 5. DOWNLOAD FILE CONTENT (Raw)
    response = client.download_file_content_without_preload_content(multipart_file_hash)
    print(f"  Status Code: {response.status}")
    print(f"  Content-Type: {response.headers.get('Content-Type', 'N/A')}")


    # 6. UPLOAD FILES BATCH
    files_to_upload = [
        {
            "name": "batch_file_1.txt",
            "content": "This is batch file 11",
            "is_public": False
        },
        {
            "name": "batch_file_2.txt",
            "content": "This is batch file 22",
            "is_public": False
        }
    ]

    upload_requests = []
    for file_info in files_to_upload:
        encoded_data = base64.b64encode(file_info["content"].encode('utf-8')).decode('utf-8')
        upload_requests.append(
            UploadFileRequest(
                name=file_info["name"],
                data=encoded_data,
                is_public=file_info["is_public"]
            )
        )

    batch_request = UploadFileBatchRequest(files=upload_requests)
    response = client.upload_file_batch(batch_request)
    print(response)


    # 7. DOWNLOAD FILES BATCH
    batch_file_hashes = ['file hash 1', 'file hash 2..']
    batch_request = DownloadFileBatchRequest(file_hashes=batch_file_hashes)
    response = client.download_file_batch(batch_request)
    print(f"Batch download completed! {len(response.files)} file(s) downloaded")

    # # 8. DELETE FILE
    uploaded_file_hash = 'file hash to delete..'
    response = client.delete_file(uploaded_file_hash)
    print(response)

    # 9. DELETE FILES BATCH
    batch_file_hashes = ['file hash 1', 'file hash 2...']
    batch_request = DeleteFileBatchRequest(file_hashes=batch_file_hashes)
    response = client.delete_file_batch(batch_request)
    print(response)

API Reference

File storage operations

• get_user_files() Get user files
• upload_file(upload_file_request) - Upload a file
• upload_file_batch(upload_file_batch_request) - Upload multiple files in batch
• upload_file_batch_multipart(files) - Upload a files in batch using multipart form data
• upload_file_multipart(file) - Upload a file using multipart form data
• download_file(file_hash) - Download a file
• download_file_batch(download_file_batch_request) - Download multiple files in batch
• download_file_content(file_hash) - Download raw file content
• delete_file(file_hash) - Delete a file
• delete_file_batch(delete_file_batch_request) - Delete multiple files in batch

Collection Management

• get_collections() - Get all available collections
• add_collection(collection) - Create a new collection
• change_collection(changes) - Update collection configuration
• remove_collection(name) - Remove a collection

Vector Operations

• create_vector(collection, vector_request) - Create a single vector
• create_vector_batch(collection, batch_request) - Create multiple vectors
• create_vector_batch_chunked(collection, batch_request) - Create vectors in chunks
• delete_vector(collection, payload) - Delete a vector
• delete_vector_batch(collection, payloads) - Delete multiple vectors

Embedding Operations

• create_vector_embedding(collection, payload) - Create text embedding
• create_vector_embedding_batch(collection, payloads) - Create text embeddings batch
• create_vector_embedding_batch_chunked(collection, payloads) - Create text embeddings in chunks
• create_image_embedding(collection, image_request) - Create image embedding
• create_image_embedding_batch(collection, batch_request) - Create image embeddings batch

Search Operations

• search_objects(collection, query, max_distance=None, max_vectors=None) - Search by text
• get_closest_objects(collection, vector_request, max_distance=None, max_vectors=None) - Search by vector
• get_closest_objects_with_distance(collection, vector_request, max_distance=None, max_vectors=None) - Search with distances
• get_closest_objects_with_filter(collection, filter_request, max_distance=None, max_vectors=None) - Search with filter
• search_images(collection, image_request, max_distance=None, max_vectors=None) - Search similar images

Import Operations

• get_default_data_with_embedding() - Get default import data
• import_default_data(collection) - Import default data

Response Types

TransactionResultBody

All write operations return a TransactionResultBody:

{
    'tx_rid': str,         # Transaction ID
    'status': str,         # 'confirmed', 'waiting', 'pending', or 'rejected'
    'reject_reason': str   # Only present if status is 'rejected' (optional)
}

GetClosestResponse

Search operations return a GetClosestResponse:

{
    'payloads': [str]      # Array of matched payloads
}

PayloadDistance

Distance-aware searches return List[PayloadDistance]:

{
    'text': str,           # Payload text
    'distance': float      # Distance from query vector
}

Error Handling

The client uses standard Python exceptions. Handle errors using try-except:

from de_console_client import ApiClient, ChromiaVectorDBApi, Configuration
from de_console_client.rest import ApiException

configuration = Configuration(
    host='https://chromia-console.chromia.dev',
    brid='YOUR_BLOCKCHAIN_RID',
    network='mainnet',
    chromia_api_key='YOUR_API_KEY'
)

try:
    with ApiClient(configuration) as api_client:
        client = ChromiaVectorDBApi(api_client)
        response = client.create_vector(
            'my_collection',
            vector_request
        )
        print('Success:', response)
except ApiException as e:
    # API-specific error occurred
    print(f'API Exception: {e.status}')
    print(f'Reason: {e.reason}')
    print(f'Body: {e.body}')
except Exception as e:
    # General error occurred
    print(f'Error: {str(e)}')