aibridgecore 1.5.10

Bridge for LLM"s

aibridgecore

aibridgecore is a Python SDK for working with multiple AI providers through a consistent set of text-generation utilities, prompt management, reusable variables, structured outputs, queue-backed execution, and provider-specific image and video modules.

Overview

• Multi-provider text generation with a shared high-level pattern
• Structured outputs for JSON, CSV, and XML workflows
• Stored prompts and reusable variables backed by SQL or MongoDB
• Redis-based queue support for asynchronous processing
• Provider-specific image generation APIs
• Provider-specific video generation APIs

Installation

pip install aibridgecore

Python 3.9 or later is required.

Configuration

Set the config file path with AIBRIDGE_CONFIG.

export AIBRIDGE_CONFIG=/absolute/path/to/aibridge_config.yaml

Minimal configuration example:

open_ai:
  equal:
    - YOUR_OPENAI_API_KEY

database: sql
message_queue: redis
redis_host: localhost
redis_port: 6379
group_name: my_consumer_group
stream_name: my_stream
no_of_threads: 1

You can also configure the SDK programmatically:

from aibridgecore import SetConfig

SetConfig.set_api_key(
    ai_service="open_ai",
    key="YOUR_OPENAI_API_KEY",
    priority="equal",
)

SetConfig.set_db_confonfig(
    database="sql",
    database_name=None,
    database_uri=None,
)

SetConfig.redis_config(
    redis_host="localhost",
    redis_port=6379,
    group_name="my_consumer_group",
    stream_name="my_stream",
    no_of_threads=1,
)

Supported ai_service keys:

• open_ai
• stable_diffusion
• cohere_api
• ai21_api
• gemini_ai
• anthropic
• grok
• deepseek
• mistral
• alibaba
• kimi

Text Generation

Primary text providers exported from aibridgecore:

• OpenAIService
• GeminiAIService
• AnthropicService
• CohereApi
• AI21labsText
• OllamaService
• GrokService
• DeepseekService
• MistralService
• AlibabaService
• KimiService

Basic generation example:

import json

from aibridgecore import OpenAIService

schema = json.dumps(
    {
        "summary": ["short summary bullet"],
        "keywords": ["keyword"],
    }
)

response = OpenAIService.generate(
    prompts=["Summarize {{topic}} for an engineering update."],
    prompt_data=[{"topic": "queue-backed AI processing"}],
    output_format=["json"],
    format_strcture=[schema],
    model="gpt-3.5-turbo",
    max_tokens=800,
    temperature=0.3,
    context=[{"role": "system", "context": "Be concise and factual."}],
)

print(response["items"]["response"][0]["data"][0])

Streaming is also available on the text provider classes that expose generate_stream(...):

from aibridgecore import OpenAIService

stream = OpenAIService.generate_stream(
    prompts=["Write a short release note for this SDK update."],
    model="gpt-3.5-turbo",
    context=[{"role": "system", "context": "Keep the tone professional."}],
)

for chunk in stream:
    print(chunk)

Structured output notes:

• output_format accepts json, csv, or xml
• format_strcture should match the expected structure for each prompt
• context entries use role and context

Prompt and Variable Management

Use prompt templates when the shape of a prompt is reusable, and use variables when part of that prompt should come from a named stored dataset.

• prompt_data injects request-specific values directly into a prompt template
• variables maps template placeholders to previously saved variable keys

Example:

from aibridgecore import PromptInsertion, VariableInsertion

saved_variable = VariableInsertion.save_variables(
    var_key="release_tones",
    var_value=["clear", "professional", "direct"],
)

saved_prompt = PromptInsertion.save_prompt(
    name="release_summary",
    prompt="Write a {{tone}} summary about {{topic}}.",
    prompt_data={"topic": "this release"},
    variables={"tone": "release_tones"},
)

prompt_record = PromptInsertion.get_prompt(id=saved_prompt["id"])
all_prompts = PromptInsertion.get_all_prompt(page=1)

variable_record = VariableInsertion.get_variable(id=saved_variable["id"])
all_variables = VariableInsertion.get_all_variable(page=1)

Common operations:

from aibridgecore import PromptInsertion, VariableInsertion

PromptInsertion.update_prompt(
    id="PROMPT_ID",
    name="updated_release_summary",
    prompt_data={"topic": "the latest SDK release"},
    variables={"tone": "release_tones"},
)

VariableInsertion.update_variables(
    id="VARIABLE_ID",
    var_key="release_tones",
    var_value=["clear", "concise", "technical"],
)

Message Queue Support

Queue-backed execution is available through Redis.

When message_queue=True, generation returns a response id instead of the final model output:

from aibridgecore import FetchAIResponse, MessageQ, OpenAIService

MessageQ.mq_deque()

queued = OpenAIService.generate(
    prompts=["Generate a short deployment checklist."],
    model="gpt-3.5-turbo",
    message_queue=True,
)

response_id = queued["response_id"]
result = FetchAIResponse.get_response(id=response_id)
print(result)

Image Generation

Image APIs are currently imported from aibridgecore.image.*, not from the top-level package.

Available image providers:

• aibridgecore.image.providers.openai.OpenAIImageProvider
• aibridgecore.image.providers.stability.StabilityImageProvider
• aibridgecore.image.providers.google_imagen.GoogleImagenProvider
• aibridgecore.image.providers.alibaba_wan_image.AlibabaWanImageProvider

The image request contract supports:

• text2img
• edit
• img2img

Mode support depends on the provider and model you use.

Example:

from aibridgecore.image.contracts import ImageGenerationRequest, ImageMode
from aibridgecore.image.providers.openai import OpenAIImageProvider

provider = OpenAIImageProvider(api_key="YOUR_OPENAI_API_KEY")

request = ImageGenerationRequest(
    prompts=["A clean product render on a studio background"],
    model="gpt-image-1",
    n=1,
    size="1024x1024",
    mode=ImageMode.TEXT_TO_IMAGE,
)

response = provider.generate(request)
artifact = response.results[0].images[0]

with open("generated_image.png", "wb") as file:
    file.write(artifact.content)

For edit and image-to-image flows, pass images=[...] and optionally masks=[...] in the request.

Video Generation

Video APIs are currently imported from aibridgecore.video.*, not from the top-level package.

Available video providers:

• aibridgecore.video.providers.openai_sora.OpenAISoraProvider
• aibridgecore.video.providers.google_veo.GoogleVeoProvider
• aibridgecore.video.providers.alibaba_wan.AlibabaWanProvider
• aibridgecore.video.providers.stability.StabilityVideoProvider
• aibridgecore.video.providers.luma.LumaVideoProvider

Video generation is asynchronous. You start a job, store the provider job id, and poll for status until the result is ready.

Current request modes:

• text2video
• img2video
• video2video is planned but not enabled yet

Example:

from aibridgecore.video.contracts import VideoGenerationRequest, VideoMode
from aibridgecore.video.providers.openai_sora import OpenAISoraProvider

provider = OpenAISoraProvider(api_key="YOUR_OPENAI_API_KEY")

request = VideoGenerationRequest(
    model="YOUR_VIDEO_MODEL",
    prompt="A slow cinematic drone shot over a rainforest canopy at sunrise",
    duration_seconds=5,
    aspect_ratio="16:9",
    mode=VideoMode.TEXT_TO_VIDEO.value,
)

job = provider.start_generation(request)
status = provider.check_status(job.provider_job_id)

print(job)
print(status)

For image-to-video workflows, set mode=VideoMode.IMG_TO_VIDEO.value and provide images=[...] in the request.