audacity-sdk

Python client for the Audacity Investments LLM gateway. Exposes the same Amazon Bedrock Converse surface so teams migrating off Bedrock can swap the client constructor and keep the rest of their call-sites unchanged.

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Installation

pip install audacity-sdk

Zero runtime dependencies — stdlib only. Requires Python 3.9+.

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Quick start

Non-streaming (Converse)

from audacity import Audacity

client = Audacity(api_key="audacity_api_…")   # or set AUDACITY_API_KEY

response = client.converse(
    modelId="gpt-5.4-mini",
    messages=[{"role": "user", "content": [{"text": "What is 2+2?"}]}],
    inferenceConfig={"maxTokens": 256, "temperature": 0.0},
)

print(response["output"]["message"]["content"][0]["text"])
print(response["stopReason"])   # "end_turn"
print(response["usage"])        # {"inputTokens": …, "outputTokens": …, "totalTokens": …}
print(response["metrics"])      # {"latencyMs": …}

Streaming (ConverseStream)

from audacity import Audacity

client = Audacity()

stream_response = client.converse_stream(
    modelId="gpt-5.4-mini",
    messages=[{"role": "user", "content": [{"text": "Write me a haiku."}]}],
)

for event in stream_response["stream"]:         # boto3 parity: response["stream"]
    if "contentBlockDelta" in event:
        delta = event["contentBlockDelta"]["delta"]
        print(delta.get("text", ""), end="", flush=True)

print()  # newline at end

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Migrating from boto3 bedrock-runtime

# BEFORE — boto3
import boto3
client = boto3.client("bedrock-runtime", region_name="us-east-1")

response = client.converse(
    modelId="anthropic.claude-3-sonnet-20240229-v1:0",
    messages=[{"role": "user", "content": [{"text": "Hi"}]}],
)

# AFTER — Audacity SDK
from audacity import Audacity
client = Audacity(api_key="audacity_api_…")   # only line that changes

response = client.converse(
    modelId="gpt-5.4-mini",                   # use Audacity model ID
    messages=[{"role": "user", "content": [{"text": "Hi"}]}],
)

Streaming diff:

# BEFORE — boto3
stream_resp = client.converse_stream(modelId=…, messages=…)
for event in stream_resp["stream"]:
    …

# AFTER — Audacity SDK (identical call-site)
stream_resp = client.converse_stream(modelId=…, messages=…)
for event in stream_resp["stream"]:
    …

---

Tool use

response = client.converse(
    modelId="gpt-5.4-mini",
    messages=[{"role": "user", "content": [{"text": "What's the weather in NYC?"}]}],
    toolConfig={
        "tools": [{
            "toolSpec": {
                "name": "get_weather",
                "description": "Get current weather for a city",
                "inputSchema": {
                    "json": {
                        "type": "object",
                        "properties": {"city": {"type": "string"}},
                        "required": ["city"],
                    }
                },
            }
        }],
        "toolChoice": {"auto": {}},
    },
)

# Assistant responds with a tool call
tool_use = response["output"]["message"]["content"][0]["toolUse"]
print(tool_use["name"])   # "get_weather"
print(tool_use["input"])  # {"city": "NYC"}

# Send tool result back
response2 = client.converse(
    modelId="gpt-5.4-mini",
    messages=[
        {"role": "user",  "content": [{"text": "What's the weather in NYC?"}]},
        {"role": "assistant", "content": response["output"]["message"]["content"]},
        {"role": "user",  "content": [
            {"toolResult": {
                "toolUseId": tool_use["toolUseId"],
                "content": [{"text": "Sunny, 72°F"}],
            }}
        ]},
    ],
)

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Images (vision models)

Bedrock-style image content blocks are supported in user messages. Pass raw bytes (encoded for you) or a URL (Audacity extension):

with open("chart.png", "rb") as f:
    image_bytes = f.read()

response = client.converse(
    modelId="gpt-5.5",
    messages=[{
        "role": "user",
        "content": [
            {"text": "What does this chart show?"},
            {"image": {"format": "png", "source": {"bytes": image_bytes}}},
        ],
    }],
)

# Or reference a hosted image directly (not available in Bedrock):
# {"image": {"format": "jpeg", "source": {"url": "https://example.com/photo.jpg"}}}

format is one of png, jpeg, gif, webp. Use a vision-capable model.

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Video (Gemini models)

Bedrock-style video content blocks are supported in user messages. Video input is only available on the Gemini family (gemini-2.5-flash, gemini-2.5-pro, gemini-3-flash-preview); every other model rejects video with an HTTP 400.

with open("demo.mp4", "rb") as f:
    video_bytes = f.read()

response = client.converse(
    modelId="gemini-2.5-flash",
    messages=[{
        "role": "user",
        "content": [
            {"text": "Summarize what happens in this video."},
            {"video": {"format": "mp4", "source": {"bytes": video_bytes}}},
        ],
    }],
)

format is one of mp4, mov, mkv, webm, flv, mpeg, mpg, wmv, three_gp. Inline (bytes) video is base64-encoded into the request — keep those files under ~20 MB.

For larger files (up to 1 GB), upload once and reference by URI:

upload = client.files.upload("large-demo.mp4", content_type="video/mp4")

response = client.converse(
    modelId="gemini-2.5-flash",
    messages=[{
        "role": "user",
        "content": [
            {"text": "Summarize this video."},
            {"video": {"format": "mp4", "source": {"uri": upload["uri"]}}},
        ],
    }],
)

client.files.upload(data, content_type=…) accepts raw bytes or a file path and returns {"file_id", "upload_url", "uri", "expires_at"}. Uploads are resumable: the helper streams the file in 8 MB chunks over a GCS resumable session and automatically resumes from the last confirmed byte after a network drop (bounded retries). Uploaded files are transient inference inputs: they expire after ~24 hours and are scoped to your API key's organization; the signed upload URL itself is valid ~15 minutes.

To control video token cost, set mediaResolution on the request — "low" processes video at ~4x fewer tokens (Gemini models; ignored elsewhere):

response = client.converse(
    modelId="gemini-2.5-flash",
    mediaResolution="low",
    messages=[…],
)

---

Image generation

Generate images from a text prompt with client.images.generate:

import base64

result = client.images.generate(
    model="gpt-image-1",
    prompt="A watercolor painting of a fox in a snowy forest",
    size="1024x1024",
    response_format="b64_json",  # or "url" (default) for a signed download link
)

with open("fox.png", "wb") as f:
    f.write(base64.b64decode(result["data"][0]["b64_json"]))
# With response_format="url": result["data"][0]["url"] (signed link, expires ~24 h)

Optional parameters: n (1–10 images), size ("WxH", model-dependent), quality (e.g. "standard", "hd"), and user. The response dict carries created, data (each entry has url or b64_json, plus revised_prompt when the provider rewrites your prompt) and optional usage token counts. Errors map to the same exception classes as converse (401 → AccessDeniedException, 429 → ThrottlingException, spend cap → ServiceQuotaExceededException).

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Prompt caching

Place a Bedrock-style cachePoint block after the stable prefix you want the provider to cache (system prompt, large documents). Everything up to the cache point is cached provider-side on Claude models; OpenAI/Gemini models cache automatically and ignore the marker. At most 4 cache points per request.

response = client.converse(
    modelId="claude-sonnet-4-5",
    system=[
        {"text": long_system_prompt},
        {"cachePoint": {"type": "default"}},
    ],
    messages=[{
        "role": "user",
        "content": [
            {"text": big_reference_document},
            {"cachePoint": {"type": "default"}},
            {"text": "Summarise the key risks."},
        ],
    }],
)

# Cache activity is reported in usage (Bedrock names):
print(response["usage"]["cacheReadInputTokens"])   # tokens served from cache
print(response["usage"]["cacheWriteInputTokens"])  # tokens written to cache

A cachePoint with nothing before it in the same message is silently ignored.

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Error handling

from audacity import Audacity
from audacity.exceptions import (
    MissingApiKeyError,
    AccessDeniedException,
    ThrottlingException,
    ServiceQuotaExceededException,
    ModelStreamErrorException,
    SdkError,
)

client = Audacity()

try:
    response = client.converse(modelId="gpt-5.4-mini", messages=[…])
except client.exceptions.ThrottlingException as e:
    print(f"Rate limited: {e.message}, retry after {e.retry_after_seconds}s")
except client.exceptions.AccessDeniedException as e:
    print(f"Auth error [{e.status_code}]: {e.message}")
except client.exceptions.ServiceQuotaExceededException as e:
    print(f"Budget exhausted: {e.message}")
except SdkError as e:
    print(f"Network/decode error: {e.message}")

Streaming errors surface when you iterate the stream:

try:
    for event in stream_response["stream"]:
        …
except client.exceptions.ModelStreamErrorException as e:
    print(f"Stream broken: {e.message}")

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Configuration & environment variables

Constructor parameter	Environment variable	Default
api_key	AUDACITY_API_KEY	— (required)
base_url	AUDACITY_BASE_URL	https://portal.audacityinvestments.com
timeout	—	120.0 seconds
max_retries	—	2 (3 total attempts)

client = Audacity(
    api_key="audacity_api_…",
    base_url="https://portal.audacityinvestments.com",
    timeout=60.0,
    max_retries=3,
)

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Retry behaviour

The SDK automatically retries transient failures with jittered exponential backoff (capped at 20 s):

• Retried: ThrottlingException (429), ModelTimeoutException (408), ServiceUnavailableException (502/503/504), InternalServerException (500), network errors.
• Never retried: AccessDeniedException, ValidationException, ResourceNotFoundException, ServiceQuotaExceededException (including BUDGET_EXCEEDED at 429/402), or any 4xx except 408/429.
• Streaming: retries apply only before the first SSE byte; after that, connection drops raise ModelStreamErrorException.