Ad network that delivers ads to the LLM's response
Project description
Adstract SDK for Python
Adstract integrates ad-enhanced prompts into LLM applications and provides the acknowledgment flow required to close the ad cycle after the final model response is produced.
Official Documentation
Full documentation is available at Adstract Documentation.
Install
pip install adstractai
Authentication
Pass an API key when initializing the client, or set the ADSTRACT_API_KEY
environment variable.
export ADSTRACT_API_KEY="adpk_live_123"
from adstractai import Adstract
client = Adstract()
Quickstart
from adstractai import Adstract, AdRequestContext
client = Adstract(api_key="adpk_live_123")
result = client.request_ad(
prompt="How do I improve analytics in my LLM app?",
context=AdRequestContext(
session_id="sess-1",
user_agent=(
"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
"(KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36"
),
user_ip="203.0.113.24",
),
)
prompt_for_model = result.prompt
llm_response = "Your final LLM response here"
ack = client.acknowledge(
enhancement_result=result,
llm_response=llm_response,
)
if ack is not None:
print(ack.ad_ack_id)
print(ack.status)
print(ack.success)
client.close()
Core Flow
The SDK integration flow has two main steps:
- Call
request_ad()orrequest_ad_async()to get anEnhancementResult. - After your LLM produces its final response, call
acknowledge()oracknowledge_async()to close the ad cycle.
EnhancementResult.prompt always gives you the prompt your application should
use next:
- enhanced prompt when ad injection succeeds;
- original prompt when the SDK falls back.
Required Request Context
request_ad() and request_ad_async() require an AdRequestContext with:
session_iduser_agentuser_ip
from adstractai.models import AdRequestContext
context = AdRequestContext(
session_id="sess-1",
user_agent="Mozilla/5.0 (X11; Linux x86_64)",
user_ip="203.0.113.24",
)
Missing required values raise MissingParameterError, or are captured in
EnhancementResult.error when raise_exception=False.
Optional Context
Pass OptionalContext to include optional targeting signals.
from adstractai import Adstract, AdRequestContext, OptionalContext
client = Adstract(api_key="adpk_live_123")
result = client.request_ad(
prompt="How do I improve analytics in my LLM app?",
context=AdRequestContext(
session_id="sess-1",
user_agent="Mozilla/5.0 (X11; Linux x86_64)",
user_ip="203.0.113.24",
),
optional_context=OptionalContext(
country="US",
region="California",
city="San Francisco",
asn=15169,
age=30,
gender="female",
),
)
Validation rules:
| Field | Rule |
|---|---|
age |
Integer between 0 and 120 |
gender |
One of "male", "female", "other" |
country |
ISO 3166-1 alpha-2 code such as "US" |
Enhancement Results
request_ad() and request_ad_async() return EnhancementResult.
Important fields:
prompt: the prompt your application should pass to the modelsession_id: the request session identifierad_response: parsed backend response when availablesuccess: whether ad enhancement succeedederror: captured failure whenraise_exception=False
if result.success:
prompt_for_model = result.prompt
else:
print(result.error)
prompt_for_model = result.prompt
Acknowledgment Results
acknowledge() and acknowledge_async() return AdAckResponse on successful
acknowledgment.
AdAckResponse includes:
ad_ack_idstatussuccess
success means the acknowledgment itself completed successfully:
status="ok"->success=Truestatus="no_ad_used"->success=Truestatus="recoverable_error"->success=False
If enhancement_result.success is False, acknowledgment is skipped and the
method returns None.
Error Handling
By default, SDK methods raise on failure.
request_ad(..., raise_exception=True)acknowledge(..., raise_exception=True)
Set raise_exception=False if you want a fallback-first integration flow.
Enhancement exceptions
from adstractai.errors import (
AdEnhancementError,
AuthenticationError,
DuplicateAdRequestError,
NoFillError,
PromptRejectedError,
)
result = client.request_ad(
prompt="My prompt",
context=context,
raise_exception=False,
)
if result.success:
print(result.prompt)
elif isinstance(result.error, PromptRejectedError):
print("Prompt not suitable for ad injection")
elif isinstance(result.error, NoFillError):
print("No ad inventory available")
elif isinstance(result.error, DuplicateAdRequestError):
print("This message already has an ad request")
elif isinstance(result.error, AuthenticationError):
print("Authentication failed")
elif isinstance(result.error, AdEnhancementError):
print(result.error)
Acknowledgment exceptions
from adstractai.errors import (
AdResponseNotFoundError,
AuthenticationError,
DuplicateAcknowledgmentError,
UnsuccessfulAdResponseError,
)
try:
ack = client.acknowledge(
enhancement_result=result,
llm_response="Final response",
)
except AuthenticationError:
print("Authentication failed")
except AdResponseNotFoundError:
print("The ad response was not found")
except UnsuccessfulAdResponseError:
print("The ad response was not created by a successful enhancement")
except DuplicateAcknowledgmentError:
print("This response was already acknowledged")
Wrapping Type
Control how ads are wrapped in the enhanced prompt. The default is "xml".
client = Adstract(api_key="adpk_live_123", wrapping_type="markdown")
Supported values:
"xml""plain""markdown"
Async Usage
import asyncio
from adstractai import Adstract, AdRequestContext
async def main() -> None:
client = Adstract(api_key="adpk_live_123")
result = await client.request_ad_async(
prompt="Need performance tips",
context=AdRequestContext(
session_id="sess-99",
user_agent=(
"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 "
"(KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36"
),
user_ip="192.0.2.1",
),
)
llm_response = "Your final LLM response here"
ack = await client.acknowledge_async(
enhancement_result=result,
llm_response=llm_response,
)
if ack is not None:
print(ack.ad_ack_id)
await client.aclose()
asyncio.run(main())
Public API
| Method | Description |
|---|---|
request_ad() |
Request ad enhancement synchronously |
request_ad_async() |
Request ad enhancement asynchronously |
acknowledge() |
Report the final LLM response and return AdAckResponse |
acknowledge_async() |
Async acknowledgment flow returning AdAckResponse |
close() |
Close the owned sync HTTP client |
aclose() |
Close the owned async HTTP client |
License
This SDK is distributed under the Adstract SDK Proprietary License. See LICENSE.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file adstractai-1.0.1.tar.gz.
File metadata
- Download URL: adstractai-1.0.1.tar.gz
- Upload date:
- Size: 23.4 kB
- Tags: Source
- Uploaded using Trusted Publishing? Yes
- Uploaded via: twine/6.1.0 CPython/3.13.7
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
5b74af110c14d4d8fa526cb95ac8ef93c7339aed55f5b46f8616de83c45473f8
|
|
| MD5 |
f61f89ec450372ba4e368fc013d323b9
|
|
| BLAKE2b-256 |
3f6c789a84abe492a563ba598630f953268d4e144518826bb76bcf99938de60b
|
Provenance
The following attestation bundles were made for adstractai-1.0.1.tar.gz:
Publisher:
publish.yml on Adstract-AI/adstract-library
-
Statement:
-
Statement type:
https://in-toto.io/Statement/v1 -
Predicate type:
https://docs.pypi.org/attestations/publish/v1 -
Subject name:
adstractai-1.0.1.tar.gz -
Subject digest:
5b74af110c14d4d8fa526cb95ac8ef93c7339aed55f5b46f8616de83c45473f8 - Sigstore transparency entry: 1115451299
- Sigstore integration time:
-
Permalink:
Adstract-AI/adstract-library@3f2485e97da1d52dd4056a73cafcd0d3828c0a05 -
Branch / Tag:
refs/tags/v1.0.1 - Owner: https://github.com/Adstract-AI
-
Access:
public
-
Token Issuer:
https://token.actions.githubusercontent.com -
Runner Environment:
github-hosted -
Publication workflow:
publish.yml@3f2485e97da1d52dd4056a73cafcd0d3828c0a05 -
Trigger Event:
push
-
Statement type:
File details
Details for the file adstractai-1.0.1-py3-none-any.whl.
File metadata
- Download URL: adstractai-1.0.1-py3-none-any.whl
- Upload date:
- Size: 18.5 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? Yes
- Uploaded via: twine/6.1.0 CPython/3.13.7
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
99157494cb1f249beb9e4b49662003ecdf5006f622dd1efd66f3fcb919e0859e
|
|
| MD5 |
14e0e7935c55a31f4ca7ed8d48ec0c9a
|
|
| BLAKE2b-256 |
3e5d5352e79759b20d1b9c4f53aa15322f8a81179afd6400ce146dc7202b3101
|
Provenance
The following attestation bundles were made for adstractai-1.0.1-py3-none-any.whl:
Publisher:
publish.yml on Adstract-AI/adstract-library
-
Statement:
-
Statement type:
https://in-toto.io/Statement/v1 -
Predicate type:
https://docs.pypi.org/attestations/publish/v1 -
Subject name:
adstractai-1.0.1-py3-none-any.whl -
Subject digest:
99157494cb1f249beb9e4b49662003ecdf5006f622dd1efd66f3fcb919e0859e - Sigstore transparency entry: 1115451307
- Sigstore integration time:
-
Permalink:
Adstract-AI/adstract-library@3f2485e97da1d52dd4056a73cafcd0d3828c0a05 -
Branch / Tag:
refs/tags/v1.0.1 - Owner: https://github.com/Adstract-AI
-
Access:
public
-
Token Issuer:
https://token.actions.githubusercontent.com -
Runner Environment:
github-hosted -
Publication workflow:
publish.yml@3f2485e97da1d52dd4056a73cafcd0d3828c0a05 -
Trigger Event:
push
-
Statement type: