genai-pyo3
Typed Python bindings for the Rust genai crate, built with pyo3 and maturin.
This release uses upstream genai v0.7.0. The Bedrock SigV4 feature is enabled
in the extension:
genai = { git = "https://github.com/jeremychone/rust-genai", tag = "v0.7.0", features = ["bedrock-sigv4"] }
What It Exposes
Routing — AdapterKind, ModelIden, Endpoint, AuthData, ServiceTarget
Client — Client, ClientBuilder
Requests — ChatRequest, ChatMessage, ChatOptions, Tool, ToolCall, JsonSpec, Binary
Responses — ChatResponse, StreamEnd, ChatStreamEvent, Usage (with prompt/completion/cache-creation detail)
Embeddings — EmbedOptions, EmbedResponse, Embedding
Errors — GenaiError and its subclasses (see below)
Entry points:
await client.achat(model, request, options=None) |
a full response |
await client.astream_chat(model, request, options=None) |
an async iterator of events |
await client.achat_via_stream(model, request, options=None) |
stream under the hood, one response out |
client.chat(model, request, options=None) |
blocking |
await client.aembed(model, text, options=None) |
one embedding |
await client.aembed_batch(model, texts, options=None) |
many |
client.resolve_service_target(model) |
where a call would go, without making it |
await client.aall_model_names(adapter_kind) |
what a provider is serving |
model is str | ModelIden | ServiceTarget everywhere.
Vertex and Bedrock
Provider namespaces route through the native 0.7.0 adapters:
from genai_pyo3 import AuthData, ChatMessage, ChatRequest, Client
request = ChatRequest(messages=[ChatMessage("user", "Hello")])
# Vertex uses a GCP OAuth bearer token; VERTEX_PROJECT_ID and optionally
# VERTEX_LOCATION control the project and region.
vertex = Client.builder().provider("vertex", auth=AuthData.from_env("VERTEX_TOKEN")).build()
await vertex.achat("vertex::gemini-2.5-flash", request)
await vertex.achat("vertex::claude-sonnet-4-6", request)
# Bedrock API keys use BEDROCK_API_KEY (or AWS_BEARER_TOKEN_BEDROCK).
await Client().achat("bedrock_api::us.anthropic.claude-sonnet-4-6", request)
# SigV4 uses the AWS credential chain. Select a named profile per client
# with AuthData.key("profile-name") when needed.
sigv4 = Client.builder().provider("bedrock_sigv4", auth=AuthData.key("my-profile")).build()
await sigv4.achat("bedrock_sigv4::us.anthropic.claude-sonnet-4-5-20250929-v1:0", request)
For Vertex, refresh the OAuth token before expiry through an auth resolver;
AuthData.from_env reads an already available token. ChatOptions also accepts
stream_header_timeout and stream_read_timeout in seconds for long-running
streams. AdapterKind.all() includes the new 0.7.0 provider names, including
Mistral, Requesty, API Route, and Gemini Interactions.
Install
Editable install with uv:
uv pip install -e .
This builds the pyo3 extension and makes genai_pyo3 importable from the active environment.
Quick Start
import asyncio
from genai_pyo3 import AuthData, ChatMessage, ChatRequest, Client
async def main() -> None:
client = (
Client.builder()
.adapter_kind("openai")
.provider("openai", auth=AuthData.from_env("OPENAI_API_KEY"))
.build()
)
request = ChatRequest(messages=[ChatMessage("user", "Say hello in one short sentence")])
response = await client.achat("gpt-4o-mini", request)
print(response.text)
asyncio.run(main())
Named endpoints
A ServiceTarget states where to go, how to authenticate, and which model to
ask for. A registry of them is just a dict, and one client serves all of it:
from genai_pyo3 import AuthData, Client, ModelIden, ServiceTarget
client = Client()
registry = {
"flash": ServiceTarget(
"https://openrouter.ai/api/v1/",
AuthData.from_env("OPENROUTER_API_KEY"),
ModelIden("openai", "deepseek-ai/DeepSeek-V4-Flash-0731"),
),
"local": ServiceTarget(
"http://localhost:8000/v1/",
AuthData.none(),
ModelIden("openai", "Qwen/Qwen3.8-27B"),
),
}
response = await client.achat(registry["flash"], request)
AuthData never reveals a key to Python and redacts it in repr(), so
logging a ServiceTarget does not log a credential.
Errors
GenaiError(RuntimeError)
├── ConfigError malformed request/options, adapter mismatch
├── AuthError missing or unresolvable credentials
├── ResolverError a resolver (including a Python callback) failed
├── ApiError the provider answered with a failure status
│ ├── BadRequestError 4xx other than 429
│ ├── RateLimitError 429
│ └── ServerError 5xx
├── TransportError connect/timeout/socket failures, dropped streams
├── StreamError malformed or error events mid-stream
├── ResponseError a well-formed reply that could not be interpreted
└── UnsupportedError feature unavailable on this adapter/model
Every instance carries:
kind |
the precise failure in rust-genai's own vocabulary — the error variant in snake_case ("http_error", "no_auth_data"), or the provider's own error type for a mid-stream failure ("overloaded_error") |
status |
HTTP status, when the provider answered with one |
status_code |
compatibility alias for status |
retryable |
conservative hint; the retry and backoff policy is yours |
body |
response body, when there was one |
headers |
response headers, when the failure carried an HTTP response |
retry_after |
seconds, parsed from headers when present |
model_iden |
the model the failure is about, when the error names one |
The exception class is the actionable category; kind is the exact cause.
Branch on the class, log the kind.
try:
response = await client.achat(target, request)
except RateLimitError as err:
await asyncio.sleep(err.retry_after or 5.0)
Truncation
stop_reason is normalised across providers — "completed",
"max_tokens", "tool_call", "content_filter", "stop_sequence",
"other" — with the provider's own wording kept in stop_reason_raw.
Without it, a truncated answer is indistinguishable from a short one.
Images and PDFs
from genai_pyo3 import Binary, ChatMessage
ChatMessage("user", content_parts=["what is in this?", Binary.from_path("chart.png")])
Transport
(Client.builder()
.proxy("http://proxy.corp:3128", scheme="https") # "all" | "http" | "https"
.timeouts(connect_seconds=10.0, read_seconds=120.0)
.gzip(False)
.tcp_nodelay(False)
.build())
Without an explicit proxy, requests ignore whatever proxy environment the process was started with — which on a locked-down network looks like an unexplained connect timeout.
sanitize_json_schema(schema, dialect) applies a provider's
constrained-decoding normalization without making a request, for seeing
what will actually be enforced before a rejection tells you.
Resolver callbacks
For credentials or routing that cannot be stated up front:
(Client.builder()
.auth_resolver(lambda iden: AuthData.key(cache[iden.adapter_kind.name]))
.model_mapper(lambda iden: ALIASES.get(iden.model_name, iden.model_name))
.service_target_resolver(lambda target: reroute(target))
.build())
Callbacks must be regular defs. They are called synchronously while
holding the GIL, so an async def would never be awaited — it is rejected
at registration — and blocking inside one stalls every other Python thread.
Cache in Python rather than doing I/O in a callback.
Development
maturin develop # build the extension into the active venv
python -m pytest # Python tests (no network; a local fake provider)
# Rust tests link against libpython, so they need the extension-module
# feature off and the interpreter feature on:
cargo test --no-default-features --features test-interpreter
cargo build does not link the cdylib on macOS (it lacks maturin's link
arguments); use cargo check or maturin develop.
Migrating
See MIGRATION.md for the move from the Client.with_*
constructors to ClientBuilder.
1 maintainer
Metadata
1 maintainer
Release files for genai-pyo3 0.7.0.5
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