vv-llm

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Universal LLM interface layer for Python. One API, 17 backends, sync & async.

pip install vv-llm

Supported Backends

OpenAI | Anthropic | DeepSeek | Gemini | Qwen | Groq | Mistral | Moonshot | MiniMax | Yi | ZhiPuAI | Baichuan | StepFun | xAI | Xiaomi | Ernie | Local

Also supports Azure OpenAI, Vertex AI, and AWS Bedrock deployments.

Quick Start

Configure

from vv_llm.settings import settings

settings.load({
    "endpoints": [
        {
            "id": "openai-default",
            "api_base": "https://api.openai.com/v1",
            "api_key": "sk-...",
        }
    ],
    "backends": {
        "openai": {
            "models": {
                "gpt-4o": {
                    "id": "gpt-4o",
                    "endpoints": ["openai-default"],
                }
            }
        }
    }
})

Typed sync (canonical request)

from vv_llm.chat_clients import create_chat_client, BackendType
from vv_llm import ChatRequest, ChatRequestOptions, ThinkingPreference

client = create_chat_client(BackendType.OpenAI, model="gpt-4o")
resp = client.create(
    ChatRequest(
        model="gpt-4o",
        messages=[{"role": "user", "content": "Explain RAG in one sentence"}],
        options=ChatRequestOptions(
            thinking=ThinkingPreference.default(),
            max_tokens=512,
        ),
    )
)
print(resp.content)

ChatRequest is the normalized runtime request. At a contract boundary, ChatRequest.from_contract(...) decodes canonical JSON (where model is required and options.stream is nested), while to_contract() omits runtime transport controls such as headers and query parameters.

Use ThinkingPreference.default() to preserve the provider default, enabled() or enabled(budget_tokens=...) to opt in, and disabled() to opt out explicitly.

Keyword API

create_completion(...) accepts keyword arguments. Pass thinking explicitly when a provider supports Anthropic-style thinking control; omit it to use the provider default:

resp = client.create_completion(
    messages=[{"role": "user", "content": "Answer directly"}],
    thinking={"type": "disabled"},
)

Middleware, Retry, And Metadata

Wrap a client with MiddlewareChatClient for middleware hooks, classified retry, and execution metadata:

from vv_llm import ChatMiddlewareV1, ChatRequest, MiddlewareChatClient, RetryPolicy

class TraceMiddleware(ChatMiddlewareV1):
    def on_request(self, context, request):
        context.attributes["trace_id"] = "request-42"
        return request

runtime = MiddlewareChatClient(
    client,
    [TraceMiddleware()],
    retry_policy=RetryPolicy(max_attempts=3, total_timeout=20),
)
result = runtime.create_with_metadata(
    ChatRequest(messages=[{"role": "user", "content": "Answer directly"}])
)

print(result.response.content)
print(result.metadata.provider, result.metadata.attempts, result.metadata.latency_ms)

ErrorKind distinguishes authentication, rate limiting, network, timeout, invalid request, context length, content policy, missing model, provider internal, serialization, and configuration failures. The default retry policy retries only transient kinds and respects retry-after-ms plus numeric or HTTP-date Retry-After, exponential backoff, jitter, and an optional total deadline.

Explicit Registry And Fallback

Fallback is opt-in and ordered. Every registration declares model capabilities, so an incompatible route is skipped without sending a request:

from vv_llm import FallbackChatClient, FallbackRoute, ProviderRegistry

registry = ProviderRegistry()
registry.register(
    "primary",
    lambda: primary_client,
    capabilities=primary_client.capabilities,
)
registry.register(
    "secondary",
    lambda: secondary_client,
    capabilities=secondary_client.capabilities,
)
runtime = FallbackChatClient(
    registry,
    [
        FallbackRoute("primary", "primary-model"),
        FallbackRoute("secondary", "secondary-model"),
    ],
)

Authentication and invalid-request errors do not fall back by default. Streaming may switch routes only while establishing the stream or before its first visible chunk; after output begins, later errors are returned without replay.

Streaming

from vv_llm import ChatRequest

for chunk in client.create(ChatRequest(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Write a haiku"}],
    stream=True,
)):
    if chunk.content:
        print(chunk.content, end="")

Async

import asyncio
from vv_llm.chat_clients import create_async_chat_client, BackendType
from vv_llm import ChatRequest

async def main():
    client = create_async_chat_client(BackendType.OpenAI, model="gpt-4o")
    resp = await client.create(ChatRequest(
        model="gpt-4o",
        messages=[{"role": "user", "content": "hello"}],
    ))
    print(resp.content)

asyncio.run(main())

HTTP transport clients

The http_client argument accepts httpx2.Client for sync calls and httpx2.AsyncClient for async calls. This lets applications provide a custom transport (for example, an offline MockTransport) while keeping OpenAI 3.x and Anthropic 1.x clients on the same HTTPX2 runtime:

import httpx2
from vv_llm.chat_clients import BackendType, create_chat_client

transport = httpx2.MockTransport(
    lambda request: httpx2.Response(200, json={"choices": []}, request=request)
)
http_client = httpx2.Client(transport=transport)
client = create_chat_client(BackendType.OpenAI, model="gpt-4o", http_client=http_client)

Use an endpoint proxy setting when vv-llm should construct the transport client itself. Legacy httpx.Client and httpx.AsyncClient instances are not accepted; use the matching HTTPX2 client type instead.

Embedding & Rerank

from vv_llm.settings import settings

settings.load({
    "endpoints": [
        {
            "id": "siliconflow",
            "api_base": "https://api.siliconflow.cn/v1",
            "api_key": "sk-...",
        }
    ],
    "backends": {},
    "embedding_backends": {
        "siliconflow": {
            "models": {
                "BAAI/bge-large-zh-v1.5": {
                    "id": "BAAI/bge-large-zh-v1.5",
                    "endpoints": ["siliconflow"],
                    "protocol": "openai_embeddings",
                }
            }
        }
    },
    "rerank_backends": {
        "siliconflow": {
            "models": {
                "BAAI/bge-reranker-v2-m3": {
                    "id": "BAAI/bge-reranker-v2-m3",
                    "endpoints": ["siliconflow"],
                    "protocol": "custom_json_http",
                    "request_mapping": {
                        "method": "POST",
                        "path": "/rerank",
                        "body_template": {
                            "model": "${model_id}",
                            "query": "${query}",
                            "documents": "${documents}",
                        },
                    },
                    "response_mapping": {
                        "results_path": "$.results[*]",
                        "field_map": {
                            "index": "$.index",
                            "relevance_score": "$.relevance_score",
                        },
                    },
                }
            }
        }
    },
})

from vv_llm.embedding_clients import create_embedding_client
from vv_llm.rerank_clients import create_rerank_client

embedding_client = create_embedding_client("siliconflow", model="BAAI/bge-large-zh-v1.5")
embedding_resp = embedding_client.create_embeddings(input="hello world")
print(len(embedding_resp.data[0].embedding))

rerank_client = create_rerank_client("siliconflow", model="BAAI/bge-reranker-v2-m3")
rerank_resp = rerank_client.rerank(
    query="Apple",
    documents=["apple", "banana", "fruit", "vegetable"],
)
print(rerank_resp.results[0].index, rerank_resp.results[0].relevance_score)

import asyncio
from vv_llm.embedding_clients import create_async_embedding_client
from vv_llm.rerank_clients import create_async_rerank_client

async def main():
    embedding_client = create_async_embedding_client("siliconflow", model="BAAI/bge-large-zh-v1.5")
    rerank_client = create_async_rerank_client("siliconflow", model="BAAI/bge-reranker-v2-m3")

    emb = await embedding_client.create_embeddings(input=["a", "b"])
    rr = await rerank_client.rerank(query="Apple", documents=["apple", "banana"])
    print(len(emb.data), len(rr.results))

asyncio.run(main())

Features

• Unified interface — canonical ChatRequest execution across all providers, with create_completion / create_stream retained for compatibility
• Embedding & rerank — unified sync/async retrieval clients with normalized outputs
• Type-safe factory — create_chat_client(BackendType.X) returns the correct client type
• Multi-endpoint — select enabled endpoints by ascending priority, preserving configuration order within each tier
• Tool calling — normalized tool/function calling across providers
• Multimodal — text + image inputs where supported
• Thinking/reasoning — access chain-of-thought from Claude, DeepSeek Reasoner, etc.
• Token counting — per-model tokenizers (tiktoken, deepseek-tokenizer, qwen-tokenizer)
• Rate limiting — RPM/TPM controls with memory, Redis, or DiskCache backends
• Context length control — automatic message truncation to fit model limits
• Prompt caching — Anthropic prompt caching support
• Retry with backoff — configurable retry logic for transient failures
• Versioned middleware — stable v1 request, response, and error hooks outside provider adapters
• Classified errors — provider-neutral error kinds with retryability and request context
• Explicit fallback — registered, ordered, capability-aware routes with no hidden provider switching
• Scripted testing — deterministic completion/error/stream scripts for conformance tests

Model endpoint bindings accept an optional priority integer of at least 1 (default: 1). Explicit endpoint_id selection takes precedence. from vv_llm.settings import order_endpoints exposes the same stable ordering: order_endpoints(endpoints, preferred_endpoint_id=None) returns a new list. A preferred endpoint moves ahead only within its priority tier.

The package includes vv-llm-contract 1.1.0. Read contract metadata, the model catalog, and integrity status through vv_llm.contract:

from vv_llm.contract import contract_info, load_catalog, verify_contract

info = contract_info()
assert info.contract_version == "1.1.0"
assert verify_contract().ok
catalog = load_catalog()

Maintainers can validate the packaged copy with pdm run contract-check and update it from a verified release directory with pdm run contract-sync --source PATH.

Python Capability Matrix

Chat Provider Matrix

Examples

Runnable examples are in examples/: basic_chat.py, streaming.py, tools.py, multimodal.py, and contract_json.py cover the main typed request paths. async_streaming.py, typed_thinking.py, middleware_metadata.py, and registry_fallback.py cover focused extensions; the last one is deterministic and offline.

Cache Usage Semantics

OpenAI-compatible chat completions report cache reads through usage.prompt_tokens_details.cached_tokens. usage.prompt_tokens remains the total input token count, so consumers can calculate uncached input as prompt_tokens - cached_tokens. This path intentionally does not populate Anthropic's cache_read_input_tokens field because Anthropic defines its base input_tokens as uncached input.

For generic OpenAI-compatible backends, omitted cache-read fields remain unknown, while an explicit cached_tokens: 0 is preserved as an observed zero. Moonshot may omit both top-level cached_tokens and prompt_tokens_details on a cold request; only in that fully omitted case does vv-llm project prompt_tokens_details.cached_tokens = 0 from the provider contract. Explicit null or invalid cache values remain unknown.

Utilities

from vv_llm.chat_clients import format_messages, get_token_counts, get_message_token_counts

Optional Dependencies

pip install 'vv-llm[redis]'      # Redis rate limiting
pip install 'vv-llm[diskcache]'  # DiskCache rate limiting
pip install 'vv-llm[server]'     # FastAPI token server
pip install 'vv-llm[vertex]'     # Google Vertex AI
pip install 'vv-llm[bedrock]'    # AWS Bedrock

Project Structure

src/vv_llm/
  _contract/      # Versioned schemas, fixtures, catalog, and consumer lock
  chat_clients/    # Per-backend clients + factory
  embedding_clients/  # Embedding clients + factory
  rerank_clients/     # Rerank clients + factory
  retrieval_clients/  # Shared retrieval client internals
  settings/        # Configuration management
  types/           # Type definitions & enums
  utilities/       # Rate limiting, retry, media processing, token counting
  server/          # Optional token counting server

tests/unit/        # Unit tests
tests/live/        # Live integration tests (requires real API keys)

User, Maintainer, And Release Workflows

Users

Install the package and configure endpoints through the public Settings API. Contract artifacts are read from the installed package; no repository checkout or local path is required at runtime.

Maintainers

pdm install -d          # Install dev dependencies
pdm run contract-check  # Validate only the vendored contract lock
pdm run contract-sync --source PATH  # Refresh from an explicit source tree
# Or: VV_LLM_CONTRACT_SOURCE=PATH pdm run contract-sync
# Compare an explicit source tree with the vendor:
python scripts/sync_contract.py --check --source PATH
pdm run lint            # Ruff linter
pdm run format-check    # Ruff format check
pdm run type-check      # Ty type checker
pdm run test            # Unit tests

For an intentional live smoke check, provide private settings through the existing tests/dev_settings.py mechanism and opt in explicitly:

VV_LLM_RUN_LIVE_TESTS=1 python tests/live/run_live_tests.py test_deepseek_contract_smoke.py

The smoke output contains only provider/model, response shape, usage counters, and exit status; it does not print credentials or response content.

Release publishers

pdm build
python scripts/smoke_wheel.py

The release CI performs contract-check, unit tests, linting, package build, and isolated wheel smoke before publication. Live API checks are intentionally not part of release CI.

License

MIT