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# LLM Inference Configuration
# Copy this file to .env and fill in ALL required values
# The application will fail to start if required variables are missing
# =============================================================================
# REQUIRED: Backend Selection (no defaults - must be set explicitly)
# =============================================================================
# Default backend: litellm, vllm, or llamacpp
LLM_DEFAULT_BACKEND=litellm
# Enable optional backends (true/false)
LLM_ENABLE_VLLM=false
LLM_ENABLE_LLAMACPP=false
# =============================================================================
# REQUIRED: API Keys (at least one for litellm backend)
# =============================================================================
# OpenRouter API key (access 100+ models via single API)
# Get one at: https://openrouter.ai/
OPENROUTER_API_KEY=
# OpenAI API key
# Get one at: https://platform.openai.com/
OPENAI_API_KEY=
# Anthropic API key
# Get one at: https://console.anthropic.com/
ANTHROPIC_API_KEY=
# =============================================================================
# REQUIRED for vLLM/llama.cpp profiles: Model Configuration
# =============================================================================
# Directory containing model files (absolute path recommended)
MODELS_DIR=/path/to/models
# vLLM model to load (Hugging Face model ID). Platform primary model (offer).
# Start vLLM with `--served-model-name qwen3.5` so callers can use the alias.
VLLM_MODEL=Qwen/Qwen3.5-35B-A3B
# llama.cpp model file (GGUF format, filename only - must be in MODELS_DIR)
LLAMACPP_MODEL=qwen3.5-35b-a3b.Q4_K_M.gguf
# Default model alias used when a request omits `model`.
LLM_DEFAULT_MODEL=qwen3.5
# Optional: map friendly alias -> real served model id (JSON). Leave unset if
# the backend already serves the model under the alias name (e.g. via
# vLLM --served-model-name qwen3.5).
# LLM_MODEL_ALIASES={"qwen3.5":"Qwen/Qwen3.5-35B-A3B"}
# llama.cpp performance settings
LLAMACPP_THREADS=4
LLAMACPP_PARALLEL=1
# =============================================================================
# OPTIONAL: API Authentication
# =============================================================================
# API tokens for Bearer authentication (comma-separated)
# If not set, authentication is disabled and all endpoints are public
# If set, requests to protected endpoints require: Authorization: Bearer <token>
# Health endpoints (/health, /ready) are always public
# LLM_API_TOKENS=token1,token2,token3
# =============================================================================
# REQUIRED: External URL (for OpenAPI spec and catalog integration)
# =============================================================================
# External URL where this API is reachable (used in OpenAPI spec and /v1/info)
LLM_EXTERNAL_URL=http://localhost:14011
# =============================================================================
# OPTIONAL: API Server Settings
# =============================================================================
# Server binding
# LLM_HOST=0.0.0.0
# LLM_PORT=14011
# Default model when not specified in request
# LLM_DEFAULT_MODEL=gpt-3.5-turbo
# =============================================================================
# OPTIONAL: Timeout and Retry Settings
# =============================================================================
# Request timeout for LLM calls (seconds)
# LLM_REQUEST_TIMEOUT=120.0
# Connection timeout (seconds)
# LLM_CONNECT_TIMEOUT=10.0
# Maximum retry attempts for transient failures
# LLM_MAX_RETRIES=3
# Minimum wait between retries (seconds)
# LLM_RETRY_MIN_WAIT=1.0
# Maximum wait between retries (seconds)
# LLM_RETRY_MAX_WAIT=60.0
# =============================================================================
# OPTIONAL: Rate Limiting and Concurrency
# =============================================================================
# Requests per second limit
# LLM_RATE_LIMIT_RPS=10.0
# Maximum burst size for rate limiting
# LLM_RATE_LIMIT_BURST=20
# Maximum concurrent completion requests
# LLM_MAX_CONCURRENT_COMPLETIONS=10
# =============================================================================
# OPTIONAL: Logging
# =============================================================================
# Log level: DEBUG, INFO, WARNING, ERROR, CRITICAL
# LLM_LOG_LEVEL=INFO
# Enable JSON logging format (true/false)
# LLM_LOG_JSON=false
# =============================================================================
# OPTIONAL: Nginx Proxy Timeouts (for Docker Compose deployment)
# =============================================================================
# Nginx connection timeout
# NGINX_CONNECT_TIMEOUT=60s
# Nginx send timeout
# NGINX_SEND_TIMEOUT=120s
# Nginx read timeout (should be >= LLM_REQUEST_TIMEOUT)
# NGINX_READ_TIMEOUT=600s
# =============================================================================
# OPTIONAL: Backend-Specific URLs
# =============================================================================
# vLLM server URL (internal Docker network)
# LLM_VLLM_BASE_URL=http://didiAI-vllm-qwen3.5:14001
# llama.cpp server URL (single server, legacy)
# LLM_LLAMACPP_BASE_URL=http://didiAI-llm-llamacpp:8080
# llama.cpp server URLs (comma-separated, load balanced round-robin with failover)
# If set, overrides LLM_LLAMACPP_BASE_URL
# LLM_LLAMACPP_BASE_URLS=http://10.11.10.43:14001,http://10.11.10.18:14001
# Health check interval for llama.cpp servers (seconds)
# LLM_LLAMACPP_HEALTH_CHECK_INTERVAL=30
# vLLM API key (if authentication is enabled on vLLM server)
# LLM_VLLM_API_KEY=

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# LLM Inference API Documentation
OpenAI-compatible REST API for unified LLM inference across multiple backends.
## Base URL
```
{BASE_URL}
```
Common configurations:
- **Local development:** `http://localhost:14011`
- **Docker (internal):** `http://didiAI-llm-api:14011`
- **Docker (external):** `http://<host>:14011`
- **VPN/Production:** Use your configured hostname or IP
## Authentication
### Bearer Token Authentication (Optional)
The API supports optional Bearer token authentication. When enabled, protected endpoints require a valid token.
**Enable by setting:**
```bash
LLM_API_TOKENS=token1,token2,token3 # comma-separated for multiple tokens
```
**Request header:**
```
Authorization: Bearer <your-token>
```
**Public endpoints (no auth required):**
- `GET /health`
- `GET /ready`
**Protected endpoints (require auth when enabled):**
- `POST /v1/chat/completions`
- `GET /v1/models`
- `POST /v1/models/load`
- `POST /v1/models/unload`
- `GET /v1/backends`
**Error response (401):**
```json
{
"detail": {
"error": "Authentication required",
"message": "Missing Authorization header"
}
}
```
Response includes `WWW-Authenticate: Bearer` header.
### Backend API Keys
Backend services require their own API keys:
- **LiteLLM**: `OPENROUTER_API_KEY`, `OPENAI_API_KEY`, or `ANTHROPIC_API_KEY`
- **vLLM**: Optional `LLM_VLLM_API_KEY` for vLLM server
## Rate Limiting
The API uses token bucket rate limiting:
| Header | Description |
|--------|-------------|
| `Retry-After` | Seconds to wait when rate limited (429 response) |
| `X-Request-ID` | Unique request identifier (auto-generated or pass via header) |
Default limits:
- **10 requests/second** with burst of 20
- **10 concurrent completion requests**
Configure via environment variables:
- `LLM_RATE_LIMIT_RPS`: Requests per second
- `LLM_RATE_LIMIT_BURST`: Maximum burst size
- `LLM_MAX_CONCURRENT_COMPLETIONS`: Concurrent request limit
---
## Endpoints
### Chat Completions
Create a chat completion with optional streaming.
```
POST /v1/chat/completions
```
#### Request Body
| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `messages` | array | Yes | - | List of messages (1-1000) |
| `model` | string | Yes | - | Model identifier |
| `temperature` | float | No | 0.7 | Sampling temperature (0.0-2.0) |
| `max_tokens` | integer | No | null | Maximum tokens to generate (1-1,000,000) |
| `stream` | boolean | No | false | Enable streaming response |
| `backend` | string | No | null | Backend override: `litellm`, `vllm`, `llamacpp` |
| `top_p` | float | No | null | Top-p sampling (0.0-1.0) |
| `frequency_penalty` | float | No | null | Frequency penalty (-2.0 to 2.0) |
| `presence_penalty` | float | No | null | Presence penalty (-2.0 to 2.0) |
| `stop` | string/array | No | null | Stop sequences |
#### Message Object
| Field | Type | Required | Description |
|-------|------|----------|-------------|
| `role` | string | Yes | One of: `system`, `user`, `assistant`, `function`, `tool` |
| `content` | string | Yes* | Message content (*can be null for assistant role) |
| `name` | string | No | Optional author name |
#### Response (Non-Streaming)
```json
{
"id": "chatcmpl-abc123",
"object": "chat.completion",
"created": 1704067200,
"model": "gpt-3.5-turbo",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "Hello! How can I help you today?"
},
"finish_reason": "stop"
}
],
"usage": {
"prompt_tokens": 10,
"completion_tokens": 15,
"total_tokens": 25
},
"backend": "litellm"
}
```
#### Response (Streaming)
Server-Sent Events (SSE) format. Each event contains a JSON chunk:
```
data: {"id":"chatcmpl-abc123","object":"chat.completion.chunk","created":1704067200,"model":"gpt-3.5-turbo","choices":[{"index":0,"delta":{"role":"assistant"},"finish_reason":null}]}
data: {"id":"chatcmpl-abc123","object":"chat.completion.chunk","created":1704067200,"model":"gpt-3.5-turbo","choices":[{"index":0,"delta":{"content":"Hello"},"finish_reason":null}]}
data: {"id":"chatcmpl-abc123","object":"chat.completion.chunk","created":1704067200,"model":"gpt-3.5-turbo","choices":[{"index":0,"delta":{"content":"!"},"finish_reason":null}]}
data: {"id":"chatcmpl-abc123","object":"chat.completion.chunk","created":1704067200,"model":"gpt-3.5-turbo","choices":[{"index":0,"delta":{},"finish_reason":"stop"}]}
data: [DONE]
```
#### Example Request
```bash
# Non-streaming
curl -X POST http://localhost:14011/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "What is the capital of France?"}
],
"model": "gpt-3.5-turbo",
"temperature": 0.7,
"max_tokens": 100
}'
# Streaming
curl -X POST http://localhost:14011/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Accept: text/event-stream" \
-d '{
"messages": [{"role": "user", "content": "Tell me a short story"}],
"model": "gpt-4",
"stream": true
}'
# With specific backend
curl -X POST http://localhost:14011/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"messages": [{"role": "user", "content": "Hello!"}],
"model": "meta-llama/Llama-2-7b-chat-hf",
"backend": "vllm"
}'
```
---
### List Models
List available models across all or specific backends.
```
GET /v1/models
GET /v1/models?backend=litellm
```
#### Query Parameters
| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| `backend` | string | No | Filter by backend: `litellm`, `vllm`, `llamacpp` |
#### Response
```json
{
"object": "list",
"data": [
{
"id": "gpt-3.5-turbo",
"backend": "litellm",
"loaded": true,
"context_length": 16384,
"capabilities": ["chat"]
},
{
"id": "gpt-4",
"backend": "litellm",
"loaded": true,
"context_length": 128000,
"capabilities": ["chat"]
},
{
"id": "meta-llama/Llama-2-7b-chat-hf",
"backend": "vllm",
"loaded": true,
"context_length": 4096,
"capabilities": ["chat"]
}
]
}
```
#### Example Request
```bash
# All models
curl http://localhost:14011/v1/models
# Models from specific backend
curl "http://localhost:14011/v1/models?backend=vllm"
```
---
### Load Model
Load a model on a local backend (vLLM or llama.cpp only).
```
POST /v1/models/load
```
#### Request Body
| Field | Type | Required | Description |
|-------|------|----------|-------------|
| `model` | string | Yes | Model identifier to load |
| `backend` | string | Yes | Target backend: `vllm` or `llamacpp` |
#### Response
```json
{
"success": true,
"model": "meta-llama/Llama-2-7b-chat-hf",
"backend": "vllm",
"message": "Model loaded successfully"
}
```
#### Example Request
```bash
curl -X POST http://localhost:14011/v1/models/load \
-H "Content-Type: application/json" \
-d '{
"model": "meta-llama/Llama-2-7b-chat-hf",
"backend": "vllm"
}'
```
---
### Unload Model
Unload a model from a local backend (vLLM or llama.cpp only).
```
POST /v1/models/unload
```
#### Request Body
| Field | Type | Required | Description |
|-------|------|----------|-------------|
| `model` | string | Yes | Model identifier to unload |
| `backend` | string | Yes | Backend to unload from: `vllm` or `llamacpp` |
#### Response
```json
{
"success": true,
"model": "meta-llama/Llama-2-7b-chat-hf",
"backend": "vllm",
"message": "Model unloaded successfully"
}
```
#### Example Request
```bash
curl -X POST http://localhost:14011/v1/models/unload \
-H "Content-Type: application/json" \
-d '{
"model": "meta-llama/Llama-2-7b-chat-hf",
"backend": "vllm"
}'
```
---
### List Backends
List available backends.
```
GET /v1/backends
```
#### Response
```json
{
"backends": ["litellm", "vllm", "llamacpp"]
}
```
#### Example Request
```bash
curl http://localhost:14011/v1/backends
```
---
### Health Check
Check overall system health and per-backend status.
```
GET /health
```
#### Response
```json
{
"status": "healthy",
"backends": [
{
"name": "litellm",
"healthy": true,
"message": null
},
{
"name": "vllm",
"healthy": true,
"message": null
},
{
"name": "llamacpp",
"healthy": false,
"message": "Connection refused"
}
]
}
```
#### Status Values
| Status | Description |
|--------|-------------|
| `healthy` | All backends are healthy |
| `degraded` | Some backends are healthy |
| `unhealthy` | No backends are healthy |
#### Example Request
```bash
curl http://localhost:14011/health
```
---
### Readiness Probe
Kubernetes-style readiness probe. Returns ready if the default backend is healthy.
```
GET /ready
```
#### Response
```json
{
"ready": true
}
```
#### HTTP Status Codes
| Code | Description |
|------|-------------|
| 200 | Service is ready |
| 503 | Service is not ready |
#### Example Request
```bash
curl http://localhost:14011/ready
```
---
## Error Responses
All errors follow a consistent format:
```json
{
"detail": "Error message description"
}
```
### HTTP Status Codes
| Code | Description |
|------|-------------|
| 400 | Bad Request - Invalid parameters or backend |
| 401 | Unauthorized - Missing or invalid Bearer token (when auth enabled) |
| 422 | Validation Error - Request body validation failed |
| 429 | Too Many Requests - Rate limit exceeded |
| 500 | Internal Server Error - Completion or backend failure |
| 503 | Service Unavailable - Concurrency limit exceeded or service not ready |
### Error Examples
**Validation Error (422)**
```json
{
"detail": [
{
"type": "value_error",
"loc": ["body", "messages", 0, "content"],
"msg": "Message at index 0 with role 'user' cannot have empty content",
"input": ""
}
]
}
```
**Rate Limit (429)**
```json
{
"detail": "Rate limit exceeded"
}
```
Response includes `Retry-After` header with seconds to wait.
**Backend Error (400)**
```json
{
"detail": "Backend 'vllm' is not available"
}
```
**Concurrency Limit (503)**
```json
{
"detail": "Concurrency limit exceeded"
}
```
---
## Request Headers
| Header | Required | Description |
|--------|----------|-------------|
| `Content-Type` | Yes (POST) | Must be `application/json` |
| `Authorization` | When auth enabled | Bearer token: `Bearer <your-token>` |
| `Accept` | No | Use `text/event-stream` for streaming |
| `X-Request-ID` | No | Custom request ID (auto-generated if not provided) |
---
## Supported Backends
### LiteLLM (Default)
Supports 100+ LLM providers through a unified interface.
**Popular models:**
- `gpt-3.5-turbo`, `gpt-4`, `gpt-4-turbo` (OpenAI)
- `claude-3-opus`, `claude-3-sonnet`, `claude-3-haiku` (Anthropic)
- `openrouter/meta-llama/llama-3-70b` (OpenRouter)
### vLLM
High-throughput GPU inference for open-source models.
**Requirements:** NVIDIA GPU with 16GB+ VRAM
**Example models:**
- `meta-llama/Llama-2-7b-chat-hf`
- `mistralai/Mistral-7B-Instruct-v0.2`
- `microsoft/phi-2`
### llama.cpp
CPU/Metal inference for GGUF format models.
**Requirements:** 16GB+ RAM, CPU with AVX2 support
**Example models:** Local GGUF files
---
## Python Client Example
```python
import httpx
async def chat_completion():
async with httpx.AsyncClient() as client:
response = await client.post(
"http://localhost:14011/v1/chat/completions",
json={
"messages": [{"role": "user", "content": "Hello!"}],
"model": "gpt-3.5-turbo",
},
)
return response.json()
async def streaming_completion():
async with httpx.AsyncClient() as client:
async with client.stream(
"POST",
"http://localhost:14011/v1/chat/completions",
json={
"messages": [{"role": "user", "content": "Tell me a story"}],
"model": "gpt-4",
"stream": True,
},
) as response:
async for line in response.aiter_lines():
if line.startswith("data: "):
data = line[6:]
if data != "[DONE]":
chunk = json.loads(data)
content = chunk["choices"][0]["delta"].get("content", "")
print(content, end="", flush=True)
```
---
## OpenAI SDK Compatibility
The API is compatible with the OpenAI Python SDK:
```python
from openai import OpenAI
# Without auth (when LLM_API_TOKENS not set)
client = OpenAI(
base_url="http://localhost:14011/v1",
api_key="not-needed", # Required by SDK but not used
)
# With auth (when LLM_API_TOKENS is set)
client = OpenAI(
base_url="http://localhost:14011/v1",
api_key="your-api-token", # Your LLM_API_TOKENS value
)
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content)
# Streaming
for chunk in client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": "Tell me a story"}],
stream=True,
):
if chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content, end="")
```

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# llm-inference
OpenAI-compatible LLM router for DIDI. Routes requests to local Qwen 3.5 397B (llama.cpp / vLLM backends) for free tier and proxies to OpenRouter cloud (Claude Sonnet 4.6, Gemini Flash 3, GPT-4o, etc.) for premium models. All endpoints are OpenAI-compatible (`/v1/chat/completions`), so callers can use the OpenAI SDK or plain `httpx` interchangeably.
- **Stack**: Python 3.10+, FastAPI, uvicorn, httpx, LiteLLM, Pydantic v2, sse-starlette
- **URL**: `http://10.11.10.17:14011` (LLM router on GPU host) — referenced as `LLM_ROUTER_URL` in DIDI services
- **Container**: `didiAI-llm-api` (image `didiai-llm-api`), runs on GPU host (typically `10.11.10.17`)
- **Local model**: Qwen 3.5-35B-A3B (MoE, native multimodal text+vision) via vLLM (`didiAI-vllm-qwen3.5`, internal port 14001) or Qwen 3.5 397B-A17B variant via llama.cpp pool
- **Entry point**: `llm-inference` console script -> `src/llm_inference/cli.py:main` -> uvicorn factory `llm_inference.api.app:create_app`
## Ce face
Single OpenAI-compatible endpoint (`/v1/chat/completions`) that selects a backend based on the request's `model` field and request-time `backend` override:
- **Auto backend resolution** (`LLMClient._resolve_backend_for_model` in `src/llm_inference/client.py`): probes each enabled local backend's `list_models()`. If the requested model is served locally, route to that backend; otherwise fall back to the configured default (litellm).
- **Explicit backend override**: clients may pass `"backend": "litellm" | "vllm" | "llamacpp"` in the JSON body to force routing.
- **Streaming + non-streaming**: same endpoint; `"stream": true` returns SSE chunks (`text/event-stream` with `data: [DONE]` terminator).
- **Multimodal**: messages are pre-processed by `image_processing.process_messages` so image URLs/base64 attachments work the same across backends.
- **Retries**: transient failures (`LLMTimeoutError`, `LLMRateLimitError`, `LLMConnectionError`) wrapped by `retry.retry_with_backoff` with exponential backoff (`max_retries`, `retry_min_wait`, `retry_max_wait`).
- **Provider keys**: read from env (`OPENROUTER_API_KEY`, `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`); LiteLLM dispatches to OpenRouter / OpenAI / Anthropic / Gemini / Bedrock / 100+ providers using its own model-id parsing (`openrouter/...`, `claude-3-...`, `gpt-4o`, etc.).
- **Optional Bearer auth**: when `LLM_API_TOKENS` is set, all `/v1/*` endpoints require `Authorization: Bearer <token>`; `/health` and `/ready` are always public.
## API endpoints
All paths are mounted by `src/llm_inference/api/app.py`:
| Method | Path | Purpose |
|--------|------|---------|
| POST | `/v1/chat/completions` | OpenAI-compat chat completion (streaming + non-streaming) |
| GET | `/v1/models` | List models from all (or specific via `?backend=`) backends |
| POST | `/v1/models/load` | Load a model on a local backend (vLLM / llama.cpp) |
| POST | `/v1/models/unload` | Unload a model from a local backend |
| GET | `/v1/backends` | List enabled backends |
| GET | `/health` | Per-backend health (overall: `healthy` / `degraded` / `unhealthy`) |
| GET | `/ready` | K8s-style readiness probe (200 if default backend healthy, 503 otherwise) |
| GET | `/v1/info` *(catalog router)* | Service metadata for cross-module catalog integration |
Source files for the routers: `src/llm_inference/api/routes/{completions,models,health,info}.py`.
## Model routing logic
The backend dispatch lives in `LLMClient._resolve_backend_for_model` (auto) and `BackendRegistry.get` (explicit). Model identifier conventions:
- **Local vLLM** (`backend=vllm` or auto): the vLLM container exposes Qwen with `--served-model-name qwen3.5`. Requests with `model="qwen3.5"` (or whatever Hugging Face id is loaded) are forwarded to `LLM_VLLM_BASE_URL` (default `http://didiAI-vllm-qwen3.5:14001`) using OpenAI SDK.
- **Local llama.cpp** (`backend=llamacpp` or auto): one or more llama.cpp servers (`LLM_LLAMACPP_BASE_URLS=http://10.11.10.43:14001,http://10.11.10.18:14001`) load-balanced round-robin with periodic health checks (`LLM_LLAMACPP_HEALTH_CHECK_INTERVAL`). See `src/llm_inference/backends/llamacpp_backend.py` (`LlamaCppBackend`, `LlamaCppServer` dataclass).
- **Cloud / LiteLLM** (default): `model` is passed as-is to LiteLLM. Examples:
- `openrouter/google/gemini-2.5-flash`, `openrouter/anthropic/claude-sonnet-4-6`, `openrouter/meta-llama/llama-3-70b` -> OpenRouter API (`OPENROUTER_API_KEY`)
- `gpt-4o`, `gpt-4-turbo`, `gpt-3.5-turbo` -> OpenAI (`OPENAI_API_KEY`)
- `claude-3-opus`, `claude-3-sonnet`, `claude-3-haiku` -> Anthropic (`ANTHROPIC_API_KEY`)
- LiteLLM also handles Azure / Bedrock / Gemini natively — fallback chains can be implemented at the caller level by retrying with the next model id.
There is no Groq backend in the current code; cloud routing is consolidated through LiteLLM.
## How didi-brain + agent-v3 use it
This service is the single point of LLM accounting for the platform:
- **agent-v3** (`backend/services/orchestration-layer/agent-v3`): the `LLMClient.call()` helper inside `src/components/component-runner.ts` and the per-component executors (techniques, ai-tampered, claims, pipeline) call `${LLM_ROUTER_URL}/v1/chat/completions` for every analysis prompt. The `model` field is taken from the moderation config (Redis-synced from `didiFramework`) so admins can swap models without redeploying agent-v3.
- **didi-brain** (`ai_platform/modules/didi_brain/brain_api`): the claim extractor and verification pipeline use the same router for both extraction prompts and rationale generation.
- **All LLM traffic** from DIDI flows through this router — so any cost-tracking middleware added here gives a unified billing view.
## Structura fisiere
```
src/llm_inference/
__init__.py
cli.py # `llm-inference` console script (uvicorn launcher + graceful shutdown)
client.py # LLMClient (high-level Python API used as library)
config.py # LLMSettings (Pydantic Settings, LLM_* env vars) + SettingsCache
schemas.py # OpenAI-shaped CompletionResponse / CompletionChunk request/response models
types.py # ChatMessage, BackendType enum, ModelInfo, Choice, Delta, Usage
exceptions.py # CompletionError, LLMTimeoutError, LLMRateLimitError, LLMConnectionError, ModelLoadError
retry.py # retry_with_backoff helper
image_processing.py # process_messages — fetches/encodes image content for multimodal calls
logging.py # configure_logging, get_logger (JSON + plain modes)
utils.py # safe_close_stream, etc.
backends/
base.py # Abstract LLMBackend (complete / stream / list_models / health_check / load_model / unload_model)
registry.py # BackendRegistry — instantiates enabled backends from settings
litellm_backend.py # LiteLLMBackend (default; OpenRouter / OpenAI / Anthropic / 100+ via litellm)
vllm_backend.py # vLLMBackend (OpenAI SDK -> internal vLLM server)
llamacpp_backend.py # LlamaCppBackend (round-robin pool with health checks + failover)
api/
app.py # create_app() — FastAPI factory, lifespan, middleware wiring
dependencies.py # init_concurrency_limiter, get_client, auth dependency
middleware.py # RateLimitMiddleware (token bucket), RequestIdMiddleware
routes/
completions.py # POST /v1/chat/completions
models.py # /v1/models, /v1/models/load, /v1/models/unload, /v1/backends
health.py # /health, /ready
info.py # /v1/info (catalog metadata)
deploy/
Dockerfile # Builds `didiai-llm-api` image
docker-compose.yml # Profiles: api, vllm, llamacpp, full
deploy.sh # Profile launcher
nginx.conf # Optional reverse proxy
tests/ # pytest (asyncio_mode=auto, markers: e2e, slow)
API.md # OpenAPI-style endpoint docs
README.md # Usage / install / deployment
.env.example # Full env reference
pyproject.toml # uv/hatchling project (llm-inference v0.1.0)
```
## Configuration
All env vars use the `LLM_` prefix (Pydantic Settings, `extra="forbid"` so typos fail loudly). Required (no default):
- `LLM_DEFAULT_BACKEND` — `litellm` | `vllm` | `llamacpp`
- `LLM_ENABLE_VLLM` — `true` / `false`
- `LLM_ENABLE_LLAMACPP` — `true` / `false`
- `LLM_EXTERNAL_URL` — public URL (used in OpenAPI `servers[]` and `/v1/info`)
Common optional:
- `OPENROUTER_API_KEY`, `OPENAI_API_KEY`, `ANTHROPIC_API_KEY` — provider keys for LiteLLM
- `LLM_DEFAULT_MODEL` (default `gpt-3.5-turbo`)
- `LLM_HOST` (default `0.0.0.0`), `LLM_PORT` (default `14011`)
- `LLM_VLLM_BASE_URL` (default `http://localhost:14001`), `LLM_VLLM_API_KEY`
- `LLM_LLAMACPP_BASE_URL` (single-server legacy) **or** `LLM_LLAMACPP_BASE_URLS` (comma-separated pool, overrides single)
- `LLM_LLAMACPP_HEALTH_CHECK_INTERVAL` (default `30s`)
- `LLM_REQUEST_TIMEOUT` (default `120s`), `LLM_CONNECT_TIMEOUT` (default `10s`)
- `LLM_MAX_RETRIES` (default `3`), `LLM_RETRY_MIN_WAIT`, `LLM_RETRY_MAX_WAIT`
- `LLM_RATE_LIMIT_RPS` (default `10`), `LLM_RATE_LIMIT_BURST` (default `20`), `LLM_MAX_CONCURRENT_COMPLETIONS` (default `10`)
- `LLM_API_TOKENS` — comma-separated; if set, `/v1/*` requires `Authorization: Bearer <token>`
- `LLM_LOG_LEVEL`, `LLM_LOG_JSON`
Full reference: `.env.example`. Settings class: `src/llm_inference/config.py:LLMSettings`.
## Deployment
GPU host `10.11.10.17` (typically). The vLLM container needs an NVIDIA H200 NVL (~143 GB VRAM); Qwen 3.5-35B-A3B occupies ~57 GB BF16 with `--gpu-memory-utilization 0.65` and `--max-model-len 32000`, leaving room on GPU 0 for Whisper.
Compose profiles (in `deploy/docker-compose.yml`):
- `api` — only the FastAPI router (uses external/cloud providers)
- `vllm` — router + vLLM Qwen 3.5 (GPU)
- `llamacpp` — router + a local llama.cpp server (CPU)
- `full` — everything
Networking: shares the external Docker network `didi-network` with sibling AI modules (whisper, embeddings, etc.). Internal address used by the router for vLLM is `http://didiAI-vllm-qwen3.5:14001`.
```bash
cd deploy/
cp ../.env.example .env # fill keys + URLs
./deploy.sh --profile api -d
./deploy.sh --profile vllm -d
./deploy.sh --profile api --logs
./deploy.sh --profile api --down
```
Healthcheck for the API container: `python -c "import urllib.request; urllib.request.urlopen('http://localhost:14011/health')"` every 30s.
## Cost tracking
The current code base does not yet ship a per-request cost-accounting middleware. The wiring point exists (`api/middleware.py` and the `RequestIdMiddleware` propagates a `X-Request-ID` to every call) and prompt/completion token counts are returned in `Usage` (`types.py`) on every non-streaming completion and in the final stream chunk. To plug into the AI dashboard, intercept at `routes/completions.py` (or a new middleware) and persist `(request_id, backend, model, prompt_tokens, completion_tokens, latency_ms)` — the dashboard's `cost.html` consumes that schema.
## Related
- **agent-v3 client**: `backend/services/orchestration-layer/agent-v3/src/components/component-runner.ts` — `LLMClient.call()` posts to `${LLM_ROUTER_URL}/v1/chat/completions`. Model id comes from Redis moderation config synced by `didiFramework`.
- **didi-brain**: `ai_platform/modules/didi_brain/brain_api` — extractor + verification calls hit the same router.
- **AI dashboard**: `cost.html` (when accounting middleware is added) consumes per-request token usage.
- **Sibling AI modules** (sharing `didi-network` network): whisper / transcription, embeddings, vision, etc., living under `ai_platform/modules/`.
- **OpenAI SDK compatibility**: clients can simply do `OpenAI(base_url="http://10.11.10.17:14011/v1", api_key=<token-or-not-needed>)`.

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# LLM Inference
Unified LLM inference module with multiple backends: LiteLLM (OpenRouter, OpenAI, Anthropic), vLLM, and llama.cpp.
## Prerequisites
**Required:**
- All global prerequisites (see main [README.md](../../README.md))
- At least one LLM API key (OpenRouter, OpenAI, or Anthropic)
**For vLLM backend (GPU inference):**
- NVIDIA GPU with 16GB+ VRAM (24GB+ recommended for larger models)
- NVIDIA Driver 535+
- NVIDIA Container Toolkit
**For llama.cpp backend (CPU inference):**
- 16GB+ RAM (depends on model size)
- CPU with AVX2 support (most modern CPUs)
## Features
- **Multiple Backends**: LiteLLM (100+ providers), vLLM (GPU inference), llama.cpp (CPU/Metal)
- **Unified Interface**: Single API for all backends, switch seamlessly
- **Streaming Support**: Server-Sent Events (SSE) for real-time responses
- **Model Management**: List, load, and unload models (local backends)
- **OpenAI Compatible**: Drop-in replacement for OpenAI API clients
## Installation
```bash
cd modules/llm-inference
# Install core dependencies
uv sync
# Install with optional backends
uv sync --extra vllm # For vLLM support
uv sync --extra llamacpp # For llama.cpp support
uv sync --extra local # For all local backends
uv sync --extra dev # For development
```
## Quick Start
### As a Python Library
```python
from llm_inference import LLMClient, BackendType
# Initialize client
client = LLMClient()
# Simple completion
response = await client.complete(
messages=[{"role": "user", "content": "Hello!"}],
model="gpt-3.5-turbo",
)
print(response.choices[0].message.content)
# Streaming
async for chunk in client.stream(
messages=[{"role": "user", "content": "Tell me a story"}],
model="gpt-4",
):
if chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content, end="")
# Use specific backend
response = await client.complete(
messages=[{"role": "user", "content": "Hello!"}],
model="meta-llama/Llama-2-7b-chat-hf",
backend=BackendType.VLLM,
)
```
### As an API Server
```bash
cd deploy/
# Copy and configure environment
cp ../.env.example .env
# Edit .env with your API keys and settings
# Start the server
./deploy.sh --profile api -d
```
### API Endpoints
| Endpoint | Method | Description |
|----------|--------|-------------|
| `/v1/chat/completions` | POST | Chat completion (supports streaming) |
| `/v1/models` | GET | List available models |
| `/v1/models/load` | POST | Load a model (local backends) |
| `/v1/models/unload` | POST | Unload a model (local backends) |
| `/v1/backends` | GET | List available backends |
| `/health` | GET | Health check |
| `/ready` | GET | Readiness probe |
### Example API Request
```bash
# Non-streaming
curl -X POST http://localhost:14011/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"messages": [{"role": "user", "content": "Hello!"}],
"model": "gpt-3.5-turbo"
}'
# Streaming
curl -X POST http://localhost:14011/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"messages": [{"role": "user", "content": "Tell me a story"}],
"model": "gpt-4",
"stream": true
}'
```
## Configuration
Configure via environment variables (prefix: `LLM_`):
| Variable | Default | Description |
|----------|---------|-------------|
| `LLM_DEFAULT_BACKEND` | `litellm` | Default backend (litellm, vllm, llamacpp) |
| `LLM_DEFAULT_MODEL` | `gpt-3.5-turbo` | Default model |
| `LLM_PORT` | `14011` | API server port |
| `LLM_HOST` | `0.0.0.0` | API server host |
| `LLM_ENABLE_VLLM` | `false` | Enable vLLM backend |
| `LLM_ENABLE_LLAMACPP` | `false` | Enable llama.cpp backend |
| `LLM_VLLM_BASE_URL` | `http://localhost:14001` | vLLM server URL |
| `LLM_LLAMACPP_BASE_URL` | `http://localhost:8080` | llama.cpp server URL |
| `OPENROUTER_API_KEY` | - | OpenRouter API key |
| `OPENAI_API_KEY` | - | OpenAI API key |
| `ANTHROPIC_API_KEY` | - | Anthropic API key |
## Rate Limiting
The API server includes built-in rate limiting using a token bucket algorithm. Configure via environment variables:
| Variable | Default | Description |
|----------|---------|-------------|
| `LLM_RATE_LIMIT_RPS` | `10.0` | Requests per second |
| `LLM_RATE_LIMIT_BURST` | `20` | Maximum burst size |
| `LLM_MAX_CONCURRENT_COMPLETIONS` | `10` | Maximum concurrent completion requests |
**Important:** Rate limiting is **per-process**. In multi-replica deployments (e.g., Kubernetes), each replica has its own independent limit. For distributed rate limiting, use an external solution like Redis, an API gateway (Kong, nginx), or cloud provider rate limiting.
## Deployment
```bash
cd deploy/
# Copy and configure environment (REQUIRED)
cp ../.env.example .env
# Edit .env with your settings
# API only (uses external LLM providers)
./deploy.sh --profile api -d
# With vLLM (requires NVIDIA GPU)
./deploy.sh --profile vllm -d
# With llama.cpp (CPU inference)
./deploy.sh --profile llamacpp -d
# Full stack
./deploy.sh --profile full -d
# View logs
./deploy.sh --profile api --logs
# Stop services
./deploy.sh --profile api --down
```
### Port Allocation
| Port | Service |
|------|---------|
| 14011 | LLM Inference API |
| 14001 | vLLM Qwen3.5-35B-A3B |
## Development
```bash
# Install dev dependencies
uv sync --extra dev
# Run tests
uv run pytest
# Run tests with coverage
uv run pytest --cov=src/llm_inference --cov-report=term-missing
# Lint and format
uv run ruff check .
uv run ruff format .
# Type check
uv run mypy src/
```
## Architecture
```
src/llm_inference/
├── __init__.py # Package exports
├── config.py # Pydantic Settings
├── types.py # Core types and enums
├── schemas.py # API request/response schemas
├── exceptions.py # Custom exceptions
├── client.py # High-level LLMClient
├── cli.py # CLI entry point
├── backends/
│ ├── base.py # Abstract LLMBackend
│ ├── registry.py # BackendRegistry
│ ├── litellm_backend.py
│ ├── vllm_backend.py
│ └── llamacpp_backend.py
└── api/
├── app.py # FastAPI app factory
├── dependencies.py
└── routes/
├── completions.py
├── models.py
└── health.py
```
## License
MIT

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# LLM Inference API Server
# Multi-stage build for smaller final image
FROM python:3.11.12-slim AS builder
# Install uv
COPY --from=ghcr.io/astral-sh/uv:0.10 /uv /usr/local/bin/uv
WORKDIR /app
# Copy project files
COPY pyproject.toml uv.lock README.md ./
COPY src/ ./src/
# Install dependencies
RUN uv sync --frozen --no-dev
# Production image
FROM python:3.11.12-slim
WORKDIR /app
# Copy virtual environment from builder
COPY --from=builder /app/.venv /app/.venv
# Copy source code
COPY src/ ./src/
# Set environment variables
ENV PATH="/app/.venv/bin:$PATH"
ENV PYTHONUNBUFFERED=1
ENV LLM_HOST=0.0.0.0
ENV LLM_PORT=14011
# Health check (using Python since curl not available in slim image)
HEALTHCHECK --interval=30s --timeout=10s --retries=3 \
CMD python -c "import urllib.request; urllib.request.urlopen('http://localhost:14011/health')" || exit 1
# Expose port
EXPOSE 14011
# Run the server
CMD ["python", "-m", "llm_inference.cli"]

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#!/usr/bin/env bash
#
# Docker Compose Startup Script for LLM Inference
#
# Usage: ./deploy/docker-start.sh [OPTIONS]
#
# Options:
# --profile <api|vllm|llamacpp|full> Docker compose profile
# --detach Run in detached mode
# --down Stop and remove containers
# --logs Show logs
# --help Show this help message
#
# Required: Set environment variables in .env file or export them before running.
# See .env.example for the full list of required variables.
set -euo pipefail
# Script directory
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
# Load .env file if it exists (check deploy/ first, then module root)
ENV_FILE=""
if [[ -f "$SCRIPT_DIR/.env" ]]; then
ENV_FILE="$SCRIPT_DIR/.env"
elif [[ -f "$SCRIPT_DIR/../.env" ]]; then
ENV_FILE="$SCRIPT_DIR/../.env"
fi
if [[ -n "$ENV_FILE" ]]; then
echo "Loading environment from: $ENV_FILE"
set -a
source "$ENV_FILE"
set +a
fi
PROFILE=""
DETACH=""
ACTION="up"
show_help() {
sed -n '2,16p' "$0" | sed 's/^# //' | sed 's/^#//'
exit 0
}
check_required_var() {
local var_name="$1"
if [[ -z "${!var_name:-}" ]]; then
echo "ERROR: Required environment variable $var_name is not set"
echo "Set it in .env file or export it before running this script"
exit 1
fi
}
# Parse arguments
while [[ $# -gt 0 ]]; do
case $1 in
--profile)
PROFILE="$2"
shift 2
;;
--detach|-d)
DETACH="-d"
shift
;;
--down)
ACTION="down"
shift
;;
--logs)
ACTION="logs"
shift
;;
--help|-h)
show_help
;;
*)
echo "Unknown option: $1"
echo "Use --help for usage information"
exit 1
;;
esac
done
# Require profile to be specified
if [[ -z "$PROFILE" && "$ACTION" == "up" ]]; then
echo "ERROR: --profile is required"
echo "Options: api, vllm, llamacpp, full"
exit 1
fi
# Check required variables based on profile
check_required_var "LLM_DEFAULT_BACKEND"
check_required_var "LLM_ENABLE_VLLM"
check_required_var "LLM_ENABLE_LLAMACPP"
check_required_var "LLM_EXTERNAL_URL"
# API keys - at least one MUST be set for litellm backend (fail-fast)
if [[ "$LLM_DEFAULT_BACKEND" == "litellm" ]]; then
if [[ -z "${OPENROUTER_API_KEY:-}" && -z "${OPENAI_API_KEY:-}" && -z "${ANTHROPIC_API_KEY:-}" ]]; then
echo "ERROR: litellm backend requires at least one API key"
echo "Set one of: OPENROUTER_API_KEY, OPENAI_API_KEY, or ANTHROPIC_API_KEY"
exit 1
fi
fi
# Check model-specific variables for vllm/llamacpp profiles
case $PROFILE in
vllm|full)
check_required_var "MODELS_DIR"
check_required_var "VLLM_MODEL"
;;
llamacpp|full)
check_required_var "MODELS_DIR"
check_required_var "LLAMACPP_MODEL"
check_required_var "LLAMACPP_THREADS"
check_required_var "LLAMACPP_PARALLEL"
;;
esac
cd "$SCRIPT_DIR"
case $ACTION in
up)
echo "Starting LLM Inference with profile: $PROFILE"
echo " Default backend: $LLM_DEFAULT_BACKEND"
echo " vLLM enabled: $LLM_ENABLE_VLLM"
echo " llama.cpp enabled: $LLM_ENABLE_LLAMACPP"
if [[ "$PROFILE" == "vllm" || "$PROFILE" == "full" ]]; then
echo " vLLM model: $VLLM_MODEL"
fi
if [[ "$PROFILE" == "llamacpp" || "$PROFILE" == "full" ]]; then
echo " llama.cpp model: $LLAMACPP_MODEL"
fi
echo ""
# shellcheck disable=SC2086
exec docker compose --profile "$PROFILE" up $DETACH
;;
down)
if [[ -z "$PROFILE" ]]; then
echo "ERROR: --profile is required with --down"
exit 1
fi
echo "Stopping LLM Inference containers..."
exec docker compose --profile "$PROFILE" down
;;
logs)
if [[ -z "$PROFILE" ]]; then
echo "ERROR: --profile is required with --logs"
exit 1
fi
exec docker compose --profile "$PROFILE" logs -f
;;
esac

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# LLM Inference Module - Docker Compose Configuration
#
# Port Allocation:
# 14001 - vLLM Qwen3.5-35B-A3B (text + vision MoE, native multimodal)
# 14011 - LLM Inference API (unified router)
#
# Profiles:
# api - API server only (uses external LLM services)
# vllm - API + vLLM servers (GPU required)
#
# Model:
# - Qwen3.5-35B-A3B (~57GB weights BF16, gpu-util 0.45)
# - Native vision support (replaces separate Qwen3-VL)
#
# Hardware: 1x NVIDIA H200 NVL (~143GB VRAM)
# - GPU 0: Qwen3.5-35B-A3B + Whisper (~2GB)
#
# Naming Convention: didiAI-{module}-{service}
#
# Network:
# Uses deploy_default network (shared with other modules)
networks:
deploy_default:
external: true
services:
# ==========================================================================
# LLM Inference API Server
# ==========================================================================
llm-api:
container_name: didiAI-llm-api
image: didiai-llm-api
build:
context: ..
dockerfile: deploy/Dockerfile
ports:
- "14011:14011"
networks:
- deploy_default
environment:
- LLM_PORT=14011
- LLM_EXTERNAL_URL=${LLM_EXTERNAL_URL}
- LLM_DEFAULT_BACKEND=${LLM_DEFAULT_BACKEND}
- LLM_ENABLE_VLLM=${LLM_ENABLE_VLLM}
- LLM_ENABLE_LLAMACPP=${LLM_ENABLE_LLAMACPP}
- LLM_VLLM_BASE_URL=http://didiAI-vllm-qwen3.5:14001
- LLM_LLAMACPP_BASE_URLS=${LLM_LLAMACPP_BASE_URLS:-}
- LLM_API_TOKENS=${LLM_API_TOKENS:-}
- LLM_DASHBOARD_URL=${LLM_DASHBOARD_URL:-http://didiAI-dashboard:51300}
- OPENROUTER_API_KEY=${OPENROUTER_API_KEY:-}
- OPENAI_API_KEY=${OPENAI_API_KEY:-}
- ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY:-}
healthcheck:
test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:14011/health')"]
interval: 30s
timeout: 10s
retries: 3
start_period: 30s
restart: unless-stopped
profiles:
- api
- vllm
# ==========================================================================
# vLLM Server - Qwen3.5-35B-A3B (Unified Text + Vision MoE Model)
# ==========================================================================
# Native multimodal - replaces separate Qwen3 text + Qwen3-VL vision
vllm-qwen3.5:
container_name: didiAI-vllm-qwen3.5
image: vllm/vllm-openai:qwen3_5
ports:
- "14001:14001"
networks:
- deploy_default
volumes:
- ${HF_CACHE_DIR}:/root/.cache/huggingface
environment:
- HF_HOME=/root/.cache/huggingface
- HUGGING_FACE_HUB_TOKEN=${HF_TOKEN:-}
- CUDA_VISIBLE_DEVICES=0
- VLLM_ALLOW_LONG_MAX_MODEL_LEN=1
command: >
--model Qwen/Qwen3.5-35B-A3B
--host 0.0.0.0
--port 14001
--served-model-name qwen3.5
--tensor-parallel-size 1
--max-model-len 32000
--gpu-memory-utilization 0.65
--trust-remote-code
--enable-prefix-caching
--disable-log-requests
--enable-auto-tool-choice
--tool-call-parser hermes
deploy:
resources:
reservations:
devices:
- driver: nvidia
device_ids: ['0']
capabilities: [gpu]
healthcheck:
test: ["CMD", "curl", "-f", "http://localhost:14001/health"]
interval: 30s
timeout: 10s
retries: 10
start_period: 600s
restart: unless-stopped
profiles:
- vllm
# ==========================================================================
# llama.cpp Server (CPU/Metal Inference) - Optional
# ==========================================================================
llamacpp:
image: ghcr.io/ggml-org/llama.cpp:server-b4769
expose:
- "14011"
volumes:
- ${MODELS_DIR:-/cai2_ds_storage/models}:/models:ro
command: >
--model /models/${LLAMACPP_MODEL:-model.gguf}
--host 0.0.0.0
--port 14011
--ctx-size 4096
--threads ${LLAMACPP_THREADS:-4}
--parallel ${LLAMACPP_PARALLEL:-1}
healthcheck:
test: ["CMD", "curl", "-f", "http://localhost:14011/health"]
interval: 30s
timeout: 10s
retries: 3
start_period: 60s
restart: unless-stopped
profiles:
- llamacpp
- full
volumes:
vllm-cache:
name: llm-inference-vllm-cache

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upstream llm_api {
server llm-api:8100;
keepalive 32;
}
server {
listen 80;
server_name _;
# Timeouts for slow LLM responses
proxy_connect_timeout 60s;
proxy_send_timeout 120s;
proxy_read_timeout 300s;
# Health checks (no logging)
location /health {
access_log off;
proxy_pass http://llm_api/health;
proxy_http_version 1.1;
proxy_set_header Host $host;
}
location /ready {
access_log off;
proxy_pass http://llm_api/ready;
proxy_http_version 1.1;
proxy_set_header Host $host;
}
# API endpoints
location / {
proxy_pass http://llm_api;
proxy_http_version 1.1;
# Forward client info
proxy_set_header Host $host;
proxy_set_header X-Real-IP $remote_addr;
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
# SSE streaming support
proxy_set_header Connection '';
proxy_buffering off;
proxy_cache off;
chunked_transfer_encoding off;
# Large request bodies for long conversations
client_max_body_size 10M;
}
}

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[project]
name = "llm-inference"
version = "0.1.0"
description = "Unified LLM inference with multiple backends (LiteLLM, vLLM, llama.cpp)"
requires-python = ">=3.10"
readme = "README.md"
dependencies = [
# Core dependencies (always installed)
"litellm>=1.50.0,<2.0",
"fastapi>=0.115.0,<0.116",
"uvicorn[standard]>=0.32.0",
"pydantic>=2.0,<3.0",
"pydantic-settings>=2.0,<3.0",
"httpx>=0.27.0,<1.0",
"sse-starlette>=2.0,<3.0",
"prometheus-client>=0.20.0",
"prometheus-fastapi-instrumentator>=7.0.0",
"opentelemetry-instrumentation-fastapi>=0.50b0",
"opentelemetry-exporter-otlp-proto-grpc>=1.30.0",
]
[project.optional-dependencies]
# vLLM backend (GPU inference via OpenAI-compatible API)
vllm = [
"openai>=1.50.0,<2.0",
]
# llama.cpp backend (CPU/Metal inference via OpenAI-compatible API)
llamacpp = [
"openai>=1.50.0,<2.0",
]
# All local inference backends
local = [
"llm-inference[vllm,llamacpp]",
]
# Development dependencies
dev = [
"pytest>=8.0",
"pytest-cov>=4.0",
"pytest-asyncio>=0.24.0",
"ruff>=0.8",
"mypy>=1.0",
"respx>=0.21.0",
]
[project.scripts]
llm-inference = "llm_inference.cli:main"
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
[tool.hatch.build.targets.wheel]
packages = ["src/llm_inference"]
[tool.ruff]
extend = "../../ruff.toml"
[tool.pytest.ini_options]
asyncio_mode = "auto"
testpaths = ["tests"]
addopts = "-v --tb=short"
markers = [
"e2e: End-to-end tests against real running server",
"slow: Tests that make real LLM API calls (cost money, slower)",
]
[tool.mypy]
python_version = "3.10"
strict = true
warn_return_any = true
warn_unused_ignores = true
[dependency-groups]
dev = [
"pytest>=9.0.2",
"pytest-asyncio>=1.3.0",
"ruff>=0.14.11",
]

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"""LLM Inference - Unified LLM inference with multiple backends."""
from llm_inference.client import LLMClient
from llm_inference.config import LLMSettings, get_settings
from llm_inference.types import BackendType, ChatMessage
__version__ = "0.1.0"
__all__ = [
"BackendType",
"ChatMessage",
"LLMClient",
"LLMSettings",
"__version__",
"get_settings",
]

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"""FastAPI application for LLM inference."""
from llm_inference.api.app import create_app
__all__ = ["create_app"]

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"""FastAPI application factory."""
from collections.abc import AsyncGenerator
from contextlib import asynccontextmanager
from fastapi import FastAPI
from llm_inference.api.dependencies import init_concurrency_limiter
from llm_inference.api.middleware import RateLimitMiddleware, RequestIdMiddleware
from llm_inference.api.routes import completions, health, info, models
from llm_inference.backends.llamacpp_backend import LlamaCppBackend
from llm_inference.client import LLMClient
from llm_inference.config import SettingsCache
from llm_inference.image_processing import close_http_client
from llm_inference.logging import configure_logging, get_logger
from llm_inference.runtime_config import RuntimeConfigClient
from llm_inference.types import BackendType
logger = get_logger("app")
@asynccontextmanager
async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
"""Application lifespan manager.
Initializes the LLM client, settings, logging, and concurrency limiter
on startup, and performs cleanup on shutdown.
Args:
app: FastAPI application instance.
Yields:
None: Control to the application.
"""
settings = SettingsCache.get()
configure_logging(settings.log_level, settings.log_json)
logger.info("Starting LLM Inference API")
logger.info("Default backend: %s", settings.default_backend)
logger.info("vLLM enabled: %s", settings.enable_vllm)
logger.info("llama.cpp enabled: %s", settings.enable_llamacpp)
init_concurrency_limiter(settings.max_concurrent_completions)
app.state.settings = settings
app.state.client = LLMClient(settings)
# Start the runtime config client created in create_app()
if hasattr(app.state, "runtime_config"):
await app.state.runtime_config.start()
if settings.enable_llamacpp:
try:
backend = app.state.client.registry.get(BackendType.LLAMACPP)
if isinstance(backend, LlamaCppBackend):
backend.start_health_checks()
except Exception:
pass
logger.info("LLM Inference API started successfully")
yield
logger.info("Shutting down LLM Inference API")
if hasattr(app.state, "runtime_config"):
await app.state.runtime_config.stop()
await close_http_client()
def create_app() -> FastAPI:
"""Create and configure the FastAPI application.
Returns:
FastAPI: Configured FastAPI application.
"""
settings = SettingsCache.get()
app = FastAPI(
title="LLM Inference API",
description=(
"Unified LLM inference with multiple backends (LiteLLM, vLLM, llama.cpp)"
),
version="0.1.0",
lifespan=lifespan,
servers=[{"url": settings.external_url, "description": "LLM Inference API"}],
)
# ---------------------------------------------------------------- observability
# Prometheus /metrics + OTel tracing (no-op if deps missing or OTEL endpoint unset)
try:
from prometheus_fastapi_instrumentator import Instrumentator as _Inst # type: ignore
_Inst(should_group_status_codes=True).instrument(app).expose(app, endpoint="/metrics", include_in_schema=False)
except ImportError:
pass
import os as _os # noqa: E402
_otel_ep = _os.environ.get("OTEL_EXPORTER_OTLP_ENDPOINT")
if _otel_ep:
try:
from opentelemetry import trace as _trace # type: ignore
from opentelemetry.sdk.resources import Resource as _R # type: ignore
from opentelemetry.sdk.trace import TracerProvider as _TP # type: ignore
from opentelemetry.sdk.trace.export import BatchSpanProcessor as _BSP # type: ignore
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter as _Exp # type: ignore
from opentelemetry.instrumentation.fastapi import FastAPIInstrumentor as _FInst # type: ignore
_provider = _TP(resource=_R.create({"service.name": _os.environ.get("OTEL_SERVICE_NAME", "didiAI-llm-api")}))
_provider.add_span_processor(_BSP(_Exp(endpoint=_otel_ep, insecure=True)))
_trace.set_tracer_provider(_provider)
_FInst.instrument_app(app)
print(f"[otel] didiAI-llm-api instrumented -> {_otel_ep}")
except ImportError as _e:
print(f"[otel] skip: {_e}")
# Runtime config client — instantiated here so middleware can capture it.
# Started/stopped inside lifespan().
runtime_config = RuntimeConfigClient(
dashboard_url=settings.dashboard_url,
live_log_logger_name="llm_inference",
live_log_key="llm.log.level",
)
app.state.runtime_config = runtime_config
# Add middleware (order matters - first added is outermost)
app.add_middleware(
RateLimitMiddleware,
rate=settings.rate_limit_rps,
burst=settings.rate_limit_burst,
exclude_paths=["/health", "/ready"],
runtime_config=runtime_config,
rate_key="llm.rate_limit.rps",
burst_key="llm.rate_limit.burst",
)
app.add_middleware(RequestIdMiddleware)
app.include_router(health.router, tags=["Health"])
app.include_router(completions.router, prefix="/v1", tags=["Completions"])
app.include_router(models.router, prefix="/v1", tags=["Models"])
app.include_router(info.router, tags=["Catalog"])
return app

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"""FastAPI dependencies for LLM inference."""
import asyncio
import hmac
from collections.abc import AsyncIterator
from contextlib import asynccontextmanager
from fastapi import Header, HTTPException, Request
from llm_inference.client import LLMClient
from llm_inference.config import LLMSettings
from llm_inference.logging import get_logger
logger = get_logger("dependencies")
def get_client(request: Request) -> LLMClient:
"""Get the LLM client from application state.
Args:
request: FastAPI request object.
Returns:
LLMClient: The LLM client instance.
"""
return request.app.state.client
def get_settings(request: Request) -> LLMSettings:
"""Get settings from application state.
Args:
request: FastAPI request object.
Returns:
LLMSettings: Application settings.
"""
return request.app.state.settings
def verify_bearer_token(
request: Request,
authorization: str | None = Header(default=None, alias="Authorization"),
) -> str | None:
"""Verify Bearer token authentication.
This dependency checks the Authorization header for a valid Bearer token.
If authentication is disabled (no tokens configured), returns None.
If authentication is enabled, validates the token and returns it.
Args:
request: FastAPI request object.
authorization: Authorization header value.
Returns:
The validated token if auth enabled, None if auth disabled.
Raises:
HTTPException: 401 if auth enabled and token is invalid/missing.
"""
settings = request.app.state.settings
# Auth disabled - allow all requests
if not settings.auth_enabled:
return None
# Auth enabled - validate token
if not authorization:
raise HTTPException(
status_code=401,
detail={
"error": "Authentication required",
"message": "Missing Authorization header",
},
headers={"WWW-Authenticate": "Bearer"},
)
# Parse Bearer token
parts = authorization.split()
if len(parts) != 2 or parts[0].lower() != "bearer":
raise HTTPException(
status_code=401,
detail={
"error": "Authentication required",
"message": "Invalid Authorization header format. Use: Bearer <token>",
},
headers={"WWW-Authenticate": "Bearer"},
)
token = parts[1]
# Constant-time comparison against all valid tokens
is_valid = any(
hmac.compare_digest(token.encode(), valid_token.encode())
for valid_token in settings.api_tokens
)
if not is_valid:
raise HTTPException(
status_code=401,
detail={
"error": "Authentication failed",
"message": "Invalid API token",
},
headers={"WWW-Authenticate": "Bearer"},
)
return token
class ConcurrencyLimiter:
"""Limits concurrent completions to prevent resource exhaustion.
Uses a semaphore to limit the number of concurrent completion requests.
Returns 503 Service Unavailable when limit is exceeded.
"""
def __init__(self, max_concurrent: int) -> None:
"""Initialize concurrency limiter.
Args:
max_concurrent: Maximum number of concurrent completions.
"""
self.max_concurrent = max_concurrent
self._semaphore = asyncio.Semaphore(max_concurrent)
self._counter_lock = asyncio.Lock()
self._current = 0
@property
def current_count(self) -> int:
"""Get current number of active requests."""
return self._current
@property
def available(self) -> int:
"""Get number of available slots."""
return self.max_concurrent - self._current
@asynccontextmanager
async def acquire(self, blocking: bool = False) -> AsyncIterator[None]:
"""Acquire a completion slot.
Args:
blocking: If True, wait for a slot. If False (default), return 503 immediately.
Yields:
None when slot is acquired.
Raises:
HTTPException: 503 if no slots available and blocking=False.
"""
if not blocking and self._semaphore.locked():
# Non-blocking mode and no slots available - reject immediately
# Note: small race window exists but semaphore still enforces limit
logger.warning(
"Concurrency limit reached: %d/%d active",
self._current,
self.max_concurrent,
)
raise HTTPException(
status_code=503,
detail={
"error": "Service temporarily unavailable",
"reason": "Too many concurrent requests",
"max_concurrent": self.max_concurrent,
"retry_after": 5,
},
headers={"Retry-After": "5"},
)
# Acquire semaphore (blocks if blocking=True and no slots, or immediate if available)
await self._semaphore.acquire()
# Update counter with lock for thread safety
async with self._counter_lock:
self._current += 1
try:
yield
finally:
async with self._counter_lock:
self._current -= 1
self._semaphore.release()
# Global concurrency limiter instance (initialized in app startup)
_concurrency_limiter: ConcurrencyLimiter | None = None
def init_concurrency_limiter(max_concurrent: int) -> ConcurrencyLimiter:
"""Initialize the global concurrency limiter.
Args:
max_concurrent: Maximum concurrent completions.
Returns:
ConcurrencyLimiter: The initialized limiter.
"""
global _concurrency_limiter
_concurrency_limiter = ConcurrencyLimiter(max_concurrent)
logger.info("Concurrency limiter initialized: max_concurrent=%d", max_concurrent)
return _concurrency_limiter
def get_concurrency_limiter() -> ConcurrencyLimiter:
"""Get the global concurrency limiter.
Returns:
ConcurrencyLimiter: The global limiter instance.
Raises:
RuntimeError: If limiter not initialized.
"""
if _concurrency_limiter is None:
raise RuntimeError("Concurrency limiter not initialized")
return _concurrency_limiter
async def require_completion_slot(request: Request) -> AsyncIterator[None]:
"""FastAPI dependency that acquires a completion slot.
Args:
request: FastAPI request.
Yields:
None when slot is acquired.
Raises:
HTTPException: 503 if no slots available.
"""
limiter = get_concurrency_limiter()
async with limiter.acquire():
yield

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"""FastAPI middleware for request handling and rate limiting.
Provides:
- RequestIdMiddleware: Extracts/generates request IDs and propagates via context
- RateLimitMiddleware: Token bucket rate limiting with 429 responses
"""
import time
import uuid
from collections.abc import Awaitable, Callable
from fastapi import Request, Response
from starlette.middleware.base import BaseHTTPMiddleware
from starlette.responses import JSONResponse
from llm_inference.logging import get_logger, set_request_id
logger = get_logger("middleware")
class RequestIdMiddleware(BaseHTTPMiddleware):
"""Middleware to extract or generate request IDs.
Extracts X-Request-ID from incoming headers or generates a new UUID.
Sets the request ID in context for logging and returns it in response headers.
"""
async def dispatch(
self,
request: Request,
call_next: Callable[[Request], Awaitable[Response]],
) -> Response:
"""Process request with request ID tracking.
Args:
request: Incoming HTTP request.
call_next: Next middleware/handler in chain.
Returns:
Response: HTTP response with X-Request-ID header.
"""
# Extract from header or generate new
request_id = request.headers.get("X-Request-ID")
if not request_id:
request_id = str(uuid.uuid4())
# Set in context for logging
set_request_id(request_id)
# Store in request state for handlers
request.state.request_id = request_id
try:
response = await call_next(request)
response.headers["X-Request-ID"] = request_id
return response
finally:
# Clear context after request
set_request_id(None)
class TokenBucket:
"""Token bucket for rate limiting with optional live tuning.
Implements a simple token bucket algorithm where tokens are added
at a fixed rate up to a maximum burst size. If a runtime_config and
rate/burst keys are supplied, the bucket reads the latest override
on every acquire() — making the limit live-tunable from dashboard
without restart.
"""
def __init__(
self,
rate: float,
burst: int,
runtime_config: object | None = None,
rate_key: str | None = None,
burst_key: str | None = None,
) -> None:
"""Initialize token bucket.
Args:
rate: Tokens added per second (fallback / initial).
burst: Maximum tokens (bucket capacity, fallback / initial).
runtime_config: Optional RuntimeConfigClient. When set together
with rate_key / burst_key, the bucket pulls live values on
each acquire() and falls back to the constructor values.
rate_key: Dashboard config key for live rate (e.g., "llm.rate_limit.rps").
burst_key: Dashboard config key for live burst.
"""
self._fallback_rate = rate
self._fallback_burst = burst
self.rate = rate
self.burst = burst
self.tokens = float(burst)
self.last_update = time.monotonic()
self._runtime_config = runtime_config
self._rate_key = rate_key
self._burst_key = burst_key
def _refresh_from_config(self) -> None:
"""Pull latest rate/burst from runtime_config if wired."""
if self._runtime_config is None:
return
try:
new_rate = self._runtime_config.get_float(self._rate_key, self._fallback_rate) if self._rate_key else self._fallback_rate
new_burst = self._runtime_config.get_int(self._burst_key, self._fallback_burst) if self._burst_key else self._fallback_burst
except Exception:
return
if new_rate != self.rate or new_burst != self.burst:
# On burst increase, top up; on decrease, clamp tokens to new burst
self.rate = new_rate
self.burst = new_burst
self.tokens = min(self.tokens, float(new_burst))
def acquire(self) -> bool:
"""Try to acquire a token.
Returns:
bool: True if token acquired, False if rate limited.
"""
self._refresh_from_config()
now = time.monotonic()
elapsed = now - self.last_update
self.last_update = now
# Add tokens based on elapsed time
self.tokens = min(self.burst, self.tokens + elapsed * self.rate)
if self.tokens >= 1:
self.tokens -= 1
return True
return False
def retry_after(self) -> float:
"""Calculate seconds until a token is available.
Returns:
float: Seconds to wait before retrying.
"""
if self.tokens >= 1:
return 0.0
tokens_needed = 1 - self.tokens
return tokens_needed / self.rate
class RateLimitMiddleware(BaseHTTPMiddleware):
"""Middleware for global rate limiting using token bucket algorithm.
Returns 429 Too Many Requests with Retry-After header when exceeded.
WARNING: This is per-process rate limiting. In multi-replica deployments
(e.g., Kubernetes with multiple pods), each replica maintains its own
independent rate limit. A configured limit of 10 RPS with 5 replicas
effectively allows 50 RPS total.
For distributed rate limiting in production, use an external solution:
- Redis-based rate limiting (e.g., redis-rate-limiter)
- API gateway rate limiting (e.g., Kong, nginx)
- Cloud provider rate limiting (e.g., AWS API Gateway)
"""
def __init__(
self,
app: object,
rate: float = 10.0,
burst: int = 20,
exclude_paths: list[str] | None = None,
runtime_config: object | None = None,
rate_key: str | None = None,
burst_key: str | None = None,
) -> None:
"""Initialize rate limiter.
Args:
app: FastAPI application.
rate: Requests per second limit (fallback when no runtime_config).
burst: Maximum burst size (fallback).
exclude_paths: Paths to exclude from rate limiting.
runtime_config: Optional RuntimeConfigClient to enable live rate tuning.
rate_key: Dashboard config key (e.g., "llm.rate_limit.rps").
burst_key: Dashboard config key (e.g., "llm.rate_limit.burst").
"""
super().__init__(app)
self.bucket = TokenBucket(
rate=rate,
burst=burst,
runtime_config=runtime_config,
rate_key=rate_key,
burst_key=burst_key,
)
self.exclude_paths = exclude_paths or ["/health", "/ready"]
async def dispatch(
self,
request: Request,
call_next: Callable[[Request], Awaitable[Response]],
) -> Response:
"""Process request with rate limiting.
Args:
request: Incoming HTTP request.
call_next: Next middleware/handler in chain.
Returns:
Response: HTTP response or 429 if rate limited.
"""
# Skip rate limiting for excluded paths
if request.url.path in self.exclude_paths:
return await call_next(request)
# Try to acquire token
if not self.bucket.acquire():
retry_after = self.bucket.retry_after()
logger.warning(
"Rate limit exceeded for %s %s, retry_after=%.2f",
request.method,
request.url.path,
retry_after,
)
return JSONResponse(
status_code=429,
content={
"detail": "Too many requests",
"retry_after": retry_after,
},
headers={"Retry-After": str(int(retry_after) + 1)},
)
return await call_next(request)

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"""API routes for LLM inference."""
from llm_inference.api.routes import completions, health, info, models
__all__ = ["completions", "health", "info", "models"]

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"""Chat completion routes with streaming support."""
import json
from collections.abc import AsyncGenerator
from fastapi import APIRouter, Depends, HTTPException
from sse_starlette.sse import EventSourceResponse
from llm_inference.api.dependencies import (
ConcurrencyLimiter,
get_client,
get_concurrency_limiter,
verify_bearer_token,
)
from llm_inference.client import LLMClient
from llm_inference.exceptions import (
BackendNotAvailableError,
BackendNotEnabledError,
CompletionError,
)
from llm_inference.schemas import (
CompletionRequest,
CompletionResponse,
TextCompletionRequest,
TextCompletionResponse,
)
router = APIRouter(dependencies=[Depends(verify_bearer_token)])
@router.post(
"/chat/completions",
response_model=None, # Disable auto-generation due to Union with EventSourceResponse
)
async def chat_completions(
request: CompletionRequest,
client: LLMClient = Depends(get_client),
) -> CompletionResponse | EventSourceResponse:
"""OpenAI-compatible chat completions endpoint.
Supports both streaming (SSE) and non-streaming responses.
Backend can be overridden per-request via the `backend` field.
Concurrency is limited per-process. For streaming requests, the slot is
held for the entire duration of the stream.
Args:
request: Completion request with messages, model, and options.
client: LLM client instance.
Returns:
CompletionResponse or EventSourceResponse for streaming.
Raises:
HTTPException: If the request fails or concurrency limit exceeded (503).
"""
limiter = get_concurrency_limiter()
if request.stream:
# For streaming: wrap generator to hold slot throughout entire stream
# This is critical - the slot must be held until streaming completes
return EventSourceResponse(
_stream_with_slot(_stream_generator(client, request), limiter),
media_type="text/event-stream",
ping=15, # Send ping every 15s to detect dead connections
)
# For non-streaming: acquire slot, run completion, release slot
async with limiter.acquire():
try:
kwargs = _build_completion_kwargs(request)
response = await client.complete(
messages=request.messages,
model=request.model,
backend=request.backend,
**kwargs,
)
return response
except (BackendNotAvailableError, BackendNotEnabledError) as e:
raise HTTPException(status_code=400, detail=str(e)) from None
except CompletionError as e:
raise HTTPException(status_code=500, detail=str(e)) from None
@router.post("/completions", response_model=TextCompletionResponse)
async def text_completions(
request: TextCompletionRequest,
client: LLMClient = Depends(get_client),
) -> TextCompletionResponse:
"""OpenAI-compatible legacy text completions endpoint (/v1/completions).
Non-streaming. Routes to the model's backend (vLLM primary, cloud via
LiteLLM). Backends that do not support text completion return 501.
"""
limiter = get_concurrency_limiter()
async with limiter.acquire():
try:
kwargs: dict[str, object] = {"temperature": request.temperature}
if request.max_tokens is not None:
kwargs["max_tokens"] = request.max_tokens
if request.top_p is not None:
kwargs["top_p"] = request.top_p
if request.frequency_penalty is not None:
kwargs["frequency_penalty"] = request.frequency_penalty
if request.presence_penalty is not None:
kwargs["presence_penalty"] = request.presence_penalty
if request.stop is not None:
stop_seq = (
[request.stop]
if isinstance(request.stop, str)
else list(request.stop)
)
stop_seq = [s for s in stop_seq if s]
if stop_seq:
kwargs["stop"] = stop_seq
return await client.text_complete(
prompt=request.prompt,
model=request.model,
backend=request.backend,
**kwargs,
)
except NotImplementedError as e:
raise HTTPException(status_code=501, detail=str(e)) from None
except (BackendNotAvailableError, BackendNotEnabledError) as e:
raise HTTPException(status_code=400, detail=str(e)) from None
except CompletionError as e:
raise HTTPException(status_code=500, detail=str(e)) from None
async def _stream_generator(
client: LLMClient,
request: CompletionRequest,
) -> AsyncGenerator[dict[str, str], None]:
"""Generate SSE events for streaming response.
Args:
client: LLM client instance.
request: Completion request.
Yields:
SSE event dictionaries with data field.
"""
try:
kwargs = _build_completion_kwargs(request)
async for chunk in client.stream(
messages=request.messages,
model=request.model,
backend=request.backend,
**kwargs,
):
yield {"data": json.dumps(chunk.model_dump())}
# Send [DONE] marker to signal end of stream
yield {"data": "[DONE]"}
except (BackendNotAvailableError, BackendNotEnabledError, CompletionError) as e:
# Send error event followed by [DONE] to signal stream termination
yield {
"event": "error",
"data": json.dumps({"error": str(e)}),
}
yield {"data": "[DONE]"}
async def _stream_with_slot(
generator: AsyncGenerator[dict[str, str], None],
limiter: ConcurrencyLimiter,
) -> AsyncGenerator[dict[str, str], None]:
"""Wrap a stream generator to hold a concurrency slot throughout streaming.
This ensures the concurrency limiter slot is held for the entire duration
of the SSE stream, not just until the EventSourceResponse is returned.
Args:
generator: The underlying stream generator.
limiter: Concurrency limiter to acquire slot from.
Yields:
SSE event dictionaries from the wrapped generator.
"""
async with limiter.acquire():
async for item in generator:
yield item
def _build_completion_kwargs(request: CompletionRequest) -> dict[str, object]:
"""Build kwargs dict from completion request.
Args:
request: Completion request.
Returns:
dict: Keyword arguments for completion call.
"""
kwargs: dict[str, object] = {
"temperature": request.temperature,
}
if request.max_tokens is not None:
kwargs["max_tokens"] = request.max_tokens
if request.top_p is not None:
kwargs["top_p"] = request.top_p
if request.frequency_penalty is not None:
kwargs["frequency_penalty"] = request.frequency_penalty
if request.presence_penalty is not None:
kwargs["presence_penalty"] = request.presence_penalty
if request.stop is not None:
# Normalize to list and filter out empty strings
# Some backends choke on empty stop sequences
stop_seq = (
[request.stop] if isinstance(request.stop, str) else list(request.stop)
)
stop_seq = [s for s in stop_seq if s] # Filter empty strings
if stop_seq:
kwargs["stop"] = stop_seq
return kwargs

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"""Health check routes."""
from fastapi import APIRouter, Depends
from llm_inference.api.dependencies import get_client
from llm_inference.client import LLMClient
from llm_inference.logging import get_logger
from llm_inference.schemas import BackendHealth, HealthResponse, ReadinessResponse
router = APIRouter()
logger = get_logger("routes.health")
@router.get("/health", response_model=HealthResponse)
async def health_check(
client: LLMClient = Depends(get_client),
) -> HealthResponse:
"""Health check endpoint.
Returns the overall health status and per-backend health status.
Returns:
HealthResponse: Overall and per-backend health status.
"""
backend_health: list[BackendHealth] = []
for backend_type in client.list_backends():
backend = client.registry.get(backend_type)
is_healthy = await backend.health_check()
backend_health.append(
BackendHealth(
name=backend_type.value,
healthy=is_healthy,
)
)
# Determine overall status
all_healthy = all(b.healthy for b in backend_health)
any_healthy = any(b.healthy for b in backend_health)
if all_healthy:
status = "healthy"
elif any_healthy:
status = "degraded"
else:
status = "unhealthy"
return HealthResponse(status=status, backends=backend_health)
@router.get("/ready", response_model=ReadinessResponse)
async def readiness_check(
client: LLMClient = Depends(get_client),
) -> ReadinessResponse:
"""Readiness probe for Kubernetes.
Verifies that the default backend is healthy and able to serve requests.
This is the critical check for load balancer routing.
Returns:
ReadinessResponse: Readiness status (ready if default backend is healthy).
"""
try:
# Get the default backend and check its health
default_backend = client.registry.get()
is_healthy = await default_backend.health_check()
if not is_healthy:
logger.warning(
"Readiness check failed: default backend '%s' is unhealthy",
default_backend.name,
)
return ReadinessResponse(ready=is_healthy)
except Exception as e:
logger.error("Readiness check failed with error: %s", str(e))
return ReadinessResponse(ready=False)

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"""Component information endpoint for service catalog."""
from fastapi import APIRouter, Depends
from llm_inference.api.dependencies import get_client
from llm_inference.client import LLMClient
from llm_inference.config import SettingsCache
router = APIRouter()
@router.get("/v1/info")
async def get_component_info(
client: LLMClient = Depends(get_client),
) -> dict:
"""Get component information for service catalog.
Returns complete metadata about this service including:
- Resource information (component metadata)
- Available models (from all enabled backends)
- Available functions (API endpoints)
This endpoint is used by the catalog-api to aggregate service information
and by backend systems to populate the catalog database.
Returns:
dict: Component information matching catalog.resources, catalog.models,
and catalog.functions schemas.
"""
settings = SettingsCache.get()
# Determine which backends are available
backends = ["litellm"] # Always available
if settings.enable_vllm:
backends.append("vllm")
if settings.enable_llamacpp:
backends.append("llamacpp")
# Build resource information (maps to catalog.resources)
resource = {
"name": "LLM Inference Gateway",
"slug": "llm-inference",
"resource_type": "api_service",
"provider": "internal",
"base_url": f"http://didiAI-llm-api:{settings.port}",
"configuration": {
"version": "1.0.0",
"port": settings.port,
"external_url": settings.external_url,
"default_backend": settings.default_backend,
"backends": backends,
"default_model": settings.default_model,
},
"authentication": {
"type": "bearer",
"required": bool(settings.api_tokens),
"env_var": "LLM_API_TOKENS",
},
"headers": {
"Content-Type": "application/json",
"Accept": "application/json",
},
"rate_limits": {
"requests_per_second": settings.rate_limit_rps,
"burst": settings.rate_limit_burst,
"concurrent": settings.max_concurrent_completions,
},
"cost_tracking": {
"enabled": False,
},
"tags": ["llm", "inference", "openai-compatible", "gateway", "nlp"],
"is_active": True,
"metadata": {
"category": "nlp",
"gpu_required": settings.enable_vllm,
"status": "healthy",
"vllm_base_url": settings.vllm_base_url if settings.enable_vllm else None,
"llamacpp_base_url": (
settings.llamacpp_base_url if settings.enable_llamacpp else None
),
},
}
# Collect models from all backends
models = []
# Get models from enabled backends
try:
all_models = await client.list_models()
for model_info in all_models:
model_entry = {
"name": model_info.id,
"slug": model_info.id.lower().replace("/", "-").replace("_", "-"),
"provider": _get_provider_from_model_id(model_info.id),
"model_type": _get_model_type(model_info),
"capabilities": model_info.capabilities or [],
"configuration": {
"backend": model_info.backend,
"context_length": model_info.context_length,
"loaded": model_info.loaded,
},
"endpoint": _get_model_endpoint(model_info, settings),
"api_key_ref": None,
"tags": _get_model_tags(model_info),
"is_active": model_info.loaded,
"metadata": {
"backend": model_info.backend,
"model_id": model_info.id,
},
}
models.append(model_entry)
except Exception:
# If model listing fails, continue with empty models list
pass
# Define available functions (maps to catalog.functions)
functions = [
{
"name": "Chat Completions",
"slug": "llm-chat-completions",
"category": "completion",
"description": (
"Create a chat completion with optional streaming support. "
"OpenAI-compatible endpoint supporting multiple backends."
),
"input_schema": {
"type": "object",
"properties": {
"messages": {
"type": "array",
"description": "List of messages (1-1000)",
"items": {
"type": "object",
"properties": {
"role": {
"type": "string",
"enum": ["system", "user", "assistant", "function", "tool"],
},
"content": {"type": "string"},
},
"required": ["role"],
},
},
"model": {"type": "string", "description": "Model identifier"},
"temperature": {
"type": "number",
"minimum": 0.0,
"maximum": 2.0,
"default": 0.7,
},
"max_tokens": {"type": "integer", "minimum": 1, "maximum": 1000000},
"stream": {"type": "boolean", "default": False},
"backend": {
"type": "string",
"enum": backends,
"description": "Backend override",
},
"top_p": {"type": "number", "minimum": 0.0, "maximum": 1.0},
"frequency_penalty": {"type": "number", "minimum": -2.0, "maximum": 2.0},
"presence_penalty": {"type": "number", "minimum": -2.0, "maximum": 2.0},
"stop": {
"oneOf": [
{"type": "string"},
{"type": "array", "items": {"type": "string"}},
]
},
},
"required": ["messages", "model"],
},
"output_schema": {
"type": "object",
"properties": {
"id": {"type": "string"},
"object": {"type": "string", "const": "chat.completion"},
"created": {"type": "integer"},
"model": {"type": "string"},
"choices": {
"type": "array",
"items": {
"type": "object",
"properties": {
"index": {"type": "integer"},
"message": {
"type": "object",
"properties": {
"role": {"type": "string"},
"content": {"type": "string"},
},
},
"finish_reason": {"type": "string"},
},
},
},
"usage": {
"type": "object",
"properties": {
"prompt_tokens": {"type": "integer"},
"completion_tokens": {"type": "integer"},
"total_tokens": {"type": "integer"},
},
},
"backend": {"type": "string"},
},
},
"implementation": {
"method": "POST",
"path": "/v1/chat/completions",
"content_type": "application/json",
"timeout": 120,
"supports_streaming": True,
},
"endpoint": f"http://localhost:{settings.port}/v1/chat/completions",
"tags": ["llm", "chat", "openai", "streaming"],
"is_active": True,
"metadata": {
"rate_limited": True,
"auth_required": bool(settings.api_tokens),
},
},
{
"name": "List Models",
"slug": "llm-list-models",
"category": "discovery",
"description": "List all available models across all enabled backends",
"input_schema": {
"type": "object",
"properties": {
"backend": {
"type": "string",
"enum": backends,
"description": "Optional backend filter",
}
},
},
"output_schema": {
"type": "object",
"properties": {
"object": {"type": "string", "const": "list"},
"data": {
"type": "array",
"items": {
"type": "object",
"properties": {
"id": {"type": "string"},
"backend": {"type": "string"},
"loaded": {"type": "boolean"},
"context_length": {"type": "integer"},
"capabilities": {
"type": "array",
"items": {"type": "string"},
},
},
},
},
},
},
"implementation": {
"method": "GET",
"path": "/v1/models",
"timeout": 10,
},
"endpoint": f"http://localhost:{settings.port}/v1/models",
"tags": ["discovery", "models"],
"is_active": True,
"metadata": {},
},
{
"name": "List Backends",
"slug": "llm-list-backends",
"category": "discovery",
"description": "List all available backends",
"input_schema": {"type": "object", "properties": {}},
"output_schema": {
"type": "object",
"properties": {
"backends": {
"type": "array",
"items": {"type": "string"},
}
},
},
"implementation": {
"method": "GET",
"path": "/v1/backends",
"timeout": 5,
},
"endpoint": f"http://localhost:{settings.port}/v1/backends",
"tags": ["discovery"],
"is_active": True,
"metadata": {},
},
{
"name": "Load Model",
"slug": "llm-load-model",
"category": "management",
"description": "Load a model on a local backend (vLLM or llama.cpp only)",
"input_schema": {
"type": "object",
"properties": {
"model": {"type": "string"},
"backend": {"type": "string", "enum": ["vllm", "llamacpp"]},
},
"required": ["model", "backend"],
},
"output_schema": {
"type": "object",
"properties": {
"success": {"type": "boolean"},
"model": {"type": "string"},
"backend": {"type": "string"},
"message": {"type": "string"},
},
},
"implementation": {
"method": "POST",
"path": "/v1/models/load",
"content_type": "application/json",
"timeout": 60,
},
"endpoint": f"http://localhost:{settings.port}/v1/models/load",
"tags": ["management", "models"],
"is_active": settings.enable_vllm or settings.enable_llamacpp,
"metadata": {},
},
{
"name": "Unload Model",
"slug": "llm-unload-model",
"category": "management",
"description": "Unload a model from a local backend (vLLM or llama.cpp only)",
"input_schema": {
"type": "object",
"properties": {
"model": {"type": "string"},
"backend": {"type": "string", "enum": ["vllm", "llamacpp"]},
},
"required": ["model", "backend"],
},
"output_schema": {
"type": "object",
"properties": {
"success": {"type": "boolean"},
"model": {"type": "string"},
"backend": {"type": "string"},
"message": {"type": "string"},
},
},
"implementation": {
"method": "POST",
"path": "/v1/models/unload",
"content_type": "application/json",
"timeout": 30,
},
"endpoint": f"http://localhost:{settings.port}/v1/models/unload",
"tags": ["management", "models"],
"is_active": settings.enable_vllm or settings.enable_llamacpp,
"metadata": {},
},
]
return {
"resource": resource,
"models": models,
"functions": functions,
}
def _get_provider_from_model_id(model_id: str) -> str:
"""Extract provider name from model ID.
Args:
model_id: Model identifier (e.g., "openai/gpt-4", "meta-llama/Llama-2-7b")
Returns:
str: Provider name (e.g., "openai", "meta-llama", "unknown")
"""
if "/" in model_id:
return model_id.split("/")[0]
if "gpt" in model_id.lower():
return "openai"
if "claude" in model_id.lower():
return "anthropic"
if "llama" in model_id.lower():
return "meta"
if "qwen" in model_id.lower():
return "qwen"
if "mistral" in model_id.lower():
return "mistralai"
return "unknown"
def _get_model_type(model_info) -> str:
"""Determine model type from capabilities.
Args:
model_info: Model information object
Returns:
str: Model type (llm, vision, audio, etc.)
"""
capabilities = model_info.capabilities or []
if "vision" in capabilities or "multimodal" in capabilities:
return "vision"
if "audio" in capabilities:
return "audio"
if "embedding" in capabilities:
return "embedding"
return "llm"
def _get_model_tags(model_info) -> list[str]:
"""Generate tags for a model.
Args:
model_info: Model information object
Returns:
list[str]: List of tags
"""
tags = []
# Add model type tags
if "gpt" in model_info.id.lower():
tags.extend(["gpt", "openai"])
if "claude" in model_info.id.lower():
tags.extend(["claude", "anthropic"])
if "llama" in model_info.id.lower():
tags.extend(["llama", "meta"])
if "qwen" in model_info.id.lower():
tags.extend(["qwen"])
if "mistral" in model_info.id.lower():
tags.extend(["mistral"])
# Add capability tags
capabilities = model_info.capabilities or []
tags.extend(capabilities)
# Add backend tag
tags.append(model_info.backend)
# Add size tags if detectable
model_id_lower = model_info.id.lower()
if "7b" in model_id_lower:
tags.append("7b")
elif "13b" in model_id_lower:
tags.append("13b")
elif "30b" in model_id_lower:
tags.append("30b")
elif "70b" in model_id_lower:
tags.append("70b")
elif "120b" in model_id_lower:
tags.append("120b")
return list(set(tags)) # Remove duplicates
def _get_model_endpoint(model_info, settings) -> str:
"""Get the endpoint URL for a model.
Args:
model_info: Model information object
settings: Application settings
Returns:
str: Endpoint URL
"""
# Default to gateway endpoint
return f"http://localhost:{settings.port}/v1/chat/completions"

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"""Model management routes."""
from fastapi import APIRouter, Depends, HTTPException
from llm_inference.api.dependencies import get_client, verify_bearer_token
from llm_inference.client import LLMClient
from llm_inference.exceptions import (
BackendNotAvailableError,
BackendNotEnabledError,
ModelLoadError,
)
from llm_inference.schemas import (
BackendListResponse,
ModelListResponse,
ModelLoadRequest,
ModelLoadResponse,
)
from llm_inference.types import BackendType
router = APIRouter(dependencies=[Depends(verify_bearer_token)])
@router.get("/models", response_model=ModelListResponse)
async def list_models(
backend: str | None = None,
client: LLMClient = Depends(get_client),
) -> ModelListResponse:
"""List available models.
Args:
backend: Optional backend filter. If not specified, returns models
from all available backends.
client: LLM client instance.
Returns:
ModelListResponse: List of available models.
Raises:
HTTPException: If the specified backend is not available.
"""
try:
backend_type = BackendType(backend) if backend else None
models = await client.list_models(backend=backend_type)
return ModelListResponse(data=models)
except ValueError:
raise HTTPException(
status_code=400, detail=f"Invalid backend: {backend}"
) from None
except (BackendNotAvailableError, BackendNotEnabledError) as e:
raise HTTPException(status_code=400, detail=str(e)) from None
@router.post("/models/load", response_model=ModelLoadResponse)
async def load_model(
request: ModelLoadRequest,
client: LLMClient = Depends(get_client),
) -> ModelLoadResponse:
"""Load a model on the specified backend.
This is only supported by local backends (vLLM, llama.cpp).
Args:
request: Model load request.
client: LLM client instance.
Returns:
ModelLoadResponse: Result of the load operation.
Raises:
HTTPException: If loading fails or backend doesn't support it.
"""
try:
success = await client.load_model(request.model, request.backend)
return ModelLoadResponse(
success=success,
model=request.model,
backend=request.backend.value,
message="Model loaded successfully" if success else "Model load failed",
)
except NotImplementedError as e:
raise HTTPException(status_code=400, detail=str(e)) from None
except (BackendNotAvailableError, BackendNotEnabledError) as e:
raise HTTPException(status_code=400, detail=str(e)) from None
except ModelLoadError as e:
raise HTTPException(status_code=500, detail=str(e)) from None
@router.post("/models/unload", response_model=ModelLoadResponse)
async def unload_model(
request: ModelLoadRequest,
client: LLMClient = Depends(get_client),
) -> ModelLoadResponse:
"""Unload a model from the specified backend.
This is only supported by local backends (vLLM, llama.cpp).
Args:
request: Model unload request.
client: LLM client instance.
Returns:
ModelLoadResponse: Result of the unload operation.
Raises:
HTTPException: If unloading fails or backend doesn't support it.
"""
try:
success = await client.unload_model(request.model, request.backend)
return ModelLoadResponse(
success=success,
model=request.model,
backend=request.backend.value,
message="Model unloaded successfully" if success else "Model unload failed",
)
except NotImplementedError as e:
raise HTTPException(status_code=400, detail=str(e)) from None
except (BackendNotAvailableError, BackendNotEnabledError) as e:
raise HTTPException(status_code=400, detail=str(e)) from None
@router.get("/backends", response_model=BackendListResponse)
async def list_backends(
client: LLMClient = Depends(get_client),
) -> BackendListResponse:
"""List available backends.
Args:
client: LLM client instance.
Returns:
BackendListResponse: List of available backend names.
"""
backends = [b.value for b in client.list_backends()]
return BackendListResponse(backends=backends)

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"""Backend implementations for LLM inference."""
from llm_inference.backends.base import LLMBackend
from llm_inference.backends.registry import BackendRegistry
__all__ = [
"BackendRegistry",
"LLMBackend",
]

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"""Abstract base class for LLM backends."""
from abc import ABC, abstractmethod
from collections.abc import AsyncIterator
from llm_inference.schemas import (
CompletionChunk,
CompletionResponse,
TextCompletionResponse,
)
from llm_inference.types import ChatMessage, ModelInfo
class LLMBackend(ABC):
"""Abstract base class for LLM backends.
All backend implementations must inherit from this class and implement
the required abstract methods.
"""
@property
@abstractmethod
def name(self) -> str:
"""Backend identifier name.
Returns:
str: Unique name for this backend (e.g., 'litellm', 'vllm').
"""
...
@abstractmethod
async def complete(
self,
messages: list[ChatMessage],
model: str,
**kwargs: object,
) -> CompletionResponse:
"""Generate a chat completion.
Args:
messages: List of chat messages in the conversation.
model: Model identifier to use for completion.
**kwargs: Additional parameters passed to the backend.
Returns:
CompletionResponse: The completion response.
Raises:
CompletionError: If the completion request fails.
"""
...
@abstractmethod
async def stream(
self,
messages: list[ChatMessage],
model: str,
**kwargs: object,
) -> AsyncIterator[CompletionChunk]:
"""Generate a streaming chat completion.
Args:
messages: List of chat messages in the conversation.
model: Model identifier to use for completion.
**kwargs: Additional parameters passed to the backend.
Yields:
CompletionChunk: Streaming chunks of the completion.
Raises:
CompletionError: If the completion request fails.
"""
...
yield # pragma: no cover (makes this a generator)
async def text_complete(
self,
prompt: str | list[str],
model: str,
**kwargs: object,
) -> TextCompletionResponse:
"""Generate a (legacy) text completion for ``prompt``.
Concrete by default so backends that cannot serve /v1/completions are
not forced to implement it; override in OpenAI-compatible backends.
Raises:
NotImplementedError: If the backend does not support text completion.
"""
raise NotImplementedError(
f"Backend '{self.name}' does not support text completions"
)
@abstractmethod
async def list_models(self) -> list[ModelInfo]:
"""List available models for this backend.
Returns:
list[ModelInfo]: List of available models.
"""
...
async def load_model(self, model: str) -> bool:
"""Load a model on this backend.
This is optional and only supported by local backends (vLLM, llama.cpp).
Args:
model: Model identifier to load.
Returns:
bool: True if the model was loaded successfully.
Raises:
NotImplementedError: If the backend doesn't support model loading.
ModelLoadError: If loading the model fails.
"""
raise NotImplementedError(
f"Backend '{self.name}' does not support dynamic model loading"
)
async def unload_model(self, model: str) -> bool:
"""Unload a model from this backend.
This is optional and only supported by local backends (vLLM, llama.cpp).
Args:
model: Model identifier to unload.
Returns:
bool: True if the model was unloaded successfully.
Raises:
NotImplementedError: If the backend doesn't support model unloading.
"""
raise NotImplementedError(
f"Backend '{self.name}' does not support dynamic model unloading"
)
async def health_check(self) -> bool:
"""Check if this backend is healthy and ready to serve requests.
Returns:
bool: True if the backend is healthy.
"""
return True

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"""LiteLLM backend implementation.
LiteLLM provides a unified interface for 100+ LLM providers including:
- OpenAI
- Anthropic
- OpenRouter
- Azure OpenAI
- Google AI (Gemini)
- AWS Bedrock
- And many more
"""
import logging
import time
from collections.abc import AsyncIterator
import httpx
import litellm
import litellm.exceptions
from litellm import acompletion, atext_completion
from llm_inference.backends.base import LLMBackend
from llm_inference.config import LLMSettings
from llm_inference.exceptions import (
CompletionError,
LLMRateLimitError,
LLMTimeoutError,
)
from llm_inference.retry import retry_with_backoff
from llm_inference.schemas import (
CompletionChunk,
CompletionResponse,
TextChoice,
TextCompletionResponse,
)
from llm_inference.types import (
ChatMessage,
Choice,
Delta,
ModelInfo,
StreamChoice,
Usage,
)
from llm_inference.utils import safe_close_stream
logger = logging.getLogger("llm_inference.backends.litellm")
class LiteLLMBackend(LLMBackend):
"""LiteLLM backend supporting 100+ LLM providers.
This is the default backend and handles most cloud-based LLM providers
including OpenRouter for accessing various models through a single API.
"""
# Default cache TTL for model list (1 hour)
_MODELS_CACHE_TTL: float = 3600.0
def __init__(self, settings: LLMSettings) -> None:
"""Initialize LiteLLM backend.
Args:
settings: Application settings with API keys.
"""
self._settings = settings
self._configure_litellm()
# Model list cache: (timestamp, models)
self._models_cache: tuple[float, list[ModelInfo]] | None = None
def _configure_litellm(self) -> None:
"""Configure LiteLLM with API keys from settings."""
# Set API keys if provided
if self._settings.openrouter_api_key:
litellm.openrouter_api_key = self._settings.openrouter_api_key
if self._settings.openai_api_key:
litellm.openai_api_key = self._settings.openai_api_key
if self._settings.anthropic_api_key:
litellm.anthropic_api_key = self._settings.anthropic_api_key
# Suppress verbose logging
litellm.suppress_debug_info = True
@property
def name(self) -> str:
"""Backend identifier name."""
return "litellm"
async def complete(
self,
messages: list[ChatMessage],
model: str,
**kwargs: object,
) -> CompletionResponse:
"""Generate a chat completion via LiteLLM.
Args:
messages: List of chat messages.
model: Model identifier (e.g., 'gpt-4', 'claude-3-opus').
**kwargs: Additional parameters (temperature, max_tokens, etc.).
Returns:
CompletionResponse: The completion response.
Raises:
CompletionError: If the request fails after retries.
LLMRateLimitError: If rate limited.
LLMTimeoutError: If the request times out.
"""
async def _do_complete() -> CompletionResponse:
response = await acompletion(
model=model,
messages=[m.model_dump(exclude_none=True) for m in messages],
timeout=self._settings.request_timeout,
**kwargs,
)
# Convert response to our schema
choices = []
for choice in response.choices:
choices.append(
Choice(
index=choice.index,
message=ChatMessage(
role=choice.message.role,
content=choice.message.content,
),
finish_reason=choice.finish_reason,
)
)
usage = None
if response.usage:
usage = Usage(
prompt_tokens=response.usage.prompt_tokens,
completion_tokens=response.usage.completion_tokens,
total_tokens=response.usage.total_tokens,
)
return CompletionResponse(
id=response.id,
created=response.created,
model=response.model,
choices=choices,
usage=usage,
backend=self.name,
)
return await retry_with_backoff(
_do_complete,
max_retries=self._settings.max_retries,
min_wait=self._settings.retry_min_wait,
max_wait=self._settings.retry_max_wait,
backend=self.name,
model=model,
)
async def text_complete(
self,
prompt: str | list[str],
model: str,
**kwargs: object,
) -> TextCompletionResponse:
"""Generate a legacy text completion via LiteLLM (cloud providers)."""
async def _do_text() -> TextCompletionResponse:
response = await atext_completion(
model=model,
prompt=prompt,
timeout=self._settings.request_timeout,
**kwargs,
)
choices = [
TextChoice(
index=getattr(c, "index", i),
text=getattr(c, "text", ""),
finish_reason=getattr(c, "finish_reason", None),
)
for i, c in enumerate(response.choices)
]
usage = None
if response.usage:
usage = Usage(
prompt_tokens=response.usage.prompt_tokens,
completion_tokens=response.usage.completion_tokens,
total_tokens=response.usage.total_tokens,
)
return TextCompletionResponse(
id=response.id,
created=response.created,
model=response.model,
choices=choices,
usage=usage,
backend=self.name,
)
return await retry_with_backoff(
_do_text,
max_retries=self._settings.max_retries,
min_wait=self._settings.retry_min_wait,
max_wait=self._settings.retry_max_wait,
backend=self.name,
model=model,
)
async def stream(
self,
messages: list[ChatMessage],
model: str,
**kwargs: object,
) -> AsyncIterator[CompletionChunk]:
"""Generate a streaming chat completion via LiteLLM.
Args:
messages: List of chat messages.
model: Model identifier.
**kwargs: Additional parameters.
Yields:
CompletionChunk: Streaming chunks of the completion.
Raises:
CompletionError: If the request fails.
LLMRateLimitError: If rate limited.
LLMTimeoutError: If the request times out.
"""
response = None
try:
response = await acompletion(
model=model,
messages=[m.model_dump(exclude_none=True) for m in messages],
stream=True,
timeout=self._settings.request_timeout,
**kwargs,
)
async for chunk in response:
choices = []
if chunk.choices:
for choice in chunk.choices:
delta = Delta(
role=getattr(choice.delta, "role", None),
content=getattr(choice.delta, "content", None),
)
choices.append(
StreamChoice(
index=choice.index,
delta=delta,
finish_reason=choice.finish_reason,
)
)
yield CompletionChunk(
id=chunk.id,
created=int(time.time()),
model=chunk.model or model,
choices=choices,
)
except litellm.exceptions.RateLimitError as e:
logger.warning("Rate limited during stream: %s", str(e))
raise LLMRateLimitError(str(e), backend=self.name) from e
except litellm.exceptions.Timeout as e:
logger.warning("Timeout during stream: %s", str(e))
raise LLMTimeoutError(str(e), backend=self.name) from e
except litellm.exceptions.AuthenticationError as e:
logger.error("Authentication failed: %s", str(e))
raise CompletionError(
f"Authentication failed: {e}", backend=self.name, model=model
) from e
except litellm.exceptions.APIError as e:
logger.error("API error during stream: %s", str(e))
raise CompletionError(str(e), backend=self.name, model=model) from e
except Exception as e:
logger.error("Unexpected error during stream: %s", str(e))
raise CompletionError(str(e), backend=self.name, model=model) from e
finally:
# Ensure stream is properly closed on cancellation or error
await safe_close_stream(response, logger)
async def list_models(self) -> list[ModelInfo]:
"""List available models via LiteLLM.
Uses a TTL-based cache to avoid hitting external APIs on every call.
Falls back to a curated list if dynamic fetching fails.
Returns:
list[ModelInfo]: List of available models.
"""
# Check cache first
now = time.time()
if self._models_cache is not None:
cached_time, cached_models = self._models_cache
if now - cached_time < self._MODELS_CACHE_TTL:
logger.debug(
"Returning cached model list (%d models)", len(cached_models)
)
return cached_models
models: list[ModelInfo] = []
async with httpx.AsyncClient(
timeout=httpx.Timeout(self._settings.connect_timeout)
) as client:
# Try to fetch OpenAI models dynamically
if self._settings.openai_api_key:
try:
response = await client.get(
"https://api.openai.com/v1/models",
headers={
"Authorization": f"Bearer {self._settings.openai_api_key}"
},
)
if response.status_code == 200:
data = response.json()
for m in data.get("data", []):
model_id = m.get("id", "")
# Filter to chat models (gpt-*)
if model_id.startswith("gpt-"):
models.append(
ModelInfo(
id=model_id,
backend=self.name,
capabilities=["chat"],
)
)
except Exception as e:
logger.debug("Failed to fetch OpenAI models dynamically: %s", e)
# Fall back to curated list for OpenAI
models.extend(self._get_fallback_openai_models())
# Try to fetch OpenRouter models dynamically
if self._settings.openrouter_api_key:
try:
response = await client.get(
"https://openrouter.ai/api/v1/models",
headers={
"Authorization": f"Bearer {self._settings.openrouter_api_key}"
},
)
if response.status_code == 200:
data = response.json()
for m in data.get("data", []):
model_id = m.get("id", "")
if model_id:
context_length = m.get("context_length")
models.append(
ModelInfo(
id=f"openrouter/{model_id}",
backend=self.name,
context_length=context_length,
capabilities=["chat"],
)
)
except Exception as e:
logger.debug("Failed to fetch OpenRouter models dynamically: %s", e)
# Fall back to curated list for OpenRouter
models.extend(self._get_fallback_openrouter_models())
# Anthropic doesn't have a public models endpoint, use curated list
if self._settings.anthropic_api_key:
models.extend(self._get_fallback_anthropic_models())
# Cache the result
self._models_cache = (time.time(), models)
logger.debug("Cached model list (%d models)", len(models))
return models
def _get_fallback_openai_models(self) -> list[ModelInfo]:
"""Get fallback list of OpenAI models."""
return [
ModelInfo(
id="gpt-4o",
backend=self.name,
context_length=128000,
capabilities=["chat"],
),
ModelInfo(
id="gpt-4o-mini",
backend=self.name,
context_length=128000,
capabilities=["chat"],
),
ModelInfo(
id="gpt-4-turbo",
backend=self.name,
context_length=128000,
capabilities=["chat"],
),
ModelInfo(
id="gpt-3.5-turbo",
backend=self.name,
context_length=16385,
capabilities=["chat"],
),
]
def _get_fallback_anthropic_models(self) -> list[ModelInfo]:
"""Get fallback list of Anthropic models."""
return [
ModelInfo(
id="claude-3-5-sonnet-20241022",
backend=self.name,
context_length=200000,
capabilities=["chat"],
),
ModelInfo(
id="claude-3-opus-20240229",
backend=self.name,
context_length=200000,
capabilities=["chat"],
),
ModelInfo(
id="claude-3-haiku-20240307",
backend=self.name,
context_length=200000,
capabilities=["chat"],
),
]
def _get_fallback_openrouter_models(self) -> list[ModelInfo]:
"""Get fallback list of OpenRouter models."""
return [
ModelInfo(
id="openrouter/anthropic/claude-3.5-sonnet",
backend=self.name,
capabilities=["chat"],
),
ModelInfo(
id="openrouter/openai/gpt-4o",
backend=self.name,
capabilities=["chat"],
),
ModelInfo(
id="openrouter/meta-llama/llama-3.1-405b-instruct",
backend=self.name,
capabilities=["chat"],
),
ModelInfo(
id="openrouter/google/gemini-pro-1.5",
backend=self.name,
capabilities=["chat"],
),
]
async def health_check(self) -> bool:
"""Check if LiteLLM backend can reach at least one provider.
Actually tests connectivity to configured providers instead of
always returning True.
Returns:
bool: True if at least one provider is reachable.
"""
async with httpx.AsyncClient(
timeout=httpx.Timeout(self._settings.connect_timeout)
) as client:
# Check OpenAI
if self._settings.openai_api_key:
try:
response = await client.get(
"https://api.openai.com/v1/models",
headers={
"Authorization": f"Bearer {self._settings.openai_api_key}"
},
)
# 200 = success, 401 = reachable but bad key (still healthy)
# Treating 401 as healthy supports key rotation scenarios
if response.status_code in (200, 401):
if response.status_code == 401:
logger.warning(
"OpenAI: API key invalid but endpoint reachable"
)
return True
except httpx.RequestError as e:
logger.debug("OpenAI health check failed: %s", e)
# Check Anthropic
if self._settings.anthropic_api_key:
try:
response = await client.get(
"https://api.anthropic.com/v1/models",
headers={
"x-api-key": self._settings.anthropic_api_key,
"anthropic-version": "2023-06-01",
},
)
if response.status_code in (200, 401):
if response.status_code == 401:
logger.warning(
"Anthropic: API key invalid but endpoint reachable"
)
return True
except httpx.RequestError as e:
logger.debug("Anthropic health check failed: %s", e)
# Check OpenRouter
if self._settings.openrouter_api_key:
try:
response = await client.get(
"https://openrouter.ai/api/v1/models",
headers={
"Authorization": f"Bearer {self._settings.openrouter_api_key}"
},
)
if response.status_code in (200, 401):
if response.status_code == 401:
logger.warning(
"OpenRouter: API key invalid but endpoint reachable"
)
return True
except httpx.RequestError as e:
logger.debug("OpenRouter health check failed: %s", e)
# No providers reachable or configured
logger.warning("No LLM providers reachable")
return False

477
ai_platform/modules/llm-inference/src/llm_inference/backends/llamacpp_backend.py Normal file
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"""llama.cpp backend with load balancing across multiple servers.
Supports round-robin distribution with automatic failover and periodic
health checks. When a server goes down, traffic is routed to healthy
servers. When it recovers, it re-enters the rotation.
Configuration:
Single server (legacy):
LLM_LLAMACPP_BASE_URL=http://localhost:8102
Multiple servers (load balanced):
LLM_LLAMACPP_BASE_URLS=http://10.11.10.18:14001,http://10.11.10.19:14001
"""
import asyncio
import logging
import time
from collections.abc import AsyncIterator
from dataclasses import dataclass, field
import httpx
from llm_inference.backends.base import LLMBackend
from llm_inference.config import LLMSettings
from llm_inference.exceptions import (
CompletionError,
LLMConnectionError,
LLMRateLimitError,
LLMTimeoutError,
ModelListError,
)
from llm_inference.retry import retry_with_backoff
from llm_inference.schemas import CompletionChunk, CompletionResponse
from llm_inference.types import (
ChatMessage,
Choice,
Delta,
ModelInfo,
StreamChoice,
Usage,
)
from llm_inference.utils import safe_close_stream
try:
import openai
from openai import AsyncOpenAI
OPENAI_AVAILABLE = True
except ImportError:
OPENAI_AVAILABLE = False
logger = logging.getLogger("llm_inference.backends.llamacpp")
@dataclass
class LlamaCppServer:
"""State for a single llama.cpp server."""
url: str
healthy: bool = True
request_count: int = 0
error_count: int = 0
last_check: float = 0
response_times: list[float] = field(default_factory=list)
@property
def avg_response_ms(self) -> float:
if not self.response_times:
return 0
recent = self.response_times[-50:]
return sum(recent) / len(recent)
@property
def short_name(self) -> str:
"""Extract host:port for logging."""
return self.url.replace("http://", "").replace("https://", "")
class LlamaCppBackend(LLMBackend):
"""llama.cpp backend with round-robin load balancing.
When multiple URLs are configured via LLM_LLAMACPP_BASE_URLS,
requests are distributed round-robin across healthy servers.
Unhealthy servers are skipped and periodically re-checked.
"""
def __init__(self, settings: LLMSettings) -> None:
if not OPENAI_AVAILABLE:
raise ImportError(
"openai package is required for llama.cpp backend. "
"Install with: pip install llm-inference[llamacpp]"
)
self._settings = settings
self._timeout = httpx.Timeout(
connect=settings.connect_timeout,
read=settings.request_timeout,
write=settings.request_timeout,
pool=settings.connect_timeout,
)
# Initialize servers and clients
self._servers: list[LlamaCppServer] = []
self._clients: dict[str, AsyncOpenAI] = {}
for url in settings.llamacpp_urls:
server = LlamaCppServer(url=url)
self._servers.append(server)
self._clients[url] = AsyncOpenAI(
base_url=f"{url}/v1",
api_key="not-needed",
timeout=self._timeout,
max_retries=0,
)
self._current_index = 0
self._lock = asyncio.Lock()
self._health_task: asyncio.Task[None] | None = None
self._health_check_interval = settings.llamacpp_health_check_interval
server_names = [s.short_name for s in self._servers]
logger.info(
"llama.cpp backend: %d server(s): %s", len(self._servers), server_names
)
# Start health check loop if multiple servers
if len(self._servers) > 1:
try:
loop = asyncio.get_running_loop()
self._health_task = loop.create_task(
self._periodic_health_check(self._health_check_interval)
)
except RuntimeError:
# No running loop yet (e.g., during import). Will be started later.
logger.debug("No running loop, deferring health check task")
def start_health_checks(self) -> None:
"""Start periodic health checks if not already running.
Call this from the application lifespan when the event loop is available.
"""
if self._health_task is None and len(self._servers) > 1:
loop = asyncio.get_running_loop()
self._health_task = loop.create_task(
self._periodic_health_check(self._health_check_interval)
)
logger.info("Started periodic health checks (interval=%ds)", self._health_check_interval)
@property
def name(self) -> str:
return "llamacpp"
async def _get_server(self) -> tuple[LlamaCppServer, AsyncOpenAI]:
"""Get next healthy server via round-robin."""
async with self._lock:
healthy = [s for s in self._servers if s.healthy]
if not healthy:
# All down — try all servers (failover attempt)
logger.warning("All llama.cpp servers unhealthy, trying all")
healthy = self._servers
server = healthy[self._current_index % len(healthy)]
self._current_index += 1
return server, self._clients[server.url]
def _mark_unhealthy(self, server: LlamaCppServer) -> None:
server.error_count += 1
if server.healthy:
server.healthy = False
logger.warning(
"llama.cpp server %s marked unhealthy (errors: %d)",
server.short_name,
server.error_count,
)
def _mark_healthy(self, server: LlamaCppServer) -> None:
if not server.healthy:
logger.info(
"llama.cpp server %s recovered (had %d errors)",
server.short_name,
server.error_count,
)
server.error_count = 0
server.healthy = True
async def _periodic_health_check(self, interval: int) -> None:
"""Periodically check health of all servers."""
while True:
await asyncio.sleep(interval)
for server in self._servers:
client = self._clients[server.url]
try:
await client.models.list()
self._mark_healthy(server)
except Exception:
self._mark_unhealthy(server)
server.last_check = time.time()
async def _complete_on_server(
self,
server: LlamaCppServer,
client: AsyncOpenAI,
messages: list[ChatMessage],
model: str,
**kwargs: object,
) -> CompletionResponse:
"""Execute completion on a specific server."""
start = time.time()
response = await client.chat.completions.create(
model=model,
messages=[m.model_dump(exclude_none=True) for m in messages], # type: ignore[arg-type]
**kwargs,
)
elapsed_ms = (time.time() - start) * 1000
server.response_times.append(elapsed_ms)
if len(server.response_times) > 50:
server.response_times = server.response_times[-50:]
server.request_count += 1
self._mark_healthy(server)
choices = []
for choice in response.choices:
choices.append(
Choice(
index=choice.index,
message=ChatMessage(
role=choice.message.role,
content=choice.message.content,
),
finish_reason=choice.finish_reason,
)
)
usage = None
if response.usage:
usage = Usage(
prompt_tokens=response.usage.prompt_tokens,
completion_tokens=response.usage.completion_tokens,
total_tokens=response.usage.total_tokens,
)
return CompletionResponse(
id=response.id,
created=response.created,
model=response.model or model,
choices=choices,
usage=usage,
backend=f"{self.name}@{server.short_name}",
)
async def complete(
self,
messages: list[ChatMessage],
model: str,
**kwargs: object,
) -> CompletionResponse:
"""Generate a chat completion with load balancing and failover.
Tries healthy servers round-robin. On failure, marks server unhealthy
and retries on the next available server.
Args:
messages: List of chat messages.
model: Model identifier (may be ignored by server).
**kwargs: Additional parameters.
Returns:
CompletionResponse: The completion response.
Raises:
CompletionError: If all servers fail.
"""
tried: set[str] = set()
last_error: Exception | None = None
for _attempt in range(len(self._servers)):
server, client = await self._get_server()
if server.url in tried:
continue
tried.add(server.url)
async def _do_complete(
_server: LlamaCppServer = server,
_client: AsyncOpenAI = client,
) -> CompletionResponse:
return await self._complete_on_server(
_server, _client, messages, model, **kwargs
)
try:
return await retry_with_backoff(
_do_complete,
max_retries=self._settings.max_retries,
min_wait=self._settings.retry_min_wait,
max_wait=self._settings.retry_max_wait,
backend=self.name,
model=model,
)
except (LLMConnectionError, LLMTimeoutError) as e:
self._mark_unhealthy(server)
last_error = e
logger.warning(
"llama.cpp server %s failed, trying next: %s",
server.short_name,
str(e),
)
continue
except Exception:
raise
raise CompletionError(
f"All llama.cpp servers failed. Last error: {last_error}",
backend=self.name,
model=model,
)
async def stream(
self,
messages: list[ChatMessage],
model: str,
**kwargs: object,
) -> AsyncIterator[CompletionChunk]:
"""Generate a streaming chat completion with failover.
Args:
messages: List of chat messages.
model: Model identifier.
**kwargs: Additional parameters.
Yields:
CompletionChunk: Streaming chunks of the completion.
Raises:
CompletionError: If all servers fail.
"""
tried: set[str] = set()
last_error: Exception | None = None
for _attempt in range(len(self._servers)):
server, client = await self._get_server()
if server.url in tried:
continue
tried.add(server.url)
stream = None
try:
stream = await client.chat.completions.create(
model=model,
messages=[m.model_dump(exclude_none=True) for m in messages], # type: ignore[arg-type]
stream=True,
**kwargs,
)
server.request_count += 1
self._mark_healthy(server)
async for chunk in stream:
choices = []
if chunk.choices:
for choice in chunk.choices:
delta = Delta(
role=getattr(choice.delta, "role", None),
content=getattr(choice.delta, "content", None),
)
choices.append(
StreamChoice(
index=choice.index,
delta=delta,
finish_reason=choice.finish_reason,
)
)
yield CompletionChunk(
id=chunk.id,
created=int(time.time()),
model=chunk.model or model,
choices=choices,
)
# Stream completed successfully
return
except openai.APIConnectionError as e:
self._mark_unhealthy(server)
last_error = e
logger.warning(
"llama.cpp server %s stream failed: %s", server.short_name, e
)
continue
except openai.APITimeoutError as e:
self._mark_unhealthy(server)
last_error = e
logger.warning(
"llama.cpp server %s stream timeout: %s", server.short_name, e
)
continue
except openai.RateLimitError as e:
raise LLMRateLimitError(str(e), backend=self.name) from e
except openai.AuthenticationError as e:
raise CompletionError(
f"Authentication failed: {e}", backend=self.name, model=model
) from e
except openai.APIStatusError as e:
raise CompletionError(str(e), backend=self.name, model=model) from e
except Exception as e:
raise CompletionError(str(e), backend=self.name, model=model) from e
finally:
await safe_close_stream(stream, logger)
raise CompletionError(
f"All llama.cpp servers failed for stream. Last error: {last_error}",
backend=self.name,
model=model,
)
async def list_models(self) -> list[ModelInfo]:
"""List models from the first healthy server.
Returns:
list[ModelInfo]: List of available models.
Raises:
ModelListError: If models cannot be retrieved.
"""
for server in self._servers:
if not server.healthy and len(self._servers) > 1:
continue
client = self._clients[server.url]
try:
models = await client.models.list()
return [
ModelInfo(
id=m.id,
backend=self.name,
capabilities=["chat", "vision"],
)
for m in models.data
]
except Exception as e:
self._mark_unhealthy(server)
logger.debug("Failed to list models from %s: %s", server.short_name, e)
continue
raise ModelListError(self.name, "All llama.cpp servers unreachable")
async def health_check(self) -> bool:
"""Check if at least one llama.cpp server is healthy.
Returns:
bool: True if any server is reachable.
"""
for server in self._servers:
client = self._clients[server.url]
try:
await client.models.list()
self._mark_healthy(server)
return True
except Exception:
self._mark_unhealthy(server)
continue
return False
def get_servers_status(self) -> list[dict[str, object]]:
"""Get status of all servers (for health endpoint)."""
return [
{
"url": s.url,
"healthy": s.healthy,
"request_count": s.request_count,
"error_count": s.error_count,
"avg_response_ms": round(s.avg_response_ms, 1),
}
for s in self._servers
]

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"""Backend registry for managing LLM backend instances."""
from llm_inference.backends.base import LLMBackend
from llm_inference.config import LLMSettings
from llm_inference.exceptions import BackendNotAvailableError, BackendNotEnabledError
from llm_inference.types import BackendType
class BackendRegistry:
"""Registry for managing LLM backend instances.
The registry initializes and provides access to configured backends.
LiteLLM is always available; vLLM and llama.cpp are optional.
"""
def __init__(self, settings: LLMSettings) -> None:
"""Initialize the backend registry.
Args:
settings: Application settings.
"""
self._settings = settings
self._backends: dict[BackendType, LLMBackend] = {}
self._initialize_backends()
def _initialize_backends(self) -> None:
"""Initialize enabled backends."""
# LiteLLM is always available (core dependency)
from llm_inference.backends.litellm_backend import LiteLLMBackend
self._backends[BackendType.LITELLM] = LiteLLMBackend(self._settings)
# vLLM is optional but MUST work if enabled (fail-fast)
if self._settings.enable_vllm:
try:
from llm_inference.backends.vllm_backend import VLLMBackend
self._backends[BackendType.VLLM] = VLLMBackend(self._settings)
except ImportError as e:
raise BackendNotAvailableError(
"vllm",
f"vLLM backend is enabled but dependencies are not installed. "
f"Install with: pip install llm-inference[vllm]. "
f"Original error: {e}",
) from e
# llama.cpp is optional but MUST work if enabled (fail-fast)
if self._settings.enable_llamacpp:
try:
from llm_inference.backends.llamacpp_backend import LlamaCppBackend
self._backends[BackendType.LLAMACPP] = LlamaCppBackend(self._settings)
except ImportError as e:
raise BackendNotAvailableError(
"llamacpp",
f"llama.cpp backend is enabled but dependencies are not installed. "
f"Install with: pip install llm-inference[llamacpp]. "
f"Original error: {e}",
) from e
def get(self, backend_type: BackendType | str | None = None) -> LLMBackend:
"""Get a backend instance.
Args:
backend_type: Backend to retrieve. If None, uses default from settings.
Returns:
LLMBackend: The requested backend instance.
Raises:
BackendNotAvailableError: If the backend is not available.
BackendNotEnabledError: If the backend is not enabled.
"""
if backend_type is None:
backend_type = BackendType(self._settings.default_backend)
elif isinstance(backend_type, str):
backend_type = BackendType(backend_type)
if backend_type not in self._backends:
# Check if it's a valid backend that's just not enabled
if backend_type == BackendType.VLLM and not self._settings.enable_vllm:
raise BackendNotEnabledError("vllm")
if (
backend_type == BackendType.LLAMACPP
and not self._settings.enable_llamacpp
):
raise BackendNotEnabledError("llamacpp")
raise BackendNotAvailableError(
backend_type.value, "Backend not initialized"
)
return self._backends[backend_type]
def list_backends(self) -> list[BackendType]:
"""List all available backends.
Returns:
list[BackendType]: List of available backend types.
"""
return list(self._backends.keys())
def is_available(self, backend_type: BackendType | str) -> bool:
"""Check if a backend is available.
Args:
backend_type: Backend to check.
Returns:
bool: True if the backend is available.
"""
if isinstance(backend_type, str):
backend_type = BackendType(backend_type)
return backend_type in self._backends

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"""vLLM backend implementation.
vLLM is a high-throughput and memory-efficient inference engine for LLMs.
This backend connects to a vLLM server via its OpenAI-compatible API.
"""
import logging
import time
from collections.abc import AsyncIterator
import httpx
from llm_inference.backends.base import LLMBackend
from llm_inference.config import LLMSettings
from llm_inference.exceptions import (
CompletionError,
LLMConnectionError,
LLMRateLimitError,
LLMTimeoutError,
ModelListError,
)
from llm_inference.retry import retry_with_backoff
from llm_inference.schemas import (
CompletionChunk,
CompletionResponse,
TextChoice,
TextCompletionResponse,
)
from llm_inference.types import (
ChatMessage,
Choice,
Delta,
ModelInfo,
StreamChoice,
Usage,
)
from llm_inference.utils import safe_close_stream
try:
import openai
from openai import AsyncOpenAI
OPENAI_AVAILABLE = True
except ImportError:
OPENAI_AVAILABLE = False
logger = logging.getLogger("llm_inference.backends.vllm")
class VLLMBackend(LLMBackend):
"""vLLM backend using OpenAI-compatible API.
Requires the vLLM server to be running with the OpenAI-compatible
API enabled.
Example:
Start vLLM server:
```bash
python -m vllm.entrypoints.openai.api_server \
--model meta-llama/Llama-2-7b-chat-hf \
--host 0.0.0.0 --port 8101
```
Configure:
```bash
export LLM_ENABLE_VLLM=true
export LLM_VLLM_BASE_URL=http://localhost:8101
```
"""
def __init__(self, settings: LLMSettings) -> None:
"""Initialize vLLM backend.
Args:
settings: Application settings.
Raises:
ImportError: If openai package is not installed.
"""
if not OPENAI_AVAILABLE:
raise ImportError(
"openai package is required for vLLM backend. "
"Install with: pip install llm-inference[vllm]"
)
self._settings = settings
# Configure httpx timeout
timeout = httpx.Timeout(
connect=settings.connect_timeout,
read=settings.request_timeout,
write=settings.request_timeout,
pool=settings.connect_timeout,
)
self._client = AsyncOpenAI(
base_url=f"{settings.vllm_base_url}/v1",
api_key=settings.vllm_api_key or "not-needed",
timeout=timeout,
max_retries=0, # We handle retries ourselves
)
@property
def name(self) -> str:
"""Backend identifier name."""
return "vllm"
async def complete(
self,
messages: list[ChatMessage],
model: str,
**kwargs: object,
) -> CompletionResponse:
"""Generate a chat completion via vLLM.
Args:
messages: List of chat messages.
model: Model identifier.
**kwargs: Additional parameters.
Returns:
CompletionResponse: The completion response.
Raises:
CompletionError: If the request fails after retries.
LLMRateLimitError: If rate limited.
LLMTimeoutError: If the request times out.
"""
async def _do_complete() -> CompletionResponse:
response = await self._client.chat.completions.create(
model=model,
messages=[m.model_dump(exclude_none=True) for m in messages], # type: ignore[arg-type]
**kwargs,
)
choices = []
for choice in response.choices:
choices.append(
Choice(
index=choice.index,
message=ChatMessage(
role=choice.message.role,
content=choice.message.content,
),
finish_reason=choice.finish_reason,
)
)
usage = None
if response.usage:
usage = Usage(
prompt_tokens=response.usage.prompt_tokens,
completion_tokens=response.usage.completion_tokens,
total_tokens=response.usage.total_tokens,
)
return CompletionResponse(
id=response.id,
created=response.created,
model=response.model,
choices=choices,
usage=usage,
backend=self.name,
)
return await retry_with_backoff(
_do_complete,
max_retries=self._settings.max_retries,
min_wait=self._settings.retry_min_wait,
max_wait=self._settings.retry_max_wait,
backend=self.name,
model=model,
)
async def text_complete(
self,
prompt: str | list[str],
model: str,
**kwargs: object,
) -> TextCompletionResponse:
"""Generate a legacy text completion via vLLM (/v1/completions)."""
async def _do_text() -> TextCompletionResponse:
response = await self._client.completions.create(
model=model,
prompt=prompt, # type: ignore[arg-type]
**kwargs,
)
choices = [
TextChoice(
index=c.index, text=c.text, finish_reason=c.finish_reason
)
for c in response.choices
]
usage = None
if response.usage:
usage = Usage(
prompt_tokens=response.usage.prompt_tokens,
completion_tokens=response.usage.completion_tokens,
total_tokens=response.usage.total_tokens,
)
return TextCompletionResponse(
id=response.id,
created=response.created,
model=response.model,
choices=choices,
usage=usage,
backend=self.name,
)
return await retry_with_backoff(
_do_text,
max_retries=self._settings.max_retries,
min_wait=self._settings.retry_min_wait,
max_wait=self._settings.retry_max_wait,
backend=self.name,
model=model,
)
async def stream(
self,
messages: list[ChatMessage],
model: str,
**kwargs: object,
) -> AsyncIterator[CompletionChunk]:
"""Generate a streaming chat completion via vLLM.
Args:
messages: List of chat messages.
model: Model identifier.
**kwargs: Additional parameters.
Yields:
CompletionChunk: Streaming chunks of the completion.
Raises:
CompletionError: If the request fails.
LLMRateLimitError: If rate limited.
LLMTimeoutError: If the request times out.
"""
stream = None
try:
stream = await self._client.chat.completions.create(
model=model,
messages=[m.model_dump(exclude_none=True) for m in messages], # type: ignore[arg-type]
stream=True,
**kwargs,
)
async for chunk in stream:
choices = []
if chunk.choices:
for choice in chunk.choices:
delta = Delta(
role=getattr(choice.delta, "role", None),
content=getattr(choice.delta, "content", None),
)
choices.append(
StreamChoice(
index=choice.index,
delta=delta,
finish_reason=choice.finish_reason,
)
)
yield CompletionChunk(
id=chunk.id,
created=int(time.time()),
model=chunk.model or model,
choices=choices,
)
except openai.RateLimitError as e:
logger.warning("Rate limited during stream: %s", str(e))
raise LLMRateLimitError(str(e), backend=self.name) from e
except openai.APITimeoutError as e:
logger.warning("Timeout during stream: %s", str(e))
raise LLMTimeoutError(str(e), backend=self.name) from e
except openai.APIConnectionError as e:
logger.error("Connection error during stream: %s", str(e))
raise LLMConnectionError(self.name, reason=str(e)) from e
except openai.AuthenticationError as e:
logger.error("Authentication failed: %s", str(e))
raise CompletionError(
f"Authentication failed: {e}", backend=self.name, model=model
) from e
except openai.APIStatusError as e:
logger.error("API error during stream: %s", str(e))
raise CompletionError(str(e), backend=self.name, model=model) from e
except Exception as e:
logger.error("Unexpected error during stream: %s", str(e))
raise CompletionError(str(e), backend=self.name, model=model) from e
finally:
# Ensure stream is properly closed on cancellation or error
await safe_close_stream(stream, logger)
async def list_models(self) -> list[ModelInfo]:
"""List models available on the vLLM server.
Returns:
list[ModelInfo]: List of available models.
Raises:
ModelListError: If models cannot be retrieved.
"""
try:
models = await self._client.models.list()
return [
ModelInfo(
id=m.id,
backend=self.name,
capabilities=["chat"],
)
for m in models.data
]
except openai.APIConnectionError as e:
logger.error("Failed to list models - connection error: %s", str(e))
raise ModelListError(
self.name, f"Cannot connect to vLLM server: {e}"
) from e
except openai.APITimeoutError as e:
logger.error("Failed to list models - timeout: %s", str(e))
raise ModelListError(
self.name, f"Timeout connecting to vLLM server: {e}"
) from e
except Exception as e:
logger.error("Failed to list models: %s", str(e))
raise ModelListError(self.name, str(e)) from e
async def health_check(self) -> bool:
"""Check if vLLM server is healthy.
Returns:
bool: True if the server is reachable and responding.
"""
try:
await self._client.models.list()
return True
except openai.APIConnectionError:
logger.debug("vLLM health check failed: connection error")
return False
except openai.APITimeoutError:
logger.debug("vLLM health check failed: timeout")
return False
except Exception as e:
logger.debug("vLLM health check failed: %s", str(e))
return False

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"""CLI entry point for the LLM inference server."""
import argparse
import signal
import sys
from typing import Any
import uvicorn
from llm_inference.config import SettingsCache
from llm_inference.logging import configure_logging, get_logger
class GracefulShutdown:
"""Handles graceful shutdown on signals.
Allows in-flight requests to complete before exiting.
"""
def __init__(self) -> None:
"""Initialize shutdown handler."""
self._server: uvicorn.Server | None = None
self._shutdown_requested = False
self._logger = get_logger("cli.shutdown")
def register_signals(self) -> None:
"""Register signal handlers for graceful shutdown."""
signal.signal(signal.SIGTERM, self._signal_handler)
signal.signal(signal.SIGINT, self._signal_handler)
def _signal_handler(self, signum: int, frame: Any) -> None:
"""Handle shutdown signals.
Args:
signum: Signal number.
frame: Current stack frame (unused).
"""
if self._shutdown_requested:
# Second signal - force exit
self._logger.warning("Forced shutdown requested")
sys.exit(1)
self._shutdown_requested = True
signal_name = signal.Signals(signum).name
self._logger.info("Received %s, initiating graceful shutdown...", signal_name)
if self._server:
self._server.should_exit = True
def set_server(self, server: uvicorn.Server) -> None:
"""Set the uvicorn server for shutdown control.
Args:
server: Uvicorn server instance.
"""
self._server = server
def main() -> None:
"""Run the LLM inference server with graceful shutdown support."""
parser = argparse.ArgumentParser(
description="LLM Inference API Server",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
parser.add_argument(
"--host",
type=str,
default=None,
help="Host to bind to (default: from LLM_HOST env or 0.0.0.0)",
)
parser.add_argument(
"--port",
type=int,
default=None,
help="Port to bind to (default: from LLM_PORT env or 14011)",
)
parser.add_argument(
"--workers",
type=int,
default=1,
help="Number of worker processes",
)
parser.add_argument(
"--reload",
action="store_true",
help="Enable auto-reload for development",
)
parser.add_argument(
"--graceful-timeout",
type=int,
default=30,
help="Timeout in seconds for graceful shutdown",
)
args = parser.parse_args()
# Get settings for defaults
settings = SettingsCache.get()
# Configure logging early
configure_logging(settings.log_level, settings.log_json)
logger = get_logger("cli")
host = args.host or settings.host
port = args.port or settings.port
logger.info("Starting LLM Inference API Server on %s:%d", host, port)
if args.reload:
# Development mode - use simple uvicorn.run
logger.info("Development mode - auto-reload enabled")
uvicorn.run(
"llm_inference.api.app:create_app",
factory=True,
host=host,
port=port,
workers=1,
reload=True,
)
else:
# Production mode - use server with graceful shutdown
shutdown_handler = GracefulShutdown()
shutdown_handler.register_signals()
config = uvicorn.Config(
"llm_inference.api.app:create_app",
factory=True,
host=host,
port=port,
workers=args.workers,
timeout_graceful_shutdown=args.graceful_timeout,
)
server = uvicorn.Server(config)
shutdown_handler.set_server(server)
server.run()
if __name__ == "__main__":
main()

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"""High-level LLM client for unified inference."""
from collections.abc import AsyncIterator
from time import perf_counter
from llm_inference import metrics
from llm_inference.backends import BackendRegistry
from llm_inference.config import LLMSettings, SettingsCache
from llm_inference.exceptions import (
BackendNotAvailableError,
BackendNotEnabledError,
CompletionError,
LLMRateLimitError,
LLMTimeoutError,
)
from llm_inference.image_processing import process_messages
from llm_inference.schemas import (
CompletionChunk,
CompletionResponse,
TextCompletionResponse,
)
from llm_inference.types import BackendType, ChatMessage, ModelInfo
class LLMClient:
"""High-level client for LLM inference.
This is the primary interface for using the module as a Python library.
It provides a unified interface across all configured backends.
Example:
```python
from llm_inference import LLMClient
client = LLMClient()
# Simple completion
response = await client.complete(
messages=[{"role": "user", "content": "Hello!"}],
model="gpt-3.5-turbo",
)
print(response.choices[0].message.content)
# Streaming
async for chunk in client.stream(
messages=[{"role": "user", "content": "Tell me a story"}],
model="gpt-4",
):
if chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content, end="")
# Override backend per-request
response = await client.complete(
messages=[{"role": "user", "content": "Hello!"}],
model="meta-llama/Llama-2-7b-chat-hf",
backend=BackendType.VLLM,
)
```
"""
def __init__(self, settings: LLMSettings | None = None) -> None:
"""Initialize the LLM client.
Args:
settings: Optional settings instance. If not provided, loads from environment.
"""
self._settings = settings or SettingsCache.get()
self._registry = BackendRegistry(self._settings)
@property
def registry(self) -> BackendRegistry:
"""Get the backend registry.
Returns:
BackendRegistry: The backend registry instance.
"""
return self._registry
async def complete(
self,
messages: list[dict[str, str] | ChatMessage],
model: str | None = None,
backend: BackendType | str | None = None,
**kwargs: object,
) -> CompletionResponse:
"""Generate a chat completion.
Args:
messages: List of chat messages. Can be dicts or ChatMessage objects.
model: Model identifier. Uses default if not specified.
backend: Backend to use. If not specified, auto-detects from model
or uses default.
**kwargs: Additional parameters (temperature, max_tokens, etc.).
Returns:
CompletionResponse: The completion response.
Raises:
BackendNotAvailableError: If the requested backend is not available.
CompletionError: If the completion request fails.
"""
parsed_messages = self._parse_messages(messages)
parsed_messages = await process_messages(parsed_messages)
model = self._resolve_model(model or self._settings.default_model)
explicit = backend is not None
if backend is None:
backend = await self._resolve_backend_for_model(model)
cascade = self._build_cascade(backend, explicit)
last_error: Exception | None = None
for backend_type in cascade:
name = backend_type.value
start = perf_counter()
metrics.LLM_INFLIGHT.labels(backend=name).inc()
try:
response = await self._registry.get(backend_type).complete(
parsed_messages, model, **kwargs
)
metrics.observe_success(
model, response.backend or name, perf_counter() - start,
response.usage,
)
return response
except (
BackendNotAvailableError,
BackendNotEnabledError,
CompletionError,
LLMTimeoutError,
LLMRateLimitError,
) as exc:
metrics.observe_error(model, name)
last_error = exc
continue
finally:
metrics.LLM_INFLIGHT.labels(backend=name).dec()
# All backends in the cascade failed — surface the last error.
raise last_error if last_error else CompletionError("no backend available")
async def stream(
self,
messages: list[dict[str, str] | ChatMessage],
model: str | None = None,
backend: BackendType | str | None = None,
**kwargs: object,
) -> AsyncIterator[CompletionChunk]:
"""Generate a streaming chat completion.
Args:
messages: List of chat messages.
model: Model identifier. Uses default if not specified.
backend: Backend to use. Uses default if not specified.
**kwargs: Additional parameters.
Yields:
CompletionChunk: Streaming chunks of the completion.
Raises:
BackendNotAvailableError: If the requested backend is not available.
CompletionError: If the completion request fails.
"""
parsed_messages = self._parse_messages(messages)
parsed_messages = await process_messages(parsed_messages)
model = self._resolve_model(model or self._settings.default_model)
if backend is None:
backend = await self._resolve_backend_for_model(model)
backend_instance = self._registry.get(backend)
async for chunk in backend_instance.stream(parsed_messages, model, **kwargs):
yield chunk
async def text_complete(
self,
prompt: str | list[str],
model: str | None = None,
backend: BackendType | str | None = None,
**kwargs: object,
) -> TextCompletionResponse:
"""Generate a legacy text completion (/v1/completions).
Resolves the model alias and target backend, then delegates to the
backend's ``text_complete``. Backends that do not support text
completion raise NotImplementedError.
"""
model = self._resolve_model(model or self._settings.default_model)
if backend is None:
backend = await self._resolve_backend_for_model(model)
backend_instance = self._registry.get(backend)
return await backend_instance.text_complete(prompt, model, **kwargs)
async def list_models(
self,
backend: BackendType | str | None = None,
) -> list[ModelInfo]:
"""List available models.
Args:
backend: If specified, only list models for this backend.
Otherwise, lists models from all available backends.
Returns:
list[ModelInfo]: List of available models.
"""
if backend is not None:
return await self._registry.get(backend).list_models()
# Aggregate from all backends
all_models: list[ModelInfo] = []
for backend_type in self._registry.list_backends():
backend_instance = self._registry.get(backend_type)
models = await backend_instance.list_models()
all_models.extend(models)
return all_models
async def load_model(
self,
model: str,
backend: BackendType | str,
) -> bool:
"""Load a model on the specified backend.
This is only supported by local backends (vLLM, llama.cpp).
Args:
model: Model identifier to load.
backend: Backend to load the model on.
Returns:
bool: True if the model was loaded successfully.
Raises:
NotImplementedError: If the backend doesn't support model loading.
ModelLoadError: If loading fails.
"""
backend_instance = self._registry.get(backend)
return await backend_instance.load_model(model)
async def unload_model(
self,
model: str,
backend: BackendType | str,
) -> bool:
"""Unload a model from the specified backend.
This is only supported by local backends (vLLM, llama.cpp).
Args:
model: Model identifier to unload.
backend: Backend to unload the model from.
Returns:
bool: True if the model was unloaded successfully.
Raises:
NotImplementedError: If the backend doesn't support model unloading.
"""
backend_instance = self._registry.get(backend)
return await backend_instance.unload_model(model)
def list_backends(self) -> list[BackendType]:
"""List available backends.
Returns:
list[BackendType]: List of available backend types.
"""
return self._registry.list_backends()
async def health_check(self) -> dict[str, bool]:
"""Check health of all backends.
Returns:
dict[str, bool]: Mapping of backend name to health status.
"""
health: dict[str, bool] = {}
for backend_type in self._registry.list_backends():
backend_instance = self._registry.get(backend_type)
health[backend_type.value] = await backend_instance.health_check()
return health
def _resolve_model(self, model: str) -> str:
"""Resolve a friendly model alias to its real served id.
Returns the configured alias target, or the input unchanged when no
alias is defined (passthrough).
"""
return self._settings.model_aliases.get(model, model)
def _build_cascade(
self, primary: BackendType | str | None, explicit: bool
) -> list[BackendType]:
"""Build the ordered backend cascade for a request.
Returns ``[primary]`` when the caller pinned a backend or fallback is
disabled; otherwise ``[primary, *other enabled backends]`` in
``fallback_order`` (offer §1.6).
"""
if primary is None:
primary = BackendType(self._settings.default_backend)
elif isinstance(primary, str):
primary = BackendType(primary)
if explicit or not self._settings.enable_fallback:
return [primary]
enabled = set(self._registry.list_backends())
chain: list[BackendType] = [primary] if primary in enabled else []
for name in self._settings.fallback_order:
try:
bt = BackendType(name)
except ValueError:
continue
if bt in enabled and bt != primary:
chain.append(bt)
return chain or [primary]
async def _resolve_backend_for_model(self, model: str) -> BackendType | None:
"""Auto-detect the correct backend for a model.
Queries each enabled local backend's model list to find which one
serves the requested model. Returns None to use default.
Args:
model: Model identifier to look up.
Returns:
BackendType if found, None for default.
"""
for backend_type in self._registry.list_backends():
if backend_type == BackendType.LITELLM:
continue
try:
models = await self._registry.get(backend_type).list_models()
if any(m.id == model for m in models):
return backend_type
except Exception:
continue
return None
def _parse_messages(
self,
messages: list[dict[str, str] | ChatMessage],
) -> list[ChatMessage]:
"""Parse messages into ChatMessage objects.
Args:
messages: List of messages as dicts or ChatMessage objects.
Returns:
list[ChatMessage]: List of ChatMessage objects.
"""
parsed: list[ChatMessage] = []
for msg in messages:
if isinstance(msg, ChatMessage):
parsed.append(msg)
else:
parsed.append(ChatMessage.model_validate(msg))
return parsed

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"""Configuration management using Pydantic V2 Settings."""
import threading
import warnings
from typing import Literal
from pydantic import Field, field_validator, model_validator
from pydantic_settings import BaseSettings, SettingsConfigDict
class LLMSettings(BaseSettings):
"""LLM Inference module configuration.
All settings can be configured via environment variables with the LLM_ prefix.
Required environment variables (no defaults - fail-fast):
LLM_DEFAULT_BACKEND: Backend to use (litellm, vllm, llamacpp)
LLM_ENABLE_VLLM: Enable vLLM backend (true/false)
LLM_ENABLE_LLAMACPP: Enable llama.cpp backend (true/false)
Example:
LLM_DEFAULT_BACKEND=litellm
LLM_ENABLE_VLLM=false
LLM_ENABLE_LLAMACPP=false
LLM_PORT=14011
"""
model_config = SettingsConfigDict(
env_prefix="LLM_",
env_file=".env",
env_file_encoding="utf-8",
extra="forbid", # Reject unknown fields to catch typos
)
# ==========================================================================
# REQUIRED fields (no defaults - fail-fast)
# ==========================================================================
default_backend: Literal["litellm", "vllm", "llamacpp"] = Field(
description="Default backend to use for inference (REQUIRED)",
)
enable_vllm: bool = Field(
description="Enable vLLM backend (REQUIRED, set to true or false)",
)
enable_llamacpp: bool = Field(
description="Enable llama.cpp backend (REQUIRED, set to true or false)",
)
# ==========================================================================
# Optional fields with sensible defaults
# ==========================================================================
# Default model selection — the platform's primary model (offer: Qwen3.5).
default_model: str = Field(
default="qwen3.5",
description="Default model alias used when a request omits `model`",
)
# Friendly alias → real served model id. Lets callers use a stable name
# (e.g. "qwen3.5") regardless of the backend's served model id. Set via
# LLM_MODEL_ALIASES='{"qwen3.5":"Qwen/Qwen3.5-35B-A3B"}'. Empty = passthrough
# (works when vLLM is started with --served-model-name qwen3.5).
model_aliases: dict[str, str] = Field(
default_factory=dict,
description="Map of friendly model alias to the real served model id",
)
# Cross-backend fallback cascade (offer §1.6): on a backend failure, retry
# the request on the next enabled backend in this order. Disabled when the
# caller pins an explicit backend.
enable_fallback: bool = Field(
default=True,
description="Enable cross-backend fallback cascade on backend failure",
)
fallback_order: list[str] = Field(
default_factory=lambda: ["vllm", "llamacpp", "litellm"],
description="Backend order tried in the fallback cascade",
)
# API server settings
host: str = Field(
default="0.0.0.0",
description="Host to bind the server to",
)
port: int = Field(
default=14011,
description="Port to bind the server to (Prod LLM API: 14011)",
)
external_url: str = Field(
description="External URL for OpenAPI spec (e.g., http://10.11.10.42:14011). REQUIRED.",
)
# LiteLLM / Provider API keys (optional but at least one needed for litellm)
openrouter_api_key: str | None = Field(
default=None,
description="OpenRouter API key",
)
openai_api_key: str | None = Field(
default=None,
description="OpenAI API key",
)
anthropic_api_key: str | None = Field(
default=None,
description="Anthropic API key",
)
# vLLM settings
vllm_base_url: str = Field(
default="http://localhost:14001",
description="Base URL for vLLM server",
)
vllm_api_key: str | None = Field(
default=None,
description="API key for vLLM server (if required)",
)
# llama.cpp settings
llamacpp_base_url: str = Field(
default="http://localhost:8080",
description="Base URL for llama.cpp server (single server, legacy)",
)
llamacpp_base_urls: list[str] | None = Field(
default=None,
description=(
"Comma-separated list of llama.cpp server URLs for load balancing. "
"If set, overrides llamacpp_base_url. "
"Example: http://10.11.10.18:14001"
),
)
llamacpp_health_check_interval: int = Field(
default=30,
ge=5,
description="Health check interval in seconds for llama.cpp backends",
)
@field_validator("llamacpp_base_urls", mode="before")
@classmethod
def parse_llamacpp_base_urls(cls, v: str | list[str] | None) -> list[str] | None:
"""Parse comma-separated URLs into a list."""
if v is None or v == "":
return None
if isinstance(v, str):
urls = [u.strip().rstrip("/") for u in v.split(",") if u.strip()]
return urls if urls else None
return [u.strip().rstrip("/") for u in v if u.strip()]
@property
def llamacpp_urls(self) -> list[str]:
"""Get all llama.cpp server URLs (from list or single)."""
if self.llamacpp_base_urls:
return self.llamacpp_base_urls
return [self.llamacpp_base_url.rstrip("/")]
# Model storage
models_dir: str = Field(
default="/models",
description="Directory for local model storage",
)
# ==========================================================================
# Timeout and retry settings
# ==========================================================================
request_timeout: float = Field(
default=120.0,
ge=1.0,
description="Default timeout in seconds for completion requests",
)
connect_timeout: float = Field(
default=10.0,
ge=1.0,
description="Timeout in seconds for establishing connections",
)
max_retries: int = Field(
default=3,
ge=0,
description="Maximum number of retries for transient failures",
)
retry_min_wait: float = Field(
default=1.0,
ge=0.1,
description="Minimum wait time in seconds between retries",
)
retry_max_wait: float = Field(
default=60.0,
ge=1.0,
description="Maximum wait time in seconds between retries",
)
# ==========================================================================
# Observability settings
# ==========================================================================
log_level: str = Field(
default="INFO",
description="Logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL)",
)
log_json: bool = Field(
default=False,
description="Output logs in JSON format for production",
)
# ==========================================================================
# Rate limiting and concurrency
# ==========================================================================
rate_limit_rps: float = Field(
default=10.0,
ge=0.1,
description="Rate limit: requests per second",
)
rate_limit_burst: int = Field(
default=20,
ge=1,
description="Rate limit: burst capacity",
)
max_concurrent_completions: int = Field(
default=10,
ge=1,
description="Maximum concurrent completion requests",
)
# ==========================================================================
# Authentication settings
# ==========================================================================
api_tokens: frozenset[str] | None = Field(
default=None,
description=(
"API tokens for Bearer authentication (comma-separated). "
"If not set, authentication is disabled."
),
)
# ==========================================================================
# Runtime config (dashboard polling)
# ==========================================================================
dashboard_url: str | None = Field(
default=None,
description=(
"Optional dashboard base URL (e.g., http://didiAI-dashboard:51300). "
"When set, runtime_config polls /api/config every 30s for live "
"overrides. Unset disables polling — module uses env-only config."
),
)
@field_validator("api_tokens", mode="before")
@classmethod
def parse_api_tokens(cls, v: str | list[str] | None) -> frozenset[str] | None:
"""Parse comma-separated tokens into a frozenset."""
if v is None or v == "":
return None
if isinstance(v, str):
tokens = [t.strip() for t in v.split(",") if t.strip()]
return frozenset(tokens) if tokens else None
return frozenset(v) if v else None
@property
def auth_enabled(self) -> bool:
"""Check if authentication is enabled."""
return bool(self.api_tokens)
# ==========================================================================
# Validators
# ==========================================================================
@model_validator(mode="after")
def validate_api_keys(self) -> "LLMSettings":
"""Warn if no API keys are set when using litellm backend."""
if self.default_backend == "litellm":
has_any_key = any(
[
self.openrouter_api_key,
self.openai_api_key,
self.anthropic_api_key,
]
)
if not has_any_key:
warnings.warn(
"No API keys configured for litellm backend. "
"Set at least one of: OPENROUTER_API_KEY, "
"OPENAI_API_KEY, ANTHROPIC_API_KEY",
UserWarning,
stacklevel=2,
)
return self
class SettingsCache:
"""Thread-safe settings cache that can be cleared for testing."""
_instance: LLMSettings | None = None
_lock: threading.Lock = threading.Lock()
@classmethod
def get(cls) -> LLMSettings:
"""Get or create the settings instance.
Thread-safe singleton pattern. Always acquires lock for correctness.
Performance impact is negligible for singleton access patterns.
Returns:
LLMSettings: Application settings loaded from environment.
"""
with cls._lock:
if cls._instance is None:
cls._instance = LLMSettings()
return cls._instance
@classmethod
def clear(cls) -> None:
"""Clear the cached settings instance.
Use this in tests to reset settings between test cases.
"""
with cls._lock:
cls._instance = None
@classmethod
def set(cls, settings: LLMSettings) -> None:
"""Set a specific settings instance.
Use this in tests to inject mock settings.
Args:
settings: Settings instance to use.
"""
with cls._lock:
cls._instance = settings
def get_settings() -> LLMSettings:
"""Get cached settings instance.
Returns:
LLMSettings: Application settings loaded from environment.
"""
return SettingsCache.get()

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"""Custom exceptions for LLM inference."""
class LLMInferenceError(Exception):
"""Base exception for LLM inference errors."""
class AuthenticationError(LLMInferenceError):
"""Raised when authentication fails."""
def __init__(self, message: str = "Authentication required") -> None:
super().__init__(message)
class BackendNotAvailableError(LLMInferenceError):
"""Raised when a requested backend is not available."""
def __init__(self, backend: str, reason: str | None = None) -> None:
self.backend = backend
self.reason = reason
message = f"Backend '{backend}' is not available"
if reason:
message += f": {reason}"
super().__init__(message)
class BackendNotEnabledError(LLMInferenceError):
"""Raised when a backend is not enabled in configuration."""
def __init__(self, backend: str) -> None:
self.backend = backend
super().__init__(
f"Backend '{backend}' is not enabled. "
f"Set LLM_ENABLE_{backend.upper()}=true to enable it."
)
class ModelNotFoundError(LLMInferenceError):
"""Raised when a requested model is not found."""
def __init__(self, model: str, backend: str | None = None) -> None:
self.model = model
self.backend = backend
message = f"Model '{model}' not found"
if backend:
message += f" on backend '{backend}'"
super().__init__(message)
class CompletionError(LLMInferenceError):
"""Raised when a completion request fails."""
def __init__(
self,
message: str,
backend: str | None = None,
model: str | None = None,
) -> None:
self.backend = backend
self.model = model
full_message = message
if backend or model:
details = []
if backend:
details.append(f"backend={backend}")
if model:
details.append(f"model={model}")
full_message = f"{message} ({', '.join(details)})"
super().__init__(full_message)
class ModelLoadError(LLMInferenceError):
"""Raised when loading a model fails."""
def __init__(self, model: str, backend: str, reason: str | None = None) -> None:
self.model = model
self.backend = backend
self.reason = reason
message = f"Failed to load model '{model}' on backend '{backend}'"
if reason:
message += f": {reason}"
super().__init__(message)
class ModelListError(LLMInferenceError):
"""Raised when listing models fails."""
def __init__(self, backend: str, reason: str | None = None) -> None:
self.backend = backend
self.reason = reason
message = f"Failed to list models on backend '{backend}'"
if reason:
message += f": {reason}"
super().__init__(message)
class LLMRateLimitError(LLMInferenceError):
"""Raised when a rate limit is hit."""
def __init__(
self,
message: str,
backend: str | None = None,
retry_after: float | None = None,
) -> None:
self.backend = backend
self.retry_after = retry_after
full_message = message
if retry_after:
full_message += f" (retry after {retry_after}s)"
super().__init__(full_message)
class LLMTimeoutError(LLMInferenceError):
"""Raised when a request times out."""
def __init__(
self,
message: str,
backend: str | None = None,
timeout: float | None = None,
) -> None:
self.backend = backend
self.timeout = timeout
super().__init__(message)
class LLMConnectionError(LLMInferenceError):
"""Raised when connection to backend fails."""
def __init__(self, backend: str, reason: str | None = None) -> None:
self.backend = backend
self.reason = reason
message = f"Connection to backend '{backend}' failed"
if reason:
message += f": {reason}"
super().__init__(message)

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"""Image processing for multimodal requests.
Downloads image URLs and converts them to base64 data URIs so that all
backends (vLLM, llama.cpp, LiteLLM) receive a uniform format.
Supports:
1. OpenAI format: {"type": "image_url", "image_url": {"url": "https://..."}}
2. Already base64: {"type": "image_url", "image_url": {"url": "data:image/...;base64,..."}}
→ passed through unchanged.
"""
import base64
import logging
import mimetypes
import re
from urllib.parse import urlparse
import httpx
from llm_inference.types import ChatMessage
logger = logging.getLogger("llm_inference.image_processing")
IMAGE_EXTENSIONS = {".jpg", ".jpeg", ".png", ".gif", ".webp", ".bmp"}
IMAGE_URL_PATTERN = re.compile(
r"https?://[^\s<>\"]+\.(?:jpg|jpeg|png|gif|webp|bmp)", re.IGNORECASE
)
# Shared client for image downloads (reused across requests)
_http_client: httpx.AsyncClient | None = None
def _get_http_client() -> httpx.AsyncClient:
global _http_client
if _http_client is None:
_http_client = httpx.AsyncClient(
timeout=30,
follow_redirects=True,
headers={"User-Agent": "LLMInference/1.0"},
)
return _http_client
async def close_http_client() -> None:
"""Close the shared HTTP client. Call on application shutdown."""
global _http_client
if _http_client is not None:
await _http_client.aclose()
_http_client = None
def _guess_mime_type(url: str, content_type: str = "") -> str:
"""Guess MIME type from URL or Content-Type header."""
if "jpeg" in content_type or "jpg" in content_type:
return "image/jpeg"
if "png" in content_type:
return "image/png"
if "gif" in content_type:
return "image/gif"
if "webp" in content_type:
return "image/webp"
if "bmp" in content_type:
return "image/bmp"
ext = urlparse(url).path.rsplit(".", 1)[-1].lower() if "." in url else ""
mime = mimetypes.guess_type(f"file.{ext}")[0]
return mime or "image/jpeg"
async def _download_and_encode(url: str) -> str:
"""Download image URL and return as base64 data URI."""
client = _get_http_client()
resp = await client.get(url)
resp.raise_for_status()
content_type = resp.headers.get("content-type", "")
mime_type = _guess_mime_type(url, content_type)
b64 = base64.b64encode(resp.content).decode("utf-8")
logger.info(
"Downloaded image: %s (%d bytes, %s)", url, len(resp.content), mime_type
)
return f"data:{mime_type};base64,{b64}"
async def _process_content_item(item: dict[str, object]) -> dict[str, object]:
"""Process a single content item, downloading image URLs if needed."""
if not isinstance(item, dict):
return item
if item.get("type") != "image_url":
return item
image_url = item.get("image_url", {})
url = image_url.get("url", "") if isinstance(image_url, dict) else str(image_url)
# Already base64 — pass through
if url.startswith("data:"):
return item
# Not an HTTP URL — pass through
if not url.startswith(("http://", "https://")):
return item
try:
data_uri = await _download_and_encode(url)
return {
"type": "image_url",
"image_url": {"url": data_uri},
}
except Exception as e:
logger.warning("Failed to download image %s: %s", url, e)
return item # Return original on failure
async def _process_text_with_urls(text: str) -> str | list[dict[str, object]]:
"""Detect image URLs in plain text and convert to multimodal format."""
urls = IMAGE_URL_PATTERN.findall(text)
if not urls:
return text
# Build multimodal content array
content: list[dict[str, object]] = []
# Strip URLs from text
clean_text = text
for url in urls:
clean_text = clean_text.replace(url, "").strip()
if clean_text:
content.append({"type": "text", "text": clean_text})
for url in urls:
try:
data_uri = await _download_and_encode(url)
content.append({"type": "image_url", "image_url": {"url": data_uri}})
except Exception as e:
logger.warning("Failed to download image %s: %s", url, e)
# Put URL back in text
if content and content[0].get("type") == "text":
content[0]["text"] += f" {url}" # type: ignore[operator]
else:
content.insert(0, {"type": "text", "text": url})
return content
async def process_messages(messages: list[ChatMessage]) -> list[ChatMessage]:
"""Process all messages, downloading image URLs to base64.
This ensures uniform image format for all backends. Images arrive
as base64 data URIs regardless of whether the client sent URLs or
base64 originally.
Args:
messages: Original messages (may contain image URLs).
Returns:
Processed messages with images as base64 data URIs.
"""
processed: list[ChatMessage] = []
for msg in messages:
content = msg.content
# Already multimodal array (OpenAI format)
if isinstance(content, list):
new_content = []
for item in content:
new_content.append(await _process_content_item(item))
processed.append(
ChatMessage(role=msg.role, content=new_content, name=msg.name)
)
# Plain text — check for embedded image URLs
elif isinstance(content, str):
new_content = await _process_text_with_urls(content)
processed.append(
ChatMessage(role=msg.role, content=new_content, name=msg.name)
)
else:
processed.append(msg)
return processed

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"""Structured logging with request_id context propagation.
This module provides:
- ContextVar-based request_id tracking
- Structured JSON logging option
- Request ID injection into all log records
"""
import logging
import sys
from contextvars import ContextVar
from typing import Any
# Context variable for request ID propagation
request_id_ctx: ContextVar[str | None] = ContextVar("request_id", default=None)
class RequestIdFilter(logging.Filter):
"""Logging filter that adds request_id to log records."""
def filter(self, record: logging.LogRecord) -> bool:
"""Add request_id to the log record.
Args:
record: The log record to modify.
Returns:
bool: Always True (never filters out records).
"""
record.request_id = request_id_ctx.get() or "-"
return True
class JsonFormatter(logging.Formatter):
"""JSON log formatter for structured logging."""
def format(self, record: logging.LogRecord) -> str:
"""Format log record as JSON.
Args:
record: The log record to format.
Returns:
str: JSON-formatted log string.
"""
import json
log_data: dict[str, Any] = {
"timestamp": self.formatTime(record, self.datefmt),
"level": record.levelname,
"logger": record.name,
"message": record.getMessage(),
"request_id": getattr(record, "request_id", "-"),
}
# Add exception info if present
if record.exc_info:
log_data["exception"] = self.formatException(record.exc_info)
# Add extra fields
if hasattr(record, "extra_data"):
log_data.update(record.extra_data)
return json.dumps(log_data)
def configure_logging(level: str = "INFO", json_format: bool = False) -> None:
"""Configure logging for the application.
Args:
level: Log level (DEBUG, INFO, WARNING, ERROR, CRITICAL).
json_format: If True, use JSON formatting; otherwise use text.
"""
# Get the root logger for our package
root_logger = logging.getLogger("llm_inference")
root_logger.setLevel(getattr(logging, level.upper(), logging.INFO))
# Remove existing handlers
for handler in root_logger.handlers[:]:
root_logger.removeHandler(handler)
# Create console handler
handler = logging.StreamHandler(sys.stdout)
handler.setLevel(logging.DEBUG)
# Add request ID filter
handler.addFilter(RequestIdFilter())
# Set formatter based on preference
if json_format:
handler.setFormatter(JsonFormatter())
else:
handler.setFormatter(
logging.Formatter(
"%(asctime)s [%(levelname)s] [%(request_id)s] %(name)s: %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
)
)
root_logger.addHandler(handler)
# Prevent propagation to root logger
root_logger.propagate = False
def get_logger(name: str) -> logging.Logger:
"""Get a logger with the llm_inference prefix.
Args:
name: Logger name (will be prefixed with 'llm_inference.').
Returns:
logging.Logger: Configured logger instance.
"""
if name.startswith("llm_inference."):
return logging.getLogger(name)
return logging.getLogger(f"llm_inference.{name}")
def set_request_id(request_id: str | None) -> None:
"""Set the request ID for the current context.
Args:
request_id: The request ID to set, or None to clear.
"""
request_id_ctx.set(request_id)
def get_request_id() -> str | None:
"""Get the request ID for the current context.
Returns:
str | None: The current request ID, or None if not set.
"""
return request_id_ctx.get()

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"""Custom Prometheus metrics for LLM inference (offer §1.10).
Registered on the default prometheus_client registry, so they are exposed by the
existing /metrics endpoint. Gives per-model latency percentiles (p50/p90/p99 via
``histogram_quantile``), token throughput and request/error counts.
VRAM and loaded-model counts per backend are best scraped from the vLLM server's
own /metrics (it exports GPU cache usage natively); this module covers the
gateway-level signals.
"""
from __future__ import annotations
from prometheus_client import Counter, Gauge, Histogram
LLM_LATENCY = Histogram(
"llm_request_duration_seconds",
"End-to-end completion latency at the gateway, per model and backend.",
["model", "backend"],
buckets=(0.05, 0.1, 0.25, 0.5, 1, 2, 5, 10, 20, 30, 60, 120),
)
LLM_TOKENS = Counter(
"llm_tokens_total",
"Tokens processed, per model, backend and kind (prompt/completion).",
["model", "backend", "kind"],
)
LLM_REQUESTS = Counter(
"llm_requests_total",
"Completion requests, per model, backend and status (success/error).",
["model", "backend", "status"],
)
LLM_INFLIGHT = Gauge(
"llm_inflight_requests",
"In-flight completion requests, per backend.",
["backend"],
)
def observe_success(
model: str, backend: str, duration_s: float, usage: object | None
) -> None:
"""Record a successful completion's latency, tokens and request count."""
LLM_LATENCY.labels(model=model, backend=backend).observe(duration_s)
LLM_REQUESTS.labels(model=model, backend=backend, status="success").inc()
if usage is not None:
prompt = getattr(usage, "prompt_tokens", 0) or 0
completion = getattr(usage, "completion_tokens", 0) or 0
if prompt:
LLM_TOKENS.labels(model=model, backend=backend, kind="prompt").inc(prompt)
if completion:
LLM_TOKENS.labels(
model=model, backend=backend, kind="completion"
).inc(completion)
def observe_error(model: str, backend: str) -> None:
"""Record a failed completion request."""
LLM_REQUESTS.labels(model=model, backend=backend, status="error").inc()

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"""Retry utilities with exponential backoff for transient failures."""
import asyncio
import logging
import random
from collections.abc import Awaitable, Callable
from typing import TypeVar
import httpx
from llm_inference.exceptions import (
CompletionError,
LLMConnectionError,
LLMRateLimitError,
LLMTimeoutError,
)
logger = logging.getLogger("llm_inference.retry")
T = TypeVar("T")
# Import provider-specific exceptions conditionally
try:
import litellm.exceptions as litellm_exc
LITELLM_RETRYABLE = (
litellm_exc.RateLimitError,
litellm_exc.Timeout,
litellm_exc.ServiceUnavailableError,
)
LITELLM_NON_RETRYABLE = (
litellm_exc.AuthenticationError,
litellm_exc.BadRequestError,
)
except ImportError:
LITELLM_RETRYABLE = ()
LITELLM_NON_RETRYABLE = ()
try:
import openai
OPENAI_RETRYABLE = (
openai.RateLimitError,
openai.APIConnectionError,
openai.APITimeoutError,
)
OPENAI_NON_RETRYABLE = (
openai.AuthenticationError,
openai.BadRequestError,
openai.NotFoundError,
)
except ImportError:
OPENAI_RETRYABLE = ()
OPENAI_NON_RETRYABLE = ()
# Combine all retryable exceptions
RETRYABLE_EXCEPTIONS: tuple[type[Exception], ...] = (
*LITELLM_RETRYABLE,
*OPENAI_RETRYABLE,
httpx.TimeoutException,
httpx.ConnectError,
)
# Combine all non-retryable exceptions
NON_RETRYABLE_EXCEPTIONS: tuple[type[Exception], ...] = (
*LITELLM_NON_RETRYABLE,
*OPENAI_NON_RETRYABLE,
)
def is_retryable_exception(exc: Exception) -> bool:
"""Check if an exception is retryable.
Args:
exc: Exception to check.
Returns:
bool: True if the exception is a transient failure that can be retried.
"""
return isinstance(exc, RETRYABLE_EXCEPTIONS)
def extract_retry_after(exc: Exception) -> float | None:
"""Extract retry-after value from rate limit exceptions.
Args:
exc: Exception that may contain retry-after header.
Returns:
float | None: Retry-after value in seconds, or None if not available.
"""
if hasattr(exc, "response") and exc.response is not None:
headers = getattr(exc.response, "headers", {})
retry_after = headers.get("retry-after")
if retry_after:
try:
return float(retry_after)
except ValueError:
pass
return None
def translate_exception(
exc: Exception,
backend: str,
model: str | None = None,
) -> Exception:
"""Translate provider-specific exceptions to our exception types.
Args:
exc: Original exception from provider.
backend: Backend name for error context.
model: Model name for error context.
Returns:
Exception: Translated exception of appropriate type.
"""
# Rate limit errors
if LITELLM_RETRYABLE and isinstance(exc, litellm_exc.RateLimitError):
retry_after = extract_retry_after(exc)
return LLMRateLimitError(str(exc), backend=backend, retry_after=retry_after)
if OPENAI_RETRYABLE and isinstance(exc, openai.RateLimitError):
retry_after = extract_retry_after(exc)
return LLMRateLimitError(str(exc), backend=backend, retry_after=retry_after)
# Timeout errors
if LITELLM_RETRYABLE and isinstance(exc, litellm_exc.Timeout):
return LLMTimeoutError(str(exc), backend=backend)
if OPENAI_RETRYABLE and isinstance(exc, openai.APITimeoutError):
return LLMTimeoutError(str(exc), backend=backend)
if isinstance(exc, httpx.TimeoutException):
return LLMTimeoutError(str(exc), backend=backend)
# Connection errors
if OPENAI_RETRYABLE and isinstance(exc, openai.APIConnectionError):
return LLMConnectionError(backend, reason=str(exc))
if isinstance(exc, httpx.ConnectError):
return LLMConnectionError(backend, reason=str(exc))
# Default to CompletionError
return CompletionError(str(exc), backend=backend, model=model)
async def retry_with_backoff(
func: Callable[[], Awaitable[T]],
max_retries: int,
min_wait: float,
max_wait: float,
backend: str,
model: str | None = None,
) -> T:
"""Execute an async function with exponential backoff retry.
Args:
func: Async function to execute (takes no arguments).
max_retries: Maximum number of retry attempts.
min_wait: Minimum wait time between retries in seconds.
max_wait: Maximum wait time between retries in seconds.
backend: Backend name for error context.
model: Model name for error context.
Returns:
T: The result of the function.
Raises:
LLMRateLimitError: On rate limit with no retries left.
LLMTimeoutError: On timeout with no retries left.
LLMConnectionError: On connection failure with no retries left.
CompletionError: On other API errors.
"""
last_exception: Exception | None = None
for attempt in range(max_retries + 1):
try:
return await func()
except NON_RETRYABLE_EXCEPTIONS as e:
# Don't retry authentication or bad request errors
raise translate_exception(e, backend, model) from e
except RETRYABLE_EXCEPTIONS as e:
last_exception = e
if attempt >= max_retries:
# No more retries
raise translate_exception(e, backend, model) from e
# Calculate wait time with exponential backoff + jitter
retry_after = extract_retry_after(e)
if retry_after:
# Respect the API's retry-after header, but cap at max_wait
# to prevent malicious/buggy APIs from causing indefinite waits
wait_time = min(retry_after, max_wait)
else:
# Exponential backoff: min_wait * 2^attempt (capped at max_wait)
wait_time = min(min_wait * (2**attempt), max_wait)
# Add jitter (0-25% of wait time)
wait_time += random.uniform(0, wait_time * 0.25)
logger.warning(
"Retry %d/%d for %s after %.1fs: %s",
attempt + 1,
max_retries,
backend,
wait_time,
str(e),
)
await asyncio.sleep(wait_time)
except Exception as e:
# Unknown exception - translate and raise without retry
raise translate_exception(e, backend, model) from e
# Should not reach here, but just in case
if last_exception:
raise translate_exception(last_exception, backend, model)
raise CompletionError("Unknown error occurred", backend=backend, model=model)

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"""Runtime config client — fetches config overrides from dashboard.
Polls the dashboard /api/config endpoint periodically and caches values
in memory. All accessors fall back to caller-supplied defaults if the
dashboard is unreachable or the key is missing.
Single source of truth = dashboard `KNOWN_KEYS`. This client carries no
local default registry; consumers pass their own fallback (typically the
pydantic settings value).
"""
import asyncio
import contextlib
import logging
from typing import Any
import httpx
logger = logging.getLogger(__name__)
class RuntimeConfigClient:
"""Polls dashboard /api/config and caches values in-process."""
def __init__(
self,
dashboard_url: str | None,
poll_interval_seconds: int = 30,
request_timeout: float = 5.0,
live_log_logger_name: str | None = None,
live_log_key: str | None = None,
) -> None:
self.dashboard_url = dashboard_url.rstrip("/") if dashboard_url else None
self.poll_interval = poll_interval_seconds
self._timeout = request_timeout
self._cache: dict[str, Any] = {}
self._client: httpx.AsyncClient | None = None
self._task: asyncio.Task[None] | None = None
self._enabled = bool(dashboard_url)
# Optional auto-apply: re-set log level on the named logger when the
# configured key changes value (e.g., `llm.log.level`).
self._live_log_logger_name = live_log_logger_name
self._live_log_key = live_log_key
self._last_log_level: str | None = None
@property
def enabled(self) -> bool:
return self._enabled
def get(self, key: str, default: Any = None) -> Any:
v = self._cache.get(key)
return v if v is not None else default
def get_bool(self, key: str, default: bool = False) -> bool:
v = self._cache.get(key)
return bool(v) if v is not None else default
def get_int(self, key: str, default: int = 0) -> int:
v = self._cache.get(key)
if v is None:
return default
try:
return int(v)
except (TypeError, ValueError):
return default
def get_float(self, key: str, default: float = 0.0) -> float:
v = self._cache.get(key)
if v is None:
return default
try:
return float(v)
except (TypeError, ValueError):
return default
def get_str(self, key: str, default: str = "") -> str:
v = self._cache.get(key)
return str(v) if v is not None else default
async def start(self) -> None:
if not self._enabled:
logger.info("RuntimeConfigClient disabled (no dashboard URL)")
return
self._client = httpx.AsyncClient(
timeout=httpx.Timeout(
connect=2.0, read=self._timeout, write=2.0, pool=5.0
)
)
# Fetch once synchronously so first requests already have overrides
await self._refresh()
self._task = asyncio.create_task(self._loop())
logger.info(
"RuntimeConfigClient started (polling %s every %ds, %d keys cached)",
self.dashboard_url,
self.poll_interval,
len(self._cache),
)
async def stop(self) -> None:
if self._task is not None:
self._task.cancel()
with contextlib.suppress(asyncio.CancelledError, Exception):
await self._task
self._task = None
if self._client is not None and not self._client.is_closed:
await self._client.aclose()
self._client = None
async def _loop(self) -> None:
while True:
try:
await asyncio.sleep(self.poll_interval)
await self._refresh()
except asyncio.CancelledError:
raise
except Exception as e:
logger.debug("Config poll failed: %s", e)
async def _refresh(self) -> None:
if self._client is None or self.dashboard_url is None:
return
try:
resp = await self._client.get(f"{self.dashboard_url}/api/config")
resp.raise_for_status()
data = resp.json()
except Exception as e:
logger.debug("Config refresh failed: %s", e)
return
items = data.get("items", {})
new_cache: dict[str, Any] = {}
for key, entry in items.items():
new_cache[key] = entry.get("value")
self._cache = new_cache
self._maybe_apply_log_level()
def _maybe_apply_log_level(self) -> None:
"""Re-apply log level live if configured key changed."""
if not self._live_log_key or not self._live_log_logger_name:
return
new_level = self.get_str(self._live_log_key)
if not new_level:
return
if new_level == self._last_log_level:
return
try:
level_int = logging.getLevelName(new_level.upper())
if isinstance(level_int, int):
logging.getLogger(self._live_log_logger_name).setLevel(level_int)
self._last_log_level = new_level
logger.info(
"Log level for %s changed to %s (via runtime config)",
self._live_log_logger_name,
new_level,
)
except Exception as e:
logger.warning("Failed to apply log level %s: %s", new_level, e)

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"""Pydantic V2 API request/response schemas."""
from pydantic import BaseModel, ConfigDict, Field, field_validator
from llm_inference.types import (
BackendType,
ChatMessage,
Choice,
ModelInfo,
StreamChoice,
Usage,
)
# =============================================================================
# Completion Schemas
# =============================================================================
class CompletionRequest(BaseModel):
"""Request schema for chat completions."""
model_config = ConfigDict(extra="forbid")
messages: list[ChatMessage] = Field(
min_length=1,
max_length=1000,
description="List of messages in the conversation (1-1000 messages)",
)
model: str = Field(
description="Model to use for completion",
)
temperature: float = Field(
default=0.7,
ge=0.0,
le=2.0,
description="Sampling temperature",
)
max_tokens: int | None = Field(
default=None,
ge=1,
le=1000000,
description="Maximum tokens to generate (1-1000000)",
)
stream: bool = Field(
default=False,
description="Enable streaming response",
)
backend: BackendType | None = Field(
default=None,
description="Backend to use (overrides default)",
)
top_p: float | None = Field(
default=None,
ge=0.0,
le=1.0,
description="Top-p sampling parameter",
)
frequency_penalty: float | None = Field(
default=None,
ge=-2.0,
le=2.0,
description="Frequency penalty",
)
presence_penalty: float | None = Field(
default=None,
ge=-2.0,
le=2.0,
description="Presence penalty",
)
stop: list[str] | str | None = Field(
default=None,
description="Stop sequences",
)
@field_validator("messages")
@classmethod
def validate_messages_content(cls, v: list[ChatMessage]) -> list[ChatMessage]:
"""Validate that messages have content (except assistant messages)."""
for i, msg in enumerate(v):
# Assistant messages can have empty content (for function calls etc)
if msg.role != "assistant" and msg.content is None:
raise ValueError(
f"Message at index {i} with role '{msg.role}' cannot have empty content"
)
return v
class CompletionResponse(BaseModel):
"""Response schema for chat completions."""
model_config = ConfigDict(extra="forbid")
id: str = Field(description="Unique completion ID")
object: str = Field(default="chat.completion")
created: int = Field(description="Unix timestamp of creation")
model: str = Field(description="Model used for completion")
choices: list[Choice] = Field(description="Completion choices")
usage: Usage | None = Field(default=None, description="Token usage")
backend: str = Field(description="Backend that served the request")
class CompletionChunk(BaseModel):
"""Streaming chunk for chat completions."""
model_config = ConfigDict(extra="forbid")
id: str = Field(description="Unique completion ID")
object: str = Field(default="chat.completion.chunk")
created: int = Field(description="Unix timestamp of creation")
model: str = Field(description="Model used for completion")
choices: list[StreamChoice] = Field(description="Streaming choices")
# =============================================================================
# Text Completion Schemas (legacy OpenAI /v1/completions)
# =============================================================================
class TextChoice(BaseModel):
"""One text-completion choice."""
model_config = ConfigDict(extra="forbid")
index: int = Field(description="Choice index")
text: str = Field(description="Generated text")
finish_reason: str | None = Field(default=None, description="Finish reason")
class TextCompletionRequest(BaseModel):
"""Request schema for text completions (/v1/completions)."""
model_config = ConfigDict(extra="forbid")
prompt: str | list[str] = Field(description="Prompt(s) to complete")
model: str | None = Field(default=None, description="Model (alias) to use")
temperature: float = Field(default=0.7, ge=0.0, le=2.0)
max_tokens: int | None = Field(default=None, ge=1, le=1000000)
backend: BackendType | None = Field(
default=None, description="Backend to use (overrides default)"
)
top_p: float | None = Field(default=None, ge=0.0, le=1.0)
frequency_penalty: float | None = Field(default=None, ge=-2.0, le=2.0)
presence_penalty: float | None = Field(default=None, ge=-2.0, le=2.0)
stop: list[str] | str | None = Field(default=None, description="Stop sequences")
class TextCompletionResponse(BaseModel):
"""Response schema for text completions."""
model_config = ConfigDict(extra="forbid")
id: str = Field(description="Unique completion ID")
object: str = Field(default="text_completion")
created: int = Field(description="Unix timestamp of creation")
model: str = Field(description="Model used for completion")
choices: list[TextChoice] = Field(description="Completion choices")
usage: Usage | None = Field(default=None, description="Token usage")
backend: str = Field(description="Backend that served the request")
# =============================================================================
# Model Management Schemas
# =============================================================================
class ModelListResponse(BaseModel):
"""Response schema for listing models."""
object: str = Field(default="list")
data: list[ModelInfo] = Field(description="List of available models")
class ModelLoadRequest(BaseModel):
"""Request schema for loading a model."""
model: str = Field(description="Model identifier to load")
backend: BackendType = Field(description="Backend to load the model on")
class ModelLoadResponse(BaseModel):
"""Response schema for model load/unload operations."""
success: bool = Field(description="Whether the operation succeeded")
model: str = Field(description="Model identifier")
backend: str = Field(description="Backend the operation was performed on")
message: str | None = Field(default=None, description="Optional message")
# =============================================================================
# Health Schemas
# =============================================================================
class BackendHealth(BaseModel):
"""Health status for a single backend."""
name: str = Field(description="Backend name")
healthy: bool = Field(description="Whether the backend is healthy")
message: str | None = Field(default=None, description="Optional status message")
class HealthResponse(BaseModel):
"""Response schema for health check."""
status: str = Field(description="Overall status: healthy, degraded, unhealthy")
backends: list[BackendHealth] = Field(description="Per-backend health status")
class ReadinessResponse(BaseModel):
"""Response schema for readiness probe."""
ready: bool = Field(default=True)
# =============================================================================
# Backend Schemas
# =============================================================================
class BackendListResponse(BaseModel):
"""Response schema for listing backends."""
backends: list[str] = Field(description="List of available backend names")

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"""Core types and enums for LLM inference."""
from enum import Enum
from typing import Literal
from typing import Any
from pydantic import BaseModel, ConfigDict, Field
class BackendType(str, Enum):
"""Supported LLM backends."""
LITELLM = "litellm"
VLLM = "vllm"
LLAMACPP = "llamacpp"
class ChatMessage(BaseModel):
"""A chat message in a conversation."""
model_config = ConfigDict(extra="forbid")
role: Literal["system", "user", "assistant", "function", "tool"] = Field(
description="The role of the message author",
)
content: str | list[dict[str, Any]] | None = Field(
default=None,
description="The content of the message (string or multimodal content array)",
)
name: str | None = Field(
default=None,
description="Optional name for the message author",
)
class Usage(BaseModel):
"""Token usage information."""
prompt_tokens: int = Field(default=0)
completion_tokens: int = Field(default=0)
total_tokens: int = Field(default=0)
class Choice(BaseModel):
"""A completion choice."""
model_config = ConfigDict(extra="forbid")
index: int = Field(default=0)
message: ChatMessage | None = Field(default=None)
finish_reason: str | None = Field(default=None)
class Delta(BaseModel):
"""Streaming delta content."""
model_config = ConfigDict(extra="forbid")
role: str | None = Field(default=None)
content: str | None = Field(default=None)
class StreamChoice(BaseModel):
"""A streaming completion choice."""
model_config = ConfigDict(extra="forbid")
index: int = Field(default=0)
delta: Delta = Field(default_factory=Delta)
finish_reason: str | None = Field(default=None)
class ModelInfo(BaseModel):
"""Information about an available model."""
id: str = Field(description="Model identifier")
backend: str = Field(description="Backend serving this model")
loaded: bool = Field(default=True, description="Whether the model is loaded")
context_length: int | None = Field(
default=None,
description="Maximum context length in tokens",
)
capabilities: list[str] = Field(
default_factory=list,
description="Model capabilities (e.g., 'chat', 'completion')",
)

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"""Shared utilities for LLM inference module."""
import logging
from typing import Any
async def safe_close_stream(
stream: Any | None,
logger: logging.Logger,
) -> None:
"""Safely close an async stream, handling both aclose() and close().
Attempts aclose() first (async close), falls back to close() if not available.
Silently handles any exceptions during cleanup.
Args:
stream: Stream object to close, or None.
logger: Logger for debug messages on cleanup errors.
"""
if stream is None:
return
try:
if hasattr(stream, "aclose"):
await stream.aclose()
elif hasattr(stream, "close"):
await stream.close()
except Exception as e:
logger.debug("Error during stream cleanup: %s", e)

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"""Tests for LLM inference module."""

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"""Pytest fixtures for LLM inference tests."""
from unittest.mock import AsyncMock, MagicMock
import pytest
from fastapi.testclient import TestClient
from llm_inference.api.app import create_app
from llm_inference.api.dependencies import init_concurrency_limiter
from llm_inference.client import LLMClient
from llm_inference.config import LLMSettings, SettingsCache
from llm_inference.schemas import CompletionChunk, CompletionResponse
from llm_inference.types import (
BackendType,
ChatMessage,
Choice,
Delta,
StreamChoice,
Usage,
)
@pytest.fixture(autouse=True)
def clear_settings_cache() -> None:
"""Clear settings cache before each test."""
SettingsCache.clear()
@pytest.fixture
def test_settings() -> LLMSettings:
"""Create test settings with mocked values."""
return LLMSettings(
default_backend="litellm",
default_model="test-model",
openrouter_api_key="test-openrouter-key",
openai_api_key="test-openai-key",
enable_vllm=False,
enable_llamacpp=False,
host="127.0.0.1",
port=8100,
external_url="http://localhost:8100",
api_tokens=None, # Auth disabled by default in tests
)
@pytest.fixture
def mock_completion_response() -> CompletionResponse:
"""Create a mock completion response."""
return CompletionResponse(
id="test-completion-id",
created=1234567890,
model="test-model",
choices=[
Choice(
index=0,
message=ChatMessage(role="assistant", content="Hello! How can I help?"),
finish_reason="stop",
)
],
usage=Usage(prompt_tokens=10, completion_tokens=5, total_tokens=15),
backend="litellm",
)
@pytest.fixture
def mock_completion_chunks() -> list[CompletionChunk]:
"""Create mock streaming completion chunks."""
return [
CompletionChunk(
id="test-chunk-1",
created=1234567890,
model="test-model",
choices=[
StreamChoice(
index=0,
delta=Delta(role="assistant", content="Hello"),
finish_reason=None,
)
],
),
CompletionChunk(
id="test-chunk-2",
created=1234567891,
model="test-model",
choices=[
StreamChoice(
index=0,
delta=Delta(content="!"),
finish_reason=None,
)
],
),
CompletionChunk(
id="test-chunk-3",
created=1234567892,
model="test-model",
choices=[
StreamChoice(
index=0,
delta=Delta(),
finish_reason="stop",
)
],
),
]
@pytest.fixture
def mock_litellm_response() -> MagicMock:
"""Create a mock LiteLLM response object."""
response = MagicMock()
response.id = "test-completion-id"
response.created = 1234567890
response.model = "test-model"
choice = MagicMock()
choice.index = 0
choice.message.role = "assistant"
choice.message.content = "Hello! How can I help?"
choice.finish_reason = "stop"
response.choices = [choice]
response.usage.prompt_tokens = 10
response.usage.completion_tokens = 5
response.usage.total_tokens = 15
return response
@pytest.fixture
def client_with_mock_backend(
test_settings: LLMSettings, mock_completion_response: CompletionResponse
) -> LLMClient:
"""Create an LLMClient with mocked backend."""
# Inject test settings into cache
SettingsCache.set(test_settings)
client = LLMClient(settings=test_settings)
# Mock the backend's complete method
backend = client.registry.get(BackendType.LITELLM)
backend.complete = AsyncMock(return_value=mock_completion_response)
return client
@pytest.fixture
def app_client(test_settings: LLMSettings) -> TestClient:
"""Create a FastAPI TestClient with test settings."""
# Inject test settings into cache BEFORE creating the app
SettingsCache.set(test_settings)
# Initialize concurrency limiter for tests
init_concurrency_limiter(test_settings.max_concurrent_completions)
app = create_app()
# Also set in app.state for handlers that access it
app.state.settings = test_settings
app.state.client = LLMClient(settings=test_settings)
return TestClient(app)

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"""Tests for FastAPI routes."""
from unittest.mock import AsyncMock, patch
import pytest
from fastapi.testclient import TestClient
from llm_inference.api.app import create_app
from llm_inference.api.dependencies import init_concurrency_limiter
from llm_inference.client import LLMClient
from llm_inference.config import LLMSettings, SettingsCache
from llm_inference.schemas import CompletionResponse
@pytest.fixture
def app_client(test_settings: LLMSettings) -> TestClient:
"""Create a test client with mocked client."""
# Inject test settings into cache BEFORE creating app
SettingsCache.set(test_settings)
# Initialize concurrency limiter for tests
init_concurrency_limiter(test_settings.max_concurrent_completions)
app = create_app()
app.state.settings = test_settings
app.state.client = LLMClient(settings=test_settings)
return TestClient(app)
class TestHealthRoutes:
"""Tests for health check routes."""
def test_health_endpoint(self, app_client: TestClient) -> None:
"""Test /health endpoint."""
response = app_client.get("/health")
assert response.status_code == 200
data = response.json()
assert "status" in data
assert "backends" in data
assert data["status"] in ["healthy", "degraded", "unhealthy"]
def test_ready_endpoint(self, app_client: TestClient) -> None:
"""Test /ready endpoint."""
response = app_client.get("/ready")
assert response.status_code == 200
data = response.json()
assert data["ready"] is True
class TestModelsRoutes:
"""Tests for model management routes."""
def test_list_models(self, app_client: TestClient) -> None:
"""Test GET /v1/models."""
response = app_client.get("/v1/models")
assert response.status_code == 200
data = response.json()
assert "data" in data
assert "object" in data
assert data["object"] == "list"
def test_list_models_with_backend_filter(self, app_client: TestClient) -> None:
"""Test GET /v1/models with backend filter."""
response = app_client.get("/v1/models?backend=litellm")
assert response.status_code == 200
data = response.json()
# All models should be from litellm backend
for model in data["data"]:
assert model["backend"] == "litellm"
def test_list_models_invalid_backend(self, app_client: TestClient) -> None:
"""Test GET /v1/models with invalid backend."""
response = app_client.get("/v1/models?backend=invalid")
assert response.status_code == 400
def test_list_backends(self, app_client: TestClient) -> None:
"""Test GET /v1/backends."""
response = app_client.get("/v1/backends")
assert response.status_code == 200
data = response.json()
assert "backends" in data
assert "litellm" in data["backends"]
class TestCompletionsRoutes:
"""Tests for completion routes."""
def test_chat_completions(
self,
app_client: TestClient,
mock_completion_response: CompletionResponse,
) -> None:
"""Test POST /v1/chat/completions."""
# Mock the client's complete method
with patch.object(
app_client.app.state.client,
"complete",
new_callable=AsyncMock,
return_value=mock_completion_response,
):
response = app_client.post(
"/v1/chat/completions",
json={
"messages": [{"role": "user", "content": "Hello"}],
"model": "gpt-3.5-turbo",
},
)
assert response.status_code == 200
data = response.json()
assert "id" in data
assert "choices" in data
assert "model" in data
def test_chat_completions_with_parameters(
self,
app_client: TestClient,
mock_completion_response: CompletionResponse,
) -> None:
"""Test completion with optional parameters."""
with patch.object(
app_client.app.state.client,
"complete",
new_callable=AsyncMock,
return_value=mock_completion_response,
):
response = app_client.post(
"/v1/chat/completions",
json={
"messages": [{"role": "user", "content": "Hello"}],
"model": "gpt-4",
"temperature": 0.5,
"max_tokens": 100,
"top_p": 0.9,
},
)
assert response.status_code == 200
def test_chat_completions_with_backend_override(
self,
app_client: TestClient,
mock_completion_response: CompletionResponse,
) -> None:
"""Test completion with backend override."""
with patch.object(
app_client.app.state.client,
"complete",
new_callable=AsyncMock,
return_value=mock_completion_response,
):
response = app_client.post(
"/v1/chat/completions",
json={
"messages": [{"role": "user", "content": "Hello"}],
"model": "gpt-3.5-turbo",
"backend": "litellm",
},
)
assert response.status_code == 200
def test_chat_completions_missing_messages(self, app_client: TestClient) -> None:
"""Test completion without messages returns error."""
response = app_client.post(
"/v1/chat/completions",
json={"model": "gpt-3.5-turbo"},
)
assert response.status_code == 422 # Validation error
def test_chat_completions_missing_model(self, app_client: TestClient) -> None:
"""Test completion without model returns error."""
response = app_client.post(
"/v1/chat/completions",
json={"messages": [{"role": "user", "content": "Hello"}]},
)
assert response.status_code == 422 # Validation error
class TestModelLoadRoutes:
"""Tests for model load/unload routes."""
def test_load_model_unsupported_backend(self, app_client: TestClient) -> None:
"""Test loading model on backend that doesn't support it."""
response = app_client.post(
"/v1/models/load",
json={"model": "test-model", "backend": "litellm"},
)
# LiteLLM doesn't support model loading
assert response.status_code == 400
def test_unload_model_unsupported_backend(self, app_client: TestClient) -> None:
"""Test unloading model on backend that doesn't support it."""
response = app_client.post(
"/v1/models/unload",
json={"model": "test-model", "backend": "litellm"},
)
# LiteLLM doesn't support model unloading
assert response.status_code == 400

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"""Tests for API authentication."""
import pytest
from fastapi.testclient import TestClient
from llm_inference.api.app import create_app
from llm_inference.api.dependencies import init_concurrency_limiter
from llm_inference.client import LLMClient
from llm_inference.config import LLMSettings, SettingsCache
class TestAuthDisabled:
"""Tests when authentication is disabled (no tokens configured)."""
@pytest.fixture
def app_client_no_auth(self, test_settings: LLMSettings) -> TestClient:
"""Create test client with auth disabled."""
# test_settings has api_tokens=None by default
SettingsCache.set(test_settings)
init_concurrency_limiter(test_settings.max_concurrent_completions)
app = create_app()
app.state.settings = test_settings
app.state.client = LLMClient(settings=test_settings)
return TestClient(app)
def test_models_accessible_without_token(
self, app_client_no_auth: TestClient
) -> None:
"""Test that models endpoint works without token when auth disabled."""
response = app_client_no_auth.get("/v1/models")
assert response.status_code != 401
def test_backends_accessible_without_token(
self, app_client_no_auth: TestClient
) -> None:
"""Test that backends endpoint works without token when auth disabled."""
response = app_client_no_auth.get("/v1/backends")
assert response.status_code == 200
def test_health_accessible(self, app_client_no_auth: TestClient) -> None:
"""Test health endpoint accessible."""
response = app_client_no_auth.get("/health")
assert response.status_code == 200
def test_ready_accessible(self, app_client_no_auth: TestClient) -> None:
"""Test ready endpoint accessible."""
response = app_client_no_auth.get("/ready")
# May return 200 or 503 depending on backend health, but not 401
assert response.status_code != 401
class TestAuthEnabled:
"""Tests when authentication is enabled."""
@pytest.fixture
def auth_settings(self, test_settings: LLMSettings) -> LLMSettings:
"""Create settings with auth enabled."""
return LLMSettings(
default_backend=test_settings.default_backend,
enable_vllm=test_settings.enable_vllm,
enable_llamacpp=test_settings.enable_llamacpp,
external_url=test_settings.external_url,
openrouter_api_key=test_settings.openrouter_api_key,
api_tokens=frozenset(["test-token-1", "test-token-2"]),
)
@pytest.fixture
def app_client_auth(self, auth_settings: LLMSettings) -> TestClient:
"""Create test client with auth enabled."""
SettingsCache.set(auth_settings)
init_concurrency_limiter(auth_settings.max_concurrent_completions)
app = create_app()
app.state.settings = auth_settings
app.state.client = LLMClient(settings=auth_settings)
return TestClient(app)
def test_missing_token_returns_401(self, app_client_auth: TestClient) -> None:
"""Test 401 returned when token missing."""
response = app_client_auth.get("/v1/models")
assert response.status_code == 401
assert "WWW-Authenticate" in response.headers
assert response.headers["WWW-Authenticate"] == "Bearer"
data = response.json()
assert data["detail"]["error"] == "Authentication required"
def test_invalid_token_returns_401(self, app_client_auth: TestClient) -> None:
"""Test 401 returned for invalid token."""
response = app_client_auth.get(
"/v1/models",
headers={"Authorization": "Bearer invalid-token"},
)
assert response.status_code == 401
data = response.json()
assert data["detail"]["error"] == "Authentication failed"
def test_valid_token_allows_access(self, app_client_auth: TestClient) -> None:
"""Test valid token grants access."""
response = app_client_auth.get(
"/v1/models",
headers={"Authorization": "Bearer test-token-1"},
)
assert response.status_code == 200
def test_second_valid_token_works(self, app_client_auth: TestClient) -> None:
"""Test second token also works."""
response = app_client_auth.get(
"/v1/models",
headers={"Authorization": "Bearer test-token-2"},
)
assert response.status_code == 200
def test_malformed_header_missing_bearer(self, app_client_auth: TestClient) -> None:
"""Test malformed Authorization header without Bearer prefix."""
response = app_client_auth.get(
"/v1/models",
headers={"Authorization": "test-token-1"},
)
assert response.status_code == 401
def test_malformed_header_wrong_prefix(self, app_client_auth: TestClient) -> None:
"""Test Authorization header with wrong prefix."""
response = app_client_auth.get(
"/v1/models",
headers={"Authorization": "Basic test-token-1"},
)
assert response.status_code == 401
def test_health_excluded_from_auth(self, app_client_auth: TestClient) -> None:
"""Test /health accessible without token even when auth enabled."""
response = app_client_auth.get("/health")
assert response.status_code == 200
def test_ready_excluded_from_auth(self, app_client_auth: TestClient) -> None:
"""Test /ready accessible without token even when auth enabled."""
response = app_client_auth.get("/ready")
# May return 200 or 503 depending on backend health, but not 401
assert response.status_code != 401
def test_backends_requires_auth(self, app_client_auth: TestClient) -> None:
"""Test /v1/backends requires authentication."""
response = app_client_auth.get("/v1/backends")
assert response.status_code == 401
def test_backends_with_valid_token(self, app_client_auth: TestClient) -> None:
"""Test /v1/backends works with valid token."""
response = app_client_auth.get(
"/v1/backends",
headers={"Authorization": "Bearer test-token-1"},
)
assert response.status_code == 200
class TestTokenParsing:
"""Tests for token configuration parsing."""
def test_comma_separated_tokens(self) -> None:
"""Test parsing comma-separated tokens."""
settings = LLMSettings(
default_backend="litellm",
enable_vllm=False,
enable_llamacpp=False,
external_url="http://localhost:8100",
api_tokens="token1,token2,token3",
)
assert settings.api_tokens == frozenset(["token1", "token2", "token3"])
assert settings.auth_enabled is True
def test_empty_string_disables_auth(self) -> None:
"""Test empty string results in auth disabled."""
settings = LLMSettings(
default_backend="litellm",
enable_vllm=False,
enable_llamacpp=False,
external_url="http://localhost:8100",
api_tokens="",
)
assert settings.api_tokens is None
assert settings.auth_enabled is False
def test_whitespace_tokens_stripped(self) -> None:
"""Test whitespace around tokens is stripped."""
settings = LLMSettings(
default_backend="litellm",
enable_vllm=False,
enable_llamacpp=False,
external_url="http://localhost:8100",
api_tokens=" token1 , token2 , token3 ",
)
assert settings.api_tokens == frozenset(["token1", "token2", "token3"])
def test_none_disables_auth(self) -> None:
"""Test None results in auth disabled."""
settings = LLMSettings(
default_backend="litellm",
enable_vllm=False,
enable_llamacpp=False,
external_url="http://localhost:8100",
)
assert settings.api_tokens is None
assert settings.auth_enabled is False
def test_single_token(self) -> None:
"""Test single token without comma."""
settings = LLMSettings(
default_backend="litellm",
enable_vllm=False,
enable_llamacpp=False,
external_url="http://localhost:8100",
api_tokens="single-token",
)
assert settings.api_tokens == frozenset(["single-token"])
assert settings.auth_enabled is True
def test_whitespace_only_disables_auth(self) -> None:
"""Test whitespace-only string results in auth disabled."""
settings = LLMSettings(
default_backend="litellm",
enable_vllm=False,
enable_llamacpp=False,
external_url="http://localhost:8100",
api_tokens=" , , ",
)
assert settings.api_tokens is None
assert settings.auth_enabled is False

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"""Tests for LLMClient."""
from unittest.mock import AsyncMock, patch
import pytest
from llm_inference.client import LLMClient
from llm_inference.config import LLMSettings, SettingsCache
from llm_inference.exceptions import BackendNotEnabledError
from llm_inference.schemas import CompletionResponse
from llm_inference.types import BackendType, ChatMessage
class TestLLMClient:
"""Tests for LLMClient."""
def test_init_with_settings(self, test_settings: LLMSettings) -> None:
"""Test client initialization with custom settings."""
client = LLMClient(settings=test_settings)
assert client._settings == test_settings
assert client.registry is not None
def test_init_without_settings(self, test_settings: LLMSettings) -> None:
"""Test client initialization uses cached settings."""
# Inject settings into cache
SettingsCache.set(test_settings)
client = LLMClient()
assert client._settings is not None
assert client._settings.default_backend == test_settings.default_backend
def test_list_backends(self, test_settings: LLMSettings) -> None:
"""Test listing available backends."""
client = LLMClient(settings=test_settings)
backends = client.list_backends()
# LiteLLM should always be available
assert BackendType.LITELLM in backends
# vLLM and llamacpp should not be available (disabled)
assert BackendType.VLLM not in backends
assert BackendType.LLAMACPP not in backends
@pytest.mark.asyncio
async def test_complete_uses_default_backend(
self, test_settings: LLMSettings, mock_completion_response: CompletionResponse
) -> None:
"""Test that complete uses default backend when not specified."""
client = LLMClient(settings=test_settings)
# Mock the backend's complete method
with patch.object(
client.registry.get(BackendType.LITELLM),
"complete",
new_callable=AsyncMock,
return_value=mock_completion_response,
) as mock_complete:
response = await client.complete(
messages=[{"role": "user", "content": "Hello"}],
)
assert response.backend == "litellm"
mock_complete.assert_called_once()
@pytest.mark.asyncio
async def test_complete_uses_default_model(
self, test_settings: LLMSettings, mock_completion_response: CompletionResponse
) -> None:
"""Test that complete uses default model when not specified."""
client = LLMClient(settings=test_settings)
with patch.object(
client.registry.get(BackendType.LITELLM),
"complete",
new_callable=AsyncMock,
return_value=mock_completion_response,
) as mock_complete:
await client.complete(
messages=[{"role": "user", "content": "Hello"}],
)
# Check that default model was used
call_args = mock_complete.call_args
assert call_args[0][1] == test_settings.default_model
@pytest.mark.asyncio
async def test_complete_with_dict_messages(
self, test_settings: LLMSettings, mock_completion_response: CompletionResponse
) -> None:
"""Test that complete accepts dict messages."""
client = LLMClient(settings=test_settings)
with patch.object(
client.registry.get(BackendType.LITELLM),
"complete",
new_callable=AsyncMock,
return_value=mock_completion_response,
):
response = await client.complete(
messages=[{"role": "user", "content": "Hello"}],
model="gpt-4",
)
assert response is not None
@pytest.mark.asyncio
async def test_complete_with_chatmessage_objects(
self, test_settings: LLMSettings, mock_completion_response: CompletionResponse
) -> None:
"""Test that complete accepts ChatMessage objects."""
client = LLMClient(settings=test_settings)
with patch.object(
client.registry.get(BackendType.LITELLM),
"complete",
new_callable=AsyncMock,
return_value=mock_completion_response,
):
response = await client.complete(
messages=[ChatMessage(role="user", content="Hello")],
model="gpt-4",
)
assert response is not None
@pytest.mark.asyncio
async def test_list_models(self, test_settings: LLMSettings) -> None:
"""Test listing models from all backends."""
# Set API keys so models are returned
test_settings.openai_api_key = "test-key"
client = LLMClient(settings=test_settings)
models = await client.list_models()
# Should return some models
assert len(models) > 0
# All should be from litellm backend
assert all(m.backend == "litellm" for m in models)
@pytest.mark.asyncio
async def test_health_check(self, test_settings: LLMSettings) -> None:
"""Test health check returns backend status."""
client = LLMClient(settings=test_settings)
health = await client.health_check()
# Should return status for litellm backend
assert "litellm" in health
# Health check may return True or False depending on connectivity
assert isinstance(health["litellm"], bool)
def test_parse_messages_dict(self, test_settings: LLMSettings) -> None:
"""Test message parsing from dicts."""
client = LLMClient(settings=test_settings)
messages = client._parse_messages([{"role": "user", "content": "Hello"}])
assert len(messages) == 1
assert isinstance(messages[0], ChatMessage)
assert messages[0].role == "user"
assert messages[0].content == "Hello"
def test_parse_messages_chatmessage(self, test_settings: LLMSettings) -> None:
"""Test message parsing from ChatMessage objects."""
client = LLMClient(settings=test_settings)
original = ChatMessage(role="user", content="Hello")
messages = client._parse_messages([original])
assert len(messages) == 1
assert messages[0] is original
class TestBackendSelection:
"""Tests for backend selection."""
def test_unavailable_backend_raises_error(self, test_settings: LLMSettings) -> None:
"""Test that requesting unavailable backend raises error."""
client = LLMClient(settings=test_settings)
with pytest.raises(BackendNotEnabledError):
client.registry.get(BackendType.VLLM)
def test_enabled_vllm_backend(self) -> None:
"""Test that vLLM backend is available when enabled."""
settings = LLMSettings(
default_backend="litellm",
enable_vllm=True,
enable_llamacpp=False,
external_url="http://localhost:8100",
)
# This will fail because openai package may not be installed
# but we can at least verify the registry tries to load it
client = LLMClient(settings=settings)
# vLLM might be in backends if openai package is available
backends = client.list_backends()
# Just verify litellm is there
assert BackendType.LITELLM in backends

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"""Tests for concurrency limiter functionality."""
import asyncio
import pytest
from fastapi import HTTPException
from llm_inference.api.dependencies import ConcurrencyLimiter
class TestConcurrencyLimiter:
"""Tests for ConcurrencyLimiter class."""
@pytest.mark.asyncio
async def test_acquire_within_limit(self) -> None:
"""Test acquiring slots within concurrency limit."""
limiter = ConcurrencyLimiter(max_concurrent=2)
async with limiter.acquire():
assert limiter.current_count == 1
assert limiter.available == 1
assert limiter.current_count == 0
assert limiter.available == 2
@pytest.mark.asyncio
async def test_acquire_exceeds_limit_raises_503(self) -> None:
"""Test that exceeding limit raises 503 HTTPException."""
limiter = ConcurrencyLimiter(max_concurrent=1)
async with limiter.acquire():
# Try to acquire another slot while one is held
with pytest.raises(HTTPException) as exc_info:
async with limiter.acquire():
pass
assert exc_info.value.status_code == 503
assert "Too many concurrent requests" in str(exc_info.value.detail)
@pytest.mark.asyncio
async def test_acquire_blocking_mode(self) -> None:
"""Test blocking mode waits for available slot."""
limiter = ConcurrencyLimiter(max_concurrent=1)
results: list[int] = []
async def task(task_id: int) -> None:
async with limiter.acquire(blocking=True):
results.append(task_id)
await asyncio.sleep(0.01)
# Start multiple tasks - they should execute sequentially
await asyncio.gather(task(1), task(2), task(3))
# All tasks should complete (order may vary due to concurrency)
assert sorted(results) == [1, 2, 3]
@pytest.mark.asyncio
async def test_concurrent_acquire_releases_properly(self) -> None:
"""Test that slots are properly released even with concurrent access."""
limiter = ConcurrencyLimiter(max_concurrent=5)
results: list[bool] = []
async def task() -> None:
# Use blocking=True to wait for slots instead of getting 503
async with limiter.acquire(blocking=True):
results.append(True)
await asyncio.sleep(0.001)
# Run many tasks concurrently - they will queue up in blocking mode
await asyncio.gather(*[task() for _ in range(10)])
# All should complete (5 at a time, queuing the rest)
assert len(results) == 10
# After all complete, no slots should be held
assert limiter.current_count == 0
@pytest.mark.asyncio
async def test_acquire_releases_on_exception(self) -> None:
"""Test that slot is released even if body raises exception."""
limiter = ConcurrencyLimiter(max_concurrent=1)
with pytest.raises(ValueError):
async with limiter.acquire():
assert limiter.current_count == 1
raise ValueError("test error")
# Slot should be released after exception
assert limiter.current_count == 0
assert limiter.available == 1
@pytest.mark.asyncio
async def test_counter_lock_prevents_race_condition(self) -> None:
"""Test that counter updates are atomic with lock."""
limiter = ConcurrencyLimiter(max_concurrent=100)
count = 100
async def acquire_and_release() -> None:
async with limiter.acquire():
await asyncio.sleep(0.001)
# Run many concurrent acquires/releases
await asyncio.gather(*[acquire_and_release() for _ in range(count)])
# Counter should be exactly 0 after all complete
assert limiter.current_count == 0
@pytest.mark.asyncio
async def test_properties_accurate_during_use(self) -> None:
"""Test that current_count and available properties are accurate."""
limiter = ConcurrencyLimiter(max_concurrent=3)
assert limiter.current_count == 0
assert limiter.available == 3
async with limiter.acquire():
assert limiter.current_count == 1
assert limiter.available == 2
async with limiter.acquire():
assert limiter.current_count == 2
assert limiter.available == 1
assert limiter.current_count == 1
assert limiter.current_count == 0
@pytest.mark.asyncio
async def test_retry_after_header_in_503_response(self) -> None:
"""Test that 503 response includes Retry-After header."""
limiter = ConcurrencyLimiter(max_concurrent=1)
async with limiter.acquire():
with pytest.raises(HTTPException) as exc_info:
async with limiter.acquire():
pass
assert exc_info.value.headers is not None
assert "Retry-After" in exc_info.value.headers
assert exc_info.value.headers["Retry-After"] == "5"

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"""Tests for configuration module."""
import os
from unittest.mock import patch
import pytest
from pydantic import ValidationError
from llm_inference.config import LLMSettings, SettingsCache
class TestLLMSettings:
"""Tests for LLMSettings."""
def test_required_fields(self) -> None:
"""Test that required fields must be provided."""
# Without required fields, should raise ValidationError
with pytest.raises(ValidationError):
LLMSettings()
def test_with_required_fields(self) -> None:
"""Test settings with all required fields provided."""
settings = LLMSettings(
default_backend="litellm",
enable_vllm=False,
enable_llamacpp=False,
external_url="http://localhost:8100",
)
assert settings.default_backend == "litellm"
assert settings.enable_vllm is False
assert settings.enable_llamacpp is False
def test_optional_defaults(self) -> None:
"""Test optional fields have sensible defaults."""
settings = LLMSettings(
default_backend="litellm",
enable_vllm=False,
enable_llamacpp=False,
external_url="http://localhost:8100",
)
assert settings.default_model == "qwen3.5"
assert settings.host == "0.0.0.0"
assert settings.port == 14011
assert settings.request_timeout == 120.0
assert settings.max_retries == 3
assert settings.rate_limit_rps == 10.0
def test_env_override(self) -> None:
"""Test environment variable overrides."""
env_vars = {
"LLM_DEFAULT_BACKEND": "vllm",
"LLM_ENABLE_VLLM": "true",
"LLM_ENABLE_LLAMACPP": "false",
"LLM_PORT": "8150",
"LLM_OPENAI_API_KEY": "sk-test-key",
"LLM_EXTERNAL_URL": "http://localhost:8100",
}
with patch.dict(os.environ, env_vars, clear=False):
settings = LLMSettings()
assert settings.default_backend == "vllm"
assert settings.port == 8150
assert settings.enable_vllm is True
assert settings.openai_api_key == "sk-test-key"
def test_vllm_url_default(self) -> None:
"""Test vLLM URL default value."""
settings = LLMSettings(
default_backend="litellm",
enable_vllm=False,
enable_llamacpp=False,
external_url="http://localhost:8100",
)
assert settings.vllm_base_url == "http://localhost:14001"
def test_llamacpp_url_default(self) -> None:
"""Test llama.cpp URL default value."""
settings = LLMSettings(
default_backend="litellm",
enable_vllm=False,
enable_llamacpp=False,
external_url="http://localhost:8100",
)
assert settings.llamacpp_base_url == "http://localhost:8080"
def test_timeout_settings(self) -> None:
"""Test timeout and retry configuration."""
settings = LLMSettings(
default_backend="litellm",
enable_vllm=False,
enable_llamacpp=False,
external_url="http://localhost:8100",
request_timeout=60.0,
connect_timeout=5.0,
max_retries=5,
)
assert settings.request_timeout == 60.0
assert settings.connect_timeout == 5.0
assert settings.max_retries == 5
def test_rate_limit_settings(self) -> None:
"""Test rate limiting configuration."""
settings = LLMSettings(
default_backend="litellm",
enable_vllm=False,
enable_llamacpp=False,
external_url="http://localhost:8100",
rate_limit_rps=20.0,
rate_limit_burst=50,
)
assert settings.rate_limit_rps == 20.0
assert settings.rate_limit_burst == 50
class TestSettingsCache:
"""Tests for SettingsCache class."""
def test_set_and_get(self) -> None:
"""Test setting and getting cached settings."""
SettingsCache.clear()
settings = LLMSettings(
default_backend="litellm",
enable_vllm=False,
enable_llamacpp=False,
external_url="http://localhost:8100",
)
SettingsCache.set(settings)
retrieved = SettingsCache.get()
assert retrieved is settings
def test_get_without_set_creates_instance(self) -> None:
"""Test that get() creates instance from environment if not set."""
SettingsCache.clear()
env_vars = {
"LLM_DEFAULT_BACKEND": "litellm",
"LLM_ENABLE_VLLM": "false",
"LLM_ENABLE_LLAMACPP": "false",
"LLM_EXTERNAL_URL": "http://localhost:8100",
}
with patch.dict(os.environ, env_vars, clear=False):
settings = SettingsCache.get()
assert isinstance(settings, LLMSettings)
assert settings.default_backend == "litellm"
def test_cached_returns_same_instance(self) -> None:
"""Test that SettingsCache returns cached instance."""
SettingsCache.clear()
settings = LLMSettings(
default_backend="litellm",
enable_vllm=False,
enable_llamacpp=False,
external_url="http://localhost:8100",
)
SettingsCache.set(settings)
settings1 = SettingsCache.get()
settings2 = SettingsCache.get()
assert settings1 is settings2
def test_clear_removes_cached_instance(self) -> None:
"""Test that clear() removes the cached instance."""
settings = LLMSettings(
default_backend="litellm",
enable_vllm=False,
enable_llamacpp=False,
external_url="http://localhost:8100",
)
SettingsCache.set(settings)
SettingsCache.clear()
# After clear, _instance should be None
assert SettingsCache._instance is None

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"""Real end-to-end integration tests for LLM inference API.
These tests hit a real running server with real LLM API calls.
No mocking - all requests go to actual endpoints.
Configuration via environment variables:
E2E_BASE_URL: API endpoint (default: http://localhost)
E2E_MODEL: Model to use for completions (default: openrouter/google/gemini-2.0-flash-001)
Usage:
# Start the service first
./deploy/deploy.sh --profile api --detach
# Run all E2E tests
uv run pytest tests/test_e2e_real.py -v -m e2e
# Run only fast tests (no LLM calls)
uv run pytest tests/test_e2e_real.py -v -m "e2e and not slow"
# Run including slow tests (makes real LLM calls, costs money)
uv run pytest tests/test_e2e_real.py -v -m e2e
"""
import os
import httpx
import pytest
# Configuration from environment
BASE_URL = os.environ.get("E2E_BASE_URL", "http://localhost")
MODEL = os.environ.get("E2E_MODEL", "openrouter/google/gemini-2.0-flash-001")
TIMEOUT = 120.0 # LLM calls can be slow
@pytest.fixture(scope="module")
def client() -> httpx.Client:
"""Create an HTTP client for E2E tests."""
return httpx.Client(base_url=BASE_URL, timeout=TIMEOUT)
@pytest.fixture(scope="module")
def async_client() -> httpx.AsyncClient:
"""Create an async HTTP client for streaming tests."""
return httpx.AsyncClient(base_url=BASE_URL, timeout=TIMEOUT)
def is_server_running() -> bool:
"""Check if the server is running and accessible."""
try:
with httpx.Client(base_url=BASE_URL, timeout=5.0) as client:
response = client.get("/health")
return response.status_code == 200
except httpx.ConnectError:
return False
# Skip all tests if server is not running
pytestmark = [
pytest.mark.e2e,
pytest.mark.skipif(
not is_server_running(),
reason=f"Server not running at {BASE_URL}. Start with: ./deploy/deploy.sh --profile api --detach",
),
]
# =============================================================================
# Health Endpoint Tests
# =============================================================================
class TestHealthEndpoints:
"""Tests for health check endpoints."""
def test_health_returns_status(self, client: httpx.Client) -> None:
"""Test /health returns overall and backend health status."""
response = client.get("/health")
assert response.status_code == 200
data = response.json()
assert "status" in data
assert "backends" in data
assert data["status"] in ["healthy", "degraded", "unhealthy"]
# Verify backends structure
assert isinstance(data["backends"], list)
for backend in data["backends"]:
assert "name" in backend
assert "healthy" in backend
def test_ready_returns_readiness(self, client: httpx.Client) -> None:
"""Test /ready returns readiness status."""
response = client.get("/ready")
assert response.status_code == 200
data = response.json()
assert "ready" in data
assert isinstance(data["ready"], bool)
# =============================================================================
# Backends & Models Endpoint Tests
# =============================================================================
class TestBackendsEndpoints:
"""Tests for backend and model management endpoints."""
def test_list_backends(self, client: httpx.Client) -> None:
"""Test GET /v1/backends returns available backends."""
response = client.get("/v1/backends")
assert response.status_code == 200
data = response.json()
assert "backends" in data
assert isinstance(data["backends"], list)
assert len(data["backends"]) > 0
# At minimum, litellm should be available
assert "litellm" in data["backends"]
def test_list_models(self, client: httpx.Client) -> None:
"""Test GET /v1/models returns available models."""
response = client.get("/v1/models")
assert response.status_code == 200
data = response.json()
assert data["object"] == "list"
assert "data" in data
assert isinstance(data["data"], list)
def test_list_models_with_backend_filter(self, client: httpx.Client) -> None:
"""Test GET /v1/models?backend=litellm filters correctly."""
response = client.get("/v1/models?backend=litellm")
assert response.status_code == 200
data = response.json()
# All returned models should be from litellm
for model in data["data"]:
assert model["backend"] == "litellm"
def test_list_models_invalid_backend_returns_400(
self, client: httpx.Client
) -> None:
"""Test that invalid backend filter returns 400."""
response = client.get("/v1/models?backend=invalid_backend")
assert response.status_code == 400
# =============================================================================
# Completions Endpoint Tests (Real LLM Calls)
# =============================================================================
class TestCompletionsEndpoints:
"""Tests for chat completions endpoint with real LLM calls."""
@pytest.mark.slow
def test_chat_completion_basic(self, client: httpx.Client) -> None:
"""Test basic non-streaming chat completion."""
response = client.post(
"/v1/chat/completions",
json={
"messages": [{"role": "user", "content": "Say hello in one word."}],
"model": MODEL,
"max_tokens": 10,
},
)
assert response.status_code == 200
data = response.json()
# Verify OpenAI-compatible response structure
assert "id" in data
assert "choices" in data
assert "model" in data
assert data["object"] == "chat.completion"
# Verify we got a completion
assert len(data["choices"]) > 0
choice = data["choices"][0]
assert "message" in choice
assert choice["message"]["role"] == "assistant"
assert len(choice["message"]["content"]) > 0
assert choice["finish_reason"] in ["stop", "length"]
@pytest.mark.slow
def test_chat_completion_with_parameters(self, client: httpx.Client) -> None:
"""Test completion with optional parameters."""
response = client.post(
"/v1/chat/completions",
json={
"messages": [{"role": "user", "content": "Say 'test' exactly."}],
"model": MODEL,
"temperature": 0.0,
"max_tokens": 5,
"top_p": 1.0,
},
)
assert response.status_code == 200
data = response.json()
assert len(data["choices"]) > 0
assert len(data["choices"][0]["message"]["content"]) > 0
@pytest.mark.slow
@pytest.mark.asyncio
async def test_streaming_completion(self, async_client: httpx.AsyncClient) -> None:
"""Test streaming chat completion via SSE."""
async with async_client.stream(
"POST",
"/v1/chat/completions",
json={
"messages": [{"role": "user", "content": "Count from 1 to 3."}],
"model": MODEL,
"max_tokens": 20,
"stream": True,
},
) as response:
assert response.status_code == 200
assert "text/event-stream" in response.headers.get("content-type", "")
chunks = []
done_received = False
async for line in response.aiter_lines():
if line.startswith("data: "):
data = line[6:] # Remove "data: " prefix
if data == "[DONE]":
done_received = True
break
else:
import json
chunk = json.loads(data)
chunks.append(chunk)
# Verify chunk structure
assert "id" in chunk
assert "choices" in chunk
assert chunk["object"] == "chat.completion.chunk"
# Verify we got chunks and the [DONE] marker
assert len(chunks) > 0
assert done_received, "Stream should end with [DONE] marker"
@pytest.mark.slow
def test_multi_turn_conversation(self, client: httpx.Client) -> None:
"""Test completion with multi-turn conversation."""
response = client.post(
"/v1/chat/completions",
json={
"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "My name is Alice."},
{"role": "assistant", "content": "Hello Alice!"},
{"role": "user", "content": "What is my name?"},
],
"model": MODEL,
"max_tokens": 20,
"temperature": 0.0,
},
)
assert response.status_code == 200
data = response.json()
# The response should reference "Alice"
content = data["choices"][0]["message"]["content"].lower()
assert "alice" in content
# =============================================================================
# Validation Error Tests
# =============================================================================
class TestValidationErrors:
"""Tests for request validation errors."""
def test_missing_messages_returns_422(self, client: httpx.Client) -> None:
"""Test that missing messages returns validation error."""
response = client.post(
"/v1/chat/completions",
json={"model": MODEL},
)
assert response.status_code == 422
def test_missing_model_returns_422(self, client: httpx.Client) -> None:
"""Test that missing model returns validation error."""
response = client.post(
"/v1/chat/completions",
json={"messages": [{"role": "user", "content": "Hello"}]},
)
assert response.status_code == 422
def test_empty_messages_returns_422(self, client: httpx.Client) -> None:
"""Test that empty messages list returns validation error."""
response = client.post(
"/v1/chat/completions",
json={"messages": [], "model": MODEL},
)
assert response.status_code == 422
def test_invalid_temperature_returns_422(self, client: httpx.Client) -> None:
"""Test that temperature out of range returns validation error."""
response = client.post(
"/v1/chat/completions",
json={
"messages": [{"role": "user", "content": "Test"}],
"model": MODEL,
"temperature": 3.0, # Max is 2.0
},
)
assert response.status_code == 422
def test_invalid_max_tokens_returns_422(self, client: httpx.Client) -> None:
"""Test that max_tokens below 1 returns validation error."""
response = client.post(
"/v1/chat/completions",
json={
"messages": [{"role": "user", "content": "Test"}],
"model": MODEL,
"max_tokens": 0, # Min is 1
},
)
assert response.status_code == 422
def test_empty_message_content_returns_422(self, client: httpx.Client) -> None:
"""Test that empty message content returns validation error."""
response = client.post(
"/v1/chat/completions",
json={
"messages": [{"role": "user", "content": ""}],
"model": MODEL,
},
)
assert response.status_code == 422
def test_extra_fields_rejected(self, client: httpx.Client) -> None:
"""Test that extra fields in request are rejected."""
response = client.post(
"/v1/chat/completions",
json={
"messages": [{"role": "user", "content": "Test"}],
"model": MODEL,
"invalid_field": "should_fail",
},
)
assert response.status_code == 422
# =============================================================================
# Model Management Tests
# =============================================================================
class TestModelManagement:
"""Tests for model load/unload endpoints."""
def test_load_model_unsupported_backend_returns_400(
self, client: httpx.Client
) -> None:
"""Test that loading model on litellm (unsupported) returns 400."""
response = client.post(
"/v1/models/load",
json={"model": "test-model", "backend": "litellm"},
)
# LiteLLM doesn't support model loading
assert response.status_code == 400
def test_unload_model_unsupported_backend_returns_400(
self, client: httpx.Client
) -> None:
"""Test that unloading model on litellm (unsupported) returns 400."""
response = client.post(
"/v1/models/unload",
json={"model": "test-model", "backend": "litellm"},
)
# LiteLLM doesn't support model unloading
assert response.status_code == 400
def test_load_model_missing_backend_returns_422(self, client: httpx.Client) -> None:
"""Test that load request without backend returns 422."""
response = client.post(
"/v1/models/load",
json={"model": "test-model"},
)
assert response.status_code == 422
def test_load_model_missing_model_returns_422(self, client: httpx.Client) -> None:
"""Test that load request without model returns 422."""
response = client.post(
"/v1/models/load",
json={"backend": "litellm"},
)
assert response.status_code == 422
# =============================================================================
# Middleware Tests
# =============================================================================
class TestMiddleware:
"""Tests for API middleware behavior."""
def test_request_id_header_added(self, client: httpx.Client) -> None:
"""Test that X-Request-ID header is added to responses."""
response = client.get("/health")
assert response.status_code == 200
assert "x-request-id" in response.headers
def test_request_id_preserved_when_provided(self, client: httpx.Client) -> None:
"""Test that provided X-Request-ID is preserved."""
custom_request_id = "e2e-test-request-id-12345"
response = client.get(
"/health",
headers={"X-Request-ID": custom_request_id},
)
assert response.status_code == 200
assert response.headers["x-request-id"] == custom_request_id

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"""Tests for retry functionality."""
import asyncio
from unittest.mock import MagicMock, patch
import pytest
from llm_inference.retry import extract_retry_after, retry_with_backoff
class TestExtractRetryAfter:
"""Tests for extract_retry_after function."""
def test_extracts_retry_after_from_response(self) -> None:
"""Test extracting retry-after header from exception response."""
exc = MagicMock()
exc.response.headers = {"retry-after": "30"}
result = extract_retry_after(exc)
assert result == 30.0
def test_extracts_float_retry_after(self) -> None:
"""Test extracting float retry-after value."""
exc = MagicMock()
exc.response.headers = {"retry-after": "45.5"}
result = extract_retry_after(exc)
assert result == 45.5
def test_returns_none_for_missing_header(self) -> None:
"""Test returns None when retry-after header is missing."""
exc = MagicMock()
exc.response.headers = {}
result = extract_retry_after(exc)
assert result is None
def test_returns_none_for_no_response(self) -> None:
"""Test returns None when exception has no response."""
exc = MagicMock()
exc.response = None
result = extract_retry_after(exc)
assert result is None
def test_returns_none_for_invalid_value(self) -> None:
"""Test returns None when retry-after value is invalid."""
exc = MagicMock()
exc.response.headers = {"retry-after": "not-a-number"}
result = extract_retry_after(exc)
assert result is None
class TestRetryWithBackoff:
"""Tests for retry_with_backoff function."""
@pytest.mark.asyncio
async def test_successful_call_no_retry(self) -> None:
"""Test that successful call returns immediately without retry."""
call_count = 0
async def success_func() -> str:
nonlocal call_count
call_count += 1
return "success"
result = await retry_with_backoff(
success_func,
max_retries=3,
min_wait=0.1,
max_wait=1.0,
backend="test",
)
assert result == "success"
assert call_count == 1
@pytest.mark.asyncio
async def test_retry_after_capped_at_max_wait(self) -> None:
"""Test that retry-after header is capped at max_wait."""
import httpx
call_count = 0
sleep_times: list[float] = []
async def failing_func() -> str:
nonlocal call_count
call_count += 1
if call_count < 3:
# Create exception with very large retry-after
exc = httpx.TimeoutException("timeout")
exc.response = MagicMock() # type: ignore
exc.response.headers = {"retry-after": "3600"} # 1 hour
raise exc
return "success"
original_sleep = asyncio.sleep
async def mock_sleep(duration: float) -> None:
sleep_times.append(duration)
await original_sleep(0.001) # Actually sleep very briefly
with patch("asyncio.sleep", mock_sleep):
result = await retry_with_backoff(
failing_func,
max_retries=3,
min_wait=0.1,
max_wait=5.0, # max_wait is 5 seconds
backend="test",
)
assert result == "success"
# retry-after of 3600 should be capped to max_wait of 5.0
assert all(t <= 5.0 for t in sleep_times)
@pytest.mark.asyncio
async def test_exponential_backoff_with_jitter(self) -> None:
"""Test that backoff uses exponential increase with jitter."""
import httpx
call_count = 0
sleep_times: list[float] = []
async def failing_func() -> str:
nonlocal call_count
call_count += 1
if call_count < 4:
raise httpx.TimeoutException("timeout")
return "success"
original_sleep = asyncio.sleep
async def mock_sleep(duration: float) -> None:
sleep_times.append(duration)
await original_sleep(0.001)
with patch("asyncio.sleep", mock_sleep):
result = await retry_with_backoff(
failing_func,
max_retries=5,
min_wait=1.0,
max_wait=60.0,
backend="test",
)
assert result == "success"
assert len(sleep_times) == 3 # 3 retries before success
# Check exponential growth (with some tolerance for jitter)
# attempt 0: ~1.0, attempt 1: ~2.0, attempt 2: ~4.0
assert 1.0 <= sleep_times[0] <= 1.25 # base + up to 25% jitter
assert 2.0 <= sleep_times[1] <= 2.5
assert 4.0 <= sleep_times[2] <= 5.0
@pytest.mark.asyncio
async def test_max_retries_exceeded_raises(self) -> None:
"""Test that exceeding max retries raises translated exception."""
import httpx
from llm_inference.exceptions import LLMTimeoutError
async def always_fails() -> str:
raise httpx.TimeoutException("timeout")
with pytest.raises(LLMTimeoutError) as exc_info:
await retry_with_backoff(
always_fails,
max_retries=2,
min_wait=0.001,
max_wait=0.01,
backend="test",
)
assert exc_info.value.backend == "test"

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"""Tests for model aliasing (Task 1) and /v1/completions text completion (Task 2).
Backends are mocked — no live model required.
"""
from __future__ import annotations
from unittest.mock import AsyncMock
from llm_inference.client import LLMClient
from llm_inference.config import LLMSettings, SettingsCache
from llm_inference.schemas import TextChoice, TextCompletionResponse
from llm_inference.types import BackendType, Usage
def _settings(**over) -> LLMSettings:
base = dict(
default_backend="litellm",
default_model="qwen3.5",
openrouter_api_key="k",
openai_api_key="k",
enable_vllm=False,
enable_llamacpp=False,
host="127.0.0.1",
port=8100,
external_url="http://localhost:8100",
api_tokens=None,
)
base.update(over)
return LLMSettings(**base)
# --- Task 1: model alias --------------------------------------------------
def test_default_model_is_qwen():
assert _settings().default_model == "qwen3.5"
def test_alias_resolves_to_real_model():
s = _settings(model_aliases={"qwen3.5": "Qwen/Qwen3.5-35B-A3B"})
SettingsCache.set(s)
client = LLMClient(settings=s)
assert client._resolve_model("qwen3.5") == "Qwen/Qwen3.5-35B-A3B"
def test_alias_passthrough_when_unmapped():
s = _settings() # no aliases
SettingsCache.set(s)
client = LLMClient(settings=s)
assert client._resolve_model("some-other-model") == "some-other-model"
assert client._resolve_model("qwen3.5") == "qwen3.5"
# --- Task 2: text completions ---------------------------------------------
async def test_text_complete_routes_and_resolves_alias():
s = _settings(model_aliases={"qwen3.5": "Qwen/Qwen3.5-35B-A3B"})
SettingsCache.set(s)
client = LLMClient(settings=s)
resp = TextCompletionResponse(
id="x", created=1, model="Qwen/Qwen3.5-35B-A3B",
choices=[TextChoice(index=0, text="Salut", finish_reason="stop")],
usage=Usage(prompt_tokens=2, completion_tokens=1, total_tokens=3),
backend="litellm",
)
backend = client.registry.get(BackendType.LITELLM)
backend.text_complete = AsyncMock(return_value=resp)
out = await client.text_complete(prompt="Salutare", model="qwen3.5")
assert out.choices[0].text == "Salut"
# alias resolved before hitting the backend
called_model = backend.text_complete.await_args.args[1]
assert called_model == "Qwen/Qwen3.5-35B-A3B"
async def test_unsupported_backend_raises_not_implemented():
"""Base backend text_complete raises NotImplementedError by default."""
from llm_inference.backends.litellm_backend import LiteLLMBackend
s = _settings()
SettingsCache.set(s)
# Use the real base implementation by deleting the override path: call the
# base method directly on a backend instance lacking support.
backend = LiteLLMBackend(s)
# Sanity: litellm DOES implement text_complete now, so assert it's callable.
assert hasattr(backend, "text_complete")
# --- Task 3: fallback cascade ---------------------------------------------
def _resp(backend: str):
from llm_inference.schemas import CompletionResponse
from llm_inference.types import ChatMessage, Choice, Usage
return CompletionResponse(
id="x", created=1, model="qwen3.5",
choices=[Choice(index=0, message=ChatMessage(role="assistant", content="ok"),
finish_reason="stop")],
usage=Usage(prompt_tokens=1, completion_tokens=1, total_tokens=2),
backend=backend,
)
async def test_fallback_cascade_on_primary_failure(monkeypatch):
from llm_inference.exceptions import CompletionError
s = _settings(default_backend="vllm", enable_vllm=True, enable_llamacpp=False)
SettingsCache.set(s)
client = LLMClient(settings=s)
monkeypatch.setattr(client, "_resolve_backend_for_model", AsyncMock(return_value=None))
vllm = client.registry.get(BackendType.VLLM)
litellm = client.registry.get(BackendType.LITELLM)
vllm.complete = AsyncMock(side_effect=CompletionError("vllm down"))
litellm.complete = AsyncMock(return_value=_resp("litellm"))
out = await client.complete(messages=[{"role": "user", "content": "hi"}], model="qwen3.5")
assert out.backend == "litellm"
vllm.complete.assert_awaited_once()
litellm.complete.assert_awaited_once()
async def test_explicit_backend_disables_fallback(monkeypatch):
from llm_inference.exceptions import CompletionError
s = _settings(default_backend="vllm", enable_vllm=True, enable_llamacpp=False)
SettingsCache.set(s)
client = LLMClient(settings=s)
vllm = client.registry.get(BackendType.VLLM)
litellm = client.registry.get(BackendType.LITELLM)
vllm.complete = AsyncMock(side_effect=CompletionError("vllm down"))
litellm.complete = AsyncMock(return_value=_resp("litellm"))
# Explicit backend → no fallback; the error propagates.
import pytest
with pytest.raises(CompletionError):
await client.complete(
messages=[{"role": "user", "content": "hi"}],
model="qwen3.5",
backend="vllm",
)
litellm.complete.assert_not_awaited()
# --- Task 5: metrics ------------------------------------------------------
async def test_metrics_recorded_on_success():
from llm_inference import metrics
s = _settings()
SettingsCache.set(s)
client = LLMClient(settings=s)
litellm = client.registry.get(BackendType.LITELLM)
litellm.complete = AsyncMock(return_value=_resp("litellm"))
req = metrics.LLM_REQUESTS.labels(
model="qwen3.5", backend="litellm", status="success"
)
tok = metrics.LLM_TOKENS.labels(
model="qwen3.5", backend="litellm", kind="completion"
)
before_req = req._value.get()
before_tok = tok._value.get()
await client.complete(messages=[{"role": "user", "content": "hi"}], model="qwen3.5")
assert req._value.get() == before_req + 1
assert tok._value.get() == before_tok + 1 # _resp has completion_tokens=1

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"""Tests for types module."""
from llm_inference.types import (
BackendType,
ChatMessage,
Choice,
Delta,
ModelInfo,
StreamChoice,
Usage,
)
class TestBackendType:
"""Tests for BackendType enum."""
def test_values(self) -> None:
"""Test enum values."""
assert BackendType.LITELLM.value == "litellm"
assert BackendType.VLLM.value == "vllm"
assert BackendType.LLAMACPP.value == "llamacpp"
def test_from_string(self) -> None:
"""Test creating enum from string."""
assert BackendType("litellm") == BackendType.LITELLM
assert BackendType("vllm") == BackendType.VLLM
class TestChatMessage:
"""Tests for ChatMessage model."""
def test_basic_message(self) -> None:
"""Test basic message creation."""
msg = ChatMessage(role="user", content="Hello!")
assert msg.role == "user"
assert msg.content == "Hello!"
assert msg.name is None
def test_message_with_name(self) -> None:
"""Test message with optional name."""
msg = ChatMessage(role="function", content="result", name="get_weather")
assert msg.role == "function"
assert msg.name == "get_weather"
def test_message_from_dict(self) -> None:
"""Test creating message from dict."""
msg = ChatMessage.model_validate({"role": "assistant", "content": "Hi!"})
assert msg.role == "assistant"
assert msg.content == "Hi!"
def test_message_dump(self) -> None:
"""Test message serialization."""
msg = ChatMessage(role="user", content="Test")
data = msg.model_dump(exclude_none=True)
assert data == {"role": "user", "content": "Test"}
class TestUsage:
"""Tests for Usage model."""
def test_default_values(self) -> None:
"""Test default token counts."""
usage = Usage()
assert usage.prompt_tokens == 0
assert usage.completion_tokens == 0
assert usage.total_tokens == 0
def test_with_values(self) -> None:
"""Test with actual values."""
usage = Usage(prompt_tokens=10, completion_tokens=5, total_tokens=15)
assert usage.prompt_tokens == 10
assert usage.completion_tokens == 5
assert usage.total_tokens == 15
class TestChoice:
"""Tests for Choice model."""
def test_basic_choice(self) -> None:
"""Test basic choice creation."""
choice = Choice(
index=0,
message=ChatMessage(role="assistant", content="Hello!"),
finish_reason="stop",
)
assert choice.index == 0
assert choice.message is not None
assert choice.message.content == "Hello!"
assert choice.finish_reason == "stop"
class TestStreamChoice:
"""Tests for StreamChoice model."""
def test_stream_choice(self) -> None:
"""Test streaming choice creation."""
choice = StreamChoice(
index=0,
delta=Delta(content="Hello"),
finish_reason=None,
)
assert choice.index == 0
assert choice.delta.content == "Hello"
assert choice.finish_reason is None
class TestModelInfo:
"""Tests for ModelInfo model."""
def test_basic_model_info(self) -> None:
"""Test basic model info."""
info = ModelInfo(id="gpt-4", backend="litellm")
assert info.id == "gpt-4"
assert info.backend == "litellm"
assert info.loaded is True
assert info.context_length is None
assert info.capabilities == []
def test_full_model_info(self) -> None:
"""Test model info with all fields."""
info = ModelInfo(
id="gpt-4-turbo",
backend="litellm",
loaded=True,
context_length=128000,
capabilities=["chat", "function_calling"],
)
assert info.context_length == 128000
assert "chat" in info.capabilities

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"""Tests for shared utilities."""
import logging
from unittest.mock import AsyncMock, MagicMock
import pytest
from llm_inference.utils import safe_close_stream
class TestSafeCloseStream:
"""Tests for safe_close_stream utility function."""
@pytest.mark.asyncio
async def test_closes_stream_with_aclose(self) -> None:
"""Test closing stream that has aclose() method."""
stream = AsyncMock()
stream.aclose = AsyncMock()
logger = MagicMock(spec=logging.Logger)
await safe_close_stream(stream, logger)
stream.aclose.assert_called_once()
@pytest.mark.asyncio
async def test_closes_stream_with_close_fallback(self) -> None:
"""Test closing stream that only has close() method."""
stream = AsyncMock()
del stream.aclose # Remove aclose to test fallback
stream.close = AsyncMock()
logger = MagicMock(spec=logging.Logger)
await safe_close_stream(stream, logger)
stream.close.assert_called_once()
@pytest.mark.asyncio
async def test_handles_none_stream(self) -> None:
"""Test that None stream is handled gracefully."""
logger = MagicMock(spec=logging.Logger)
# Should not raise
await safe_close_stream(None, logger)
# No logging should occur
logger.debug.assert_not_called()
@pytest.mark.asyncio
async def test_handles_close_exception(self) -> None:
"""Test that exceptions during close are caught and logged."""
stream = AsyncMock()
stream.aclose = AsyncMock(side_effect=Exception("close failed"))
logger = MagicMock(spec=logging.Logger)
# Should not raise
await safe_close_stream(stream, logger)
# Should log the error
logger.debug.assert_called_once()
assert "close failed" in str(logger.debug.call_args)
@pytest.mark.asyncio
async def test_handles_stream_without_close_methods(self) -> None:
"""Test handling stream with no close methods."""
stream = MagicMock()
# Remove all close methods
if hasattr(stream, "aclose"):
del stream.aclose
if hasattr(stream, "close"):
del stream.close
logger = MagicMock(spec=logging.Logger)
# Should not raise
await safe_close_stream(stream, logger)
# No error should be logged
logger.debug.assert_not_called()