neuravec-llm 0.1.1

Async Azure OpenAI toolkit — structured output, agent pipelines, vision, token tracking

llm_service

Async Python toolkit for Azure OpenAI (Foundry). One module the team imports everywhere — handles config, model detection, concurrency, structured output, agent pipelines, vision, token tracking.

Installation

pip install httpx pyyaml pydantic

Clone the repo and import directly, or copy llm_service/ into your project.

Quick start

# config.yaml
api_key: ${AZURE_OPENAI_KEY}
endpoint: https://your-resource.openai.azure.com
model_name: gpt-4.1

import asyncio
from llm_service import LLMConfig, LLMClient

async def main():
    cfg = LLMConfig.from_yaml("config.yaml")
    async with LLMClient(cfg) as llm:
        answer = await llm.chat(
            "Summarize this document.",
            system="You extract key facts."
        )
        print(answer)
        print(llm.usage.summary())

asyncio.run(main())

In a Jupyter notebook asyncio.run() is not needed — just await directly:

cfg = LLMConfig.from_yaml("config.yaml")
async with LLMClient(cfg) as llm:
    answer = await llm.chat("Hello!")

Features

Batch (concurrent)

results = await llm.batch(
    ["What is ETL?", "What is RAG?", "What is a vector DB?"],
    system="Answer in 1-2 sentences.",
)

Semaphore (default 8) controls how many requests fly in parallel.

Structured output (Pydantic)

from pydantic import BaseModel, Field

class Invoice(BaseModel):
    number: str = Field(description="Invoice number")
    total: float = Field(description="Gross amount")
    currency: str = Field(description="Currency code")

invoice = await llm.structured(
    f"Extract data from this invoice:\n\n{document}",
    Invoice,
)
print(invoice.number, invoice.total, invoice.currency)

Two modes:

• strict=True (default) — Azure constrains output to the schema server-side
• strict=False — JSON mode + post-validation with Pydantic

JSON output (no schema)

data = await llm.chat_json("Return top 3 cities in Poland as JSON with 'cities' key")
# → {"cities": [{"name": "Warsaw", ...}, ...]}

Pipeline (agent chains)

Multiple agents working on the same input, with dependencies:

from llm_service import Pipeline, Step

pipe = Pipeline(
    # Layer 1 — run in parallel (no depends_on)
    Step(name="analyst", system="You are a business analyst.", output=AnalystView),
    Step(name="lawyer",  system="You are a corporate lawyer.", output=LegalView),
    # Layer 2 — waits for both
    Step(
        name="critic",
        system="You are a critical reviewer.",
        prompt="Review:\n\n{analyst}\n\n{lawyer}\n\nOriginal:\n{input}",
        output=Verdict,
        depends_on=["analyst", "lawyer"],
    ),
)

async with LLMClient(cfg) as llm:
    result = await pipe.run(llm, input="Proposal to acquire startup ABC...")
    print(result.output("critic"))
    print(result.trace())

Pipeline completed in 4.2s

  [OK] analyst: 1.8s, 450 tokens, $0.0018
  [OK] lawyer: 2.1s, 520 tokens, $0.0022
  [OK] critic: 1.9s, 380 tokens, $0.0014

  Total: 1,350 tokens, $0.0054 USD

Pipelines are reusable — define once, run on many documents (including via asyncio.gather).

Vision / multimodal

# From file
answer = await llm.chat("Describe this image.", images=["photo.png"])

# Structured OCR from scan
invoice = await llm.structured(
    "Extract invoice data from this scan.",
    Invoice,
    images=["scan.jpg"],
    image_detail="high",
)

# Compare two images
result = await llm.structured(
    "Compare these two images.",
    ComparisonResult,
    images=["v1.png", "v2.png"],
)

Accepts file paths (auto base64), URLs (passthrough), or raw bytes.

Token tracking and cost estimation

async with LLMClient(cfg) as llm:
    await llm.batch(prompts)
    print(llm.usage.summary())

Requests: 50
Tokens:   12,340 (prompt: 8,200, completion: 4,140)
          reasoning: 1,200 (included in completion)
Cost:     $0.0495 USD

Built-in pricing for gpt-4.1, gpt-4o, o-series, gpt-5.x families.

Model auto-detection

The client automatically adapts to the model:

Model	temperature	system role	token param	reasoning_effort
gpt-4.1	supported	system	max_completion_tokens	stripped
gpt-5.4-mini	stripped	system	max_completion_tokens	medium default
o3, o4-mini	stripped	developer	max_completion_tokens	medium default

Unsupported params are silently dropped — no need for model-specific code.

Error handling

from llm_service import LLMError

try:
    result = await llm.chat("...")
except LLMError as e:
    print(e)

LLMError: Rate limit exceeded
  HTTP 429
  Azure code: RateLimitExceeded
  Model: gpt-4.1
  Hint: Rate limit — za duzo requestow. Zmniejsz concurrency lub dodaj retry.
  Retries: 3
    attempt 1: HTTP 429 after 1.2s
    attempt 2: HTTP 429 after 0.8s
    attempt 3: HTTP 429 after 0.9s

Retries (408, 429, 5xx, timeouts) with exponential backoff + Retry-After header support.

Configuration

YAML (recommended)

api_key: ${AZURE_OPENAI_KEY}
endpoint: https://your-resource.openai.azure.com
model_name: gpt-4.1

# All optional:
# temperature: 0.7
# max_tokens: 4096
# reasoning_effort: medium     # for reasoning models: low|medium|high
# concurrency: 8               # max parallel requests
# timeout: 120                 # seconds per request
# retries: 3

Supports ${ENV_VAR} and ${ENV_VAR:default} substitution.

Code

cfg = LLMConfig(
    api_key=os.environ["AZURE_OPENAI_KEY"],
    endpoint="https://your-resource.openai.azure.com",
    model_name="gpt-4.1",
)

Only api_key, endpoint, model_name are required. Everything else has sensible defaults.

Project structure

llm_service/
  __init__.py      — public API exports
  config.py        — YAML config loader with env-var substitution
  models.py        — model capability registry (reasoning vs standard)
  client.py        — async httpx client, semaphore, retry, error handling
  structured.py    — Pydantic ↔ JSON schema, response parsing
  pipeline.py      — agent chains with DAG execution
  usage.py         — token counting, cost estimation
  vision.py        — image encoding for multimodal requests

config.example.yaml  — config template
examples.py          — usage examples for .py scripts
examples.ipynb       — usage examples for Jupyter notebooks

Examples

See examples.py (scripts) and examples.ipynb (notebooks) for complete working examples covering all features.

Dependencies

• httpx — async HTTP client
• pyyaml — YAML config loading
• pydantic — structured output validation

No dependency on the openai Python SDK. Direct HTTP to Azure OpenAI REST API.