jsmithpkp-llm-client-kit 0.1.3

Cached LLM client with Ollama + Anthropic provider dispatch, fixture mode, and JSON-schema-strict completions.

llm-client-kit

Cached, fixture-aware LLM client with Ollama (default) and Anthropic provider dispatch. Carved out of resume-builder/src/resume_builder/llm_client.py so it can be shared across internal Python apps without each app rolling its own dispatch + caching layer.

PyPI: jsmithpkp-llm-client-kit. Import path stays llm_client_kit (PyPI distribution name and Python import name are allowed to differ).

Install

pip install jsmithpkp-llm-client-kit

For the Anthropic provider path, install the extra:

pip install 'jsmithpkp-llm-client-kit[anthropic]'

Public API

from llm_client_kit import (
    LLMClient,
    LLMResponse,
    LLMEndpointUnreachableError,
    AnthropicProviderError,
    PROVIDER_OLLAMA,
    PROVIDER_ANTHROPIC,
)

Construct from env (resume-builder-compatible)

client = LLMClient.from_env()  # reads RESUME_BUILDER_LLM_* env vars
result = client.complete_json(
    namespace="my_stage",
    system_prompt="You are a JSON-only classifier...",
    user_payload={"subject": "...", "body": "..."},
)

Env vars honored:

Env var	Default	Notes
RESUME_BUILDER_LLM_PROVIDER	ollama	ollama or anthropic
RESUME_BUILDER_LLM_API_URL	provider-default	OpenAI-compat for Ollama; ignored by Anthropic adapter
RESUME_BUILDER_LLM_MODEL	llama3.1:8b / claude-sonnet-4-6	per-provider default
RESUME_BUILDER_LLM_API_KEY / ANTHROPIC_API_KEY	unset	required for Anthropic
RESUME_BUILDER_LLM_TIMEOUT_SECONDS	90	per-request timeout
RESUME_BUILDER_LLM_TIMEOUT_<NAMESPACE>	unset	per-stage timeout override
RESUME_BUILDER_LLM_MAX_TOKENS	2048	Anthropic only
RESUME_BUILDER_LLM_FIXTURE	0	fixture mode (no network calls; cache-only)
RESUME_BUILDER_LLM_CACHE_DIR	.llm_cache / tests/fixtures/llm_cache (fixture mode)	response cache location

The env var prefix RESUME_BUILDER_LLM_* is preserved from the carve-out source for v0.1.0 backward compat. A future release will parameterize the prefix so consumers can use their own namespace.

Construct directly (no env)

from pathlib import Path
from llm_client_kit import LLMClient, PROVIDER_OLLAMA

client = LLMClient(
    endpoint="http://localhost:11434/v1/chat/completions",
    model="llama3.2:3b",
    cache_dir=Path("./.cache"),
    fixture_mode=False,
    timeout_seconds=30.0,
    provider=PROVIDER_OLLAMA,
)

Error contract

complete_json documents three failure modes:

• ValueError — invalid / non-object JSON response, malformed cache payload.
• RuntimeError — provider-agnostic failures (cache I/O, fixture-mode miss) AND Ollama-path transport/parse failures.
• LLMEndpointUnreachableError (RuntimeError subclass) — connection refused / DNS failure / host unreachable, on the Ollama path.
• AnthropicProviderError (NOT a RuntimeError subclass) — any failure on the Anthropic path. Intentionally outside the (RuntimeError, ValueError) fallback chain: a paid API call halting loudly is better than silently degrading.

See class docstrings for the full rationale.

Releasing

Releases are automated. Tag-triggered workflow at .github/workflows/release.yml:

1. Bump version in pyproject.toml and __version__ in src/llm_client_kit/__init__.py on a PR.
2. After the PR merges, tag vX.Y.Z on main and push the tag.
3. The release workflow runs gitleaks against the working tree AND full git history — any finding aborts the workflow before the build. Then python -m build produces sdist + wheel, and pypa/gh-action-pypi-publish uploads via PyPI Trusted Publisher OIDC (no API token stored in repo secrets).

# example bump-and-release flow
git switch main && git pull
# (PR merged that bumps version to 0.1.4)
git tag v0.1.4
git push origin v0.1.4
# watch: gh run watch

The secret-scan step is mandatory and is the only thing standing between an accidentally-committed credential and a published wheel. Do not edit the workflow to skip it without first understanding what it catches.