tegwick
c0b7e54ed1
render-hall requires --entry and no longer walks the corpus. image plan without --entry stays an inventory. LLM-WP-0010 records that existing seats are historic and that the cooldown publisher comes later. Assistant: grok Assistant-Session: 01a11ab6-1534-7d63-8606-662b92a80ed6
283 lines
9 KiB
Markdown
283 lines
9 KiB
Markdown
# llm-connect
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Pluggable LLM adapters for Python and the commandline. Supports OpenRouter, Gemini,
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OpenAI, and the Claude Code CLI out of the box, with a clean abstract interface for adding
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your own.
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## Quick start
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```python
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from llm_connect import create_adapter, RunConfig
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adapter = create_adapter("gemini", model="gemini-2.5-flash")
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config = RunConfig(temperature=0.7, max_tokens=1000)
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response = adapter.execute_prompt("Summarise the value chain concept.", config)
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print(response.content)
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```
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## Installation
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```bash
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pip install -e /path/to/llm-connect # local editable install
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# or, once published:
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pip install llm-connect
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```
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**Requires:** Python 3.10+, `toml`
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## Providers
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| Provider key | Class | Notes |
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|---|---|---|
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| `"openrouter"` | `OpenRouterAdapter` | OpenAI-compatible endpoint; supports all OpenRouter models |
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| `"gemini"` | `GeminiAdapter` | Google Generative Language REST API; supports free tier |
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```python
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from llm_connect import create_adapter
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# OpenRouter (default model: moonshotai/kimi-k3)
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adapter = create_adapter("openrouter")
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adapter = create_adapter("openrouter", model="anthropic/claude-sonnet-4")
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# Gemini (uses GEMINI_API_KEY env var or apikey-geminifree.txt)
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adapter = create_adapter("gemini", model="gemini-2.5-flash")
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# OpenAI (uses OPENAI_API_KEY env var)
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adapter = create_adapter("openai", model="gpt-4.1-mini")
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# Claude Code CLI (uses locally installed claude binary)
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adapter = create_adapter("claude-code")
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```
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## CLI
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```bash
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# Run a prompt; content on stdout, tokens + €/USD cost on stderr
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llm-connect run "Summarise the value chain concept."
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llm-connect run "Hello" --provider mock --model moonshotai/kimi-k3 --json
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# Estimate cost without calling a provider
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llm-connect cost estimate --model moonshotai/kimi-k3 \
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--prompt-tokens 1000 --completion-tokens 500
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# Weekly spend (Mon-based; default timezone Europe/Berlin)
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llm-connect spend week # current week: Mon 00:00 → now
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llm-connect spend week --last # previous week: Mon → Sun
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llm-connect spend week --json
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# Provider prepaid / key remaining (backend-scoped; does not change defaults)
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llm-connect balance # current default provider
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llm-connect balance --provider openrouter # one-shot backend select
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llm-connect balance --json
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# Square text-to-image. The model and the quote are required when a cap is set.
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# Which model and quote: docs/image-model-selection.md
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# Render the one hall entry that is about to be published. Do not backfill the corpus.
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llm-connect image render-hall --hall ../hall-of-helix --entry entries/<file>.md
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# Receipt JSON on stdout; image bytes in --output. Credentials stay in the env.
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llm-connect image render \
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--prompt "precise technical illustration of a gear, no text" \
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--model "<image-model>" \
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--output visuals/gear.jpg \
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--max-cost-usd 0.15 \
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--quote-usd 0.08
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```
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Usage events append to a JSONL ledger (default
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`~/.local/share/llm-connect/usage.jsonl`; override with `--ledger` or
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`LLM_CONNECT_USAGE_LEDGER`). Costs are list-price estimates (USD rate table +
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EUR via snapshot or `LLM_CONNECT_EUR_PER_USD`).
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`balance` is **provider-scoped**: it reports the selected backend’s prepaid /
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key remaining (OpenRouter today). `--provider` applies only to that
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invocation and does not switch the library default provider or model.
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Unsupported backends fail clearly instead of falling back to OpenRouter.
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| Env | Purpose |
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|---|---|
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| `LLM_CONNECT_USAGE_LEDGER` | Usage JSONL path; enables library/server auto-recording when set |
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| `LLM_CONNECT_EUR_PER_USD` | Euros per one USD (FX override) |
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| `LLM_CONNECT_TZ` | Timezone for weekly spend windows (default `Europe/Berlin`) |
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## API keys
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Keys are resolved in this order (first found wins):
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1. Explicit `api_key` argument to the constructor
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2. Environment variable (e.g. `OPENROUTER_API_KEY`, `GEMINI_API_KEY`, `OPENAI_API_KEY`)
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3. Key file in the project root (e.g. `apikey-openrouter.txt`, `apikey-geminifree.txt`)
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## Core types
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### `RunConfig`
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Controls a single LLM call.
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```python
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from llm_connect import RunConfig
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config = RunConfig(
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model_name="gemini-2.5-flash", # overrides adapter default
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temperature=0.3,
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max_tokens=2000,
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timeout_seconds=60,
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)
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```
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| Field | Default | Description |
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|---|---|---|
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| `model_name` | `"gpt-4"` | Model identifier (adapter may override) |
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| `temperature` | `0.7` | Sampling temperature |
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| `max_tokens` | `2000` | Maximum output tokens |
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| `model_params` | `{}` | Portable extras translated by each adapter; see `docs/adapter-model-params.md` |
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| `max_depth` | `3` | Max nesting depth for recursive calls |
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| `skip_if_exists` | `True` | Skip if identical input hash already processed |
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| `timeout_seconds` | `300` | Request timeout |
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### `LLMResponse`
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Returned by every `execute_prompt` call.
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```python
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response = adapter.execute_prompt(prompt, config)
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print(response.content) # generated text
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print(response.model) # model actually used
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print(response.usage) # {"prompt_tokens": …, "completion_tokens": …, "total_tokens": …}
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print(response.finish_reason) # "stop", "length", etc.
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```
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## Server diagnostics
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Serve mode can include a debug envelope without changing normal responses:
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```bash
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LLM_CONNECT_DEBUG=1 python -m llm_connect.server --provider openrouter
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curl 'http://127.0.0.1:8080/execute?debug=1' -d '{"prompt":"hi"}'
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```
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Set `LLM_CONNECT_AUDIT_DIR=/path/to/audit` to write per-call replay records,
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then parse one without another provider call:
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```bash
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python -m llm_connect.replay /path/to/audit/record.json --json
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```
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## Server runtime profiles
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Serve mode enables named runtime profiles by default. A client can send
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`config.model_name="custodian-triage-balanced"` and the server resolves it to
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the configured provider/model before calling the adapter.
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Useful runtime environment variables:
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```bash
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LLM_CONNECT_HOST=0.0.0.0
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LLM_CONNECT_PORT=8080
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LLM_CONNECT_PROVIDER=openrouter
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LLM_CONNECT_MODEL=google/gemini-2.5-flash
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LLM_CONNECT_CUSTODIAN_TRIAGE_PROVIDER=openrouter
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LLM_CONNECT_CUSTODIAN_TRIAGE_MODEL=google/gemini-2.5-flash
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```
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For local smoke tests without provider credentials:
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```bash
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export LLM_CONNECT_MOCK_RESPONSE="$(python -c 'import json; print(json.dumps(json.load(open("fixtures/activity_core/daily-triage-valid-content.json"))))')"
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python -m llm_connect.server --provider mock
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python scripts/smoke_activity_core_endpoint.py --url http://127.0.0.1:8080
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```
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Disable profile dispatch with `--disable-profiles`. Set
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`LLM_CONNECT_STRICT_PROFILES=1` or pass `--strict-profiles` to reject direct
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model names that are not configured profiles.
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## Writing your own adapter
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```python
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from llm_connect import LLMAdapter, RunConfig, LLMResponse
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class MyAdapter(LLMAdapter):
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def execute_prompt(self, prompt: str, config: RunConfig) -> LLMResponse:
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# call your API here
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return LLMResponse(content="...", model="my-model")
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def validate_config(self, config: RunConfig) -> bool:
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return True
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```
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## TOML configuration chain
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The `resolve_llm()` function walks a 7-level priority chain to pick a
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provider and model. This is used by the `llm-helper` integration but is also
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available standalone:
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```python
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from llm_connect.toml_config import resolve_llm
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resolved = resolve_llm(app_name="myapp")
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print(resolved.provider, resolved.model, resolved.provider_source)
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```
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Priority order (highest first):
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1. CLI flags (`cli_provider`, `cli_model` arguments)
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2. Env var `{APP_NAME}_HELPER_MODEL` (model only)
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3. User preference — `~/.config/{app_name}/config.toml` `[llm.preference]`
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4. Directory preference — `.{app_name}.toml` `[llm.preference]`
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5. Directory default — `.{app_name}.toml` `[llm.default]`
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6. User default — `~/.config/{app_name}/config.toml` `[llm.default]`
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7. Hardcoded fallback — `gemini / gemini-2.5-flash`
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Example config file (`~/.config/myapp/config.toml`):
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```toml
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[llm.default]
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provider = "gemini"
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model = "gemini-2.5-flash"
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[llm.preference]
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provider = "openrouter"
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model = "anthropic/claude-sonnet-4"
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```
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## Embeddings
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```python
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from llm_connect import create_embedding_adapter, EmbeddingCache
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adapter = create_embedding_adapter("openai", model="text-embedding-3-small")
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cache = EmbeddingCache(cache_dir=".embeddings")
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# Get embedding (cached after first call)
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vec = cache.get_or_compute("my text", lambda t: adapter.embed([t])[0])
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```
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## Exceptions
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```python
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from llm_connect.exceptions import (
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LLMError, # base
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LLMConfigurationError,# bad key, unknown provider
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LLMAPIError, # HTTP error from provider (has .status_code)
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LLMRateLimitError, # 429
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LLMTimeoutError, # request timed out
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LLMSubprocessError, # claude CLI failed (has .return_code, .stderr)
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)
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```
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## Testing
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```python
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from llm_connect import MockLLMAdapter, RunConfig
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mock = MockLLMAdapter(mock_response="Test response")
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config = RunConfig()
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response = mock.execute_prompt("any prompt", config)
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assert response.content == "Test response"
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assert mock.call_count == 1
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```
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## Origin
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Extracted from the [markitect](https://github.com/worsch/markitect) project.
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The `markitect.llm` module remains a re-export shim pointing here.
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