Implement LLM-WP-0007: Kimi K3 default and EUR spend reporting
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Add moonshotai/kimi-k3 as OpenRouter basemodel default after live smoke,
USD→EUR cost conversion, append-only usage ledger, and CLI run/cost/spend
week commands with token and euro reporting.
This commit is contained in:
tegwick 2026-08-03 22:00:34 +02:00
parent f3121c3f1f
commit 09aa1f3604
21 changed files with 1396 additions and 103 deletions

147
README.md
View file

@ -35,7 +35,8 @@ pip install llm-connect
```python
from llm_connect import create_adapter
# OpenRouter
# OpenRouter (default model: moonshotai/kimi-k3)
adapter = create_adapter("openrouter")
adapter = create_adapter("openrouter", model="anthropic/claude-sonnet-4")
# Gemini (uses GEMINI_API_KEY env var or apikey-geminifree.txt)
@ -48,6 +49,34 @@ adapter = create_adapter("openai", model="gpt-4.1-mini")
adapter = create_adapter("claude-code")
```
## CLI
```bash
# Run a prompt; content on stdout, tokens + €/USD cost on stderr
llm-connect run "Summarise the value chain concept."
llm-connect run "Hello" --provider mock --model moonshotai/kimi-k3 --json
# Estimate cost without calling a provider
llm-connect cost estimate --model moonshotai/kimi-k3 \
--prompt-tokens 1000 --completion-tokens 500
# Weekly spend (Mon-based; default timezone Europe/Berlin)
llm-connect spend week # current week: Mon 00:00 → now
llm-connect spend week --last # previous week: Mon → Sun
llm-connect spend week --json
```
Usage events append to a JSONL ledger (default
`~/.local/share/llm-connect/usage.jsonl`; override with `--ledger` or
`LLM_CONNECT_USAGE_LEDGER`). Costs are list-price estimates (USD rate table +
EUR via snapshot or `LLM_CONNECT_EUR_PER_USD`).
| Env | Purpose |
|---|---|
| `LLM_CONNECT_USAGE_LEDGER` | Usage JSONL path; enables library/server auto-recording when set |
| `LLM_CONNECT_EUR_PER_USD` | Euros per one USD (FX override) |
| `LLM_CONNECT_TZ` | Timezone for weekly spend windows (default `Europe/Berlin`) |
## API keys
Keys are resolved in this order (first found wins):
@ -73,15 +102,15 @@ config = RunConfig(
)
```
| Field | Default | Description |
|---|---|---|
| `model_name` | `"gpt-4"` | Model identifier (adapter may override) |
| `temperature` | `0.7` | Sampling temperature |
| `max_tokens` | `2000` | Maximum output tokens |
| `model_params` | `{}` | Portable extras translated by each adapter; see `docs/adapter-model-params.md` |
| `max_depth` | `3` | Max nesting depth for recursive calls |
| `skip_if_exists` | `True` | Skip if identical input hash already processed |
| `timeout_seconds` | `300` | Request timeout |
| Field | Default | Description |
|---|---|---|
| `model_name` | `"gpt-4"` | Model identifier (adapter may override) |
| `temperature` | `0.7` | Sampling temperature |
| `max_tokens` | `2000` | Maximum output tokens |
| `model_params` | `{}` | Portable extras translated by each adapter; see `docs/adapter-model-params.md` |
| `max_depth` | `3` | Max nesting depth for recursive calls |
| `skip_if_exists` | `True` | Skip if identical input hash already processed |
| `timeout_seconds` | `300` | Request timeout |
### `LLMResponse`
@ -92,55 +121,55 @@ response = adapter.execute_prompt(prompt, config)
print(response.content) # generated text
print(response.model) # model actually used
print(response.usage) # {"prompt_tokens": …, "completion_tokens": …, "total_tokens": …}
print(response.finish_reason) # "stop", "length", etc.
```
## Server diagnostics
Serve mode can include a debug envelope without changing normal responses:
```bash
LLM_CONNECT_DEBUG=1 python -m llm_connect.server --provider openrouter
curl 'http://127.0.0.1:8080/execute?debug=1' -d '{"prompt":"hi"}'
```
Set `LLM_CONNECT_AUDIT_DIR=/path/to/audit` to write per-call replay records,
then parse one without another provider call:
```bash
python -m llm_connect.replay /path/to/audit/record.json --json
```
## Server runtime profiles
Serve mode enables named runtime profiles by default. A client can send
`config.model_name="custodian-triage-balanced"` and the server resolves it to
the configured provider/model before calling the adapter.
Useful runtime environment variables:
```bash
LLM_CONNECT_HOST=0.0.0.0
LLM_CONNECT_PORT=8080
LLM_CONNECT_PROVIDER=openrouter
LLM_CONNECT_MODEL=google/gemini-2.5-flash
LLM_CONNECT_CUSTODIAN_TRIAGE_PROVIDER=openrouter
LLM_CONNECT_CUSTODIAN_TRIAGE_MODEL=google/gemini-2.5-flash
```
For local smoke tests without provider credentials:
```bash
export LLM_CONNECT_MOCK_RESPONSE="$(python -c 'import json; print(json.dumps(json.load(open("fixtures/activity_core/daily-triage-valid-content.json"))))')"
python -m llm_connect.server --provider mock
python scripts/smoke_activity_core_endpoint.py --url http://127.0.0.1:8080
```
Disable profile dispatch with `--disable-profiles`. Set
`LLM_CONNECT_STRICT_PROFILES=1` or pass `--strict-profiles` to reject direct
model names that are not configured profiles.
## Writing your own adapter
print(response.finish_reason) # "stop", "length", etc.
```
## Server diagnostics
Serve mode can include a debug envelope without changing normal responses:
```bash
LLM_CONNECT_DEBUG=1 python -m llm_connect.server --provider openrouter
curl 'http://127.0.0.1:8080/execute?debug=1' -d '{"prompt":"hi"}'
```
Set `LLM_CONNECT_AUDIT_DIR=/path/to/audit` to write per-call replay records,
then parse one without another provider call:
```bash
python -m llm_connect.replay /path/to/audit/record.json --json
```
## Server runtime profiles
Serve mode enables named runtime profiles by default. A client can send
`config.model_name="custodian-triage-balanced"` and the server resolves it to
the configured provider/model before calling the adapter.
Useful runtime environment variables:
```bash
LLM_CONNECT_HOST=0.0.0.0
LLM_CONNECT_PORT=8080
LLM_CONNECT_PROVIDER=openrouter
LLM_CONNECT_MODEL=google/gemini-2.5-flash
LLM_CONNECT_CUSTODIAN_TRIAGE_PROVIDER=openrouter
LLM_CONNECT_CUSTODIAN_TRIAGE_MODEL=google/gemini-2.5-flash
```
For local smoke tests without provider credentials:
```bash
export LLM_CONNECT_MOCK_RESPONSE="$(python -c 'import json; print(json.dumps(json.load(open("fixtures/activity_core/daily-triage-valid-content.json"))))')"
python -m llm_connect.server --provider mock
python scripts/smoke_activity_core_endpoint.py --url http://127.0.0.1:8080
```
Disable profile dispatch with `--disable-profiles`. Set
`LLM_CONNECT_STRICT_PROFILES=1` or pass `--strict-profiles` to reject direct
model names that are not configured profiles.
## Writing your own adapter
```python
from llm_connect import LLMAdapter, RunConfig, LLMResponse