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Generic Ollama-compatible LLM chat adapter. The lower-level
counterpart to orchestrator-llm: orchestrator-llm wraps the LLM
in a planning prompt that emits a structured F∆I Plan; this
module is the plain-prompt adapter that flows compose for
summarisation, translation, free-form Q&A, etc.
Capability surface:
Inputs:
prompt : text
endpoint : text (Ollama /api/chat URL)
model : text
api_key : text (optional bearer token)
system_prompt : text (optional)
Outputs:
response : text (assistant reply)
model_endpoint : text (audit correlation)
model_name : text (audit correlation)
model_digest : text (Ollama /api/show probe; empty
for non-Ollama or transient failures)
Permissions: net to localhost / 127.0.0.1 / api.openai.com /
api.anthropic.com.
The audit-field trio (endpoint + name + digest) closes the same
forensic gap that orchestrator-llm v0.3.1 closed for plan
generation: any historical chat invocation can be traced to the
exact model that produced it.
Implementation reuses the same defensive Ollama client pattern
from orchestrator-llm — derive_show_url + extract_show_digest
+ best-effort probe_model_digest. Duplication accepted at the
two-module mark; a shared crate refactor lands once a third
module needs the same plumbing.
12 host-side tests cover prompt building, Ollama-shaped
response parsing, URL transform, digest extraction (top-level
+ nested), end-to-end success, end-to-end probe-failure
swallow, end-to-end skip-for-non-Ollama, and the missing-input
guards.
Wasm artifact: 294 KB. Verified to build with v1.0 fai:platform
imports baked in.
Bootstrapped via 'fai new module llm.chat' (workspace v0.10.13)
which now produces an SDK-based template directly.
Signed-off-by: flemming-it <sf@flemming.it>
62 lines
2 KiB
Markdown
62 lines
2 KiB
Markdown
# llm-chat
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F∆I module providing the `llm.chat` capability — a generic
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Ollama-compatible LLM chat adapter.
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## Capability
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| Field | Value |
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|-------|-------|
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| Capability | `llm.chat@0.1.0` |
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| Inputs | `prompt: text`, `endpoint: text`, `model: text`, `api_key: text` (opt), `system_prompt: text` (opt) |
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| Outputs | `response: text`, `model_endpoint: text`, `model_name: text`, `model_digest: text` |
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| Permissions | `net: localhost`, `net: 127.0.0.1`, `net: api.openai.com`, `net: api.anthropic.com` |
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| Status (in store index) | `alpha` |
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## Why a separate module from orchestrator-llm
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`orchestrator-llm` wraps an LLM call inside a planning prompt
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that emits a structured F∆I `Plan`. `llm.chat` is the lower-
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level adapter: it sends a prompt and returns the assistant text
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verbatim. Flows that need a generic chat step (summarisation,
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translation, free-form Q&A) compose `llm.chat` directly; flows
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that need plan generation use `orchestrator-llm`.
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## Audit fields
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`model_endpoint`, `model_name`, and `model_digest` are emitted
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on every successful invocation. Together they answer the audit
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question "which exact model produced this response?" The digest
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probe targets Ollama's `/api/show` and is best-effort — non-
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Ollama endpoints and transient probe failures yield an empty
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digest rather than blocking the response. Cf. F∆I Platform
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`docs/advanced/compliance-gaps.md` Gap 1.
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## Build
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```bash
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cargo build --release --target wasm32-wasip2
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```
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## Test
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```bash
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cargo test # 11 tests, all host-side
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cargo build --release --target wasm32-wasip2
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```
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## SDK source
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The Cargo.toml git-deps `fai-module-sdk` from
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`https://git.flemming.ws/fai/module-sdk.git`. The Forgejo
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instance has `REQUIRE_SIGNIN_VIEW=true`, so anonymous clones
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fail; CI uses the `MODULE_SDK_PAT` actions secret + `git config
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url.X.insteadOf Y` to authenticate cargo's git fetch
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transparently. Local dev uses the same pattern via env vars.
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## License
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Apache-2.0.
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Author: Dr. Stefan Flemming, Flemming.AI <platform@flemming.ai>
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Repository: https://git.flemming.ws/fai-modules/llm-chat
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