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Explains the 'why this module instead of inline HTTP' answer operators ask when they see llm.chat for the first time: audit, permission posture, endpoint portability. Signed-off-by: flemming-it <sf@flemming.it>
104 lines
3.7 KiB
Markdown
104 lines
3.7 KiB
Markdown
# llm.chat
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Generic Ollama-compatible LLM chat adapter. The building-block
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module any flow uses when it needs a one-shot LLM completion —
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classify, extract-fields, rewrite, decide — instead of rolling
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its own HTTP client.
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## Capability
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- `llm.chat@0.1.0`
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## Inputs
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| Name | Type | Description |
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| ---------------- | ---- | --------------------------------------------------------------- |
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| `prompt` | text | The user-facing prompt. |
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| `endpoint` | text | Ollama-shaped `/api/chat` URL (e.g. `http://localhost:11434/api/chat`). |
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| `model` | text | Model identifier (e.g. `qwen2.5:14b`, `llama3.1:8b`). |
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| `api_key` | text | Optional bearer token for cloud-hosted endpoints. |
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| `system_prompt` | text | Optional system message. Empty = use the model's default. |
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## Outputs
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| Name | Type | Description |
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| ---------------- | ---- | ------------------------------------------------------------------- |
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| `response` | text | The assistant's plain-text reply. |
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| `model_endpoint` | text | The endpoint the response was generated against. |
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| `model_name` | text | The model identifier as supplied to the LLM API. |
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| `model_digest` | text | SHA-256 digest of the served Ollama model. Empty for cloud APIs. |
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Together the three `model_*` outputs answer the audit
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question: "which exact model produced this response?" — that
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audit trail is the reason flows use this module rather than
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spawning their own HTTP calls.
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## Permissions
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```yaml
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permissions:
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- "net: localhost"
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- "net: 127.0.0.1"
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- "net: api.openai.com"
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- "net: api.anthropic.com"
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```
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Loopback (local Ollama) by default. Cloud endpoints require an
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operator-policy override in `~/.fai/config.yaml#security.max_permissions`.
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## Why this module instead of inline HTTP
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Three reasons:
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1. **Audit.** Every LLM call surfaces `model_endpoint`,
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`model_name`, `model_digest` as separate outputs that land
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in the hash-chained audit log alongside the response. A
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regulator-facing reproducibility check just compares the
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digest field to the model snapshot.
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2. **Permission posture.** The operator sees `llm.chat` in
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the installed modules + its declared `net:` list. A
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home-grown HTTP module would either hide its endpoints
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or be a fresh review surface every time.
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3. **Endpoint portability.** The endpoint + model are flow
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inputs, not compile-time constants. The same flow runs
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against `localhost:11434` in dev and a production
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inference cluster in prod just by swapping the input.
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## Limits in v0.1.0
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- No streaming. The whole reply is buffered before the output
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step fires.
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- No tool-call / function-call surface. A flow needing tool
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use composes multiple `llm.chat` steps with prompt
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engineering, or uses MCP via the bridge.
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- Cloud-provider adapters for OpenAI and Anthropic are
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deferred until a flow actually needs them. Today the
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Ollama wire-format is the only target.
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## Example flow
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```yaml
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name: classify-incoming
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inputs:
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text: text
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steps:
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- id: classify
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use: llm.chat@^0
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with:
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prompt: $inputs.text
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system_prompt: |
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Classify the text as one of: question, complaint,
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feedback, spam. Answer with the label only.
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endpoint: "http://localhost:11434/api/chat"
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model: "qwen2.5:14b"
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outputs:
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category: $classify.response
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audit_model: $classify.model_digest
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```
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## Build
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```bash
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cargo build --release --target wasm32-wasip2
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# Output: target/wasm32-wasip2/release/llm_chat.wasm
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```
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