ai-llm-integration
SolidUse when integrating an LLM provider into an application with streaming, structured outputs, tool calls, embeddings, multi-model routing, retries, caching, and usage metering.
AI & Automation 25 stars
6 forks Updated 3 days ago MIT
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# LLM API Integration
## Inputs
| Input | Required | Purpose |
|---|---|---|
| Domain evidence | yes | provider and model requirements, application runtime, data classification, tool schemas, latency/cost limits, and failure policy |
## Outputs
- Produce: provider adapter contract, structured-output and tool-call handling, retry controls, usage telemetry, and integration tests.
## Capability and permission boundaries
Default to read-only analysis. Read only scoped records; redact secrets and regulated data. Writes, execution, network calls, production configuration, customer communication, billing changes, and delegation require explicit authority and an identified owner. Never widen tenant, time-window, or system scope implicitly.
## Degraded mode
When required telemetry, evidence, execution, network access, or write authority is unavailable, return a partial result with each unassessed item labelled, preserve the safest existing state, and state the evidence or approval needed to continue. Never convert missing evidence into a pass.
## Decision rules
| Condition | Action |
|---|---|
| Scope, owner, or threshold is missing | Stop the affected decision and request it |
| Evidence is incomplete but read-only analysis is safe | Produce a qualified partial result and gap list |
| A mutation exceeds authority or tenant boundary | Block it and route for approval |
| Evidence meets the stated threshold | Issue the output with provenance and owner |
## Anti-Patterns
- Treating absen...
Details
- Author
- peterbamuhigire
- Repository
- peterbamuhigire/chwezi-dev-engine
- Created
- 7 months ago
- Last Updated
- 3 days ago
- Language
- HTML
- License
- MIT
Integrates with
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