kami
77c28ba313
They are shared infra (the orchestrator uses them for workflow repo-knowledge, not just the router chat layer), so [router.embedder]/[router.l3] was misleading. Loader/writer use the new names; legacy [router.*] still read as a fallback. Also notes the STEERING LLM-reformulation cost as a ponytail follow-up.
145 lines
5.9 KiB
TOML
145 lines
5.9 KiB
TOML
# CORREX Configuration Sample
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# Place at ~/.config/correx/config.toml
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[server]
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host = "localhost"
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port = 8080
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[tui]
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theme = "dark"
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session_list_limit = 5
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[cli]
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default_output = "human"
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[tools]
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sandbox_root = "~/.config/correx/sandbox"
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working_dir = "/tmp"
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default_system_prompt_path = "~/.config/correx/prompts/default_system.md"
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[tools.shell]
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enabled = true
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allowed_executables = ["bash", "sh", "python3", "node"]
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[tools.file_read]
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enabled = true
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[tools.file_write]
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enabled = true
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[tools.file_edit]
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enabled = true
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# ─────────────────────────────────────────────────────────────
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# Managed model configuration (Slice 1: correx spawns + owns llama-server)
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#
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# When [[models]] is present, correx launches llama-server at boot and kills it on
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# shutdown. Use this instead of (or alongside) [[providers]].
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#
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# [models] — global settings for the managed llama-server process
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# [[models]] — one entry per model file; correx will load the defaultModel at startup.
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# ─────────────────────────────────────────────────────────────
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[models]
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default_model = "mistral-7b" # which [[models]] entry to load at boot
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llama_server_bin = "llama-server" # path to the llama-server binary (default: llama-server)
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host = "127.0.0.1" # host for llama-server to bind / correx to connect
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port = 10000 # port for llama-server
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[[models]]
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id = "mistral-7b"
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model_path = "~/models/mistral-7b-gguf/model.gguf"
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context_size = 8192
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capabilities = { General = 1.0, Coding = 0.7, Reasoning = 0.6, Summarization = 0.8, ToolCalling = 0.5 }
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# Example: additional model (swap via TUI in a later slice)
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# [[models]]
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# id = "codellama-7b"
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# model_path = "~/models/codellama-7b-gguf/model.gguf"
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# context_size = 4096
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# capabilities = { General = 0.8, Coding = 1.0, Reasoning = 0.7, Summarization = 0.6, ToolCalling = 0.7 }
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# ─────────────────────────────────────────────────────────────
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# Legacy static provider configuration (array of tables)
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# Use [[providers]] when correx should connect to an already-running llama-server
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# (i.e. you launch the server yourself externally).
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# If [[models]] is configured, [[providers]] entries are registered as additional
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# static providers alongside the managed one.
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# ─────────────────────────────────────────────────────────────
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# [[providers]]
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# id = "local-llama"
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# type = "llamacpp"
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# model_id = "mistral-7b"
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# model_path = "~/models/mistral-7b-gguf/model.gguf"
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# url = "http://127.0.0.1:10000"
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# capabilities = { General = 1.0, Coding = 0.7, Reasoning = 0.6, Summarization = 0.8, ToolCalling = 0.5 }
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# Project-scoped, cross-session memory (distilled decision journal + repo map).
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# When enabled, decisions are distilled to durable per-repo memory at session end and
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# retrieved at the next session's start via the router L3 path.
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[project]
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enabled = false
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root = "" # repo root key; empty = current working dir
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memory_k = 5 # distilled decisions retrieved per session start
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# Repo-map indexer (emits a ranked file/symbol index as a recorded observation at session
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# start; a top-K slice rides in context as droppable L3 memory — paths + symbol names only).
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max_depth = 4 # directory recursion cap
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inject_top_k = 30 # entries injected into context (ranked by score)
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# ignore_globs = [".git", "node_modules", "build", "target", ".gradle", "dist", ".idea"]
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# Router configuration (optional, defaults shown below)
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[router]
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conversation_keep_last = 6 # how many recent chat turns to keep in context
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retrieval_k = 5 # L3 memory hits to retrieve per query
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token_budget = 4096 # context budget for router prompts
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# Sampling/length for router chat + steering replies.
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[router.generation]
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temperature = 0.7
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top_p = 0.9
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max_tokens = 512
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# Router narration (the live workflow commentary). max_tokens is higher than chat so
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# reasoning models can finish "thinking" and still emit the line; max_per_run caps how
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# many narrations fire per workflow run.
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[router.narration]
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temperature = 0.7
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top_p = 0.9
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max_tokens = 1024
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max_per_run = 100
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# Embedder + L3 vector memory. Shared infra: the orchestrator uses these for workflow
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# repo-knowledge retrieval, not just the router chat layer — hence top-level, not [router.*].
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# (Legacy [router.embedder] / [router.l3] are still read as a fallback.)
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[embedder]
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backend = "noop" # or "llamacpp"
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dimension = 1536
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# url = "http://127.0.0.1:11000"
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# model_id = "nomic-embed-text"
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[l3]
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backend = "in_memory" # or "turbovec"
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# persist_path = "~/.config/correx/l3/index.tq"
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# python_executable = "python3"
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# script_path = "~/.config/correx/python/turbovec_sidecar.py"
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# dim = 1536
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# bit_width = 4
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# Custom artifact kinds. Each entry points to a JSON file holding the kind's JSON
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# schema (object type, typed properties, required, additionalProperties). Workflows
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# may then declare `produces` slots of this kind; LLM-emitted kinds are validated
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# against the declared schema. schema_path is absolute, ~-relative, or relative to
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# this config file's directory.
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# [[artifacts]]
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# id = "review_report"
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# schema_path = "schemas/review_report.json"
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# llm_emitted = true
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#
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# The bundled examples/workflows/review_loop.toml uses this kind: the reviewer emits a
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# review_report whose `verdict` field gates the implementer↔reviewer loop via
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# artifact_field_equals transitions. See docs/schemas/review_report.json for the schema.
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[[artifacts]]
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id = "execution_plan"
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schema_path = "schemas/execution_plan.json"
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llm_emitted = true
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