Appendix C: LLM Included or Not

Dr Strange calls an external language model for a specific, bounded set of features. Everything else — the graph store, vector and keyword indexes, hybrid retrieval over pre-computed vectors, graph algorithms, time-travel, the change feed, backup, and code digestion through the parser plugins (a repository that yields only parsed facts is ingested without a single model call) — runs with no model at all.

This appendix delineates exactly which features require model support, how to run Dr Strange with none, and how to point the model features at a local model instead of a hosted API. In all cases, provider API keys are read from the server’s environment (or the [llm] configuration section), never from a request or tool parameter.

What features need LLM support

A model is invoked in two situations: to embed text (turn a string into a vector), and to chat (generate structure or a plan). The features that depend on one or both:

Everything else requires no model:

• Storing and searching vectors you already have. A vector is an ordinary property; declare an index and search it with a literal vector (SEARCH … NEAR $vec). Only text queries need embedding.
• Keyword search. BM25 is purely lexical.
• Graph queries and traversal. MATCH, SEARCH … NEAR $vec, plane.query, plane.neighbors, graph.seed / graph.expand.
• Graph algorithms. PageRank, components, shortest path, Louvain.
• Time-travel, the change feed, and backup/restore.
• The hybrid keyword and graph channels, and the vector channel when given a literal vector.

In short: the model is needed only to turn text into vectors, and to drive ask and digest. If you embed your data with your own pipeline and query by literal vector, keyword, and graph, Dr Strange needs no model.

Running Dr Strange without LLM

There are two independent ways to run without a model: simply not using the model features, and building a binary with the model code excluded entirely.

Not using the model features

The model-backed operations call a provider only when invoked. Supply no provider keys and avoid ask, digest, and text-embedding queries, and the rest of the system is fully functional. Embed your data with your own pipeline, store the vectors as properties, and query by:

• literal-vector similarity — SEARCH (d:Doc) ON embedding NEAR $vec TOPK 10,
• keyword — a BM25 index (index keyword …),
• graph — MATCH, traversal, and the graph algorithms.

A served instance still exposes ask and digest, but they return a clear error when no provider key is configured; nothing else is affected.

Building without the model code

The command-line tool gates the model features behind the digest Cargo feature, which pulls in the LLM crate. Build without it for a lean binary that has no model dependency at all:

$ cargo build --release -p drsg --no-default-features --features native-backend

The resulting drsg omits the ask and digest commands; every other command — planes, import/export, queries, indexes, hybrid (keyword and literal-vector channels), algorithms, snapshot/restore, and serve — is unchanged.

Use local LLM / models

The model features do not require a hosted API. A provider is either a preset name or a base URL, so any OpenAI-compatible endpoint — including a local one — can serve chat and embeddings. A base URL is an operator’s setting — the CLI flags and the configuration file take one; a request to drsg serve’s JSON-RPC API may name only a preset, or the URL the server was configured with.

Ollama

Ollama exposes an OpenAI-compatible API locally. The built-in ollama preset points at http://localhost:11434/v1, needs no key, and defaults to llama3.1 for chat and nomic-embed-text for embeddings:

$ ollama pull llama3.1
$ ollama pull nomic-embed-text

$ drsg --db graph.drsg ask "which companies does Ada work for?" \
    --plane social --chat ollama --embed ollama

$ drsg --db graph.drsg digest notes.md --plane social --apply \
    --chat ollama --embed ollama

Override the models with --model and --embed-model as needed.

Any OpenAI-compatible server

A local inference server that speaks the OpenAI API — vLLM, LM Studio, llama.cpp’s server, and others — is addressed by passing its base URL as the provider, with the model named explicitly:

$ drsg --db graph.drsg ask "…" --plane social \
    --chat  http://localhost:8000/v1 --model       my-chat-model \
    --embed http://localhost:8000/v1 --embed-model my-embed-model

If the endpoint requires a key, set it in the environment and name its variable with the key-environment options; a keyless local server needs none.

In the dashboard, server, and MCP

The same providers apply everywhere the model is used. The AIgest page and the semantic search selector list the presets, including ollama; the server and the MCP host read the provider keys (or none, for a keyless local server) from their environment. Chat and embedding providers are chosen independently, so a local chat model may be paired with a local — or hosted — embedding model, and vice versa.