knowledgeweaver 0.1.0

CLI-first cognitive indexing pipeline

KnowledgeWeaver

KnowledgeWeaver was inspired by Andrej Karpathy's LLM Wiki note — the idea that LLMs should compile sources into a persistent intermediate layer instead of re-reading raw text on every query. In KnowledgeWeaver, that intermediate layer is made of Cognodes: small, typed units of knowledge that stay readable on disk.

Why Cognodes

Most RAG systems chunk raw documents at query time. KnowledgeWeaver does the structure work up front: each source becomes a set of typed Cognodes such as concept, fact, experience, narrative, opinion, and known_unknown. Those units can point to each other through typed relations such as depends_on, instantiates, and evidences.

That gives you:

• readable markdown you can grep, diff, and version
• structure-first retrieval over typed fields and relations
• a compiled Postgres index you can delete and rebuild from markdown at any time

See cognodes/examples/karpathy-llm-wiki for a worked example.

What You Can Do

• kw distill: turn raw text or files into Cognode markdown
• kw query-cognode: answer questions from Cognode markdown on disk
• kw index: compile Cognodes into Postgres
• kw query: query the Postgres compiled index
• kw eval-distill: run YAML distillation evals

Quickstart

Option A: local CLI loop

Before you start, make sure you have:

• Python 3.10+
• Docker Desktop or Docker Engine with docker compose
• Ollama installed and running at http://localhost:11434/v1
• local Ollama models configured for both generation and embeddings

Notes:

• The bundled Postgres setup is started through Docker, so you do not need to install Postgres separately if you use the commands below.
• The default config is written to ~/.knowledgeweaver/kw.yaml and the generated Docker env file is written to ~/.knowledgeweaver/docker-compose.env.
• If your Ollama model names differ from the defaults, edit ~/.knowledgeweaver/kw.yaml after kw config init so the extraction, query_*, and embedding model entries match what you have installed locally.
• Run the Docker command from the repo root so docker-compose.postgres.yml and Dockerfile.postgres are available.

pip install .
kw config init
# edit ~/.knowledgeweaver/kw.yaml if you need different Ollama model names
kw config validate
# start the bundled pgvector/Postgres instance on port 55432
docker compose --env-file ~/.knowledgeweaver/docker-compose.env -f docker-compose.postgres.yml up -d --build
kw db init
kw distill --text "Revenue dropped by 4% in Q2 due to lower enterprise renewals."
kw query-cognode "What caused the Q2 revenue drop?"
kw index
kw query "What caused the Q2 revenue drop?"

The default config assumes:

• Ollama at http://localhost:11434/v1
• local Postgres credentials stored in ~/.knowledgeweaver/docker-compose.env
• config at ~/.knowledgeweaver/kw.yaml

Option B: using with agents

Claude Code auto-registers the bundled skills under skills/cognode-distillation/ and skills/cognode-query/.

For Codex or other agents, point them at the skill files directly and keep the Cognode markdown as the canonical artifact. The intended workflow is still:

1. distill raw text into Cognodes
2. save Cognodes as markdown
3. query markdown directly with kw query-cognode or compile into Postgres with kw index

Common Commands

Show config resolution:

kw config path
kw config show
kw config validate

Distill:

kw distill --input ./notes/article.txt
kw distill --text "Revenue dropped by 4% in Q2 due to lower enterprise renewals."
cat article.txt | kw distill --stdin
kw distill --input ./inbox --recursive

Query markdown directly:

kw query-cognode "What caused the Q2 revenue drop?"

Compile and query Postgres:

kw index
kw query "What caused the Q2 revenue drop?"
kw query "What caused the Q2 revenue drop?" --debug

Initialize Postgres schema explicitly:

kw db init
kw db init --profile postgres_local

Useful flags:

• --config: use a non-default config file
• --profile: select a profile from the config
• --doc-id, --title, --author, --source-uri, --created-at: attach metadata during distillation
• --top-k: control retrieval breadth for query and query-cognode

Configuration

kw looks for config in this order:

1. --config <path>
2. $KW_HOME/kw.yaml if KW_HOME is set, otherwise ~/.knowledgeweaver/kw.yaml
3. ./kw.yaml

Inside the config, kw_home is the root for generated files:

• ${kw_home}/cognodes
• ${kw_home}/cognodes/manifests
• ${kw_home}/logs
• ${kw_home}/docker-compose.env

After changing kw_home, run kw config validate again so kw refreshes docker-compose.env.

Docker

This repo includes:

• docker-compose.postgres.yml
• Dockerfile.postgres

Start the bundled local Postgres:

kw config validate
docker compose --env-file ~/.knowledgeweaver/docker-compose.env -f docker-compose.postgres.yml up -d --build
kw db init

The bundled setup uses port 55432.

Providers

Supported provider styles:

• ollama
• openai-compatible
• anthropic

For each model under profiles.<name>.models.<key>, you usually set:

• provider
• model
• api_base
• one auth method: api_key, api_key_env, oauth_token, or oauth_token_env
• optional tuning fields such as timeout_seconds, temperature, max_tokens, and dimensions

kw config show redacts inline secrets and DSN passwords before printing.

Notes

• Use kw query-cognode when you want the markdown-only workflow.
• Use kw query when you want structure-first retrieval over the compiled Postgres index.
• Rebuilding Postgres should never require editing Cognodes.
• Changing kw_home moves Cognodes, manifests, logs, and Docker helper files together.

Project Policy

• Contributing guide
• Code of conduct
• Security policy