File size: 2,381 Bytes
4554903 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | #!/usr/bin/env bash
# Rivet v2 setup — Ollama + model + memory DB.
set -euo pipefail
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
MODEL_NAME="rivet"
BASE_MODEL="qwen2.5-coder:32b"
MODELFILE="$SCRIPT_DIR/Modelfile"
REQUIREMENTS="$SCRIPT_DIR/requirements.txt"
MEMORY_DB="$SCRIPT_DIR/data/rivet_memory.db"
echo "== Rivet setup =="
# 1. Ollama
if ! command -v ollama >/dev/null 2>&1; then
echo "Ollama not found — installing..."
if [[ "$(uname -s)" == "Darwin" ]]; then
echo "No unattended installer for macOS. Install from https://ollama.com/download and re-run this script." >&2
exit 1
fi
curl -fsSL https://ollama.com/install.sh | sh
else
echo "Ollama found: $(command -v ollama)"
fi
if ! curl -fsS http://localhost:11434/api/tags >/dev/null 2>&1; then
echo "Starting ollama serve..."
nohup ollama serve >/tmp/ollama-serve.log 2>&1 &
for _ in $(seq 1 30); do
curl -fsS http://localhost:11434/api/tags >/dev/null 2>&1 && break
sleep 1
done
fi
# 2. Pull base model
echo "Pulling $BASE_MODEL (this can take a while)..."
ollama pull "$BASE_MODEL"
# 3. Create the Rivet model from the Modelfile
if [[ ! -f "$MODELFILE" ]]; then
echo "Modelfile not found at $MODELFILE" >&2
exit 1
fi
echo "Creating '$MODEL_NAME' model from $MODELFILE..."
ollama create "$MODEL_NAME" -f "$MODELFILE"
# 4. Python deps (memory.py itself is stdlib-only; this covers anything
# added later, e.g. the HTTP server's `requests` dependency)
if [[ -f "$REQUIREMENTS" ]] && grep -qv '^\s*#\|^\s*$' "$REQUIREMENTS"; then
echo "Installing Python dependencies from $REQUIREMENTS..."
python3 -m pip install --user -r "$REQUIREMENTS"
else
echo "No Python dependencies to install."
fi
# 5. Initialize the memory database
echo "Initializing memory database..."
python3 "$SCRIPT_DIR/engine/memory.py" init --db "$MEMORY_DB"
# 6. Instructions
cat <<EOF
== Rivet is ready ==
Model: $MODEL_NAME (base: $BASE_MODEL)
Memory: $MEMORY_DB
Start Rivet directly:
ollama run $MODEL_NAME
Or run it through the discipline-gated HTTP endpoint:
python3 "$SCRIPT_DIR/../scaffold/rivet_serve.py" --model $MODEL_NAME
In Python, load a user's memory before a session:
from engine.memory import MemoryStore
store = MemoryStore("$MEMORY_DB")
system_block = store.to_system_block(user_id, first_message)
EOF
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