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#!/usr/bin/env bash
set -e
# Parse command line arguments
MODEL_PATH=""
MODEL_NAME=""
PROMPTS_FILE=""
# First argument is always model path
if [ $# -gt 0 ] && [[ "$1" != --* ]]; then
MODEL_PATH="$1"
shift
fi
# Parse remaining arguments
while [[ $# -gt 0 ]]; do
case $1 in
--prompts-file|-pf)
PROMPTS_FILE="$2"
shift 2
;;
*)
# If MODEL_NAME not set and this isn't a flag, use as model name
if [ -z "$MODEL_NAME" ] && [[ "$1" != --* ]]; then
MODEL_NAME="$1"
fi
shift
;;
esac
done
# Set defaults
MODEL_PATH="${MODEL_PATH:-"$EMBEDDING_MODEL_PATH"}"
MODEL_NAME="${MODEL_NAME:-$(basename "$MODEL_PATH")}"
CONVERTED_MODEL_PATH="${CONVERTED_EMBEDDING_PATH:-"$CONVERTED_EMBEDDING_MODEL"}"
CONVERTED_MODEL_NAME="${CONVERTED_MODEL_NAME:-$(basename "$CONVERTED_MODEL_PATH" .gguf)}"
if [ -t 0 ]; then
CPP_EMBEDDINGS="data/llamacpp-${CONVERTED_MODEL_NAME}-embeddings.bin"
else
# Process piped JSON data and convert to binary (matching logits.cpp format)
TEMP_FILE=$(mktemp /tmp/tmp.XXXXXX.binn)
python3 -c "
import json
import sys
import struct
data = json.load(sys.stdin)
# Flatten all embeddings completely
flattened = []
for item in data:
embedding = item['embedding']
for token_embedding in embedding:
flattened.extend(token_embedding)
print(f'Total embedding values: {len(flattened)}', file=sys.stderr)
# Write as binary floats - matches logitc.cpp fwrite format
with open('$TEMP_FILE', 'wb') as f:
for value in flattened:
f.write(struct.pack('f', value))
"
CPP_EMBEDDINGS="$TEMP_FILE"
trap "rm -f $TEMP_FILE" EXIT
fi
# Build the semantic_check.py command
SEMANTIC_CMD="python scripts/utils/semantic_check.py --model-path $MODEL_PATH \
--python-embeddings data/pytorch-${MODEL_NAME}-embeddings.bin \
--cpp-embeddings $CPP_EMBEDDINGS"
# Add prompts file if specified, otherwise use default prompt
if [ -n "$PROMPTS_FILE" ]; then
SEMANTIC_CMD="$SEMANTIC_CMD --prompts-file \"$PROMPTS_FILE\""
else
SEMANTIC_CMD="$SEMANTIC_CMD --prompt \"Hello world today\""
fi
# Execute the command
eval $SEMANTIC_CMD
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