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Tool2Vec

Generate Tool2Vec Embeddings

For generating tool2vec embeddings, you need to provide the path to the dataset, output path, output filename and model. The available models are: text-embedding-3-small, e5-small, e5-base, e5-large, and mxbai-large. To use OpenAI model, you need to set the AZURE_ENDPOINT, AZURE_OPENAI_API_KEY, and AZURE_OPENAI_API_VERSION environment variables. For other models, you need to provide the path to the model checkpoint.

For example, to generate embeddings using the text-embedding-3-small model with the dataset located at ...json and save the embeddings to ...pkl, you can run the following command:

AZURE_ENDPOINT="..."
AZURE_OPENAI_API_KEY="..."
AZURE_OPENAI_API_VERSION="..."
DATA_PATH="...json"
OUTPUT_PATH="..."
OUTPUT_FILE_NAME="....pkl"

python embedding_generator.py \
    --data_path ${DATA_PATH} \
    --output_path "..." \
    --output_file_name "..." \
    --model "azure"

To generate embeddings using the e5-small model with the dataset located at ...json and save the embeddings to ...pkl, you can run the following command:

DATA_PATH="...json"
OUTPUT_PATH="..."
OUTPUT_FILE_NAME="....pkl"

python embedding_generator.py \
    --data_path ${DATA_PATH} \
    --output_path "..." \
    --output_file_name "..." \
    --model "e5-small" \
    --checkpoint_path "..." \
    --use_checkpoint

Model Evaluation

VAL_DATA_PATH="...json"
T2V_EMBEDDING_DATA_PATH="...pkl"
OUTPUT_FILE_NAME="....txt"

python evaluate_t2v_embedding.py \
    --valid_data_path ${VAL_DATA_PATH} \
    --tool_embedding_dir ${T2V_EMBEDDING_DATA_PATH} \
    --output_file_name ${OUTPUT_FILE_NAME}