This repository contains the code that Defog uses for the evaluation of generated SQL. It's based off the schema from the Spider, but with a new set of hand-selected questions and queries grouped by query category. For an in-depth look into our process of creating this evaluation approach, see this.

Our testing procedure comprises the following steps. For each question/query pair:

1. We generate a SQL query (possibly from an LLM).
2. We run both the "gold" query and the generated query on their respective Postgres database to obtain 2 dataframes with the results.
3. We compare the 2 dataframes using an "exact" and a "subset" match. TODO add link to blogpost.
4. We log these alongside other metrics of interest (e.g. tokens used, latency) and aggregate the results for reporting.

This is a comprehensive set of instructions that assumes basic familiarity with the command line, Docker, running SQL queries on a database, and common Python data manipulation libraries (e.g. pandas).

Firstly, install all Python libraries listed in the requirements.txt file. You would also need to download the spacy model used in the NER heuristic for our metadata-pruning method. Also, you would need to clone the repository where we store our database data and schema, and install the library.

pip install -r requirements.txt
python -m spacy download en_core_web_sm
git clone https://github.com/defog-ai/defog-data.git
cd defog-data
pip install -e .

Next, you would need to set up the databases that the queries are executed on. We use Postgres here, since it is the most common OSS database with the widest distribution and usage in production. In addition, we would recommend using Docker to do this, as it is the easiest way to get started. You can install Docker here.

Once you have Docker installed, you can create the Docker container and start the Postgres database using the following commands. We recommend mounting a volume on data/postgres to persist the data, as well as data/export to make it easier to import the data. To create the container, run:

mkdir data/postgres data/export
docker create --name postgres-sql-eval -e POSTGRES_PASSWORD=postgres -p 5432:5432 -v $(pwd)/data/postgres:/var/lib/postgresql/data -v $(pwd)/data/export:/export postgres:14-alpine

To start the container, run:

docker start postgres-sql-eval

If you want to reset the Postgres server instance's state (e.g. memory leaks from transient connections), you can turn it off (and start it back up after):

docker stop postgres-sql-eval
# see that the container is still there:
docker container list -a

Some notes:

• You would need to stop other Postgres instances listening on port 5432 before running the above command.
• You only need to run the docker create ... once to create the image, and then subsequently only docker start/stop postgres-sql-eval.
• The data is persisted in data/postgres, so turning it off isn't critical. On the other hand, if you delete the data/postgres folder, then all is lost T.T
• While we will use Docker for deploying Postgres and the initialization, you are free to modify the scripts/instructions to work with your local installation.

The data for importing is in the defog-data repository which we cloned earlier. Each folder contains the metadata and data corresponding to a single database (e.g. academic contains all the data required to reload the 'academic' database). We assume that you have a psql client installed locally. We will create a new database in our postgres instance for each of the 7 SQL databases with the following commands:

# set the following environment variables
cd defog-data # if you're not already in the defog-data directory
export DBPASSWORD="postgres"
export DBUSER="postgres"
export DBHOST="localhost"
export DBPORT=5432
./setup.sh

If you have a private dataset that you do not want to make publicly available but would still like to repurpose the code here for evaluations, you can do so by following the steps below.

• Begin by creating a separate git repository for your private data, that has a setup.py file, similar to defog-data.
• Create the metadata and data files, and import them into your database. This is to allow our evaluation framework to run the generated queries with some actual data. You can refer to defog-data's metadata objects for the schema, and setup.sh as an example on how import the data into your database. We do not prescribe any specific folder structure, and leave it to you to decide how you want to organize your data, so long as you can import it into your database easily.
• To use our metadata pruning utilities, you would need to have the following defined:
  • A way to load your embeddings. In our case, we call a function load_embeddings from defog-data's supplementary module to load a dictionary of database name to a tuple of the 2D embedding matrix (num examples x embedding dimension) and the associated text metadata for each row/example. If you would like to see how we generate this tuple, you may refer to generate_embeddings in the defog-data repository.
  • A way to load columns associated with various named entities. In our case, we call a dictionary columns_ner of database name to a nested dictionary that maps each named entity type to a list of column metadata strings that are associated with that named entity type. You can refer to the raw data here for an example of how we generate this dictionary.
  • A way to define joinable columns between tables. In our case, we call a dictionary columns_join of database name to a nested dictionary of table tuples to column name tuples. You can refer to the raw data here for an example of how we generate this dictionary.

Once all of the 3 above steps have completed, you would need to

• Install your data library as a dependency, by running pip install -e . (-e to automatically incorporate edits without reinstalling)
• Replace the associated function calls and variables in prune_metadata_str with your own imported functions and variables. Note that you might not name your package/module defog_data_private.supplementary, so do modify accordingly.

Some things to take note of:

• If you do not populate your database with data (ie only create the tables without inserting data), you would return empty dataframes most of the time (regardless of whether the query generated was what you want), and it would result in results matching all the time and generate a lot of false positives. Hence, you might want to consider populating your database with some meaningful data that would return different results if the queries should be different from what you want.
• If testing out on your private data, you would also need to change the questions file to point to your own questions file (tailored to your database schema).

To test your own query generator with our framework, you would need to extend Query Generator and implement the generate_query method to return the query of interest. We create a new class for each question/query pair to isolate each pair's runtime state against the others when running concurrently. You can also reference OpenAIQueryGenerator which implements Query Generator and uses a simple prompt to send a message over to OpenAI's API. Feel free to extend it for your own use.

If there are functions that are generally useful for all query generators, they can be placed in the utils folder. If you need to incorporate specific verbose templates (e.g. for prompt testing), you can store them in the prompts folder, and later import them. Being able to version control the prompts in a central place has been a productivity win for our team.

Having implemented the query generator, the next piece of abstraction would be the runner. The runner calls the query generator, and is responsible for handling the configuration of work (e.g. parallelization / batching / model selected etc.) to the query generator for each question/query pair. We have provided 2 most common runners: eval/openai_runner.py for calling OpenAI's API (with parallelization support) and eval/hf_runner.py for calling a local Hugging Face model. When testing your own query generator with an existing runner, you can replace the qg_class in the runner's code with your own query generator class.

Remember to have your API key (OPENAI_API_KEY or ANTHROPIC_API_KEY) set as an environment variable before running the test if you plan to call the OpenAI or Anthropic/other LLM API's accordingly.

To test it out with just 10 questions (instead of all 200), parallelized across 5 :

python main.py \
  -q data/questions_gen.csv \
  -o results/my_query_generator.csv \
  -g oa \
  -f prompts/prompt_openai.md \
  -m gpt-3.5-turbo-0613 \
  -n 10 \
  -p 5

To test out the full suite of questions for claude-2:

python main.py \
  -q data/questions_gen.csv \
  -o results/claude-2.csv \
  -g anthropic \
  -f prompts/prompt_anthropic.md \
  -m claude-2

To test it out with our fine-tuned sql model with just 10 questions (instead of all 200):

# use the -W option to ignore warnings about sequential use of transformers pipeline
python -W ignore main.py \
  -q data/questions_gen.csv \
  -o results/results.csv \
  -g hf \
  -f prompts/prompt.md \
  -m defog/sqlcoder2 \
  -n 10

We also support loading a peft adapter here as well via the -a flag. Note that the loading of the adapter with the model will take slightly longer than usual.

We also have a vllm runner which uses the VLLM engine to run the inference altogether as a single batch. It is much faster to do so especially when num_beams > 1. You would have to pass in a single set of merged model weights, and the model architecture needs to be supported by vllm. Here's a sample command:

python -W ignore main.py \
  -q data/questions_gen.csv \
  -o "results/results.csv" \
  -g vllm \
  -f "prompts/prompt.md" \
  -m defog/sqlcoder2

If you'd like to test out a few prompts in a single run (to save the few minutes spent loading the model into GPU at the start of each run), you can specify a list of prompt files in --prompt_file (e.g. -f prompts/prompt-1.md prompts/prompt-2.md prompts/prompt-3.md), as well as a corresponding list of output files in --output_file (e.g. -o results/results-1.csv results/results-2.csv results/results-3.csv). The number of prompts and output files must be the same. Here's a sample command:

python -W ignore main.py \
  -q data/questions_gen.csv \
  -o results/results_1.csv results/results_2.csv \
  -g vllm \
  -f prompts/prompt_1.md prompts/prompt_2.md \
  -m defog/sqlcoder2

While you can do the same for the other runners, the time savings are most significant when loading a large model locally, vs calling an always-on API.

To test it out with just 10 questions (instead of all 200), parallelized across 3 calls:

mkdir results
python main.py \
  -q data/questions_gen.csv \
  -o results/results.csv \
  -g api \
  -b 5 \
  -f prompts/prompt.md \
  --url YOUR_API_URL \
  -p 3 \
  -n 10

You can use the following flags in the command line to change the configurations of your evaluation runs.

To better understand your query generator's performance, you can explore the results generated and aggregated for the various metrics that you care about. Happy iterating!

We welcome contributions to our project, specifically:

• Dataset
  • Adding new database schema/data
• Framework code
  • New query generators/runners (in the query_generators and eval folders respectively)
  • Improving existing generators/runners (e.g. adding new metrics)

Please see CONTRIBUTING.md for more information.