Metadata-Version: 2.1
Name: blendsql
Version: 0.0.26
Summary: Query language for blending SQL logic and LLM reasoning across multi-modal data. [Findings of ACL 2024]
Author-email: Parker Glenn <parkervg5@gmail.com>
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Project-URL: Documentation, https://parkervg.github.io/blendsql/
Keywords: language models,query language,symbolic ai,sql
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Text Processing
Classifier: Programming Language :: Python :: 3
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Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: guidance
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<br>
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<div align="center"><picture>
  <source media="(prefers-color-scheme: dark)" srcset="docs/img/logo_dark.png">
  <img alt="blendsql" src="docs/img/logo_light.png" width=350">
</picture>
<p align="center">
    <i> SQL 🤝 LLMs </i>
  </p>
<b><h3>Check out our <a href="https://parkervg.github.io/blendsql/" target="_blank">online documentation</a> for a more comprehensive overview.</h3></b>

<i>Results from the paper are available [here](https://github.com/parkervg/blendsql/tree/research-paper/research/paper-results)</i>

</div>
<br/>

```
pip install blendsql
```

BlendSQL is a *superset of SQLite* for problem decomposition and hybrid question-answering with LLMs. 

As a result, we can *Blend* together...

- 🥤 ...operations over heterogeneous data sources (e.g. tables, text, images)
- 🥤 ...the structured & interpretable reasoning of SQL with the generalizable reasoning of LLMs

It can be viewed as an inversion of the typical text-to-SQL paradigm, where a user calls a LLM, and the LLM calls a SQL program.

**Now, the user is given the control to oversee all calls (LLM + SQL) within a unified query language.**

![comparison](docs/img/comparison.jpg)

For example, imagine we have the following table titled `parks`, containing [info on national parks in the United States](https://en.wikipedia.org/wiki/List_of_national_parks_of_the_United_States).

We can use BlendSQL to build a travel planning LLM chatbot to help us navigate the options below.


| **Name**        | **Image**                                                                       | **Location**       | **Area**                          | **Recreation Visitors (2022)** | **Description**                                                                                                                          |
|-----------------|---------------------------------------------------------------------------------|--------------------|-----------------------------------|--------------------------------|------------------------------------------------------------------------------------------------------------------------------------------|
| Death Valley    | ![death_valley.jpeg](./docs/img/national_parks_example/death_valley.jpeg)       | California, Nevada | 3,408,395.63 acres (13,793.3 km2) | 1,128,862                      | Death Valley is the hottest, lowest, and driest place in the United States, with daytime temperatures that have exceeded 130 °F (54 °C). |
| Everglades      | ![everglades.jpeg](./docs/img/national_parks_example/everglades.jpeg)           | Alaska             | 7,523,897.45 acres (30,448.1 km2) | 9,457                          | The country's northernmost park protects an expanse of pure wilderness in Alaska's Brooks Range and has no park facilities.              |
| New River Gorge | ![new_river_gorge.jpeg](./docs/img/national_parks_example/new_river_gorge.jpeg) | West Virgina       | 7,021 acres (28.4 km2)            | 1,593,523                      | The New River Gorge is the deepest river gorge east of the Mississippi River.                                                            |
 | Katmai          | ![katmai.jpg](./docs/img/national_parks_example/katmai.jpg)                     | Alaska             |  3,674,529.33 acres (14,870.3 km2)                                 | 33,908 | This park on the Alaska Peninsula protects the Valley of Ten Thousand Smokes, an ash flow formed by the 1912 eruption of Novarupta.  |

BlendSQL allows us to ask the following questions by injecting "ingredients", which are callable functions denoted by double curly brackets (`{{`, `}}`).

_Which parks don't have park facilities?_
```sql
SELECT "Name", "Description" FROM parks
  WHERE {{
      LLMMap(
          'Does this location have park facilities?',
          context='parks::Description'
      )
  }} = FALSE
```
| Name            | Description                                                                                                                            |
|:----------------|:---------------------------------------------------------------------------------------------------------------------------------------|
| Everglades      | The country's northernmost park protects an expanse of pure wilderness in Alaska's Brooks Range and has no park facilities.            |
<hr>

_What does the largest park in Alaska look like?_

```sql
SELECT "Name",
{{ImageCaption('parks::Image')}} as "Image Description", 
{{
    LLMMap(
        question='Size in km2?',
        context='parks::Area'
    )
}} as "Size in km" FROM parks
WHERE "Location" = 'Alaska'
ORDER BY "Size in km" DESC LIMIT 1
```

| Name       | Image Description                                       |   Size in km |
|:-----------|:--------------------------------------------------------|-------------:|
| Everglades | A forest of tall trees with a sunset in the background. |      30448.1 |

<hr>

_Which state is the park in that protects an ash flow?_

```sql
SELECT "Location", "Name" AS "Park Protecting Ash Flow" FROM parks 
    WHERE "Name" = {{
      LLMQA(
        'Which park protects an ash flow?',
        context=(SELECT "Name", "Description" FROM parks),
        options="parks::Name"
      ) 
  }}
```
| Location   | Park Protecting Ash Flow   |
|:-----------|:---------------------------|
| Alaska     | Katmai                     |

<hr>

_How many parks are located in more than 1 state?_

```sql
SELECT COUNT(*) FROM parks
    WHERE {{LLMMap('How many states?', 'parks::Location')}} > 1
```
|   Count |
|--------:|
|       1 |
<hr>

_What's the difference in visitors for those parks with a superlative in their description vs. those without?_
```sql
SELECT SUM(CAST(REPLACE("Recreation Visitors (2022)", ',', '') AS integer)) AS "Total Visitors", 
{{LLMMap('Contains a superlative?', 'parks::Description', options='t;f')}} AS "Description Contains Superlative",
GROUP_CONCAT(Name, ', ') AS "Park Names"
FROM parks
GROUP BY "Description Contains Superlative"
```
| Total Visitors |   Description Contains Superlative | Park Names                    |
|---------------:|-----------------------------------:|:------------------------------|
|          43365 |                                  0 | Everglades, Katmai            |
|        2722385 |                                  1 | Death Valley, New River Gorge |
<hr>

Now, we have an intermediate representation for our LLM to use that is explainable, debuggable, and [very effective at hybrid question-answering tasks](https://arxiv.org/abs/2402.17882).

For in-depth descriptions of the above queries, check out our [documentation](https://parkervg.github.io/blendsql/).

### Features

- Supports many DBMS 💾
  - SQLite, PostgreSQL, DuckDB, Pandas (aka duckdb in a trenchcoat)
- Supports many models ✨
  - Transformers, OpenAI, Anthropic, Ollama
- Easily extendable to [multi-modal usecases](./examples/vqa-ingredient.ipynb) 🖼️
- Smart parsing optimizes what is passed to external functions 🧠
  - Traverses abstract syntax tree with [sqlglot](https://github.com/tobymao/sqlglot) to minimize LLM function calls 🌳
- Constrained decoding with [guidance](https://github.com/guidance-ai/guidance) 🚀
- LLM function caching, built on [diskcache](https://grantjenks.com/docs/diskcache/) 🔑

## Quickstart

```python
import pandas as pd

from blendsql import blend, LLMMap, LLMQA, LLMJoin
from blendsql.db import Pandas
from blendsql.models import TransformersLLM

# Load model
model = TransformersLLM('Qwen/Qwen1.5-0.5B')

# Prepare our local database
db = Pandas(
  {
    "w": pd.DataFrame(
      (
        ['11 jun', 'western districts', 'bathurst', 'bathurst ground', '11-0'],
        ['12 jun', 'wallaroo & university nsq', 'sydney', 'cricket ground',
         '23-10'],
        ['5 jun', 'northern districts', 'newcastle', 'sports ground', '29-0']
      ),
      columns=['date', 'rival', 'city', 'venue', 'score']
    ),
    "documents": pd.DataFrame(
      (
        ['bathurst, new south wales',
         'bathurst /ˈbæθərst/ is a city in the central tablelands of new south wales , australia . it is about 200 kilometres ( 120 mi ) west-northwest of sydney and is the seat of the bathurst regional council .'],
        ['sydney',
         'sydney ( /ˈsɪdni/ ( listen ) sid-nee ) is the state capital of new south wales and the most populous city in australia and oceania . located on australia s east coast , the metropolis surrounds port jackson.'],
        ['newcastle, new south wales',
         'the newcastle ( /ˈnuːkɑːsəl/ new-kah-səl ) metropolitan area is the second most populated area in the australian state of new south wales and includes the newcastle and lake macquarie local government areas .']
      ),
      columns=['title', 'content']
    )
  }
)

# Write BlendSQL query
blendsql = """
SELECT * FROM w
WHERE city = {{
    LLMQA(
        'Which city is located 120 miles west of Sydney?',
        (SELECT * FROM documents WHERE content LIKE '%sydney%'),
        options='w::city'
    )
}}
"""
smoothie = blend(
  query=blendsql,
  db=db,
  ingredients={LLMMap, LLMQA, LLMJoin},
  default_model=model,
  # Optional args below
  infer_gen_constraints=True,
  verbose=True
)
print(smoothie.df)
# ┌────────┬───────────────────┬──────────┬─────────────────┬─────────┐
# │ date   │ rival             │ city     │ venue           │ score   │
# ├────────┼───────────────────┼──────────┼─────────────────┼─────────┤
# │ 11 jun │ western districts │ bathurst │ bathurst ground │ 11-0    │
# └────────┴───────────────────┴──────────┴─────────────────┴─────────┘
print(smoothie.meta.prompts)
# [
#   {
#       'answer': 'bathurst',
#       'question': 'Which city is located 120 miles west of Sydney?',
#       'context': [
#           {'title': 'bathurst, new south wales', 'content': 'bathurst /ˈbæθərst/ is a city in the central tablelands of new south wales , australia . it is about...'},
#           {'title': 'sydney', 'content': 'sydney ( /ˈsɪdni/ ( listen ) sid-nee ) is the state capital of new south wales and the most populous city in...'}
#       ]
#    }
# ]
```
<hr>

### Citation

```bibtex
@article{glenn2024blendsql,
      title={BlendSQL: A Scalable Dialect for Unifying Hybrid Question Answering in Relational Algebra},
      author={Parker Glenn and Parag Pravin Dakle and Liang Wang and Preethi Raghavan},
      year={2024},
      eprint={2402.17882},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```

### Acknowledgements
Special thanks to those below for inspiring this project. Definitely recommend checking out the linked work below, and citing when applicable!

- The authors of [Binding Language Models in Symbolic Languages](https://arxiv.org/abs/2210.02875)
  - This paper was the primary inspiration for BlendSQL.
- The authors of [EHRXQA: A Multi-Modal Question Answering Dataset for Electronic Health Records with Chest X-ray Images](https://arxiv.org/pdf/2310.18652)
  - As far as I can tell, the first publication to propose unifying model calls within SQL 
  - Served as the inspiration for the [vqa-ingredient.ipynb](./examples/vqa-ingredient.ipynb) example
- The authors of [Grammar Prompting for Domain-Specific Language Generation with Large Language Models](https://arxiv.org/abs/2305.19234)
- The maintainers of the [Guidance](https://github.com/guidance-ai/guidance) library for powering the constrained decoding capabilities of BlendSQL
