| Name | blendsql JSON |
| Version |
0.0.45
JSON |
| download |
| home_page | None |
| Summary | Query language for blending SQL and LLMs across structured + unstructured data, with type constraints. |
| upload_time | 2025-07-11 02:31:58 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | Apache-2.0 |
| keywords |
language models
query language
symbolic ai
sql
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<div align="right">
<a href="https://opensource.org/licenses/Apache-2.0"><img src="https://img.shields.io/badge/License-Apache_2.0-blue.svg" /></a>
<a><img src="https://img.shields.io/github/last-commit/parkervg/blendsql?color=green"/></a>
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<a><img src="https://img.shields.io/badge/python-3.10%20%7C%203.11%20%7C%203.12%20%7C%203.13-blue"/></a>
<br>
</div>
<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/reference/functions/" target="_blank">online documentation</a> for a more comprehensive overview.</h3></b>
<b><h4>Join our <a href="https://discord.gg/vCv7ak3WrU" target="_blank">Discord server</a> for more discussion!</h4></b>
</div>
<br/>
# 💻 Installation
```
pip install blendsql
```
# ⭐ Quickstart
```python
import pandas as pd
from blendsql import BlendSQL
from blendsql.models import TransformersLLM, LiteLLM
USE_LOCAL_CONSTRAINED_MODEL = False
# Load model, either a local transformers model, or remote provider via LiteLLM
if USE_LOCAL_CONSTRAINED_MODEL:
model = TransformersLLM(
"meta-llama/Llama-3.2-3B-Instruct", config={"device_map": "auto"}
) # Local models enable BlendSQL's predicate-guided constrained decoding
else:
model = LiteLLM("openai/gpt-4o-mini")
# Prepare our BlendSQL connection
bsql = BlendSQL(
{
"People": pd.DataFrame(
{
"Name": [
"George Washington",
"John Adams",
"Thomas Jefferson",
"James Madison",
"James Monroe",
"Alexander Hamilton",
"Sabrina Carpenter",
"Charli XCX",
"Elon Musk",
"Michelle Obama",
"Elvis Presley",
],
"Known_For": [
"Established federal government, First U.S. President",
"XYZ Affair, Alien and Sedition Acts",
"Louisiana Purchase, Declaration of Independence",
"War of 1812, Constitution",
"Monroe Doctrine, Missouri Compromise",
"Created national bank, Federalist Papers",
"Nonsense, Emails I Cant Send, Mean Girls musical",
"Crash, How Im Feeling Now, Boom Clap",
"Tesla, SpaceX, Twitter/X acquisition",
"Lets Move campaign, Becoming memoir",
"14 Grammys, King of Rock n Roll",
],
}
),
"Eras": pd.DataFrame({"Years": ["1700-1800", "1800-1900", "1900-2000", "2000-Now"]}),
},
model=model,
verbose=True,
)
smoothie = bsql.execute(
"""
SELECT * FROM People P
WHERE P.Name IN {{
LLMQA('First 3 presidents of the U.S?', quantifier='{3}')
}}
""",
infer_gen_constraints=True, # Is `True` by default
)
print(smoothie.df)
# ┌───────────────────┬───────────────────────────────────────────────────────┐
# │ Name │ Known_For │
# ├───────────────────┼───────────────────────────────────────────────────────┤
# │ George Washington │ Established federal government, First U.S. Preside... │
# │ John Adams │ XYZ Affair, Alien and Sedition Acts │
# │ Thomas Jefferson │ Louisiana Purchase, Declaration of Independence │
# └───────────────────┴───────────────────────────────────────────────────────┘
print(smoothie.summary())
# ┌────────────┬──────────────────────┬─────────────────┬─────────────────────┐
# │ Time (s) │ # Generation Calls │ Prompt Tokens │ Completion Tokens │
# ├────────────┼──────────────────────┼─────────────────┼─────────────────────┤
# │ 1.25158 │ 1 │ 296 │ 16 │
# └────────────┴──────────────────────┴─────────────────┴─────────────────────┘
smoothie = bsql.execute(
"""
SELECT GROUP_CONCAT(Name, ', ') AS 'Names',
{{
LLMMap(
'In which time period was this person born?',
p.Name,
options=Eras.Years
)
}} AS Born
FROM People p
GROUP BY Born
""",
)
print(smoothie.df)
# ┌───────────────────────────────────────────────────────┬───────────┐
# │ Names │ Born │
# ├───────────────────────────────────────────────────────┼───────────┤
# │ George Washington, John Adams, Thomas Jefferson, J... │ 1700-1800 │
# │ Sabrina Carpenter, Charli XCX, Elon Musk, Michelle... │ 2000-Now │
# │ Elvis Presley │ 1900-2000 │
# └───────────────────────────────────────────────────────┴───────────┘
print(smoothie.summary())
# ┌────────────┬──────────────────────┬─────────────────┬─────────────────────┐
# │ Time (s) │ # Generation Calls │ Prompt Tokens │ Completion Tokens │
# ├────────────┼──────────────────────┼─────────────────┼─────────────────────┤
# │ 1.03858 │ 2 │ 544 │ 75 │
# └────────────┴──────────────────────┴─────────────────┴─────────────────────┘
smoothie = bsql.execute("""
SELECT {{
LLMQA(
'Describe BlendSQL in 50 words.',
context=(
SELECT content[0:5000] AS "README"
FROM read_text('https://raw.githubusercontent.com/parkervg/blendsql/main/README.md')
)
)
}} AS answer
""")
print(smoothie.df)
# ┌─────────────────────────────────────────────────────┐
# │ answer │
# ├─────────────────────────────────────────────────────┤
# │ BlendSQL is a Python library that combines SQL a... │
# └─────────────────────────────────────────────────────┘
print(smoothie.summary())
# ┌────────────┬──────────────────────┬─────────────────┬─────────────────────┐
# │ Time (s) │ # Generation Calls │ Prompt Tokens │ Completion Tokens │
# ├────────────┼──────────────────────┼─────────────────┼─────────────────────┤
# │ 4.07617 │ 1 │ 1921 │ 50 │
# └────────────┴──────────────────────┴─────────────────┴─────────────────────┘
```
# 📰 News
- (5/30/25) Created a [Discord server](https://discord.gg/vCv7ak3WrU)
- (5/6/25): New blog post: [Language Models, SQL, and Types, Oh My!](https://parkervg.github.io/misc/2025/05/05/sql-llms.html)
- (5/1/15): Single-page [function documentation](https://parkervg.github.io/blendsql/reference/functions/)
- (3/16/25) Use BlendSQL with 100+ LLM APIs, using [LiteLLM](https://github.com/BerriAI/litellm)!
- (10/26/24) New tutorial! [blendsql-by-example.ipynb](examples/blendsql-by-example.ipynb)
- (10/18/24) Concurrent async requests in 0.0.29! OpenAI and Anthropic `LLMMap` calls are speedy now.
- Customize max concurrent async calls via `blendsql.config.set_async_limit(10)`
# Summary
BlendSQL is a *superset of SQL* 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
**Now, the user is given the control to oversee all calls (LLM + SQL) within a unified query language.**
# Features
- Supports many DBMS 💾
- SQLite, PostgreSQL, DuckDB, Pandas (aka duckdb in a trenchcoat)
- Supports local & remote models ✨
- Transformers, OpenAI, Anthropic, Ollama, and 100+ more!
- Easily extendable to [multi-modal usecases](./examples/vqa-ingredient.ipynb) 🖼
- Write your normal queries - 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) 🚀
- When using local models, we only generate syntactically valid outputs according to query syntax + database contents
- LLM function caching, built on [diskcache](https://grantjenks.com/docs/diskcache/) 🔑
# Example
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 |  | 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 |  | 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 |  | 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 |  | 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 p
WHERE {{
LLMMap(
'Does this location have park facilities?',
p.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(Image)}} as "Image Description",
{{
LLMMap(
question='Size in km2?',
values=Area
)
}} as "Size in km" FROM parks p
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=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?', Location)}} > 1
```
| Count |
|--------:|
| 1 |
<hr>
_Give me some info about the park in the state that Sarah Palin was governor of._
```sql
SELECT "Name", "Location", "Description" FROM parks
/* Will gather relevant context via WebSearch, and automatically constrain output
to one of the values in 'parks.Location'*/
WHERE Location = {{WebSearchQA('Which state was Sarah Palin governor of?')}}
```
| Name | Location | Description |
|:-----------|:-----------|:------------------------------------------------------------------------------------------------------------------------------------|
| Everglades | Alaska | The country's northernmost park protects an expanse of pure wilderness in Alaska's Brooks Range and has no park facilities. |
| Katmai | Alaska | This park on the Alaska Peninsula protects the Valley of Ten Thousand Smokes, an ash flow formed by the 1912 eruption of Novarupta. |
<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?', 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/).
# 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}
}
```
# Few-Shot Prompting
For the LLM-based ingredients in BlendSQL, few-shot prompting can be vital. In `LLMMap`, `LLMQA` and `LLMJoin`, we provide an interface to pass custom few-shot examples and dynamically retrieve those top-`k` most relevant examples at runtime, given the current inference example.
#### `LLMMap`
- [Default examples](./blendsql/ingredients/builtin/map/default_examples.json)
- [All possible fields](./blendsql/ingredients/builtin/map/examples.py)
```python
from blendsql import BlendSQL
from blendsql.ingredients.builtin import LLMMap, DEFAULT_MAP_FEW_SHOT
ingredients = {
LLMMap.from_args(
few_shot_examples=[
*DEFAULT_MAP_FEW_SHOT,
{
"question": "Is this a sport?",
"mapping": {
"Soccer": True,
"Chair": False,
"Banana": False,
"Golf": True
},
# Below are optional
"column_name": "Items",
"table_name": "Table",
"return_type": "boolean"
}
],
num_few_shot_examples=2,
# How many inference values to pass to model at once
batch_size=5,
)
}
bsql = BlendSQL(db, ingredients=ingredients)
```
#### `LLMQA`
- [Default examples](./blendsql/ingredients/builtin/qa/default_examples.json)
- [All possible fields](./blendsql/ingredients/builtin/qa/examples.py)
```python
from blendsql import BlendSQL
from blendsql.ingredients.builtin import LLMQA, DEFAULT_QA_FEW_SHOT
ingredients = {
LLMQA.from_args(
few_shot_examples=[
*DEFAULT_QA_FEW_SHOT,
{
"question": "Which weighs the most?",
"context": {
{
"Animal": ["Dog", "Gorilla", "Hamster"],
"Weight": ["20 pounds", "350 lbs", "100 grams"]
}
},
"answer": "Gorilla",
# Below are optional
"options": ["Dog", "Gorilla", "Hamster"]
}
],
# Will fetch `k` most relevant few-shot examples using embedding-based retriever
num_few_shot_examples=2,
# Lambda to turn the pd.DataFrame to a serialized string
context_formatter=lambda df: df.to_markdown(
index=False
)
)
}
bsql = BlendSQL(db, ingredients=ingredients)
```
#### `LLMJoin`
- [Default examples](./blendsql/ingredients/builtin/join/default_examples.json)
- [All possible fields](./blendsql/ingredients/builtin/join/examples.py)
```python
from blendsql import BlendSQL
from blendsql.ingredients.builtin import LLMJoin, DEFAULT_JOIN_FEW_SHOT
ingredients = {
LLMJoin.from_args(
few_shot_examples=[
*DEFAULT_JOIN_FEW_SHOT,
{
"join_criteria": "Join the state to its capital.",
"left_values": ["California", "Massachusetts", "North Carolina"],
"right_values": ["Sacramento", "Boston", "Chicago"],
"mapping": {
"California": "Sacramento",
"Massachusetts": "Boston",
"North Carolina": "-"
}
}
],
num_few_shot_examples=2
)
}
bsql = BlendSQL(db, ingredients=ingredients)
```
# 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
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"description": "<div align=\"right\">\n<a href=\"https://opensource.org/licenses/Apache-2.0\"><img src=\"https://img.shields.io/badge/License-Apache_2.0-blue.svg\" /></a>\n<a><img src=\"https://img.shields.io/github/last-commit/parkervg/blendsql?color=green\"/></a>\n<a><img src=\"https://img.shields.io/endpoint?url=https://gist.githubusercontent.com/parkervg/e24f1214fdff3ab086b829b5f01f85a8/raw/covbadge.json\"/></a>\n<a><img src=\"https://img.shields.io/badge/python-3.10%20%7C%203.11%20%7C%203.12%20%7C%203.13-blue\"/></a>\n<br>\n</div>\n\n<div align=\"center\"><picture>\n <source media=\"(prefers-color-scheme: dark)\" srcset=\"docs/img/logo_dark.png\">\n <img alt=\"blendsql\" src=\"docs/img/logo_light.png\" width=350\">\n</picture>\n<p align=\"center\">\n <i> SQL \ud83e\udd1d LLMs </i>\n </p>\n<b><h3>Check out our <a href=\"https://parkervg.github.io/blendsql/reference/functions/\" target=\"_blank\">online documentation</a> for a more comprehensive overview.</h3></b>\n<b><h4>Join our <a href=\"https://discord.gg/vCv7ak3WrU\" target=\"_blank\">Discord server</a> for more discussion!</h4></b>\n</div>\n<br/>\n\n# \ud83d\udcbb Installation\n```\npip install blendsql\n```\n\n# \u2b50 Quickstart\n```python\nimport pandas as pd\n\nfrom blendsql import BlendSQL\nfrom blendsql.models import TransformersLLM, LiteLLM\n\nUSE_LOCAL_CONSTRAINED_MODEL = False\n\n# Load model, either a local transformers model, or remote provider via LiteLLM\nif USE_LOCAL_CONSTRAINED_MODEL:\n model = TransformersLLM(\n \"meta-llama/Llama-3.2-3B-Instruct\", config={\"device_map\": \"auto\"}\n ) # Local models enable BlendSQL's predicate-guided constrained decoding\nelse:\n model = LiteLLM(\"openai/gpt-4o-mini\")\n\n# Prepare our BlendSQL connection\nbsql = BlendSQL(\n {\n \"People\": pd.DataFrame(\n {\n \"Name\": [\n \"George Washington\",\n \"John Adams\",\n \"Thomas Jefferson\",\n \"James Madison\",\n \"James Monroe\",\n \"Alexander Hamilton\",\n \"Sabrina Carpenter\",\n \"Charli XCX\",\n \"Elon Musk\",\n \"Michelle Obama\",\n \"Elvis Presley\",\n ],\n \"Known_For\": [\n \"Established federal government, First U.S. President\",\n \"XYZ Affair, Alien and Sedition Acts\",\n \"Louisiana Purchase, Declaration of Independence\",\n \"War of 1812, Constitution\",\n \"Monroe Doctrine, Missouri Compromise\",\n \"Created national bank, Federalist Papers\",\n \"Nonsense, Emails I Cant Send, Mean Girls musical\",\n \"Crash, How Im Feeling Now, Boom Clap\",\n \"Tesla, SpaceX, Twitter/X acquisition\",\n \"Lets Move campaign, Becoming memoir\",\n \"14 Grammys, King of Rock n Roll\",\n ],\n }\n ),\n \"Eras\": pd.DataFrame({\"Years\": [\"1700-1800\", \"1800-1900\", \"1900-2000\", \"2000-Now\"]}),\n },\n model=model,\n verbose=True,\n)\n\nsmoothie = bsql.execute(\n \"\"\"\n SELECT * FROM People P\n WHERE P.Name IN {{\n LLMQA('First 3 presidents of the U.S?', quantifier='{3}')\n }}\n \"\"\",\n infer_gen_constraints=True, # Is `True` by default\n)\n\nprint(smoothie.df)\n# \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n# \u2502 Name \u2502 Known_For \u2502\n# \u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n# \u2502 George Washington \u2502 Established federal government, First U.S. Preside... \u2502\n# \u2502 John Adams \u2502 XYZ Affair, Alien and Sedition Acts \u2502\n# \u2502 Thomas Jefferson \u2502 Louisiana Purchase, Declaration of Independence \u2502\n# \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\nprint(smoothie.summary())\n# \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n# \u2502 Time (s) \u2502 # Generation Calls \u2502 Prompt Tokens \u2502 Completion Tokens \u2502\n# \u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n# \u2502 1.25158 \u2502 1 \u2502 296 \u2502 16 \u2502\n# \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n\nsmoothie = bsql.execute(\n \"\"\"\n SELECT GROUP_CONCAT(Name, ', ') AS 'Names',\n {{\n LLMMap(\n 'In which time period was this person born?',\n p.Name,\n options=Eras.Years\n )\n }} AS Born\n FROM People p\n GROUP BY Born\n \"\"\",\n)\n\nprint(smoothie.df)\n# \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n# \u2502 Names \u2502 Born \u2502\n# \u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n# \u2502 George Washington, John Adams, Thomas Jefferson, J... \u2502 1700-1800 \u2502\n# \u2502 Sabrina Carpenter, Charli XCX, Elon Musk, Michelle... \u2502 2000-Now \u2502\n# \u2502 Elvis Presley \u2502 1900-2000 \u2502\n# \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\nprint(smoothie.summary())\n# \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n# \u2502 Time (s) \u2502 # Generation Calls \u2502 Prompt Tokens \u2502 Completion Tokens \u2502\n# \u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n# \u2502 1.03858 \u2502 2 \u2502 544 \u2502 75 \u2502\n# \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\nsmoothie = bsql.execute(\"\"\"\n SELECT {{\n LLMQA(\n 'Describe BlendSQL in 50 words.',\n context=(\n SELECT content[0:5000] AS \"README\"\n FROM read_text('https://raw.githubusercontent.com/parkervg/blendsql/main/README.md')\n )\n )\n }} AS answer\n\"\"\")\n\nprint(smoothie.df)\n# \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n# \u2502 answer \u2502\n# \u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n# \u2502 BlendSQL is a Python library that combines SQL a... \u2502\n# \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\nprint(smoothie.summary())\n\n# \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n# \u2502 Time (s) \u2502 # Generation Calls \u2502 Prompt Tokens \u2502 Completion Tokens \u2502\n# \u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n# \u2502 4.07617 \u2502 1 \u2502 1921 \u2502 50 \u2502\n# \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n```\n\n# \ud83d\udcf0 News\n- (5/30/25) Created a [Discord server](https://discord.gg/vCv7ak3WrU)\n- (5/6/25): New blog post: [Language Models, SQL, and Types, Oh My!](https://parkervg.github.io/misc/2025/05/05/sql-llms.html)\n- (5/1/15): Single-page [function documentation](https://parkervg.github.io/blendsql/reference/functions/)\n- (3/16/25) Use BlendSQL with 100+ LLM APIs, using [LiteLLM](https://github.com/BerriAI/litellm)!\n- (10/26/24) New tutorial! [blendsql-by-example.ipynb](examples/blendsql-by-example.ipynb)\n- (10/18/24) Concurrent async requests in 0.0.29! OpenAI and Anthropic `LLMMap` calls are speedy now.\n - Customize max concurrent async calls via `blendsql.config.set_async_limit(10)`\n\n# Summary\n\nBlendSQL is a *superset of SQL* for problem decomposition and hybrid question-answering with LLMs.\n\nAs a result, we can *Blend* together...\n\n- \ud83e\udd64 ...operations over heterogeneous data sources (e.g. tables, text, images)\n- \ud83e\udd64 ...the structured & interpretable reasoning of SQL with the generalizable reasoning of LLMs\n\n\n\n**Now, the user is given the control to oversee all calls (LLM + SQL) within a unified query language.**\n\n# Features\n\n- Supports many DBMS \ud83d\udcbe\n - SQLite, PostgreSQL, DuckDB, Pandas (aka duckdb in a trenchcoat)\n- Supports local & remote models \u2728\n - Transformers, OpenAI, Anthropic, Ollama, and 100+ more!\n- Easily extendable to [multi-modal usecases](./examples/vqa-ingredient.ipynb) \ud83d\uddbc\n- Write your normal queries - smart parsing optimizes what is passed to external functions \ud83e\udde0\n - Traverses abstract syntax tree with [sqlglot](https://github.com/tobymao/sqlglot) to minimize LLM function calls \ud83c\udf33\n- Constrained decoding with [guidance](https://github.com/guidance-ai/guidance) \ud83d\ude80\n - When using local models, we only generate syntactically valid outputs according to query syntax + database contents\n- LLM function caching, built on [diskcache](https://grantjenks.com/docs/diskcache/) \ud83d\udd11\n\n# Example\nFor 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).\n\nWe can use BlendSQL to build a travel planning LLM chatbot to help us navigate the options below.\n\n\n| **Name** | **Image** | **Location** | **Area** | **Recreation Visitors (2022)** | **Description** |\n|-----------------|---------------------------------------------------------------------------------|--------------------|-----------------------------------|--------------------------------|------------------------------------------------------------------------------------------------------------------------------------------|\n| Death Valley |  | 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 \u00b0F (54 \u00b0C). |\n| Everglades |  | 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. |\n| New River Gorge |  | 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. |\n | Katmai |  | 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. |\n\nBlendSQL allows us to ask the following questions by injecting \"ingredients\", which are callable functions denoted by double curly brackets (`{{`, `}}`).\n\n_Which parks don't have park facilities?_\n```sql\nSELECT \"Name\", \"Description\" FROM parks p\n WHERE {{\n LLMMap(\n 'Does this location have park facilities?',\n p.Description\n )\n }} = FALSE\n```\n| Name | Description |\n|:----------------|:---------------------------------------------------------------------------------------------------------------------------------------|\n| Everglades | The country's northernmost park protects an expanse of pure wilderness in Alaska's Brooks Range and has no park facilities. |\n<hr>\n\n_What does the largest park in Alaska look like?_\n\n```sql\nSELECT \"Name\",\n{{ImageCaption(Image)}} as \"Image Description\",\n{{\n LLMMap(\n question='Size in km2?',\n values=Area\n )\n}} as \"Size in km\" FROM parks p\nWHERE \"Location\" = 'Alaska'\nORDER BY \"Size in km\" DESC LIMIT 1\n```\n\n| Name | Image Description | Size in km |\n|:-----------|:--------------------------------------------------------|-------------:|\n| Everglades | A forest of tall trees with a sunset in the background. | 30448.1 |\n\n<hr>\n\n_Which state is the park in that protects an ash flow?_\n\n```sql\nSELECT Location, Name AS \"Park Protecting Ash Flow\" FROM parks\n WHERE Name = {{\n LLMQA(\n 'Which park protects an ash flow?',\n context=(SELECT Name, Description FROM parks),\n options=Name\n )\n }}\n```\n| Location | Park Protecting Ash Flow |\n|:-----------|:---------------------------|\n| Alaska | Katmai |\n\n<hr>\n\n_How many parks are located in more than 1 state?_\n\n```sql\nSELECT COUNT(*) FROM parks\n WHERE {{LLMMap('How many states?', Location)}} > 1\n```\n| Count |\n|--------:|\n| 1 |\n<hr>\n\n_Give me some info about the park in the state that Sarah Palin was governor of._\n```sql\nSELECT \"Name\", \"Location\", \"Description\" FROM parks\n/* Will gather relevant context via WebSearch, and automatically constrain output \n to one of the values in 'parks.Location'*/\nWHERE Location = {{WebSearchQA('Which state was Sarah Palin governor of?')}}\n```\n| Name | Location | Description |\n|:-----------|:-----------|:------------------------------------------------------------------------------------------------------------------------------------|\n| Everglades | Alaska | The country's northernmost park protects an expanse of pure wilderness in Alaska's Brooks Range and has no park facilities. |\n| Katmai | Alaska | This park on the Alaska Peninsula protects the Valley of Ten Thousand Smokes, an ash flow formed by the 1912 eruption of Novarupta. |\n<hr>\n\n_What's the difference in visitors for those parks with a superlative in their description vs. those without?_\n```sql\nSELECT SUM(CAST(REPLACE(\"Recreation Visitors (2022)\", ',', '') AS integer)) AS \"Total Visitors\",\n{{LLMMap('Contains a superlative?', Description, options=('t', 'f'))}} AS \"Description Contains Superlative\",\nGROUP_CONCAT(Name, ', ') AS \"Park Names\"\nFROM parks\nGROUP BY \"Description Contains Superlative\"\n```\n| Total Visitors | Description Contains Superlative | Park Names |\n|---------------:|-----------------------------------:|:------------------------------|\n| 43365 | 0 | Everglades, Katmai |\n| 2722385 | 1 | Death Valley, New River Gorge |\n<hr>\n\nNow, 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).\n\nFor in-depth descriptions of the above queries, check out our [documentation](https://parkervg.github.io/blendsql/).\n\n\n# Citation\n\n```bibtex\n@article{glenn2024blendsql,\n title={BlendSQL: A Scalable Dialect for Unifying Hybrid Question Answering in Relational Algebra},\n author={Parker Glenn and Parag Pravin Dakle and Liang Wang and Preethi Raghavan},\n year={2024},\n eprint={2402.17882},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n```\n\n# Few-Shot Prompting\nFor the LLM-based ingredients in BlendSQL, few-shot prompting can be vital. In `LLMMap`, `LLMQA` and `LLMJoin`, we provide an interface to pass custom few-shot examples and dynamically retrieve those top-`k` most relevant examples at runtime, given the current inference example.\n#### `LLMMap`\n- [Default examples](./blendsql/ingredients/builtin/map/default_examples.json)\n- [All possible fields](./blendsql/ingredients/builtin/map/examples.py)\n\n```python\nfrom blendsql import BlendSQL\nfrom blendsql.ingredients.builtin import LLMMap, DEFAULT_MAP_FEW_SHOT\n\ningredients = {\n LLMMap.from_args(\n few_shot_examples=[\n *DEFAULT_MAP_FEW_SHOT,\n {\n \"question\": \"Is this a sport?\",\n \"mapping\": {\n \"Soccer\": True,\n \"Chair\": False,\n \"Banana\": False,\n \"Golf\": True\n },\n # Below are optional\n \"column_name\": \"Items\",\n \"table_name\": \"Table\",\n \"return_type\": \"boolean\"\n }\n ],\n num_few_shot_examples=2,\n # How many inference values to pass to model at once\n batch_size=5,\n )\n}\n\nbsql = BlendSQL(db, ingredients=ingredients)\n```\n\n#### `LLMQA`\n- [Default examples](./blendsql/ingredients/builtin/qa/default_examples.json)\n- [All possible fields](./blendsql/ingredients/builtin/qa/examples.py)\n\n```python\nfrom blendsql import BlendSQL\nfrom blendsql.ingredients.builtin import LLMQA, DEFAULT_QA_FEW_SHOT\n\ningredients = {\n LLMQA.from_args(\n few_shot_examples=[\n *DEFAULT_QA_FEW_SHOT,\n {\n \"question\": \"Which weighs the most?\",\n \"context\": {\n {\n \"Animal\": [\"Dog\", \"Gorilla\", \"Hamster\"],\n \"Weight\": [\"20 pounds\", \"350 lbs\", \"100 grams\"]\n }\n },\n \"answer\": \"Gorilla\",\n # Below are optional\n \"options\": [\"Dog\", \"Gorilla\", \"Hamster\"]\n }\n ],\n # Will fetch `k` most relevant few-shot examples using embedding-based retriever\n num_few_shot_examples=2,\n # Lambda to turn the pd.DataFrame to a serialized string\n context_formatter=lambda df: df.to_markdown(\n index=False\n )\n )\n}\n\nbsql = BlendSQL(db, ingredients=ingredients)\n```\n\n#### `LLMJoin`\n- [Default examples](./blendsql/ingredients/builtin/join/default_examples.json)\n- [All possible fields](./blendsql/ingredients/builtin/join/examples.py)\n\n```python\nfrom blendsql import BlendSQL\nfrom blendsql.ingredients.builtin import LLMJoin, DEFAULT_JOIN_FEW_SHOT\n\ningredients = {\n LLMJoin.from_args(\n few_shot_examples=[\n *DEFAULT_JOIN_FEW_SHOT,\n {\n \"join_criteria\": \"Join the state to its capital.\",\n \"left_values\": [\"California\", \"Massachusetts\", \"North Carolina\"],\n \"right_values\": [\"Sacramento\", \"Boston\", \"Chicago\"],\n \"mapping\": {\n \"California\": \"Sacramento\",\n \"Massachusetts\": \"Boston\",\n \"North Carolina\": \"-\"\n }\n }\n ],\n num_few_shot_examples=2\n )\n}\n\nbsql = BlendSQL(db, ingredients=ingredients)\n```\n\n\n# Acknowledgements\nSpecial thanks to those below for inspiring this project. Definitely recommend checking out the linked work below, and citing when applicable!\n\n- The authors of [Binding Language Models in Symbolic Languages](https://arxiv.org/abs/2210.02875)\n - This paper was the primary inspiration for BlendSQL.\n- 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)\n - As far as I can tell, the first publication to propose unifying model calls within SQL\n - Served as the inspiration for the [vqa-ingredient.ipynb](./examples/vqa-ingredient.ipynb) example\n- The authors of [Grammar Prompting for Domain-Specific Language Generation with Large Language Models](https://arxiv.org/abs/2305.19234)\n- The maintainers of the [Guidance](https://github.com/guidance-ai/guidance) library for powering the constrained decoding capabilities of BlendSQL\n",
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