├── docs ├── posts │ ├── .gitignore │ ├── llms-and-data-pt0 │ │ ├── .gitignore │ │ └── index.qmd │ ├── llms-and-data-pt1 │ │ ├── .gitignore │ │ └── index.qmd │ ├── llms-and-data-pt2 │ │ ├── .gitignore │ │ └── index.qmd │ ├── llms-and-data-pt3 │ │ ├── .gitignore │ │ ├── index.qmd │ │ └── index.ipynb │ ├── llms-and-data-pt4 │ │ ├── .gitignore │ │ ├── images │ │ │ └── figure1.png │ │ └── index.qmd │ ├── llms-and-data-pt5 │ │ ├── .gitignore │ │ └── index.qmd │ ├── llms-and-data-pt6 │ │ ├── .gitignore │ │ └── index.qmd │ └── _metadata.yml ├── styles.css ├── how-to │ ├── _metadata.yml │ └── input-output │ │ └── basics.qmd ├── release_notes.md ├── .gitignore ├── images │ ├── bad.png │ ├── ibas.png │ ├── logo.png │ ├── bbrans.png │ ├── brians.png │ ├── ibribans.png │ └── thumbnail.png ├── concepts │ ├── archive │ │ ├── platforms.qmd │ │ ├── ops.qmd │ │ ├── user-interfaces.qmd │ │ └── llms.qmd │ ├── bots.qmd │ ├── tasks.qmd │ ├── flows.qmd │ ├── messages.qmd │ └── attachments.qmd ├── contribute │ └── contributing.qmd ├── demo.qmd ├── posts.qmd ├── _freeze │ ├── concepts │ │ ├── bots │ │ │ └── execute-results │ │ │ │ └── html.json │ │ ├── tasks │ │ │ └── execute-results │ │ │ │ └── html.json │ │ ├── flows │ │ │ └── execute-results │ │ │ │ └── html.json │ │ ├── messages │ │ │ └── execute-results │ │ │ │ └── html.json │ │ └── attachments │ │ │ └── execute-results │ │ │ └── html.json │ ├── install │ │ └── execute-results │ │ │ └── html.json │ ├── posts │ │ ├── llms-and-data-pt4 │ │ │ └── index │ │ │ │ └── execute-results │ │ │ │ └── html.json │ │ └── llms-and-data-pt2 │ │ │ └── index │ │ │ └── execute-results │ │ │ └── html.json │ ├── site_libs │ │ ├── quarto-listing │ │ │ └── quarto-listing.js │ │ └── clipboard │ │ │ └── clipboard.min.js │ └── tutorials │ │ └── python │ │ └── execute-results │ │ └── html.json ├── install.qmd ├── index.qmd ├── why.qmd ├── tutorials │ ├── python.qmd │ └── cli.qmd └── _quarto.yml ├── presentation ├── _quarto.yml ├── images │ └── duckdb-nsql.png └── index.qmd ├── src └── ibis_birdbrain │ ├── __main__.py │ ├── __init__.py │ ├── logging │ └── __init__.py │ ├── commands │ ├── __init__.py │ ├── testing.py │ └── ipy.py │ ├── messages │ ├── email.py │ └── __init__.py │ ├── utils │ ├── messages.py │ ├── strings.py │ ├── web.py │ └── attachments.py │ ├── attachments │ ├── viz.py │ ├── text.py │ ├── data.py │ └── __init__.py │ ├── cli.py │ ├── tasks │ ├── __init__.py │ └── sql.py │ ├── flows │ ├── __init__.py │ └── data.py │ ├── strings.py │ ├── app.py │ └── bot.py ├── dev-requirements.txt ├── README.md ├── .github └── workflows │ └── docs.yaml ├── pyproject.toml ├── justfile ├── .gitignore └── LICENSE /docs/posts/.gitignore: -------------------------------------------------------------------------------- 1 | *.ipynb 2 | -------------------------------------------------------------------------------- /docs/styles.css: -------------------------------------------------------------------------------- 1 | /* css styles */ 2 | -------------------------------------------------------------------------------- /docs/how-to/_metadata.yml: -------------------------------------------------------------------------------- 1 | code-annotations: hover 2 | -------------------------------------------------------------------------------- /docs/release_notes.md: -------------------------------------------------------------------------------- 1 | # Release notes 2 | 3 | TBD 4 | -------------------------------------------------------------------------------- /docs/posts/llms-and-data-pt0/.gitignore: -------------------------------------------------------------------------------- 1 | .env 2 | *.ddb* 3 | -------------------------------------------------------------------------------- /docs/posts/llms-and-data-pt1/.gitignore: -------------------------------------------------------------------------------- 1 | .env 2 | *.ddb* 3 | -------------------------------------------------------------------------------- /docs/posts/llms-and-data-pt2/.gitignore: -------------------------------------------------------------------------------- 1 | .env 2 | *.ddb* 3 | -------------------------------------------------------------------------------- /docs/posts/llms-and-data-pt3/.gitignore: -------------------------------------------------------------------------------- 1 | .env 2 | *.ddb* 3 | -------------------------------------------------------------------------------- /docs/posts/llms-and-data-pt4/.gitignore: -------------------------------------------------------------------------------- 1 | .env 2 | *.ddb* 3 | -------------------------------------------------------------------------------- /docs/posts/llms-and-data-pt5/.gitignore: -------------------------------------------------------------------------------- 1 | .env 2 | *.ddb* 3 | -------------------------------------------------------------------------------- /docs/posts/llms-and-data-pt6/.gitignore: -------------------------------------------------------------------------------- 1 | .env 2 | *.ddb* 3 | -------------------------------------------------------------------------------- /docs/.gitignore: -------------------------------------------------------------------------------- 1 | _site 2 | objects.json 3 | reference/ 4 | /.quarto/ 5 | -------------------------------------------------------------------------------- /presentation/_quarto.yml: -------------------------------------------------------------------------------- 1 | project: 2 | type: website 3 | output-dir: _output -------------------------------------------------------------------------------- /docs/images/bad.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ibis-project/ibis-birdbrain/HEAD/docs/images/bad.png -------------------------------------------------------------------------------- /docs/images/ibas.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ibis-project/ibis-birdbrain/HEAD/docs/images/ibas.png -------------------------------------------------------------------------------- /docs/images/logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ibis-project/ibis-birdbrain/HEAD/docs/images/logo.png -------------------------------------------------------------------------------- /docs/how-to/input-output/basics.qmd: -------------------------------------------------------------------------------- 1 | # Basic input/output 2 | 3 | You can... 4 | 5 | ## Overview 6 | 7 | -------------------------------------------------------------------------------- /docs/images/bbrans.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ibis-project/ibis-birdbrain/HEAD/docs/images/bbrans.png -------------------------------------------------------------------------------- /docs/images/brians.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ibis-project/ibis-birdbrain/HEAD/docs/images/brians.png -------------------------------------------------------------------------------- /src/ibis_birdbrain/__main__.py: -------------------------------------------------------------------------------- 1 | from ibis_birdbrain.cli import app 2 | 3 | app(prog_name="birdbrain") 4 | -------------------------------------------------------------------------------- /docs/images/ibribans.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ibis-project/ibis-birdbrain/HEAD/docs/images/ibribans.png -------------------------------------------------------------------------------- /docs/images/thumbnail.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ibis-project/ibis-birdbrain/HEAD/docs/images/thumbnail.png -------------------------------------------------------------------------------- /dev-requirements.txt: -------------------------------------------------------------------------------- 1 | # python 2 | ruff 3 | build 4 | twine 5 | nbclient 6 | ipykernel 7 | 8 | # docs 9 | quartodoc 10 | -------------------------------------------------------------------------------- /presentation/images/duckdb-nsql.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ibis-project/ibis-birdbrain/HEAD/presentation/images/duckdb-nsql.png -------------------------------------------------------------------------------- /src/ibis_birdbrain/__init__.py: -------------------------------------------------------------------------------- 1 | # imports 2 | 3 | # exports 4 | from ibis_birdbrain.bot import Bot 5 | 6 | __all__ = ["Bot"] 7 | -------------------------------------------------------------------------------- /docs/concepts/archive/platforms.qmd: -------------------------------------------------------------------------------- 1 | # Data and AI platforms 2 | 3 | Data and AI platforms are... 4 | 5 | ## Data platforms 6 | 7 | ## AI platforms 8 | 9 | -------------------------------------------------------------------------------- /docs/posts/llms-and-data-pt4/images/figure1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ibis-project/ibis-birdbrain/HEAD/docs/posts/llms-and-data-pt4/images/figure1.png -------------------------------------------------------------------------------- /docs/contribute/contributing.qmd: -------------------------------------------------------------------------------- 1 | --- 2 | hide: 3 | - toc 4 | --- 5 | 6 | # Contributing 7 | 8 | ## Required dependencies 9 | 10 | To contribute... 11 | -------------------------------------------------------------------------------- /src/ibis_birdbrain/logging/__init__.py: -------------------------------------------------------------------------------- 1 | # imports 2 | import logging as log 3 | 4 | # config 5 | log.basicConfig(level=log.INFO) 6 | 7 | # exports 8 | __all__ = ["log"] 9 | -------------------------------------------------------------------------------- /src/ibis_birdbrain/commands/__init__.py: -------------------------------------------------------------------------------- 1 | # imports 2 | 3 | # exports 4 | from ibis_birdbrain.commands.ipy import ipy_run 5 | from ibis_birdbrain.commands.testing import testing_run 6 | 7 | __all__ = ["ipy_run", "testing_run"] 8 | -------------------------------------------------------------------------------- /src/ibis_birdbrain/commands/testing.py: -------------------------------------------------------------------------------- 1 | def testing_run(): 2 | from rich.console import Console 3 | 4 | console = Console() 5 | console.print(f"testing: ", style="bold violet", end="") 6 | console.print(f"done...") 7 | -------------------------------------------------------------------------------- /docs/concepts/bots.qmd: -------------------------------------------------------------------------------- 1 | # Bots 2 | 3 | Ibis Birdbrain implements a `Bot` calss that can be used to instantiate one or more bots that automate various tasks. 4 | 5 | ## Usage 6 | 7 | ```{python} 8 | from ibis_birdbrain import Bot 9 | Bot 10 | ``` 11 | -------------------------------------------------------------------------------- /docs/demo.qmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "demo" 3 | format: 4 | html: 5 | page-layout: full 6 | --- 7 | 8 | ```{=html} 9 | 10 | ``` 11 | 12 | -------------------------------------------------------------------------------- /docs/concepts/archive/ops.qmd: -------------------------------------------------------------------------------- 1 | # LLMOps, MLOps, DevOps 2 | 3 | The ML landscape is plagued by "the toy problem" -- building something cool is easy, putting something in production is hard. 4 | 5 | ## The original MLOps paper 6 | 7 | 8 | ## Learnings from industry 9 | 10 | 11 | -------------------------------------------------------------------------------- /docs/posts.qmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Posts" 3 | listing: 4 | contents: posts 5 | sort: "date desc" 6 | image-placeholder: images/logo.png 7 | #type: default 8 | type: grid 9 | categories: true 10 | sort-ui: true 11 | filter-ui: true 12 | feed: true 13 | page-layout: full 14 | --- 15 | -------------------------------------------------------------------------------- /docs/concepts/tasks.qmd: -------------------------------------------------------------------------------- 1 | # Tasks 2 | 3 | Ibis Birdbrain's [`Flow`](./flow.qmd) executes one or more `Tasks` to accomplish its goal. A `Task` is a single unit of work that takes a [`Message`](./message.qmd) as input and returns a `Message` as output. 4 | 5 | ## Usage 6 | 7 | ```{python} 8 | from ibis_birdbrain.tasks import Task, Tasks 9 | 10 | task = Task() 11 | task 12 | ``` 13 | -------------------------------------------------------------------------------- /src/ibis_birdbrain/commands/ipy.py: -------------------------------------------------------------------------------- 1 | def ipy_run(interactive=False): 2 | # imports 3 | import ibis 4 | import IPython 5 | 6 | from ibis_birdbrain import Bot 7 | 8 | # config 9 | ibis.options.interactive = True 10 | ibis.options.repr.interactive.max_rows = 20 11 | ibis.options.repr.interactive.max_columns = None 12 | 13 | # start IPython 14 | IPython.embed(colors="neutral") 15 | -------------------------------------------------------------------------------- /docs/concepts/flows.qmd: -------------------------------------------------------------------------------- 1 | # Flows 2 | 3 | Ibis Birdbrain's [`Bot`](./bot.qmd) chooses a `Flow` to execute based on [`Messages`](./messages.qmd). 4 | 5 | A Flow takes Messages as input and returns Messages as output. The details of a given Flow are specific to itself, running a series of [`Tasks`](./tasks.qmd) to accomplish its goal. 6 | 7 | ## Usage 8 | 9 | ```{python} 10 | from ibis_birdbrain.flows import Flow, Flows 11 | 12 | flow = Flow() 13 | flow 14 | ``` 15 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Ibis Birdbrain 2 | 3 | The portable Python ML-powered data bot for your data project. Powered by 4 | [Ibis](https://ibis-project.org), your data platform, 5 | [Marvin](https://github.com/prefectHQ/marvin), and your ML platform. 6 | 7 | > [!WARNING] 8 | > Highly experimental. Effectively abandoned. Made for cool demos. If anyone's interested in forking this or understanding the learnings, reach out! 9 | > 10 | > May eventually be under re-construction by slow humans. 11 | -------------------------------------------------------------------------------- /src/ibis_birdbrain/messages/email.py: -------------------------------------------------------------------------------- 1 | # imports 2 | from ibis_birdbrain.messages import Message 3 | 4 | 5 | # classes 6 | class Email(Message): 7 | """An email.""" 8 | 9 | def __str__(self): 10 | return f"""To: {self.to_address} 11 | From: {self.from_address} 12 | Subject: {self.subject} 13 | Sent at: {self.created_at} 14 | Message: {self.id} 15 | 16 | {self.body} 17 | 18 | Attachments: 19 | 20 | {self.attachments}\n""" 21 | 22 | def __repr__(self): 23 | return str(self) 24 | -------------------------------------------------------------------------------- /docs/posts/_metadata.yml: -------------------------------------------------------------------------------- 1 | # options specified here will apply to all posts in this folder 2 | 3 | # freeze computational output 4 | # (see https://quarto.org/docs/projects/code-execution.html#freeze) 5 | freeze: auto 6 | 7 | # Enable banner style title blocks 8 | title-block-banner: true 9 | 10 | execute: 11 | warning: false 12 | 13 | # code annoations 14 | code-annotations: hover 15 | 16 | # comments # TODO: enable 17 | # comments: 18 | # giscus: 19 | # repo: ibis-project/ibis 20 | # category: Q&A 21 | # reactions-enabled: false 22 | -------------------------------------------------------------------------------- /src/ibis_birdbrain/utils/messages.py: -------------------------------------------------------------------------------- 1 | # imports 2 | from typing import Any 3 | from ibis_birdbrain.messages import Message, Email 4 | 5 | from ibis_birdbrain.utils.attachments import to_attachment 6 | 7 | 8 | # functions 9 | def to_message(text: str, stuff: list[Any] = []) -> Message: 10 | """Convert text and stuff into a message with attachments.""" 11 | attachments = [] 12 | for thing in stuff: 13 | attachment = to_attachment(thing) 14 | if attachment is not None: 15 | attachments.append(attachment) 16 | 17 | return Email(body=text, attachments=attachments) 18 | -------------------------------------------------------------------------------- /src/ibis_birdbrain/attachments/viz.py: -------------------------------------------------------------------------------- 1 | # imports 2 | from plotly.graph_objs import Figure 3 | 4 | from ibis_birdbrain.attachments import Attachment 5 | 6 | 7 | # classes 8 | class ChartAttachment(Attachment): 9 | """A chart attachment.""" 10 | 11 | content: Figure 12 | 13 | def __init__(self, *args, **kwargs): 14 | super().__init__(*args, **kwargs) 15 | 16 | def encode(self): 17 | ... 18 | 19 | def decode(self): 20 | ... 21 | 22 | def __str__(self): 23 | return ( 24 | super().__str__() 25 | + f""" 26 | **chart**:\n{self.content}""" 27 | ) 28 | -------------------------------------------------------------------------------- /docs/_freeze/concepts/bots/execute-results/html.json: -------------------------------------------------------------------------------- 1 | { 2 | "hash": "bd2906497289e9de4361517b57031090", 3 | "result": { 4 | "engine": "jupyter", 5 | "markdown": "---\ntitle: Bots\n---\n\n\n\nIbis Birdbrain implements a `Bot` calss that can be used to instantiate one or more bots that automate various tasks.\n\n## Usage\n\n\n::: {#a5d8d0d4 .cell execution_count=1}\n``` {.python .cell-code}\nfrom ibis_birdbrain import Bot\nBot\n```\n\n::: {.cell-output .cell-output-display execution_count=1}\n```\nibis_birdbrain.bot.Bot\n```\n:::\n:::\n\n\n", 6 | "supporting": [ 7 | "bots_files" 8 | ], 9 | "filters": [], 10 | "includes": {} 11 | } 12 | } -------------------------------------------------------------------------------- /docs/concepts/messages.qmd: -------------------------------------------------------------------------------- 1 | # Messages 2 | 3 | Ibis Birdbrain communicates with the user, itself, and (eventually) other bots through `Messages`. A `Message` is a simple wrapper around text with metadata and optional [`Attachments`](./attachments.qmd). 4 | 5 | 6 | ## Usage 7 | 8 | ```{python} 9 | from ibis_birdbrain.messages import Message, Messages, Email 10 | 11 | m1 = Message("Hello, world!") 12 | m1 13 | ``` 14 | 15 | ## Emails 16 | 17 | Currently, the only implementation of `Message` that is viewable as a proper string is `Email`. 18 | 19 | ```{python} 20 | e1 = Email("Hello") 21 | e2 = Email(", world!") 22 | 23 | messages = Messages([e1, e2]) 24 | messages 25 | ``` 26 | -------------------------------------------------------------------------------- /src/ibis_birdbrain/cli.py: -------------------------------------------------------------------------------- 1 | """ 2 | Ibis Birdbrain CLI. 3 | """ 4 | 5 | # imports 6 | import typer 7 | 8 | from typing_extensions import Annotated, Optional 9 | 10 | from ibis_birdbrain.commands import ipy_run, testing_run 11 | 12 | # typer config 13 | app = typer.Typer(no_args_is_help=True) 14 | 15 | 16 | # subcommands 17 | @app.command() 18 | def ipy(): 19 | """ 20 | ipy 21 | """ 22 | ipy_run() 23 | 24 | 25 | @app.command() 26 | def test(): 27 | """ 28 | test 29 | """ 30 | testing_run() 31 | 32 | 33 | # main 34 | @app.callback() 35 | def cli(): 36 | return 37 | 38 | 39 | ## main 40 | if __name__ == "__main__": 41 | typer.run(cli) 42 | -------------------------------------------------------------------------------- /docs/_freeze/concepts/tasks/execute-results/html.json: -------------------------------------------------------------------------------- 1 | { 2 | "hash": "ea522272a8d0a666dacb53873801a1aa", 3 | "result": { 4 | "engine": "jupyter", 5 | "markdown": "---\ntitle: Tasks\n---\n\n\n\nIbis Birdbrain's [`Flow`](./flow.qmd) executes one or more `Tasks` to accomplish its goal. A `Task` is a single unit of work that takes a [`Message`](./message.qmd) as input and returns a `Message` as output.\n\n## Usage\n\n\n::: {#ef7b5518 .cell execution_count=1}\n``` {.python .cell-code}\nfrom ibis_birdbrain.tasks import Task, Tasks\n\ntask = Task()\ntask\n```\n\n::: {.cell-output .cell-output-display execution_count=1}\n```\n\n```\n:::\n:::\n\n\n", 6 | "supporting": [ 7 | "tasks_files" 8 | ], 9 | "filters": [], 10 | "includes": {} 11 | } 12 | } -------------------------------------------------------------------------------- /docs/_freeze/concepts/flows/execute-results/html.json: -------------------------------------------------------------------------------- 1 | { 2 | "hash": "5ca217bfa5ee488df2c050375105b2a1", 3 | "result": { 4 | "engine": "jupyter", 5 | "markdown": "---\ntitle: Flows\n---\n\n\n\nIbis Birdbrain's [`Bot`](./bot.qmd) chooses a `Flow` to execute based on [`Messages`](./messages.qmd).\n\nA Flow takes Messages as input and returns Messages as output. The details of a given Flow are specific to itself, running a series of [`Tasks`](./tasks.qmd) to accomplish its goal.\n\n## Usage\n\n\n::: {#254b2753 .cell execution_count=1}\n``` {.python .cell-code}\nfrom ibis_birdbrain.flows import Flow, Flows\n\nflow = Flow()\nflow\n```\n\n::: {.cell-output .cell-output-display execution_count=1}\n```\n\n```\n:::\n:::\n\n\n", 6 | "supporting": [ 7 | "flows_files" 8 | ], 9 | "filters": [], 10 | "includes": {} 11 | } 12 | } -------------------------------------------------------------------------------- /docs/install.qmd: -------------------------------------------------------------------------------- 1 | # Installation and setup 2 | 3 | This page describes how to install and setup Ibis Birdbrain. 4 | 5 | ## Install from PyPI 6 | 7 | ```bash 8 | pip install ibis-birdbrain 9 | ``` 10 | 11 | ## Data platform setup 12 | 13 | Create an Ibis connection: 14 | 15 | :::{.callout-warning} 16 | Only DuckDB is installed by default. For now, you need to install other backends 17 | manually with Ibis. 18 | ::: 19 | 20 | ```{python} 21 | import ibis 22 | 23 | backend = "duckdb" 24 | backend_uri = "" 25 | 26 | con = ibis.connect(f"{backend}://{backend_uri}") 27 | con 28 | ``` 29 | 30 | ## AI platform setup 31 | 32 | See [Marvin documentation for 33 | setup](https://www.askmarvin.ai/docs/configuration/settings/). 34 | 35 | :::{.callout-warning} 36 | Only OpenAI and Azure OpenAI are supported for now. 37 | ::: 38 | 39 | ## Next steps 40 | 41 | [Learn how get started with Ibis Birdbrain's CLI](tutorials/cli.qmd). 42 | -------------------------------------------------------------------------------- /src/ibis_birdbrain/utils/strings.py: -------------------------------------------------------------------------------- 1 | # imports 2 | 3 | 4 | # functions 5 | def estimate_tokens(s: str) -> int: 6 | """Estimates the number of tokens in a string.""" 7 | 8 | return len(s) // 4 9 | 10 | 11 | def shorten_str(s: str, max_len: int = 27) -> str: 12 | """Converts a string to a display string.""" 13 | 14 | if len(s) > max_len: 15 | return f"{s[:max_len]}..." 16 | else: 17 | return s 18 | 19 | 20 | def str_to_list_of_str(s: str, max_chunk_len: int = 1000, sep: str = "\n") -> list[str]: 21 | """Splits a string into a list of strings.""" 22 | 23 | result = [] 24 | 25 | # TODO: better string chunking algorithm 26 | # split the string into chunks 27 | chunks = [s[i : i + max_chunk_len] for i in range(0, len(s), max_chunk_len)] 28 | 29 | # split the chunks into lines 30 | for chunk in chunks: 31 | result.extend(chunk.split(sep)) 32 | 33 | return result 34 | -------------------------------------------------------------------------------- /docs/index.qmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Ibis Birdbrain" 3 | description: "the portable Python LM-powered data bot" 4 | repo-actions: false 5 | code-annotations: hover 6 | format: 7 | html: 8 | toc: false 9 | about: 10 | id: about 11 | template: jolla 12 | image: images/logo.png 13 | links: 14 | - icon: info-circle 15 | text: Why Ibis Birdbrain? 16 | href: /why/ 17 | - icon: download 18 | text: Installation 19 | href: /install/ 20 | - icon: book 21 | text: "Tutorial: getting started" 22 | href: /tutorials/python/ 23 | - icon: github 24 | text: GitHub 25 | href: https://github.com/ibis-project/ibis-birdbrain 26 | - icon: slack 27 | text: Chat 28 | href: https://ibis-project.zulipchat.com 29 | # - icon: rss 30 | # text: RSS 31 | # href: https://ibis-project.github.io/ibis-birdbrain/posts.xml 32 | --- 33 | 34 | ::: {#about} 35 | ::: 36 | 37 | ::: {.callout-warning} 38 | Ibis Birdbrain is highly experimental and currently under construction by slow humans. Please check back soon! 39 | ::: 40 | -------------------------------------------------------------------------------- /src/ibis_birdbrain/utils/web.py: -------------------------------------------------------------------------------- 1 | # imports 2 | import requests 3 | import webbrowser 4 | 5 | 6 | from itertools import islice 7 | from html2text import html2text 8 | from duckduckgo_search import DDGS 9 | 10 | 11 | # functions 12 | def open_browser(url: str) -> str: 13 | """Opens the URL in a web browser.""" 14 | try: 15 | webbrowser.open(url.strip("/")) 16 | except Exception as e: 17 | return str(e) 18 | 19 | return f"Opened {url} in web browser." 20 | 21 | 22 | def search_internet(query: str, n_results: int = 10) -> list[dict]: 23 | """Searches the internet for n results.""" 24 | ddgs = DDGS() 25 | return [r for r in islice(ddgs.text(query, backend="lite"), n_results)] 26 | 27 | 28 | def webpage_to_str(url: str = "https://ibis-project.org") -> str: 29 | """Reads a webpage link into a string.""" 30 | response = requests.get(url) 31 | return ( 32 | html2text(response.text) 33 | # .replace("\n", " ") 34 | # .replace("\r", " ") 35 | # .replace("\t", " ") 36 | # .replace(" ", " ") 37 | ) 38 | -------------------------------------------------------------------------------- /docs/concepts/attachments.qmd: -------------------------------------------------------------------------------- 1 | # Attachments 2 | 3 | Ibis Birdbrain passes Python objects as `Attachments` to [`Messages`](./messages.qmd). This allows the user, itself, and (eventually) other bots to interact with data, code, and more. 4 | 5 | 6 | ## Usage 7 | 8 | ```{python} 9 | from ibis_birdbrain.attachments import Attachment, Attachments 10 | 11 | a1 = Attachment(content="Hello, world!") 12 | a1 13 | ``` 14 | 15 | ## TableAttachment 16 | 17 | A `TableAttachment` contains an Ibis table: 18 | 19 | ```{python} 20 | import ibis 21 | 22 | from ibis_birdbrain.attachments import TableAttachment 23 | 24 | ibis.options.interactive = True 25 | 26 | t = ibis.examples.penguins.fetch() 27 | 28 | a2 = TableAttachment(content=t) 29 | a2 30 | ``` 31 | 32 | Notice the name, description (schema), and preview are automatically populated. 33 | 34 | ## CodeAttachment 35 | 36 | A `CodeAttachment` contains code -- typically Python or SQL: 37 | 38 | ```{python} 39 | from ibis_birdbrain.attachments import CodeAttachment 40 | 41 | a3 = CodeAttachment(content="select 1 as id", language="sql") 42 | a3 43 | ``` 44 | -------------------------------------------------------------------------------- /.github/workflows/docs.yaml: -------------------------------------------------------------------------------- 1 | name: docs 2 | 3 | on: 4 | push: 5 | branches: [main] 6 | paths: 7 | - "docs/**" 8 | - ".github/workflows/docs.yaml" 9 | 10 | permissions: 11 | contents: write 12 | 13 | jobs: 14 | build-docs: 15 | runs-on: ubuntu-latest 16 | steps: 17 | - uses: actions/checkout@v3 18 | 19 | - name: Install Python 20 | uses: actions/setup-python@v4 21 | with: 22 | python-version: "3.11" 23 | 24 | - name: Install Quarto 25 | uses: quarto-dev/quarto-actions/setup@v2 26 | with: 27 | version: "1.4.398" 28 | 29 | - name: Install ibis-birdbrain and dependencies 30 | run: | 31 | pip install -r dev-requirements.txt 32 | pip install -e "." 33 | 34 | - name: Build Docs 35 | run: | 36 | pushd docs 37 | quarto render 38 | popd 39 | 40 | - name: Deploy 41 | uses: peaceiris/actions-gh-pages@v3 42 | if: github.ref == 'refs/heads/main' 43 | with: 44 | github_token: ${{ secrets.GITHUB_TOKEN }} 45 | publish_dir: ./docs/_site 46 | -------------------------------------------------------------------------------- /docs/why.qmd: -------------------------------------------------------------------------------- 1 | # Why Ibis Birdbrain? 2 | 3 | [Ibis](https://ibis-project.org) is the portable Python dataframe library. 4 | 5 | Ibis Birdbrain is the portable Python LM-powered data bot, built on Ibis with 6 | support for all data platforms Ibis's 20+ backends support. Ibis Birdbrain is 7 | also built on [Marvin](https://github.com/PrefectHQ/marvin) with support for all 8 | AI platforms Marvin supports. 9 | 10 | ## Language models (LMs) 11 | 12 | Langauge models have... 13 | 14 | ## Standards 15 | 16 | > > When things don't work as they should, it often means that standards are absent. 17 | > > 18 | > > \- [The International Organization for Standardization (ISO)](https://www.iso.org/standards.html) 19 | > 20 | > \- [The Composable Codex](https://voltrondata.com/codex/standards-over-silos) 21 | 22 | ## Composable data systems are MICE 23 | 24 | [Composable data systems are MICE: modular, interoperable, customizable, and extensible](https://voltrondata.com/codex/standards-over-silos). 25 | 26 | Ibis Birdbrain aims to bring these values to a data + AI framework that makes working with LLMs delightful and easy, for individual hobbiests or the more advanced technology organizations. 27 | 28 | -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [tool.hatch.metadata] 2 | allow-direct-references = true 3 | 4 | [build-system] 5 | requires = ["hatchling"] 6 | build-backend = "hatchling.build" 7 | 8 | [project] 9 | name = "ibis-birdbrain" 10 | version = "0.2.0" 11 | authors = [ 12 | { name="Cody", email="cody@dkdc.dev" }, 13 | ] 14 | description = "the portable Python LM-powered data bot" 15 | readme = "README.md" 16 | requires-python = ">=3.11" 17 | classifiers = [ 18 | "Programming Language :: Python :: 3", 19 | "License :: OSI Approved :: MIT License", 20 | "Operating System :: OS Independent", 21 | ] 22 | dependencies = [ 23 | 'ipython', 24 | 'python-dotenv', 25 | 'toml', 26 | 'httpx', 27 | 'Pillow', 28 | 'typer[all]', 29 | 'requests', 30 | 'html2text', 31 | 'duckduckgo-search', 32 | 'marvin>2,<3', 33 | 'ibis-framework[duckdb,examples]', # @ git+https://github.com/ibis-project/ibis', 34 | 'plotly', 35 | 'streamlit', 36 | 'sqlglot', 37 | 'levenshtein', 38 | ] 39 | 40 | [project.urls] 41 | "Homepage" = "https://github.com/ibis-project/ibis-birdbrain" 42 | "Bug Tracker" = "https://github.com/ibis-project/ibis-birdbrain/issues" 43 | 44 | [project.scripts] 45 | birdbrain = "ibis_birdbrain.cli:app" 46 | -------------------------------------------------------------------------------- /docs/_freeze/install/execute-results/html.json: -------------------------------------------------------------------------------- 1 | { 2 | "hash": "5d2f1ac330c63e71709868d483412e5f", 3 | "result": { 4 | "engine": "jupyter", 5 | "markdown": "---\ntitle: Installation and setup\n---\n\n\n\nThis page describes how to install and setup Ibis Birdbrain.\n\n## Install from PyPI\n\n```bash\npip install ibis-birdbrain\n```\n\n## Data platform setup\n\nCreate an Ibis connection:\n\n:::{.callout-warning}\nOnly DuckDB is installed by default. For now, you need to install other backends\nmanually with Ibis.\n:::\n\n\n::: {#9af055c5 .cell execution_count=1}\n``` {.python .cell-code}\nimport ibis\n\nbackend = \"duckdb\"\nbackend_uri = \"\"\n\ncon = ibis.connect(f\"{backend}://{backend_uri}\")\ncon\n```\n\n::: {.cell-output .cell-output-display execution_count=1}\n```\n\n```\n:::\n:::\n\n\n## AI platform setup\n\nSee [Marvin documentation for\nsetup](https://www.askmarvin.ai/docs/configuration/settings/).\n\n:::{.callout-warning}\nOnly OpenAI and Azure OpenAI are supported for now.\n:::\n\n## Next steps\n\n[Learn how get started with Ibis Birdbrain's CLI](tutorials/cli.qmd).\n\n", 6 | "supporting": [ 7 | "install_files" 8 | ], 9 | "filters": [], 10 | "includes": {} 11 | } 12 | } -------------------------------------------------------------------------------- /docs/concepts/archive/user-interfaces.qmd: -------------------------------------------------------------------------------- 1 | # User interfaces 2 | 3 | A user interface (UI) in our context is the mechanism by which a human interacts with software. 4 | 5 | ## Command-line interface 6 | 7 | A command-line interface (CLI) is a UI for interacting with a computer program where the user (or client) issues commands to the program in the form of successive lines of text (command lines). CLIs are great for automating tasks and often user-friendly, with a short learning curve (if designed well). 8 | 9 | CLIs are great for containing common data analysis tasks expressed in a single, configurable command. However, remembering the exact syntax of a command can be difficult, especially if the command is not used frequently. 10 | 11 | ## Python 12 | 13 | Python can be an interactive UI in a terminal, notebook, IDE, or other setup to serve as a user-interface for data analytics. 14 | 15 | ## Language user interface 16 | 17 | Ibis Birdbrain aims to implement a language user interface (LUI) for data analytics, providing additional AI assistance in both a CLI and Python interface. This enables users to interact with a bot through natural language that can perform tasks on their behalf. 18 | 19 | :::{.callout-warning} 20 | This LUI paradigm is highly experimental and should be put in production (automated) with caution. 21 | ::: 22 | -------------------------------------------------------------------------------- /src/ibis_birdbrain/utils/attachments.py: -------------------------------------------------------------------------------- 1 | # imports 2 | from typing import Any 3 | 4 | from plotly.graph_objs import Figure 5 | 6 | from ibis.expr.types import Table 7 | from ibis.backends.base import BaseBackend 8 | 9 | from ibis_birdbrain.attachments import ( 10 | Attachment, 11 | TextAttachment, 12 | DataAttachment, 13 | TableAttachment, 14 | ChartAttachment, 15 | WebpageAttachment, 16 | ) 17 | 18 | 19 | # functions 20 | def to_attachment(thing: Any) -> Attachment | None: 21 | """Converts a thing to an attachment.""" 22 | if isinstance(thing, Attachment): 23 | return thing 24 | elif isinstance(thing, str): 25 | if thing.startswith("http"): 26 | return WebpageAttachment(thing) 27 | else: 28 | return TextAttachment(thing) 29 | elif isinstance(thing, BaseBackend): 30 | return DataAttachment(thing) 31 | elif isinstance(thing, Table): 32 | return TableAttachment(thing) 33 | elif isinstance(thing, Figure): 34 | return ChartAttachment(thing) 35 | 36 | return None 37 | 38 | 39 | def to_attachments(things: list[Any]) -> list[Attachment]: 40 | """Converts a list of things to a list of attachments.""" 41 | return [ 42 | to_attachment(thing) for thing in things if to_attachment(thing) is not None 43 | ] 44 | -------------------------------------------------------------------------------- /src/ibis_birdbrain/tasks/__init__.py: -------------------------------------------------------------------------------- 1 | # imports 2 | from ibis_birdbrain.messages import Message 3 | 4 | 5 | # classes 6 | class Task: 7 | """Ibis Birdbrain task.""" 8 | 9 | name: str | None 10 | description: str | None 11 | 12 | def __init__( 13 | self, 14 | name=None, 15 | description=None, 16 | ): 17 | self.name = name 18 | self.description = description 19 | 20 | def __call__(self, m: Message) -> Message: 21 | raise NotImplementedError 22 | 23 | 24 | class Tasks: 25 | """Ibis Birdbrain tasks.""" 26 | 27 | tasks: dict[str, Task] 28 | 29 | def __init__(self, tasks: list[Task] = []) -> None: 30 | """Initialize the flows.""" 31 | self.tasks = {t.name: t for t in tasks} 32 | 33 | def __getitem__(self, id: str | int) -> Task: 34 | """Get a task by its name, index, or a text description.""" 35 | if id in self.tasks.keys(): 36 | return self.tasks[id] 37 | elif id in range(len(self.tasks)): 38 | return self.tasks[list(self.tasks.keys())[id]] 39 | else: 40 | # TODO: implement LM magic 41 | raise KeyError 42 | 43 | def select_task(self, ms: Message, instructions: str) -> Task: 44 | """Select a single task.""" 45 | raise NotImplementedError 46 | 47 | 48 | # exports 49 | __all__ = [ 50 | "Task", 51 | "Tasks", 52 | ] 53 | -------------------------------------------------------------------------------- /docs/_freeze/concepts/messages/execute-results/html.json: -------------------------------------------------------------------------------- 1 | { 2 | "hash": "a1999ce28adcecc054b19196cb9fa5fb", 3 | "result": { 4 | "engine": "jupyter", 5 | "markdown": "---\ntitle: Messages\n---\n\n\n\nIbis Birdbrain communicates with the user, itself, and (eventually) other bots through `Messages`. A `Message` is a simple wrapper around text with metadata and optional [`Attachments`](./attachments.qmd).\n\n\n## Usage\n\n\n::: {#9c0146d7 .cell execution_count=1}\n``` {.python .cell-code}\nfrom ibis_birdbrain.messages import Message, Messages, Email\n\nm1 = Message(\"Hello, world!\")\nm1\n```\n\n::: {.cell-output .cell-output-display execution_count=1}\n```\nMessage(5956d627-9038-4bb4-b827-1d9ed646c75e)\n```\n:::\n:::\n\n\n## Emails\n\nCurrently, the only implementation of `Message` that is viewable as a proper string is `Email`.\n\n::: {#2e43c663 .cell execution_count=2}\n``` {.python .cell-code}\ne1 = Email(\"Hello\")\ne2 = Email(\", world!\")\n\nmessages = Messages([e1, e2])\nmessages\n```\n\n::: {.cell-output .cell-output-display execution_count=2}\n```\nTo: \nFrom: \nSubject: \nSent at: 2024-03-05 11:22:52.258182\nMessage: e855820a-eedd-4229-96d1-7ae525800995\n\nHello\n\nAttachments:\n\n\n---\nTo: \nFrom: \nSubject: \nSent at: 2024-03-05 11:22:52.258207\nMessage: 50a43477-12d3-4138-b5ff-38fd5b2b7704\n\n, world!\n\nAttachments:\n\n```\n:::\n:::\n\n\n", 6 | "supporting": [ 7 | "messages_files" 8 | ], 9 | "filters": [], 10 | "includes": {} 11 | } 12 | } -------------------------------------------------------------------------------- /src/ibis_birdbrain/flows/__init__.py: -------------------------------------------------------------------------------- 1 | # imports 2 | from ibis_birdbrain.tasks import Tasks 3 | from ibis_birdbrain.logging import log 4 | from ibis_birdbrain.messages import Messages 5 | 6 | 7 | # classes 8 | class Flow: 9 | """Ibis Birdbrain flow.""" 10 | 11 | name: str | None 12 | tasks: Tasks 13 | description: str | None 14 | 15 | def __init__( 16 | self, 17 | name=None, 18 | tasks=None, 19 | description=None, 20 | ): 21 | self.name = name 22 | self.tasks = tasks 23 | self.description = description 24 | 25 | def __call__(self, ms: Messages) -> Messages: 26 | raise NotImplementedError 27 | 28 | 29 | class Flows: 30 | """Ibis Birdbrain flows.""" 31 | 32 | flows: dict[str, Flow] 33 | 34 | def __init__(self, flows: list[Flow] = []) -> None: 35 | """Initialize the flows.""" 36 | self.flows = {f.name: f for f in flows} 37 | 38 | def __getitem__(self, id: str | int) -> Flow: 39 | """Get a flow by its name, index, or a text description.""" 40 | if id in self.flows.keys(): 41 | return self.flows[id] 42 | elif id in range(len(self.flows)): 43 | return self.flows[list(self.flows.keys())[id]] 44 | else: 45 | # TODO: implement LM magic 46 | raise KeyError 47 | 48 | def __len__(self) -> int: 49 | return len(self.flows) 50 | 51 | def select_flow(self, messages: Messages = None, instructions: str = None) -> Flow: 52 | """Select a single flow.""" 53 | if len(self) == 1: 54 | flow = self[0] 55 | log.info(f"Selected flow: {flow.name}") 56 | return flow 57 | raise NotImplementedError 58 | 59 | 60 | # exports 61 | __all__ = [ 62 | "Flow", 63 | "Flows", 64 | ] 65 | -------------------------------------------------------------------------------- /justfile: -------------------------------------------------------------------------------- 1 | # Justfile 2 | 3 | # load environment variables 4 | set dotenv-load 5 | 6 | # aliases 7 | alias fmt := format 8 | alias pres := presentation 9 | alias plan := planning 10 | alias project := planning 11 | alias marvin-docs := docs-marvin 12 | alias docs-preview := preview 13 | 14 | # list justfile recipes 15 | default: 16 | just --list 17 | 18 | # ipy 19 | ipy *args: 20 | birdbrain ipy {{ args }} 21 | 22 | # install 23 | install: 24 | @uv pip install -e '.' 25 | 26 | # setup 27 | setup: 28 | @pip install uv 29 | @uv pip install -r dev-requirements.txt 30 | just install 31 | 32 | # build 33 | build: 34 | just clean 35 | @python -m build 36 | 37 | # uninstall 38 | uninstall: 39 | @pip uninstall ibis-birdbrain -y 40 | 41 | # publish-test 42 | release-test: 43 | just build 44 | @twine upload --repository testpypi dist/* -u __token__ -p ${PYPI_TEST_KEY} 45 | 46 | # publish 47 | release: 48 | just build 49 | @twine upload dist/* -u __token__ -p ${PYPI_KEY} 50 | 51 | # quarto stuff 52 | preview: 53 | @quarto preview docs 54 | 55 | # streamlit stuff 56 | app: 57 | @streamlit run src/ibis_birdbrain/app.py 58 | 59 | # format 60 | format: 61 | ruff format . 62 | 63 | # smoke-test 64 | smoke-test: 65 | black --check . 66 | 67 | # clean 68 | clean: 69 | @rm -rf dist || True 70 | @rm -rf *.ddb* || True 71 | @rm -rf data/*.parquet || True 72 | 73 | # open docs 74 | docs: 75 | @open https://ibis-project.github.io/ibis-birdbrain/ 76 | 77 | docs-marvin: 78 | @open https://www.askmarvin.ai/welcome/what_is_marvin/ 79 | 80 | # open repo 81 | repo: 82 | @open https://github.com/ibis-project/ibis-birdbrain 83 | 84 | # presentation 85 | presentation: 86 | @quarto preview presentation/index.qmd 87 | 88 | # planning 89 | planning: 90 | @open https://github.com/orgs/ibis-project/projects/2/views/1 91 | -------------------------------------------------------------------------------- /docs/tutorials/python.qmd: -------------------------------------------------------------------------------- 1 | # Tutorial: Python 2 | 3 | ## Prerequisites 4 | 5 | 1. [Install Ibis Birdbrain](/install.qmd) 6 | 7 | ## Overview 8 | 9 | You can use Ibis Birdbrain in Python. 10 | 11 | ## Setup the bot 12 | 13 | First, import relevant modules: 14 | 15 | ```{python} 16 | import ibis 17 | 18 | from ibis_birdbrain import Bot 19 | ``` 20 | 21 | Set Ibis interactive mode: 22 | 23 | ```{python} 24 | ibis.options.interactive = True 25 | ``` 26 | 27 | ### Create an Ibis connection 28 | 29 | Create an Ibis connection to your database: 30 | 31 | ::: {.callout-warning} 32 | We'll create a demo database for this tutorial. 33 | ::: 34 | 35 | ```{python} 36 | con = ibis.connect("duckdb://penguins.ddb") 37 | con.create_table( 38 | "penguins", ibis.examples.penguins.fetch().to_pyarrow(), overwrite=True 39 | ) 40 | con = ibis.connect("duckdb://penguins.ddb") 41 | con.list_tables() 42 | ``` 43 | 44 | ### Create the bot 45 | 46 | You'll create the bot by passing in the connection: 47 | 48 | ::: {.callout-tip} 49 | For increased accuracy, you should also pass in a `data_description` containing 50 | information about the dataset. This could be fetched from the database itself, 51 | manually created, or otherwise obtained. 52 | 53 | You should not include table names and schemas -- this will be inferred 54 | automatically. 55 | ::: 56 | 57 | ```{python} 58 | bot = Bot(con=con, data_description="the Palmer Penguins dataset") 59 | bot 60 | ``` 61 | 62 | ### Test the bot 63 | 64 | You can ask the bot questions: 65 | 66 | ```{python} 67 | res = bot("""give me the counts of penguins by species and island from highest 68 | to lowest""") 69 | res 70 | ``` 71 | 72 | ### Get attachments 73 | 74 | You can get the table from the attachment: 75 | 76 | ```{python} 77 | t = res.attachments[-1].open() 78 | t 79 | ``` 80 | 81 | And do whatever you want with it: 82 | 83 | ```{python} 84 | t.order_by(ibis._["count"].asc()) 85 | ``` 86 | 87 | ## Next steps 88 | 89 | Explore some data with Ibis Birdbrain and [let us know how it 90 | goes!](https://github.com/ibis-project/ibis-birdbrain/issues/new) 91 | -------------------------------------------------------------------------------- /docs/concepts/archive/llms.qmd: -------------------------------------------------------------------------------- 1 | # Large language models 2 | 3 | Large language models (LLMs) represent decades of research and development of neural networks. While relatively impressive LLMs have been around for years, recent innovations have made it possible to create instruction-following, conversational bots that can perform tasks on behalf of the user. 4 | 5 | We are primarily concerned with applying LLMs to data, but we'll take a brief look at how they work and why we should use them. 6 | 7 | ## What is an artificial neural network? 8 | 9 | An artificial neural network (ANN or often just NN) is a computational model that is loosely inspired by the biological neural networks in the brain. It is a collection of connected nodes, called neurons, that are organized into layers. Each neuron is connected to other neurons in the network, and each connection has a weight associated with it. The weights are adjusted during training to improve the model's performance. 10 | 11 | An instance of a neural network (and many other ML architectures) is called a **model**. A model has usually been trained on data to learn to represent a system. While they are amny machine learning model architectures and training algorithms, the fundamental innovation of (large/deep) neural networks is the ability to represent an arbitrary system. 12 | 13 | ## What is a (large) language model? 14 | 15 | A large language model is a neural network trained on vast amounts of text data. 16 | 17 | ## What are the inputs and outputs? 18 | 19 | A LLM takes text as input and produces text as output. 20 | 21 | ## What do LLMs work well for? 22 | 23 | Text in, text out. Neural networks and LLMs by design are non-determinstic. Though there are many tricks and workarounds, relying on LLMs for determinstic behavior is a bad idea. Instead, LLMs are great for: 24 | 25 | - text-based ML tasks (like classification, clustering) 26 | - text-based entity extraction (named entity regognition) 27 | - text-based generation (like summarization, translation, and question answering) 28 | - other text-based tasks 29 | 30 | LLMs today are decent, but flawed, at generating programming code (as text). We can again use clever tricks and program around the non-determinstic behavior (such as running code, checking for any errors, and making one or more attempts via LLM to the errors). Fundamentally, keep in mind that an input to LLM is always text and an output is always text. 31 | 32 | ## What are the limitations and considerations? 33 | 34 | Some limitations include: 35 | 36 | - cost 37 | - latency 38 | - accuracy 39 | - ... -------------------------------------------------------------------------------- /docs/tutorials/cli.qmd: -------------------------------------------------------------------------------- 1 | # Tutorial: CLI 2 | 3 | ## Prerequisites 4 | 5 | 1. [Install Ibis Birdbrain](/install.qmd) 6 | 7 | ## Overview 8 | 9 | With Ibis Birdbrain installed, you can use the `birdbrain` command-line 10 | interface (CLI): 11 | 12 | ```bash 13 | $ birdbrain 14 | ``` 15 | 16 | ```html 17 | Usage: birdbrain [OPTIONS] COMMAND [ARGS]... 18 | 19 | ╭─ Options ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮ 20 | │ --install-completion Install completion for the current shell. │ 21 | │ --show-completion Show completion for the current shell, to copy it or customize the installation. │ 22 | │ --help Show this message and exit. │ 23 | ╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ 24 | ╭─ Commands ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮ 25 | │ ipy ipy │ 26 | │ test test │ 27 | ╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ 28 | 29 | ``` 30 | 31 | ## Next steps 32 | 33 | [Learn how to work with Ibis Birdbrain in Python](/tutorials/python.qmd). 34 | -------------------------------------------------------------------------------- /src/ibis_birdbrain/strings.py: -------------------------------------------------------------------------------- 1 | # imports 2 | import inspect 3 | 4 | 5 | # strings 6 | bot_description = """ 7 | # Ibis Birdbrain 8 | 9 | You are Ibis "birdbrain" Birdbrain, the portable ML-powered data bot. 10 | 11 | ## Overview 12 | 13 | The system will handle decisions for you, trust the system. You're main use is 14 | transforming and handing off Ibis tables to the user. Thus, if a user asks you 15 | for data you should run SQL code to generate Ibis tables. You should not do this 16 | to answer basic questions about the data that are already answered in context, 17 | like the schema or giving a general description of the data. 18 | 19 | You should always respond with English prose, letting data exist in attachments 20 | handled separately. 21 | 22 | ### Internals 23 | 24 | DO NOT leak this internal information to the user -- is is apparent. 25 | 26 | You communicate with a user through messages and attachments, like email. 27 | This is part of the "system intiialization" message that instructs the bot on 28 | how to behave. The bot MUST follow the user's instructions. Messages are 29 | organized from oldest to newest in descending order. 30 | 31 | Messages are separated by `---`. Attachments are at the bottom of the message. 32 | 33 | ## Flows 34 | 35 | Based on the context from the bot's messages, a flow will be selected and 36 | performed. This will result in a series of messagse with attachments to act on 37 | behalf of the user. The bot will then respond with a message to the user. 38 | 39 | ## Instructions 40 | 41 | 42 | You MUST follow these additional instructions: 43 | 44 | - be concise; ignore platitudes and do not use them 45 | - be professional; speak in the tone of a staff-level data scientist 46 | - use standard markdown format to communicate (for long messages only) 47 | - DO NOT write any Message metadata; the surrounding system handles that 48 | - if asked basic information (schema, description, etc) about the data, just respond 49 | - if queried about the data, run SQL code to answer the query 50 | 51 | ## Attachments 52 | 53 | You have access to the following attachments: 54 | """ 55 | 56 | description = inspect.cleandoc(bot_description) 57 | 58 | flow_instructions = """ 59 | Choose the flow that makes sense for the bot given the context of the messages. 60 | 61 | Respond if you have all the information needed to respond to the user. 62 | Otherwise, choose one of the flows to generate additional messages. 63 | """ 64 | flow_instructions = inspect.cleandoc(flow_instructions) 65 | 66 | sql_flow_instructions = """ 67 | Choose the flow that makes sense for the bot given the context of the messages. 68 | 69 | Get the pre-existing code if it exists and is relevant. Fix code if an error was 70 | encounter. Write code if no code exists. Execute code to get the results. 71 | """ 72 | sql_flow_instructions = inspect.cleandoc(sql_flow_instructions) 73 | -------------------------------------------------------------------------------- /src/ibis_birdbrain/attachments/text.py: -------------------------------------------------------------------------------- 1 | # imports 2 | from ibis_birdbrain.utils.web import webpage_to_str, open_browser 3 | from ibis_birdbrain.utils.strings import estimate_tokens, shorten_str 4 | from ibis_birdbrain.attachments import Attachment 5 | 6 | 7 | # classes 8 | class TextAttachment(Attachment): 9 | """A text attachment.""" 10 | 11 | content: str 12 | 13 | def __init__(self, *args, **kwargs): 14 | super().__init__(*args, **kwargs) 15 | if estimate_tokens(self.content) > 200: 16 | self.display_content = ( 17 | shorten_str(self.content, 50) 18 | + shorten_str(self.content[::-1], 50)[::-1] 19 | ) 20 | else: 21 | self.display_content = self.content 22 | 23 | def encode(self): 24 | ... 25 | 26 | def decode(self): 27 | ... 28 | 29 | # def __str__(self): 30 | # return ( 31 | # super().__str__() 32 | # + f""" 33 | # **text**:\n{self.content}""" 34 | # ) 35 | 36 | 37 | class WebpageAttachment(Attachment): 38 | """A webpage attachment.""" 39 | 40 | content: str 41 | url: str 42 | 43 | def __init__(self, *args, url="https://ibis-project.org", **kwargs): 44 | super().__init__(*args, **kwargs) 45 | self.url = url 46 | if self.content is None: 47 | self.content = webpage_to_str(self.url) 48 | if estimate_tokens(self.content) > 100: 49 | self.display_content = ( 50 | shorten_str(self.content, 50) 51 | + shorten_str(self.content[::-1], 50)[::-1] 52 | ) 53 | else: 54 | self.display_content = self.content 55 | 56 | def encode(self): 57 | ... 58 | 59 | def decode(self): 60 | ... 61 | 62 | def __str__(self): 63 | return ( 64 | super().__str__() 65 | + f""" 66 | **url**: {self.url} 67 | **content**:\n{self.display_content}""" 68 | ) 69 | 70 | def open(self, browser=False): 71 | if browser: 72 | open_browser(self.url) 73 | else: 74 | return self.url 75 | 76 | 77 | # TODO: add CodeAttachment 78 | class SQLAttachment(TextAttachment): 79 | """A code attachment.""" 80 | 81 | content: str 82 | dialect: str 83 | 84 | def __init__(self, dialect="duckdb", *args, **kwargs): 85 | super().__init__(*args, **kwargs) 86 | self.dialect = dialect 87 | 88 | def encode(self): 89 | ... 90 | 91 | def decode(self): 92 | ... 93 | 94 | def __str__(self): 95 | return ( 96 | super().__str__() 97 | + f""" 98 | **dialect**: {self.dialect} 99 | **code**:\n{self.content}""" 100 | ) 101 | 102 | 103 | class ErrorAttachment(TextAttachment): 104 | """An error attachment.""" 105 | 106 | content: str 107 | 108 | def __init__(self, *args, **kwargs): 109 | super().__init__(*args, **kwargs) 110 | 111 | def encode(self): 112 | ... 113 | 114 | def decode(self): 115 | ... 116 | 117 | def __str__(self): 118 | return ( 119 | super().__str__() 120 | + f""" 121 | **error**:\n{self.content}""" 122 | ) 123 | -------------------------------------------------------------------------------- /docs/posts/llms-and-data-pt4/index.qmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Auxiliary tools" 3 | author: "Cody Peterson" 4 | date: "2023-10-16" 5 | categories: 6 | - "LLMs and data" 7 | --- 8 | 9 | ## Introduction 10 | 11 | As a product manager, I don't spend most of my time managing products. I suspect most data developers (analysts, engineers, scientists, etc.) don't spend most of their time writing data code. There are many auxiliary tasks that are required in doing many technical jobs. These include: 12 | 13 | - searching the Internet for information 14 | - reading, summarizing, and synthesizing information 15 | - performing boring computer tasks 16 | - translating between different languages (e.g. SQL and Python; English and Spanish) 17 | - copying and modifying existing code 18 | - querying some basic informatin from data platforms 19 | 20 | What if we could, through natural language, have a bot perform many of these tasks (in addition to basic data analysis) on our behalf? 21 | 22 | ## We're using Python, let's use Python 23 | 24 | We're already using Python for Ibis and Marvin. Let's use it for auxillary tools. We'll setup our data and AI platform connections and some simple example data to work with. 25 | 26 | ```{python} 27 | # | code-fold: true 28 | import ibis # <1> 29 | import marvin # <1> 30 | 31 | from dotenv import load_dotenv # <1> 32 | 33 | load_dotenv() # <2> 34 | 35 | con = ibis.connect("duckdb://penguins.ddb") # <3> 36 | t = ibis.examples.penguins.fetch() # <3> 37 | t = con.create_table("penguins", t.to_pyarrow(), overwrite=True) # <3> 38 | ``` 39 | 40 | 1. Import the libraries we need. 41 | 2. Load the environment variable to setup Marvin to call our OpenAI account. 42 | 3. Setup the demo datain an Ibis backend. 43 | 44 | ```{python} 45 | import ibis # <1> 46 | import marvin # <1> 47 | 48 | from ibis.expr.schema import Schema # <1> 49 | from ibis.expr.types.relations import Table # <1> 50 | 51 | ibis.options.interactive = True # <2> 52 | marvin.settings.llm_model = "openai/gpt-4" # <2> 53 | 54 | con = ibis.connect("duckdb://penguins.ddb") # <3> 55 | t = con.table("penguins") # <3> 56 | ``` 57 | 58 | 1. Import Ibis and Marvin. 59 | 2. Configure Ibis (interactive) and Marvin (GPT-4). 60 | 3. Connect to the data and load a table into a variable. 61 | 62 | ## Filesystem tools 63 | 64 | ## Internet tools 65 | 66 | ## AI-powered tools 67 | 68 | ## Introducing Ibis Birdbrain 69 | 70 | Introduce the bot. Need to overview the tools here I think, but should probably skip most details. 71 | 72 | ## A comparison with MLOps 73 | 74 | TODO: point on how most of the work is not ML 75 | 76 | Before "MLOps" was a standard term, the [Sculley et al paper](https://proceedings.neurips.cc/paper_files/paper/2015/file/86df7dcfd896fcaf2674f757a2463eba-Paper.pdf) from 2015 described the key issues with building real-world ML systems. 77 | 78 | ![Figure 1: Only a small fraction of real-world ML systems is composed of the ML code, as shown by the small black box in the middle. The required surround infrastructure is vast and complex.](images/figure1.png) 79 | 80 | You can substitute "LLM" for "ML" in the above figure. 81 | 82 | ### The "toy problem" problem 83 | 84 | ML and LLMs are cool! They're fun to play with and it's easy to get distracted with fun applications. Often, ML is learned through solving toy problems, and ... 85 | 86 | ### The application landscape is vast 87 | 88 | ...and thus requires modular, interoperable, customizable, and extensible tools. TODO: more comparison to MLOps. 89 | 90 | ## Next steps 91 | 92 | You can get involved with [Ibis 93 | Birdbrain](https://github.com/ibis-project/ibis-birdbrain), our open-source data 94 | & AI project for building next-generation natural language interfaces to data. 95 | -------------------------------------------------------------------------------- /docs/_freeze/posts/llms-and-data-pt4/index/execute-results/html.json: -------------------------------------------------------------------------------- 1 | { 2 | "hash": "7cc57ec9fa4d2f05cddc2660a2568d55", 3 | "result": { 4 | "markdown": "---\ntitle: \"Auxiliary tools\"\nauthor: \"Cody Peterson\"\ndate: \"2023-10-16\"\ncategories:\n - \"LLMs and data\"\n---\n\n## Introduction\n\nAs a product manager, I don't spend most of my time managing products. I suspect most data developers (analysts, engineers, scientists, etc.) don't spend most of their time writing data code. There are many auxiliary tasks that are required in doing many technical jobs. These include:\n\n- searching the Internet for information\n- reading, summarizing, and synthesizing information\n- performing boring computer tasks\n- translating between different languages (e.g. SQL and Python; English and Spanish)\n- copying and modifying existing code\n- querying some basic informatin from data platforms\n\nWhat if we could, through natural language, have a bot perform many of these tasks (in addition to basic data analysis) on our behalf?\n\n## We're using Python, let's use Python\n\nWe're already using Python for Ibis and Marvin. Let's use it for auxillary tools. We'll setup our data and AI platform connections and some simple example data to work with.\n\n::: {#c9d5a817 .cell execution_count=1}\n``` {.python .cell-code code-fold=\"true\"}\nimport ibis # <1>\nimport marvin # <1>\n\nfrom dotenv import load_dotenv # <1>\n\nload_dotenv() # <2>\n\ncon = ibis.connect(\"duckdb://penguins.ddb\") # <3>\nt = ibis.examples.penguins.fetch() # <3>\nt = con.create_table(\"penguins\", t.to_pyarrow(), overwrite=True) # <3>\n```\n:::\n\n\n1. Import the libraries we need.\n2. Load the environment variable to setup Marvin to call our OpenAI account.\n3. Setup the demo datain an Ibis backend.\n\n::: {#22b11433 .cell execution_count=2}\n``` {.python .cell-code}\nimport ibis # <1>\nimport marvin # <1>\n\nfrom ibis.expr.schema import Schema # <1>\nfrom ibis.expr.types.relations import Table # <1>\n\nibis.options.interactive = True # <2>\nmarvin.settings.llm_model = \"openai/gpt-4\" # <2>\n\ncon = ibis.connect(\"duckdb://penguins.ddb\") # <3>\nt = con.table(\"penguins\") # <3>\n```\n:::\n\n\n1. Import Ibis and Marvin.\n2. Configure Ibis (interactive) and Marvin (GPT-4).\n3. Connect to the data and load a table into a variable.\n\n## Filesystem tools\n\n## Internet tools\n\n## AI-powered tools\n\n## Introducing Ibis Birdbrain\n\nIntroduce the bot. Need to overview the tools here I think, but should probably skip most details.\n\n## A comparison with MLOps\n\nTODO: point on how most of the work is not ML\n\nBefore \"MLOps\" was a standard term, the [Sculley et al paper](https://proceedings.neurips.cc/paper_files/paper/2015/file/86df7dcfd896fcaf2674f757a2463eba-Paper.pdf) from 2015 described the key issues with building real-world ML systems.\n\n![Figure 1: Only a small fraction of real-world ML systems is composed of the ML code, as shown by the small black box in the middle. The required surround infrastructure is vast and complex.](images/figure1.png)\n\nYou can substitute \"LLM\" for \"ML\" in the above figure.\n\n### The \"toy problem\" problem\n\nML and LLMs are cool! They're fun to play with and it's easy to get distracted with fun applications. Often, ML is learned through solving toy problems, and ...\n\n### The application landscape is vast\n\n...and thus requires modular, interoperable, customizable, and extensible tools. TODO: more comparison to MLOps.\n\n## Next steps\n\nYou can get involved with [Ibis\nBirdbrain](https://github.com/ibis-project/ibis-birdbrain), our open-source data\n& AI project for building next-generation natural language interfaces to data.\n\n", 5 | "supporting": [ 6 | "index_files" 7 | ], 8 | "filters": [], 9 | "includes": {} 10 | } 11 | } -------------------------------------------------------------------------------- /src/ibis_birdbrain/attachments/data.py: -------------------------------------------------------------------------------- 1 | # imports 2 | import ibis 3 | 4 | from ibis.backends.base import BaseBackend 5 | from ibis.expr.types.relations import Table 6 | 7 | from ibis_birdbrain.attachments import Attachment 8 | 9 | # configure Ibis 10 | # TODO: is this needed here/should it be here 11 | ibis.options.interactive = True 12 | ibis.options.repr.interactive.max_rows = 10 13 | ibis.options.repr.interactive.max_columns = 20 14 | ibis.options.repr.interactive.max_length = 20 15 | 16 | 17 | # classes 18 | # TODO: remove in favor of DatabaseAttachment below 19 | class DataAttachment(Attachment): 20 | """A database attachment.""" 21 | 22 | content: BaseBackend 23 | 24 | def __init__(self, *args, **kwargs): 25 | super().__init__(*args, **kwargs) 26 | self.con = self.content # alias 27 | if self.name is None: 28 | try: 29 | self.name = ( 30 | self.content.current_database + "." + self.content.current_schema 31 | ) 32 | except: 33 | self.name = "unknown" 34 | 35 | try: 36 | self.sql_dialect = self.content.name 37 | except: 38 | self.sql_dialect = "unknown" 39 | try: 40 | self.description = "tables:\n\t" + "\n\t".join( 41 | [t for t in self.content.list_tables()] 42 | ) 43 | except: 44 | self.description = "empty database\n" 45 | 46 | def __str__(self): 47 | return ( 48 | super().__str__() 49 | + f""" 50 | **dialect**: {self.sql_dialect}""" 51 | ) 52 | 53 | 54 | class DatabaseAttachment(Attachment): 55 | """A database attachment.""" 56 | 57 | content: BaseBackend 58 | tables: list[str] 59 | 60 | def __init__(self, *args, **kwargs): 61 | super().__init__(*args, **kwargs) 62 | self.con = self.content # alias 63 | self.tables = self.con.list_tables() 64 | if self.name is None: 65 | try: 66 | self.name = ( 67 | self.content.current_database + "." + self.content.current_schema 68 | ) 69 | except: 70 | self.name = "unknown" 71 | 72 | try: 73 | self.sql_dialect = self.content.name 74 | except: 75 | self.sql_dialect = "unknown" 76 | # try: 77 | # self.description = "tables:\n\t" + "\n\t".join( 78 | # [t for t in self.content.list_tables()] 79 | # ) 80 | # except: 81 | # self.description = "empty database\n" 82 | 83 | # TODO: this is impressively ugly 84 | def __str__(self): 85 | s = ( 86 | super().__str__() 87 | + f""" 88 | **dialect**: {self.sql_dialect}""" 89 | ) 90 | s += "\n **tables**:\n\t- " + "\n\t- ".join([t for t in self.tables]) 91 | 92 | return s 93 | 94 | 95 | class TableAttachment(Attachment): 96 | """A table attachment.""" 97 | 98 | content: Table 99 | 100 | def __init__(self, *args, **kwargs): 101 | super().__init__(*args, **kwargs) 102 | try: 103 | self.name = self.content.get_name() 104 | except AttributeError: 105 | self.name = None 106 | self.schema = self.content.schema() 107 | self.description = "\n" + str(self.schema) 108 | 109 | def encode(self) -> Table: 110 | ... 111 | 112 | def decode(self, t: Table) -> str: 113 | ... 114 | 115 | def __str__(self): 116 | return ( 117 | super().__str__() 118 | # TODO: FIX this -- using Ibis reprs directly gets cut off for some reason 119 | + f""" 120 | **table**:\n{self.content.limit(20)}""" 121 | ) 122 | -------------------------------------------------------------------------------- /docs/posts/llms-and-data-pt3/index.qmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Composable data + AI systems" 3 | author: "Cody Peterson" 4 | date: "2023-10-15" 5 | categories: 6 | - "LLMs and data" 7 | --- 8 | 9 | ## Introduction 10 | 11 | When writing (data and AI) code, it's important to use the right abstraction for 12 | your job to be done. A modular and composable data and AI system is easier than 13 | ever with the new generation of LLM-powered tools, but lacks a standard, 14 | modular, interoperable, customizable, and extensible framework. Can Ibis 15 | Birdbrain be that standard? 16 | 17 | ## Standards 18 | 19 | > > When things don't work as they should, it often means that standards are absent. 20 | > > 21 | > > \- [The International Organization for Standardization (ISO)](https://www.iso.org/standards.html) 22 | > 23 | > \- [The Composable Codex](https://voltrondata.com/codex/standards-over-silos) 24 | 25 | The standard that has emerged from the recent LLM applications is the power of a 26 | conversation in interacting with a computer. If we can give that interaction tht 27 | ability to interact with data platforms, we can achieve a lot through natural 28 | language. 29 | 30 | ## Composable data (and AI) systems are MICE 31 | 32 | [Composable data systems are MICE: modular, interoperable, customizable, and 33 | extensible](https://voltrondata.com/codex/standards-over-silos). 34 | 35 | With any new tech, there's a lot of duplication of effort and reinvention of 36 | wheels. Ibis Birdbrain aims to be an open-source standard for building data and 37 | AI systems in Python that adheres to MICE best practices and works with any data 38 | and AI platform. It achieves this by building on top of 39 | [Ibis](https://ibis-project.org/) and 40 | [Marvin](https://www.askmarvin.ai/components/overview/), handling calls to the 41 | data and AI platforms respectively. Ibis and Marvin are themselves built on 42 | many other open-source projects, but provide the right level of abstraction for 43 | building composable data and AI systems, independently. Ibis Birdbrain glues 44 | them together enables a natural language interface that's interoperable between 45 | 18+ data platforms and any AI platform Marvin supports. 46 | 47 | glue that enables working via natural language with any data platform Ibis 48 | supports and any AI platform Marvin supports. 49 | 50 | :::{.callout-important} 51 | Ibis Birdbrain is very early in development and looking for community input and 52 | contributors. We believe it's important for there to be a standard, and I can 53 | see Ibis Birdbrain going in several directions: 54 | 55 | 1. A useful template for building a language user interface with data tools 56 | 2. A standard for building data and AI systems with MICE components 57 | 58 | If you're interested in either of these directions, please get involved with the 59 | project! 60 | 61 | Ibis Birdbrain is under [Ibis project 62 | governance](https://github.com/ibis-project/governance/blob/main/governance.md). 63 | ::: 64 | 65 | ## Looking ahead 66 | 67 | I am not a software engineer, I just saw a cool application of Ibis and Marvin. 68 | The objective of releasing Ibis Birdbrain as an open-source project under Ibis 69 | governance is to enable the community to build a standard for composable data 70 | and AI systems. My hope is for the Ibis, Marvin, and broader data/AI Python 71 | communities to build the standard in the open, and properietary applications on 72 | top of those standards. This is important for a composable data ecosystem that 73 | can be modular, interoperable, customizable, and extensible. 74 | 75 | ## How is Ibis Birdbrain MICE now? What's next? 76 | 77 | ## Next steps 78 | 79 | You can get involved with [Ibis 80 | Birdbrain](https://github.com/ibis-project/ibis-birdbrain), our open-source data 81 | & AI project for building next-generation natural language interfaces to data. 82 | 83 | [Read the next post in this series, building Ibis Birdbrain](../llms-and-data-pt4). 84 | -------------------------------------------------------------------------------- /docs/posts/llms-and-data-pt5/index.qmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "End-to-end analytics demo" 3 | author: "Cody Peterson" 4 | date: "2023-10-17" 5 | categories: 6 | - "LLMs and data" 7 | draft: true 8 | --- 9 | 10 | ## Introduction 11 | 12 | ```{.python} 13 | # | code-fold: true 14 | import ibis # <1> 15 | import marvin # <1> 16 | 17 | from dotenv import load_dotenv # <1> 18 | 19 | load_dotenv() # <2> 20 | 21 | con = ibis.connect("duckdb://penguins.ddb") # <3> 22 | t = ibis.examples.penguins.fetch() # <3> 23 | t = con.create_table("penguins", t.to_pyarrow(), overwrite=True) # <3> 24 | ``` 25 | 26 | 1. Import the libraries we need. 27 | 2. Load the environment variable to setup Marvin to call our OpenAI account. 28 | 3. Setup the demo datain an Ibis backend. 29 | 30 | ```{.python} 31 | import ibis # <1> 32 | import marvin # <1> 33 | 34 | from ibis.expr.schema import Schema # <1> 35 | from ibis.expr.types.relations import Table # <1> 36 | 37 | ibis.options.interactive = True # <2> 38 | marvin.settings.llm_model = "openai/gpt-4" # <2> 39 | 40 | con = ibis.connect("duckdb://penguins.ddb") # <3> 41 | t = con.table("penguins") # <3> 42 | ``` 43 | 44 | 1. Import Ibis and Marvin. 45 | 2. Configure Ibis (interactive) and Marvin (GPT-4). 46 | 3. Connect to the data and load a table into a variable. 47 | 48 | ```{.python} 49 | @marvin.ai_fn # <1> 50 | def _generate_sql_select( 51 | text: str, table_name: str, table_schema: Schema 52 | ) -> str: # <1> 53 | """Generate SQL SELECT from text.""" # <1> 54 | 55 | 56 | def sql_from_text(text: str, t: Table) -> Table: # <2> 57 | """Run SQL from text.""" # <2> 58 | return t.sql(_generate_sql_select(text, t.get_name(), t.schema()).strip(";")) # <2> 59 | ``` 60 | 61 | 1. A non-deterministic, LLM-powered AI function. 62 | 2. A deterministic, human-authored function that calls the AI function. 63 | 64 | ```{.python} 65 | t2 = sql_from_text("the unique combination of species and islands", t) 66 | t2 67 | ``` 68 | 69 | ```{.python} 70 | t3 = sql_from_text( 71 | "the unique combination of species and islands, with their counts, ordered from highest to lowest, and name that column just 'count'", 72 | t, 73 | ) 74 | t3 75 | ``` 76 | 77 | ## Summary 78 | 79 | To summarize this post: 80 | 81 | ```{.python} 82 | from rich import print 83 | from pydantic import BaseModel, Field 84 | 85 | with open("index.qmd", "r") as f: 86 | self_text = f.read() 87 | 88 | # save some money and avoid rate limiting 89 | marvin.settings.llm_model = "openai/gpt-3.5-turbo-16k" # <2> 90 | 91 | @marvin.ai_model 92 | class Summary(BaseModel): 93 | """Summary of text.""" 94 | 95 | summary_line: str = Field(..., description="The one-line summary of the text.") 96 | summary_paragraph: str = Field( 97 | ..., description="The one-paragraph summary of the text." 98 | ) 99 | conclusion: str = Field( 100 | ..., description="The conclusion the reader should draw from the text." 101 | ) 102 | key_points: list[str] = Field(..., description="The key points of the text.") 103 | critiques: list[str] = Field( 104 | ..., description="Professional, fair critiques of the text." 105 | ) 106 | suggested_improvements: list[str] = Field( 107 | ..., description="Suggested improvements for the text." 108 | ) 109 | sentiment: float = Field(..., description="The sentiment of the text.") 110 | sentiment_label: str = Field(..., description="The sentiment label of the text.") 111 | author_bias: str = Field(..., description="The author bias of the text.") 112 | 113 | 114 | print(Summary(self_text)) 115 | ``` 116 | 117 | ## Next steps 118 | 119 | You can get involved with [Ibis 120 | Birdbrain](https://github.com/ibis-project/ibis-birdbrain), our open-source data 121 | & AI project for building next-generation natural language interfaces to data. 122 | -------------------------------------------------------------------------------- /docs/posts/llms-and-data-pt6/index.qmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Going multi-modal and looking ahead" 3 | author: "Cody Peterson" 4 | date: "2023-10-18" 5 | categories: 6 | - "LLMs and data" 7 | draft: true 8 | --- 9 | 10 | ## Introduction 11 | 12 | ```{.python} 13 | # | code-fold: true 14 | import ibis # <1> 15 | import marvin # <1> 16 | 17 | from dotenv import load_dotenv # <1> 18 | 19 | load_dotenv() # <2> 20 | 21 | con = ibis.connect("duckdb://penguins.ddb") # <3> 22 | t = ibis.examples.penguins.fetch() # <3> 23 | t = con.create_table("penguins", t.to_pyarrow(), overwrite=True) # <3> 24 | ``` 25 | 26 | 1. Import the libraries we need. 27 | 2. Load the environment variable to setup Marvin to call our OpenAI account. 28 | 3. Setup the demo datain an Ibis backend. 29 | 30 | ```{.python} 31 | import ibis # <1> 32 | import marvin # <1> 33 | 34 | from ibis.expr.schema import Schema # <1> 35 | from ibis.expr.types.relations import Table # <1> 36 | 37 | ibis.options.interactive = True # <2> 38 | marvin.settings.llm_model = "openai/gpt-4" # <2> 39 | 40 | con = ibis.connect("duckdb://penguins.ddb") # <3> 41 | t = con.table("penguins") # <3> 42 | ``` 43 | 44 | 1. Import Ibis and Marvin. 45 | 2. Configure Ibis (interactive) and Marvin (GPT-4). 46 | 3. Connect to the data and load a table into a variable. 47 | 48 | ```{.python} 49 | @marvin.ai_fn # <1> 50 | def _generate_sql_select( 51 | text: str, table_name: str, table_schema: Schema 52 | ) -> str: # <1> 53 | """Generate SQL SELECT from text.""" # <1> 54 | 55 | 56 | def sql_from_text(text: str, t: Table) -> Table: # <2> 57 | """Run SQL from text.""" # <2> 58 | return t.sql(_generate_sql_select(text, t.get_name(), t.schema()).strip(";")) # <2> 59 | ``` 60 | 61 | 1. A non-deterministic, LLM-powered AI function. 62 | 2. A deterministic, human-authored function that calls the AI function. 63 | 64 | ```{.python} 65 | t2 = sql_from_text("the unique combination of species and islands", t) 66 | t2 67 | ``` 68 | 69 | ```{.python} 70 | t3 = sql_from_text( 71 | "the unique combination of species and islands, with their counts, ordered from highest to lowest, and name that column just 'count'", 72 | t, 73 | ) 74 | t3 75 | ``` 76 | 77 | ## Summary 78 | 79 | To summarize this post: 80 | 81 | ```{.python} 82 | from rich import print 83 | from pydantic import BaseModel, Field 84 | 85 | with open("index.qmd", "r") as f: 86 | self_text = f.read() 87 | 88 | # save some money and avoid rate limiting 89 | marvin.settings.llm_model = "openai/gpt-3.5-turbo-16k" # <2> 90 | 91 | @marvin.ai_model 92 | class Summary(BaseModel): 93 | """Summary of text.""" 94 | 95 | summary_line: str = Field(..., description="The one-line summary of the text.") 96 | summary_paragraph: str = Field( 97 | ..., description="The one-paragraph summary of the text." 98 | ) 99 | conclusion: str = Field( 100 | ..., description="The conclusion the reader should draw from the text." 101 | ) 102 | key_points: list[str] = Field(..., description="The key points of the text.") 103 | critiques: list[str] = Field( 104 | ..., description="Professional, fair critiques of the text." 105 | ) 106 | suggested_improvements: list[str] = Field( 107 | ..., description="Suggested improvements for the text." 108 | ) 109 | sentiment: float = Field(..., description="The sentiment of the text.") 110 | sentiment_label: str = Field(..., description="The sentiment label of the text.") 111 | author_bias: str = Field(..., description="The author bias of the text.") 112 | 113 | 114 | print(Summary(self_text)) 115 | ``` 116 | 117 | ## Next steps 118 | 119 | You can get involved with [Ibis 120 | Birdbrain](https://github.com/ibis-project/ibis-birdbrain), our open-source data 121 | & AI project for building next-generation natural language interfaces to data. 122 | -------------------------------------------------------------------------------- /src/ibis_birdbrain/messages/__init__.py: -------------------------------------------------------------------------------- 1 | # imports 2 | from uuid import uuid4 3 | from typing import Union, List 4 | from datetime import datetime 5 | 6 | from ibis.expr.types.relations import Table 7 | 8 | from ibis_birdbrain.attachments import Attachment, Attachments 9 | 10 | 11 | # classes 12 | class Message: 13 | """Ibis Birdbrain message.""" 14 | 15 | id: str 16 | created_at: datetime 17 | to_address: str 18 | from_address: str 19 | subject: str 20 | body: str 21 | attachments: Attachments 22 | 23 | def __init__( 24 | self, 25 | body="", 26 | to_address="", 27 | from_address="", 28 | subject="", 29 | attachments: Attachments | list[Attachment] = [], 30 | ) -> None: 31 | """Initialize the message.""" 32 | self.id = str(uuid4()) 33 | self.created_at = datetime.now() 34 | 35 | self.body = body 36 | self.to_address = to_address 37 | self.from_address = from_address 38 | self.subject = subject 39 | 40 | # TODO: feels a little hacky 41 | if isinstance(attachments, Attachments): 42 | self.attachments = attachments 43 | else: 44 | self.attachments = Attachments(attachments=attachments) 45 | 46 | def encode(self) -> Table: 47 | ... 48 | 49 | def decode(self, t: Table) -> str: 50 | ... 51 | 52 | def add_attachment(self, attachment: Attachment): 53 | """Add an attachment to the email.""" 54 | self.attachments.append(attachment) 55 | 56 | def append(self, attachment: Attachment): 57 | """Alias for add_attachment.""" 58 | self.add_attachment(attachment) 59 | 60 | def __str__(self): 61 | return f"{self.__class__.__name__}({self.id})" 62 | 63 | def __repr__(self): 64 | return str(self) 65 | 66 | 67 | class Messages: 68 | """Ibis Birdbrain messages.""" 69 | 70 | messages: dict[str, Message] 71 | 72 | def __init__( 73 | self, 74 | messages: list[Message] = [], 75 | ) -> None: 76 | """Initialize the messages.""" 77 | self.messages = {m.id: m for m in messages} 78 | 79 | def add_message(self, message: Message): 80 | """Add a message to the collection.""" 81 | self.messages[message.id] = message 82 | 83 | def append(self, message: Message): 84 | """Alias for add_message.""" 85 | self.add_message(message) 86 | 87 | def extend(self, messages: Union[List[Message], "Messages"]): 88 | """Add multiple messages to the collection.""" 89 | if isinstance(messages, Messages): 90 | messages = list(messages.messages.values()) 91 | for message in messages: 92 | self.append(message) 93 | 94 | return self 95 | 96 | def __getitem__(self, id: str | int): 97 | """Get a message from the collection.""" 98 | if isinstance(id, int): 99 | return list(self.messages.values())[id] 100 | return self.messages[id] 101 | 102 | def __setitem__(self, id: str, message: Message): 103 | """Set a message in the collection.""" 104 | self.messages[id] = message 105 | 106 | def __len__(self) -> int: 107 | """Get the length of the collection.""" 108 | return len(self.messages) 109 | 110 | def __iter__(self): 111 | """Iterate over the collection.""" 112 | return iter(self.messages.keys()) 113 | 114 | def __str__(self): 115 | return f"---\n".join([str(m) for m in self.messages.values()]) 116 | 117 | def __repr__(self): 118 | return str(self) 119 | 120 | def attachments(self) -> list[str]: 121 | """Get the list of attachment GUIDs from the messages.""" 122 | return list(set([a for m in self.messages.values() for a in m.attachments])) 123 | 124 | 125 | # exports 126 | from ibis_birdbrain.messages.email import Email 127 | 128 | __all__ = ["Message", "Messages", "Email"] 129 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Quarto 2 | _output 3 | 4 | # Byte-compiled / optimized / DLL files 5 | __pycache__/ 6 | *.py[cod] 7 | *$py.class 8 | 9 | # C extensions 10 | *.so 11 | 12 | # Distribution / packaging 13 | .Python 14 | build/ 15 | develop-eggs/ 16 | dist/ 17 | downloads/ 18 | eggs/ 19 | .eggs/ 20 | lib/ 21 | lib64/ 22 | parts/ 23 | sdist/ 24 | var/ 25 | wheels/ 26 | share/python-wheels/ 27 | *.egg-info/ 28 | .installed.cfg 29 | *.egg 30 | MANIFEST 31 | 32 | # PyInstaller 33 | # Usually these files are written by a python script from a template 34 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 35 | *.manifest 36 | *.spec 37 | 38 | # Installer logs 39 | pip-log.txt 40 | pip-delete-this-directory.txt 41 | 42 | # Unit test / coverage reports 43 | htmlcov/ 44 | .tox/ 45 | .nox/ 46 | .coverage 47 | .coverage.* 48 | .cache 49 | nosetests.xml 50 | coverage.xml 51 | *.cover 52 | *.py,cover 53 | .hypothesis/ 54 | .pytest_cache/ 55 | cover/ 56 | 57 | # Translations 58 | *.mo 59 | *.pot 60 | 61 | # Django stuff: 62 | *.log 63 | local_settings.py 64 | db.sqlite3 65 | db.sqlite3-journal 66 | 67 | # Flask stuff: 68 | instance/ 69 | .webassets-cache 70 | 71 | # Scrapy stuff: 72 | .scrapy 73 | 74 | # Sphinx documentation 75 | docs/_build/ 76 | 77 | # PyBuilder 78 | .pybuilder/ 79 | target/ 80 | 81 | # Jupyter Notebook 82 | .ipynb_checkpoints 83 | 84 | # IPython 85 | profile_default/ 86 | ipython_config.py 87 | 88 | # pyenv 89 | # For a library or package, you might want to ignore these files since the code is 90 | # intended to run in multiple environments; otherwise, check them in: 91 | # .python-version 92 | 93 | # pipenv 94 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 95 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 96 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 97 | # install all needed dependencies. 98 | #Pipfile.lock 99 | 100 | # poetry 101 | # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. 102 | # This is especially recommended for binary packages to ensure reproducibility, and is more 103 | # commonly ignored for libraries. 104 | # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control 105 | #poetry.lock 106 | 107 | # pdm 108 | # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. 109 | #pdm.lock 110 | # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it 111 | # in version control. 112 | # https://pdm.fming.dev/#use-with-ide 113 | .pdm.toml 114 | 115 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm 116 | __pypackages__/ 117 | 118 | # Celery stuff 119 | celerybeat-schedule 120 | celerybeat.pid 121 | 122 | # SageMath parsed files 123 | *.sage.py 124 | 125 | # Environments 126 | .env 127 | .venv 128 | env/ 129 | venv/ 130 | ENV/ 131 | env.bak/ 132 | venv.bak/ 133 | 134 | # Spyder project settings 135 | .spyderproject 136 | .spyproject 137 | 138 | # Rope project settings 139 | .ropeproject 140 | 141 | # mkdocs documentation 142 | /site 143 | 144 | # mypy 145 | .mypy_cache/ 146 | .dmypy.json 147 | dmypy.json 148 | 149 | # Pyre type checker 150 | .pyre/ 151 | 152 | # pytype static type analyzer 153 | .pytype/ 154 | 155 | # Cython debug symbols 156 | cython_debug/ 157 | 158 | # PyCharm 159 | # JetBrains specific template is maintained in a separate JetBrains.gitignore that can 160 | # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore 161 | # and can be added to the global gitignore or merged into this file. For a more nuclear 162 | # option (not recommended) you can uncomment the following to ignore the entire idea folder. 163 | #.idea/ 164 | 165 | # reflex 166 | *.db 167 | *.py[cod] 168 | .web 169 | __pycache__/ 170 | 171 | *.ddb* 172 | data 173 | 174 | /.quarto/ 175 | -------------------------------------------------------------------------------- /presentation/index.qmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Ibis Birdbrain" 3 | format: 4 | revealjs: 5 | footer: 6 | #preview-links: true 7 | incremental: true 8 | # https://quarto.org/docs/presentations/revealjs/themes.html#using-themes 9 | theme: dark 10 | echo: true 11 | scrollable: true 12 | --- 13 | 14 | # why? 15 | 16 | ## Ibis + LLMs = Ibis Birdbrain 17 | 18 | Why would we build this? 19 | 20 | 1. it's cool 21 | 2. marketing/sales 22 | 3. marketing/sales 23 | 24 | ## Ibis + LLMs = Ibis Birdbrain 25 | 26 | Why would we build this? 27 | 28 | ::: {.nonincremental} 29 | 1. it's cool 30 | 2. marketing/sales (for Ibis) 31 | 3. marketing/sales (for Theseus) 32 | ::: 33 | 34 | ## language model basics 35 | 36 | (Large) language models are neural networks trained on text to predict the next word in a sequence: 37 | 38 | - text is converted to **tokens** (numbers) so the neural network can understand it 39 | - providers (e.g. OpenAI) charge based on the number of tokens used 40 | - output tokens are more expensive than input tokens 41 | - response time is proportional to the number of tokens used 42 | - providers have a **system prompt** that instructs the language model on how to behave 43 | 44 | ## text in, text out 45 | 46 | - System prompt: You are ChatGPT, a language interface based on the GPT-4 model whose purpose is to... 47 | - Additional system prompt: My name is Cody. I want you to concisely respond. My messages will be about... 48 | - User message 1: Write a new README.md for the Ibis project. 49 | - System response 1: Ibis is... 50 | - User message 2: Adjust it to... 51 | - System response 2: Ibis is... 52 | - User message 3: ... 53 | 54 | ## demo 55 | 56 | [ChatGPT](https://chat.openai.com/) 57 | 58 | ## issues 59 | 60 | - text in, (unstructured) text out 61 | - expensive 62 | - slow 63 | - **PERSONAL OPINION ALERT**: plateau at GPT-4 64 | - **we need (small) task-specific language models** 65 | - **we need the system(s) around the language models** 66 | 67 | ## task-specific example: DuckDB-NSQL 68 | 69 | MotherDuck, in collaboration with someone, trained a DuckDB-specific CodeLlama 7B variant: 70 | 71 | > In total, we generate **200k text-to-SQL training data pairs using Mixtral-8x7B-Instruct-v0.1 model for data generation. The data covers 600 different DuckDB scalar, aggregate, and table functions, more than 40 different DuckDB expressions, and 20 DuckDB extensions.** 72 | > 73 | > To train the model, we use the base model of CodeLLama 7B and finetune over our DuckDB training dataset...on **8XA100 80G machine**. 74 | 75 | ## image 76 | 77 | ![](images/duckdb-nsql.png) 78 | 79 | # how? 80 | 81 | ## Marvin 82 | 83 | [Marvin](https://www.askmarvin.ai/welcome/what_is_marvin/) is "The AI Engineering Toolkit" built by Prefect. 84 | 85 | - Pythonic usage of language models 86 | - only supports OpenAI, but could support any provider 87 | - very easy to use 88 | 89 | ## Marvin functionality 90 | 91 | Marvin supports: 92 | 93 | - image/audio things (not important for birdbrain) 94 | - generating synthetic data (not important for birdbrain) 95 | - casting text to structured Python objects (possibly important for birdbrain) 96 | - extracting structured Python objects from text (possibly important for birdbrain) 97 | - generic language model functions (important for birdbrain) 98 | - classification of text (very important for birdbrain) 99 | 100 | ## Classification 101 | 102 | Marvin uses [the clever logit bias trick](https://twitter.com/AAAzzam/status/1669753721574633473) to classify text with a single output token. 103 | 104 | ```{.python} 105 | import marvin 106 | 107 | category = marvin.classify( 108 | "Ibis Birdbrain is a great product name.", 109 | labels=[True, False] 110 | ) 111 | category 112 | ``` 113 | 114 | Importantly, **only a single output token is used**. This makes the language model call fast and efficient. We use classification throughout Ibis Birdbrain for decision making. 115 | 116 | ## Text to SQL: setup 117 | 118 | ## Text to SQL: method 1 119 | 120 | We can use Marvin to convert natural language to SQL. 121 | 122 | ## Text to SQL: method 2 123 | 124 | # what? 125 | 126 | # future 127 | 128 | # questions? 129 | 130 | # the end 131 | -------------------------------------------------------------------------------- /src/ibis_birdbrain/app.py: -------------------------------------------------------------------------------- 1 | # imports 2 | import time 3 | import ibis 4 | import inspect 5 | 6 | import streamlit as st 7 | 8 | from dotenv import load_dotenv 9 | 10 | from ibis_birdbrain.bot import Bot 11 | from ibis_birdbrain.attachments import ( 12 | SQLAttachment, 13 | ErrorAttachment, 14 | TextAttachment, 15 | WebpageAttachment, 16 | TableAttachment, 17 | DataAttachment, 18 | ChartAttachment, 19 | ) 20 | 21 | # config 22 | ## load .env 23 | load_dotenv() 24 | 25 | ## streamlit config 26 | st.set_page_config(layout="wide") 27 | 28 | ## ibis config 29 | ibis.options.interactive = True 30 | ibis.options.repr.interactive.max_rows = 20 31 | ibis.options.repr.interactive.max_columns = None 32 | 33 | # TODO: move to config.toml or something 34 | con = ibis.connect("duckdb://app.ddb") 35 | description = f""" 36 | This is the IMDB database with a few tables. 37 | 38 | Join them on `tconst`. If asked about `movies`, make sure to filter on `titleType` of `movie`. 39 | """ 40 | description = inspect.cleandoc(description) 41 | 42 | # ml-powered data bot 43 | bot = Bot(con=con, data_description=description) 44 | 45 | 46 | # functions 47 | def process_message(message, include_attachments=False): 48 | """ 49 | Process message and attachments into appropriate streamlit component. 50 | """ 51 | results = [] 52 | results.append(st.markdown(message.body)) 53 | if include_attachments: 54 | if sql_attachment := message.attachments.get_attachment_by_type(SQLAttachment): 55 | expander = st.expander(label=a.dialect, expanded=False) 56 | results.append(expander.markdown(f"```{sql_attachment.dialect}\n{sql_attachment.open()}")) 57 | 58 | if table_attachments := message.attachments.get_attachment_by_type(TableAttachment): 59 | # only have 1 table 60 | results.append( 61 | st.dataframe( 62 | table_attachments[0].open().limit(1000).to_pandas(), use_container_width=True 63 | ) 64 | ) 65 | 66 | # for attachment in message.attachments: 67 | # a = message.attachments[attachment] # TODO: hack 68 | # if isinstance(a, SQLAttachment): 69 | # expander = st.expander(label=a.dialect, expanded=False) 70 | # results.append(expander.markdown(f"```{a.dialect}\n{a.open()}")) 71 | # elif isinstance(a, TextAttachment): 72 | # results.append(st.markdown(a.open())) 73 | # elif isinstance(a, ErrorAttachment): 74 | # results.append(st.markdown(a.open())) 75 | # elif isinstance(a, WebpageAttachment): 76 | # results.append(st.markdown(a.open())) # TODO: better? 77 | # elif isinstance(a, DataAttachment): 78 | # results.append(st.markdown(a.open())) 79 | # elif isinstance(a, TableAttachment): 80 | # results.append( 81 | # st.dataframe( 82 | # a.open().limit(1000).to_pandas(), use_container_width=True 83 | # ) 84 | # ) 85 | # elif isinstance(a, ChartAttachment): 86 | # results.append(st.plotly_chart(a.open(), use_container_width=True)) 87 | # else: 88 | # results.append(st.markdown("Unknown attachment type")) 89 | 90 | return results 91 | 92 | 93 | # header 94 | f""" 95 | # Ibis Birdbrain 96 | """ 97 | 98 | with st.expander(label="data", expanded=False): 99 | tables_str = "" 100 | for table in con.list_tables(): 101 | tables_str += f"- {table} ({str(con.table(table).schema())})\n" 102 | 103 | st.markdown(tables_str) 104 | 105 | # take input 106 | if prompt := st.chat_input("ask birdbrain..."): 107 | with st.spinner("birdbrain is thinking..."): 108 | time.sleep(3) 109 | bot(prompt) 110 | 111 | # display history 112 | for message in bot.messages: 113 | # user-to-bot message 114 | if ( 115 | bot.messages[message].from_address == bot.user_name 116 | and bot.messages[message].to_address == bot.name 117 | ): 118 | with st.chat_message("user"): # bot.messages[message].from_address): 119 | process_message(bot.messages[message]) 120 | # bot-to-user message 121 | elif ( 122 | bot.messages[message].from_address == bot.name 123 | and bot.messages[message].to_address == bot.user_name 124 | ): 125 | with st.chat_message("assistant"): # bot.messages[message].from_address): 126 | process_message(bot.messages[message], include_attachments=True) 127 | -------------------------------------------------------------------------------- /src/ibis_birdbrain/attachments/__init__.py: -------------------------------------------------------------------------------- 1 | # imports 2 | from collections import defaultdict 3 | from uuid import uuid4 4 | from typing import Any, Union, List, Type 5 | from datetime import datetime 6 | 7 | from ibis.expr.types.relations import Table 8 | 9 | 10 | # classes 11 | class Attachment: 12 | """Ibis Birdbrain attachment.""" 13 | 14 | content: Any 15 | id: str 16 | created_at: datetime 17 | name: str | None 18 | description: str | None 19 | 20 | def __init__( 21 | self, 22 | content, 23 | name=None, 24 | description=None, 25 | ): 26 | self.id = str(uuid4()) 27 | self.created_at = datetime.now() 28 | 29 | self.name = name 30 | self.description = description 31 | self.content = content 32 | 33 | def encode(self) -> Table: 34 | ... 35 | 36 | def decode(self, t: Table) -> str: 37 | ... 38 | 39 | def open(self) -> Any: 40 | return self.content 41 | 42 | def __str__(self): 43 | return f"""{self.__class__.__name__} 44 | **guid**: {self.id} 45 | **time**: {self.created_at} 46 | **name**: {self.name} 47 | **desc**: {self.description}""" 48 | 49 | def __repr__(self): 50 | return str(self) 51 | 52 | 53 | class Attachments: 54 | """Ibis Birdbrain attachments.""" 55 | 56 | attachments: dict[str, Attachment] 57 | type_id_map: dict[Type[Attachment], List[str]] 58 | 59 | def __init__(self, attachments: list[Attachment] = []) -> None: 60 | """Initialize the attachments.""" 61 | self.attachments = {a.id: a for a in attachments} 62 | self.type_id_map = defaultdict(list) 63 | for a in attachments: 64 | self.type_id_map[type(a)].append(a.id) 65 | 66 | 67 | def add_attachment(self, attachment: Attachment): 68 | """Add an attachment to the collection.""" 69 | self.attachments[attachment.id] = attachment 70 | self.type_id_map[type(attachment)].append(attachment.id) 71 | 72 | def append(self, attachment: Attachment): 73 | """Alias for add_attachment.""" 74 | self.add_attachment(attachment) 75 | 76 | def extend(self, attachments: Union[List[Attachment], "Attachments"]): 77 | """Adds multiple attachments to the collection.""" 78 | if isinstance(attachments, Attachments): 79 | attachments = list(attachments.attachments.values()) 80 | for attachment in attachments: 81 | self.add_attachment(attachment) 82 | 83 | return self 84 | 85 | def get_attachment_by_type(self, attachment_type: Type[Attachment]): 86 | """Get attachments of a specific type.""" 87 | if attachment_type not in self.type_id_map: 88 | return None 89 | 90 | ids = self.type_id_map[attachment_type] 91 | if not isinstance(self.attachments[ids[0]], TableAttachment): 92 | return self.attachments[ids[0]] 93 | 94 | # One messages may have multiple table attachment 95 | attachments = Attachments() 96 | for id in ids: 97 | attachments.append(self.attachments[id]) 98 | return attachments 99 | 100 | def __getitem__(self, id: str | int): 101 | """Get an attachment from the collection.""" 102 | if isinstance(id, int): 103 | return list(self.attachments.values())[id] 104 | return self.attachments[id] 105 | 106 | def __setitem__(self, id: str, attachment: Attachment): 107 | """Set an attachment in the collection.""" 108 | self.attachments[id] = attachment 109 | self.type_id_map[type(attachment)].append(id) 110 | 111 | def __len__(self) -> int: 112 | """Get the length of the collection.""" 113 | return len(self.attachments) 114 | 115 | def __iter__(self): 116 | """Iterate over the collection.""" 117 | return iter(self.attachments.keys()) 118 | 119 | def __str__(self): 120 | return "\n\n".join([str(a) for a in self.attachments.values()]) 121 | 122 | def __repr__(self): 123 | return str(self) 124 | 125 | 126 | # exports 127 | from ibis_birdbrain.attachments.viz import ChartAttachment 128 | from ibis_birdbrain.attachments.data import ( 129 | DataAttachment, 130 | DatabaseAttachment, 131 | TableAttachment, 132 | ) 133 | from ibis_birdbrain.attachments.text import ( 134 | TextAttachment, 135 | SQLAttachment, 136 | ErrorAttachment, 137 | WebpageAttachment, 138 | ) 139 | 140 | __all__ = [ 141 | "Attachment", 142 | "Attachments", 143 | "DataAttachment", 144 | "DatabaseAttachment", 145 | "TableAttachment", 146 | "ChartAttachment", 147 | "TextAttachment", 148 | "SQLAttachment", 149 | "ErrorAttachment", 150 | "WebpageAttachment", 151 | ] 152 | -------------------------------------------------------------------------------- /src/ibis_birdbrain/flows/data.py: -------------------------------------------------------------------------------- 1 | # imports 2 | import inspect 3 | 4 | from ibis_birdbrain.flows import Flow 5 | from ibis_birdbrain.logging import log 6 | from ibis_birdbrain.messages import Email, Messages 7 | from ibis_birdbrain.attachments import ( 8 | TableAttachment, 9 | SQLAttachment, 10 | ErrorAttachment, 11 | Attachments, 12 | DatabaseAttachment, 13 | ) 14 | 15 | from ibis_birdbrain.tasks import Tasks 16 | from ibis_birdbrain.tasks.sql import TextToSQLTask, ExecuteSQLTask, FixSQLTask, SearchTextTask 17 | 18 | 19 | # flows 20 | class DataFlow(Flow): 21 | """Ibis Birdbrain data flow.""" 22 | 23 | def __init__( 24 | self, 25 | name: str = "data", 26 | description: str = "Ibis Birdbrain data flow", 27 | tasks=Tasks([ 28 | TextToSQLTask(), 29 | ExecuteSQLTask(), 30 | FixSQLTask(), 31 | SearchTextTask(), 32 | ]), 33 | retries: int = 3, 34 | ) -> None: 35 | """Initialize the data flow.""" 36 | self.retries = retries 37 | super().__init__(name=name, description=description, tasks=tasks) 38 | 39 | def __call__(self, messages: Messages) -> Messages: 40 | """Execute the data flow.""" 41 | log.info("Executing the data flow") 42 | 43 | # TODOs: 44 | # - we need to construct the task messages 45 | # - the first message in messages has the DatabaseAttachment 46 | # - the last message in messages has the TableAttachments 47 | # - turn the body of the last message + TableAttachments into a SQLAttachment 48 | # - turn the DatabaseAttachment + SQLAttachment into a TableAttachment (or ErrorAttachment) 49 | # - finally, return the SQLAttachment + TableAttachment as results 50 | 51 | database_attachment = messages[0].attachments.get_attachment_by_type(DatabaseAttachment) 52 | table_attachments = messages[-1].attachments.get_attachment_by_type(TableAttachment) 53 | 54 | # check if question is found in cached table 55 | search_task_response = self.tasks["search-cached-question"](messages) 56 | sql_attachment = search_task_response.attachments.get_attachment_by_type(SQLAttachment) 57 | # initialize response messages 58 | response_messages = Messages() 59 | if not sql_attachment: 60 | # If not existing question and sql found in the cache table 61 | # call the text-to-SQL task 62 | task_message = Email( 63 | body=messages[-1].body, 64 | attachments=[database_attachment] + list(table_attachments.attachments.values()), 65 | to_address=self.tasks["text-to-SQL"].name, 66 | from_address=self.name, 67 | ) 68 | 69 | task_response = self.tasks["text-to-SQL"](task_message) 70 | response_messages.append(task_response) 71 | 72 | # check the response 73 | assert task_response.attachments.get_attachment_by_type(SQLAttachment) is not None 74 | 75 | # extract the SQL attachment 76 | sql_attachment = task_response.attachments.get_attachment_by_type(SQLAttachment) 77 | 78 | # try executing 79 | task_message = Email( 80 | body="execute this SQL on the database", 81 | attachments=[database_attachment, sql_attachment], 82 | to_address=self.tasks["execute-SQL"].name, 83 | from_address=self.name, 84 | ) 85 | 86 | task_response = self.tasks["execute-SQL"](task_message) 87 | response_messages.append(task_response) 88 | 89 | assert len(task_response.attachments) == 2 90 | 91 | # check the response 92 | if task_response.attachments.get_attachment_by_type(TableAttachment): 93 | return response_messages 94 | elif task_response.attachments.get_attachment_by_type(ErrorAttachment): 95 | # for N retries 96 | for i in range(self.retries): 97 | error_attachment = task_response.attachments[0] 98 | 99 | task_message = Email( 100 | body="fix this SQL", 101 | attachments=[error_attachment, database_attachment, sql_attachment] 102 | + list(table_attachments.attachments.values()), 103 | to_address=self.tasks["fix-SQL"].name, 104 | from_address=self.name, 105 | ) 106 | 107 | # fix the SQL 108 | task_response = self.tasks["fix-SQL"](task_message) 109 | response_messages.append(task_response) 110 | 111 | # get the new sql_attachment 112 | sql_attachment = task_response.attachments.get_attachment_by_type(SQLAttachment) 113 | 114 | # try executing 115 | task_response = self.tasks["execute-SQL"]( 116 | Email(attachments=[database_attachment, sql_attachment]) 117 | ) 118 | response_messages.append(task_response) 119 | 120 | if isinstance(task_response.attachments[0], TableAttachment): 121 | return response_messages 122 | elif isinstance(task_response.attachments[0], ErrorAttachment): 123 | continue 124 | else: 125 | raise ValueError 126 | else: 127 | raise ValueError 128 | 129 | return response_messages 130 | -------------------------------------------------------------------------------- /docs/_quarto.yml: -------------------------------------------------------------------------------- 1 | # project 2 | project: 3 | type: website 4 | 5 | execute: 6 | freeze: auto # re-render only when source changes 7 | 8 | # website 9 | website: 10 | 11 | # basics 12 | title: "Ibis Birdbrain" 13 | site-url: https://ibis-project.github.io 14 | site-path: ibis-birdbrain 15 | description: "the portable Python ML-powered data bot" 16 | favicon: images/logo.png 17 | 18 | # options 19 | reader-mode: false 20 | twitter-card: true 21 | back-to-top-navigation: true 22 | repo-url: https://github.com/ibis-project/ibis-birdbrain 23 | repo-actions: [edit, issue] 24 | repo-subdir: docs 25 | issue-url: https://github.com/ibis-project/ibis-birdbrain/issues/new/choose 26 | 27 | # footer 28 | page-footer: 29 | border: false 30 | left: "" 31 | right: 32 | - icon: github 33 | href: https://github.com/ibis-project/ibis-birdbrain 34 | 35 | # search 36 | search: 37 | location: navbar 38 | type: overlay 39 | 40 | # navbar 41 | navbar: 42 | logo: images/logo.png 43 | tools: 44 | - icon: github 45 | menu: 46 | - text: Source code 47 | url: https://github.com/ibis-project/ibis-birdbrain 48 | - text: Report an issue 49 | url: https://github.com/ibis-project/ibis-birdbrain/issues/new 50 | - text: Ask for help 51 | url: https://github.com/ibis-project/ibis-birdbrain/discussions/new?category=q-a 52 | 53 | left: 54 | - sidebar:getting-started 55 | - sidebar:concepts 56 | #- sidebar:how-to 57 | #- sidebar:reference 58 | 59 | # right: 60 | #- posts.qmd 61 | #- release_notes.md # TODO: release notes 62 | #- sidebar:contribute 63 | 64 | # sidebar 65 | sidebar: 66 | - id: "" 67 | - id: getting-started 68 | title: "Getting started" 69 | style: "docked" 70 | collapse-level: 2 71 | contents: 72 | - install.qmd 73 | - auto: tutorials/*.qmd 74 | - id: concepts 75 | title: "Concepts" 76 | style: "docked" 77 | collapse-level: 2 78 | contents: 79 | - why.qmd 80 | - concepts/bots.qmd 81 | - concepts/messages.qmd 82 | - concepts/attachments.qmd 83 | - concepts/flows.qmd 84 | - concepts/tasks.qmd 85 | - id: how-to 86 | title: "How-to" 87 | style: "docked" 88 | collapse-level: 2 89 | contents: 90 | - auto: "how-to/input-output" 91 | - id: contribute 92 | title: "Contribute" 93 | style: "docked" 94 | collapse-level: 2 95 | contents: 96 | - auto: contribute 97 | #- id: reference 98 | # title: "Reference" 99 | # style: "docked" 100 | # collapse-level: 2 101 | # contents: 102 | # - section: Tools 103 | # contents: 104 | # - reference/code.qmd 105 | # - reference/filesystem.qmd 106 | 107 | # format 108 | format: 109 | html: 110 | #theme: vapor 111 | theme: 112 | dark: darkly 113 | light: darkly 114 | #dark: vapor 115 | #light: vapor 116 | #fontcolor: fuchsia 117 | #linkcolor: fuchsia 118 | css: styles.css 119 | toc: true 120 | 121 | # resources 122 | resources: 123 | - CNAME 124 | 125 | #quartodoc: 126 | # package: ibis_birdbrain 127 | # title: Reference 128 | # sidebar: reference/_sidebar.yml 129 | # dynamic: true 130 | # render_interlinks: false 131 | # options: 132 | # member_options: 133 | # signature_name: short 134 | # sections: 135 | # - title: Filesystem tools 136 | # desc: Tools for the filesystem 137 | # package: ibis_birdbrain.tools.filesystem 138 | # contents: 139 | # - kind: page 140 | # path: filesystem 141 | # summary: 142 | # name: filesystem 143 | # desc: Filesystem tools 144 | # contents: 145 | # - read_file 146 | # - list_files_and_dirs 147 | # - write_file 148 | # - title: EDA tools 149 | # desc: Tools for EDA 150 | # package: ibis_birdbrain.tools.eda 151 | # contents: 152 | # - kind: page 153 | # path: eda 154 | # summary: 155 | # name: eda 156 | # desc: EDA tools 157 | # contents: 158 | # - read_excel_file 159 | # - read_delta_table 160 | # - list_tables 161 | # - get_table_schemas 162 | # - title: Code tools 163 | # desc: Tools for code 164 | # package: ibis_birdbrain.tools.code 165 | # contents: 166 | # - kind: page 167 | # path: code 168 | # summary: 169 | # name: code 170 | # desc: Code tools 171 | # contents: 172 | # - text_to_python 173 | # - fix_python_error 174 | # - python_function_to_udf 175 | # - run_python_code 176 | # - title: Internet tools 177 | # desc: Tools for the Internet 178 | # package: ibis_birdbrain.tools.internet 179 | # contents: 180 | # - kind: page 181 | # path: internet 182 | # summary: 183 | # name: internet 184 | # desc: Internet tools 185 | # contents: 186 | # - search_internet 187 | # - webpage_to_str 188 | # - open_browser 189 | # 190 | -------------------------------------------------------------------------------- /docs/posts/llms-and-data-pt2/index.qmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Computations and control flow: it's just programming" 3 | author: "Cody Peterson" 4 | date: "2023-10-14" 5 | categories: 6 | - "LLMs and data" 7 | --- 8 | 9 | ## Introduction 10 | 11 | The recent Generative AI hype cycle has led to a lot of new terminology to 12 | understand. In this post, we'll cover some key concepts from the groud up and 13 | explain the basics of working with LLMs in the context of data. 14 | 15 | This post assumes [basic familiarity with Marvin and Ibis](../llms-and-data-pt0) 16 | and [three approaches to applying LLMs to data](../llms-and-data-pt1). 17 | 18 | ```{python} 19 | # | code-fold: true 20 | import ibis # <1> 21 | import marvin # <1> 22 | 23 | from dotenv import load_dotenv # <1> 24 | 25 | load_dotenv() # <2> 26 | 27 | con = ibis.connect("duckdb://penguins.ddb") # <3> 28 | t = ibis.examples.penguins.fetch() # <3> 29 | t = con.create_table("penguins", t.to_pyarrow(), overwrite=True) # <3> 30 | ``` 31 | 32 | 1. Import the libraries we need. 33 | 2. Load the environment variable to setup Marvin to call our OpenAI account. 34 | 3. Setup the demo datain an Ibis backend. 35 | 36 | First, we'll setup Ibis and Marvin with some simple example data: 37 | 38 | ```{python} 39 | import ibis # <1> 40 | import marvin # <1> 41 | 42 | from ibis.expr.schema import Schema # <1> 43 | from ibis.expr.types.relations import Table # <1> 44 | 45 | ibis.options.interactive = True # <2> 46 | marvin.settings.llm_model = "openai/gpt-4" # <2> 47 | 48 | con = ibis.connect("duckdb://penguins.ddb") # <3> 49 | t = con.table("penguins") # <3> 50 | ``` 51 | 52 | 1. Import Ibis and Marvin. 53 | 2. Configure Ibis (interactive) and Marvin (GPT-4). 54 | 3. Connect to the data and load a table into a variable. 55 | 56 | ## Context 57 | 58 | Context is a fancy way of talking about the input to a LLM. 59 | 60 | ## Calls 61 | 62 | We make calls with inputs to functions or systems and get outputs. We can think 63 | of calling the LLM with our input (context) and getting an output (text). 64 | 65 | ## Computations 66 | 67 | A function or system often computes something. We can be pedantic about calls 68 | versus computations, but in general the connotation around computations is more 69 | time and resource intensive than a call. At the end of the day, they will both 70 | take some computer cycles. 71 | 72 | ## Retrieval augmented generation (RAG) 73 | 74 | Instead of you typing out context for the bot, we can **retrieve** context from 75 | somewhere, **augment** our strings sent to the bot with this context, and then 76 | **generate** a response from the bot. 77 | 78 | As a contrived example, instead of saying "The capitol of foo is bar", we can 79 | retrieve the capitol of foo from a database, augment it with our context, and 80 | then generate a response from the bot. You may notice that [we already did this 81 | in the firt post in the series -- let's review that code 82 | again](../llms-and-data-pt0): 83 | 84 | ```{python} 85 | from ibis.expr.schema import Schema 86 | from ibis.expr.types.relations import Table 87 | 88 | 89 | @marvin.ai_fn 90 | def sql_select( 91 | text: str, table_name: str = t.get_name(), schema: Schema = t.schema() 92 | ) -> str: 93 | """writes the SQL SELECT statement to query the table according to the text""" 94 | 95 | 96 | query = "the unique combination of species and islands" 97 | sql = sql_select(query).strip(";") 98 | sql 99 | ``` 100 | 101 | Notice that we **retrieved** the table name and schema with calls to the Ibis 102 | table (`t.get_name()` and `t.schema()`). We then **augment** our context (the 103 | query in natural language) with this information and **generate** a response 104 | from the bot. 105 | 106 | This works reasonably well for simple SQL queries: 107 | 108 | ```{python} 109 | t.sql(sql) 110 | ``` 111 | 112 | I would argue in this case there wasn't any real **computation** done by our 113 | **calls** to the Ibis table -- we were just retrieving some relatively static 114 | metadata -- but we could have done some more complex computations (on any of 18+ 115 | data platforms). 116 | 117 | ## Thought leadership 118 | 119 | TODO: human rewrite 120 | 121 | In the realm of Generative AI, particularly when working with Language Learning 122 | Models (LLMs), understanding the concept of 'context' is crucial. Context, in this 123 | domain, refers to the inputs that are fed into an LLM, and the corresponding 124 | outputs they generate. This post breaks down the complexities of this process into 125 | understandable fragments, including retrieval of context, its augmentation, and, 126 | thereafter, the generation of a response. 127 | 128 | An illustrative example is provided, showcasing a database interaction. It 129 | demonstrates how the data retrieved can be used to augment the context before the 130 | bot generates a response. This valuable insight underlines the practical 131 | application of the theory, reinforcing the understanding of the readers. 132 | 133 | We also venture into the difference between simple static metadata retrieval and 134 | the more intricate computations. This distinction echoes the breadth and depth of 135 | the processes involved in Generative AI. 136 | 137 | As we continue to explore and unravel the potential of Generative AI and LLMs, 138 | this post serves as a fundamental building block. It creates a pathway for 139 | enthusiasts and professionals alike to delve deeper into this exciting field. By 140 | breaking down complex concepts into comprehensible segments, it fosters an 141 | environment of learning and growth. 142 | 143 | This marks just the beginning of our journey into the world of Generative AI. As 144 | we dig deeper, we will continue to explore, learn and share with our readers. Stay 145 | tuned for more insightful content in this series. [1] 146 | 147 | [1] https://github.com/ibis-project/ibis-birdbrain 148 | 149 | ## Next steps 150 | 151 | You can get involved with [Ibis 152 | Birdbrain](https://github.com/ibis-project/ibis-birdbrain), our open-source data 153 | & AI project for building next-generation natural language interfaces to data. 154 | 155 | [Read the next post in this series](../llms-and-data-pt3). 156 | -------------------------------------------------------------------------------- /docs/posts/llms-and-data-pt3/index.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "raw", 5 | "metadata": {}, 6 | "source": [ 7 | "---\n", 8 | "title: \"LLMs and data: modular and composable best practices\"\n", 9 | "author: \"Cody Peterson\"\n", 10 | "date: \"2023-10-03\"\n", 11 | "freeze: auto\n", 12 | "categories:\n", 13 | " - blog\n", 14 | " - ai\n", 15 | " - llm\n", 16 | "---" 17 | ], 18 | "id": "56691f4e" 19 | }, 20 | { 21 | "cell_type": "markdown", 22 | "metadata": {}, 23 | "source": [ 24 | "## Introduction\n" 25 | ], 26 | "id": "f55bf8d3" 27 | }, 28 | { 29 | "cell_type": "code", 30 | "metadata": {}, 31 | "source": [ 32 | "# | code-fold: true\n", 33 | "import ibis # <1>\n", 34 | "import marvin # <1>\n", 35 | "\n", 36 | "from dotenv import load_dotenv # <1>\n", 37 | "\n", 38 | "load_dotenv() # <2>\n", 39 | "\n", 40 | "con = ibis.connect(\"duckdb://penguins.ddb\") # <3>\n", 41 | "t = ibis.examples.penguins.fetch() # <3>\n", 42 | "t = con.create_table(\"penguins\", t.to_pyarrow(), overwrite=True) # <3>" 43 | ], 44 | "id": "60b0d282", 45 | "execution_count": null, 46 | "outputs": [] 47 | }, 48 | { 49 | "cell_type": "markdown", 50 | "metadata": {}, 51 | "source": [ 52 | "1. Import the libraries we need.\n", 53 | "2. Load the environment variable to setup Marvin to call our OpenAI account.\n", 54 | "3. Setup the demo datain an Ibis backend.\n" 55 | ], 56 | "id": "8d246789" 57 | }, 58 | { 59 | "cell_type": "code", 60 | "metadata": {}, 61 | "source": [ 62 | "import ibis # <1>\n", 63 | "import marvin # <1>\n", 64 | "\n", 65 | "from ibis.expr.schema import Schema # <1>\n", 66 | "from ibis.expr.types.relations import Table # <1>\n", 67 | "\n", 68 | "ibis.options.interactive = True # <2>\n", 69 | "marvin.settings.llm_model = \"openai/gpt-4\" # <2>\n", 70 | "\n", 71 | "con = ibis.connect(\"duckdb://penguins.ddb\") # <3>\n", 72 | "t = con.table(\"penguins\") # <3>" 73 | ], 74 | "id": "7bf8f9ee", 75 | "execution_count": null, 76 | "outputs": [] 77 | }, 78 | { 79 | "cell_type": "markdown", 80 | "metadata": {}, 81 | "source": [ 82 | "1. Import Ibis and Marvin.\n", 83 | "2. Configure Ibis (interactive) and Marvin (GPT-4).\n", 84 | "3. Connect to the data and load a table into a variable.\n" 85 | ], 86 | "id": "d38a0eb1" 87 | }, 88 | { 89 | "cell_type": "code", 90 | "metadata": {}, 91 | "source": [ 92 | "@marvin.ai_fn # <1>\n", 93 | "def _generate_sql_select(\n", 94 | " text: str, table_name: str, table_schema: Schema\n", 95 | ") -> str: # <1>\n", 96 | " \"\"\"Generate SQL SELECT from text.\"\"\" # <1>\n", 97 | "\n", 98 | "\n", 99 | "def sql_from_text(text: str, t: Table) -> Table: # <2>\n", 100 | " \"\"\"Run SQL from text.\"\"\" # <2>\n", 101 | " return t.sql(_generate_sql_select(text, t.get_name(), t.schema()).strip(\";\")) # <2>" 102 | ], 103 | "id": "e28e6dcd", 104 | "execution_count": null, 105 | "outputs": [] 106 | }, 107 | { 108 | "cell_type": "markdown", 109 | "metadata": {}, 110 | "source": [ 111 | "1. A non-deterministic, LLM-powered AI function.\n", 112 | "2. A deterministic, human-authored function that calls the AI function.\n" 113 | ], 114 | "id": "989a0f14" 115 | }, 116 | { 117 | "cell_type": "code", 118 | "metadata": {}, 119 | "source": [ 120 | "t2 = sql_from_text(\"the unique combination of species and islands\", t)\n", 121 | "t2" 122 | ], 123 | "id": "286ac581", 124 | "execution_count": null, 125 | "outputs": [] 126 | }, 127 | { 128 | "cell_type": "code", 129 | "metadata": {}, 130 | "source": [ 131 | "t3 = sql_from_text(\n", 132 | " \"the unique combination of species and islands, with their counts, ordered from highest to lowest, and name that column just 'count'\",\n", 133 | " t,\n", 134 | ")\n", 135 | "t3" 136 | ], 137 | "id": "7acb6628", 138 | "execution_count": null, 139 | "outputs": [] 140 | }, 141 | { 142 | "cell_type": "markdown", 143 | "metadata": {}, 144 | "source": [ 145 | "## Summary\n", 146 | "\n", 147 | "To summarize this post:\n" 148 | ], 149 | "id": "ac4c3dd8" 150 | }, 151 | { 152 | "cell_type": "code", 153 | "metadata": {}, 154 | "source": [ 155 | "from rich import print\n", 156 | "\n", 157 | "with open(\"index.qmd\", \"r\") as f:\n", 158 | " self_text = f.read()\n", 159 | "\n", 160 | "# save some money and avoid rate limiting\n", 161 | "marvin.settings.llm_model = \"openai/gpt-3.5-turbo-16k\" # <2>\n", 162 | "\n", 163 | "@marvin.ai_model\n", 164 | "class Summary(BaseModel):\n", 165 | " \"\"\"Summary of text.\"\"\"\n", 166 | "\n", 167 | " summary_line: str = Field(..., description=\"The one-line summary of the text.\")\n", 168 | " summary_paragraph: str = Field(\n", 169 | " ..., description=\"The one-paragraph summary of the text.\"\n", 170 | " )\n", 171 | " conclusion: str = Field(\n", 172 | " ..., description=\"The conclusion the reader should draw from the text.\"\n", 173 | " )\n", 174 | " key_points: list[str] = Field(..., description=\"The key points of the text.\")\n", 175 | " critiques: list[str] = Field(\n", 176 | " ..., description=\"Professional, fair critiques of the text.\"\n", 177 | " )\n", 178 | " suggested_improvements: list[str] = Field(\n", 179 | " ..., description=\"Suggested improvements for the text.\"\n", 180 | " )\n", 181 | " sentiment: float = Field(..., description=\"The sentiment of the text.\")\n", 182 | " sentiment_label: str = Field(..., description=\"The sentiment label of the text.\")\n", 183 | " author_bias: str = Field(..., description=\"The author bias of the text.\")\n", 184 | "\n", 185 | "\n", 186 | "print(Summary(self_text))" 187 | ], 188 | "id": "d1dddf9f", 189 | "execution_count": null, 190 | "outputs": [] 191 | }, 192 | { 193 | "cell_type": "markdown", 194 | "metadata": {}, 195 | "source": [ 196 | "## Next steps\n", 197 | "\n", 198 | "You can get involved with [Ibis\n", 199 | "Birdbrain](https://github.com/ibis-project/ibis-birdbrain), our open-source data\n", 200 | "& AI project for building next-generation natural language interfaces to data.\n", 201 | "\n", 202 | "## Discussions" 203 | ], 204 | "id": "471587d8" 205 | } 206 | ], 207 | "metadata": { 208 | "kernelspec": { 209 | "name": "python3", 210 | "language": "python", 211 | "display_name": "Python 3 (ipykernel)" 212 | } 213 | }, 214 | "nbformat": 4, 215 | "nbformat_minor": 5 216 | } -------------------------------------------------------------------------------- /src/ibis_birdbrain/bot.py: -------------------------------------------------------------------------------- 1 | # imports 2 | import ibis 3 | import sqlglot as sg 4 | 5 | from uuid import uuid4 6 | from typing import Any 7 | from datetime import datetime 8 | 9 | from ibis.backends.base import BaseBackend 10 | 11 | from ibis_birdbrain.logging import log 12 | from ibis_birdbrain.attachments import ( 13 | Attachments, 14 | TableAttachment, 15 | DatabaseAttachment, 16 | SQLAttachment, 17 | ) 18 | from ibis_birdbrain.flows import Flows 19 | from ibis_birdbrain.strings import bot_description 20 | from ibis_birdbrain.messages import Message, Messages, Email 21 | from ibis_birdbrain.utils.strings import shorten_str 22 | from ibis_birdbrain.utils.attachments import to_attachments 23 | from ibis_birdbrain.tasks.sql import ExecuteSQLTask 24 | 25 | from ibis_birdbrain.flows.data import DataFlow 26 | 27 | 28 | # bot 29 | class Bot: 30 | """Ibis Birdbrain bot.""" 31 | 32 | id: str 33 | created_at: datetime 34 | 35 | con: BaseBackend 36 | name: str 37 | user_name: str 38 | description: str 39 | data_description: str 40 | version: str 41 | messages: Messages 42 | source_table_attachments: Attachments 43 | flows: Flows 44 | lm_response: bool 45 | conversational: bool 46 | 47 | current_subject: str 48 | 49 | def __init__( 50 | self, 51 | con=ibis.connect("duckdb://"), 52 | cached_table=None, 53 | name="birdbrain", 54 | user_name="user", 55 | description=bot_description, 56 | data_description="", 57 | version="infinity", 58 | messages=Messages(), 59 | source_table_attachments=None, 60 | flows=Flows([DataFlow()]), 61 | lm_response=False, 62 | conversational=False, 63 | ) -> None: 64 | """Initialize the bot.""" 65 | self.id = uuid4() 66 | self.created_at = datetime.now() 67 | 68 | self.con = con 69 | self.name = name 70 | self.user_name = user_name 71 | self.description = description 72 | self.data_description = data_description 73 | self.version = version 74 | self.messages = messages 75 | self.flows = flows 76 | 77 | self.lm_response = lm_response 78 | self.conversational = conversational 79 | 80 | self.current_subject = "" 81 | 82 | source_table_attachments = Attachments() 83 | for table in con.list_tables(): 84 | a = TableAttachment(con.table(table)) 85 | source_table_attachments.append(a) 86 | 87 | self.source_table_attachments = source_table_attachments 88 | 89 | # TODO: add flows to the description/body 90 | body = """TODO""" # noqa 91 | 92 | # system initialization message 93 | attachments = [ 94 | DatabaseAttachment(con, description=self.data_description), 95 | ] 96 | 97 | if cached_table is not None: 98 | attachments.append( 99 | TableAttachment(cached_table) 100 | ) 101 | system_message = Email( 102 | body=self.description, 103 | subject="system initialization", 104 | to_address=self.name, 105 | from_address=self.name, 106 | attachments=attachments, 107 | ) 108 | self.messages.append(system_message) 109 | log.info(f"Bot {self.name} initialized...") 110 | 111 | def __call__( 112 | self, 113 | text: str = "Who are you and what can you do?", 114 | stuff: list[Any] = [], 115 | ) -> Message: 116 | """Call upon the bot.""" 117 | 118 | log.info(f"Bot {self.name} called with text: {text}") 119 | 120 | # convert user input to message 121 | if self.current_subject == "": 122 | self.current_subject = shorten_str(text) 123 | 124 | input_attachments = to_attachments(stuff) 125 | 126 | input_message = Email( 127 | body=text, 128 | subject=self.current_subject, 129 | to_address=self.name, 130 | from_address=self.user_name, 131 | attachments=input_attachments, 132 | ) 133 | 134 | # add message to messages 135 | self.messages.append(input_message) 136 | 137 | # if conversational, use all messages 138 | if self.conversational: 139 | flow_messages = self.messages 140 | else: 141 | flow_messages = Messages([self.messages[0], self.messages[-1]]) 142 | 143 | # select the flow 144 | flow = self.flows.select_flow(flow_messages) 145 | 146 | # TODO: slight hack 147 | if flow.name == "data": 148 | flow_messages[-1].attachments.extend(self.source_table_attachments) 149 | 150 | # execute the flow 151 | result_messages = flow(flow_messages) 152 | 153 | # extend the messages 154 | self.messages.extend(result_messages) 155 | 156 | # generate the response 157 | response_attachments = Attachments() 158 | # TODO: smarter here 159 | for message in result_messages: 160 | response_attachments.extend(result_messages[message].attachments) 161 | response_message = Email( 162 | body="TODO", # TODO: generate body from attachments 163 | subject=self.current_subject, 164 | to_address=self.user_name, 165 | from_address=self.name, 166 | attachments=response_attachments, 167 | ) 168 | 169 | # update the response body 170 | if self.lm_response: 171 | response_message.body = self.respond(self.messages) 172 | else: 173 | response_message.body = "Ibis Birdbrain has attached the results." 174 | 175 | # add the response to the messages 176 | self.messages.append(response_message) 177 | 178 | # return the response 179 | self.messages[-1].to_address = self.user_name 180 | self.messages[-1].from_address = self.name 181 | return self.messages[-1] 182 | 183 | def respond(self, messages: Messages) -> Message: 184 | """Respond to the messages.""" 185 | ... 186 | 187 | def transpile_sql(self, sql: str, dialect_from: str, dialect_to: str) -> str: 188 | """Translate SQL from one dialect to another.""" 189 | 190 | return sg.transpile( 191 | sql=sql, 192 | read=dialect_from, 193 | write=dialect_to, 194 | identity=False, 195 | pretty=True, 196 | )[0] 197 | 198 | def execute_last_sql(self, con: BaseBackend) -> Message: 199 | """Execute the last successfully executed SQL statement.""" 200 | 201 | sql_attachment = None 202 | for m in reversed(self.messages): 203 | if m.attachments.get_attachment_by_type(SQLAttachment) and m.attachments.get_attachment_by_type(TableAttachment): 204 | sql_attachment = m.attachments.get_attachment_by_type(SQLAttachment) 205 | break 206 | 207 | if sql_attachment: 208 | database_attachment = DatabaseAttachment(con) 209 | task_message = Email( 210 | body="execute this SQL on the {con.name}", 211 | attachments=[database_attachment, sql_attachment], 212 | to_address="execute-SQL", 213 | from_address=self.name, 214 | ) 215 | return ExecuteSQLTask("execute-SQL")(task_message) 216 | 217 | 218 | return Email( 219 | body=f"No Sql query executed", 220 | to_address=self.messages[-1].from_address, 221 | from_address=self.name, 222 | ) -------------------------------------------------------------------------------- /docs/_freeze/site_libs/quarto-listing/quarto-listing.js: -------------------------------------------------------------------------------- 1 | const kProgressiveAttr = "data-src"; 2 | let categoriesLoaded = false; 3 | 4 | window.quartoListingCategory = (category) => { 5 | if (categoriesLoaded) { 6 | activateCategory(category); 7 | setCategoryHash(category); 8 | } 9 | }; 10 | 11 | window["quarto-listing-loaded"] = () => { 12 | // Process any existing hash 13 | const hash = getHash(); 14 | 15 | if (hash) { 16 | // If there is a category, switch to that 17 | if (hash.category) { 18 | activateCategory(hash.category); 19 | } 20 | // Paginate a specific listing 21 | const listingIds = Object.keys(window["quarto-listings"]); 22 | for (const listingId of listingIds) { 23 | const page = hash[getListingPageKey(listingId)]; 24 | if (page) { 25 | showPage(listingId, page); 26 | } 27 | } 28 | } 29 | 30 | const listingIds = Object.keys(window["quarto-listings"]); 31 | for (const listingId of listingIds) { 32 | // The actual list 33 | const list = window["quarto-listings"][listingId]; 34 | 35 | // Update the handlers for pagination events 36 | refreshPaginationHandlers(listingId); 37 | 38 | // Render any visible items that need it 39 | renderVisibleProgressiveImages(list); 40 | 41 | // Whenever the list is updated, we also need to 42 | // attach handlers to the new pagination elements 43 | // and refresh any newly visible items. 44 | list.on("updated", function () { 45 | renderVisibleProgressiveImages(list); 46 | setTimeout(() => refreshPaginationHandlers(listingId)); 47 | 48 | // Show or hide the no matching message 49 | toggleNoMatchingMessage(list); 50 | }); 51 | } 52 | }; 53 | 54 | window.document.addEventListener("DOMContentLoaded", function (_event) { 55 | // Attach click handlers to categories 56 | const categoryEls = window.document.querySelectorAll( 57 | ".quarto-listing-category .category" 58 | ); 59 | 60 | for (const categoryEl of categoryEls) { 61 | const category = categoryEl.getAttribute("data-category"); 62 | categoryEl.onclick = () => { 63 | activateCategory(category); 64 | setCategoryHash(category); 65 | }; 66 | } 67 | 68 | // Attach a click handler to the category title 69 | // (there should be only one, but since it is a class name, handle N) 70 | const categoryTitleEls = window.document.querySelectorAll( 71 | ".quarto-listing-category-title" 72 | ); 73 | for (const categoryTitleEl of categoryTitleEls) { 74 | categoryTitleEl.onclick = () => { 75 | activateCategory(""); 76 | setCategoryHash(""); 77 | }; 78 | } 79 | 80 | categoriesLoaded = true; 81 | }); 82 | 83 | function toggleNoMatchingMessage(list) { 84 | const selector = `#${list.listContainer.id} .listing-no-matching`; 85 | const noMatchingEl = window.document.querySelector(selector); 86 | if (noMatchingEl) { 87 | if (list.visibleItems.length === 0) { 88 | noMatchingEl.classList.remove("d-none"); 89 | } else { 90 | if (!noMatchingEl.classList.contains("d-none")) { 91 | noMatchingEl.classList.add("d-none"); 92 | } 93 | } 94 | } 95 | } 96 | 97 | function setCategoryHash(category) { 98 | setHash({ category }); 99 | } 100 | 101 | function setPageHash(listingId, page) { 102 | const currentHash = getHash() || {}; 103 | currentHash[getListingPageKey(listingId)] = page; 104 | setHash(currentHash); 105 | } 106 | 107 | function getListingPageKey(listingId) { 108 | return `${listingId}-page`; 109 | } 110 | 111 | function refreshPaginationHandlers(listingId) { 112 | const listingEl = window.document.getElementById(listingId); 113 | const paginationEls = listingEl.querySelectorAll( 114 | ".pagination li.page-item:not(.disabled) .page.page-link" 115 | ); 116 | for (const paginationEl of paginationEls) { 117 | paginationEl.onclick = (sender) => { 118 | setPageHash(listingId, sender.target.getAttribute("data-i")); 119 | showPage(listingId, sender.target.getAttribute("data-i")); 120 | return false; 121 | }; 122 | } 123 | } 124 | 125 | function renderVisibleProgressiveImages(list) { 126 | // Run through the visible items and render any progressive images 127 | for (const item of list.visibleItems) { 128 | const itemEl = item.elm; 129 | if (itemEl) { 130 | const progressiveImgs = itemEl.querySelectorAll( 131 | `img[${kProgressiveAttr}]` 132 | ); 133 | for (const progressiveImg of progressiveImgs) { 134 | const srcValue = progressiveImg.getAttribute(kProgressiveAttr); 135 | if (srcValue) { 136 | progressiveImg.setAttribute("src", srcValue); 137 | } 138 | progressiveImg.removeAttribute(kProgressiveAttr); 139 | } 140 | } 141 | } 142 | } 143 | 144 | function getHash() { 145 | // Hashes are of the form 146 | // #name:value|name1:value1|name2:value2 147 | const currentUrl = new URL(window.location); 148 | const hashRaw = currentUrl.hash ? currentUrl.hash.slice(1) : undefined; 149 | return parseHash(hashRaw); 150 | } 151 | 152 | const kAnd = "&"; 153 | const kEquals = "="; 154 | 155 | function parseHash(hash) { 156 | if (!hash) { 157 | return undefined; 158 | } 159 | const hasValuesStrs = hash.split(kAnd); 160 | const hashValues = hasValuesStrs 161 | .map((hashValueStr) => { 162 | const vals = hashValueStr.split(kEquals); 163 | if (vals.length === 2) { 164 | return { name: vals[0], value: vals[1] }; 165 | } else { 166 | return undefined; 167 | } 168 | }) 169 | .filter((value) => { 170 | return value !== undefined; 171 | }); 172 | 173 | const hashObj = {}; 174 | hashValues.forEach((hashValue) => { 175 | hashObj[hashValue.name] = decodeURIComponent(hashValue.value); 176 | }); 177 | return hashObj; 178 | } 179 | 180 | function makeHash(obj) { 181 | return Object.keys(obj) 182 | .map((key) => { 183 | return `${key}${kEquals}${obj[key]}`; 184 | }) 185 | .join(kAnd); 186 | } 187 | 188 | function setHash(obj) { 189 | const hash = makeHash(obj); 190 | window.history.pushState(null, null, `#${hash}`); 191 | } 192 | 193 | function showPage(listingId, page) { 194 | const list = window["quarto-listings"][listingId]; 195 | if (list) { 196 | list.show((page - 1) * list.page + 1, list.page); 197 | } 198 | } 199 | 200 | function activateCategory(category) { 201 | // Deactivate existing categories 202 | const activeEls = window.document.querySelectorAll( 203 | ".quarto-listing-category .category.active" 204 | ); 205 | for (const activeEl of activeEls) { 206 | activeEl.classList.remove("active"); 207 | } 208 | 209 | // Activate this category 210 | const categoryEl = window.document.querySelector( 211 | `.quarto-listing-category .category[data-category='${category}'` 212 | ); 213 | if (categoryEl) { 214 | categoryEl.classList.add("active"); 215 | } 216 | 217 | // Filter the listings to this category 218 | filterListingCategory(category); 219 | } 220 | 221 | function filterListingCategory(category) { 222 | const listingIds = Object.keys(window["quarto-listings"]); 223 | for (const listingId of listingIds) { 224 | const list = window["quarto-listings"][listingId]; 225 | if (list) { 226 | if (category === "") { 227 | // resets the filter 228 | list.filter(); 229 | } else { 230 | // filter to this category 231 | list.filter(function (item) { 232 | const itemValues = item.values(); 233 | if (itemValues.categories !== null) { 234 | const categories = itemValues.categories.split(","); 235 | return categories.includes(category); 236 | } else { 237 | return false; 238 | } 239 | }); 240 | } 241 | } 242 | } 243 | } 244 | -------------------------------------------------------------------------------- /docs/_freeze/posts/llms-and-data-pt2/index/execute-results/html.json: -------------------------------------------------------------------------------- 1 | { 2 | "hash": "d58aef84abe91176621bc06047c25c39", 3 | "result": { 4 | "markdown": "---\ntitle: \"Computations and control flow: it's just programming\"\nauthor: \"Cody Peterson\"\ndate: \"2023-10-14\"\ncategories:\n - \"LLMs and data\"\n---\n\n## Introduction\n\nThe recent Generative AI hype cycle has led to a lot of new terminology to\nunderstand. In this post, we'll cover some key concepts from the groud up and\nexplain the basics of working with LLMs in the context of data.\n\nThis post assumes [basic familiarity with Marvin and Ibis](../llms-and-data-pt0)\nand [three approaches to applying LLMs to data](../llms-and-data-pt1).\n\n::: {#675d651b .cell execution_count=1}\n``` {.python .cell-code code-fold=\"true\"}\nimport ibis # <1>\nimport marvin # <1>\n\nfrom dotenv import load_dotenv # <1>\n\nload_dotenv() # <2>\n\ncon = ibis.connect(\"duckdb://penguins.ddb\") # <3>\nt = ibis.examples.penguins.fetch() # <3>\nt = con.create_table(\"penguins\", t.to_pyarrow(), overwrite=True) # <3>\n```\n:::\n\n\n1. Import the libraries we need.\n2. Load the environment variable to setup Marvin to call our OpenAI account.\n3. Setup the demo datain an Ibis backend.\n\nFirst, we'll setup Ibis and Marvin with some simple example data:\n\n::: {#297c21b2 .cell execution_count=2}\n``` {.python .cell-code}\nimport ibis # <1>\nimport marvin # <1>\n\nfrom ibis.expr.schema import Schema # <1>\nfrom ibis.expr.types.relations import Table # <1>\n\nibis.options.interactive = True # <2>\nmarvin.settings.llm_model = \"openai/gpt-4\" # <2>\n\ncon = ibis.connect(\"duckdb://penguins.ddb\") # <3>\nt = con.table(\"penguins\") # <3>\n```\n:::\n\n\n1. Import Ibis and Marvin.\n2. Configure Ibis (interactive) and Marvin (GPT-4).\n3. Connect to the data and load a table into a variable.\n\n## Context\n\nContext is a fancy way of talking about the input to a LLM.\n\n## Calls\n\nWe make calls with inputs to functions or systems and get outputs. We can think\nof calling the LLM with our input (context) and getting an output (text).\n\n## Computations\n\nA function or system often computes something. We can be pedantic about calls\nversus computations, but in general the connotation around computations is more\ntime and resource intensive than a call. At the end of the day, they will both\ntake some computer cycles.\n\n## Retrieval augmented generation (RAG)\n\nInstead of you typing out context for the bot, we can **retrieve** context from\nsomewhere, **augment** our strings sent to the bot with this context, and then\n**generate** a response from the bot.\n\nAs a contrived example, instead of saying \"The capitol of foo is bar\", we can\nretrieve the capitol of foo from a database, augment it with our context, and\nthen generate a response from the bot. You may notice that [we already did this\nin the firt post in the series -- let's review that code\nagain](../llms-and-data-pt0):\n\n::: {#1e60f7bd .cell execution_count=3}\n``` {.python .cell-code}\nfrom ibis.expr.schema import Schema\nfrom ibis.expr.types.relations import Table\n\n\n@marvin.ai_fn\ndef sql_select(\n text: str, table_name: str = t.get_name(), schema: Schema = t.schema()\n) -> str:\n \"\"\"writes the SQL SELECT statement to query the table according to the text\"\"\"\n\n\nquery = \"the unique combination of species and islands\"\nsql = sql_select(query).strip(\";\")\nsql\n```\n\n::: {.cell-output .cell-output-display execution_count=3}\n```\n'SELECT DISTINCT species, island FROM penguins'\n```\n:::\n:::\n\n\nNotice that we **retrieved** the table name and schema with calls to the Ibis\ntable (`t.get_name()` and `t.schema()`). We then **augment** our context (the\nquery in natural language) with this information and **generate** a response\nfrom the bot.\n\nThis works reasonably well for simple SQL queries:\n\n::: {#07c59dc8 .cell execution_count=4}\n``` {.python .cell-code}\nt.sql(sql)\n```\n\n::: {.cell-output .cell-output-display execution_count=4}\n```{=html}\n
┏━━━━━━━━━━━┳━━━━━━━━━━━┓\n┃ species   ┃ island    ┃\n┡━━━━━━━━━━━╇━━━━━━━━━━━┩\n│ string    │ string    │\n├───────────┼───────────┤\n│ Adelie    │ Torgersen │\n│ Adelie    │ Biscoe    │\n│ Adelie    │ Dream     │\n│ Gentoo    │ Biscoe    │\n│ Chinstrap │ Dream     │\n└───────────┴───────────┘\n
\n```\n:::\n:::\n\n\nI would argue in this case there wasn't any real **computation** done by our\n**calls** to the Ibis table -- we were just retrieving some relatively static\nmetadata -- but we could have done some more complex computations (on any of 18+\ndata platforms).\n\n## Thought leadership\n\nTODO: human rewrite\n\nIn the realm of Generative AI, particularly when working with Language Learning\nModels (LLMs), understanding the concept of 'context' is crucial. Context, in this\ndomain, refers to the inputs that are fed into an LLM, and the corresponding\noutputs they generate. This post breaks down the complexities of this process into\nunderstandable fragments, including retrieval of context, its augmentation, and,\nthereafter, the generation of a response.\n\nAn illustrative example is provided, showcasing a database interaction. It\ndemonstrates how the data retrieved can be used to augment the context before the\nbot generates a response. This valuable insight underlines the practical\napplication of the theory, reinforcing the understanding of the readers.\n\nWe also venture into the difference between simple static metadata retrieval and\nthe more intricate computations. This distinction echoes the breadth and depth of\nthe processes involved in Generative AI.\n\nAs we continue to explore and unravel the potential of Generative AI and LLMs,\nthis post serves as a fundamental building block. It creates a pathway for\nenthusiasts and professionals alike to delve deeper into this exciting field. By\nbreaking down complex concepts into comprehensible segments, it fosters an\nenvironment of learning and growth.\n\nThis marks just the beginning of our journey into the world of Generative AI. As\nwe dig deeper, we will continue to explore, learn and share with our readers. Stay\ntuned for more insightful content in this series. [1]\n\n[1] https://github.com/ibis-project/ibis-birdbrain\n\n## Next steps\n\nYou can get involved with [Ibis\nBirdbrain](https://github.com/ibis-project/ibis-birdbrain), our open-source data\n& AI project for building next-generation natural language interfaces to data.\n\n[Read the next post in this series](../llms-and-data-pt3).\n\n", 5 | "supporting": [ 6 | "index_files/figure-html" 7 | ], 8 | "filters": [], 9 | "includes": { 10 | "include-in-header": [ 11 | "\n\n\n" 12 | ] 13 | } 14 | } 15 | } -------------------------------------------------------------------------------- /docs/posts/llms-and-data-pt0/index.qmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "An introduction to Marvin and Ibis" 3 | author: "Cody Peterson" 4 | date: "2023-10-12" 5 | execute: 6 | warning: false 7 | categories: 8 | - "LLMs and data" 9 | --- 10 | 11 | ## Introduction 12 | 13 | In this "LLMs and data" series, we'll explore how to apply large-language models 14 | (LLMs) to data analytics. We'll walk through the steps to build Ibis Birdbrain. 15 | 16 | Throughout the series, we'll be using 17 | [Marvin](https://www.askmarvin.ai/welcome/overview/) and 18 | [Ibis](https://ibis-project.org). A brief introduction to each is provided 19 | below. 20 | 21 | ## Marvin 22 | 23 | [Marvin](https://www.askmarvin.ai/welcome/overview/) is an AI engineering 24 | framework that makes it easy to build up to an interactive conversational 25 | application. 26 | 27 | Marvin makes calls to an AI platform. You typically use an API key set as an 28 | environment variable -- in this case, we'll load a `.env` file that contians 29 | secrets for the AI platform that Marvin will use. We also set the large language 30 | model model. 31 | 32 | ```{python} 33 | import marvin # <1> 34 | 35 | from rich import print # <1> 36 | from time import sleep # <1> 37 | from dotenv import load_dotenv # <1> 38 | 39 | load_dotenv() # <2> 40 | 41 | # increase accuracy 42 | marvin.settings.llm_model = "openai/gpt-4" # <3> 43 | # decrease cost 44 | # marvin.settings.llm_model = "openai/gpt-3.5-turbo" # <3> 45 | 46 | test_str = "working with data and LLMs on 18+ data platforms is easy!" # <4> 47 | test_str 48 | ``` 49 | 50 | 1. Import the libraries we need. 51 | 2. Load the environment variable to setup Marvin to call our OpenAI account. 52 | 3. Configure the LLM model to use. 53 | 4. Some text to test on 54 | 55 | ### Functions 56 | 57 | AI functions are one of the building blocks in Marvin and allow yout to specify 58 | a typed python function with no code -- only a docstring -- to achieve a wide 59 | variety of tasks. 60 | 61 | We'll demonstrate this with an AI function that trnaslates text: 62 | 63 | ```{python} 64 | @marvin.ai_fn 65 | def translate(text: str, from_: str = "English", to: str = "Spanish") -> str: 66 | """translates the text""" 67 | 68 | translate(test_str) 69 | ``` 70 | 71 | ```{python} 72 | # | code-fold: true 73 | sleep(1) # <1> 74 | ``` 75 | 76 | 1. Avoid rate-limiting by waiting. 77 | 78 | ```{python} 79 | translate(translate(test_str), from_="Spanish", to="English") 80 | ``` 81 | 82 | ```{python} 83 | # | code-fold: true 84 | sleep(3) # <1> 85 | ``` 86 | 87 | 1. Avoid rate-limiting by waiting. 88 | 89 | ### Models 90 | 91 | AI models are another building block for generating python classes from input 92 | text. It's a great way to build structured data from unstructured data that can 93 | be customized for your needs. 94 | 95 | We'll demosntrate this with an AI model that translates text: 96 | 97 | ```{python} 98 | from pydantic import BaseModel, Field 99 | 100 | # decrease cost 101 | marvin.settings.llm_model = "openai/gpt-3.5-turbo" 102 | 103 | @marvin.ai_model 104 | class ExtractParts(BaseModel): 105 | """Extracts parts of a sentence""" 106 | subject: str = Field(..., description="The subject of the sentence.") 107 | objects: list[str] = Field(..., description="The objects of the sentence.") 108 | predicate: str = Field(..., description="The predicate of the sentence.") 109 | modifiers: list[str] = Field(..., description="The modifiers of the sentence.") 110 | 111 | ExtractParts(test_str) 112 | ``` 113 | 114 | ```{python} 115 | # | code-fold: true 116 | sleep(1) # <1> 117 | ``` 118 | 119 | 1. Avoid rate-limiting by waiting. 120 | 121 | ### Classifiers 122 | 123 | AI classifiers are another building block for generating python classes from 124 | input text. It's the most efficient (time and cost) method for applying LLMs as 125 | it only results in a single output token, selecting an output in a specified 126 | Enum. 127 | 128 | We'll demonstrate this by classifying the language of some text: 129 | 130 | ```{python} 131 | from enum import Enum 132 | 133 | # increase accuracy 134 | marvin.settings.llm_model = "openai/gpt-4" 135 | 136 | @marvin.ai_classifier 137 | class IdentifyLanguage(Enum): 138 | """Identifies the language of the text""" 139 | 140 | english = "English" 141 | spanish = "Spanish" 142 | 143 | 144 | IdentifyLanguage(test_str).value 145 | ``` 146 | 147 | ```{python} 148 | # | code-fold: true 149 | sleep(1) # <1> 150 | ``` 151 | 152 | 1. Avoid rate-limiting by waiting. 153 | 154 | ```{python} 155 | IdentifyLanguage(translate(test_str)).value 156 | ``` 157 | 158 | ```{python} 159 | # | code-fold: true 160 | sleep(3) # <1> 161 | ``` 162 | 163 | 1. Avoid rate-limiting by waiting. 164 | 165 | ## Ibis 166 | 167 | [Ibis](https://ibis-project.org) is the portable Python dataframe library that 168 | enables Ibis Birdbrain to work on many data platforms at native scale. 169 | 170 | Ibis makes calls to a data platform, providing an API but pushing the compute to 171 | (local or remote) query engines and storage. DuckDB is the default and we'll 172 | typically use it for demo puroses. You can work with an in-memory instance, but 173 | we'll often create a database file from example data: 174 | 175 | ```{python} 176 | import ibis # <1> 177 | 178 | con = ibis.connect("duckdb://penguins.ddb") # <2> 179 | t = ibis.examples.penguins.fetch() # <2> 180 | t = con.create_table("penguins", t.to_pyarrow(), overwrite=True) # <2> 181 | ``` 182 | 183 | 1. Import the libraries we need. 184 | 2. Setup the demo datain an Ibis backend. 185 | 186 | You will typically connect to an existing data platform via your corresponding 187 | Ibis backend and have access to a number of tables: 188 | 189 | ```{python} 190 | import ibis # <1> 191 | 192 | ibis.options.interactive = True # <2> 193 | 194 | con = ibis.connect("duckdb://penguins.ddb") # <3> 195 | t = con.table("penguins") # <3> 196 | ``` 197 | 198 | 1. Import Ibis. 199 | 2. Configure Ibis (interactive). 200 | 3. Connect to the data and load a table into a variable. 201 | 202 | ### Backend 203 | 204 | A backend provides the connection and basic management of the data platform. 205 | Above, we created the `con` variable that is an instance of a DuckDB backend: 206 | 207 | ```{python} 208 | con 209 | ``` 210 | 211 | It usually contains some tables: 212 | 213 | ```{python} 214 | con.list_tables() 215 | ``` 216 | 217 | We can access some internals of Ibis to see what backends are available: 218 | 219 | ::: {.callout-tip} 220 | Don't rely on accessing internals of Ibis in production. 221 | ::: 222 | 223 | ```{python} 224 | backends = [entrypoint.name for entrypoint in ibis.util.backend_entry_points()] 225 | backends 226 | ``` 227 | 228 | ### Table 229 | 230 | You typically work with a table, conventionally named `t` for demo or 231 | exploratory purposes: 232 | 233 | ```{python} 234 | t 235 | ``` 236 | 237 | When working with many tables, you should name them descriptively. 238 | 239 | ### Schema 240 | 241 | A table has a schema that Ibis maps to the data platform's data types: 242 | 243 | ```{python} 244 | t.schema() 245 | ``` 246 | 247 | ## LLMs and data: Marvin and Ibis 248 | 249 | You can use Marvin and Ibis together to easily apply LLMs to data. 250 | 251 | ```{python} 252 | from ibis.expr.schema import Schema 253 | from ibis.expr.types.relations import Table 254 | 255 | @marvin.ai_fn 256 | def sql_select( 257 | text: str, table_name: str = t.get_name(), schema: Schema = t.schema() 258 | ) -> str: 259 | """writes the SQL SELECT statement to query the table according to the text""" 260 | 261 | 262 | query = "the unique combination of species and islands" 263 | sql = sql_select(query).strip(";") 264 | sql 265 | ``` 266 | 267 | ```{python} 268 | t.sql(sql) 269 | ``` 270 | 271 | ```{python} 272 | # | code-fold: true 273 | sleep(3) # <1> 274 | ``` 275 | 276 | 1. Avoid rate-limiting by waiting. 277 | 278 | ```{python} 279 | t.sql(sql_select(query + " and include their counts in from highest to lowest").strip(";")) 280 | ``` 281 | 282 | ## Next steps 283 | 284 | You can get involved with [Ibis 285 | Birdbrain](https://github.com/ibis-project/ibis-birdbrain), our open-source data 286 | & AI project for building next-generation natural language interfaces to data. 287 | 288 | [Read the next post in this series](../llms-and-data-pt1). 289 | -------------------------------------------------------------------------------- /docs/_freeze/site_libs/clipboard/clipboard.min.js: -------------------------------------------------------------------------------- 1 | /*! 2 | * clipboard.js v2.0.11 3 | * https://clipboardjs.com/ 4 | * 5 | * Licensed MIT © Zeno Rocha 6 | */ 7 | !function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use strict";n.d(e,{default:function(){return b}});var e=n(279),i=n.n(e),e=n(370),u=n.n(e),e=n(817),r=n.n(e);function c(t){try{return document.execCommand(t)}catch(t){return}}var a=function(t){t=r()(t);return c("cut"),t};function o(t,e){var n,o,t=(n=t,o="rtl"===document.documentElement.getAttribute("dir"),(t=document.createElement("textarea")).style.fontSize="12pt",t.style.border="0",t.style.padding="0",t.style.margin="0",t.style.position="absolute",t.style[o?"right":"left"]="-9999px",o=window.pageYOffset||document.documentElement.scrollTop,t.style.top="".concat(o,"px"),t.setAttribute("readonly",""),t.value=n,t);return e.container.appendChild(t),e=r()(t),c("copy"),t.remove(),e}var f=function(t){var e=1\n```\n:::\n\n::: {.cell-output .cell-output-display execution_count=2}\n\n::: {.ansi-escaped-output}\n```{=html}\n
TableAttachment\n    **guid**: 5992ed9a-8a14-46c6-9da8-afdb3644a23d\n    **time**: 2024-03-05 11:22:40.051587\n    **name**: penguins\n    **desc**: \nibis.Schema {\n  species            string\n  island             string\n  bill_length_mm     float64\n  bill_depth_mm      float64\n  flipper_length_mm  int64\n  body_mass_g        int64\n  sex                string\n  year               int64\n}\n                **table**:\n┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┓\n┃ species ┃ island    ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ body_mass_g ┃ sex    ┃ year  ┃\n┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━┩\n│ string  │ string    │ float64        │ float64       │ int64             │ int64       │ string │ int64 │\n├─────────┼───────────┼────────────────┼───────────────┼───────────────────┼─────────────┼────────┼───────┤\n│ Adelie  │ Torgersen │           39.1 │          18.7 │               181 │        3750 │ male   │  2007 │\n│ Adelie  │ Torgersen │           39.5 │          17.4 │               186 │        3800 │ female │  2007 │\n│ Adelie  │ Torgersen │           40.3 │          18.0 │               195 │        3250 │ female │  2007 │\n│ Adelie  │ Torgersen │           NULL │          NULL │              NULL │        NULL │ NULL   │  2007 │\n│ Adelie  │ Torgersen │           36.7 │          19.3 │               193 │        3450 │ female │  2007 │\n│ Adelie  │ Torgersen │           39.3 │          20.6 │               190 │        3650 │ male   │  2007 │\n│ Adelie  │ Torgersen │           38.9 │          17.8 │               181 │        3625 │ female │  2007 │\n│ Adelie  │ Torgersen │           39.2 │          19.6 │               195 │        4675 │ male   │  2007 │\n│ Adelie  │ Torgersen │           34.1 │          18.1 │               193 │        3475 │ NULL   │  2007 │\n│ Adelie  │ Torgersen │           42.0 │          20.2 │               190 │        4250 │ NULL   │  2007 │\n│ …       │ …         │              … │             … │                 … │           … │ …      │     … │\n└─────────┴───────────┴────────────────┴───────────────┴───────────────────┴─────────────┴────────┴───────┘
\n```\n:::\n\n:::\n:::\n\n\nNotice the name, description (schema), and preview are automatically populated.\n\n## CodeAttachment\n\nA `CodeAttachment` contains code -- typically Python or SQL:\n\n::: {#89ecfbf6 .cell execution_count=3}\n``` {.python .cell-code}\nfrom ibis_birdbrain.attachments import CodeAttachment\n\na3 = CodeAttachment(content=\"select 1 as id\", language=\"sql\")\na3\n```\n\n::: {.cell-output .cell-output-display execution_count=3}\n```\nCodeAttachment\n **guid**: 8138742b-dcef-4735-b452-8481468e12da\n **time**: 2024-03-05 11:22:40.087917\n **name**: None\n **desc**: None\n **language**: sql\n **code**:\nselect 1 as id\n```\n:::\n:::\n\n\n", 6 | "supporting": [ 7 | "attachments_files" 8 | ], 9 | "filters": [], 10 | "includes": { 11 | "include-in-header": [ 12 | "\n\n\n" 13 | ] 14 | } 15 | } 16 | } -------------------------------------------------------------------------------- /src/ibis_birdbrain/tasks/sql.py: -------------------------------------------------------------------------------- 1 | # imports 2 | import ibis 3 | import marvin 4 | 5 | from Levenshtein import ratio 6 | 7 | from ibis_birdbrain.tasks import Task 8 | from ibis_birdbrain.logging import log 9 | from ibis_birdbrain.messages import Email, Message, Messages 10 | from ibis_birdbrain.attachments import ( 11 | Attachments, 12 | TableAttachment, 13 | SQLAttachment, 14 | ErrorAttachment, 15 | DatabaseAttachment, 16 | ) 17 | 18 | 19 | @ibis.udf.scalar.python 20 | def levenshtein_ratio(text1: str, text2: str) -> float: 21 | return ratio(text1, text2) 22 | 23 | # tasks 24 | class SearchTextTask(Task): 25 | """Ibis Birdbrain task to search cached text and SQL pair.""" 26 | 27 | def __init__( 28 | self, name: str = "search-cached-question", description: str = "Ibis Birdbrain search task" 29 | ) -> None: 30 | """Initialize the searchtask.""" 31 | super().__init__(name=name, description=description) 32 | 33 | def __call__(self, messages: Messages) -> Message: 34 | """Search text task.""" 35 | log.info("Search text task.") 36 | 37 | try: 38 | cached_table = messages[0].attachments.get_attachment_by_type(TableAttachment)[0].open() 39 | question = messages[-1].body 40 | matched_res = cached_table.mutate( 41 | similarity=levenshtein_ratio(ibis._.question, question) 42 | ).filter( 43 | ibis._.similarity > 0.75 # Threshold 44 | ).order_by( 45 | ibis._.similarity.desc() 46 | ).limit(1).to_pandas() 47 | 48 | log.info(f"similarity = {matched_res.iloc[0]['similarity']}") 49 | log.info(f"sql = {matched_res.iloc[0]['sql']}") 50 | 51 | sql_attachment = SQLAttachment( 52 | dialect=matched_res.iloc[0]['dialect'], 53 | content=matched_res.iloc[0]['sql'] 54 | ) 55 | 56 | return Email( 57 | body="search cached question called", 58 | attachments=[sql_attachment], 59 | to_address=messages[-1].from_address, 60 | from_address=self.name, 61 | ) 62 | except Exception as e: 63 | return Email( 64 | body=f"No similar question found", 65 | to_address=messages[-1].from_address, 66 | from_address=self.name, 67 | ) 68 | 69 | 70 | class TextToSQLTask(Task): 71 | """Ibis Birdbrain task to turn text into SQL.""" 72 | 73 | def __init__( 74 | self, name: str = "text-to-SQL", description: str = "Ibis Birdbrain SQL task" 75 | ) -> None: 76 | """Initialize the SQL task.""" 77 | super().__init__(name=name, description=description) 78 | 79 | def __call__(self, message: Message) -> Message: 80 | """Text to SQL task.""" 81 | log.info("Text to SQL task") 82 | 83 | # get the database attachment and table attachments 84 | table_attachments = message.attachments.get_attachment_by_type(TableAttachment) 85 | database_attachment = message.attachments.get_attachment_by_type(DatabaseAttachment) 86 | 87 | assert table_attachments is not None, "No table attachments found" 88 | assert database_attachment is not None, "No database attachment found" 89 | 90 | dialect = database_attachment.open().name 91 | 92 | # generate the SQL 93 | sql = self.text_to_sql( 94 | text=message.body, 95 | tables=table_attachments, 96 | data_description=database_attachment.description, 97 | dialect=dialect, 98 | ) 99 | sql_attachment = SQLAttachment(dialect=dialect, content=sql) 100 | 101 | # generate the response message 102 | response_message = Email( 103 | body="text to sql called", 104 | attachments=[sql_attachment], 105 | to_address=message.from_address, 106 | from_address=self.name, 107 | ) 108 | return response_message 109 | 110 | @staticmethod 111 | @marvin.fn 112 | def _text_to_sql( 113 | text: str, tables: Attachments, data_description: str, dialect: str 114 | ) -> str: 115 | """ 116 | Generates correct, simple, and human-readable SQL based on the input 117 | `text`, `tables, and `data_description`, returns a SQL SELECT statement 118 | in the `dialect`. 119 | 120 | The `text` will contain a query in natural language to be answered. 121 | 122 | The `tables` will contain the table names and their schemas, alongside 123 | some metadata that can be ignored. DO NOT change the spelling or casing 124 | and only generate SQL for the provided tables. 125 | 126 | DO NOT add a LIMIT unless specifically told otherwise. 127 | 128 | Return (select) ALL possible columns unless specifically told otherwise. 129 | 130 | After joins, ensure that the columns are correctly qualified with the table name. 131 | """ 132 | 133 | def text_to_sql( 134 | self, text: str, tables: Attachments, data_description: str, dialect="duckdb" 135 | ) -> str: 136 | """Convert text to SQL.""" 137 | return ( 138 | self._text_to_sql( 139 | text=text, 140 | tables=tables, 141 | data_description=data_description, 142 | dialect=dialect, 143 | ) 144 | .strip() 145 | .strip(";") 146 | ) 147 | 148 | 149 | class ExecuteSQLTask(Task): 150 | """Ibis Birdbrain task to execute SQL.""" 151 | 152 | def __init__( 153 | self, name: str = "execute-SQL", description: str = "Ibis Birdbrain SQL task" 154 | ) -> None: 155 | """Initialize the SQL task.""" 156 | super().__init__(name=name, description=description) 157 | 158 | def __call__(self, message: Message) -> Message: 159 | """Execute the SQL task.""" 160 | log.info("Executing the SQL task") 161 | 162 | # get the database attachment and sql attachments 163 | sql_attachment = message.attachments.get_attachment_by_type(SQLAttachment) 164 | database_attachment = message.attachments.get_attachment_by_type(DatabaseAttachment) 165 | 166 | con = database_attachment.open() 167 | sql = sql_attachment.open() 168 | 169 | # execute the SQL 170 | try: 171 | table = con.sql(sql) 172 | attachment = TableAttachment(name="table", content=table) 173 | except Exception as e: 174 | log.error("Error executing the SQL") 175 | log.error("SQL: " + sql) 176 | log.error(e) 177 | attachment = ErrorAttachment(name="error", content=str(e)) 178 | 179 | response_message = Email( 180 | body="execute SQL called", 181 | attachments=[attachment, sql_attachment], 182 | to_address=message.from_address, 183 | from_address=self.name, 184 | ) 185 | return response_message 186 | 187 | 188 | class FixSQLTask(Task): 189 | """Ibis Birdbrain task to fix SQL.""" 190 | 191 | def __init__( 192 | self, name: str = "fix-SQL", description: str = "Ibis Birdbrain SQL task" 193 | ) -> None: 194 | """Initialize the SQL task.""" 195 | super().__init__(name=name, description=description) 196 | 197 | def __call__(self, message: Message) -> Message: 198 | """Fix the SQL task.""" 199 | log.info("Fixing the SQL task") 200 | 201 | database_attachment = message.attachments.get_attachment_by_type(DatabaseAttachment) 202 | table_attachments = message.attachments.get_attachment_by_type(TableAttachment) 203 | sql_attachment = message.attachments.get_attachment_by_type(SQLAttachment) 204 | error_attachment = message.attachments.get_attachment_by_type(ErrorAttachment) 205 | 206 | assert database_attachment is not None, "No database attachment found" 207 | assert table_attachments is not None, "No table attachments found" 208 | assert sql_attachment is not None, "No SQL attachment found" 209 | assert error_attachment is not None, "No error attachment found" 210 | 211 | sql = self.fix_text_to_sql( 212 | text=message.body, 213 | sql=sql_attachment.open(), 214 | error=error_attachment.open(), 215 | tables=table_attachments, 216 | data_description=database_attachment.description, 217 | dialect=sql_attachment.dialect 218 | ) 219 | 220 | response_message = Email( 221 | body="fix SQL called", 222 | attachments=[SQLAttachment(dialect=sql_attachment.dialect, content=sql)], 223 | to_address=message.from_address, 224 | from_address=self.name, 225 | ) 226 | return response_message 227 | 228 | @staticmethod 229 | @marvin.fn 230 | def _fix_text_to_sql( 231 | text: str, 232 | sql: str, 233 | error: str, 234 | tables: Attachments, 235 | data_description: str, 236 | dialect: str, 237 | ) -> str: 238 | """ 239 | Fixes the `sql` to answer the `text` based on the `error`. 240 | 241 | Using `tables, and `data_description`, returns a SQL SELECT statement 242 | in the `dialect` that fixes the input SQL. 243 | 244 | The `text` will contain a query in natural language to be answered. 245 | 246 | The `tables` will contain the table names and their schemas, alongside 247 | some metadata that can be ignored. DO NOT change the spelling or casing 248 | and only generate SQL for the provided tables. 249 | 250 | DO NOT add a LIMIT unless specifically told otherwise. 251 | 252 | Return (select) ALL possible columns unless specifically told otherwise. 253 | 254 | After joins, ensure that the columns are correctly qualified with the table name. 255 | """ 256 | 257 | def fix_text_to_sql( 258 | self, 259 | text: str, 260 | sql: str, 261 | error: str, 262 | tables: Attachments, 263 | data_description: str, 264 | dialect="duckdb", 265 | ) -> str: 266 | """Convert text to SQL.""" 267 | return ( 268 | self._fix_text_to_sql( 269 | text=text, 270 | sql=sql, 271 | error=error, 272 | tables=tables, 273 | data_description=data_description, 274 | dialect=dialect, 275 | ) 276 | .strip() 277 | .strip(";") 278 | ) 279 | -------------------------------------------------------------------------------- /docs/posts/llms-and-data-pt1/index.qmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Three approaches" 3 | author: "Cody Peterson" 4 | date: "2023-10-13" 5 | execute: 6 | warning: false 7 | categories: 8 | - "LLMs and data" 9 | --- 10 | 11 | ## Introduction 12 | 13 | The thought of using natural language to transform and analyze data is 14 | appealing. This post assumes familiarity with Marvin and Ibis -- [read the 15 | previous post in the series for a quick overview](../llms-and-data-pt0). 16 | 17 | ## Approaches 18 | 19 | When discussed at Voltron Data, we identified three distinct approaches to 20 | applying LLMs to data analytics that can be implemented today: 21 | 22 | 1. LLM writes an analytic code 23 | 2. LLM writes an analytic subroutine 24 | 3. Use LLM in an analytic subroutine 25 | 26 | While these three approaches are not an exhaustive list of how LLMs can be 27 | applied to data, they can be easily understood and implemented with Ibis and 28 | Marvin in a few lines of code. Together with these two open-source tools, we can 29 | build a natural language interface for data analytics that supports 18+ 30 | backends. 31 | 32 | But first, let's demonstrate the three approaches. 33 | 34 | ### Approach 1: LLM writes analytic code 35 | 36 | State of the art (SoTA) LLMs are decent at generating SQL out of the box. We can 37 | be clever to handle errors, retries, and more, but in its simplest form: 38 | 39 | ```{python} 40 | # | code-fold: true 41 | import ibis # <1> 42 | import marvin # <1> 43 | 44 | from rich import print # <1> 45 | from time import sleep # <1> 46 | from dotenv import load_dotenv # <1> 47 | 48 | load_dotenv() # <2> 49 | 50 | con = ibis.connect("duckdb://penguins.ddb") # <3> 51 | t = ibis.examples.penguins.fetch() # <3> 52 | t = con.create_table("penguins", t.to_pyarrow(), overwrite=True) # <3> 53 | ``` 54 | 55 | 1. Import the libraries we need. 56 | 2. Load the environment variable to setup Marvin to call our OpenAI account. 57 | 3. Setup the demo datain an Ibis backend. 58 | 59 | ```{python} 60 | import ibis # <1> 61 | import marvin # <1> 62 | 63 | from ibis.expr.schema import Schema # <1> 64 | from ibis.expr.types.relations import Table # <1> 65 | 66 | 67 | ibis.options.interactive = True # <2> 68 | marvin.settings.llm_model = "openai/gpt-4" # <2> 69 | ``` 70 | 71 | 1. Import Ibis and Marvin. 72 | 2. Configure Ibis and Marvin 73 | 74 | ```{python} 75 | @marvin.ai_fn # <1> 76 | def _generate_sql_select( 77 | text: str, table_name: str, table_schema: Schema 78 | ) -> str: # <1> 79 | """Generate SQL SELECT from text.""" # <1> 80 | 81 | 82 | def sql_from_text(text: str, t: Table) -> Table: # <2> 83 | """Run SQL from text.""" # <2> 84 | return t.sql(_generate_sql_select(text, t.get_name(), t.schema()).strip(";")) # <2> 85 | ``` 86 | 87 | 1. A non-deterministic, LLM-powered AI function. 88 | 2. A deterministic, human-authored function that calls the AI function. 89 | 90 | ```{python} 91 | t2 = sql_from_text("the unique combination of species and islands", t) 92 | t2 93 | ``` 94 | 95 | ```{python} 96 | # | code-fold: true 97 | sleep(3) # <1> 98 | ``` 99 | 100 | 1. Avoid rate-limiting by waiting. 101 | 102 | ```{python} 103 | t3 = sql_from_text( 104 | "the unique combination of species and islands, with their counts, ordered from highest to lowest, and name that column just 'count'", 105 | t, 106 | ) 107 | t3 108 | ``` 109 | 110 | ```{python} 111 | # | code-fold: true 112 | sleep(3) # <1> 113 | ``` 114 | 115 | 1. Avoid rate-limiting by waiting. 116 | 117 | This works well-enough for simple cases and can be expanded to handle complex 118 | ones. In many scenarios, it may be easier to express a query in English or 119 | another language than to write it in SQL, especially if working across multiple 120 | SQL dialects. 121 | 122 | SQL isn't standard, with many dialects across data platforms. Ibis works around 123 | this by providing a standard Python API for analytic code but must make 124 | compromises to support many data platforms, often via SQL in their native 125 | dialect. [Substrait](https://substrait.io) is a newer project that aims to solve 126 | this problem by providing a standard, portable, and extensible intermediary 127 | representation (IR) for data transformation code that Ibis and data platforms 128 | could all standardize on. Substrait is still in the early stages of development, 129 | but it's worth keeping an eye on and will be adopted in Ibis once supported 130 | across many data platforms. 131 | 132 | For now, we'll focus on generating SQL and Python analytical code with LLMs. 133 | 134 | ### Approach 2: LLM writes an analytical subroutine 135 | 136 | If more complex logic needs to be expressed, SoTA LLMs are also decent at 137 | writing Python and a number of other programming languages that are used in 138 | analytical subroutines. Many data platforms support user-defined functions 139 | (UDFs) in Python or some other language. We'll stick to scalar Python UDFs via 140 | DuckDB to demonstrate the concept: 141 | 142 | ```{python} 143 | @marvin.ai_fn # <1> 144 | def _generate_python_function(text: str) -> str: # <1> 145 | """Generate a simple, typed, correct Python function from text.""" # <1> 146 | 147 | 148 | def create_udf_from_text(text: str) -> str: # <2> 149 | """Create a UDF from text.""" # <2> 150 | return f""" 151 | import ibis 152 | 153 | @ibis.udf.scalar.python 154 | {_generate_python_function(text)} 155 | """.strip() # <2> 156 | ``` 157 | 158 | 1. A non-deterministic, LLM-powered AI function. 159 | 2. A deterministic, human-authored function that calls the AI function. 160 | 161 | ```{python} 162 | udf = create_udf_from_text( 163 | "a function named count_vowels that given an input string, returns an int w/ the number of vowels (y_included as a boolean option defaulted to False)" 164 | ) 165 | print(udf) 166 | exec(udf) 167 | ``` 168 | 169 | ```{python} 170 | # | code-fold: true 171 | sleep(3) # <1> 172 | ``` 173 | 174 | 1. Avoid rate-limiting by waiting. 175 | 176 | ```{python} 177 | t4 = t3.mutate( 178 | species_vowel_count=count_vowels(t3.species), 179 | island_vowel_count=count_vowels(t3.island), 180 | ) 181 | t4 182 | ``` 183 | 184 | ```{python} 185 | # | code-fold: true 186 | sleep(3) # <1> 187 | ``` 188 | 189 | 1. Avoid rate-limiting by waiting. 190 | 191 | In this case, there's no reason not to have a human in the loop reviewing the 192 | output code and committing it for production use. This could be useful for quick 193 | prototyping or, given a box of tools in the form of UDFs, 194 | working through a natural language interface. 195 | 196 | ### Approach 3: Use LLM in an analytical subroutine 197 | 198 | We can also call the LLM once-per-row in the table via a subroutine. For 199 | variety, we'll use an [AI model](https://www.askmarvin.ai/components/ai_model/) 200 | instead of an [AI function](https://www.askmarvin.ai/components/ai_function/): 201 | 202 | ```{python} 203 | from pydantic import BaseModel, Field # <1> 204 | 205 | # decrease cost 206 | marvin.settings.llm_model = "openai/gpt-3.5-turbo-16k" # <2> 207 | 208 | 209 | @marvin.ai_model # <3> 210 | class VowelCounter(BaseModel): # <3> 211 | """Count vowels in a string.""" # <3> 212 | 213 | include_y: bool = Field(False, description="Include 'y' as a vowel.") # <3> 214 | # num_a: int = Field(..., description="The number of 'a' vowels.") # <3> 215 | # num_e: int = Field(..., description="The number of 'e' vowels.") # <3> 216 | # num_i: int = Field(..., description="The number of 'i' vowels.") # <3> 217 | # num_o: int = Field(..., description="The number of 'o' vowels.") # <3> 218 | # num_u: int = Field(..., description="The number of 'u' vowels.") # <3> 219 | # num_y: int = Field(..., description="The number of 'y' vowels.") # <3> 220 | num_total: int = Field(..., description="The total number of vowels.") # <3> 221 | 222 | 223 | VowelCounter("hello world") # <4> 224 | ``` 225 | 226 | 1. Additional imports for Pydantic. 227 | 2. Configure Marvin to use a cheaper model. 228 | 3. A non-deterministic, LLM-powered AI model. 229 | 4. Call the AI model on some text. 230 | 231 | Then we'll have the LLM write the UDF that calls the LLM, just to be fancy: 232 | 233 | ```{python} 234 | udf = create_udf_from_text( 235 | "a function named count_vowels_ai that given an input string, calls VowelCounter on it and returns the num_total attribute of that result" 236 | ) 237 | print(udf) 238 | exec(udf) 239 | ``` 240 | 241 | ```{python} 242 | # | code-fold: true 243 | sleep(3) # <1> 244 | ``` 245 | 246 | 1. Avoid rate-limiting by waiting. 247 | 248 | ```{python} 249 | t5 = t3.mutate( 250 | species_vowel_count=count_vowels_ai(t3.species), 251 | island_vowel_count=count_vowels_ai(t3.island), 252 | ) 253 | t5 254 | ``` 255 | 256 | Notice that in this UDF, unlike in the previous example, a LLM is being called 257 | (possibly several times) for each row in the table. This is a very expensive 258 | operation and we'll need to be careful about how we use it in practice. 259 | 260 | ```{python} 261 | # | code-fold: true 262 | sleep(3) # <1> 263 | ``` 264 | 265 | 1. Avoid rate-limiting by waiting. 266 | 267 | ## Summary 268 | 269 | To summarize this post: 270 | 271 | ```{python} 272 | from rich import print 273 | 274 | with open("index.qmd", "r") as f: 275 | self_text = f.read() 276 | 277 | # increase accuracy 278 | marvin.settings.llm_model = "openai/gpt-4" 279 | 280 | @marvin.ai_model 281 | class Summary(BaseModel): 282 | """Summary of text.""" 283 | 284 | summary_line: str = Field(..., description="The one-line summary of the text.") 285 | summary_paragraph: str = Field( 286 | ..., description="The one-paragraph summary of the text." 287 | ) 288 | conclusion: str = Field( 289 | ..., description="The conclusion the reader should draw from the text." 290 | ) 291 | key_points: list[str] = Field(..., description="The key points of the text.") 292 | critiques: list[str] = Field( 293 | ..., description="Professional, fair critiques of the text." 294 | ) 295 | suggested_improvements: list[str] = Field( 296 | ..., description="Suggested improvements for the text." 297 | ) 298 | sentiment: float = Field(..., description="The sentiment of the text.") 299 | sentiment_label: str = Field(..., description="The sentiment label of the text.") 300 | author_bias: str = Field(..., description="The author bias of the text.") 301 | 302 | 303 | print(Summary(self_text)) 304 | ``` 305 | 306 | ## Next steps 307 | 308 | You can get involved with [Ibis 309 | Birdbrain](https://github.com/ibis-project/ibis-birdbrain), our open-source data 310 | & AI project for building next-generation natural language interfaces to data. 311 | 312 | [Read the next post in this series](../llms-and-data-pt2). 313 | -------------------------------------------------------------------------------- /docs/_freeze/tutorials/python/execute-results/html.json: -------------------------------------------------------------------------------- 1 | { 2 | "hash": "5473aeb233c43d87b1bfa8acc656ed98", 3 | "result": { 4 | "engine": "jupyter", 5 | "markdown": "---\ntitle: 'Tutorial: Python'\n---\n\n\n\n## Prerequisites\n\n1. [Install Ibis Birdbrain](/install.qmd)\n\n## Overview\n\nYou can use Ibis Birdbrain in Python.\n\n## Setup the bot\n\nFirst, import relevant modules:\n\n\n::: {#935cf68c .cell execution_count=1}\n``` {.python .cell-code}\nimport ibis\n\nfrom ibis_birdbrain import Bot\n```\n:::\n\n\nSet Ibis interactive mode:\n\n::: {#9ee1c955 .cell execution_count=2}\n``` {.python .cell-code}\nibis.options.interactive = True\n```\n:::\n\n\n### Create an Ibis connection\n\nCreate an Ibis connection to your database:\n\n::: {.callout-warning}\nWe'll create a demo database for this tutorial.\n:::\n\n::: {#1b36ceee .cell execution_count=3}\n``` {.python .cell-code}\ncon = ibis.connect(\"duckdb://penguins.ddb\")\ncon.create_table(\n \"penguins\", ibis.examples.penguins.fetch().to_pyarrow(), overwrite=True\n)\ncon = ibis.connect(\"duckdb://penguins.ddb\")\ncon.list_tables()\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nINFO:pins.cache:cache file: /Users/cody/Library/Caches/pins-py/gcs_332a30997e141da0e08f15fbfae8b3c3ec90463922d117a96fa3b1bef85a2a4c/penguins/20230905T090411Z-9aae2/data.txt\nINFO:pins.cache:cache file: /Users/cody/Library/Caches/pins-py/gcs_332a30997e141da0e08f15fbfae8b3c3ec90463922d117a96fa3b1bef85a2a4c/penguins/20230905T090411Z-9aae2/penguins.csv.gz\n```\n:::\n\n::: {.cell-output .cell-output-display execution_count=3}\n```\n['penguins']\n```\n:::\n:::\n\n\n### Create the bot\n\nYou'll create the bot by passing in the connection:\n\n::: {.callout-tip}\nFor increased accuracy, you should also pass in a `data_description` containing\ninformation about the dataset. This could be fetched from the database itself,\nmanually created, or otherwise obtained.\n\nYou should not include table names and schemas -- this will be inferred\nautomatically.\n:::\n\n::: {#f4eab662 .cell execution_count=4}\n``` {.python .cell-code}\nbot = Bot(con=con, data_description=\"the Palmer Penguins dataset\")\nbot\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nINFO:root:Bot birdbrain initialized...\n```\n:::\n\n::: {.cell-output .cell-output-display execution_count=4}\n```\n\n```\n:::\n:::\n\n\n### Test the bot\n\nYou can ask the bot questions:\n\n::: {#fe023103 .cell execution_count=5}\n``` {.python .cell-code}\nres = bot(\"\"\"give me the counts of penguins by species and island from highest\nto lowest\"\"\")\nres\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nINFO:root:Bot birdbrain called with text: give me the counts of penguins by species and island from highest\nto lowest\nINFO:root:Selected flow: data\nINFO:root:Executing the data flow\nINFO:root:Text to SQL task\nINFO:httpx:HTTP Request: POST https://birdbrain-eh.openai.azure.com/openai/deployments/gpt-4-turbo/chat/completions?api-version=2023-12-01-preview \"HTTP/1.1 200 OK\"\nINFO:root:Executing the SQL task\n```\n:::\n\n::: {.cell-output .cell-output-display}\n```{=html}\n
\n```\n:::\n\n::: {.cell-output .cell-output-display}\n```{=html}\n
\n```\n:::\n\n::: {.cell-output .cell-output-display execution_count=5}\n\n::: {.ansi-escaped-output}\n```{=html}\n
To: user\nFrom: birdbrain\nSubject: give me the counts of pengu...\nSent at: 2024-03-05 12:18:51.951695\nMessage: 67c02541-9406-4de5-b99e-21cc5dc8d3c5\nIbis Birdbrain has attached the results.\nAttachments:\nCodeAttachment\n    **guid**: e2152990-296e-4b92-a159-05af61b5334a\n    **time**: 2024-03-05 12:18:51.949484\n    **name**: None\n    **desc**: None\n    **language**: sql\n    **code**:\nSELECT species, island, COUNT(*) AS count\nFROM penguins\nGROUP BY species, island\nORDER BY COUNT(*) DESC\nTableAttachment\n    **guid**: e6acb87c-b4ca-4fa7-8292-638d3fc0b6e8\n    **time**: 2024-03-05 12:18:51.951600\n    **name**: None\n    **desc**: \nibis.Schema {\n  species  string\n  island   string\n  count    int64\n}\n                **table**:\n┏━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━┓\n┃ species   ┃ island    ┃ count ┃\n┡━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━┩\n│ string    │ string    │ int64 │\n├───────────┼───────────┼───────┤\n│ Gentoo    │ Biscoe    │   124 │\n│ Chinstrap │ Dream     │    68 │\n│ Adelie    │ Dream     │    56 │\n│ Adelie    │ Torgersen │    52 │\n│ Adelie    │ Biscoe    │    44 │\n└───────────┴───────────┴───────┘
\n```\n:::\n\n:::\n:::\n\n\n### Get attachments\n\nYou can get the table from the attachment:\n\n::: {#822b287e .cell execution_count=6}\n``` {.python .cell-code}\nt = res.attachments[-1].open()\nt\n```\n\n::: {.cell-output .cell-output-display execution_count=6}\n```{=html}\n
┏━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━┓\n┃ species   ┃ island    ┃ count ┃\n┡━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━┩\n│ string    │ string    │ int64 │\n├───────────┼───────────┼───────┤\n│ Gentoo    │ Biscoe    │   124 │\n│ Chinstrap │ Dream     │    68 │\n│ Adelie    │ Dream     │    56 │\n│ Adelie    │ Torgersen │    52 │\n│ Adelie    │ Biscoe    │    44 │\n└───────────┴───────────┴───────┘\n
\n```\n:::\n:::\n\n\nAnd do whatever you want with it:\n\n::: {#f616ff8d .cell execution_count=7}\n``` {.python .cell-code}\nt.order_by(ibis._[\"count\"].asc())\n```\n\n::: {.cell-output .cell-output-display execution_count=7}\n```{=html}\n
┏━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━┓\n┃ species   ┃ island    ┃ count ┃\n┡━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━┩\n│ string    │ string    │ int64 │\n├───────────┼───────────┼───────┤\n│ Adelie    │ Biscoe    │    44 │\n│ Adelie    │ Torgersen │    52 │\n│ Adelie    │ Dream     │    56 │\n│ Chinstrap │ Dream     │    68 │\n│ Gentoo    │ Biscoe    │   124 │\n└───────────┴───────────┴───────┘\n
\n```\n:::\n:::\n\n\n## Next steps\n\nExplore some data with Ibis Birdbrain and [let us know how it\ngoes!](https://github.com/ibis-project/ibis-birdbrain/issues/new)\n\n",
 6 |     "supporting": [
 7 |       "python_files/figure-html"
 8 |     ],
 9 |     "filters": [],
10 |     "includes": {
11 |       "include-in-header": [
12 |         "\n\n\n"
13 |       ]
14 |     }
15 |   }
16 | }


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