├── tests └── __init__.py ├── gpt_pdf_bot ├── __init__.py ├── shared.py ├── types.py ├── ingest.py └── bot.py ├── .gitignore ├── .env.example ├── static └── example.png ├── papers ├── Machine Learning at Scale.pdf ├── Hidden Technical Debt in Machine Learning Systems.pdf ├── A Few Useful Things to Know about Machine Learning.pdf ├── Software Engineering Practices for Machine Learning.pdf ├── Operationalizing Machine Learning - An Interview Study.pdf ├── Rules of Machine Learning - Best Practices for ML Engineering.pdf ├── What’s your ML Test Score - A rubric for ML production systems.pdf ├── Machine Learning - The High-Interest Credit Card of Technical Debt.pdf └── The ML Test Score - A Rubric for ML Production Readiness and Technical Debt Reduction.pdf ├── pyproject.toml ├── main.py ├── README.md └── poetry.lock /tests/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /gpt_pdf_bot/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | .env 2 | .idea 3 | -------------------------------------------------------------------------------- /.env.example: -------------------------------------------------------------------------------- 1 | OPENAI_API_KEY=YOUR_KEY_HERE -------------------------------------------------------------------------------- /static/example.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gjreda/scratch-pdf-bot/HEAD/static/example.png -------------------------------------------------------------------------------- /papers/Machine Learning at Scale.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gjreda/scratch-pdf-bot/HEAD/papers/Machine Learning at Scale.pdf -------------------------------------------------------------------------------- /papers/Hidden Technical Debt in Machine Learning Systems.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gjreda/scratch-pdf-bot/HEAD/papers/Hidden Technical Debt in Machine Learning Systems.pdf -------------------------------------------------------------------------------- /papers/A Few Useful Things to Know about Machine Learning.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gjreda/scratch-pdf-bot/HEAD/papers/A Few Useful Things to Know about Machine Learning.pdf -------------------------------------------------------------------------------- /papers/Software Engineering Practices for Machine Learning.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gjreda/scratch-pdf-bot/HEAD/papers/Software Engineering Practices for Machine Learning.pdf -------------------------------------------------------------------------------- /papers/Operationalizing Machine Learning - An Interview Study.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gjreda/scratch-pdf-bot/HEAD/papers/Operationalizing Machine Learning - An Interview Study.pdf -------------------------------------------------------------------------------- /papers/Rules of Machine Learning - Best Practices for ML Engineering.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gjreda/scratch-pdf-bot/HEAD/papers/Rules of Machine Learning - Best Practices for ML Engineering.pdf -------------------------------------------------------------------------------- /papers/What’s your ML Test Score - A rubric for ML production systems.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gjreda/scratch-pdf-bot/HEAD/papers/What’s your ML Test Score - A rubric for ML production systems.pdf -------------------------------------------------------------------------------- /papers/Machine Learning - The High-Interest Credit Card of Technical Debt.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gjreda/scratch-pdf-bot/HEAD/papers/Machine Learning - The High-Interest Credit Card of Technical Debt.pdf -------------------------------------------------------------------------------- /papers/The ML Test Score - A Rubric for ML Production Readiness and Technical Debt Reduction.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gjreda/scratch-pdf-bot/HEAD/papers/The ML Test Score - A Rubric for ML Production Readiness and Technical Debt Reduction.pdf -------------------------------------------------------------------------------- /gpt_pdf_bot/shared.py: -------------------------------------------------------------------------------- 1 | from dotenv import load_dotenv 2 | from typing import List 3 | import openai 4 | import os 5 | 6 | load_dotenv() 7 | 8 | 9 | def embed_text(texts: List[str]) -> List[List[float]]: 10 | res = openai.Embedding.create( 11 | engine="text-embedding-ada-002", 12 | input=texts, 13 | api_key=os.environ["OPENAI_API_KEY"], 14 | ) 15 | return [record["embedding"] for record in res["data"]] 16 | -------------------------------------------------------------------------------- /gpt_pdf_bot/types.py: -------------------------------------------------------------------------------- 1 | from dataclasses import dataclass, field 2 | from typing import Any, List, Dict 3 | 4 | 5 | @dataclass 6 | class Chunk: 7 | text: str 8 | metadata: Dict[str, Any] = field(default_factory=dict) 9 | 10 | 11 | @dataclass 12 | class Page: 13 | text: str 14 | page_num: int 15 | 16 | 17 | @dataclass 18 | class Document: 19 | source: str 20 | metadata: Dict[str, Any] 21 | pages: List[Page] = field(default_factory=list) 22 | -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [tool.poetry] 2 | name = "gpt-pdf-bot" 3 | version = "0.1.0" 4 | description = "" 5 | authors = ["Greg Reda "] 6 | readme = "README.md" 7 | packages = [{include = "gpt_pdf_bot"}] 8 | 9 | [tool.poetry.dependencies] 10 | python = "^3.9,<3.13" 11 | openai = "^0.27.6" 12 | pypdf = "^3.8.1" 13 | lancedb = "^0.1.2" 14 | python-dotenv = "^1.0.0" 15 | pandas = "^2.1.1" 16 | 17 | 18 | [tool.poetry.group.dev.dependencies] 19 | ipython = "^8.13.2" 20 | 21 | [build-system] 22 | requires = ["poetry-core"] 23 | build-backend = "poetry.core.masonry.api" 24 | -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | from argparse import ArgumentParser 2 | from pathlib import Path 3 | 4 | from gpt_pdf_bot.bot import ChatBot 5 | from gpt_pdf_bot.ingest import PdfIngestionPipeline 6 | 7 | 8 | def main(pdf_directory: str): 9 | pipeline = PdfIngestionPipeline(pdf_directory=pdf_directory) 10 | pipeline.run() 11 | 12 | bot = ChatBot(table_name=pdf_directory) 13 | bot.run() 14 | 15 | 16 | if __name__ == "__main__": 17 | parser = ArgumentParser() 18 | parser.add_argument( 19 | "--pdf_directory", help="Path to directory containing PDFs to ingest" 20 | ) 21 | args = parser.parse_args() 22 | 23 | path = Path(args.pdf_directory) 24 | if not path.exists(): 25 | raise ValueError(f"Directory {path} does not exist") 26 | 27 | main(pdf_directory=path.name) 28 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # GPT PDF Chatbot 2 | 3 | This is a prototype of a chatbot that can answer questions about PDFs. It uses [OpenAI's API](https://openai.com/blog/openai-api/) for language modeling, and [LanceDB](https://lancedb.github.io/lancedb/) for vector storage and retrieval. 4 | 5 | ## Setup 6 | 7 | This uses [Poetry](https://python-poetry.org/) for dependency management. To install dependencies: 8 | 9 | ``` 10 | $ poetry install 11 | ``` 12 | 13 | You'll also need to create a `.env` file and add your `OPENAI_API_KEY` to it (see `.env.example`). 14 | 15 | ## Usage 16 | 17 | The command below will run the pipeline on the `papers` directory, which contains a few PDFs. It will then start a REPL where you can ask questions about the PDFs. You can exit the Q&A loop by typing "exit" or cmd/ctrl + c. 18 | 19 | ``` 20 | $ poetry run python main.py --pdf_directory=papers 21 | ``` 22 | 23 | Note that the LanceDB database is included in this repo, so the creation and storage of embeddings within the ingestion code will be skipped unless you delete the `.lancedb` directory. This is included to make it easier to run the code without having to wait for the embeddings to be generated. 24 | 25 | ## Demo 26 | 27 | I wrote a brief blog post and recorded a demo video of this project [here](https://gregreda.com/2023/10/26/prototyping-a-pdf-chatbot-from-scratch/). 28 | 29 | [![Demo video](https://img.youtube.com/vi/r4LAQbu3sd0/0.jpg)](https://www.youtube.com/watch?v=r4LAQbu3sd0) 30 | -------------------------------------------------------------------------------- /gpt_pdf_bot/ingest.py: -------------------------------------------------------------------------------- 1 | from dotenv import load_dotenv 2 | from pathlib import Path 3 | from typing import List 4 | import lancedb 5 | import pypdf 6 | 7 | from gpt_pdf_bot.shared import embed_text 8 | from gpt_pdf_bot.types import Chunk, Document, Page 9 | 10 | load_dotenv() 11 | 12 | 13 | class PdfIngestionPipeline: 14 | def __init__(self, pdf_directory: str): 15 | self.pdf_directory = Path(pdf_directory) 16 | self.db = lancedb.connect(uri=".lancedb") 17 | 18 | def run(self) -> None: 19 | print(f"Running ingestion pipeline on {self.pdf_directory}", end="\n\n") 20 | documents = self.read_pdfs() 21 | print(f"Read {len(documents)} documents", end="\n\n") 22 | 23 | print("Splitting documents into chunks", end="\n\n") 24 | chunks = self.chunk_documents(documents=documents) 25 | 26 | print("Creating and persisting embeddings", end="\n\n") 27 | table = self.create_and_persist_embeddings(chunks=chunks) 28 | 29 | def read_pdfs(self) -> List[Document]: 30 | documents = [] 31 | 32 | for pdf in self.pdf_directory.glob("*.pdf"): 33 | print(f"Reading {pdf.name}") 34 | reader = pypdf.PdfReader(pdf) 35 | doc = Document( 36 | source=pdf.name, 37 | metadata=reader.metadata, 38 | ) 39 | 40 | for i, page in enumerate(reader.pages): 41 | doc.pages.append( 42 | Page( 43 | page_num=i, 44 | text=page.extract_text(), 45 | ) 46 | ) 47 | 48 | documents.append(doc) 49 | return documents 50 | 51 | def chunk_documents( 52 | self, 53 | documents: List[Document], 54 | chunk_size: int = 1000, 55 | chunk_overlap: int = 200, 56 | ) -> List[Chunk]: 57 | # TODO: This is a naive implementation that is not smart about splitting on word boundaries 58 | chunks = [] 59 | 60 | for doc in documents: 61 | for page in doc.pages: 62 | for i in range(0, len(page.text), chunk_size - chunk_overlap): 63 | chunks.append( 64 | Chunk( 65 | text=page.text[i : i + chunk_size], 66 | metadata={"source": doc.source, "page_num": page.page_num}, 67 | ) 68 | ) 69 | return chunks 70 | 71 | def create_and_persist_embeddings(self, chunks: List[Chunk]): 72 | # check if table exists 73 | # TODO: this is a naive way to not recompute embeddings for the same pdfs 74 | if self.pdf_directory.name in self.db.table_names(): 75 | return self.db[self.pdf_directory.name] 76 | 77 | texts = [chunk.text for chunk in chunks] 78 | embeddings = embed_text(texts=texts) 79 | 80 | merged = [] 81 | for chunk, emb in zip(chunks, embeddings): 82 | merged.append( 83 | { 84 | "text": chunk.text, 85 | "metadata": chunk.metadata, 86 | "vector": emb, 87 | } 88 | ) 89 | 90 | table = self.db.create_table(self.pdf_directory.name, data=merged) 91 | print(f"Wrote {len(embeddings)} embeddings to {table.name} table") 92 | return table 93 | -------------------------------------------------------------------------------- /gpt_pdf_bot/bot.py: -------------------------------------------------------------------------------- 1 | from dotenv import load_dotenv 2 | from typing import List 3 | import lancedb 4 | import openai 5 | import os 6 | 7 | from gpt_pdf_bot.shared import embed_text 8 | 9 | 10 | load_dotenv() 11 | 12 | 13 | class ChatBot: 14 | def __init__(self, table_name: str): 15 | self.db = lancedb.connect(uri=".lancedb") 16 | self.table = self.db[table_name] 17 | 18 | def run(self): 19 | while True: 20 | query = input("\nAsk me a question: ") 21 | 22 | if query == "exit": 23 | break 24 | 25 | print(f"Searching for {query}", end="\n\n") 26 | context = self.retrieve_context(query=query) 27 | print(f"Found {len(context)} results", end="\n\n") 28 | 29 | texts = self.retrieve_text_from_context(context=context) 30 | annotations = self.create_annotations_from_context(context=context) 31 | 32 | prompt = self.generate_prompt(query=query, context=texts) 33 | 34 | response = self.ask_gpt(prompt, annotations=annotations) 35 | print(response, end="\n\n") 36 | 37 | def retrieve_context(self, query: str, limit: int = 5) -> List[str]: 38 | emb = embed_text([query])[0] 39 | 40 | # TODO: return and pass metadata so we can display the source and page number 41 | context = self.table.search(emb).limit(limit).to_df() 42 | return context 43 | 44 | def retrieve_text_from_context(self, context: List[str]) -> List[str]: 45 | """Extracts only the text from the context, so it can be used in the prompt""" 46 | # curly braces will mess up prompt templating, so remove them 47 | texts = context["text"].str.replace("{", "").str.replace("}", "").tolist() 48 | return texts 49 | 50 | def create_annotations_from_context(self, context: List[str]) -> List[str]: 51 | """Extracts the document metadata from the context, 52 | so it can be displayed with the LLM's response""" 53 | metadata = context["metadata"].tolist() 54 | 55 | annotations = [] 56 | for meta in metadata: 57 | source = meta["source"] 58 | page_num = meta["page_num"] 59 | annotations.append(f"- file name: {source}, page number: {page_num}") 60 | return annotations 61 | 62 | def generate_prompt(self, query: str, context: List[str]) -> str: 63 | # TODO: this is a naive implementation that doesn't handle the case where the context is too long 64 | prepared_context = "\n\n---\n\n".join(context) 65 | prompt_tmpl = f"""Answer the question based on the context below. 66 | {context} 67 | 68 | Question: {query} 69 | Answer:""" 70 | return prompt_tmpl.format(query=query, context=prepared_context) 71 | 72 | def prepare_messages_for_chat(self, text: str) -> list: 73 | messages = [ 74 | {"role": "user", "content": text}, 75 | ] 76 | return messages 77 | 78 | def ask_gpt(self, prompt: str, annotations: List[str]) -> str: 79 | res = openai.ChatCompletion.create( 80 | model="gpt-3.5-turbo", 81 | messages=self.prepare_messages_for_chat(prompt), 82 | temperature=0.7, 83 | api_key=os.environ["OPENAI_API_KEY"], 84 | ) 85 | response = res["choices"][0]["message"]["content"].strip() 86 | annotations = "\n".join(annotations).strip() 87 | 88 | answer = f"Answer: \n\n{response}\n\n---\n\n" 89 | answer += f"Here are the sources I used:\n{annotations}" 90 | return answer 91 | -------------------------------------------------------------------------------- /poetry.lock: -------------------------------------------------------------------------------- 1 | [[package]] 2 | name = "aiohttp" 3 | version = "3.8.4" 4 | description = "Async http client/server framework (asyncio)" 5 | category = "main" 6 | optional = false 7 | python-versions = ">=3.6" 8 | 9 | [package.dependencies] 10 | aiosignal = ">=1.1.2" 11 | async-timeout = ">=4.0.0a3,<5.0" 12 | attrs = ">=17.3.0" 13 | charset-normalizer = ">=2.0,<4.0" 14 | frozenlist = ">=1.1.1" 15 | multidict = ">=4.5,<7.0" 16 | yarl = ">=1.0,<2.0" 17 | 18 | [package.extras] 19 | speedups = ["Brotli", "aiodns", "cchardet"] 20 | 21 | [[package]] 22 | name = "aiosignal" 23 | version = "1.3.1" 24 | description = "aiosignal: a list of registered asynchronous callbacks" 25 | category = "main" 26 | optional = false 27 | python-versions = ">=3.7" 28 | 29 | [package.dependencies] 30 | frozenlist = ">=1.1.0" 31 | 32 | [[package]] 33 | name = "annotated-types" 34 | version = "0.6.0" 35 | description = "Reusable constraint types to use with typing.Annotated" 36 | category = "main" 37 | optional = false 38 | python-versions = ">=3.8" 39 | 40 | [[package]] 41 | name = "appnope" 42 | version = "0.1.3" 43 | description = "Disable App Nap on macOS >= 10.9" 44 | category = "dev" 45 | optional = false 46 | python-versions = "*" 47 | 48 | [[package]] 49 | name = "asttokens" 50 | version = "2.2.1" 51 | description = "Annotate AST trees with source code positions" 52 | category = "dev" 53 | optional = false 54 | python-versions = "*" 55 | 56 | [package.dependencies] 57 | six = "*" 58 | 59 | [package.extras] 60 | test = ["astroid", "pytest"] 61 | 62 | [[package]] 63 | name = "async-timeout" 64 | version = "4.0.2" 65 | description = "Timeout context manager for asyncio programs" 66 | category = "main" 67 | optional = false 68 | python-versions = ">=3.6" 69 | 70 | [[package]] 71 | name = "attr" 72 | version = "0.3.2" 73 | description = "Simple decorator to set attributes of target function or class in a DRY way." 74 | category = "main" 75 | optional = false 76 | python-versions = "*" 77 | 78 | [[package]] 79 | name = "attrs" 80 | version = "23.1.0" 81 | description = "Classes Without Boilerplate" 82 | category = "main" 83 | optional = false 84 | python-versions = ">=3.7" 85 | 86 | [package.extras] 87 | cov = ["attrs[tests]", "coverage[toml] (>=5.3)"] 88 | dev = ["attrs[docs,tests]", "pre-commit"] 89 | docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"] 90 | tests = ["attrs[tests-no-zope]", "zope-interface"] 91 | tests-no-zope = ["cloudpickle", "hypothesis", "mypy (>=1.1.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"] 92 | 93 | [[package]] 94 | name = "backcall" 95 | version = "0.2.0" 96 | description = "Specifications for callback functions passed in to an API" 97 | category = "dev" 98 | optional = false 99 | python-versions = "*" 100 | 101 | [[package]] 102 | name = "certifi" 103 | version = "2023.5.7" 104 | description = "Python package for providing Mozilla's CA Bundle." 105 | category = "main" 106 | optional = false 107 | python-versions = ">=3.6" 108 | 109 | [[package]] 110 | name = "charset-normalizer" 111 | version = "3.1.0" 112 | description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet." 113 | category = "main" 114 | optional = false 115 | python-versions = ">=3.7.0" 116 | 117 | [[package]] 118 | name = "colorama" 119 | version = "0.4.6" 120 | description = "Cross-platform colored terminal text." 121 | category = "main" 122 | optional = false 123 | python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7" 124 | 125 | [[package]] 126 | name = "decorator" 127 | version = "5.1.1" 128 | description = "Decorators for Humans" 129 | category = "main" 130 | optional = false 131 | python-versions = ">=3.5" 132 | 133 | [[package]] 134 | name = "executing" 135 | version = "1.2.0" 136 | description = "Get the currently executing AST node of a frame, and other information" 137 | category = "dev" 138 | optional = false 139 | python-versions = "*" 140 | 141 | [package.extras] 142 | tests = ["asttokens", "littleutils", "pytest", "rich"] 143 | 144 | [[package]] 145 | name = "frozenlist" 146 | version = "1.3.3" 147 | description = "A list-like structure which implements collections.abc.MutableSequence" 148 | category = "main" 149 | optional = false 150 | python-versions = ">=3.7" 151 | 152 | [[package]] 153 | name = "idna" 154 | version = "3.4" 155 | description = "Internationalized Domain Names in Applications (IDNA)" 156 | category = "main" 157 | optional = false 158 | python-versions = ">=3.5" 159 | 160 | [[package]] 161 | name = "ipython" 162 | version = "8.13.2" 163 | description = "IPython: Productive Interactive Computing" 164 | category = "dev" 165 | optional = false 166 | python-versions = ">=3.9" 167 | 168 | [package.dependencies] 169 | appnope = {version = "*", markers = "sys_platform == \"darwin\""} 170 | backcall = "*" 171 | colorama = {version = "*", markers = "sys_platform == \"win32\""} 172 | decorator = "*" 173 | jedi = ">=0.16" 174 | matplotlib-inline = "*" 175 | pexpect = {version = ">4.3", markers = "sys_platform != \"win32\""} 176 | pickleshare = "*" 177 | prompt-toolkit = ">=3.0.30,<3.0.37 || >3.0.37,<3.1.0" 178 | pygments = ">=2.4.0" 179 | stack-data = "*" 180 | traitlets = ">=5" 181 | typing-extensions = {version = "*", markers = "python_version < \"3.10\""} 182 | 183 | [package.extras] 184 | all = ["black", "curio", "docrepr", "ipykernel", "ipyparallel", "ipywidgets", "matplotlib", "matplotlib (!=3.2.0)", "nbconvert", "nbformat", "notebook", "numpy (>=1.21)", "pandas", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "qtconsole", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "trio", "typing-extensions"] 185 | black = ["black"] 186 | doc = ["docrepr", "ipykernel", "matplotlib", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "typing-extensions"] 187 | kernel = ["ipykernel"] 188 | nbconvert = ["nbconvert"] 189 | nbformat = ["nbformat"] 190 | notebook = ["ipywidgets", "notebook"] 191 | parallel = ["ipyparallel"] 192 | qtconsole = ["qtconsole"] 193 | test = ["pytest (<7.1)", "pytest-asyncio", "testpath"] 194 | test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.21)", "pandas", "pytest (<7.1)", "pytest-asyncio", "testpath", "trio"] 195 | 196 | [[package]] 197 | name = "jedi" 198 | version = "0.18.2" 199 | description = "An autocompletion tool for Python that can be used for text editors." 200 | category = "dev" 201 | optional = false 202 | python-versions = ">=3.6" 203 | 204 | [package.dependencies] 205 | parso = ">=0.8.0,<0.9.0" 206 | 207 | [package.extras] 208 | docs = ["Jinja2 (==2.11.3)", "MarkupSafe (==1.1.1)", "Pygments (==2.8.1)", "alabaster (==0.7.12)", "babel (==2.9.1)", "chardet (==4.0.0)", "commonmark (==0.8.1)", "docutils (==0.17.1)", "future (==0.18.2)", "idna (==2.10)", "imagesize (==1.2.0)", "mock (==1.0.1)", "packaging (==20.9)", "pyparsing (==2.4.7)", "pytz (==2021.1)", "readthedocs-sphinx-ext (==2.1.4)", "recommonmark (==0.5.0)", "requests (==2.25.1)", "six (==1.15.0)", "snowballstemmer (==2.1.0)", "sphinx (==1.8.5)", "sphinx-rtd-theme (==0.4.3)", "sphinxcontrib-serializinghtml (==1.1.4)", "sphinxcontrib-websupport (==1.2.4)", "urllib3 (==1.26.4)"] 209 | qa = ["flake8 (==3.8.3)", "mypy (==0.782)"] 210 | testing = ["Django (<3.1)", "attrs", "colorama", "docopt", "pytest (<7.0.0)"] 211 | 212 | [[package]] 213 | name = "lancedb" 214 | version = "0.1.16" 215 | description = "lancedb" 216 | category = "main" 217 | optional = false 218 | python-versions = ">=3.8" 219 | 220 | [package.dependencies] 221 | aiohttp = "*" 222 | attr = "*" 223 | pydantic = "*" 224 | pylance = "0.5.10" 225 | ratelimiter = "*" 226 | retry = "*" 227 | semver = "*" 228 | tqdm = "*" 229 | 230 | [package.extras] 231 | dev = ["black", "pre-commit", "ruff"] 232 | docs = ["mkdocs", "mkdocs-jupyter", "mkdocs-material", "mkdocstrings[python]"] 233 | tests = ["pandas (>=1.4)", "pytest", "pytest-asyncio", "pytest-mock"] 234 | 235 | [[package]] 236 | name = "matplotlib-inline" 237 | version = "0.1.6" 238 | description = "Inline Matplotlib backend for Jupyter" 239 | category = "dev" 240 | optional = false 241 | python-versions = ">=3.5" 242 | 243 | [package.dependencies] 244 | traitlets = "*" 245 | 246 | [[package]] 247 | name = "multidict" 248 | version = "6.0.4" 249 | description = "multidict implementation" 250 | category = "main" 251 | optional = false 252 | python-versions = ">=3.7" 253 | 254 | [[package]] 255 | name = "numpy" 256 | version = "1.24.3" 257 | description = "Fundamental package for array computing in Python" 258 | category = "main" 259 | optional = false 260 | python-versions = ">=3.8" 261 | 262 | [[package]] 263 | name = "numpy" 264 | version = "1.26.1" 265 | description = "Fundamental package for array computing in Python" 266 | category = "main" 267 | optional = false 268 | python-versions = "<3.13,>=3.9" 269 | 270 | [[package]] 271 | name = "openai" 272 | version = "0.27.6" 273 | description = "Python client library 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