updated

.gitattributes0

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

.gitignore

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+<<<<<<< HEAD
+.env
+.vscode
+.chroma
+__pycache__
+=======
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+# Usually these files are written by a python script from a template
+# before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+# For a library or package, you might want to ignore these files since the code is
+# intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+# However, in case of collaboration, if having platform-specific dependencies or dependencies
+# having no cross-platform support, pipenv may install dependencies that don't work, or not
+# install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+# This is especially recommended for binary packages to ensure reproducibility, and is more
+# commonly ignored for libraries.
+# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+# in version control.
+# https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+# and can be added to the global gitignore or merged into this file. For a more nuclear
+# option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+>>>>>>> 32c2df8e7f2fc8b1b13d677999659b2cf0a15856

Dockerfile

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+FROM python:3.9
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+ PATH=/home/user/.local/bin:$PATH
+WORKDIR $HOME/app
+COPY --chown=user . $HOME/app
+COPY ./requirements.txt ~/app/requirements.txt
+RUN pip install -r requirements.txt
+COPY . .
+CMD ["chainlit", "run", "app.py", "--port", "7860"]

LICENSE

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app.py

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+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.document_loaders import PyMuPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import Chroma
+from langchain.chains import RetrievalQAWithSourcesChain
+from langchain.chat_models import ChatOpenAI
+from langchain.prompts.chat import (
+ ChatPromptTemplate,
+ SystemMessagePromptTemplate,
+ HumanMessagePromptTemplate,
+)
+import os
+import arxiv
+import chainlit as cl
+from chainlit import user_session
+
+@cl.langchain_factory(use_async=True)
+async def init():
+ arxiv_query = None
+
+ # Wait for the user to ask an Arxiv question
+ while arxiv_query == None:
+ arxiv_query = await cl.AskUserMessage(
+ content="Please enter a topic to begin!", timeout=15
+ ).send()
+
+ # Obtain the top 3 results from Arxiv for the query
+ search = arxiv.Search(
+ query=arxiv_query["content"],
+ max_results=3,
+ sort_by=arxiv.SortCriterion.Relevance,
+ )
+
+ await cl.Message(content="Downloading and chunking articles...").send()
+ # download each of the pdfs
+ pdf_data = []
+ for result in search.results():
+ loader = PyMuPDFLoader(result.pdf_url)
+ loaded_pdf = loader.load()
+
+ for document in loaded_pdf:
+ document.metadata["source"] = result.entry_id
+ document.metadata["file_path"] = result.pdf_url
+ document.metadata["title"] = result.title
+ pdf_data.append(document)
+
+ # Create a Chroma vector store
+ embeddings = OpenAIEmbeddings(
+ disallowed_special=(),
+ )
+ 
+ # If operation takes too long, make_async allows to run in a thread
+ # docsearch = await cl.make_async(Chroma.from_documents)(pdf_data, embeddings) 
+ docsearch = Chroma.from_documents(pdf_data, embeddings)
+
+ # Create a chain that uses the Chroma vector store
+ chain = RetrievalQAWithSourcesChain.from_chain_type(
+ ChatOpenAI(
+ model_name="gpt-3.5-turbo-16k",
+ temperature=0,
+ ),
+ chain_type="stuff",
+ retriever=docsearch.as_retriever(),
+ return_source_documents=True,
+ )
+
+ # Let the user know that the system is ready
+ await cl.Message(
+ content=f"We found a few papers about `{arxiv_query['content']}` you can now ask questions!"
+ ).send()
+
+ return chain
+
+
+@cl.langchain_postprocess
+async def process_response(res):
+ answer = res["answer"]
+ source_elements_dict = {}
+ source_elements = []
+ for idx, source in enumerate(res["source_documents"]):
+ title = source.metadata["title"]
+
+ if title not in source_elements_dict:
+ source_elements_dict[title] = {
+ "page_number": [source.metadata["page"]],
+ "url": source.metadata["file_path"],
+ }
+
+ else:
+ source_elements_dict[title]["page_number"].append(source.metadata["page"])
+
+ # sort the page numbers
+ source_elements_dict[title]["page_number"].sort()
+
+ for title, source in source_elements_dict.items():
+ # create a string for the page numbers
+ page_numbers = ", ".join([str(x) for x in source["page_number"]])
+ text_for_source = f"Page Number(s): {page_numbers}\nURL: {source['url']}"
+ source_elements.append(
+ cl.Text(name=title, content=text_for_source, display="inline")
+ )
+
+ await cl.Message(content=answer, elements=source_elements).send()

chainlit.md

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+# ⚠️ Warning ⚠️
+
+You will need a OpenAI API key to use this app due to large context size!
+
+# Welcome to AskArxiv powered by Chainlit!
+
+In this app, you'll be able to enter a topic - and then ask ~30 papers from Arxiv about that topic!
+
+### Link To Demo
+
+[Hugging Face Space](https://huggingface.co/spaces/ml-maker-space/ArxivChainLitDemo)

requirements.txt

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+arxiv==1.4.7
+langchain==0.0.202
+chainlit==0.4.1
+openai
+chromadb
+tiktoken
+pymupdf