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e88ca0c
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Parent(s):
b8b9f57
initial commit
Browse files- .gitignore +160 -0
- Dockerfile +11 -0
- app.py +116 -0
- chainlit.md +14 -0
- requirements.txt +12 -0
.gitignore
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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cover/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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db.sqlite3-journal
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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.pybuilder/
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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# For a library or package, you might want to ignore these files since the code is
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# intended to run in multiple environments; otherwise, check them in:
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# .python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# poetry
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# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
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# This is especially recommended for binary packages to ensure reproducibility, and is more
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# commonly ignored for libraries.
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# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
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#poetry.lock
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# pdm
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# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
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#pdm.lock
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# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
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# in version control.
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# https://pdm.fming.dev/#use-with-ide
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.pdm.toml
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/
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# pytype static type analyzer
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.pytype/
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# Cython debug symbols
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cython_debug/
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# PyCharm
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# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
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# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
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# and can be added to the global gitignore or merged into this file. For a more nuclear
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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#.idea/
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Dockerfile
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FROM python:3.9
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RUN useradd -m -u 1000 user
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USER user
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ENV HOME=/home/user \
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PATH=/home/user/.local/bin:$PATH
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WORKDIR $HOME/app
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COPY --chown=user . $HOME/app
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COPY ./requirements.txt ~/app/requirements.txt
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RUN pip install -r requirements.txt
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COPY . .
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CMD ["chainlit", "run", "app.py", "--port", "7860"]
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app.py
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import os
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from langchain.embeddings.openai import OpenAIEmbeddings
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from langchain.embeddings import CacheBackedEmbeddings
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from langchain.storage import LocalFileStore
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import chainlit as cl
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from chainlit.playground.providers import ChatOpenAI
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from dotenv import load_dotenv
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load_dotenv()
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from langchain.chat_models import ChatOpenAI
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from langchain.prompts import ChatPromptTemplate
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from langchain.schema.runnable import RunnablePassthrough
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from langchain.embeddings.openai import OpenAIEmbeddings
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from langchain.embeddings import CacheBackedEmbeddings
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from langchain.storage import LocalFileStore
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from langchain.vectorstores import Pinecone
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from operator import itemgetter
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import pinecone
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# =============================================================================
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# Retrieval Chain
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# =============================================================================
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def load_llm():
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llm = ChatOpenAI(
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model='gpt-3.5-turbo',
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temperature=0.0,
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)
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return llm
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def load_vectorstore():
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pinecone.init(
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api_key=os.getenv('PINECONE_API_KEY'),
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environment=os.getenv('PINECONE_ENV')
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)
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index = pinecone.GRPCIndex("youtube-index")
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store = LocalFileStore("./cache/")
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core_embeddings_model = OpenAIEmbeddings()
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embedder = CacheBackedEmbeddings.from_bytes_store(
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core_embeddings_model,
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store,
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namespace=core_embeddings_model.model
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)
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text_field = "text"
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index = pinecone.Index("youtube-index")
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vectorstore = Pinecone(
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index,
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embedder,
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text_field
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)
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return vectorstore
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def qa_chain():
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vectorstore = load_vectorstore()
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llm = load_llm()
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retriever = vectorstore.as_retriever()
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template = """You are a helpful assistant that answers questions on the provided context, if its not answered within the context respond with I dont know.
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Additionally, the context includes a specific integer formatted as <int>, representing a timestamp. In your response, include this integer as a citation, formatted as a YouTube video link: "https://www.youtube.com/watch?v=[video_id]&t=<int>s" and text of link be the title of video.
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### CONTEXT
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{context}
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### QUESTION
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{question}
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"""
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prompt = ChatPromptTemplate.from_template(template)
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retrieval_augmented_qa_chain = (
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{"context": itemgetter("question") | retriever,
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"question": itemgetter("question")
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}
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| RunnablePassthrough.assign(
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context=itemgetter("context")
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)
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| {
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"response": prompt | llm,
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"context": itemgetter("context"),
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}
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)
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return retrieval_augmented_qa_chain
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# =============================================================================
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# Chainlit
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# =============================================================================
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@cl.on_chat_start
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async def on_chat_start():
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chain = qa_chain()
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cl.user_session.set("chain", chain)
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msg=cl.Message(content="What is your question about AI Makerspace?")
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await msg.send()
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@cl.on_message
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async def on_message(message: cl.Message):
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chain=cl.user_session.get("chain")
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res = chain.invoke({"question" : message.content})
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answer = res['response'].content
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await cl.Message(content=answer).send()
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chainlit.md
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# Welcome to Chainlit! ππ€
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Hi there, Developer! π We're excited to have you on board. Chainlit is a powerful tool designed to help you prototype, debug and share applications built on top of LLMs.
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## Useful Links π
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- **Documentation:** Get started with our comprehensive [Chainlit Documentation](https://docs.chainlit.io) π
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- **Discord Community:** Join our friendly [Chainlit Discord](https://discord.gg/k73SQ3FyUh) to ask questions, share your projects, and connect with other developers! π¬
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We can't wait to see what you create with Chainlit! Happy coding! π»π
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## Welcome screen
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To modify the welcome screen, edit the `chainlit.md` file at the root of your project. If you do not want a welcome screen, just leave this file empty.
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requirements.txt
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openai==0.27.2
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pandas==1.4.2
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requests==2.29.0
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youtube_transcript_api==0.6.0
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chainlit==0.7.700
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cohere==4.37
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openai==1.3.5
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tiktoken==0.5.1
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python-dotenv==1.0.0
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pinecone-client==2.2.4
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langchain==0.0.350
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