test

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"]
+

app.py

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+# AI MAKERSPACE PREPR 
+# Date: 2024-5-16
+
+# Basic Imports & Setup
+import os
+from openai import AsyncOpenAI
+
+# Using Chainlit for our UI
+import chainlit as cl
+from chainlit.prompt import Prompt, PromptMessage
+from chainlit.playground.providers import ChatOpenAI
+
+# Getting the API key from the .env file
+from dotenv import load_dotenv
+load_dotenv()
+
+# RAG pipeline imports and setup code
+# Get the DeveloperWeek PDF file (future implementation: direct download from URL)
+from langchain.document_loaders import PyMuPDFLoader
+
+# Adjust the URL to the direct download format
+file_id = "1JeA-w4kvbI3GHk9Dh_j19_Q0JUDE7hse"
+direct_url = f"https://drive.google.com/uc?export=download&id={file_id}"
+
+# Now load the document using the direct URL
+docs = PyMuPDFLoader(direct_url).load()
+
+import tiktoken
+def tiktoken_len(text):
+    tokens = tiktoken.encoding_for_model("gpt-3.5-turbo").encode(
+        text,
+    )
+    return len(tokens)
+
+# Split the document into chunks
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+
+text_splitter = RecursiveCharacterTextSplitter(
+    chunk_size = 500,           # 500 tokens per chunk, experiment with this value
+    chunk_overlap = 50,        # 50 tokens overlap between chunks, experiment with this value
+    length_function = tiktoken_len,
+)
+
+split_chunks = text_splitter.split_documents(docs)
+
+# Load the embeddings model
+from langchain_openai.embeddings import OpenAIEmbeddings
+
+embedding_model = OpenAIEmbeddings(model="text-embedding-3-small")
+
+# Load the vector store and retriever from Qdrant
+from langchain_community.vectorstores import Qdrant
+
+qdrant_vectorstore = Qdrant.from_documents(
+    split_chunks,
+    embedding_model,
+    location=":memory:",
+    collection_name="Prepr",
+)
+
+qdrant_retriever = qdrant_vectorstore.as_retriever()
+
+from langchain_openai import ChatOpenAI
+openai_chat_model = ChatOpenAI(model="gpt-3.5-turbo")
+
+from langchain_core.prompts import ChatPromptTemplate
+
+RAG_PROMPT = """
+SYSTEM:
+You are a professional personal assistant.
+You are a helpful personal assistant who provides information about conferences.
+You like to provide helpful responses to busy professionals who ask questions about conferences.
+
+You can have a long conversation with the user about conferences.
+When to talk with the user about conferences, it can be a "transactional conversation" with a prompt-response format with one prompt from the user followed by a response by you.
+
+Here is an example of a transactional conversation:
+User: When is the conference?
+You: The conference is on June 1st, 2024. What else would you like to know?
+
+It can also be a chain of questions and answers where you and the user continues the chain until they say "Got it".
+Here is an example of a transactional conversation:
+User: What sessions should I attend?
+You: You should attend the keynote session by Bono. Would you like to know more?
+User: Yes
+You: The keynote session by Bono is on June 1st, 2024. What else would you like?
+
+If asked a question about a sessions, you can provide detailed information about the session.
+If there are multiple sessions, you can provide information about each session.
+
+The format of session related replies is:
+Title:
+Description:
+Speaker:
+Background:
+Date:
+Topics to Be Covered:
+Questions to Ask:
+
+CONTEXT:
+{context}
+
+QUERY:
+{question}
+Most questions are about the date, location, and purpose of the conference.
+You may be asked for fine details about the conference regarding the speakers, sponsors, and attendees.
+You are capable of looking up information and providing detailed responses.
+When asked a question about a conference, you should provide a detailed response.
+After completing your response, you should ask the user if they would like to know more about the conference by asking "Hope that helps".
+If the user says "yes", you should provide more information about the conference. If the user says "no", you should say "Goodbye! or ask if they would like to provide feedback.
+If you are asked a question about Cher, you should respond with "Rock on With Your Bad Self!".
+If you can not answer the question, you should say "I am sorry, I do not have that information, but I am always here to help you with any other questions you may have.".
+"""
+rag_prompt = ChatPromptTemplate.from_template(RAG_PROMPT)
+
+from operator import itemgetter
+from langchain.schema.output_parser import StrOutputParser
+from langchain.schema.runnable import RunnablePassthrough
+
+retrieval_augmented_qa_chain = (
+    {"context": itemgetter("question") | qdrant_retriever, "question": itemgetter("question")}
+    | RunnablePassthrough.assign(context=itemgetter("context"))
+    | {"response": rag_prompt | openai_chat_model, "context": itemgetter("context")}
+)
+
+# Chainlit App
+@cl.on_chat_start
+async def start_chat():
+    settings = {
+        "model": "gpt-3.5-turbo",
+        "temperature": 0,
+        "max_tokens": 500,
+        "top_p": 1,
+        "frequency_penalty": 0,
+        "presence_penalty": 0,
+    }
+    cl.user_session.set("settings", settings)
+
+@cl.on_message
+async def main(message: cl.Message):
+    chainlit_question = message.content
+    #chainlit_question = "What was the total value of 'Cash and cash equivalents' as of December 31, 2023?"
+    response = retrieval_augmented_qa_chain.invoke({"question": chainlit_question})
+    chainlit_answer = response["response"].content
+
+    msg = cl.Message(content=chainlit_answer)
+    await msg.send()

chainlit.md

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+# ProPrepPal
+
+## Welcome to ProPrepPal your personal preparation assistant
+
+## I can help you prepare for a conference, a meeting or an interview
+
+How can I help you today?

requirements.txt

@@ -0,0 +1,206 @@
+aiofiles==23.2.1
+aiohttp==3.9.5
+aiosignal==1.3.1
+altair==5.3.0
+annotated-types==0.6.0
+anyio==3.7.1
+appdirs==1.4.4
+argon2-cffi==23.1.0
+argon2-cffi-bindings==21.2.0
+arrow==1.3.0
+async-lru==2.0.4
+asyncer==0.0.2
+attrs==23.2.0
+Babel==2.14.0
+backoff==2.2.1
+beautifulsoup4==4.12.3
+bidict==0.23.1
+bleach==6.1.0
+blinker==1.8.1
+cachetools==5.3.3
+certifi==2024.2.2
+cffi==1.16.0
+chainlit
+charset-normalizer==3.3.2
+click==8.1.7
+cohere==4.37
+contourpy==1.2.1
+curl_cffi==0.6.2
+cycler==0.12.1
+dataclasses-json==0.5.14
+datasets==2.19.0
+defusedxml==0.7.1
+Deprecated==1.2.14
+dill==0.3.8
+dirtyjson==1.0.8
+distro==1.9.0
+docker==7.0.0
+docker-pycreds==0.4.0
+duckduckgo_search==5.3.0
+fastapi==0.100.1
+fastapi-socketio==0.0.10
+fastavro==1.9.4
+fastjsonschema==2.19.1
+filelock==3.13.4
+filetype==1.2.0
+fonttools==4.51.0
+fqdn==1.5.1
+frozenlist==1.4.1
+fsspec==2024.3.1
+gitdb==4.0.11
+GitPython==3.1.43
+googleapis-common-protos==1.63.0
+grandalf==0.8
+greenlet==3.0.3
+grpcio==1.62.2
+grpcio-tools==1.62.2
+h11==0.14.0
+h2==4.1.0
+hpack==4.0.0
+httpcore==0.17.3
+httpx
+huggingface-hub==0.22.2
+hyperframe==6.0.1
+idna==3.6
+importlib-metadata==6.11.0
+install==1.3.5
+ipywidgets==8.1.2
+isoduration==20.11.0
+Jinja2==3.1.3
+joblib==1.4.0
+json5==0.9.25
+jsonpatch==1.33
+jsonpointer==2.4
+jsonschema==4.21.1
+jsonschema-specifications==2023.12.1
+jupyter==1.0.0
+jupyter-console==6.6.3
+jupyter-events==0.10.0
+jupyter-lsp==2.2.5
+jupyter_server==2.14.0
+jupyter_server_terminals==0.5.3
+jupyterlab
+jupyterlab_pygments==0.3.0
+jupyterlab_server==2.26.0
+jupyterlab_widgets==3.0.10
+kiwisolver==1.4.5
+langchain==0.1.17
+langchain-community==0.0.36
+langchain-core==0.1.50
+langchain-openai==0.1.6
+langchain-text-splitters==0.0.1
+langchainhub==0.1.15
+langsmith==0.1.48
+Lazify==0.4.0
+markdown-it-py==3.0.0
+MarkupSafe==2.1.5
+marshmallow==3.21.1
+matplotlib==3.8.4
+mdurl==0.1.2
+mistune==3.0.2
+multidict==6.0.5
+multiprocess==0.70.16
+mypy-extensions==1.0.0
+nbclient==0.10.0
+nbconvert==7.16.3
+nbformat==5.10.4
+networkx
+nltk==3.8.1
+notebook==7.1.2
+notebook_shim==0.2.4
+numpy==1.26.4
+openai==1.25.1
+opentelemetry-api==1.24.0
+opentelemetry-exporter-otlp==1.24.0
+opentelemetry-exporter-otlp-proto-common==1.24.0
+opentelemetry-exporter-otlp-proto-grpc==1.24.0
+opentelemetry-exporter-otlp-proto-http==1.24.0
+opentelemetry-instrumentation==0.45b0
+opentelemetry-proto==1.24.0
+opentelemetry-sdk==1.24.0
+opentelemetry-semantic-conventions==0.45b0
+orjson==3.10.1
+overrides==7.7.0
+packaging==23.2
+pandas==2.2.2
+pandocfilters==1.5.1
+pillow==10.3.0
+plotly==5.22.0
+portalocker==2.8.2
+prometheus_client==0.20.0
+protobuf==4.25.3
+pyarrow==16.0.0
+pyarrow-hotfix==0.6
+pycparser==2.22
+pydantic==2.6.4
+pydantic_core==2.16.3
+pydeck==0.9.0
+PyJWT==2.8.0
+PyMuPDF==1.24.2
+PyMuPDFb==1.24.1
+pyparsing==3.1.2
+pypdf==4.2.0
+pysbd==0.3.4
+python-dotenv==1.0.0
+python-engineio==4.9.0
+python-graphql-client==0.4.3
+python-json-logger==2.0.7
+python-magic==0.4.27
+python-multipart==0.0.6
+python-socketio==5.11.2
+pytz==2024.1
+PyYAML==6.0.1
+qdrant-client==1.9.1
+qtconsole==5.5.1
+QtPy==2.4.1
+ragas==0.1.7
+referencing==0.34.0
+regex==2024.4.16
+requests==2.31.0
+rfc3339-validator==0.1.4
+rfc3986-validator==0.1.1
+rich==13.7.1
+rpds-py==0.18.0
+scikit-learn==1.4.2
+scipy==1.13.0
+Send2Trash==1.8.3
+sentry-sdk==1.45.0
+setproctitle==1.3.3
+simple-websocket==1.0.0
+smmap==5.0.1
+sniffio==1.3.1
+soupsieve==2.5
+SQLAlchemy==2.0.29
+starlette==0.27.0
+streamlit==1.33.0
+striprtf==0.0.26
+syncer==2.0.3
+tenacity==8.2.3
+terminado==0.18.1
+threadpoolctl==3.4.0
+tiktoken==0.6.0
+tinycss2==1.2.1
+toml==0.10.2
+tomli==2.0.1
+toolz==0.12.1
+tqdm==4.66.2
+types-python-dateutil==2.9.0.20240316
+types-requests==2.31.0.20240406
+typing-inspect==0.9.0
+tzdata==2024.1
+uptrace==1.24.0
+uri-template==1.3.0
+urllib3==2.2.1
+uvicorn==0.23.2
+wandb==0.16.6
+watchfiles==0.20.0
+webcolors==1.13
+webencodings==0.5.1
+websocket-client==1.7.0
+websockets==12.0
+widgetsnbextension==4.0.10
+wikipedia==1.4.0
+wrapt==1.16.0
+wsproto==1.2.0
+xxhash==3.4.1
+yarl==1.9.4