Commit
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Parent(s):
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Upload 14 files
Browse files- .chainlit/config.toml +72 -0
- .gitattributes +5 -0
- __pycache__/model.cpython-311.pyc +0 -0
- chainlit.md +8 -0
- conversession e.g/ChatBot Conversession img-1.png +0 -0
- conversession e.g/ChatBot Conversession img-2.png +0 -0
- conversession e.g/ChatBot Conversession img-3.pdf +3 -0
- conversession e.g/ChatBot Conversession img-3.png +3 -0
- conversession e.g/ChatBot Conversession vid.mp4 +3 -0
- data/71763-gale-encyclopedia-of-medicine.-vol.-1.-2nd-ed.pdf +3 -0
- ingest.py +23 -0
- model.py +98 -0
- requirements.txt +11 -0
- vectorstores/db_faiss/index.faiss +3 -0
- vectorstores/db_faiss/index.pkl +3 -0
.chainlit/config.toml
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[project]
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# If true (default), the app will be available to anonymous users.
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# If false, users will need to authenticate and be part of the project to use the app.
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public = true
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# The project ID (found on https://cloud.chainlit.io).
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# The project ID is required when public is set to false or when using the cloud database.
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#id = ""
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# Uncomment if you want to persist the chats.
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# local will create a database in your .chainlit directory (requires node.js installed).
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# cloud will use the Chainlit cloud database.
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# custom will load use your custom client.
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# database = "local"
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# Whether to enable telemetry (default: true). No personal data is collected.
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enable_telemetry = true
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# List of environment variables to be provided by each user to use the app.
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user_env = []
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# Duration (in seconds) during which the session is saved when the connection is lost
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session_timeout = 3600
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# Enable third parties caching (e.g LangChain cache)
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cache = false
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# Follow symlink for asset mount (see https://github.com/Chainlit/chainlit/issues/317)
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# follow_symlink = false
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# Chainlit server address
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# chainlit_server = ""
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[UI]
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# Name of the app and chatbot.
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name = "Chatbot"
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# Description of the app and chatbot. This is used for HTML tags.
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# description = ""
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# The default value for the expand messages settings.
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default_expand_messages = false
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# Hide the chain of thought details from the user in the UI.
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hide_cot = false
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# Link to your github repo. This will add a github button in the UI's header.
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github = "https://github.com/ThisIs-Developer"
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# Override default MUI light theme. (Check theme.ts)
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[UI.theme.light]
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#background = "#FAFAFA"
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#paper = "#FFFFFF"
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[UI.theme.light.primary]
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#main = "#F80061"
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#dark = "#980039"
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#light = "#FFE7EB"
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# Override default MUI dark theme. (Check theme.ts)
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[UI.theme.dark]
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#background = "#FAFAFA"
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#paper = "#FFFFFF"
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[UI.theme.dark.primary]
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#main = "#F80061"
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#dark = "#980039"
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#light = "#FFE7EB"
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[meta]
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generated_by = "0.6.402"
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.gitattributes
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@@ -33,3 +33,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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conversession[[:space:]]e.g/ChatBot[[:space:]]Conversession[[:space:]]img-3.pdf filter=lfs diff=lfs merge=lfs -text
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conversession[[:space:]]e.g/ChatBot[[:space:]]Conversession[[:space:]]img-3.png filter=lfs diff=lfs merge=lfs -text
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conversession[[:space:]]e.g/ChatBot[[:space:]]Conversession[[:space:]]vid.mp4 filter=lfs diff=lfs merge=lfs -text
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data/71763-gale-encyclopedia-of-medicine.-vol.-1.-2nd-ed.pdf filter=lfs diff=lfs merge=lfs -text
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vectorstores/db_faiss/index.faiss filter=lfs diff=lfs merge=lfs -text
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__pycache__/model.cpython-311.pyc
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Binary file (4.52 kB). View file
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chainlit.md
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# Llama-2-GGML Medical Chatbot! 🚀🤖
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Llama-2-GGML Medical Chatbot is a medical chatbot that uses the **Llama-2-7B-Chat-GGML** model which is a *large language model (LLM)* that has been fine-tuned on a dataset of medical text and PDF **"The GALE ENCYCLOPEDIA of MEDICINE"** is a comprehensive medical reference that provides information on a wide range of medical topics.
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### The chatbot is still under development
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## Useful Links 🔗
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- **Model:** Know more about model [Llama-2-7B-Chat-GGML](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML) 📚
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- **GitHub:** Check out the repository [ThisIs-Developer/Llama-2-GGML-Medical-Chatbot](https://github.com/ThisIs-Developer/Llama-2-GGML-Medical-Chatbot) feel free to commit in the github repo ! 💬
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conversession e.g/ChatBot Conversession img-1.png
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conversession e.g/ChatBot Conversession img-2.png
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conversession e.g/ChatBot Conversession img-3.pdf
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version https://git-lfs.github.com/spec/v1
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oid sha256:a1c678b11a684144d33f839c9a108455f47c1c9d0ab96f7bb86a454bbc11df9b
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size 10246322
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conversession e.g/ChatBot Conversession img-3.png
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Git LFS Details
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conversession e.g/ChatBot Conversession vid.mp4
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version https://git-lfs.github.com/spec/v1
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oid sha256:23cd2ec7b5e1665a21a1020c2a9ef4f380266407845e39909665725fd4ab0536
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size 4579961
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data/71763-gale-encyclopedia-of-medicine.-vol.-1.-2nd-ed.pdf
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version https://git-lfs.github.com/spec/v1
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oid sha256:753cd53b7a3020bbd91f05629b0e3ddcfb6a114d7bbedb22c2298b66f5dd00cc
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size 16127037
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ingest.py
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from langchain.document_loaders import PyPDFLoader, DirectoryLoader
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from langchain.embeddings import HuggingFaceEmbeddings
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from langchain.vectorstores import FAISS
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DATA_PATH="data/"
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DB_FAISS_PATH="vectorstores/db_faiss"
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def create_vector_db():
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loader = DirectoryLoader(DATA_PATH, glob='*.pdf', loader_cls=PyPDFLoader)
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documents =loader.load()
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text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
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texts = text_splitter.split_documents(documents)
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embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2",
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model_kwargs = {'device': 'cpu'})
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db = FAISS.from_documents(texts, embeddings)
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db.save_local(DB_FAISS_PATH)
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if __name__ == "__main__":
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create_vector_db()
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model.py
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import asyncio
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from langchain.document_loaders import PyPDFLoader, DirectoryLoader
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from langchain import PromptTemplate
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from langchain.embeddings import HuggingFaceEmbeddings
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from langchain.vectorstores import FAISS
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from langchain.llms import CTransformers
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from langchain.chains import RetrievalQA
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import chainlit as cl
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DB_FAISS_PATH = 'vectorstores/db_faiss'
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custom_prompt_template = """Use the following pieces of information to answer the user's question.
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If you don't know the answer, just say that you don't know, don't try to make up an answer.
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Context: {context}
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Question: {question}
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Only return the helpful answer below and nothing else.
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Helpful answer:
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"""
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def set_custom_prompt():
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"""
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Prompt template for QA retrieval for each vectorstore
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"""
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prompt = PromptTemplate(template=custom_prompt_template,
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input_variables=['context', 'question'])
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return prompt
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# Retrieval QA Chain
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def retrieval_qa_chain(llm, prompt, db):
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qa_chain = RetrievalQA.from_chain_type(llm=llm,
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chain_type='stuff',
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retriever=db.as_retriever(search_kwargs={'k': 2}),
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return_source_documents=True,
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chain_type_kwargs={'prompt': prompt}
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)
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return qa_chain
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# Loading the model
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def load_llm():
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# Load the locally downloaded model here
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llm = CTransformers(
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model="TheBloke/Llama-2-7B-Chat-GGML",
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model_type="llama",
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max_new_tokens=512,
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temperature=0.5
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)
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return llm
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# QA Model Function
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async def qa_bot():
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embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2",
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model_kwargs={'device': 'cpu'})
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db = FAISS.load_local(DB_FAISS_PATH, embeddings)
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llm = load_llm()
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qa_prompt = set_custom_prompt()
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qa = retrieval_qa_chain(llm, qa_prompt, db)
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return qa
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# Output function
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async def final_result(query):
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qa_result = await qa_bot()
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response = await qa_result({'query': query})
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return response
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# chainlit code
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@cl.on_chat_start
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async def start():
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chain = await qa_bot()
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# msg = cl.Message(content="Starting the bot...")
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# await msg.send()
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# msg.content = "Hi, Welcome to Medical Bot. What is your query?"
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# await msg.update()
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cl.user_session.set("chain", chain)
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@cl.on_message
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async def main(message):
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chain = cl.user_session.get("chain")
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cb = cl.AsyncLangchainCallbackHandler(
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stream_final_answer=True, answer_prefix_tokens=["FINAL", "ANSWER"]
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)
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cb.answer_reached = True
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res = await chain.acall(message, callbacks=[cb])
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answer = res["result"]
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sources = res["source_documents"]
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if sources:
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answer += f"\nSources:" + str(sources)
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else:
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answer += "\nNo sources found"
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await cl.Message(content=answer).send()
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if __name__ == "__main__":
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asyncio.run(cl.main())
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requirements.txt
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pypdf==3.15.5
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accelerate==0.22.0
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bitsandbytes==0.41.1
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chainlit==0.6.402
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ctransformers==0.2.26
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faiss-cpu==1.7.4
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huggingface-hub==0.16.4
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langchain==0.0.281
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sentence-transformers==2.2.2
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torch==2.0.1
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transformers==4.33.0
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vectorstores/db_faiss/index.faiss
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version https://git-lfs.github.com/spec/v1
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oid sha256:41b1dd53e3fc2abc2535c8c24111b40ede2386c32a1604eaec17f3232646e7ee
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size 10983981
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vectorstores/db_faiss/index.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:4007c732db0ecbd2a226c55a6f83f1bb9bf8d899079a2e52b971f8da3d78cea5
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size 3567746
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