Upload 9 files

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* 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
+data/71763-gale-encyclopedia-of-medicine.-vol.-1.-2nd-ed.pdf filter=lfs diff=lfs merge=lfs -text
+vectorstore/db_faiss/index.faiss filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 AI Anytime
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,14 +1,104 @@
+
+
+# Llama2 Medical Bot
+
+The Llama2 Medical Bot is a powerful tool designed to provide medical information by answering user queries using state-of-the-art language models and vector stores. This README will guide you through the setup and usage of the Llama2 Medical Bot.
+
+## Table of Contents
+
+- [Introduction](#langchain-medical-bot)
+- [Table of Contents](#table-of-contents)
+- [Prerequisites](#prerequisites)
+- [Installation](#installation)
+- [Getting Started](#getting-started)
+- [Usage](#usage)
+- [Contributing](#contributing)
+- [License](#license)
+
+## Prerequisites
+
+Before you can start using the Llama2 Medical Bot, make sure you have the following prerequisites installed on your system:
+
+- Python 3.6 or higher
+- Required Python packages (you can install them using pip):
+    - langchain
+    - chainlit
+    - sentence-transformers
+    - faiss
+    - PyPDF2 (for PDF document loading)
+
+## Installation
+
+1. Clone this repository to your local machine.
+
+    ```bash
+    git clone https://github.com/your-username/langchain-medical-bot.git
+    cd langchain-medical-bot
+    ```
+
+2. Create a Python virtual environment (optional but recommended):
+
+    ```bash
+    python -m venv venv
+    source venv/bin/activate  # On Windows, use: venv\Scripts\activate
+    ```
+
+3. Install the required Python packages:
+
+    ```bash
+    pip install -r requirements.txt
+    ```
+
+4. Download the required language models and data. Please refer to the Langchain documentation for specific instructions on how to download and set up the language model and vector store.
+
+5. Set up the necessary paths and configurations in your project, including the `DB_FAISS_PATH` variable and other configurations as per your needs.
+
+## Getting Started
+
+To get started with the Llama2 Medical Bot, you need to:
+
+1. Set up your environment and install the required packages as described in the Installation section.
+
+2. Configure your project by updating the `DB_FAISS_PATH` variable and any other custom configurations in the code.
+
+3. Prepare the language model and data as per the Langchain documentation.
+
+4. Start the bot by running the provided Python script or integrating it into your application.
+
+## Usage
+
+The Llama2 Medical Bot can be used for answering medical-related queries. To use the bot, you can follow these steps:
+
+1. Start the bot by running your application or using the provided Python script.
+
+2. Send a medical-related query to the bot.
+
+3. The bot will provide a response based on the information available in its database.
+
+4. If sources are found, they will be provided alongside the answer.
+
+5. The bot can be customized to return specific information based on the query and context provided.
+
+## Contributing
+
+Contributions to the Llama2 Medical Bot are welcome! If you'd like to contribute to the project, please follow these steps:
+
+1. Fork the repository to your own GitHub account.
+
+2. Create a new branch for your feature or bug fix.
+
+3. Make your changes and ensure that the code passes all tests.
+
+4. Create a pull request to the main repository, explaining your changes and improvements.
+
+5. Your pull request will be reviewed, and if approved, it will be merged into the main codebase.
+
+## License
+
+This project is licensed under the MIT License.
+
 ---
-title: NewChatBot1
-emoji: 👀
-colorFrom: green
-colorTo: yellow
-sdk: gradio
-sdk_version: 5.9.1
-app_file: app.py
-pinned: false
-license: apache-2.0
-short_description: NewChatBot1
----
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+For more information on how to use, configure, and extend the Llama2 Medical Bot, please refer to the Langchain documentation or contact the project maintainers.
+
+Happy coding with Llama2 Medical Bot! 🚀

chainlit.md

@@ -0,0 +1,11 @@
+# Welcome to Llama2 Med-Bot! 🚀🤖
+
+Hi there, 👋 We're excited to have you on board. This is a powerful bot designed to help you ask queries related to your data/knowledge.
+
+## Useful Links 🔗
+
+- **Data:** This is the data which has been used as a knowledge base. [Knowledge Base](https://docs.chainlit.io) 📚
+- **Join AI Anytime Community:** Join our friendly [WhatsApp Group](https://discord.gg/ZThrUxbAYw) to ask questions, share your projects, and connect with other developers! 💬
+
+Happy chatting! 💻😊
+

data/71763-gale-encyclopedia-of-medicine.-vol.-1.-2nd-ed.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:753cd53b7a3020bbd91f05629b0e3ddcfb6a114d7bbedb22c2298b66f5dd00cc
+size 16127037

ingest.py

@@ -0,0 +1,28 @@
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain_community.document_loaders import PyPDFLoader, DirectoryLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter 
+
+DATA_PATH = 'data/'
+DB_FAISS_PATH = 'vectorstore/db_faiss'
+
+# Create vector database
+def create_vector_db():
+    loader = DirectoryLoader(DATA_PATH,
+                             glob='*.pdf',
+                             loader_cls=PyPDFLoader)
+
+    documents = loader.load()
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500,
+                                                   chunk_overlap=50)
+    texts = text_splitter.split_documents(documents)
+
+    embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2',
+                                       model_kwargs={'device': 'cpu'})
+
+    db = FAISS.from_documents(texts, embeddings)
+    db.save_local(DB_FAISS_PATH)
+
+if __name__ == "__main__":
+    create_vector_db()
+

model.py

@@ -0,0 +1,95 @@
+from langchain_community.document_loaders import PyPDFLoader, DirectoryLoader
+from langchain.prompts import PromptTemplate
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain_community.llms import CTransformers
+from langchain.chains import RetrievalQA
+import chainlit as cl
+
+DB_FAISS_PATH = 'vectorstore/db_faiss'
+
+custom_prompt_template = """Use the following pieces of information to answer the user's question.
+If you don't know the answer, just say that you don't know, don't try to make up an answer.
+
+Context: {context}
+Question: {question}
+
+Only return the helpful answer below and nothing else.
+Helpful answer:
+"""
+
+def set_custom_prompt():
+    """
+    Prompt template for QA retrieval for each vectorstore
+    """
+    prompt = PromptTemplate(template=custom_prompt_template,
+                            input_variables=['context', 'question'])
+    return prompt
+
+#Retrieval QA Chain
+def retrieval_qa_chain(llm, prompt, db):
+    qa_chain = RetrievalQA.from_chain_type(llm=llm,
+                                       chain_type='stuff',
+                                       retriever=db.as_retriever(search_kwargs={'k': 2}),
+                                       return_source_documents=True,
+                                       chain_type_kwargs={'prompt': prompt}
+                                       )
+    return qa_chain
+
+#Loading the model
+def load_llm():
+    # Load the locally downloaded model here
+    llm = CTransformers(
+        model = "TheBloke/Llama-2-7B-Chat-GGML",
+        model_type="llama",
+        max_new_tokens = 512,
+        temperature = 0.5
+    )
+    return llm
+
+#QA Model Function
+def qa_bot():
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2",
+                                       model_kwargs={'device': 'cpu'})
+    db = FAISS.load_local(DB_FAISS_PATH, embeddings)
+    llm = load_llm()
+    qa_prompt = set_custom_prompt()
+    qa = retrieval_qa_chain(llm, qa_prompt, db)
+
+    return qa
+
+#output function
+def final_result(query):
+    qa_result = qa_bot()
+    response = qa_result({'query': query})
+    return response
+
+#chainlit code
+@cl.on_chat_start
+async def start():
+    chain = qa_bot()
+    msg = cl.Message(content="Starting the bot...")
+    await msg.send()
+    msg.content = "Hi, Welcome to Medical Bot. What is your query?"
+    await msg.update()
+
+    cl.user_session.set("chain", chain)
+
+@cl.on_message
+async def main(message: cl.Message):
+    chain = cl.user_session.get("chain") 
+    cb = cl.AsyncLangchainCallbackHandler(
+        stream_final_answer=True, answer_prefix_tokens=["FINAL", "ANSWER"]
+    )
+    cb.answer_reached = True
+    res = await chain.acall(message.content, callbacks=[cb])
+    answer = res["result"]
+    sources = res["source_documents"]
+
+    if sources:
+        answer += f"\nSources:" + str(sources)
+    else:
+        answer += "\nNo sources found"
+
+    await cl.Message(content=answer).send()
+

requirements.txt

@@ -0,0 +1,11 @@
+pypdf
+langchain
+torch
+accelerate
+bitsandbytes
+ctransformers
+sentence_transformers
+faiss_cpu
+chainlit
+huggingface_hub
+langchain_community

vectorstore/db_faiss/index.faiss

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3c219be0c422137d6354fdf0db6f2a2fe719ba536215b2dcba2366723f00b6e9
+size 10983981

vectorstore/db_faiss/index.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d75f6e95d75f5bad95668fcd18f2daffb0d562d33784e6228e5c0f785605ee0c
+size 3567746