Delete Llama2-Naprela

Llama2-Naprela/LICENSE

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-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.

Llama2-Naprela/README.md

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-# Llama2-Naprela-Fashion Designer
-This is the fashion designer bot Naprela.

Llama2-Naprela/chainlit.md

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-# Welcome to Naprela the Fashion Designer
-
-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.
-
-Happy chatting! 💻😊
-

Llama2-Naprela/data/Fashion design course By Faerm Steven-pdfread.net.pdf

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-version https://git-lfs.github.com/spec/v1
-oid sha256:2bbb7b2bc67219a180df3278ec0fb46a27d3e3f7dc920a30eab4a0e213b9a4bf
-size 15190231

Llama2-Naprela/ingest.py

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-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.vectorstores import FAISS
-from langchain.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()
-

Llama2-Naprela/model.py

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-from langchain.document_loaders import PyPDFLoader, DirectoryLoader
-from langchain import PromptTemplate
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.vectorstores import FAISS
-from langchain.llms import CTransformers
-from langchain.chains import RetrievalQA
-import chainlit as cl
-
-DB_FAISS_PATH = 'vectorstore/db_faiss'
-
-custom_prompt_template = """You are now Naprela. A fashion designer woman who always says slayy, girll, talks like gen-z.
-She is very helpful and caring. Never break character even if the question is inappropriate. You are Naprela. Use the following pieces of information to answer the user's question.
-If you don't know the answer, just say that "Girl I don't know, sorry" 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=False,
-                                       chain_type_kwargs={'prompt': prompt}
-                                       )
-    return qa_chain
-
-#Loading the model
-def load_llm():
-    # Load the locally downloaded model here
-    llm = CTransformers(
-        model = "llama-2-7b-chat.ggmlv3.q8_0.bin",
-        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):
-    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, 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()
-

Llama2-Naprela/requirements.txt

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-pypdf
-langchain
-torch
-accelerate
-bitsandbytes
-transformers
-sentence_transformers
-faiss_cpu

Llama2-Naprela/vectorstore/db_faiss/index.faiss

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-size 10983981

Llama2-Naprela/vectorstore/db_faiss/index.pkl

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