added models and dockerfile

Dockerfile

@@ -0,0 +1,11 @@
+FROM python:3.9
+
+WORKDIR /app
+
+COPY ./requirement.txt /app/
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY . /app/
+
+CMD ["python", "ingest.py"]
+CMD ["chainlit", "run", "model.py" "-w"]

ingest.py

@@ -0,0 +1,28 @@
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.document_loaders import PyPDFLoader, DirectoryLoader
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+
+DATA_PATH = "data/"
+DB_FAISS_PATH = "vectorstores/db_faiss"
+
+#model path:
+#https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/blob/main/llama-2-7b-chat.ggmlv3.q8_0.bin
+
+#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/allMiniLM-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 +1,92 @@
+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 = "vectorstores/db_faiss"
+
+custom_prompt_template = """Use the following pieces of information to answer the user's questions.
+If you don't know the answer, don't try to make up an answer, just say that you do not know it.
+
+Context: {}
+Question: {question}
+
+Only returns the helpful answer below and nothing else.
+Helpful answer:
+"""
+
+def set_custom_prompt():
+    """
+    Prompt template for QA retrieval for each vector stores
+    """
+    
+    prompt = PromptTemplate(template = custom_prompt_template, input_variable = ['context', 'question'])
+    return prompt
+
+def load_llm():
+    llm = CTransformers(
+        model = "llama-2-7b-chat.ggmlv3.q8_0.bin",
+        model_type = "llama",
+        max_new_tokens = 512,
+        temperature = 0.5
+    )
+    return llm
+
+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
+ 
+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
+
+def final_result(query):
+    qa_result = qa_bot()
+    response = qa_result({'query': query})
+    return response
+
+
+## Chainlit ##
+
+@cl.on_chat_start
+async def start():
+    chain = qa_bot()
+    msg = cl.Message(content="Starting the bot...")
+    await msg.send()
+    msg.content = "Hi! I am Jarvis, what's 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,
+        stream_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 += f"\nNo sources found"
+    
+    await cl.Message(content = answer).send()

requirements.txt

@@ -0,0 +1,8 @@
+pypdf
+langchain
+chainlit
+torch
+accelerate
+transformers
+sentence_transformers
+faiss_cpu