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app.py

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+import together
+
+# set your API key
+together.api_key = "c9909567768fbf1a69fbd94c758e432f0a05a6755c32dced992ac6640a8cfd79"
+
+# list available models and descriptons
+models = together.Models.list()
+
+together.Models.start("togethercomputer/llama-2-7b-chat")
+
+from langchain.llms import Together
+
+
+llm = Together(
+    model="togethercomputer/llama-2-7b-chat",
+    temperature=0.7,
+    max_tokens=128,
+    top_k=1,
+    together_api_key="c9909567768fbf1a69fbd94c758e432f0a05a6755c32dced992ac6640a8cfd79"
+)
+
+from langchain.vectorstores import Chroma
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+
+from langchain.chains import RetrievalQA
+from langchain.document_loaders import TextLoader
+from langchain.document_loaders import PyPDFLoader
+from langchain.document_loaders import DirectoryLoader
+
+loader = PyPDFLoader('/Production-Table - Sheet1 (2).pdf')
+
+documents = loader.load()
+#splitting the text into
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+texts = text_splitter.split_documents(documents)
+from langchain.embeddings import HuggingFaceBgeEmbeddings
+
+model_name = "BAAI/bge-base-en"
+encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
+
+model_norm = HuggingFaceBgeEmbeddings(
+    model_name=model_name,
+    model_kwargs={'device': 'cuda'},
+    encode_kwargs=encode_kwargs
+)
+
+
+# Embed and store the texts
+# Supplying a persist_directory will store the embeddings on disk
+
+persist_directory = 'db'
+
+## Here is the nmew embeddings being used
+embedding = model_norm
+
+vectordb = Chroma.from_documents(documents=texts,
+                                 embedding=embedding,
+                                 persist_directory=persist_directory)
+
+retriever = vectordb.as_retriever(search_kwargs={"k": 5})
+## Default LLaMA-2 prompt style
+B_INST, E_INST = "[INST]", "[/INST]"
+B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
+DEFAULT_SYSTEM_PROMPT = """\
+You are a helpful, respectful and honest assistant of a production company. You should honestly answer the user's query using the knowledge of the company's production documents uploaded.
+
+If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information."""
+
+def get_prompt(instruction, new_system_prompt=DEFAULT_SYSTEM_PROMPT ):
+    SYSTEM_PROMPT = B_SYS + new_system_prompt + E_SYS
+    prompt_template =  B_INST + SYSTEM_PROMPT + instruction + E_INST
+    return prompt_template
+
+sys_prompt = """You are a helpful, respectful and honest assistant of a production company. You should honestly answer the user's query using the knowledge of the company's production documents uploaded.
+
+If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information."""
+
+instruction = """CONTEXT:/n/n {context}/n
+
+Question: {question}"""
+get_prompt(instruction, sys_prompt)
+
+from langchain.prompts import PromptTemplate
+prompt_template = get_prompt(instruction, sys_prompt)
+
+llama_prompt = PromptTemplate(
+    template=prompt_template, input_variables=["context", "question"]
+)
+from langchain.schema import prompt
+# create the chain to answer questions
+qa_chain = RetrievalQA.from_chain_type(llm=llm,
+                                       chain_type="stuff",
+                                       retriever=retriever,
+                                       chain_type_kwargs=chain_type_kwargs,
+                                       return_source_documents=True)
+## Cite sources
+
+import textwrap
+
+def wrap_text_preserve_newlines(text, width=110):
+    # Split the input text into lines based on newline characters
+    lines = text.split('\n')
+
+    # Wrap each line individually
+    wrapped_lines = [textwrap.fill(line, width=width) for line in lines]
+
+    # Join the wrapped lines back together using newline characters
+    wrapped_text = '\n'.join(wrapped_lines)
+
+    return wrapped_text
+
+def process_llm_response(llm_response):
+    print(wrap_text_preserve_newlines(llm_response['result']))
+    print('\n\nSources:')
+    for source in llm_response["source_documents"]:
+        print(source.metadata['source'])
+import gradio as gr
+
+with gr.Blocks() as demo:
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox()
+    clear = gr.Button("Clear")
+
+    def user(user_message, history):
+        return "", history + [[user_message, None]]
+
+    def bot(history):
+        print("Question: ", history[-1][0])
+        #wrap_text_preserve_newlines(llm_response['result'])
+        #bot_message = process_llm_response(qa_chain(history[-1][0]))
+        bot_message = wrap_text_preserve_newlines((qa_chain(history[-1][0]))['result'])
+        print("Response: ", bot_message)
+        history[-1][1] = ""
+        history[-1][1] += bot_message
+        return history
+
+
+    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(bot, chatbot, chatbot)
+    clear.click(lambda: None, None, chatbot, queue=False)
+
+demo.queue()
+demo.launch(debug = True)                                               

requirements.txt

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+langchain
+huggingface_hub
+tiktoken
+chromadb
+PyPDF2
+pypdf
+sentence_transformers
+together
+gradio