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| from langchain.prompts import PromptTemplate | |
| from langchain_community.llms import HuggingFaceEndpoint | |
| from langchain.vectorstores import Chroma | |
| from langchain_community.embeddings.sentence_transformer import SentenceTransformerEmbeddings | |
| import os | |
| from langchain.prompts.chat import ( | |
| ChatPromptTemplate, | |
| HumanMessagePromptTemplate, | |
| SystemMessagePromptTemplate, | |
| ) # Docs:- https://python.langchain.com/docs/modules/model_io/prompts/message_prompts | |
| #import chromadb | |
| # LLM Generator | |
| from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler | |
| from langchain.chains import ConversationalRetrievalChain | |
| from langchain.memory import ChatMessageHistory, ConversationSummaryBufferMemory, ConversationBufferMemory | |
| from langchain_experimental.chat_models import Llama2Chat | |
| # Docs:- https://python.langchain.com/docs/integrations/chat/llama2_chat | |
| HUGGINGFACEHUB_API_TOKEN = HF_ACCESS_TOKEN | |
| #os.environ["HUGGINGFACEHUB_API_TOKEN"] = HUGGINGFACEHUB_API_TOKEN | |
| # Implement another function to pass an array of PDFs / CSVs / Excels | |
| from rag_pipeline import instantiate_rag | |
| retriever = instantiate_rag() | |
| #persist_directory="Data/chroma" | |
| #chroma_client = chromadb.PersistentClient(persist_directory=persist_directory) | |
| #embedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2") | |
| #vectors = Chroma(persist_directory = persist_directory, embedding_function = embedding_function) | |
| #retriever = vectors.as_retriever() #(k=6) | |
| # Set the url to your Inference Endpoint below | |
| #your_endpoint_url = "https://fayjubiy2xqn36z0.us-east-1.aws.endpoints.huggingface.cloud" | |
| #how you can access HuggingFaceEndpoint integration of the free Serverless Endpoints API. | |
| repo_id = "mistralai/Mistral-7B-Instruct-v0.2" | |
| llm = HuggingFaceEndpoint( | |
| #endpoint_url=f"{your_endpoint_url}", | |
| repo_id=repo_id, | |
| #max_length=128, | |
| max_new_tokens=512, | |
| token=HUGGINGFACEHUB_API_TOKEN, | |
| temperature=0.1, | |
| repetition_penalty=1.1, | |
| #context_length: 4096, # Set to max for Chat Summary, Llama-2 has a max context length of 4096, | |
| stream=True, | |
| callbacks=[StreamingStdOutCallbackHandler()], | |
| #top_k=10, | |
| #top_p=0.95, | |
| ) | |
| model = Llama2Chat(llm=llm) | |
| memory = ConversationBufferMemory( | |
| llm=llm, | |
| memory_key="chat_history", | |
| return_messages=True, | |
| output_key='answer', | |
| input_key='question') | |
| # Prompt Context Reference : https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF , https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ/discussions/5#64b81e9b15ebeb44419a2b9e | |
| # Reference:- https://github.com/langchain-ai/langchain/issues/5462 | |
| system_message_template = """You're a Mental Health Specialist. Support those with Depressive Disorder. | |
| Listen compassionately, respond helpfully. For casual talk, be friendly. For facts, use context. | |
| If unsure, say, 'Out of my knowledge.' Always stay direct. | |
| If you cannot find the answer from the pieces of context, just say that you don't know, don't try to make up an answer. | |
| ---------------- | |
| {context}""" | |
| messages = [ | |
| SystemMessagePromptTemplate.from_template(system_message_template), | |
| HumanMessagePromptTemplate.from_template("{question}") | |
| ] | |
| qa_prompt = ChatPromptTemplate.from_messages(messages) | |
| qa_prompt.pretty_print() | |
| condense_question = """Given the following conversation and a follow-up message, | |
| rephrase the follow-up message to a stand-alone question or instruction that | |
| represents the user's intent precisely, add context needed if necessary to generate a complete and | |
| unambiguous question, only based on the on the Follow up Question and chat history, don't make up messages. | |
| Maintain the same question intent as the follow up input message.\n | |
| Chat History: | |
| {chat_history}\n | |
| Follow Up Input: {question} | |
| Standalone question:""" | |
| condense_question_prompt = PromptTemplate.from_template(condense_question) | |
| condense_question_prompt.pretty_print() | |
| retrieval_chain = ConversationalRetrievalChain.from_llm( | |
| llm = llm, | |
| retriever=retriever, | |
| memory = memory, | |
| return_source_documents=False, | |
| verbose=True, | |
| #condense_question_prompt=condense_question_prompt, | |
| # chain_type = "stuff", | |
| combine_docs_chain_kwargs={'prompt': qa_prompt}, # https://github.com/langchain-ai/langchain/issues/6879 | |
| ) | |
| human_inputs = ['Nothing logged yet'] | |
| ai_responses = ['Nothing logged yet'] | |
| history = ChatMessageHistory() | |
| def llm_generation(question: str): | |
| llm_answer = retrieval_chain.invoke({'question':question, 'chat_history':history.messages})['answer'] #Answer = Dict Key = Latest response by the AI | |
| history.add_user_message(question) | |
| history.add_ai_message(llm_answer) | |
| return llm_answer | |
| # Decide wether to place this in streamlit.py | |
| # or make a new post_process.py and import that to streamlit | |
| def extract_dialogues(text): | |
| ''' | |
| returns a two lists for human and ai dialogues, | |
| ''' | |
| human_dialogues = [] | |
| ai_dialogues = [] | |
| lines = text.split('\n') | |
| # Iterate through each line | |
| for line in lines: | |
| # Remove leading and trailing whitespace | |
| line = line.strip() | |
| # Check if the line starts with 'Human:' or 'AI:' | |
| if line.startswith('Human:'): | |
| # Extract the text after 'Human:' | |
| human_dialogues.append(line[len('Human:'):].strip()) | |
| elif line.startswith('AI:'): | |
| # Extract the text after 'AI:' | |
| ai_dialogues.append(line[len('AI:'):].strip()) | |
| return human_dialogues, ai_dialogues | |
| def update_list(): | |
| global human_inputs, ai_responses | |
| human_responses, ai_responses = extract_dialogues(memory.buffer_as_str) | |
| return 'responses updated' | |
| def is_depressed(): | |
| '''' | |
| returns wether according to human inputs the person is depressed or not | |
| ''' | |
| # Implement Classification | |
| all_user_inputs = ''.join(human_inputs) | |
| from nlp_models import sentiment_class, pattern_classification, corelation_analysis | |
| is_depressed = sentiment_class(all_user_inputs) | |
| return 'Not so depressed' if is_depressed[0][1] > 0.5 else 'is_depressed' |