"""
Python Backend API to chat with private data
08/14/2023
D.M. Theekshana Samaradiwakara
"""
import os
import time
from dotenv import load_dotenv
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.llms import GPT4All
from langchain.llms import HuggingFaceHub
from langchain.chat_models import ChatOpenAI
from langchain.chat_models import ChatAnyscale
# from langchain.retrievers.self_query.base import SelfQueryRetriever
# from langchain.chains.query_constructor.base import AttributeInfo
# from chromaDb import load_store
from faissDb import load_FAISS_store
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain, ConversationalRetrievalChain
from conversationBufferWindowMemory import ConversationBufferWindowMemory
load_dotenv()
#gpt4 all model
gpt4all_model_path = os.environ.get('GPT4ALL_MODEL_PATH')
model_n_ctx = os.environ.get('MODEL_N_CTX')
model_n_batch = int(os.environ.get('MODEL_N_BATCH',8))
target_source_chunks = int(os.environ.get('TARGET_SOURCE_CHUNKS',4))
openai_api_key = os.environ.get('OPENAI_API_KEY')
anyscale_api_key = os.environ.get('ANYSCALE_ENDPOINT_TOKEN')
verbose = os.environ.get('VERBOSE')
# activate/deactivate the streaming StdOut callback for LLMs
callbacks = [StreamingStdOutCallbackHandler()]
import re
def is_valid_open_ai_api_key(secretKey):
if re.search("^sk-[a-zA-Z0-9]{32,}$", secretKey ):
return True
else: return False
def get_local_LLAMA2():
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("NousResearch/Llama-2-13b-chat-hf",
# use_auth_token=True,
)
model = AutoModelForCausalLM.from_pretrained("NousResearch/Llama-2-13b-chat-hf",
device_map='auto',
torch_dtype=torch.float16,
use_auth_token=True,
# load_in_8bit=True,
# load_in_4bit=True
)
from transformers import pipeline
pipe = pipeline("text-generation",
model=model,
tokenizer= tokenizer,
torch_dtype=torch.bfloat16,
device_map="auto",
max_new_tokens = 512,
do_sample=True,
top_k=30,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id
)
from langchain import HuggingFacePipeline
LLAMA2 = HuggingFacePipeline(pipeline = pipe, model_kwargs = {'temperature':0})
print(f"\n\n> torch.cuda.is_available(): {torch.cuda.is_available()}")
print("\n\n> local LLAMA2 loaded")
return LLAMA2
memory = ConversationBufferWindowMemory(
memory_key="chat_history",
input_key="question",
output_key = "answer",
return_messages=True,
k=3
)
class QAPipeline:
def __init__(self):
print("\n\n> Initializing QAPipeline:")
self.llm_name = None
self.llm = None
self.dataset_name = None
self.vectorstore = None
self.qa_chain = None
def run_agent(self,query, model, dataset, openai_api_key=None):
try:
if (self.llm_name != model) or (self.dataset_name != dataset) or (self.qa_chain == None):
self.set_model(model, openai_api_key)
self.set_vectorstore(dataset)
self.set_qa_chain()
# Get the answer from the chain
start = time.time()
res = self.qa_chain(query)
# answer, docs = res['result'],res['source_documents']
end = time.time()
# Print the result
print("\n\n> Question:")
print(query)
print(f"\n> Answer (took {round(end - start, 2)} s.):")
print( res)
return res
except Exception as e:
# logger.error(f"Answer retrieval failed with {e}")
print(f"> QAPipeline run_agent Error : {e}")#, icon=":books:")
return
def set_model(self, model_type, openai_api_key):
if model_type != self.llm_name:
match model_type:
case "gpt4all":
# self.llm = GPT4All(model=gpt4all_model_path, n_ctx=model_n_ctx, backend='gptj', n_batch=model_n_batch, callbacks=callbacks, verbose=verbose)
self.llm = GPT4All(model=gpt4all_model_path, max_tokens=model_n_ctx, backend='gptj', n_batch=model_n_batch, callbacks=callbacks, verbose=verbose)
# self.llm = HuggingFaceHub(repo_id="nomic-ai/gpt4all-j", model_kwargs={"temperature":0.001, "max_length":1024})
case "google/flan-t5-xxl":
self.llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.001, "max_length":1024})
case "tiiuae/falcon-7b-instruct":
self.llm = HuggingFaceHub(repo_id=model_type, model_kwargs={"temperature":0.001, "max_length":1024})
case "openai":
print(f"> openai_api_key: {openai_api_key}")
if is_valid_open_ai_api_key(openai_api_key):
self.llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0, openai_api_key=openai_api_key )
else: return KeyError("openai_api_key is not valid")
case "Deci/DeciLM-6b":
self.llm = ChatOpenAI(model_name="Deci/DeciLM-6b", temperature=0)
case "local/LLAMA2":
self.llm = get_local_LLAMA2()
case "anyscale/Llama-2-13b-chat-hf":
self.llm = ChatAnyscale(anyscale_api_key=anyscale_api_key,temperature=0, model_name='meta-llama/Llama-2-13b-chat-hf', streaming=False)
case "anyscale/Llama-2-70b-chat-hf":
self.llm = ChatAnyscale(anyscale_api_key=anyscale_api_key,temperature=0, model_name='meta-llama/Llama-2-70b-chat-hf', streaming=False)
case _default:
# raise exception if model_type is not supported
raise Exception(f"Model type {model_type} is not supported. Please choose a valid one")
self.llm_name = model_type
def set_vectorstore(self, dataset):
if dataset != self.dataset_name:
# self.vectorstore = load_store(dataset)
self.vectorstore = load_FAISS_store()
print("\n\n> vectorstore loaded:")
self.dataset_name = dataset
def set_qa_chain(self):
print(f"\n> creating agent_chain")
try:
# Define a custom prompt
B_INST, E_INST = "[INST]", "[/INST]"
B_SYS, E_SYS = "<>\n", "\n<>\n\n"
retrieval_qa_template = (
"""<>
You are the AI assistant of company boardpac which provide services to company board members related to banking and financial sector.
please answer the question based on the chat history provided below.
: {chat_history}
Identify the type of the question using following 3 types and answer accordingly.
Answer should be short and simple as possible.
Dont add any extra details that is not mentioned in the context.
If the user asks questions like welcome messages, greetings and goodbyes.
Just reply accordingly with a short and simple answer as possible.
Dont use context information provided below to answer the question.
Start the answer with code word Boardpac AI(chat):
If the question doesn't belong to type 1 or type 3, that means if the question is not about greetings or Banking and Financial Services say that the question is out of your domain.
Start the answer with code word Boardpac AI(OD):
If the question is related to Banking and Financial Services Sector like Banking & Financial regulations, legal framework, governance framework, compliance requirements as per Central Bank regulations.
please answer the question based only on the information provided in following central bank documents published in various years.
The published year is mentioned as the metadata 'year' of each source document.
Please notice that content of a one document of a past year can updated by a new document from a recent year.
Always try to answer with latest information and mention the year which information extracted.
If you dont know the answer say you dont know, dont try to makeup answers.
Start the answer with code word Boardpac AI(QA):
<>
[INST]
{context}
Question : {question}[/INST]"""
)
retrieval_qa_chain_prompt = PromptTemplate(
input_variables=["question", "context", "chat_history"],
template=retrieval_qa_template
)
self.qa_chain = ConversationalRetrievalChain.from_llm(
llm=self.llm,
chain_type="stuff",
retriever = self.vectorstore.as_retriever(),
# retriever = self.vectorstore.as_retriever(search_kwargs={"k": target_source_chunks}
return_source_documents= True,
get_chat_history=lambda h : h,
combine_docs_chain_kwargs={"prompt": retrieval_qa_chain_prompt},
verbose=True,
memory=memory,
)
print(f"\n> agent_chain created")
except Exception as e:
# logger.error(f"Answer retrieval failed with {e}")
print(f"> QAPipeline set_qa_chain_with_agent Error : {e}")#, icon=":books:")
return