Spaces:
Runtime error
Runtime error
File size: 14,586 Bytes
39de480 dfb1f95 39de480 027bfbf 3715d20 027bfbf 3715d20 39de480 dfb1f95 39de480 027bfbf 2bb1521 39de480 027bfbf 39de480 3715d20 39de480 027bfbf 39de480 3715d20 027bfbf 3715d20 027bfbf 3715d20 027bfbf 3715d20 027bfbf 3715d20 39de480 23257c7 2bb1521 dfb1f95 39de480 2bb1521 39de480 3715d20 027bfbf e74d5aa cb81a79 027bfbf a84d7a2 027bfbf e74d5aa 027bfbf e74d5aa 95390bd cb81a79 027bfbf a84d7a2 027bfbf 95390bd 027bfbf 95390bd 027bfbf 95390bd 027bfbf 95390bd 027bfbf 95390bd 027bfbf 3715d20 027bfbf e74d5aa 3715d20 027bfbf 3715d20 027bfbf 99f2e6a 027bfbf |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 |
"""
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.chains import RetrievalQA
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.agents import ZeroShotAgent, Tool, AgentExecutor
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain, ConversationalRetrievalChain
from conversationBufferWindowMemory import ConversationBufferWindowMemory
from langchain.memory import ReadOnlySharedMemory
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()]
memory = ConversationBufferWindowMemory(
memory_key="chat_history",
input_key="question",
return_messages=True,
k=3
)
readonlymemory = ReadOnlySharedMemory(memory=memory)
class Singleton:
__instance = None
@staticmethod
def getInstance():
""" Static access method. """
if Singleton.__instance == None:
Singleton()
return Singleton.__instance
def __init__(self):
""" Virtually private constructor. """
if Singleton.__instance != None:
raise Exception("This class is a singleton!")
else:
Singleton.__instance = QAPipeline()
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
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
self.agent = None
def run(self,query, model, dataset):
if (self.llm_name != model) or (self.dataset_name != dataset) or (self.qa_chain == None):
self.set_model(model)
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
def run_agent(self,query, model, dataset):
try:
if (self.llm_name != model) or (self.dataset_name != dataset) or (self.agent == None):
self.set_model(model)
self.set_vectorstore(dataset)
self.set_qa_chain_with_agent()
# Get the answer from the chain
start = time.time()
res = self.agent(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["output"]
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):
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":
self.llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)
case "Deci/DeciLM-6b-instruct":
self.llm = ChatOpenAI(model_name="Deci/DeciLM-6b-instruct", temperature=0)
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):
self.qa_chain = RetrievalQA.from_chain_type(
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
)
def set_qa_chain_with_agent(self):
try:
# Define a custom prompt
general_qa_template = (
"""[INST]<<SYS>> You are the AI of company boardpac which provide services to company board members related to banking and financial sector. You should only continue the conversation and reply to users questions like welcomes, greetings and goodbyes.
If you dont know the answer say you dont know, dont try to makeup answers. Answer should be short and simple as possible. Start the answer with code word Boardpac AI (chat): <</SYS>>
Conversation: {chat_history}
Question: {question} [/INST]"""
)
general_qa_chain_prompt = PromptTemplate(input_variables=["question", "chat_history"], template=general_qa_template)
general_qa_chain = LLMChain(
llm=self.llm,
prompt=general_qa_chain_prompt,
verbose=True,
memory=readonlymemory, # use the read-only memory to prevent the tool from modifying the memory
)
general_qa_chain_tool = Tool(
name="general qa",
func= general_qa_chain.run,
description='''use this when only you need to answer questions like welcomes, greetings and goodbyes.
Input should be a fully formed question.''',
return_direct=True,
)
# Define a custom prompt
retrieval_qa_template = (
"""[INST]<<SYS>> You are the AI of company boardpac which provide services to company board members. Only answer questions 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 on the chat history and context information provided below related to central bank acts published in various years. The published year is mentioned as the metadata 'year' of each source document.
The content of a bank act of a past year can updated by a bank act from a latest 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. Answer should be short and simple as possible. Start the answer with code word Boardpac AI (QA): <</SYS>>
Conversation: {chat_history}
Context: {context}
Question : {question} [/INST]"""
)
retrieval_qa_chain_prompt = PromptTemplate(
input_variables=["question", "context", "chat_history"],
template=retrieval_qa_template
)
document_combine_prompt = PromptTemplate(
input_variables=["source","year", "page","page_content"],
template=
"""<doc> source: {source}, year: {year}, page: {page}, page content: {page_content} </doc>"""
)
bank_regulations_qa = ConversationalRetrievalChain.from_llm(
llm=self.llm,
chain_type="stuff",
retriever = self.vectorstore.as_retriever(),
# retriever = self.vectorstore.as_retriever(
# search_type="mmr",
# search_kwargs={
# 'k': 6,
# # 'lambda_mult': 0.1,
# 'fetch_k': 50},
# # search_type="similarity_score_threshold",
# # search_kwargs={"score_threshold": .5}
# ),
return_source_documents= True,
return_generated_question= True,
get_chat_history=lambda h : h,
combine_docs_chain_kwargs={
"prompt": retrieval_qa_chain_prompt,
"document_prompt": document_combine_prompt,
},
verbose=True,
memory=readonlymemory, # use the read-only memory to prevent the tool from modifying the memory
)
bank_regulations_qa_tool = Tool(
name="bank regulations",
func= lambda question: bank_regulations_qa({"question": question}),
description='''Use this more when you need to answer questions about Banking and Financial Services Sector like Banking & Financial regulations, legal framework, governance framework, compliance requirements as per Central Bank regulations.
Input should be a fully formed question.''',
return_direct=True,
)
tools = [
bank_regulations_qa_tool,
general_qa_chain_tool
]
prefix = """<<SYS>> You are the AI of company boardpac which provide services to company board members related to banking and financial sector. Have a conversation with the user, answering the following questions as best you can. You have access to the following tools:"""
suffix = """Begin! "
{agent_scratchpad}
<chat history>: {chat_history}
<</SYS>>
[INST]
<Question>: {question}
[/INST]"""
agent_prompt = ZeroShotAgent.create_prompt(
tools,
prefix=prefix,
suffix=suffix,
input_variables=["question", "chat_history", "agent_scratchpad"],
)
llm_chain = LLMChain(llm=self.llm, prompt=agent_prompt)
agent = ZeroShotAgent(
llm_chain=llm_chain,
tools=tools,
verbose=True,
)
agent_chain = AgentExecutor.from_agent_and_tools(
agent=agent,
tools=tools,
verbose=True,
memory=memory,
handle_parsing_errors=True,
)
self.agent = agent_chain
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
|