File size: 3,249 Bytes
3ac9dae |
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 |
import abc
import os
import time
import urllib
from queue import Queue
from threading import Thread
from typing import List, Optional
from langchain.chains.base import Chain
from app_modules.llm_loader import LLMLoader, TextIteratorStreamer
from app_modules.utils import remove_extra_spaces
class LLMInference(metaclass=abc.ABCMeta):
llm_loader: LLMLoader
chain: Chain
def __init__(self, llm_loader):
self.llm_loader = llm_loader
self.chain = None
@abc.abstractmethod
def create_chain(self) -> Chain:
pass
def get_chain(self) -> Chain:
if self.chain is None:
self.chain = self.create_chain()
return self.chain
def run_chain(self, chain, inputs, callbacks: Optional[List] = []):
return chain(inputs, callbacks)
def call_chain(
self,
inputs,
streaming_handler,
q: Queue = None,
testing: bool = False,
):
print(inputs)
if self.llm_loader.streamer.for_huggingface:
self.llm_loader.lock.acquire()
try:
self.llm_loader.streamer.reset(q)
chain = self.get_chain()
result = (
self._run_chain_with_streaming_handler(
chain, inputs, streaming_handler, testing
)
if streaming_handler is not None
else self.run_chain(chain, inputs)
)
if "answer" in result:
result["answer"] = remove_extra_spaces(result["answer"])
base_url = os.environ.get("PDF_FILE_BASE_URL")
if base_url is not None and len(base_url) > 0:
documents = result["source_documents"]
for doc in documents:
source = doc.metadata["source"]
title = source.split("/")[-1]
doc.metadata["url"] = f"{base_url}{urllib.parse.quote(title)}"
return result
finally:
if self.llm_loader.streamer.for_huggingface:
self.llm_loader.lock.release()
def _execute_chain(self, chain, inputs, q, sh):
q.put(self.run_chain(chain, inputs, callbacks=[sh]))
def _run_chain_with_streaming_handler(
self, chain, inputs, streaming_handler, testing
):
que = Queue()
t = Thread(
target=self._execute_chain,
args=(chain, inputs, que, streaming_handler),
)
t.start()
if self.llm_loader.streamer.for_huggingface:
count = (
2
if "chat_history" in inputs and len(inputs.get("chat_history")) > 0
else 1
)
while count > 0:
try:
for token in self.llm_loader.streamer:
if not testing:
streaming_handler.on_llm_new_token(token)
self.llm_loader.streamer.reset()
count -= 1
except Exception:
if not testing:
print("nothing generated yet - retry in 0.5s")
time.sleep(0.5)
t.join()
return que.get()
|