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[Feature] Add document retrieval QA (#5020)
* add langchain * add langchain * Add files via upload * add langchain * fix style * fix style: remove extra space * add pytest; modified retriever * add pytest; modified retriever * add tests to build_on_pr.yml * fix build_on_pr.yml * fix build on pr; fix environ vars * seperate unit tests for colossalqa from build from pr * fix container setting; fix environ vars * commented dev code * add incremental update * remove stale code * fix style * change to sha3 224 * fix retriever; fix style; add unit test for document loader * fix ci workflow config * fix ci workflow config * add set cuda visible device script in ci * fix doc string * fix style; update readme; refactored * add force log info * change build on pr, ignore colossalqa * fix docstring, captitalize all initial letters * fix indexing; fix text-splitter * remove debug code, update reference * reset previous commit * update LICENSE update README add key-value mode, fix bugs * add files back * revert force push * remove junk file * add test files * fix retriever bug, add intent classification * change conversation chain design * rewrite prompt and conversation chain * add ui v1 * ui v1 * fix atavar * add header * Refactor the RAG Code and support Pangu * Refactor the ColossalQA chain to Object-Oriented Programming and the UI demo. * resolved conversation. tested scripts under examples. web demo still buggy * fix ci tests * Some modifications to add ChatGPT api * modify llm.py and remove unnecessary files * Delete applications/ColossalQA/examples/ui/test_frontend_input.json * Remove OpenAI api key * add colossalqa * move files * move files * move files * move files * fix style * Add Readme and fix some bugs. * Add something to readme and modify some code * modify a directory name for clarity * remove redundant directory * Correct a type in llm.py * fix AI prefix * fix test_memory.py * fix conversation * fix some erros and typos * Fix a missing import in RAG_ChatBot.py * add colossalcloud LLM wrapper, correct issues in code review --------- Co-authored-by: YeAnbang <anbangy2@outlook.com> Co-authored-by: Orion-Zheng <zheng_zian@u.nus.edu> Co-authored-by: Zian(Andy) Zheng <62330719+Orion-Zheng@users.noreply.github.com> Co-authored-by: Orion-Zheng <zhengzian@u.nus.edu>
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YeAnbang
GitHub
183
applications/ColossalQA/colossalqa/local/llm.py
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183
applications/ColossalQA/colossalqa/local/llm.py
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"""
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API and LLM warpper class for running LLMs locally
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Usage:
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import os
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model_path = os.environ.get("ZH_MODEL_PATH")
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model_name = "chatglm2"
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colossal_api = ColossalAPI(model_name, model_path)
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llm = ColossalLLM(n=1, api=colossal_api)
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TEST_PROMPT_CHATGLM="续写文章：惊蛰一过，春寒加剧。先是料料峭峭，继而雨季开始，"
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logger.info(llm(TEST_PROMPT_CHATGLM, max_new_tokens=100), verbose=True)
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"""
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from typing import Any, List, Mapping, Optional
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import torch
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from colossalqa.local.utils import get_response, post_http_request
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from colossalqa.mylogging import get_logger
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from langchain.callbacks.manager import CallbackManagerForLLMRun
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from langchain.llms.base import LLM
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from transformers import AutoModelForCausalLM, AutoTokenizer
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logger = get_logger()
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class ColossalAPI:
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"""
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API for calling LLM.generate
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"""
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__instances = dict()
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def __init__(self, model_type: str, model_path: str, ckpt_path: str = None) -> None:
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"""
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Configurate model
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"""
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if model_type + model_path + (ckpt_path or "") in ColossalAPI.__instances:
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return
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else:
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ColossalAPI.__instances[model_type + model_path + (ckpt_path or "")] = self
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self.model_type = model_type
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self.model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.float16, trust_remote_code=True)
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if ckpt_path is not None:
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state_dict = torch.load(ckpt_path)
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self.model.load_state_dict(state_dict)
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self.model.to(torch.cuda.current_device())
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# Configurate tokenizer
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self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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self.model.eval()
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@staticmethod
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def get_api(model_type: str, model_path: str, ckpt_path: str = None):
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if model_type + model_path + (ckpt_path or "") in ColossalAPI.__instances:
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return ColossalAPI.__instances[model_type + model_path + (ckpt_path or "")]
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else:
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return ColossalAPI(model_type, model_path, ckpt_path)
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def generate(self, input: str, **kwargs) -> str:
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"""
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Generate response given the prompt
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Args:
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input: input string
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**kwargs: language model keyword type arguments, such as top_k, top_p, temperature, max_new_tokens...
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Returns:
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output: output string
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"""
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if self.model_type in ["chatglm", "chatglm2"]:
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inputs = {
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k: v.to(torch.cuda.current_device()) for k, v in self.tokenizer(input, return_tensors="pt").items()
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}
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else:
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inputs = {
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"input_ids": self.tokenizer(input, return_tensors="pt")["input_ids"].to(torch.cuda.current_device())
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}
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output = self.model.generate(**inputs, **kwargs)
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output = output.cpu()
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prompt_len = inputs["input_ids"].size(1)
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response = output[0, prompt_len:]
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output = self.tokenizer.decode(response, skip_special_tokens=True)
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return output
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class VllmAPI:
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def __init__(self, host: str = "localhost", port: int = 8077) -> None:
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# Configurate api for model served through web
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self.host = host
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self.port = port
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self.url = f"http://{self.host}:{self.port}/generate"
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def generate(self, input: str, **kwargs):
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output = get_response(post_http_request(input, self.url, **kwargs))[0]
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return output[len(input) :]
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class ColossalLLM(LLM):
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"""
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Langchain LLM wrapper for a local LLM
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"""
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n: int
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api: Any
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kwargs = {"max_new_tokens": 100}
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@property
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def _llm_type(self) -> str:
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return "custom"
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def _call(
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self,
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prompt: str,
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> str:
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logger.info(f"kwargs:{kwargs}\nstop:{stop}\nprompt:{prompt}", verbose=self.verbose)
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for k in self.kwargs:
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if k not in kwargs:
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kwargs[k] = self.kwargs[k]
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generate_args = {k: kwargs[k] for k in kwargs if k not in ["stop", "n"]}
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out = self.api.generate(prompt, **generate_args)
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if isinstance(stop, list) and len(stop) != 0:
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for stopping_words in stop:
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if stopping_words in out:
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out = out.split(stopping_words)[0]
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logger.info(f"{prompt}{out}", verbose=self.verbose)
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return out
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@property
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def _identifying_params(self) -> Mapping[str, int]:
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"""Get the identifying parameters."""
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return {"n": self.n}
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class VllmLLM(LLM):
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"""
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Langchain LLM wrapper for a local LLM
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"""
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n: int
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api: Any
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kwargs = {"max_new_tokens": 100}
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@property
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def _llm_type(self) -> str:
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return "custom"
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def _call(
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self,
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prompt: str,
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> str:
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for k in self.kwargs:
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if k not in kwargs:
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kwargs[k] = self.kwargs[k]
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logger.info(f"kwargs:{kwargs}\nstop:{stop}\nprompt:{prompt}", verbose=self.verbose)
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generate_args = {k: kwargs[k] for k in kwargs if k in ["n", "max_tokens", "temperature", "stream"]}
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out = self.api.generate(prompt, **generate_args)
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if len(stop) != 0:
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for stopping_words in stop:
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if stopping_words in out:
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out = out.split(stopping_words)[0]
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logger.info(f"{prompt}{out}", verbose=self.verbose)
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return out
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def set_host_port(self, host: str = "localhost", port: int = 8077, **kwargs) -> None:
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if "max_tokens" not in kwargs:
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kwargs["max_tokens"] = 100
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self.kwargs = kwargs
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self.api = VllmAPI(host=host, port=port)
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@property
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def _identifying_params(self) -> Mapping[str, int]:
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"""Get the identifying parameters."""
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return {"n": self.n}
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