import glob
import inspect
import os
import pathlib
import pickle
import shutil
import subprocess
import sys
import tempfile
import traceback
import uuid
import zipfile
from collections import defaultdict
from datetime import datetime
from functools import reduce
from operator import concat
from joblib import Parallel, delayed
from utils import wrapped_partial, EThread, import_matplotlib, sanitize_filename, makedirs, get_url, flatten_list, \
get_device
import_matplotlib()
import numpy as np
import pandas as pd
import requests
from langchain.chains.qa_with_sources import load_qa_with_sources_chain
# , GCSDirectoryLoader, GCSFileLoader
# , OutlookMessageLoader # GPL3
# ImageCaptionLoader, # use our own wrapper
# ReadTheDocsLoader, # no special file, some path, so have to give as special option
from langchain.document_loaders import PyPDFLoader, TextLoader, CSVLoader, PythonLoader, TomlLoader, \
UnstructuredURLLoader, UnstructuredHTMLLoader, UnstructuredWordDocumentLoader, UnstructuredMarkdownLoader, \
EverNoteLoader, UnstructuredEmailLoader, UnstructuredODTLoader, UnstructuredPowerPointLoader, \
UnstructuredEPubLoader, UnstructuredImageLoader, UnstructuredRTFLoader, ArxivLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.chains.question_answering import load_qa_chain
from langchain.docstore.document import Document
from langchain import PromptTemplate
from langchain.vectorstores import Chroma
def get_db(sources, use_openai_embedding=False, db_type='faiss', persist_directory="db_dir", langchain_mode='notset',
hf_embedding_model="sentence-transformers/all-MiniLM-L6-v2"):
if not sources:
return None
# get embedding model
embedding = get_embedding(use_openai_embedding, hf_embedding_model=hf_embedding_model)
# Create vector database
if db_type == 'faiss':
db = FAISS.from_documents(sources, embedding)
elif db_type == 'chroma':
collection_name = langchain_mode.replace(' ', '_')
os.makedirs(persist_directory, exist_ok=True)
db = Chroma.from_documents(documents=sources,
embedding=embedding,
persist_directory=persist_directory,
collection_name=collection_name,
anonymized_telemetry=False)
db.persist()
# FIXME: below just proves can load persistent dir, regenerates its embedding files, so a bit wasteful
if False:
db = Chroma(embedding_function=embedding,
persist_directory=persist_directory,
collection_name=collection_name)
else:
raise RuntimeError("No such db_type=%s" % db_type)
return db
def add_to_db(db, sources, db_type='faiss', avoid_dup=True):
if not sources:
return db
if db_type == 'faiss':
db.add_documents(sources)
elif db_type == 'chroma':
if avoid_dup:
collection = db.get()
metadata_sources = set([x['source'] for x in collection['metadatas']])
sources = [x for x in sources if x.metadata['source'] not in metadata_sources]
if len(sources) == 0:
return db
db.add_documents(documents=sources)
db.persist()
else:
raise RuntimeError("No such db_type=%s" % db_type)
return db
def get_embedding(use_openai_embedding, hf_embedding_model="sentence-transformers/all-MiniLM-L6-v2"):
# Get embedding model
if use_openai_embedding:
assert os.getenv("OPENAI_API_KEY") is not None, "Set ENV OPENAI_API_KEY"
from langchain.embeddings import OpenAIEmbeddings
embedding = OpenAIEmbeddings()
else:
# to ensure can fork without deadlock
from langchain.embeddings import HuggingFaceEmbeddings
device, torch_dtype, context_class = get_device_dtype()
model_kwargs = dict(device=device)
embedding = HuggingFaceEmbeddings(model_name=hf_embedding_model, model_kwargs=model_kwargs)
return embedding
def get_answer_from_sources(chain, sources, question):
return chain(
{
"input_documents": sources,
"question": question,
},
return_only_outputs=True,
)["output_text"]
def get_llm(use_openai_model=False, model_name=None, model=None,
tokenizer=None, stream_output=False,
max_new_tokens=256,
temperature=0.1,
repetition_penalty=1.0,
top_k=40,
top_p=0.7,
prompt_type=None,
):
if use_openai_model:
from langchain.llms import OpenAI
llm = OpenAI(temperature=0)
model_name = 'openai'
streamer = None
elif model_name in ['gptj', 'llama']:
from gpt4all_llm import get_llm_gpt4all
llm = get_llm_gpt4all(model_name, model=model, max_new_tokens=max_new_tokens,
temperature=temperature,
repetition_penalty=repetition_penalty,
top_k=top_k,
top_p=top_p,
)
streamer = None
prompt_type = 'plain'
else:
from transformers import AutoTokenizer, AutoModelForCausalLM
if model is None:
# only used if didn't pass model in
assert model_name is None
assert tokenizer is None
model_name = 'h2oai/h2ogpt-oasst1-512-12b'
# model_name = 'h2oai/h2ogpt-oig-oasst1-512-6.9b'
# model_name = 'h2oai/h2ogpt-oasst1-512-20b'
tokenizer = AutoTokenizer.from_pretrained(model_name)
device, torch_dtype, context_class = get_device_dtype()
with context_class(device):
load_8bit = True
# FIXME: for now not to spread across hetero GPUs
# device_map={"": 0} if load_8bit and device == 'cuda' else "auto"
device_map = {"": 0} if device == 'cuda' else "auto"
model = AutoModelForCausalLM.from_pretrained(model_name,
device_map=device_map,
torch_dtype=torch_dtype,
load_in_8bit=load_8bit)
gen_kwargs = dict(max_new_tokens=max_new_tokens, return_full_text=True, early_stopping=False)
if stream_output:
skip_prompt = False
from generate import H2OTextIteratorStreamer
decoder_kwargs = {}
streamer = H2OTextIteratorStreamer(tokenizer, skip_prompt=skip_prompt, block=False, **decoder_kwargs)
gen_kwargs.update(dict(streamer=streamer))
else:
streamer = None
if 'h2ogpt' in model_name or prompt_type == 'human_bot':
from h2oai_pipeline import H2OTextGenerationPipeline
pipe = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer, **gen_kwargs)
# pipe.task = "text-generation"
# below makes it listen only to our prompt removal, not built in prompt removal that is less general and not specific for our model
pipe.task = "text2text-generation"
prompt_type = 'human_bot'
else:
# only for non-instruct tuned cases when ok with just normal next token prediction
from transformers import pipeline
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, **gen_kwargs)
from langchain.llms import HuggingFacePipeline
llm = HuggingFacePipeline(pipeline=pipe)
return llm, model_name, streamer, prompt_type
def get_device_dtype():
# torch.device("cuda") leads to cuda:x cuda:y mismatches for multi-GPU consistently
import torch
n_gpus = torch.cuda.device_count() if torch.cuda.is_available else 0
device = 'cpu' if n_gpus == 0 else 'cuda'
# from utils import NullContext
# context_class = NullContext if n_gpus > 1 or n_gpus == 0 else context_class
context_class = torch.device
torch_dtype = torch.float16 if device == 'cuda' else torch.float32
return device, torch_dtype, context_class
def get_wiki_data(title, first_paragraph_only, text_limit=None, take_head=True):
"""
Get wikipedia data from online
:param title:
:param first_paragraph_only:
:param text_limit:
:param take_head:
:return:
"""
filename = 'wiki_%s_%s_%s_%s.data' % (first_paragraph_only, title, text_limit, take_head)
url = f"https://en.wikipedia.org/w/api.php?format=json&action=query&prop=extracts&explaintext=1&titles={title}"
if first_paragraph_only:
url += "&exintro=1"
import json
if not os.path.isfile(filename):
data = requests.get(url).json()
json.dump(data, open(filename, 'wt'))
else:
data = json.load(open(filename, "rt"))
page_content = list(data["query"]["pages"].values())[0]["extract"]
if take_head is not None and text_limit is not None:
page_content = page_content[:text_limit] if take_head else page_content[:-text_limit]
title_url = str(title).replace(' ', '_')
return Document(
page_content=page_content,
metadata={"source": f"https://en.wikipedia.org/wiki/{title_url}"},
)
def get_wiki_sources(first_para=True, text_limit=None):
"""
Get specific named sources from wikipedia
:param first_para:
:param text_limit:
:return:
"""
default_wiki_sources = ['Unix', 'Microsoft_Windows', 'Linux']
wiki_sources = list(os.getenv('WIKI_SOURCES', default_wiki_sources))
return [get_wiki_data(x, first_para, text_limit=text_limit) for x in wiki_sources]
def get_github_docs(repo_owner, repo_name):
"""
Access github from specific repo
:param repo_owner:
:param repo_name:
:return:
"""
with tempfile.TemporaryDirectory() as d:
subprocess.check_call(
f"git clone --depth 1 https://github.com/{repo_owner}/{repo_name}.git .",
cwd=d,
shell=True,
)
git_sha = (
subprocess.check_output("git rev-parse HEAD", shell=True, cwd=d)
.decode("utf-8")
.strip()
)
repo_path = pathlib.Path(d)
markdown_files = list(repo_path.glob("*/*.md")) + list(
repo_path.glob("*/*.mdx")
)
for markdown_file in markdown_files:
with open(markdown_file, "r") as f:
relative_path = markdown_file.relative_to(repo_path)
github_url = f"https://github.com/{repo_owner}/{repo_name}/blob/{git_sha}/{relative_path}"
yield Document(page_content=f.read(), metadata={"source": github_url})
def get_dai_pickle(dest="."):
from huggingface_hub import hf_hub_download
# True for case when locally already logged in with correct token, so don't have to set key
token = os.getenv('HUGGINGFACE_API_TOKEN', True)
path_to_zip_file = hf_hub_download('h2oai/dai_docs', 'dai_docs.pickle', token=token, repo_type='dataset')
shutil.copy(path_to_zip_file, dest)
def get_dai_docs(from_hf=False, get_pickle=True):
"""
Consume DAI documentation, or consume from public pickle
:param from_hf: get DAI docs from HF, then generate pickle for later use by LangChain
:param get_pickle: Avoid raw DAI docs, just get pickle directly from HF
:return:
"""
import pickle
if get_pickle:
get_dai_pickle()
dai_store = 'dai_docs.pickle'
dst = "working_dir_docs"
if not os.path.isfile(dai_store):
from create_data import setup_dai_docs
dst = setup_dai_docs(dst=dst, from_hf=from_hf)
import glob
files = list(glob.glob(os.path.join(dst, '*rst'), recursive=True))
basedir = os.path.abspath(os.getcwd())
from create_data import rst_to_outputs
new_outputs = rst_to_outputs(files)
os.chdir(basedir)
pickle.dump(new_outputs, open(dai_store, 'wb'))
else:
new_outputs = pickle.load(open(dai_store, 'rb'))
sources = []
for line, file in new_outputs:
# gradio requires any linked file to be with app.py
sym_src = os.path.abspath(os.path.join(dst, file))
sym_dst = os.path.abspath(os.path.join(os.getcwd(), file))
if os.path.lexists(sym_dst):
os.remove(sym_dst)
os.symlink(sym_src, sym_dst)
itm = Document(page_content=line, metadata={"source": file})
# NOTE: yield has issues when going into db, loses metadata
# yield itm
sources.append(itm)
return sources
import distutils.spawn
have_tesseract = distutils.spawn.find_executable("tesseract")
have_libreoffice = distutils.spawn.find_executable("libreoffice")
import pkg_resources
try:
assert pkg_resources.get_distribution('arxiv') is not None
assert pkg_resources.get_distribution('pymupdf') is not None
have_arxiv = True
except (pkg_resources.DistributionNotFound, AssertionError):
have_arxiv = False
image_types = ["png", "jpg", "jpeg"]
non_image_types = ["pdf", "txt", "csv", "toml", "py", "rst", "rtf",
"md", "html",
"enex", "eml", "epub", "odt", "pptx", "ppt",
"zip", "urls",
]
# "msg", GPL3
if have_libreoffice:
non_image_types.extend(["docx", "doc"])
file_types = non_image_types + image_types
def add_meta(docs1, file):
file_extension = pathlib.Path(file).suffix
if not isinstance(docs1, list):
docs1 = [docs1]
[x.metadata.update(dict(input_type=file_extension, date=str(datetime.now))) for x in docs1]
def file_to_doc(file, base_path=None, verbose=False, fail_any_exception=False, chunk=True, chunk_size=512,
is_url=False, is_txt=False,
enable_captions=True,
captions_model=None,
enable_ocr=False, caption_loader=None,
headsize=50):
if file is None:
if fail_any_exception:
raise RuntimeError("Unexpected None file")
else:
return []
doc1 = [] # in case no support, or disabled support
if base_path is None and not is_txt and not is_url:
# then assume want to persist but don't care which path used
# can't be in base_path
dir_name = os.path.dirname(file)
base_name = os.path.basename(file)
# if from gradio, will have its own temp uuid too, but that's ok
base_name = sanitize_filename(base_name) + "_" + str(uuid.uuid4())[:10]
base_path = os.path.join(dir_name, base_name)
if is_url:
if file.lower().startswith('arxiv:'):
query = file.lower().split('arxiv:')
if len(query) == 2 and have_arxiv:
query = query[1]
docs1 = ArxivLoader(query=query, load_max_docs=20, load_all_available_meta=True).load()
# ensure string, sometimes None
[[x.metadata.update({k: str(v)}) for k, v in x.metadata.items()] for x in docs1]
query_url = f"https://arxiv.org/abs/{query}"
[x.metadata.update(
dict(source=x.metadata.get('entry_id', query_url), query=query_url,
input_type='arxiv', head=x.metadata.get('Title', ''), date=str(datetime.now))) for x in
docs1]
else:
docs1 = []
else:
docs1 = UnstructuredURLLoader(urls=[file]).load()
[x.metadata.update(dict(input_type='url', date=str(datetime.now))) for x in docs1]
doc1 = chunk_sources(docs1, chunk_size=chunk_size)
elif is_txt:
base_path = "user_paste"
source_file = os.path.join(base_path, "_%s" % str(uuid.uuid4())[:10])
makedirs(os.path.dirname(source_file), exist_ok=True)
with open(source_file, "wt") as f:
f.write(file)
metadata = dict(source=source_file, date=str(datetime.now()), input_type='pasted txt')
doc1 = Document(page_content=file, metadata=metadata)
elif file.endswith('.html') or file.endswith('.mhtml'):
docs1 = UnstructuredHTMLLoader(file_path=file).load()
add_meta(docs1, file)
doc1 = chunk_sources(docs1, chunk_size=chunk_size)
elif (file.endswith('.docx') or file.endswith('.doc')) and have_libreoffice:
docs1 = UnstructuredWordDocumentLoader(file_path=file).load()
add_meta(docs1, file)
doc1 = chunk_sources(docs1, chunk_size=chunk_size)
elif file.endswith('.odt'):
docs1 = UnstructuredODTLoader(file_path=file).load()
add_meta(docs1, file)
doc1 = chunk_sources(docs1, chunk_size=chunk_size)
elif file.endswith('pptx') or file.endswith('ppt'):
docs1 = UnstructuredPowerPointLoader(file_path=file).load()
add_meta(docs1, file)
doc1 = chunk_sources(docs1, chunk_size=chunk_size)
elif file.endswith('.txt'):
# use UnstructuredFileLoader ?
doc1 = TextLoader(file, encoding="utf8", autodetect_encoding=True).load()
add_meta(doc1, file)
elif file.endswith('.rtf'):
docs1 = UnstructuredRTFLoader(file).load()
add_meta(docs1, file)
doc1 = chunk_sources(docs1, chunk_size=chunk_size)
elif file.endswith('.md'):
docs1 = UnstructuredMarkdownLoader(file).load()
add_meta(docs1, file)
doc1 = chunk_sources(docs1, chunk_size=chunk_size)
elif file.endswith('.enex'):
doc1 = EverNoteLoader(file).load()
add_meta(doc1, file)
elif file.endswith('.epub'):
docs1 = UnstructuredEPubLoader(file).load()
add_meta(docs1, file)
doc1 = chunk_sources(docs1, chunk_size=chunk_size)
elif file.endswith('.jpeg') or file.endswith('.jpg') or file.endswith('.png'):
docs1 = []
if have_tesseract and enable_ocr:
# OCR, somewhat works, but not great
docs1.extend(UnstructuredImageLoader(file).load())
add_meta(docs1, file)
if enable_captions:
# BLIP
if caption_loader is not None and not isinstance(caption_loader, (str, bool)):
# assumes didn't fork into this process with joblib, else can deadlock
caption_loader.set_image_paths([file])
docs1c = caption_loader.load()
add_meta(docs1c, file)
[x.metadata.update(dict(head=x.page_content[:headsize].strip())) for x in docs1c]
docs1.extend(docs1c)
else:
from image_captions import H2OImageCaptionLoader
caption_loader = H2OImageCaptionLoader(caption_gpu=caption_loader == 'gpu',
blip_model=captions_model,
blip_processor=captions_model)
caption_loader.set_image_paths([file])
docs1c = caption_loader.load()
add_meta(docs1c, file)
[x.metadata.update(dict(head=x.page_content[:headsize].strip())) for x in docs1c]
docs1.extend(docs1c)
for doci in docs1:
doci.metadata['source'] = doci.metadata['image_path']
if docs1:
doc1 = chunk_sources(docs1, chunk_size=chunk_size)
elif file.endswith('.msg'):
raise RuntimeError("Not supported, GPL3 license")
# docs1 = OutlookMessageLoader(file).load()
# docs1[0].metadata['source'] = file
elif file.endswith('.eml'):
try:
docs1 = UnstructuredEmailLoader(file).load()
add_meta(docs1, file)
doc1 = chunk_sources(docs1, chunk_size=chunk_size)
except ValueError as e:
if 'text/html content not found in email' in str(e):
# e.g. plain/text dict key exists, but not
# doc1 = TextLoader(file, encoding="utf8").load()
docs1 = UnstructuredEmailLoader(file, content_source="text/plain").load()
add_meta(docs1, file)
doc1 = chunk_sources(docs1, chunk_size=chunk_size)
else:
raise
# elif file.endswith('.gcsdir'):
# doc1 = GCSDirectoryLoader(project_name, bucket, prefix).load()
# elif file.endswith('.gcsfile'):
# doc1 = GCSFileLoader(project_name, bucket, blob).load()
elif file.endswith('.rst'):
with open(file, "r") as f:
doc1 = Document(page_content=f.read(), metadata={"source": file})
add_meta(doc1, file)
elif file.endswith('.pdf'):
# Some PDFs return nothing or junk from PDFMinerLoader
# e.g. Beyond fine-tuning_ Classifying high resolution mammograms using function-preserving transformations _ Elsevier Enhanced Reader.pdf
doc1 = PyPDFLoader(file).load_and_split()
add_meta(doc1, file)
elif file.endswith('.csv'):
doc1 = CSVLoader(file).load()
add_meta(doc1, file)
elif file.endswith('.py'):
doc1 = PythonLoader(file).load()
add_meta(doc1, file)
elif file.endswith('.toml'):
doc1 = TomlLoader(file).load()
add_meta(doc1, file)
elif file.endswith('.urls'):
with open(file, "r") as f:
docs1 = UnstructuredURLLoader(urls=f.readlines()).load()
add_meta(docs1, file)
doc1 = chunk_sources(docs1, chunk_size=chunk_size)
elif file.endswith('.zip'):
with zipfile.ZipFile(file, 'r') as zip_ref:
# don't put into temporary path, since want to keep references to docs inside zip
# so just extract in path where
zip_ref.extractall(base_path)
# recurse
doc1 = path_to_docs(base_path, verbose=verbose, fail_any_exception=fail_any_exception)
else:
raise RuntimeError("No file handler for %s" % os.path.basename(file))
# allow doc1 to be list or not. If not list, did not chunk yet, so chunk now
if not isinstance(doc1, list):
if chunk:
docs = chunk_sources([doc1], chunk_size=chunk_size)
else:
docs = [doc1]
else:
docs = doc1
assert isinstance(docs, list)
return docs
def path_to_doc1(file, verbose=False, fail_any_exception=False, return_file=True, chunk=True, chunk_size=512,
is_url=False, is_txt=False,
enable_captions=True,
captions_model=None,
enable_ocr=False, caption_loader=None):
if verbose:
if is_url:
print("Ingesting URL: %s" % file, flush=True)
elif is_txt:
print("Ingesting Text: %s" % file, flush=True)
else:
print("Ingesting file: %s" % file, flush=True)
res = None
try:
# don't pass base_path=path, would infinitely recurse
res = file_to_doc(file, base_path=None, verbose=verbose, fail_any_exception=fail_any_exception,
chunk=chunk, chunk_size=chunk_size,
is_url=is_url, is_txt=is_txt,
enable_captions=enable_captions,
captions_model=captions_model,
enable_ocr=enable_ocr,
caption_loader=caption_loader)
except BaseException as e:
print("Failed to ingest %s due to %s" % (file, traceback.format_exc()))
if fail_any_exception:
raise
else:
exception_doc = Document(
page_content='',
metadata={"source": file, "exception": str(e), "traceback": traceback.format_exc()})
res = [exception_doc]
if return_file:
base_tmp = "temp_path_to_doc1"
if not os.path.isdir(base_tmp):
os.makedirs(base_tmp, exist_ok=True)
filename = os.path.join(base_tmp, str(uuid.uuid4()) + ".tmp.pickle")
with open(filename, 'wb') as f:
pickle.dump(res, f)
return filename
return res
def path_to_docs(path_or_paths, verbose=False, fail_any_exception=False, n_jobs=-1,
chunk=True, chunk_size=512,
url=None, text=None,
enable_captions=True,
captions_model=None,
caption_loader=None,
enable_ocr=False,
):
globs_image_types = []
globs_non_image_types = []
if path_or_paths is None:
return []
elif url:
globs_non_image_types = [url]
elif text:
globs_non_image_types = [text]
elif isinstance(path_or_paths, str):
# single path, only consume allowed files
path = path_or_paths
# Below globs should match patterns in file_to_doc()
[globs_image_types.extend(glob.glob(os.path.join(path, "./**/*.%s" % ftype), recursive=True))
for ftype in image_types]
[globs_non_image_types.extend(glob.glob(os.path.join(path, "./**/*.%s" % ftype), recursive=True))
for ftype in non_image_types]
else:
# list/tuple of files (consume what can, and exception those that selected but cannot consume so user knows)
assert isinstance(path_or_paths, (list, tuple)), "Wrong type for path_or_paths: %s" % type(path_or_paths)
# reform out of allowed types
globs_image_types.extend(flatten_list([[x for x in path_or_paths if x.endswith(y)] for y in image_types]))
# could do below:
# globs_non_image_types = flatten_list([[x for x in path_or_paths if x.endswith(y)] for y in non_image_types])
# But instead, allow fail so can collect unsupported too
set_globs_image_types = set(globs_image_types)
globs_non_image_types.extend([x for x in path_or_paths if x not in set_globs_image_types])
# could use generator, but messes up metadata handling in recursive case
if caption_loader and not isinstance(caption_loader, (bool, str)) and \
caption_loader.device != 'cpu' or \
get_device() == 'cuda':
# to avoid deadlocks, presume was preloaded and so can't fork due to cuda context
n_jobs_image = 1
else:
n_jobs_image = n_jobs
return_file = True # local choice
is_url = url is not None
is_txt = text is not None
kwargs = dict(verbose=verbose, fail_any_exception=fail_any_exception,
return_file=return_file,
chunk=chunk, chunk_size=chunk_size,
is_url=is_url,
is_txt=is_txt,
enable_captions=enable_captions,
captions_model=captions_model,
caption_loader=caption_loader,
enable_ocr=enable_ocr,
)
if n_jobs != 1 and len(globs_non_image_types) > 1:
# avoid nesting, e.g. upload 1 zip and then inside many files
# harder to handle if upload many zips with many files, inner parallel one will be disabled by joblib
documents = Parallel(n_jobs=n_jobs, verbose=10 if verbose else 0, backend='multiprocessing')(
delayed(path_to_doc1)(file, **kwargs) for file in globs_non_image_types
)
else:
documents = [path_to_doc1(file, **kwargs) for file in globs_non_image_types]
# do images separately since can't fork after cuda in parent, so can't be parallel
if n_jobs_image != 1 and len(globs_image_types) > 1:
# avoid nesting, e.g. upload 1 zip and then inside many files
# harder to handle if upload many zips with many files, inner parallel one will be disabled by joblib
image_documents = Parallel(n_jobs=n_jobs, verbose=10 if verbose else 0, backend='multiprocessing')(
delayed(path_to_doc1)(file, **kwargs) for file in globs_image_types
)
else:
image_documents = [path_to_doc1(file, **kwargs) for file in globs_image_types]
# add image docs in
documents += image_documents
if return_file:
# then documents really are files
files = documents.copy()
documents = []
for fil in files:
with open(fil, 'rb') as f:
documents.extend(pickle.load(f))
# remove temp pickle
os.remove(fil)
else:
documents = reduce(concat, documents)
return documents
def prep_langchain(persist_directory, load_db_if_exists, db_type, use_openai_embedding, langchain_mode, user_path,
hf_embedding_model, n_jobs=-1, kwargs_make_db={}):
"""
do prep first time, involving downloads
# FIXME: Add github caching then add here
:return:
"""
assert langchain_mode not in ['MyData'], "Should not prep scratch data"
if os.path.isdir(persist_directory):
print("Prep: persist_directory=%s exists, using" % persist_directory, flush=True)
db = get_existing_db(persist_directory, load_db_if_exists, db_type, use_openai_embedding, langchain_mode,
hf_embedding_model)
else:
print("Prep: persist_directory=%s does not exist, regenerating" % persist_directory, flush=True)
db = None
if langchain_mode in ['All', 'DriverlessAI docs']:
# FIXME: Could also just use dai_docs.pickle directly and upload that
get_dai_docs(from_hf=True)
if langchain_mode in ['All', 'wiki']:
get_wiki_sources(first_para=kwargs_make_db['first_para'], text_limit=kwargs_make_db['text_limit'])
langchain_kwargs = kwargs_make_db.copy()
langchain_kwargs.update(locals())
db = make_db(**langchain_kwargs)
return db
def get_existing_db(persist_directory, load_db_if_exists, db_type, use_openai_embedding, langchain_mode,
hf_embedding_model):
if load_db_if_exists and db_type == 'chroma' and os.path.isdir(persist_directory) and os.path.isdir(
os.path.join(persist_directory, 'index')):
print("DO Loading db: %s" % langchain_mode, flush=True)
embedding = get_embedding(use_openai_embedding, hf_embedding_model=hf_embedding_model)
db = Chroma(persist_directory=persist_directory, embedding_function=embedding,
collection_name=langchain_mode.replace(' ', '_'))
print("DONE Loading db: %s" % langchain_mode, flush=True)
return db
return None
def make_db(**langchain_kwargs):
func_names = list(inspect.signature(_make_db).parameters)
missing_kwargs = [x for x in func_names if x not in langchain_kwargs]
defaults_db = {k: v.default for k, v in dict(inspect.signature(run_qa_db).parameters).items()}
for k in missing_kwargs:
if k in defaults_db:
langchain_kwargs[k] = defaults_db[k]
# final check for missing
missing_kwargs = [x for x in func_names if x not in langchain_kwargs]
assert not missing_kwargs, "Missing kwargs: %s" % missing_kwargs
# only keep actual used
langchain_kwargs = {k: v for k, v in langchain_kwargs.items() if k in func_names}
return _make_db(**langchain_kwargs)
def _make_db(use_openai_embedding=False,
hf_embedding_model="sentence-transformers/all-MiniLM-L6-v2",
first_para=False, text_limit=None, chunk=False, chunk_size=1024,
langchain_mode=None,
user_path=None,
db_type='faiss',
load_db_if_exists=False,
db=None,
n_jobs=-1):
persist_directory = 'db_dir_%s' % langchain_mode # single place, no special names for each case
if not db and load_db_if_exists and db_type == 'chroma' and os.path.isdir(persist_directory) and os.path.isdir(
os.path.join(persist_directory, 'index')):
assert langchain_mode not in ['MyData'], "Should not load MyData db this way"
print("Loading db", flush=True)
embedding = get_embedding(use_openai_embedding, hf_embedding_model=hf_embedding_model)
db = Chroma(persist_directory=persist_directory, embedding_function=embedding,
collection_name=langchain_mode.replace(' ', '_'))
elif not db:
assert langchain_mode not in ['MyData'], "Should not make MyData db this way"
sources = []
print("Generating sources", flush=True)
if langchain_mode in ['wiki_full', 'All', "'All'"]:
from read_wiki_full import get_all_documents
small_test = None
print("Generating new wiki", flush=True)
sources1 = get_all_documents(small_test=small_test, n_jobs=os.cpu_count() // 2)
print("Got new wiki", flush=True)
if chunk:
sources1 = chunk_sources(sources1, chunk_size=chunk_size)
print("Chunked new wiki", flush=True)
sources.extend(sources1)
if langchain_mode in ['wiki', 'All', "'All'"]:
sources1 = get_wiki_sources(first_para=first_para, text_limit=text_limit)
if chunk:
sources1 = chunk_sources(sources1, chunk_size=chunk_size)
sources.extend(sources1)
if langchain_mode in ['github h2oGPT', 'All', "'All'"]:
# sources = get_github_docs("dagster-io", "dagster")
sources1 = get_github_docs("h2oai", "h2ogpt")
# FIXME: always chunk for now
sources1 = chunk_sources(sources1, chunk_size=chunk_size)
sources.extend(sources1)
if langchain_mode in ['DriverlessAI docs', 'All', "'All'"]:
sources1 = get_dai_docs(from_hf=True)
if chunk and False: # FIXME: DAI docs are already chunked well, should only chunk more if over limit
sources1 = chunk_sources(sources1, chunk_size=chunk_size)
sources.extend(sources1)
if langchain_mode in ['All', 'UserData']:
if user_path:
# chunk internally for speed over multiple docs
sources1 = path_to_docs(user_path, n_jobs=n_jobs, chunk=chunk, chunk_size=chunk_size)
sources.extend(sources1)
else:
print("Chose UserData but user_path is empty/None", flush=True)
if False and langchain_mode in ['urls', 'All', "'All'"]:
# from langchain.document_loaders import UnstructuredURLLoader
# loader = UnstructuredURLLoader(urls=urls)
urls = ["https://www.birdsongsf.com/who-we-are/"]
from langchain.document_loaders import PlaywrightURLLoader
loader = PlaywrightURLLoader(urls=urls, remove_selectors=["header", "footer"])
sources1 = loader.load()
sources.extend(sources1)
if not sources:
print("langchain_mode %s has no sources, not making db" % langchain_mode, flush=True)
return None
print("Generating db", flush=True)
db = get_db(sources, use_openai_embedding=use_openai_embedding, db_type=db_type,
persist_directory=persist_directory, langchain_mode=langchain_mode,
hf_embedding_model=hf_embedding_model)
print("Generated db", flush=True)
return db
source_prefix = "Sources [Score | Link]:"
source_postfix = "End Sources"
def run_qa_db(**kwargs):
func_names = list(inspect.signature(_run_qa_db).parameters)
# hard-coded defaults
kwargs['answer_with_sources'] = True
kwargs['sanitize_bot_response'] = True
kwargs['show_rank'] = False
missing_kwargs = [x for x in func_names if x not in kwargs]
assert not missing_kwargs, "Missing kwargs: %s" % missing_kwargs
# only keep actual used
kwargs = {k: v for k, v in kwargs.items() if k in func_names}
return _run_qa_db(**kwargs)
def _run_qa_db(query=None,
use_openai_model=False, use_openai_embedding=False,
first_para=False, text_limit=None, k=4, chunk=False, chunk_size=1024,
user_path=None,
db_type='faiss',
model_name=None, model=None, tokenizer=None,
hf_embedding_model="sentence-transformers/all-MiniLM-L6-v2",
stream_output=False,
prompter=None,
prompt_type=None,
answer_with_sources=True,
cut_distanct=1.1,
sanitize_bot_response=True,
show_rank=False,
load_db_if_exists=False,
db=None,
max_new_tokens=256,
temperature=0.1,
repetition_penalty=1.0,
top_k=40,
top_p=0.7,
langchain_mode=None,
n_jobs=-1):
"""
:param query:
:param use_openai_model:
:param use_openai_embedding:
:param first_para:
:param text_limit:
:param k:
:param chunk:
:param chunk_size:
:param user_path: user path to glob recursively from
:param db_type: 'faiss' for in-memory db or 'chroma' for persistent db
:param model_name: model name, used to switch behaviors
:param model: pre-initialized model, else will make new one
:param tokenizer: pre-initialized tokenizer, else will make new one. Required not None if model is not None
:param answer_with_sources
:return:
"""
# FIXME: For All just go over all dbs instead of a separate db for All
db = make_db(**locals())
prompt_type = prompter.prompt_type if prompter is not None else prompt_type
llm, model_name, streamer, prompt_type_out = get_llm(use_openai_model=use_openai_model, model_name=model_name,
model=model, tokenizer=tokenizer,
stream_output=stream_output,
max_new_tokens=max_new_tokens,
temperature=temperature,
repetition_penalty=repetition_penalty,
top_k=top_k,
top_p=top_p,
prompt_type=prompt_type,
)
if model_name in ['llama', 'gptj']:
# FIXME: for now, streams to stdout/stderr currently
stream_output = False
if not use_openai_model and prompt_type not in ['plain'] or model_name in ['llama', 'gptj']:
# instruct-like, rather than few-shot prompt_type='plain' as default
# but then sources confuse the model with how inserted among rest of text, so avoid
prefix = ""
if langchain_mode in ['Disabled', 'ChatLLM', 'LLM']:
use_context = False
template = """%s{context}{question}""" % prefix
else:
use_context = True
template = """%s
==
{context}
==
{question}""" % prefix
prompt = PromptTemplate(
# input_variables=["summaries", "question"],
input_variables=["context", "question"],
template=template,
)
chain = load_qa_chain(llm, prompt=prompt)
else:
chain = load_qa_with_sources_chain(llm)
use_context = True
if query is None:
query = "What are the main differences between Linux and Windows?"
# https://github.com/hwchase17/langchain/issues/1946
# FIXME: Seems to way to get size of chroma db to limit k to avoid
# Chroma collection MyData contains fewer than 4 elements.
# type logger error
k_db = 1000 if db_type == 'chroma' else k # k=100 works ok too for
if db and use_context:
docs_with_score = db.similarity_search_with_score(query, k=k_db)[:k]
# cut off so no high distance docs/sources considered
docs = [x[0] for x in docs_with_score if x[1] < cut_distanct]
scores = [x[1] for x in docs_with_score if x[1] < cut_distanct]
if len(scores) > 0:
print("Distance: min: %s max: %s mean: %s median: %s" %
(scores[0], scores[-1], np.mean(scores), np.median(scores)), flush=True)
else:
docs = []
scores = []
if not docs and use_context:
return None
common_words_file = "data/NGSL_1.2_stats.csv.zip"
if os.path.isfile(common_words_file):
df = pd.read_csv("data/NGSL_1.2_stats.csv.zip")
import string
reduced_query = query.translate(str.maketrans(string.punctuation, ' ' * len(string.punctuation))).strip()
reduced_query_words = reduced_query.split(' ')
set_common = set(df['Lemma'].values.tolist())
num_common = len([x.lower() in set_common for x in reduced_query_words])
frac_common = num_common / len(reduced_query)
# FIXME: report to user bad query that uses too many common words
print("frac_common: %s" % frac_common, flush=True)
if langchain_mode in ['Disabled', 'ChatLLM', 'LLM']:
chain_kwargs = dict(input_documents=[], question=query)
else:
chain_kwargs = dict(input_documents=docs, question=query)
if stream_output:
answer = None
assert streamer is not None
target = wrapped_partial(chain, chain_kwargs)
import queue
bucket = queue.Queue()
thread = EThread(target=target, streamer=streamer, bucket=bucket)
thread.start()
outputs = ""
prompt = None # FIXME
try:
for new_text in streamer:
# print("new_text: %s" % new_text, flush=True)
if bucket.qsize() > 0 or thread.exc:
thread.join()
outputs += new_text
if prompter: # and False: # FIXME: pipeline can already use prompter
output1 = prompter.get_response(outputs, prompt=prompt,
sanitize_bot_response=sanitize_bot_response)
yield output1
else:
yield outputs
except BaseException:
# if any exception, raise that exception if was from thread, first
if thread.exc:
raise thread.exc
raise
finally:
# in case no exception and didn't join with thread yet, then join
if not thread.exc:
answer = thread.join()
# in case raise StopIteration or broke queue loop in streamer, but still have exception
if thread.exc:
raise thread.exc
# FIXME: answer is not string outputs from streamer. How to get actual final output?
# answer = outputs
else:
answer = chain(chain_kwargs)
if not use_context:
ret = answer['output_text']
yield ret
elif answer is not None:
print("query: %s" % query, flush=True)
print("answer: %s" % answer['output_text'], flush=True)
# link
answer_sources = [(max(0.0, 1.5 - score) / 1.5, get_url(doc)) for score, doc in
zip(scores, answer['input_documents'])]
answer_sources_dict = defaultdict(list)
[answer_sources_dict[url].append(score) for score, url in answer_sources]
answers_dict = {}
for url, scores_url in answer_sources_dict.items():
answers_dict[url] = np.max(scores_url)
answer_sources = [(score, url) for url, score in answers_dict.items()]
answer_sources.sort(key=lambda x: x[0], reverse=True)
if show_rank:
# answer_sources = ['%d | %s' % (1 + rank, url) for rank, (score, url) in enumerate(answer_sources)]
# sorted_sources_urls = "Sources [Rank | Link]:
" + "
".join(answer_sources)
answer_sources = ['%s' % url for rank, (score, url) in enumerate(answer_sources)]
sorted_sources_urls = "Ranked Sources:
" + "
".join(answer_sources)
else:
answer_sources = ['
%.2g | %s' % (score, url) for score, url in answer_sources]
sorted_sources_urls = f"{source_prefix}{source_postfix}"
if not answer['output_text'].endswith('\n'):
answer['output_text'] += '\n'
if answer_with_sources:
ret = answer['output_text'] + '\n' + sorted_sources_urls
else:
ret = answer['output_text']
yield ret
return
def chunk_sources(sources, chunk_size=1024):
source_chunks = []
# Below for known separator
# splitter = CharacterTextSplitter(separator=" ", chunk_size=chunk_size, chunk_overlap=0)
splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=0)
for source in sources:
# print(source.metadata['source'], flush=True)
for chunky in splitter.split_text(source.page_content):
source_chunks.append(Document(page_content=chunky, metadata=source.metadata))
return source_chunks
def get_db_from_hf(dest=".", db_dir='db_dir_DriverlessAI_docs.zip'):
from huggingface_hub import hf_hub_download
# True for case when locally already logged in with correct token, so don't have to set key
token = os.getenv('HUGGINGFACE_API_TOKEN', True)
path_to_zip_file = hf_hub_download('h2oai/db_dirs', db_dir, token=token, repo_type='dataset')
import zipfile
with zipfile.ZipFile(path_to_zip_file, 'r') as zip_ref:
zip_ref.extractall(dest)
return path_to_zip_file
# Note dir has space in some cases, while zip does not
some_db_zips = [['db_dir_DriverlessAI_docs.zip', 'db_dir_DriverlessAI docs', 'CC-BY-NC license'],
['db_dir_UserData.zip', 'db_dir_UserData', 'CC-BY license for ArXiv'],
['db_dir_github_h2oGPT.zip', 'db_dir_github h2oGPT', 'ApacheV2 license'],
['db_dir_wiki.zip', 'db_dir_wiki', 'CC-BY-SA Wikipedia license'],
# ['db_dir_wiki_full.zip', 'db_dir_wiki_full.zip', '23GB, 05/04/2023 CC-BY-SA Wiki license'],
]
all_db_zips = some_db_zips + \
[['db_dir_wiki_full.zip', 'db_dir_wiki_full.zip', '23GB, 05/04/2023 CC-BY-SA Wiki license'],
]
def get_some_dbs_from_hf(dest='.', db_zips=None):
if db_zips is None:
db_zips = some_db_zips
for db_dir, dir_expected, license1 in db_zips:
path_to_zip_file = get_db_from_hf(dest=dest, db_dir=db_dir)
assert os.path.isfile(path_to_zip_file), "Missing zip in %s" % path_to_zip_file
if dir_expected:
assert os.path.isdir(os.path.join(dest, dir_expected)), "Missing path for %s" % dir_expected
assert os.path.isdir(os.path.join(dest, dir_expected, 'index')), "Missing index in %s" % dir_expected
if __name__ == '__main__':
pass