import glob
import inspect
import os
import pathlib
import pickle
import queue
import random
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 langchain.embeddings import HuggingFaceInstructEmbeddings
from tqdm import tqdm
from enums import DocumentChoices
from generate import gen_hyper
from prompter import non_hf_types, PromptType
from utils import wrapped_partial, EThread, import_matplotlib, sanitize_filename, makedirs, get_url, flatten_list, \
get_device, ProgressParallel, remove, hash_file, clear_torch_cache
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.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", load_db_if_exists=True,
langchain_mode='notset',
collection_name=None,
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)
assert collection_name is not None or langchain_mode != 'notset'
if collection_name is None:
collection_name = langchain_mode.replace(' ', '_')
# Create vector database
if db_type == 'faiss':
from langchain.vectorstores import FAISS
db = FAISS.from_documents(sources, embedding)
elif db_type == 'weaviate':
import weaviate
from weaviate.embedded import EmbeddedOptions
from langchain.vectorstores import Weaviate
if os.getenv('WEAVIATE_URL', None):
client = _create_local_weaviate_client()
else:
client = weaviate.Client(
embedded_options=EmbeddedOptions()
)
index_name = collection_name.capitalize()
db = Weaviate.from_documents(documents=sources, embedding=embedding, client=client, by_text=False,
index_name=index_name)
elif db_type == 'chroma':
assert persist_directory is not None
os.makedirs(persist_directory, exist_ok=True)
# see if already actually have persistent db, and deal with possible changes in embedding
db = get_existing_db(None, persist_directory, load_db_if_exists, db_type, use_openai_embedding, langchain_mode,
hf_embedding_model, verbose=False)
if db is None:
db = Chroma.from_documents(documents=sources,
embedding=embedding,
persist_directory=persist_directory,
collection_name=collection_name,
anonymized_telemetry=False)
db.persist()
clear_embedding(db)
save_embed(db, use_openai_embedding, hf_embedding_model)
else:
# then just add
db, num_new_sources, new_sources_metadata = add_to_db(db, sources, db_type=db_type,
use_openai_embedding=use_openai_embedding,
hf_embedding_model=hf_embedding_model)
else:
raise RuntimeError("No such db_type=%s" % db_type)
return db
def _get_unique_sources_in_weaviate(db):
batch_size = 100
id_source_list = []
result = db._client.data_object.get(class_name=db._index_name, limit=batch_size)
while result['objects']:
id_source_list += [(obj['id'], obj['properties']['source']) for obj in result['objects']]
last_id = id_source_list[-1][0]
result = db._client.data_object.get(class_name=db._index_name, limit=batch_size, after=last_id)
unique_sources = {source for _, source in id_source_list}
return unique_sources
def add_to_db(db, sources, db_type='faiss',
avoid_dup_by_file=False,
avoid_dup_by_content=True,
use_openai_embedding=False,
hf_embedding_model=None):
assert hf_embedding_model is not None
num_new_sources = len(sources)
if not sources:
return db, num_new_sources, []
if db_type == 'faiss':
db.add_documents(sources)
elif db_type == 'weaviate':
# FIXME: only control by file name, not hash yet
if avoid_dup_by_file or avoid_dup_by_content:
unique_sources = _get_unique_sources_in_weaviate(db)
sources = [x for x in sources if x.metadata['source'] not in unique_sources]
num_new_sources = len(sources)
if num_new_sources == 0:
return db, num_new_sources, []
db.add_documents(documents=sources)
elif db_type == 'chroma':
collection = db.get()
# files we already have:
metadata_files = set([x['source'] for x in collection['metadatas']])
if avoid_dup_by_file:
# Too weak in case file changed content, assume parent shouldn't pass true for this for now
raise RuntimeError("Not desired code path")
sources = [x for x in sources if x.metadata['source'] not in metadata_files]
if avoid_dup_by_content:
# look at hash, instead of page_content
# migration: If no hash previously, avoid updating,
# since don't know if need to update and may be expensive to redo all unhashed files
metadata_hash_ids = set(
[x['hashid'] for x in collection['metadatas'] if 'hashid' in x and x['hashid'] not in ["None", None]])
# avoid sources with same hash
sources = [x for x in sources if x.metadata.get('hashid') not in metadata_hash_ids]
# get new file names that match existing file names. delete existing files we are overridding
dup_metadata_files = set([x.metadata['source'] for x in sources if x.metadata['source'] in metadata_files])
print("Removing %s duplicate files from db because ingesting those as new documents" % len(
dup_metadata_files), flush=True)
client_collection = db._client.get_collection(name=db._collection.name,
embedding_function=db._collection._embedding_function)
for dup_file in dup_metadata_files:
dup_file_meta = dict(source=dup_file)
try:
client_collection.delete(where=dup_file_meta)
except KeyError:
pass
num_new_sources = len(sources)
if num_new_sources == 0:
return db, num_new_sources, []
db.add_documents(documents=sources)
db.persist()
clear_embedding(db)
save_embed(db, use_openai_embedding, hf_embedding_model)
else:
raise RuntimeError("No such db_type=%s" % db_type)
new_sources_metadata = [x.metadata for x in sources]
return db, num_new_sources, new_sources_metadata
def create_or_update_db(db_type, persist_directory, collection_name,
sources, use_openai_embedding, add_if_exists, verbose, hf_embedding_model):
if db_type == 'weaviate':
import weaviate
from weaviate.embedded import EmbeddedOptions
if os.getenv('WEAVIATE_URL', None):
client = _create_local_weaviate_client()
else:
client = weaviate.Client(
embedded_options=EmbeddedOptions()
)
index_name = collection_name.replace(' ', '_').capitalize()
if client.schema.exists(index_name) and not add_if_exists:
client.schema.delete_class(index_name)
if verbose:
print("Removing %s" % index_name, flush=True)
elif db_type == 'chroma':
if not os.path.isdir(persist_directory) or not add_if_exists:
if os.path.isdir(persist_directory):
if verbose:
print("Removing %s" % persist_directory, flush=True)
remove(persist_directory)
if verbose:
print("Generating db", flush=True)
if not add_if_exists:
if verbose:
print("Generating db", flush=True)
else:
if verbose:
print("Loading and updating db", flush=True)
db = get_db(sources,
use_openai_embedding=use_openai_embedding,
db_type=db_type,
persist_directory=persist_directory,
langchain_mode=collection_name,
hf_embedding_model=hf_embedding_model)
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)
if 'instructor' in hf_embedding_model:
encode_kwargs = {'normalize_embeddings': True}
embedding = HuggingFaceInstructEmbeddings(model_name=hf_embedding_model,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs)
else:
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,
do_sample=False,
temperature=0.1,
top_k=40,
top_p=0.7,
num_beams=1,
max_new_tokens=256,
min_new_tokens=1,
early_stopping=False,
max_time=180,
repetition_penalty=1.0,
num_return_sequences=1,
prompt_type=None,
prompt_dict=None,
prompter=None,
verbose=False,
):
if use_openai_model:
from langchain.llms import OpenAI
llm = OpenAI(temperature=0)
model_name = 'openai'
streamer = None
prompt_type = 'plain'
elif model_name in non_hf_types:
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,
verbose=verbose,
)
streamer = None
prompt_type = 'plain'
else:
from transformers import AutoTokenizer, AutoModelForCausalLM
if model is None:
# only used if didn't pass model in
assert tokenizer is None
prompt_type = 'human_bot'
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)
max_max_tokens = tokenizer.model_max_length
gen_kwargs = dict(do_sample=do_sample,
temperature=temperature,
top_k=top_k,
top_p=top_p,
num_beams=num_beams,
max_new_tokens=max_new_tokens,
min_new_tokens=min_new_tokens,
early_stopping=early_stopping,
max_time=max_time,
repetition_penalty=repetition_penalty,
num_return_sequences=num_return_sequences,
return_full_text=True,
handle_long_generation='hole')
assert len(set(gen_hyper).difference(gen_kwargs.keys())) == 0
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
from h2oai_pipeline import H2OTextGenerationPipeline
pipe = H2OTextGenerationPipeline(model=model, use_prompter=True,
prompter=prompter,
prompt_type=prompt_type,
prompt_dict=prompt_dict,
sanitize_bot_response=True,
chat=False, stream_output=stream_output,
tokenizer=tokenizer,
max_input_tokens=max_max_tokens - max_new_tokens,
**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"
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
try:
assert pkg_resources.get_distribution('pymupdf') is not None
have_pymupdf = True
except (pkg_resources.DistributionNotFound, AssertionError):
have_pymupdf = 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
hashid = hash_file(file)
if not isinstance(docs1, list):
docs1 = [docs1]
[x.metadata.update(dict(input_type=file_extension, date=str(datetime.now), hashid=hashid)) 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=chunk, 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.lower().endswith('.html') or file.lower().endswith('.mhtml'):
docs1 = UnstructuredHTMLLoader(file_path=file).load()
add_meta(docs1, file)
doc1 = chunk_sources(docs1, chunk=chunk, chunk_size=chunk_size)
elif (file.lower().endswith('.docx') or file.lower().endswith('.doc')) and have_libreoffice:
docs1 = UnstructuredWordDocumentLoader(file_path=file).load()
add_meta(docs1, file)
doc1 = chunk_sources(docs1, chunk=chunk, chunk_size=chunk_size)
elif file.lower().endswith('.odt'):
docs1 = UnstructuredODTLoader(file_path=file).load()
add_meta(docs1, file)
doc1 = chunk_sources(docs1, chunk=chunk, chunk_size=chunk_size)
elif file.lower().endswith('pptx') or file.lower().endswith('ppt'):
docs1 = UnstructuredPowerPointLoader(file_path=file).load()
add_meta(docs1, file)
doc1 = chunk_sources(docs1, chunk=chunk, chunk_size=chunk_size)
elif file.lower().endswith('.txt'):
# use UnstructuredFileLoader ?
docs1 = TextLoader(file, encoding="utf8", autodetect_encoding=True).load()
# makes just one, but big one
doc1 = chunk_sources(docs1, chunk=chunk, chunk_size=chunk_size)
add_meta(doc1, file)
elif file.lower().endswith('.rtf'):
docs1 = UnstructuredRTFLoader(file).load()
add_meta(docs1, file)
doc1 = chunk_sources(docs1, chunk=chunk, chunk_size=chunk_size)
elif file.lower().endswith('.md'):
docs1 = UnstructuredMarkdownLoader(file).load()
add_meta(docs1, file)
doc1 = chunk_sources(docs1, chunk=chunk, chunk_size=chunk_size)
elif file.lower().endswith('.enex'):
docs1 = EverNoteLoader(file).load()
add_meta(doc1, file)
doc1 = chunk_sources(docs1, chunk=chunk, chunk_size=chunk_size)
elif file.lower().endswith('.epub'):
docs1 = UnstructuredEPubLoader(file).load()
add_meta(docs1, file)
doc1 = chunk_sources(docs1, chunk=chunk, chunk_size=chunk_size)
elif file.lower().endswith('.jpeg') or file.lower().endswith('.jpg') or file.lower().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']
doci.metadata['hash'] = hash_file(doci.metadata['source'])
if docs1:
doc1 = chunk_sources(docs1, chunk=chunk, chunk_size=chunk_size)
elif file.lower().endswith('.msg'):
raise RuntimeError("Not supported, GPL3 license")
# docs1 = OutlookMessageLoader(file).load()
# docs1[0].metadata['source'] = file
elif file.lower().endswith('.eml'):
try:
docs1 = UnstructuredEmailLoader(file).load()
add_meta(docs1, file)
doc1 = chunk_sources(docs1, chunk=chunk, 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=chunk, chunk_size=chunk_size)
else:
raise
# elif file.lower().endswith('.gcsdir'):
# doc1 = GCSDirectoryLoader(project_name, bucket, prefix).load()
# elif file.lower().endswith('.gcsfile'):
# doc1 = GCSFileLoader(project_name, bucket, blob).load()
elif file.lower().endswith('.rst'):
with open(file, "r") as f:
doc1 = Document(page_content=f.read(), metadata={"source": file})
add_meta(doc1, file)
elif file.lower().endswith('.pdf'):
env_gpt4all_file = ".env_gpt4all"
from dotenv import dotenv_values
env_kwargs = dotenv_values(env_gpt4all_file)
pdf_class_name = env_kwargs.get('PDF_CLASS_NAME', 'PyMuPDFParser')
if have_pymupdf and pdf_class_name == 'PyMuPDFParser':
# GPL, only use if installed
from langchain.document_loaders import PyMuPDFLoader
# load() still chunks by pages, but every page has title at start to help
doc1 = PyMuPDFLoader(file).load()
else:
# open-source fallback
# load() still chunks by pages, but every page has title at start to help
doc1 = PyPDFLoader(file).load()
# Some PDFs return nothing or junk from PDFMinerLoader
add_meta(doc1, file)
elif file.lower().endswith('.csv'):
doc1 = CSVLoader(file).load()
add_meta(doc1, file)
elif file.lower().endswith('.py'):
doc1 = PythonLoader(file).load()
add_meta(doc1, file)
elif file.lower().endswith('.toml'):
doc1 = TomlLoader(file).load()
add_meta(doc1, file)
elif file.lower().endswith('.urls'):
with open(file, "r") as f:
docs1 = UnstructuredURLLoader(urls=f.readlines()).load()
add_meta(docs1, file)
doc1 = chunk_sources(docs1, chunk=chunk, chunk_size=chunk_size)
elif file.lower().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 list of length one, don't trust and chunk it
if not isinstance(doc1, list):
if chunk:
docs = chunk_sources([doc1], chunk=chunk, chunk_size=chunk_size)
else:
docs = [doc1]
elif isinstance(doc1, list) and len(doc1) == 1:
if chunk:
docs = chunk_sources(doc1, chunk=chunk, 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,
existing_files=[],
existing_hash_ids={},
):
globs_image_types = []
globs_non_image_types = []
if not path_or_paths and not url and not text:
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])
# filter out any files to skip (e.g. if already processed them)
# this is easy, but too aggressive in case a file changed, so parent probably passed existing_files=[]
assert not existing_files, "DEV: assume not using this approach"
if existing_files:
set_skip_files = set(existing_files)
globs_image_types = [x for x in globs_image_types if x not in set_skip_files]
globs_non_image_types = [x for x in globs_non_image_types if x not in set_skip_files]
if existing_hash_ids:
# assume consistent with add_meta() use of hash_file(file)
# also assume consistent with get_existing_hash_ids for dict creation
# assume hashable values
existing_hash_ids_set = set(existing_hash_ids.items())
hash_ids_all_image = set({x: hash_file(x) for x in globs_image_types}.items())
hash_ids_all_non_image = set({x: hash_file(x) for x in globs_non_image_types}.items())
# don't use symmetric diff. If file is gone, ignore and don't remove or something
# just consider existing files (key) having new hash or not (value)
new_files_image = set(dict(hash_ids_all_image - existing_hash_ids_set).keys())
new_files_non_image = set(dict(hash_ids_all_non_image - existing_hash_ids_set).keys())
globs_image_types = [x for x in globs_image_types if x in new_files_image]
globs_non_image_types = [x for x in globs_non_image_types if x in new_files_non_image]
# 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 = ProgressParallel(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 tqdm(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 = ProgressParallel(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 tqdm(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"
db_dir_exists = os.path.isdir(persist_directory)
if db_dir_exists and user_path is None:
print("Prep: persist_directory=%s exists, using" % persist_directory, flush=True)
db = get_existing_db(None, persist_directory, load_db_if_exists, db_type, use_openai_embedding, langchain_mode,
hf_embedding_model)
else:
if db_dir_exists and user_path is not None:
print("Prep: persist_directory=%s exists, user_path=%s passed, adding any changed or new documents" % (
persist_directory, user_path), flush=True)
elif not db_dir_exists:
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, num_new_sources, new_sources_metadata = make_db(**langchain_kwargs)
return db
import posthog
posthog.disabled = True
class FakeConsumer(object):
def __init__(self, *args, **kwargs):
pass
def run(self):
pass
def pause(self):
pass
def upload(self):
pass
def next(self):
pass
def request(self, batch):
pass
posthog.Consumer = FakeConsumer
def check_update_chroma_embedding(db, use_openai_embedding, hf_embedding_model, langchain_mode):
changed_db = False
if load_embed(db) != (use_openai_embedding, hf_embedding_model):
print("Detected new embedding, updating db: %s" % langchain_mode, flush=True)
# handle embedding changes
db_get = db.get()
sources = [Document(page_content=result[0], metadata=result[1] or {})
for result in zip(db_get['documents'], db_get['metadatas'])]
# delete index, has to be redone
persist_directory = db._persist_directory
shutil.move(persist_directory, persist_directory + "_" + str(uuid.uuid4()) + ".bak")
db_type = 'chroma'
load_db_if_exists = False
db = get_db(sources, use_openai_embedding=use_openai_embedding, db_type=db_type,
persist_directory=persist_directory, load_db_if_exists=load_db_if_exists,
langchain_mode=langchain_mode,
collection_name=None,
hf_embedding_model=hf_embedding_model)
if False:
# below doesn't work if db already in memory, so have to switch to new db as above
# upsert does new embedding, but if index already in memory, complains about size mismatch etc.
client_collection = db._client.get_collection(name=db._collection.name,
embedding_function=db._collection._embedding_function)
client_collection.upsert(ids=db_get['ids'], metadatas=db_get['metadatas'], documents=db_get['documents'])
changed_db = True
print("Done updating db for new embedding: %s" % langchain_mode, flush=True)
return db, changed_db
def get_existing_db(db, persist_directory, load_db_if_exists, db_type, use_openai_embedding, langchain_mode,
hf_embedding_model, verbose=False, check_embedding=True):
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')):
if db is None:
if verbose:
print("DO Loading db: %s" % langchain_mode, flush=True)
embedding = get_embedding(use_openai_embedding, hf_embedding_model=hf_embedding_model)
from chromadb.config import Settings
client_settings = Settings(anonymized_telemetry=False,
chroma_db_impl="duckdb+parquet",
persist_directory=persist_directory)
db = Chroma(persist_directory=persist_directory, embedding_function=embedding,
collection_name=langchain_mode.replace(' ', '_'),
client_settings=client_settings)
if verbose:
print("DONE Loading db: %s" % langchain_mode, flush=True)
else:
if verbose:
print("USING already-loaded db: %s" % langchain_mode, flush=True)
if check_embedding:
db_trial, changed_db = check_update_chroma_embedding(db, use_openai_embedding, hf_embedding_model,
langchain_mode)
if changed_db:
db = db_trial
# only call persist if really changed db, else takes too long for large db
db.persist()
clear_embedding(db)
save_embed(db, use_openai_embedding, hf_embedding_model)
return db
return None
def clear_embedding(db):
# don't keep on GPU, wastes memory, push back onto CPU and only put back on GPU once again embed
db._embedding_function.client.cpu()
clear_torch_cache()
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 save_embed(db, use_openai_embedding, hf_embedding_model):
embed_info_file = os.path.join(db._persist_directory, 'embed_info')
with open(embed_info_file, 'wb') as f:
pickle.dump((use_openai_embedding, hf_embedding_model), f)
return use_openai_embedding, hf_embedding_model
def load_embed(db):
embed_info_file = os.path.join(db._persist_directory, 'embed_info')
if os.path.isfile(embed_info_file):
with open(embed_info_file, 'rb') as f:
use_openai_embedding, hf_embedding_model = pickle.load(f)
else:
# migration, assume defaults
use_openai_embedding, hf_embedding_model = False, "sentence-transformers/all-MiniLM-L6-v2"
return use_openai_embedding, hf_embedding_model
def get_persist_directory(langchain_mode):
return 'db_dir_%s' % langchain_mode # single place, no special names for each case
def _make_db(use_openai_embedding=False,
hf_embedding_model="sentence-transformers/all-MiniLM-L6-v2",
first_para=False, text_limit=None,
chunk=True, chunk_size=512,
langchain_mode=None,
user_path=None,
db_type='faiss',
load_db_if_exists=True,
db=None,
n_jobs=-1,
verbose=False):
persist_directory = get_persist_directory(langchain_mode)
# see if can get persistent chroma db
db_trial = get_existing_db(db, persist_directory, load_db_if_exists, db_type, use_openai_embedding, langchain_mode,
hf_embedding_model, verbose=verbose)
if db_trial is not None:
db = db_trial
sources = []
if not db and langchain_mode not in ['MyData'] or \
user_path is not None and \
langchain_mode in ['UserData']:
# Should not make MyData db this way, why avoided, only upload from UI
assert langchain_mode not in ['MyData'], "Should not make MyData db this way"
if verbose:
if langchain_mode in ['UserData']:
if user_path is not None:
print("Checking if changed or new sources in %s, and generating sources them" % user_path,
flush=True)
elif db is None:
print("user_path not passed and no db, no sources", flush=True)
else:
print("user_path not passed, using only existing db, no new sources", flush=True)
else:
print("Generating %s sources" % langchain_mode, 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=chunk, 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=chunk, 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=chunk, 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=chunk, chunk_size=chunk_size)
sources.extend(sources1)
if langchain_mode in ['All', 'UserData']:
if user_path:
if db is not None:
# NOTE: Ignore file names for now, only go by hash ids
# existing_files = get_existing_files(db)
existing_files = []
existing_hash_ids = get_existing_hash_ids(db)
else:
# pretend no existing files so won't filter
existing_files = []
existing_hash_ids = []
# chunk internally for speed over multiple docs
# FIXME: If first had old Hash=None and switch embeddings,
# then re-embed, and then hit here and reload so have hash, and then re-embed.
sources1 = path_to_docs(user_path, n_jobs=n_jobs, chunk=chunk, chunk_size=chunk_size,
existing_files=existing_files, existing_hash_ids=existing_hash_ids)
new_metadata_sources = set([x.metadata['source'] for x in sources1])
if new_metadata_sources:
print("Loaded %s new files as sources to add to UserData" % len(new_metadata_sources), flush=True)
if verbose:
print("Files added: %s" % '\n'.join(new_metadata_sources), flush=True)
sources.extend(sources1)
print("Loaded %s sources for potentially adding to UserData" % len(sources), flush=True)
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:
if verbose:
if db is not None:
print("langchain_mode %s has no new sources, nothing to add to db" % langchain_mode, flush=True)
else:
print("langchain_mode %s has no sources, not making new db" % langchain_mode, flush=True)
return db, 0, []
if verbose:
if db is not None:
print("Generating db", flush=True)
else:
print("Adding to db", flush=True)
if not db:
if sources:
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)
if verbose:
print("Generated db", flush=True)
else:
print("Did not generate db since no sources", flush=True)
new_sources_metadata = [x.metadata for x in sources]
elif user_path is not None and langchain_mode in ['UserData']:
print("Existing db, potentially adding %s sources from user_path=%s" % (len(sources), user_path), flush=True)
db, num_new_sources, new_sources_metadata = add_to_db(db, sources, db_type=db_type,
use_openai_embedding=use_openai_embedding,
hf_embedding_model=hf_embedding_model)
print("Existing db, added %s new sources from user_path=%s" % (num_new_sources, user_path), flush=True)
else:
new_sources_metadata = [x.metadata for x in sources]
return db, len(new_sources_metadata), new_sources_metadata
def get_existing_files(db):
collection = db.get()
metadata_sources = set([x['source'] for x in collection['metadatas']])
return metadata_sources
def get_existing_hash_ids(db):
collection = db.get()
# assume consistency, that any prior hashed source was single hashed file at the time among all source chunks
metadata_hash_ids = {x['source']: x.get('hashid') for x in collection['metadatas']}
return metadata_hash_ids
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}
try:
return _run_qa_db(**kwargs)
finally:
clear_torch_cache()
def _run_qa_db(query=None,
use_openai_model=False, use_openai_embedding=False,
first_para=False, text_limit=None, top_k_docs=4, chunk=True, chunk_size=512,
user_path=None,
detect_user_path_changes_every_query=False,
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,
prompt_dict=None,
answer_with_sources=True,
cut_distanct=1.1,
sanitize_bot_response=True,
show_rank=False,
load_db_if_exists=False,
db=None,
do_sample=False,
temperature=0.1,
top_k=40,
top_p=0.7,
num_beams=1,
max_new_tokens=256,
min_new_tokens=1,
early_stopping=False,
max_time=180,
repetition_penalty=1.0,
num_return_sequences=1,
langchain_mode=None,
document_choice=[DocumentChoices.All_Relevant.name],
n_jobs=-1,
verbose=False,
cli=False):
"""
: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' or 'weaviate' 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:
"""
assert query is not None
assert prompter is not None or prompt_type is not None or model is None # if model is None, then will generate
if prompter is not None:
prompt_type = prompter.prompt_type
prompt_dict = prompter.prompt_dict
if model is not None:
assert prompt_type is not None
if prompt_type == PromptType.custom.name:
assert prompt_dict is not None # should at least be {} or ''
else:
prompt_dict = ''
assert len(set(gen_hyper).difference(inspect.signature(get_llm).parameters)) == 0
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,
do_sample=do_sample,
temperature=temperature,
top_k=top_k,
top_p=top_p,
num_beams=num_beams,
max_new_tokens=max_new_tokens,
min_new_tokens=min_new_tokens,
early_stopping=early_stopping,
max_time=max_time,
repetition_penalty=repetition_penalty,
num_return_sequences=num_return_sequences,
prompt_type=prompt_type,
prompt_dict=prompt_dict,
prompter=prompter,
verbose=verbose,
)
if model_name in non_hf_types:
# FIXME: for now, streams to stdout/stderr currently
stream_output = False
use_context = False
scores = []
chain = None
if isinstance(document_choice, str):
# support string as well
document_choice = [document_choice]
# get first DocumentChoices as command to use, ignore others
doc_choices_set = set([x.name for x in list(DocumentChoices)])
cmd = [x for x in document_choice if x in doc_choices_set]
cmd = None if len(cmd) == 0 else cmd[0]
# now have cmd, filter out for only docs
document_choice = [x for x in document_choice if x not in doc_choices_set]
func_names = list(inspect.signature(get_similarity_chain).parameters)
sim_kwargs = {k: v for k, v in locals().items() if k in func_names}
missing_kwargs = [x for x in func_names if x not in sim_kwargs]
assert not missing_kwargs, "Missing: %s" % missing_kwargs
docs, chain, scores, use_context = get_similarity_chain(**sim_kwargs)
if cmd in [DocumentChoices.All_Relevant_Only_Sources.name, DocumentChoices.Only_All_Sources.name]:
formatted_doc_chunks = '\n\n'.join([get_url(x) + '\n\n' + x.page_content for x in docs])
yield formatted_doc_chunks, ''
return
if chain is None and model_name not in non_hf_types:
# can only return if HF type
return
if stream_output:
answer = None
assert streamer is not None
import queue
bucket = queue.Queue()
thread = EThread(target=chain, 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()
if not use_context:
ret = answer['output_text']
extra = ''
yield ret, extra
elif answer is not None:
ret, extra = get_sources_answer(query, answer, scores, show_rank, answer_with_sources, verbose=verbose)
yield ret, extra
return
def get_similarity_chain(query=None,
use_openai_model=False, use_openai_embedding=False,
first_para=False, text_limit=None, top_k_docs=4, chunk=True, chunk_size=512,
user_path=None,
detect_user_path_changes_every_query=False,
db_type='faiss',
model_name=None,
hf_embedding_model="sentence-transformers/all-MiniLM-L6-v2",
prompt_type=None,
prompt_dict=None,
cut_distanct=1.1,
load_db_if_exists=False,
db=None,
langchain_mode=None,
document_choice=[DocumentChoices.All_Relevant.name],
n_jobs=-1,
# beyond run_db_query:
llm=None,
verbose=False,
cmd=None,
):
# determine whether use of context out of docs is planned
if not use_openai_model and prompt_type not in ['plain'] or model_name in non_hf_types:
if langchain_mode in ['Disabled', 'ChatLLM', 'LLM']:
use_context = False
else:
use_context = True
else:
use_context = True
# https://github.com/hwchase17/langchain/issues/1946
# FIXME: Seems to way to get size of chroma db to limit top_k_docs to avoid
# Chroma collection MyData contains fewer than 4 elements.
# type logger error
k_db = 1000 if db_type == 'chroma' else top_k_docs # top_k_docs=100 works ok too for
# FIXME: For All just go over all dbs instead of a separate db for All
if not detect_user_path_changes_every_query and db is not None:
# avoid looking at user_path during similarity search db handling,
# if already have db and not updating from user_path every query
# but if db is None, no db yet loaded (e.g. from prep), so allow user_path to be whatever it was
user_path = None
db, num_new_sources, new_sources_metadata = make_db(use_openai_embedding=use_openai_embedding,
hf_embedding_model=hf_embedding_model,
first_para=first_para, text_limit=text_limit,
chunk=chunk,
chunk_size=chunk_size,
langchain_mode=langchain_mode,
user_path=user_path,
db_type=db_type,
load_db_if_exists=load_db_if_exists,
db=db,
n_jobs=n_jobs,
verbose=verbose)
if db and use_context:
if not isinstance(db, Chroma):
# only chroma supports filtering
filter_kwargs = {}
else:
# if here then some cmd + documents selected or just documents selected
if len(document_choice) >= 2:
or_filter = [{"source": {"$eq": x}} for x in document_choice]
filter_kwargs = dict(filter={"$or": or_filter})
elif len(document_choice) == 1:
# degenerate UX bug in chroma
one_filter = [{"source": {"$eq": x}} for x in document_choice][0]
filter_kwargs = dict(filter=one_filter)
else:
# shouldn't reach
filter_kwargs = {}
if cmd == DocumentChoices.Just_LLM.name:
docs = []
scores = []
elif cmd == DocumentChoices.Only_All_Sources.name:
if isinstance(db, Chroma):
db_get = db._collection.get(where=filter_kwargs.get('filter'))
else:
db_get = db.get()
# similar to langchain's chroma's _results_to_docs_and_scores
docs_with_score = [(Document(page_content=result[0], metadata=result[1] or {}), 0)
for result in zip(db_get['documents'], db_get['metadatas'])][:top_k_docs]
docs = [x[0] for x in docs_with_score]
scores = [x[1] for x in docs_with_score]
else:
docs_with_score = db.similarity_search_with_score(query, k=k_db, **filter_kwargs)[:top_k_docs]
# 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 and verbose:
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 and model_name not in non_hf_types:
# if HF type and have no docs, can bail out
return docs, None, [], False
if cmd in [DocumentChoices.All_Relevant_Only_Sources.name, DocumentChoices.Only_All_Sources.name]:
# no LLM use
return docs, None, [], False
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) if reduced_query else 0
# FIXME: report to user bad query that uses too many common words
if verbose:
print("frac_common: %s" % frac_common, flush=True)
if len(docs) == 0:
# avoid context == in prompt then
use_context = False
if not use_openai_model and prompt_type not in ['plain'] or model_name in non_hf_types:
# 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'] or not use_context:
template = """%s{context}{question}""" % prefix
else:
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)
if not use_context:
chain_kwargs = dict(input_documents=[], question=query)
else:
chain_kwargs = dict(input_documents=docs, question=query)
target = wrapped_partial(chain, chain_kwargs)
return docs, target, scores, use_context
def get_sources_answer(query, answer, scores, show_rank, answer_with_sources, verbose=False):
if verbose:
print("query: %s" % query, flush=True)
print("answer: %s" % answer['output_text'], flush=True)
if len(answer['input_documents']) == 0:
extra = ''
ret = answer['output_text'] + extra
return ret, extra
# 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:
extra = '\n' + sorted_sources_urls
else:
extra = ''
ret = answer['output_text'] + extra
return ret, extra
def chunk_sources(sources, chunk=True, chunk_size=512):
if not chunk:
return sources
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:
persist_directory = os.path.dirname(zip_ref.namelist()[0])
remove(persist_directory)
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
def _create_local_weaviate_client():
WEAVIATE_URL = os.getenv('WEAVIATE_URL', "http://localhost:8080")
WEAVIATE_USERNAME = os.getenv('WEAVIATE_USERNAME')
WEAVIATE_PASSWORD = os.getenv('WEAVIATE_PASSWORD')
WEAVIATE_SCOPE = os.getenv('WEAVIATE_SCOPE', "offline_access")
resource_owner_config = None
if WEAVIATE_USERNAME is not None and WEAVIATE_PASSWORD is not None:
resource_owner_config = weaviate.AuthClientPassword(
username=WEAVIATE_USERNAME,
password=WEAVIATE_PASSWORD,
scope=WEAVIATE_SCOPE
)
try:
client = weaviate.Client(WEAVIATE_URL, auth_client_secret=resource_owner_config)
except Exception as e:
print(f"Failed to create Weaviate client: {e}")
return None
if __name__ == '__main__':
pass