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add `privategpt` example

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examples/privategpt/LICENSE 0 → 100644
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examples/privategpt/README.md 0 → 100644
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# privateGPT with Llama 2 Uncensored
> Note: this example is a simplified version of [PrivateGPT](https://github.com/imartinez/privateGPT) that works with Llama 2 Uncensored.
### Setup
```shell
pip install -r requirements.txt
```
### Getting WeWork's latest quarterly report
```
curl https://d18rn0p25nwr6d.cloudfront.net/CIK-0001813756/975b3e9b-268e-4798-a9e4-2a9a7c92dc10.pdf -o source_documents/wework.pdf
```
### Ingesting data
```shell
python ingest.py
```
Output should look like this:
```shell
Creating new vectorstore
Loading documents from source_documents
Loading new documents: 100%|██████████████████████| 1/1 [00:01<00:00, 1.73s/it]
Loaded 1 new documents from source_documents
Split into 90 chunks of text (max. 500 tokens each)
Creating embeddings. May take some minutes...
Using embedded DuckDB with persistence: data will be stored in: db
Ingestion complete! You can now run privateGPT.py to query your documents
```
### Ask Questions!
```shell
python privateGPT.py
Enter a query: How many locations does WeWork have?
> Answer (took 17.7 s.):
As of June 2023, WeWork has 777 locations worldwide, including 610 Consolidated Locations (as defined in the section entitled Key Performance Indicators).
```
## Adding your own data
Put any and all your files into the `source_documents` directory
The supported extensions are:
- `.csv`: CSV,
- `.docx`: Word Document,
- `.doc`: Word Document,
- `.enex`: EverNote,
- `.eml`: Email,
- `.epub`: EPub,
- `.html`: HTML File,
- `.md`: Markdown,
- `.msg`: Outlook Message,
- `.odt`: Open Document Text,
- `.pdf`: Portable Document Format (PDF),
- `.pptx` : PowerPoint Document,
- `.ppt` : PowerPoint Document,
- `.txt`: Text file (UTF-8),
examples/privategpt/constants.py 0 → 100644
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import os
from dotenv import load_dotenv
from chromadb.config import Settings
load_dotenv()
# Define the folder for storing database
PERSIST_DIRECTORY = os.environ.get('PERSIST_DIRECTORY', 'db')
# Define the Chroma settings
CHROMA_SETTINGS = Settings(
chroma_db_impl='duckdb+parquet',
persist_directory=PERSIST_DIRECTORY,
anonymized_telemetry=False
)
examples/privategpt/ingest.py 0 → 100755
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#!/usr/bin/env python3
import os
import glob
from typing import List
from dotenv import load_dotenv
from multiprocessing import Pool
from tqdm import tqdm
from langchain.document_loaders import (
CSVLoader,
EverNoteLoader,
PyMuPDFLoader,
TextLoader,
UnstructuredEmailLoader,
UnstructuredEPubLoader,
UnstructuredHTMLLoader,
UnstructuredMarkdownLoader,
UnstructuredODTLoader,
UnstructuredPowerPointLoader,
UnstructuredWordDocumentLoader,
)
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.docstore.document import Document
from constants import CHROMA_SETTINGS
load_dotenv()
# Load environment variables
persist_directory = os.environ.get('PERSIST_DIRECTORY', 'db')
source_directory = os.environ.get('SOURCE_DIRECTORY', 'source_documents')
embeddings_model_name = os.environ.get('EMBEDDINGS_MODEL_NAME', 'all-MiniLM-L6-v2')
chunk_size = 500
chunk_overlap = 50
# Custom document loaders
class MyElmLoader(UnstructuredEmailLoader):
"""Wrapper to fallback to text/plain when default does not work"""
def load(self) -> List[Document]:
"""Wrapper adding fallback for elm without html"""
try:
try:
doc = UnstructuredEmailLoader.load(self)
except ValueError as e:
if 'text/html content not found in email' in str(e):
# Try plain text
self.unstructured_kwargs["content_source"]="text/plain"
doc = UnstructuredEmailLoader.load(self)
else:
raise
except Exception as e:
# Add file_path to exception message
raise type(e)(f"{self.file_path}: {e}") from e
return doc
# Map file extensions to document loaders and their arguments
LOADER_MAPPING = {
".csv": (CSVLoader, {}),
# ".docx": (Docx2txtLoader, {}),
".doc": (UnstructuredWordDocumentLoader, {}),
".docx": (UnstructuredWordDocumentLoader, {}),
".enex": (EverNoteLoader, {}),
".eml": (MyElmLoader, {}),
".epub": (UnstructuredEPubLoader, {}),
".html": (UnstructuredHTMLLoader, {}),
".md": (UnstructuredMarkdownLoader, {}),
".odt": (UnstructuredODTLoader, {}),
".pdf": (PyMuPDFLoader, {}),
".ppt": (UnstructuredPowerPointLoader, {}),
".pptx": (UnstructuredPowerPointLoader, {}),
".txt": (TextLoader, {"encoding": "utf8"}),
# Add more mappings for other file extensions and loaders as needed
}
def load_single_document(file_path: str) -> List[Document]:
ext = "." + file_path.rsplit(".", 1)[-1]
if ext in LOADER_MAPPING:
loader_class, loader_args = LOADER_MAPPING[ext]
loader = loader_class(file_path, **loader_args)
return loader.load()
raise ValueError(f"Unsupported file extension '{ext}'")
def load_documents(source_dir: str, ignored_files: List[str] = []) -> List[Document]:
"""
Loads all documents from the source documents directory, ignoring specified files
"""
all_files = []
for ext in LOADER_MAPPING:
all_files.extend(
glob.glob(os.path.join(source_dir, f"**/*{ext}"), recursive=True)
)
filtered_files = [file_path for file_path in all_files if file_path not in ignored_files]
with Pool(processes=os.cpu_count()) as pool:
results = []
with tqdm(total=len(filtered_files), desc='Loading new documents', ncols=80) as pbar:
for i, docs in enumerate(pool.imap_unordered(load_single_document, filtered_files)):
results.extend(docs)
pbar.update()
return results
def process_documents(ignored_files: List[str] = []) -> List[Document]:
"""
Load documents and split in chunks
"""
print(f"Loading documents from {source_directory}")
documents = load_documents(source_directory, ignored_files)
if not documents:
print("No new documents to load")
exit(0)
print(f"Loaded {len(documents)} new documents from {source_directory}")
text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
texts = text_splitter.split_documents(documents)
print(f"Split into {len(texts)} chunks of text (max. {chunk_size} tokens each)")
return texts
def does_vectorstore_exist(persist_directory: str) -> bool:
"""
Checks if vectorstore exists
"""
if os.path.exists(os.path.join(persist_directory, 'index')):
if os.path.exists(os.path.join(persist_directory, 'chroma-collections.parquet')) and os.path.exists(os.path.join(persist_directory, 'chroma-embeddings.parquet')):
list_index_files = glob.glob(os.path.join(persist_directory, 'index/*.bin'))
list_index_files += glob.glob(os.path.join(persist_directory, 'index/*.pkl'))
# At least 3 documents are needed in a working vectorstore
if len(list_index_files) > 3:
return True
return False
def main():
# Create embeddings
embeddings = HuggingFaceEmbeddings(model_name=embeddings_model_name)
if does_vectorstore_exist(persist_directory):
# Update and store locally vectorstore
print(f"Appending to existing vectorstore at {persist_directory}")
db = Chroma(persist_directory=persist_directory, embedding_function=embeddings, client_settings=CHROMA_SETTINGS)
collection = db.get()
texts = process_documents([metadata['source'] for metadata in collection['metadatas']])
print(f"Creating embeddings. May take some minutes...")
db.add_documents(texts)
else:
# Create and store locally vectorstore
print("Creating new vectorstore")
texts = process_documents()
print(f"Creating embeddings. May take some minutes...")
db = Chroma.from_documents(texts, embeddings, persist_directory=persist_directory, client_settings=CHROMA_SETTINGS)
db.persist()
db = None
print(f"Ingestion complete! You can now run privateGPT.py to query your documents")
if __name__ == "__main__":
main()
examples/privategpt/poetry.lock 0 → 100644
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examples/privategpt/privateGPT.py 0 → 100755
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#!/usr/bin/env python3
from dotenv import load_dotenv
from langchain.chains import RetrievalQA
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.vectorstores import Chroma
from langchain.llms import GPT4All, Ollama
import os
import argparse
import time
load_dotenv()
model = os.environ.get("MODEL", "llama2-uncensored")
embeddings_model_name = os.environ.get("EMBEDDINGS_MODEL_NAME", "all-MiniLM-L6-v2")
persist_directory = os.environ.get("PERSIST_DIRECTORY", "db")
target_source_chunks = int(os.environ.get('TARGET_SOURCE_CHUNKS',4))
from constants import CHROMA_SETTINGS
def main():
# Parse the command line arguments
args = parse_arguments()
embeddings = HuggingFaceEmbeddings(model_name=embeddings_model_name)
db = Chroma(persist_directory=persist_directory, embedding_function=embeddings, client_settings=CHROMA_SETTINGS)
retriever = db.as_retriever(search_kwargs={"k": target_source_chunks})
# activate/deactivate the streaming StdOut callback for LLMs
callbacks = [] if args.mute_stream else [StreamingStdOutCallbackHandler()]
llm = Ollama(model=model, callbacks=callbacks)
qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents= not args.hide_source)
# Interactive questions and answers
while True:
query = input("\nEnter a query: ")
if query == "exit":
break
if query.strip() == "":
continue
# Get the answer from the chain
start = time.time()
res = qa(query)
answer, docs = res['result'], [] if args.hide_source else res['source_documents']
end = time.time()
# Print the result
print("\n\n> Question:")
print(query)
print(f"\n> Answer (took {round(end - start, 2)} s.):")
print(answer)
# Print the relevant sources used for the answer
for document in docs:
print("\n> " + document.metadata["source"] + ":")
print(document.page_content)
def parse_arguments():
parser = argparse.ArgumentParser(description='privateGPT: Ask questions to your documents without an internet connection, '
'using the power of LLMs.')
parser.add_argument("--hide-source", "-S", action='store_true',
help='Use this flag to disable printing of source documents used for answers.')
parser.add_argument("--mute-stream", "-M",
action='store_true',
help='Use this flag to disable the streaming StdOut callback for LLMs.')
return parser.parse_args()
if __name__ == "__main__":
main()
examples/privategpt/pyproject.toml 0 → 100644
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[tool.poetry]
name = "privategpt"
version = "0.1.0"
description = ""
authors = ["Ivan Martinez <ivanmartit@gmail.com>"]
license = "Apache Version 2.0"
readme = "README.md"
[tool.poetry.dependencies]
python = "^3.10"
langchain = "0.0.261"
gpt4all = "^1.0.3"
chromadb = "^0.3.26"
PyMuPDF = "^1.22.5"
python-dotenv = "^1.0.0"
unstructured = "^0.8.0"
extract-msg = "^0.41.5"
tabulate = "^0.9.0"
pandoc = "^2.3"
pypandoc = "^1.11"
tqdm = "^4.65.0"
[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
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aiohttp==3.8.4 ; python_version >= "3.10" and python_version < "4.0" \
--hash=sha256:03543dcf98a6619254b409be2d22b51f21ec66272be4ebda7b04e6412e4b2e14 \
--hash=sha256:03baa76b730e4e15a45f81dfe29a8d910314143414e528737f8589ec60cf7391 \
--hash=sha256:0a63f03189a6fa7c900226e3ef5ba4d3bd047e18f445e69adbd65af433add5a2 \
--hash=sha256:10c8cefcff98fd9168cdd86c4da8b84baaa90bf2da2269c6161984e6737bf23e \
--hash=sha256:147ae376f14b55f4f3c2b118b95be50a369b89b38a971e80a17c3fd623f280c9 \
--hash=sha256:176a64b24c0935869d5bbc4c96e82f89f643bcdf08ec947701b9dbb3c956b7dd \
--hash=sha256:17b79c2963db82086229012cff93ea55196ed31f6493bb1ccd2c62f1724324e4 \
--hash=sha256:1a45865451439eb320784918617ba54b7a377e3501fb70402ab84d38c2cd891b \
--hash=sha256:1b3ea7edd2d24538959c1c1abf97c744d879d4e541d38305f9bd7d9b10c9ec41 \
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aiosignal==1.3.1 ; python_version >= "3.10" and python_version < "4.0" \
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anyio==3.7.1 ; python_version >= "3.10" and python_version < "4.0" \
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argilla==0.0.1 ; python_version >= "3.10" and python_version < "4.0" \
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async-timeout==4.0.2 ; python_version >= "3.10" and python_version < "4.0" \
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attrs==23.1.0 ; python_version >= "3.10" and python_version < "4.0" \
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backoff==2.2.1 ; python_version >= "3.10" and python_version < "4.0" \
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beautifulsoup4==4.12.2 ; python_version >= "3.10" and python_version < "4.0" \
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certifi==2023.5.7 ; python_version >= "3.10" and python_version < "4.0" \
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cffi==1.15.1 ; python_version >= "3.10" and python_version < "4.0" \
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chardet==5.1.0 ; python_version >= "3.10" and python_version < "4.0" \
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charset-normalizer==3.2.0 ; python_version >= "3.10" and python_version < "4.0" \
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chromadb==0.3.26 ; python_version >= "3.10" and python_version < "4.0" \
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click==8.1.4 ; python_version >= "3.10" and python_version < "4.0" \
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clickhouse-connect==0.6.6 ; python_version >= "3.10" and python_version < "4.0" \
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colorama==0.4.6 ; python_version >= "3.10" and python_version < "4.0" and platform_system == "Windows" \
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colorclass==2.2.2 ; python_version >= "3.10" and python_version < "4.0" \
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coloredlogs==15.0.1 ; python_version >= "3.10" and python_version < "4.0" \
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compressed-rtf==1.0.6 ; python_version >= "3.10" and python_version < "4.0" \
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cryptography==41.0.1 ; python_version >= "3.10" and python_version < "4.0" \
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dataclasses-json==0.5.9 ; python_version >= "3.10" and python_version < "4.0" \
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duckdb==0.8.1 ; python_version >= "3.10" and python_version < "4.0" \
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easygui==0.98.3 ; python_version >= "3.10" and python_version < "4.0" \
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ebcdic==1.1.1 ; python_version >= "3.10" and python_version < "4.0" \
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et-xmlfile==1.1.0 ; python_version >= "3.10" and python_version < "4.0" \
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exceptiongroup==1.1.2 ; python_version >= "3.10" and python_version < "3.11" \
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extract-msg==0.41.5 ; python_version >= "3.10" and python_version < "4.0" \
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fastapi==0.100.0 ; python_version >= "3.10" and python_version < "4.0" \
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filetype==1.2.0 ; python_version >= "3.10" and python_version < "4.0" \
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flatbuffers==23.5.26 ; python_version >= "3.10" and python_version < "4.0" \
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frozenlist==1.3.3 ; python_version >= "3.10" and python_version < "4.0" \
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gpt4all==1.0.3 ; python_version >= "3.10" and python_version < "4.0" \
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greenlet==2.0.2 ; python_version >= "3.10" and python_version < "4.0" and (platform_machine == "win32" or platform_machine == "WIN32" or platform_machine == "AMD64" or platform_machine == "amd64" or platform_machine == "x86_64" or platform_machine == "ppc64le" or platform_machine == "aarch64") \
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httptools==0.6.0 ; python_version >= "3.10" and python_version < "4.0" \
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humanfriendly==10.0 ; python_version >= "3.10" and python_version < "4.0" \
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idna==3.4 ; python_version >= "3.10" and python_version < "4.0" \
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langchain==0.0.261 ; python_version >= "3.10" and python_version < "4.0" \
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lz4==4.3.2 ; python_version >= "3.10" and python_version < "4.0" \
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markdown==3.4.3 ; python_version >= "3.10" and python_version < "4.0" \
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marshmallow-enum==1.5.1 ; python_version >= "3.10" and python_version < "4.0" \
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marshmallow==3.19.0 ; python_version >= "3.10" and python_version < "4.0" \
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monotonic==1.6 ; python_version >= "3.10" and python_version < "4.0" \
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mpmath==1.3.0 ; python_version >= "3.10" and python_version < "4.0" \
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msg-parser==1.2.0 ; python_version >= "3.10" and python_version < "4.0" \
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mypy-extensions==1.0.0 ; python_version >= "3.10" and python_version < "4.0" \
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nltk==3.8.1 ; python_version >= "3.10" and python_version < "4.0" \
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numexpr==2.8.4 ; python_version >= "3.10" and python_version < "4.0" \
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numpy==1.25.1 ; python_version >= "3.10" and python_version < "4.0" \
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olefile==0.46 ; python_version >= "3.10" and python_version < "4.0" \
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oletools==0.60.1 ; python_version >= "3.10" and python_version < "4.0" \
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onnxruntime==1.15.1 ; python_version >= "3.10" and python_version < "4.0" \
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openapi-schema-pydantic==1.2.4 ; python_version >= "3.10" and python_version < "4.0" \
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openpyxl==3.1.2 ; python_version >= "3.10" and python_version < "4.0" \
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overrides==7.3.1 ; python_version >= "3.10" and python_version < "4.0" \
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packaging==23.1 ; python_version >= "3.10" and python_version < "4.0" \
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pandas==2.0.3 ; python_version >= "3.10" and python_version < "4.0" \
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pandoc==2.3 ; python_version >= "3.10" and python_version < "4.0" \
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pcodedmp==1.2.6 ; python_version >= "3.10" and python_version < "4.0" \
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pdf2image==1.16.3 ; python_version >= "3.10" and python_version < "4.0" \
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pdfminer-six==20221105 ; python_version >= "3.10" and python_version < "4.0" \
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pillow==10.0.0 ; python_version >= "3.10" and python_version < "4.0" \
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plumbum==1.8.2 ; python_version >= "3.10" and python_version < "4.0" \
--hash=sha256:3ad9e5f56c6ec98f6f7988f7ea8b52159662ea9e915868d369dbccbfca0e367e \
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ply==3.11 ; python_version >= "3.10" and python_version < "4.0" \
--hash=sha256:00c7c1aaa88358b9c765b6d3000c6eec0ba42abca5351b095321aef446081da3 \
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posthog==3.0.1 ; python_version >= "3.10" and python_version < "4.0" \
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protobuf==4.23.4 ; python_version >= "3.10" and python_version < "4.0" \
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pulsar-client==3.2.0 ; python_version >= "3.10" and python_version < "4.0" \
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pycparser==2.21 ; python_version >= "3.10" and python_version < "4.0" \
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pydantic==1.10.11 ; python_version >= "3.10" and python_version < "4.0" \
--hash=sha256:008c5e266c8aada206d0627a011504e14268a62091450210eda7c07fabe6963e \
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pymupdf==1.22.5 ; python_version >= "3.10" and python_version < "4.0" \
--hash=sha256:01119edb7e4c3dd8c154d237b8ac927bd359eea8d31468f9a89aa308b5bca04e \
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pypandoc==1.11 ; python_version >= "3.10" and python_version < "4.0" \
--hash=sha256:7f6d68db0e57e0f6961bec2190897118c4d305fc2d31c22cd16037f22ee084a5 \
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pyparsing==2.4.7 ; python_version >= "3.10" and python_version < "4.0" \
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pyreadline3==3.4.1 ; sys_platform == "win32" and python_version >= "3.10" and python_version < "4.0" \
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python-dateutil==2.8.2 ; python_version >= "3.10" and python_version < "4.0" \
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python-docx==0.8.11 ; python_version >= "3.10" and python_version < "4.0" \
--hash=sha256:1105d233a0956dd8dd1e710d20b159e2d72ac3c301041b95f4d4ceb3e0ebebc4
python-dotenv==1.0.0 ; python_version >= "3.10" and python_version < "4.0" \
--hash=sha256:a8df96034aae6d2d50a4ebe8216326c61c3eb64836776504fcca410e5937a3ba \
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python-magic==0.4.27 ; python_version >= "3.10" and python_version < "4.0" \
--hash=sha256:c1ba14b08e4a5f5c31a302b7721239695b2f0f058d125bd5ce1ee36b9d9d3c3b \
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python-pptx==0.6.21 ; python_version >= "3.10" and python_version < "4.0" \
--hash=sha256:7798a2aaf89563565b3c7120c0acfe9aff775db0db3580544e3bf4840c2e378f
pytz==2023.3 ; python_version >= "3.10" and python_version < "4.0" \
--hash=sha256:1d8ce29db189191fb55338ee6d0387d82ab59f3d00eac103412d64e0ebd0c588 \
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pywin32==306 ; platform_system == "Windows" and platform_python_implementation != "PyPy" and python_version >= "3.10" and python_version < "4.0" \
--hash=sha256:06d3420a5155ba65f0b72f2699b5bacf3109f36acbe8923765c22938a69dfc8d \
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requests==2.31.0 ; python_version >= "3.10" and python_version < "4.0" \
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rtfde==0.0.2 ; python_version >= "3.10" and python_version < "4.0" \
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six==1.16.0 ; python_version >= "3.10" and python_version < "4.0" \
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sniffio==1.3.0 ; python_version >= "3.10" and python_version < "4.0" \
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soupsieve==2.4.1 ; python_version >= "3.10" and python_version < "4.0" \
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sqlalchemy==2.0.18 ; python_version >= "3.10" and python_version < "4.0" \
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starlette==0.27.0 ; python_version >= "3.10" and python_version < "4.0" \
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sympy==1.12 ; python_version >= "3.10" and python_version < "4.0" \
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tabulate==0.9.0 ; python_version >= "3.10" and python_version < "4.0" \
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tenacity==8.2.2 ; python_version >= "3.10" and python_version < "4.0" \
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tokenizers==0.13.3 ; python_version >= "3.10" and python_version < "4.0" \
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tqdm==4.65.0 ; python_version >= "3.10" and python_version < "4.0" \
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typing-extensions==4.7.1 ; python_version >= "3.10" and python_version < "4.0" \
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typing-inspect==0.9.0 ; python_version >= "3.10" and python_version < "4.0" \
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tzdata==2023.3 ; python_version >= "3.10" and python_version < "4.0" \
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tzlocal==5.0.1 ; python_version >= "3.10" and python_version < "4.0" \
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unstructured==0.8.0 ; python_version >= "3.10" and python_version < "4.0" \
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urllib3==2.0.3 ; python_version >= "3.10" and python_version < "4.0" \
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uvicorn[standard]==0.22.0 ; python_version >= "3.10" and python_version < "4.0" \
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uvloop==0.17.0 ; (sys_platform != "win32" and sys_platform != "cygwin") and platform_python_implementation != "PyPy" and python_version >= "3.10" and python_version < "4.0" \
--hash=sha256:0949caf774b9fcefc7c5756bacbbbd3fc4c05a6b7eebc7c7ad6f825b23998d6d \
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watchfiles==0.19.0 ; python_version >= "3.10" and python_version < "4.0" \
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websockets==11.0.3 ; python_version >= "3.10" and python_version < "4.0" \
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