Add application file

.gitignore

@@ -0,0 +1,172 @@
+# OSX
+.DS_STORE
+
+# Models
+models/
+
+# Local Chroma db
+.chroma/
+db/
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+.brev/

LICENSE

@@ -0,0 +1,201 @@
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README.md

@@ -1,12 +1,2 @@
----
-title: Docx
-emoji: 🌖
-colorFrom: yellow
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.21.0
-app_file: app.py
-pinned: false
----
+# PrivateGPT App
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,122 @@
+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
+import os
+from fastapi import FastAPI, UploadFile, File
+from typing import List, Optional
+import urllib.parse
+from langchain.llms import HuggingFacePipeline
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import transformers
+from torch import cuda, bfloat16 
+
+
+
+load_dotenv()
+
+embeddings_model_name = "all-MiniLM-L6-v2"
+persist_directory = "db"
+model = "tiiuae/falcon-7b-instruct"
+
+
+# model_type = os.environ.get('MODEL_TYPE')
+# model_path = os.environ.get('MODEL_PATH')
+# model_n_ctx = os.environ.get('MODEL_N_CTX')
+source_directory = os.environ.get('SOURCE_DIRECTORY', 'source_documents')
+
+from constants import CHROMA_SETTINGS
+
+# async def test_embedding():
+#     # Create the folder if it doesn't exist
+#     os.makedirs(source_directory, exist_ok=True)
+#     # Create a sample.txt file in the source_documents directory
+#     file_path = os.path.join("source_documents", "test.txt")
+#     with open(file_path, "w") as file:
+#         file.write("This is a test.")
+#     # Run the ingest.py command
+#     os.system('python ingest.py --collection test')
+#     # Delete the sample.txt file
+#     os.remove(file_path)
+#     print("embeddings working")
+
+# async def model_download():
+#     match model_type:
+#         case "LlamaCpp":
+#             url = "https://gpt4all.io/models/ggml-gpt4all-l13b-snoozy.bin"
+#         case "GPT4All":
+#             url = "https://gpt4all.io/models/ggml-gpt4all-j-v1.3-groovy.bin"
+#     folder = "models"
+#     parsed_url = urllib.parse.urlparse(url)
+#     filename = os.path.join(folder, os.path.basename(parsed_url.path))
+#     # Check if the file already exists
+#     if os.path.exists(filename):
+#         print("File already exists.")
+#         return
+#     # Create the folder if it doesn't exist
+#     os.makedirs(folder, exist_ok=True)
+#     # Run wget command to download the file
+#     os.system(f"wget {url} -O {filename}")
+#     global model_path 
+#     model_path = filename
+#     os.environ['MODEL_PATH'] = filename
+#     print("model downloaded")
+    
+
+# Starting the app with embedding and llm download
+# @app.on_event("startup")
+# async def startup_event():
+#     await test_embedding()
+#     await model_download()
+
+
+# # Example route
+# @app.get("/")
+# async def root():
+#     return {"message": "Hello, the APIs are now ready for your embeds and queries!"}
+
+def embed_documents(files, collection_name: Optional[str] = None):
+
+    saved_files = []
+    # Save the files to the specified folder
+    for file in files:
+        print(file)
+        os.makedirs(source_directory, exist_ok= True)
+        file_path = os.path.join(source_directory, file.name)
+        saved_files.append(file_path)
+        
+        with open(file_path, "wb") as f:
+            f.write(file.read())
+        
+        if collection_name is None:
+            # Handle the case when the collection_name is not defined
+            collection_name = file.filename
+    
+    os.system(f'python ingest.py --collection {collection_name}')
+    
+    # Delete the contents of the folder
+    [os.remove(os.path.join(source_directory, file.name)) or os.path.join(source_directory, file.name) for file in files]
+    
+    return {"message": "Files embedded successfully", "saved_files": saved_files}
+
+def retrieve_documents(query: str, collection_name:str):
+    target_source_chunks = 4
+    mute_stream = ""
+    embeddings = HuggingFaceEmbeddings(model_name=embeddings_model_name)
+    db = Chroma(persist_directory=persist_directory,collection_name=collection_name, embedding_function=embeddings, client_settings=CHROMA_SETTINGS)
+    retriever = db.as_retriever(search_kwargs={"k": target_source_chunks})
+    # Prepare the LLM
+    callbacks = [] if mute_stream else [StreamingStdOutCallbackHandler()]
+
+    llm = HuggingFacePipeline.from_model_id(model_id=model, task="text-generation", device=0, model_kwargs={"temperature":0.1,"trust_remote_code": True, "max_length":100000, "top_p":0.15, "top_k":0, "repetition_penalty":1.1, "num_return_sequences":1,})
+
+    qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=False)
+    
+    # Get the answer from the chain
+    res = qa(query)
+    print(res)   
+    answer = res['result']
+
+    st.subheader("Results")
+    st.text(answer)

constants.py

@@ -0,0 +1,15 @@
+import os
+from dotenv import load_dotenv
+from chromadb.config import Settings
+
+load_dotenv()
+
+# Define the folder for storing database
+PERSIST_DIRECTORY = "db"
+
+# Define the Chroma settings
+CHROMA_SETTINGS = Settings(
+        chroma_db_impl='duckdb+parquet',
+        persist_directory=PERSIST_DIRECTORY,
+        anonymized_telemetry=False
+)

example.env

@@ -0,0 +1,6 @@
+PERSIST_DIRECTORY=db
+MODEL_TYPE=GPT4All
+MODEL_PATH=models/ggml-gpt4all-j-v1.3-groovy.bin
+EMBEDDINGS_MODEL_NAME=all-MiniLM-L6-v2
+MODEL_N_CTX=1000
+API_BASE_URL=http://localhost:8000

ingest.py

@@ -0,0 +1,108 @@
+import os
+import glob
+from typing import List
+from dotenv import load_dotenv
+import argparse
+
+from langchain.document_loaders import (
+    CSVLoader,
+    EverNoteLoader,
+    PDFMinerLoader,
+    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()
+
+embeddings_model_name = "all-MiniLM-L6-v2"
+persist_directory = "db"
+model = "tiiuae/falcon-7b-instruct"
+# Map file extensions to document loaders and their arguments
+LOADER_MAPPING = {
+    ".csv": (CSVLoader, {}),
+    # ".docx": (Docx2txtLoader, {}),
+    ".docx": (UnstructuredWordDocumentLoader, {}),
+    ".enex": (EverNoteLoader, {}),
+    ".eml": (UnstructuredEmailLoader, {}),
+    ".epub": (UnstructuredEPubLoader, {}),
+    ".html": (UnstructuredHTMLLoader, {}),
+    ".md": (UnstructuredMarkdownLoader, {}),
+    ".odt": (UnstructuredODTLoader, {}),
+    ".pdf": (PDFMinerLoader, {}),
+    ".pptx": (UnstructuredPowerPointLoader, {}),
+    ".txt": (TextLoader, {"encoding": "utf8"}),
+    # Add more mappings for other file extensions and loaders as needed
+}
+
+
+load_dotenv()
+
+
+def load_single_document(file_path: str) -> 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()[0]
+
+    raise ValueError(f"Unsupported file extension '{ext}'")
+
+
+def load_documents(source_dir: str) -> List[Document]:
+    # Loads all documents from source documents directory
+    all_files = []
+    for ext in LOADER_MAPPING:
+        all_files.extend(
+            glob.glob(os.path.join(source_dir, f"**/*{ext}"), recursive=True)
+        )
+    return [load_single_document(file_path) for file_path in all_files]
+
+
+def main(collection):
+    # Load environment variables
+    embeddings_model_name = "all-MiniLM-L6-v2"
+    persist_directory = "db"
+    model = "tiiuae/falcon-7b-instruct"
+    source_directory = "source_documents"
+    os.makedirs(source_directory, exist_ok=True)
+    # Load documents and split in chunks
+    print(f"Loading documents from {source_directory}")
+    chunk_size = 500
+    chunk_overlap = 50
+    documents = load_documents(source_directory)
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
+    texts = text_splitter.split_documents(documents)
+    print(f"Loaded {len(documents)} documents from {source_directory}")
+    print(f"Split into {len(texts)} chunks of text (max. {chunk_size} characters each)")
+
+    # Create embeddings
+    embeddings = HuggingFaceEmbeddings(model_name=embeddings_model_name)
+    
+    # Create and store locally vectorstore
+    db = Chroma.from_documents(texts, embeddings, collection_name=collection, persist_directory=persist_directory, client_settings=CHROMA_SETTINGS)
+    db.persist()
+    db = None
+
+
+if __name__ == "__main__":
+    # Create the argument parser
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--collection", help="Saves the embedding in a collection name as specified")
+
+    # Parse the command-line arguments
+    args = parser.parse_args()
+
+    main(args.collection)

privateGPT.py

@@ -0,0 +1,57 @@
+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, LlamaCpp
+import os
+
+load_dotenv()
+
+embeddings_model_name = os.environ.get("EMBEDDINGS_MODEL_NAME")
+persist_directory = os.environ.get('PERSIST_DIRECTORY')
+
+model_type = os.environ.get('MODEL_TYPE')
+model_path = os.environ.get('MODEL_PATH')
+model_n_ctx = os.environ.get('MODEL_N_CTX')
+
+from constants import CHROMA_SETTINGS
+
+def main():
+    embeddings = HuggingFaceEmbeddings(model_name=embeddings_model_name)
+    db = Chroma(persist_directory=persist_directory, embedding_function=embeddings, client_settings=CHROMA_SETTINGS)
+    retriever = db.as_retriever()
+    # Prepare the LLM
+    callbacks = [StreamingStdOutCallbackHandler()]
+    match model_type:
+        case "LlamaCpp":
+            llm = LlamaCpp(model_path=model_path, n_ctx=model_n_ctx, callbacks=callbacks, verbose=False)
+        case "GPT4All":
+            llm = GPT4All(model=model_path, n_ctx=model_n_ctx, backend='gptj', callbacks=callbacks, verbose=False)
+        case _default:
+            print(f"Model {model_type} not supported!")
+            exit;
+    qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=True)
+    # Interactive questions and answers
+    while True:
+        query = input("\nEnter a query: ")
+        if query == "exit":
+            break
+        
+        # Get the answer from the chain
+        res = qa(query)    
+        answer, docs = res['result'], res['source_documents']
+
+        # Print the result
+        print("\n\n> Question:")
+        print(query)
+        print("\n> Answer:")
+        print(answer)
+        
+        # Print the relevant sources used for the answer
+        for document in docs:
+            print("\n> " + document.metadata["source"] + ":")
+            print(document.page_content)
+
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -0,0 +1,15 @@
+transformers
+einops
+accelerate
+xformers
+chromadb==0.3.26
+duckdb==0.8.0
+pdfminer.six==20221105
+unstructured==0.6.6
+extract-msg==0.41.1
+tabulate==0.9.0
+pandoc==2.3
+pypandoc==1.11 
+langchain==0.0.177
+streamlit
+sentence_transformers

streamlit_app.py

@@ -0,0 +1,94 @@
+from dotenv import load_dotenv
+import os
+import streamlit as st
+import requests
+from typing import List
+import json
+import socket
+from urllib3.connection import HTTPConnection
+from app import embed_documents, retrieve_documents
+
+API_BASE_URL = os.environ.get("API_BASE_URL")
+
+load_dotenv()
+
+embeddings_model_name = "all-MiniLM-L6-v2"
+persist_directory = "db"
+model = "tiiuae/falcon-7b-instruct"
+
+from constants import CHROMA_SETTINGS
+import chromadb
+
+def list_of_collections():
+    client = chromadb.Client(CHROMA_SETTINGS)
+    return (client.list_collections())
+    
+def main():
+    st.title("PrivateGPT App: Document Embedding and Retrieval")
+    
+    # Document upload section
+    st.header("Document Upload")
+    files = st.file_uploader("Upload document", accept_multiple_files=True)
+    # collection_name = st.text_input("Collection Name") not working for some reason
+    if st.button("Embed"):
+        embed_documents(files, "collection_name")
+    
+    # Query section
+    st.header("Document Retrieval")
+    collection_names = get_collection_names()
+    selected_collection = st.selectbox("Select a document", collection_names)
+    if selected_collection:
+        query = st.text_input("Query")
+        if st.button("Retrieve"):
+            retrieve_documents(query, selected_collection)
+
+# def embed_documents(files:List[st.runtime.uploaded_file_manager.UploadedFile], collection_name:str):
+#     endpoint = f"{API_BASE_URL}/embed"
+#     files_data = [("files", file) for file in files]
+#     data = {"collection_name": collection_name}
+
+#     response = requests.post(endpoint, files=files_data, data=data)
+#     if response.status_code == 200:
+#         st.success("Documents embedded successfully!")
+#     else:
+#         st.error("Document embedding failed.")
+#         st.write(response.text)
+
+
+def get_collection_names():
+
+    collections = list_of_collections()
+    return [collection.name for collection in collections]
+
+
+
+# def retrieve_documents(query: str, collection_name: str):
+#     endpoint = f"{API_BASE_URL}/retrieve"
+#     data = {"query": query, "collection_name": collection_name}
+
+#     # Modify socket options for the HTTPConnection class
+#     HTTPConnection.default_socket_options = (
+#         HTTPConnection.default_socket_options + [
+#             (socket.SOL_SOCKET, socket.SO_KEEPALIVE, 1),
+#             (socket.SOL_TCP, socket.TCP_KEEPIDLE, 45),
+#             (socket.SOL_TCP, socket.TCP_KEEPINTVL, 10),
+#             (socket.SOL_TCP, socket.TCP_KEEPCNT, 6)
+#         ]
+#     )
+    
+#     response = requests.post(endpoint, params=data)
+#     if response.status_code == 200:
+#         result = response.json()
+#         st.subheader("Results")
+#         st.text(result["results"])
+        
+#         st.subheader("Documents")
+#         for doc in result["docs"]:
+#             st.text(doc)
+#     else:
+#         st.error("Failed to retrieve documents.")
+#         st.write(response.text)
+
+
+if __name__ == "__main__":
+    main()