Add application file

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app.py

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+import streamlit as st
+from ingest import parse_document, create_embeddings
+from run_localGPT import generate_answer
+
+# Function to run the pipeline
+def run_pipeline(document):
+    # Parse the document and create embeddings
+    embeddings = create_embeddings(parse_document(document))
+
+    # Generate an answer using the local LLM and the extracted context
+    answer = generate_answer(embeddings)
+
+    return answer
+
+# Streamlit app
+def main():
+    st.title("Local GPT Pipeline")
+    st.write("How does it work?\n"
+             "Selecting the right local models and the power of LangChain, "
+             "you can run the entire pipeline locally without any data leaving your environment, and with reasonable performance.")
+
+    # Input section
+    document = st.text_area("Document")
+
+    # Run the pipeline when the "Run" button is clicked
+    if st.button("Run"):
+        answer = run_pipeline(document)
+        st.write("Answer:", answer)
+
+if __name__ == "__main__":
+    main()

constants.py

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+import os
+# from dotenv import load_dotenv
+from chromadb.config import Settings
+
+# load_dotenv()
+ROOT_DIRECTORY = os.path.dirname(os.path.realpath(__file__))
+
+# Define the folder for storing database
+SOURCE_DIRECTORY = f"{ROOT_DIRECTORY}/SOURCE_DOCUMENTS"
+
+PERSIST_DIRECTORY = f"{ROOT_DIRECTORY}/DB"
+
+# Define the Chroma settings
+CHROMA_SETTINGS = Settings(
+        chroma_db_impl='duckdb+parquet',
+        persist_directory=PERSIST_DIRECTORY,
+        anonymized_telemetry=False
+)

ingest.py

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+import os
+import click
+from typing import List
+
+from langchain.document_loaders import TextLoader, PDFMinerLoader, CSVLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import Chroma
+from langchain.docstore.document import Document
+from constants import CHROMA_SETTINGS, SOURCE_DIRECTORY, PERSIST_DIRECTORY
+from langchain.embeddings import HuggingFaceInstructEmbeddings
+
+
+def load_single_document(file_path: str) -> Document:
+    # Loads a single document from a file path
+    if file_path.endswith(".txt"):
+        loader = TextLoader(file_path, encoding="utf8")
+    elif file_path.endswith(".pdf"):
+        loader = PDFMinerLoader(file_path)
+    elif file_path.endswith(".csv"):
+        loader = CSVLoader(file_path)
+    return loader.load()[0]
+
+
+def load_documents(source_dir: str) -> List[Document]:
+    # Loads all documents from source documents directory
+    all_files = os.listdir(source_dir)
+    return [load_single_document(f"{source_dir}/{file_path}") for file_path in all_files if file_path[-4:] in ['.txt', '.pdf', '.csv'] ]
+
+
+@click.command()
+@click.option('--device_type', default='gpu', help='device to run on, select gpu or cpu')
+def main(device_type, ):
+    # load the instructorEmbeddings
+    if device_type in ['cpu', 'CPU']:
+        device='cpu'
+    else:
+        device='cuda'
+
+    # Load documents and split in chunks
+    print(f"Loading documents from {SOURCE_DIRECTORY}")
+    documents = load_documents(SOURCE_DIRECTORY)
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    texts = text_splitter.split_documents(documents)
+    print(f"Loaded {len(documents)} documents from {SOURCE_DIRECTORY}")
+    print(f"Split into {len(texts)} chunks of text")
+
+    # Create embeddings
+    embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl",
+                                                model_kwargs={"device": device})
+    
+    db = Chroma.from_documents(texts, embeddings, persist_directory=PERSIST_DIRECTORY, client_settings=CHROMA_SETTINGS)
+    db.persist()
+    db = None
+
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+langchain==0.0.166
+chromadb==0.3.22
+llama-cpp-python
+urllib3==1.26.6
+pdfminer.six==20221105
+InstructorEmbedding
+sentence-transformers
+faiss-cpu
+huggingface_hub
+transformers
+protobuf==3.20.0
+accelerate
+bitsandbytes
+click

run_localGPT.py

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+from langchain.chains import RetrievalQA
+# from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+from langchain.vectorstores import Chroma
+from langchain.embeddings import HuggingFaceInstructEmbeddings
+from langchain.llms import HuggingFacePipeline
+from constants import CHROMA_SETTINGS, PERSIST_DIRECTORY
+from transformers import LlamaTokenizer, LlamaForCausalLM, pipeline
+import click
+
+from constants import CHROMA_SETTINGS
+
+def load_model():
+    '''
+    Select a model on huggingface. 
+    If you are running this for the first time, it will download a model for you. 
+    subsequent runs will use the model from the disk. 
+    '''
+    model_id = "TheBloke/vicuna-7B-1.1-HF"
+    tokenizer = LlamaTokenizer.from_pretrained(model_id)
+
+    model = LlamaForCausalLM.from_pretrained(model_id,
+                                            #   load_in_8bit=True, # set these options if your GPU supports them!
+                                            #   device_map=1#'auto',
+                                            #   torch_dtype=torch.float16,
+                                            #   low_cpu_mem_usage=True
+                                              )
+
+    pipe = pipeline(
+        "text-generation",
+        model=model, 
+        tokenizer=tokenizer, 
+        max_length=2048,
+        temperature=0,
+        top_p=0.95,
+        repetition_penalty=1.15
+    )
+
+    local_llm = HuggingFacePipeline(pipeline=pipe)
+
+    return local_llm
+
+@click.command()
+@click.option('--device_type', default='gpu', help='device to run on, select gpu or cpu')
+def main(device_type, ):
+    # load the instructorEmbeddings
+    if device_type in ['cpu', 'CPU']:
+        device='cpu'
+    else:
+        device='cuda'
+
+    print(f"Running on: {device}")
+        
+    embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl",
+                                                model_kwargs={"device": device})
+    # load the vectorstore
+    db = Chroma(persist_directory=PERSIST_DIRECTORY, embedding_function=embeddings, client_settings=CHROMA_SETTINGS)
+    retriever = db.as_retriever()
+    # Prepare the LLM
+    # callbacks = [StreamingStdOutCallbackHandler()]
+    # load the LLM for generating Natural Language responses. 
+    llm = load_model()
+    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
+        print("----------------------------------SOURCE DOCUMENTS---------------------------")
+        for document in docs:
+            print("\n> " + document.metadata["source"] + ":")
+            print(document.page_content)
+        print("----------------------------------SOURCE DOCUMENTS---------------------------")
+
+
+if __name__ == "__main__":
+    main()