75 lines
2.8 KiB
Python
Executable File
75 lines
2.8 KiB
Python
Executable File
#!/usr/bin/env python3
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from langchain.chains import RetrievalQA
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from langchain.embeddings import HuggingFaceEmbeddings
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from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
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from langchain.vectorstores import Chroma
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from langchain.llms import Ollama
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import chromadb
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import os
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import argparse
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import time
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model = os.environ.get("MODEL", "llama2-uncensored")
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# For embeddings model, the example uses a sentence-transformers model
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# https://www.sbert.net/docs/pretrained_models.html
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# "The all-mpnet-base-v2 model provides the best quality, while all-MiniLM-L6-v2 is 5 times faster and still offers good quality."
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embeddings_model_name = os.environ.get("EMBEDDINGS_MODEL_NAME", "all-MiniLM-L6-v2")
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persist_directory = os.environ.get("PERSIST_DIRECTORY", "db")
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target_source_chunks = int(os.environ.get('TARGET_SOURCE_CHUNKS',4))
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from constants import CHROMA_SETTINGS
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def main():
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# Parse the command line arguments
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args = parse_arguments()
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embeddings = HuggingFaceEmbeddings(model_name=embeddings_model_name)
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db = Chroma(persist_directory=persist_directory, embedding_function=embeddings)
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retriever = db.as_retriever(search_kwargs={"k": target_source_chunks})
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# activate/deactivate the streaming StdOut callback for LLMs
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callbacks = [] if args.mute_stream else [StreamingStdOutCallbackHandler()]
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llm = Ollama(model=model, callbacks=callbacks)
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qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents= not args.hide_source)
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# Interactive questions and answers
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while True:
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query = input("\nEnter a query: ")
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if query == "exit":
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break
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if query.strip() == "":
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continue
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# Get the answer from the chain
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start = time.time()
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res = qa(query)
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answer, docs = res['result'], [] if args.hide_source else res['source_documents']
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end = time.time()
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# Print the result
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print("\n\n> Question:")
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print(query)
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print(answer)
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# Print the relevant sources used for the answer
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for document in docs:
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print("\n> " + document.metadata["source"] + ":")
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print(document.page_content)
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def parse_arguments():
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parser = argparse.ArgumentParser(description='privateGPT: Ask questions to your documents without an internet connection, '
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'using the power of LLMs.')
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parser.add_argument("--hide-source", "-S", action='store_true',
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help='Use this flag to disable printing of source documents used for answers.')
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parser.add_argument("--mute-stream", "-M",
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action='store_true',
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help='Use this flag to disable the streaming StdOut callback for LLMs.')
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return parser.parse_args()
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if __name__ == "__main__":
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main()
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