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Upload 3 files
Browse files- app.py +16 -0
- rag_pipeline.py +36 -0
- requirements.txt +7 -0
app.py
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import gradio as gr
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from rag_pipeline import load_rag_chain
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rag_chain = load_rag_chain()
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def ask_question(query):
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result = rag_chain.invoke(query)
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return result['result']
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iface = gr.Interface(fn=ask_question,
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inputs=gr.Textbox(lines=3, label="Ask a Question"),
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outputs="text",
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title="Custom PDF RAG Chatbot")
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if __name__ == "__main__":
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iface.launch()
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rag_pipeline.py
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from pathlib import Path
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from langchain.chains import RetrievalQA
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from transformers import pipeline, AutoTokenizer
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from langchain_community.vectorstores import Chroma
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from langchain_community.document_loaders import DirectoryLoader
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from langchain_huggingface import HuggingFaceEmbeddings, HuggingFacePipeline
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def load_rag_chain():
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pdf_dir = Path("data")
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loader = DirectoryLoader(str(pdf_dir), glob="*.pdf")
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text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
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pages = loader.load_and_split(text_splitter=text_splitter)
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embeddings = HuggingFaceEmbeddings(
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model_name="sentence-transformers/all-MiniLM-L6-v2",
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model_kwargs={"device": "cpu"},
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)
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vectordb_dir = "chroma_db"
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vectordb = Chroma.from_documents(pages, embeddings, persist_directory=vectordb_dir)
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retriever = vectordb.as_retriever(search_type="mmr", search_kwargs={"k": 5})
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hf_pipeline = pipeline(
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"text-generation",
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model="mistralai/Mistral-7B-Instruct-v0.2",
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tokenizer=AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2"),
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max_new_tokens=512,
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temperature=0.3,
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return_full_text=True,
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device=-1,
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)
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llm = HuggingFacePipeline(pipeline=hf_pipeline)
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qa_chain = RetrievalQA.from_llm(llm=llm, retriever=retriever)
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return qa_chain
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requirements.txt
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gradio
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transformers
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sentence-transformers
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langchain
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langchain-community
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langchain-huggingface
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chromadb
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