Use next tier model for better results; parse document from PDF.
Browse files- .gitattributes +1 -34
- app.py +16 -6
- rdna3-shader-instruction-set-architecture-feb-2023_0.pdf +3 -0
- requirements.txt +1 -0
.gitattributes
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app.py
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import gradio as gr
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from langchain import HuggingFaceHub
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from langchain.chains.question_answering import load_qa_chain
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from langchain.document_loaders import
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from langchain.embeddings import HuggingFaceEmbeddings
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from langchain.llms import HuggingFacePipeline
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from langchain.vectorstores import FAISS
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print("Loading documents")
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loader =
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documents = loader.load()
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print("Creating chunks")
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splitter = RecursiveCharacterTextSplitter(chunk_size=
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chunks = splitter.split_documents(documents)
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print("Creating database")
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print("Loading model")
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llm = HuggingFacePipeline.from_model_id(
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model_id="google/flan-t5-
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task="text2text-generation",
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model_kwargs={"temperature": 0, "max_length":
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chain = load_qa_chain(llm, chain_type="stuff")
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def ask(question):
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answers = db.similarity_search(question, k=
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result = chain.run(input_documents=answers, question=question)
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return result
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import gradio as gr
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from langchain import HuggingFaceHub
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from langchain.chains.question_answering import load_qa_chain
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from langchain.document_loaders import PyMuPDFLoader
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from langchain.embeddings import HuggingFaceEmbeddings
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from langchain.llms import HuggingFacePipeline
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from langchain.vectorstores import FAISS
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# Number of search results to query from the vector database.
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SIMILARITY_SEARCH_COUNT = 2
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# Size of each document chunk in number of characters.
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CHUNK_SIZE = 1000
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# Maximum number of output tokens.
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MODEL_MAX_LENGTH = 300
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print("Loading documents")
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loader = PyMuPDFLoader("rdna3-shader-instruction-set-architecture-feb-2023_0.pdf")
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documents = loader.load()
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print("Creating chunks")
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splitter = RecursiveCharacterTextSplitter(chunk_size=CHUNK_SIZE, chunk_overlap=0)
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chunks = splitter.split_documents(documents)
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print("Creating database")
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print("Loading model")
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llm = HuggingFacePipeline.from_model_id(
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model_id="google/flan-t5-large",
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task="text2text-generation",
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model_kwargs={"temperature": 0, "max_length": MODEL_MAX_LENGTH})
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chain = load_qa_chain(llm, chain_type="stuff")
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def ask(question):
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answers = db.similarity_search(question, k=SIMILARITY_SEARCH_COUNT)
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result = chain.run(input_documents=answers, question=question)
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return result
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rdna3-shader-instruction-set-architecture-feb-2023_0.pdf
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version https://git-lfs.github.com/spec/v1
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oid sha256:bff84b0bc818446356e73ba894149b8c810549fa240a9872b46179f412fcd13b
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size 3246429
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requirements.txt
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faiss-cpu
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sentence_transformers
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protobuf==3.20.1
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faiss-cpu
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sentence_transformers
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protobuf==3.20.1
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pymupdf
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