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from langchain.embeddings.openai import OpenAIEmbeddings |
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from langchain.vectorstores import Chroma |
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from langchain.text_splitter import CharacterTextSplitter |
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from langchain.chains.question_answering import load_qa_chain |
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from langchain.llms import OpenAI |
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import os |
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with open("guide1.txt") as f: |
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hitchhikersguide = f.read() |
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text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0, separator = "\n") |
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texts = text_splitter.split_text(hitchhikersguide) |
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embeddings = OpenAIEmbeddings() |
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docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{"source": str(i)} for i in range(len(texts))]).as_retriever() |
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chain = load_qa_chain(OpenAI(temperature=0), chain_type="stuff") |
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def make_inference(query): |
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docs = docsearch.get_relevant_documents(query) |
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return(chain.run(input_documents=docs, question=query)) |
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if __name__ == "__main__": |
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import gradio as gr |
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gr.Interface( |
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make_inference, |
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[ |
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gr.inputs.Textbox(lines=2, label="Query"), |
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], |
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gr.outputs.Textbox(label="Response"), |
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title="🗣️TalkToMyDoc📄", |
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description="🗣️TalkToMyDoc📄 is a tool that allows you to ask questions about a document. In this case - Hitch Hitchhiker's Guide to the Galaxy.", |
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).launch() |
