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app.py

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

requirements.txt

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+langchain
+openai
+tiktoken
+chromadb