Create app.py

app.py

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+from langchain.text_splitter import CharacterTextSplitter
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import Chroma
+from langchain import HuggingFacePipeline
+from langchain.chains import RetrievalQA
+from transformers import AutoTokenizer
+from langchain.retrievers import WikipediaRetriever
+import pickle
+import os
+
+retriever = WikipediaRetriever(lang="en")
+
+data = retriever.get_relevant_documents(query="Economics")
+
+bloomz_tokenizer = AutoTokenizer.from_pretrained('bigscience/bloomz-1b7')
+
+text_splitter = CharacterTextSplitter.from_huggingface_tokenizer(bloomz_tokenizer, chunk_size=100, chunk_overlap=0, separator='\n')
+
+documents = text_splitter.split_documents(data)
+
+embeddings = HuggingFaceEmbeddings()
+
+persist_directory = "vector_db"
+
+vectordb = Chroma.from_documents(documents=documents, embedding=embeddings, persist_directory=persist_directory)
+
+vectordb.persist()
+vectordb = None
+
+vectordb_persist = Chroma(persist_directory=persist_directory, embedding_function=embeddings)
+
+llm = HuggingFacePipeline.from_model_id(
+    model_id="bigscience/bloomz-1b7",
+    task="text-generation",
+    model_kwargs={"temperature" : 0, "max_length" : 500})
+
+doc_retriever = vectordb_persist.as_retriever()
+
+wikipedia_qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=doc_retriever)
+
+def make_inference(query):
+    inference = wikipedia_qa.run(query)
+    return inference
+
+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="Ask_Wikipedia about Economics",
+        description="️Building a QA application to Wikipedia",
+    ).launch()