End-to-end working version

privateGPT.py

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+from gpt4all_j import GPT4All_J
+from langchain.chains import RetrievalQA
+from langchain.embeddings import LlamaCppEmbeddings
+from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+from langchain.vectorstores import Chroma
+
+def main():        
+    # Load stored vectorstore
+    llama = LlamaCppEmbeddings(model_path="./models/ggml-model-q4_0.bin")
+    persist_directory = 'db'
+    db = Chroma(persist_directory=persist_directory, embedding_function=llama)
+    retriever = db.as_retriever()
+    # Prepare the LLM
+    callbacks = [StreamingStdOutCallbackHandler()]
+    llm = GPT4All_J(model='./models/ggml-gpt4all-j-v1.3-groovy.bin', callbacks=callbacks, verbose=False)
+    qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever)
+    # Interactive questions and answers
+    while True:
+        query = input("Enter a query: ")
+        if query == "exit":
+            break
+        qa.run(query)    
+
+if __name__ == "__main__":
+    main()