Add application file

app.py

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+import os
+from langchain.prompts import PromptTemplate
+from langchain.llms import CTransformers
+from langchain.vectorstores import Chroma
+from langchain.embeddings import HuggingFaceBgeEmbeddings
+from langchain.chains import RetrievalQA
+import gradio as gr
+
+# Mount Google Drive
+# from google.colab import drive
+# drive.mount('/content/drive/')
+# !ls /content/drive/My\ Drive/stores/enron_cosine/
+
+# The model used to generate responses based on retrieved content from the database in response to user inquiries. 
+local_llm = "TheBloke/zephyr-7B-beta-GGUF"
+
+config = {
+    # Explicitly set the max_seq_len
+    "max_seq_len" : 4096,
+    "max_answer_len" : 1024,
+    "max_new_token": 1024,
+    "repetition_penalty": 1.1,
+    "temperature": 0.1,
+    "top_k": 50,
+    "top_p": 0.9,
+    "stream": True,
+    "threads": int(os.cpu_count() / 2),
+}
+
+llm_init = CTransformers(model=local_llm, model_type="mistral", lib="avx2", **config)
+
+prompt_template = """Use the following piece of information to answers the question asked by the user.
+Don't try to make up the answer if you don't know the answer, simply say I don't know.
+
+Context: {context}
+Question: {question}
+
+Only helpful answer below.
+Helpful answer:
+"""
+
+# The model to create the embeddings of the user queries
+model_name = "BAAI/bge-large-en"
+model_kwargs = {"device": "cpu"}
+encode_kwargs = {"normalize_embeddings": False}
+
+embeddings = HuggingFaceBgeEmbeddings(
+    model_name=model_name,
+    model_kwargs=model_kwargs,
+    encode_kwargs=encode_kwargs,
+)
+
+prompt = PromptTemplate(
+    template=prompt_template, input_variables=["context", "question"]
+)
+
+load_vector_store = Chroma(
+    persist_directory="./stores/enron_cosine", embedding_function=embeddings
+)
+
+retriever = load_vector_store.as_retriever(search_kwargs={"k": 1})
+
+#query = "In what context is mentioned Natural Gas Storage Overview?"
+#semantic_search = retriever.get_relevant_documents(query)
+#print(semantic_search)
+
+# chain_type_kwargs = {"prompt": prompt}
+
+# qa = RetrievalQA.from_chain_type(
+#     llm=llm_init,
+#     chain_type="stuff",
+#     retriever=retriever,
+#     verbose=True,
+#     chain_type_kwargs=chain_type_kwargs,
+#     return_source_documents=True,
+# )
+
+
+sample_query = []
+
+def get_response(input):
+    query = input
+    chain_type_kwargs = {"prompt": prompt}
+    qa = RetrievalQA.from_chain_type(
+        llm=llm_init,
+        chain_type="stuff",
+        retriever=retriever,
+        verbose=True,
+        chain_type_kwargs=chain_type_kwargs,
+        return_source_documents=True,
+    )
+    response = qa(query)
+    print("Response:", response)
+    return response
+
+input = gr.Text(
+    label="Query",
+    show_label=True,
+    max_lines=2,
+    container=False,
+    placeholder="Enter your question",
+)
+
+gIface = gr.Interface(
+    fn=get_response,
+    inputs=input,
+    outputs="text",
+    title="Enron emails RAG AI",
+    description="RAG demo using Zephyr 7B Beta and Langchain",
+    examples=sample_query,
+    allow_flagging="never",
+)
+
+gIface.launch()
+

requirements.txt

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+torch
+ctransformers
+sentence_transformers
+chromadb
+langchain
+pypdf
+gradio