First commit.

app.py

@@ -0,0 +1,143 @@
+# 1. Using langchain Vector store
+# https://python.langchain.com/v0.1/docs/modules/data_connection/vectorstores/
+# VectorStore - FAISS 
+# 2. Embedding - HuggingFaceInferenceAPIEmbeddings with "BAAI/bge-base-en-v1.5"
+# 3. llm use mistral and llama.
+# "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.2"
+# "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-8B-Instruct"
+
+import gradio as gr
+import os
+from langchain.prompts import ChatPromptTemplate
+from langchain_community.llms.huggingface_endpoint import HuggingFaceEndpoint
+from langchain.schema.runnable import RunnablePassthrough
+from langchain_community.document_loaders import TextLoader
+from langchain_text_splitters import CharacterTextSplitter
+from langchain_community.vectorstores import FAISS
+from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings
+
+API_TOKEN = os.environ.get('HUGGINGFACE_API_KEY')
+HF_API_KEY = API_TOKEN
+
+llm_urls = { 
+    "Mistral 7B": "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.2", 
+    "Llama 8B": "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-8B-Instruct"
+}
+
+def initialize_vector_store_retriever(file):
+    # Load the document, split it into chunks, embed each chunk and load it into the vector store.
+    #raw_documents = TextLoader('./llm.txt').load()
+    raw_documents = TextLoader(file).load()
+    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+    documents = text_splitter.split_documents(raw_documents)
+
+    API_URL = "https://api-inference.huggingface.co/models/BAAI/bge-base-en-v1.5"
+    embeddings = HuggingFaceInferenceAPIEmbeddings(
+        endpoint_url=API_URL,
+        api_key=HF_API_KEY,
+    )
+    db = FAISS.from_documents(documents, embeddings)
+    retriever = db.as_retriever()
+    return retriever
+
+def generate_llm_rag_prompt() -> ChatPromptTemplate:
+    #template = "<s>[INST] {context} {prompt} [/INST]"
+    template = "<s>[INST] <<SYS>>{system}<</SYS>>{context} {prompt} [/INST]"
+
+    prompt_template = ChatPromptTemplate.from_template(template)
+    return prompt_template
+
+
+    
+def create_chain(retriever, llm):
+
+    url = llm_urls[llm]
+    model_endpoint = HuggingFaceEndpoint(
+        endpoint_url=url,
+        huggingfacehub_api_token=HF_API_KEY,
+        task="text2text-generation",
+        max_new_tokens=200
+    )
+
+    if retriever != None:
+        def get_system(input):    
+            return "You are a helpful and honest assistant. Please, respond concisely and truthfully."
+
+        retrieval = {"context": retriever, "prompt": RunnablePassthrough(), "system": get_system}
+        chain = retrieval | generate_llm_rag_prompt() | model_endpoint 
+        return chain, model_endpoint
+    else:
+        return None, model_endpoint
+
+
+def query(question_text, llm, session_data):
+    if question_text == "":
+        without_rag_text = "Query result without RAG is not available. Enter a question first."
+        rag_text = "Query result with RAG is not available. Enter a question first."
+        return without_rag_text, rag_text
+    
+    if len(session_data)>0:
+        retriever = session_data[0]
+    else:
+        retriever = None     
+    chain, model_endpoint = create_chain(retriever, llm)
+    without_rag_text = "Query result without RAG:\n\n" + model_endpoint(question_text).strip()
+    if (retriever == None):
+        rag_text = "Query result With RAG is not available. Load Vector Store first."
+    else:
+        ans = chain.invoke(question_text).strip()
+        s = ans
+        s = [s.split("[INST] <<SYS>>")[1] for s in s.split("[/SYS]>[/INST]") if s.find("[INST] <<SYS>>") >=0]
+        if len(s) >= 2:
+            s = s[1:-1]
+        else:
+            s = ans
+        rag_text = "Query result With RAG:\n\n" + "".join(s)
+    return without_rag_text, rag_text
+
+def upload_file(file, session_data): 
+    #file_paths = [file.name for file in files]
+    #file = files[0]
+    session_data = [initialize_vector_store_retriever(file)]
+    return gr.File(value=file, visible=True), session_data
+
+def initialize_vector_store(session_data):
+    session_data = [initialize_vector_store_retriever()]
+    return session_data
+    
+with gr.Blocks() as demo:
+    gr.HTML("""<h1 align="center">Retrieval Augmented Generation</h1>""")
+    session_data = gr.State([])
+
+    file_output = gr.File(visible=False)
+    upload_button = gr.UploadButton("Click to Upload a text File to Vector Store", file_types=["text"], file_count="single")
+    upload_button.upload(upload_file, [upload_button, session_data], [file_output, session_data])
+
+    #initialize_VS_button = gr.Button("Load text file to Vector Store")
+    with gr.Row():
+        with gr.Column(scale=4):    
+            question_text = gr.Textbox(show_label=False, placeholder="Ask a question", lines=2)    
+        with gr.Column(scale=1):    
+            llm_Choice = gr.Radio(["Llama 8B", "Mistral 7B"], value="Mistral 7B", label="Select lanaguage model:", info="")    
+    query_Button = gr.Button("Query")
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            without_rag_text = gr.Textbox(show_label=False, placeholder="Query result without using RAG", lines=15)
+        with gr.Column(scale=1):
+            rag_text = gr.Textbox(show_label=False, placeholder="Query result with RAG", lines=15)
+
+    #initialize_VS_button.click(
+    #    initialize_vector_store,
+    #    [session_data],
+    #    [session_data],
+    #    #show_progress=True,
+    #)     
+    query_Button.click(
+        query,
+        [question_text, llm_Choice, session_data],
+        [without_rag_text, rag_text],
+        #show_progress=True,
+    )
+
+demo.queue().launch(share=False, inbrowser=True)

requirements.txt

@@ -0,0 +1,4 @@
+gradio
+faiss-cpu
+langchain
+