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