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{
"cells": [
{
"cell_type": "markdown",
"id": "9267529d",
"metadata": {},
"source": [
"A mini version of LISA in a Jupyter notebook for easier testing and playing around."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "adcfdba2",
"metadata": {},
"outputs": [],
"source": [
"# import some packages\n",
"import os\n",
"\n",
"from dotenv import load_dotenv\n",
"from langchain.document_loaders import PyPDFLoader\n",
"from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
"from langchain.embeddings import HuggingFaceEmbeddings\n",
"from langchain.vectorstores import FAISS\n",
"from langchain.chains import ConversationalRetrievalChain\n",
"from langchain.llms import HuggingFaceTextGenInference\n",
"from langchain.chains.conversation.memory import (\n",
" ConversationBufferWindowMemory,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "2d85c6d9",
"metadata": {},
"outputs": [],
"source": [
"# Set api keys\n",
"load_dotenv(\"API.env\") # put all the API tokens here, such as openai, huggingface...\n",
"HUGGINGFACEHUB_API_TOKEN = os.getenv(\"HUGGINGFACEHUB_API_TOKEN\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ffd3db32",
"metadata": {},
"outputs": [],
"source": [
"# Set inference link, use this online one for easier reproduce\n",
"inference_api_url = 'https://api-inference.huggingface.co/models/HuggingFaceH4/zephyr-7b-beta'\n",
"# Recommend using better LLMs, such as Mixtral 7x8B\n",
"\n",
"llm = HuggingFaceTextGenInference(\n",
" verbose=True, # Provides detailed logs of operation\n",
" max_new_tokens=1024, # Maximum number of token that can be generated.\n",
" top_p=0.95, # Threshold for controlling randomness in text generation process. \n",
" typical_p=0.95, #\n",
" temperature=0.1, # For choosing probable words.\n",
" inference_server_url=inference_api_url, # URL des Inferenzservers\n",
" timeout=120, # Timeout for connection with the url\n",
" )\n",
"\n",
"# Alternative, you can load model locally, e.g.:\n",
"# model_path = \"where/you/store/local/models/zephyr-7b-beta\" # change this to your model path\n",
"# model = AutoModelForCausalLM.from_pretrained(model_path, device_map=\"auto\")\n",
"# tokenizer = AutoTokenizer.from_pretrained(model_path)\n",
"# pipe = pipeline(\n",
"# \"text-generation\", model=model, tokenizer=tokenizer, max_new_tokens=1024, model_kwargs={\"temperature\":0.1}\n",
"# )\n",
"# llm = HuggingFacePipeline(pipeline=pipe)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "2d5bacd5",
"metadata": {},
"outputs": [],
"source": [
"# Function for reading and chunking text\n",
"def load_pdf_as_docs(pdf_path, loader_module=None):\n",
" \"\"\"Load and parse pdf files.\"\"\"\n",
" \n",
" if pdf_path.endswith('.pdf'): # single file\n",
" pdf_docs = [pdf_path]\n",
" else: # a directory\n",
" pdf_docs = [os.path.join(pdf_path, f) for f in os.listdir(pdf_path) if f.endswith('.pdf')]\n",
" \n",
" docs = []\n",
" \n",
" if loader_module is None: # Set PDFLoader\n",
" loader_module = PyPDFLoader\n",
" for pdf in pdf_docs:\n",
" loader = loader_module(pdf)\n",
" doc = loader.load()\n",
" docs.extend(doc)\n",
" \n",
" return docs\n",
"\n",
"def get_doc_chunks(docs, splitter=None):\n",
" \"\"\"Split docs into chunks.\"\"\"\n",
" \n",
" if splitter is None:\n",
" splitter = RecursiveCharacterTextSplitter(\n",
" separators=[\"\\n\\n\", \"\\n\"], chunk_size=256, chunk_overlap=128\n",
" )\n",
" chunks = splitter.split_documents(docs)\n",
" \n",
" return chunks"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "8cd31248",
"metadata": {},
"outputs": [],
"source": [
"# Specify the directory containing your PDFs\n",
"directory = \"data/documents\" # change to your pdf directory\n",
"\n",
"# Find and parse all PDFs in the directory\n",
"pdf_docs = load_pdf_as_docs(directory, PyPDFLoader)\n",
"\n",
"document_chunks = get_doc_chunks(pdf_docs)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7bf62c76",
"metadata": {},
"outputs": [],
"source": [
"# Set embedding\n",
"embeddings = HuggingFaceEmbeddings(model_name='BAAI/bge-base-en-v1.5') # choose the one you like\n",
"\n",
"# Set vectorstore, e.g. FAISS\n",
"texts = [\"LISA - Lithium Ion Solid-state Assistant\"]\n",
"vectorstore = FAISS.from_texts(texts, embeddings) # this is a workaround as FAISS cannot be initialized by 'FAISS(embedding_function=embeddings)', waiting for Langchain fix\n",
"# You may also use Chroma\n",
"# vectorstore = Chroma(embedding_function=embeddings)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "e5796990",
"metadata": {},
"outputs": [],
"source": [
"# Create retrievers\n",
"# Some advanced RAG, with parent document retriever, hybrid-search and rerank\n",
"\n",
"# 1. ParentDocumentRetriever. Note: this will take a long time (~several minutes)\n",
"\n",
"from langchain.storage import InMemoryStore\n",
"from langchain.retrievers import ParentDocumentRetriever\n",
"# For local storage, ref: https://stackoverflow.com/questions/77385587/persist-parentdocumentretriever-of-langchain\n",
"store = InMemoryStore()\n",
"\n",
"parent_splitter = RecursiveCharacterTextSplitter(separators=[\"\\n\\n\", \"\\n\"], chunk_size=512, chunk_overlap=128)\n",
"child_splitter = RecursiveCharacterTextSplitter(separators=[\"\\n\\n\", \"\\n\"], chunk_size=256, chunk_overlap=64)\n",
"\n",
"parent_doc_retriver = ParentDocumentRetriever(\n",
" vectorstore=vectorstore,\n",
" docstore=store,\n",
" child_splitter=child_splitter,\n",
" parent_splitter=parent_splitter,\n",
")\n",
"parent_doc_retriver.add_documents(pdf_docs)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "bc299740",
"metadata": {},
"outputs": [],
"source": [
"# 2. Hybrid search\n",
"from langchain.retrievers import BM25Retriever\n",
"\n",
"bm25_retriever = BM25Retriever.from_documents(document_chunks, k=5) # 1/2 of dense retriever, experimental value"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "2eb8bc8f",
"metadata": {},
"outputs": [],
"source": [
"# 3. Rerank\n",
"\"\"\"\n",
"Ref:\n",
"https://medium.aiplanet.com/advanced-rag-cohere-re-ranker-99acc941601c\n",
"https://github.com/langchain-ai/langchain/issues/13076\n",
"good to read:\n",
"https://teemukanstren.com/2023/12/25/llmrag-based-question-answering/\n",
"\"\"\"\n",
"from __future__ import annotations\n",
"from typing import Dict, Optional, Sequence\n",
"from langchain.schema import Document\n",
"from langchain.pydantic_v1 import Extra, root_validator\n",
"\n",
"from langchain.callbacks.manager import Callbacks\n",
"from langchain.retrievers.document_compressors.base import BaseDocumentCompressor\n",
"\n",
"from sentence_transformers import CrossEncoder\n",
"\n",
"model_name = \"BAAI/bge-reranker-large\"\n",
"\n",
"class BgeRerank(BaseDocumentCompressor):\n",
" model_name:str = model_name\n",
" \"\"\"Model name to use for reranking.\"\"\" \n",
" top_n: int = 10 \n",
" \"\"\"Number of documents to return.\"\"\"\n",
" model:CrossEncoder = CrossEncoder(model_name)\n",
" \"\"\"CrossEncoder instance to use for reranking.\"\"\"\n",
"\n",
" def bge_rerank(self,query,docs):\n",
" model_inputs = [[query, doc] for doc in docs]\n",
" scores = self.model.predict(model_inputs)\n",
" results = sorted(enumerate(scores), key=lambda x: x[1], reverse=True)\n",
" return results[:self.top_n]\n",
"\n",
"\n",
" class Config:\n",
" \"\"\"Configuration for this pydantic object.\"\"\"\n",
"\n",
" extra = Extra.forbid\n",
" arbitrary_types_allowed = True\n",
"\n",
" def compress_documents(\n",
" self,\n",
" documents: Sequence[Document],\n",
" query: str,\n",
" callbacks: Optional[Callbacks] = None,\n",
" ) -> Sequence[Document]:\n",
" \"\"\"\n",
" Compress documents using BAAI/bge-reranker models.\n",
"\n",
" Args:\n",
" documents: A sequence of documents to compress.\n",
" query: The query to use for compressing the documents.\n",
" callbacks: Callbacks to run during the compression process.\n",
"\n",
" Returns:\n",
" A sequence of compressed documents.\n",
" \"\"\"\n",
" \n",
" if len(documents) == 0: # to avoid empty api call\n",
" return []\n",
" doc_list = list(documents)\n",
" _docs = [d.page_content for d in doc_list]\n",
" results = self.bge_rerank(query, _docs)\n",
" final_results = []\n",
" for r in results:\n",
" doc = doc_list[r[0]]\n",
" doc.metadata[\"relevance_score\"] = r[1]\n",
" final_results.append(doc)\n",
" return final_results\n",
" \n",
" \n",
"from langchain.retrievers import ContextualCompressionRetriever"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "af780912",
"metadata": {},
"outputs": [],
"source": [
"# Stack all the retrievers together\n",
"from langchain.retrievers import EnsembleRetriever\n",
"# Ensemble all above\n",
"ensemble_retriever = EnsembleRetriever(retrievers=[bm25_retriever, parent_doc_retriver], weights=[0.5, 0.5])\n",
"\n",
"# Rerank\n",
"compressor = BgeRerank()\n",
"rerank_retriever = ContextualCompressionRetriever(\n",
" base_compressor=compressor, base_retriever=ensemble_retriever\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "beb9ab21",
"metadata": {},
"outputs": [],
"source": [
"## Now begin to build Q&A system\n",
"class RAGChain:\n",
" def __init__(\n",
" self, memory_key=\"chat_history\", output_key=\"answer\", return_messages=True\n",
" ):\n",
" self.memory_key = memory_key\n",
" self.output_key = output_key\n",
" self.return_messages = return_messages\n",
"\n",
" def create(self, retriver, llm):\n",
" memory = ConversationBufferWindowMemory(\n",
" memory_key=self.memory_key,\n",
" return_messages=self.return_messages,\n",
" output_key=self.output_key,\n",
" )\n",
"\n",
" # https://github.com/langchain-ai/langchain/issues/4608\n",
" conversation_chain = ConversationalRetrievalChain.from_llm(\n",
" llm=llm,\n",
" retriever=retriver,\n",
" memory=memory,\n",
" return_source_documents=True,\n",
" rephrase_question=False, # disable rephrase, for test purpose\n",
" get_chat_history=lambda x: x,\n",
" )\n",
" \n",
" return conversation_chain\n",
" \n",
" \n",
"rag_chain = RAGChain()\n",
"lisa_qa_conversation = rag_chain.create(rerank_retriever, llm)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "59159951",
"metadata": {},
"outputs": [],
"source": [
"# Now begin to ask question\n",
"question = \"Please name two common solid electrolytes.\"\n",
"result = lisa_qa_conversation({\"question\":question, \"chat_history\": []})\n",
"print(result[\"answer\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f5e3c7b5",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "d736960b",
"metadata": {},
"outputs": [],
"source": [
"# The rests are for Gradio GUI\n",
"\n",
"import gradio as gr\n",
"import time\n",
"from pathlib import Path\n",
"\n",
"# Gradio utils\n",
"def add_text(history, text):\n",
" \"\"\"Add conversation to history message.\"\"\"\n",
" history = history + [(text, None)]\n",
" yield history, \"\"\n",
"\n",
"\n",
"def bot_lisa(history):\n",
" \"\"\"Get answer from LLM.\"\"\"\n",
" result = lisa_qa_conversation(\n",
" {\n",
" \"question\": history[-1][0], # or \"query\" if RetrievalQA\n",
" \"chat_history\": history[:-1],\n",
" }\n",
" )\n",
" print(f\"Answer: {result['answer']}\")\n",
" print(f\"Source document: {result['source_documents']}\") # for debug\n",
" # Citation post-processing\n",
" answer_text = result[\"answer\"].strip()\n",
" history[-1][1] = \"\" # Fake stream, TODO: implement streaming\n",
" for character in result[\"answer\"].strip():\n",
" time.sleep(0.002)\n",
" history[-1][1] += character\n",
" yield history, \"citation place holder\"\n",
"\n",
"\n",
"def bot(history, qa_conversation):\n",
" \"\"\"Get answer from LLM.\"\"\"\n",
" # print(\"id of qa conver\", id(qa_conversation)) # for debug\n",
" if qa_conversation is None:\n",
" gr.Warning(\"Please upload a document first.\")\n",
" \n",
" result = qa_conversation(\n",
" {\n",
" \"question\": history[-1][0], # or \"query\" if RetrievalQA\n",
" \"chat_history\": history[:-1],\n",
" }\n",
" )\n",
" print(f\"Source document: {result['source_documents']}\") # for debug\n",
" history[-1][1] = \"\" # Fake stream, TODO: implement streaming\n",
" for character in result[\"answer\"].strip():\n",
" time.sleep(0.002)\n",
" history[-1][1] += character\n",
" yield history\n",
"\n",
"\n",
"# Ref: https://huggingface.co./spaces/fffiloni/langchain-chat-with-pdf\n",
"def document_changes(doc_path):#, repo_id):\n",
" if doc_path is None:\n",
" gr.Warning(\"Please choose a document first and wait until uploaded.\")\n",
" return \"Please choose a document and wait until uploaded.\", None # for langchain_status, qa_conversation\n",
" \n",
" print(\"now reading document\")\n",
" print(f\"file is located at {doc_path[0]}\")\n",
" \n",
" file_extension = Path(doc_path[0]).suffix\n",
" if file_extension == \".pdf\":\n",
" pdf_docs = load_pdf_as_docs(doc_path[0])\n",
" document_chunks = get_doc_chunks(pdf_docs)\n",
" elif file_extension == \".xml\":\n",
" raise\n",
" # documents = load_xml_as_docs(doc_path[0])\n",
" \n",
" print(\"now creating vectordatabase\")\n",
" \n",
" texts = [\"LISA - Lithium Ion Solid-state Assistant\"]\n",
" vectorstore = FAISS.from_texts(texts, embeddings)\n",
"\n",
" store = InMemoryStore()\n",
"\n",
" parent_splitter = RecursiveCharacterTextSplitter(separators=[\"\\n\\n\", \"\\n\"], chunk_size=512, chunk_overlap=256)\n",
" child_splitter = RecursiveCharacterTextSplitter(separators=[\"\\n\\n\", \"\\n\"], chunk_size=256, chunk_overlap=128)\n",
"\n",
" parent_doc_retriver = ParentDocumentRetriever(\n",
" vectorstore=vectorstore,\n",
" docstore=store,\n",
" child_splitter=child_splitter,\n",
" parent_splitter=parent_splitter,\n",
" )\n",
" parent_doc_retriver.add_documents(pdf_docs)\n",
"\n",
" bm25_retriever = BM25Retriever.from_documents(document_chunks, k=5) # 1/2 of dense retriever, experimental value\n",
"\n",
" # Ensemble all above\n",
" ensemble_retriever = EnsembleRetriever(retrievers=[bm25_retriever, parent_doc_retriver], weights=[0.5, 0.5])\n",
"\n",
" compressor = BgeRerank()\n",
" rerank_retriever = ContextualCompressionRetriever(\n",
" base_compressor=compressor, base_retriever=ensemble_retriever\n",
" )\n",
"\n",
" rag_chain = RAGChain()\n",
" qa_conversation = rag_chain.create(rerank_retriever, llm)\n",
" \n",
" print(\"now getting llm model\")\n",
" \n",
"\n",
" file_name = Path(doc_path[0]).name # First file\n",
" return f\"Ready for {file_name}\", qa_conversation\n",
"\n",
"\n",
"# Main gradio UI\n",
"def main():\n",
" # Gradio interface\n",
" with gr.Blocks() as demo:\n",
" ######################################################################\n",
" # LISA chat tab\n",
"\n",
" # Title info\n",
" gr.Markdown(\"## LISA\")\n",
" gr.Markdown(\"Q&A system with RAG.\")\n",
"\n",
" with gr.Tab(\"LISA\"):\n",
" # Chatbot\n",
" chatbot = gr.Chatbot(\n",
" [],\n",
" elem_id=\"chatbot\",\n",
" label=\"Document Assistant (chat-history context is not supported at the moment, fixing...)\",\n",
" bubble_full_width=False,\n",
" show_copy_button=True,\n",
" likeable=True,\n",
" ) # .style(height=750)\n",
" with gr.Row():\n",
" with gr.Column(scale=80):\n",
" user_txt = gr.Textbox(\n",
" label=\"Question\",\n",
" placeholder=\"Type question and press Enter\",\n",
" ) # .style(container=False)\n",
" with gr.Column(scale=10):\n",
" submit_btn = gr.Button(\"Submit\", variant=\"primary\")\n",
" with gr.Column(scale=10):\n",
" clear_btn = gr.Button(\"Clear\", variant=\"stop\")\n",
" # Reference (citations)\n",
" with gr.Accordion(\"Advanced - Document references\", open=False):\n",
" doc_citation = gr.Markdown()\n",
" # alternative: https://www.gradio.app/guides/creating-a-chatbot-fast\n",
" gr.Examples(\n",
" examples=[\n",
" \"Please name two common solid electrolytes.\",\n",
" \"Please name two common oxide solid electrolytes.\",\n",
" \"Please tell me what is solid-state battery.\",\n",
" \"How to synthesize gc-LPSC?\",\n",
" \"Please tell me the purpose of Kadi4Mat.\",\n",
" \"Who is working on Kadi4Mat?\",\n",
" \"Can you recommend a paper to get a deeper understanding of Kadi4Mat?\",\n",
" # \"How to synthesize gc-LPSC, e.g., glass-ceramic Li5.5PS4.5Cl1.5?\",\n",
" ],\n",
" inputs=user_txt,\n",
" outputs=chatbot,\n",
" fn=add_text,\n",
" # cache_examples=True,\n",
" )\n",
"\n",
" # Manage functions\n",
" user_txt.submit(add_text, [chatbot, user_txt], [chatbot, user_txt]).then(\n",
" bot_lisa, chatbot, [chatbot, doc_citation]\n",
" )\n",
"\n",
" submit_btn.click(\n",
" add_text,\n",
" [chatbot, user_txt],\n",
" [chatbot, user_txt],\n",
" # concurrency_limit=8,\n",
" queue=False,\n",
" ).then(bot_lisa, chatbot, [chatbot, doc_citation])\n",
"\n",
" clear_btn.click(lambda: None, None, chatbot, queue=False)\n",
"\n",
" ######################################################################\n",
"\n",
" ######################################################################\n",
" # Document-based QA\n",
"\n",
" with gr.Tab(\"Document-based Q&A\"):\n",
" qa_conversation = gr.State()\n",
" \n",
" with gr.Row():\n",
" with gr.Column(scale=3, variant=\"load_file_panel\"):\n",
" with gr.Row():\n",
" gr.HTML(\n",
" \"Upload a pdf/xml file, click the Load file button and when everything is ready, you can start asking questions about the document.\"\n",
" )\n",
" with gr.Row():\n",
" uploaded_doc = gr.File(\n",
" label=\"Upload pdf/xml file (single)\",\n",
" file_count=\"multiple\", # For better looking, but only support 1 file\n",
" file_types=[\".pdf\", \".xml\"],\n",
" type=\"filepath\",\n",
" height=100,\n",
" )\n",
"\n",
" with gr.Row():\n",
" langchain_status = gr.Textbox(\n",
" label=\"Status\", placeholder=\"\", interactive=False\n",
" )\n",
" load_document = gr.Button(\"Load file\")\n",
"\n",
" with gr.Column(scale=7, variant=\"chat_panel\"):\n",
" chatbot = gr.Chatbot(\n",
" [],\n",
" elem_id=\"chatbot\",\n",
" # label=\"Document Assistant (chat-history context is not supported at the moment, fixing...)\",\n",
" label=\"Document Assistant (chat-history context is not supported at the moment, fixing...)\",\n",
" show_copy_button=True,\n",
" likeable=True,\n",
" ) # .style(height=350)\n",
" docqa_question = gr.Textbox(\n",
" label=\"Question\",\n",
" placeholder=\"Type question and press Enter/click Submit\",\n",
" )\n",
" with gr.Row():\n",
" with gr.Column(scale=50):\n",
" docqa_submit_btn = gr.Button(\"Submit\", variant=\"primary\")\n",
" with gr.Column(scale=50):\n",
" docqa_clear_btn = gr.Button(\"Clear\", variant=\"stop\")\n",
" \n",
" gr.Examples(\n",
" examples=[\n",
" \"Summarize the paper\",\n",
" \"Summarize the paper in 3 bullet points\",\n",
" \"What are the contributions of this paper\",\n",
" \"Explain the practical implications of this paper\",\n",
" \"Methods used in this paper\",\n",
" \"What data has been used in this paper\",\n",
" \"Results of the paper\",\n",
" \"Conclusions from the paper\",\n",
" \"Limitations of this paper\",\n",
" \"Future works suggested in this paper\",\n",
" ],\n",
" inputs=docqa_question,\n",
" outputs=chatbot,\n",
" fn=add_text,\n",
" # cache_examples=True,\n",
" )\n",
"\n",
" load_document.click(\n",
" document_changes,\n",
" inputs=[uploaded_doc], # , repo_id],\n",
" outputs=[langchain_status, qa_conversation],#, docqa_db, docqa_retriever],\n",
" queue=False,\n",
" )\n",
" \n",
" docqa_question.submit(add_text, [chatbot, docqa_question], [chatbot, docqa_question]).then(\n",
" bot, [chatbot, qa_conversation], chatbot\n",
" )\n",
" docqa_submit_btn.click(add_text, [chatbot, docqa_question], [chatbot, docqa_question]).then(\n",
" bot, [chatbot, qa_conversation], chatbot\n",
" )\n",
"\n",
" gr.Markdown(\"*Notes: The model may produce incorrect statements. Users should treat these outputs as suggestions or starting points, not as definitive or accurate facts.\")\n",
"\n",
" ######################################################################\n",
"\n",
" demo.queue().launch(share=True)\n",
" \n",
" \n",
"main()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e2864a11",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "lisa",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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