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| # Install required packages first: | |
| # pip install langchain huggingface_hub pandas faiss-cpu numpy transformers torch accelerate bitsandbytes gradio | |
| import pandas as pd | |
| import numpy as np | |
| import gradio as gr | |
| import torch | |
| from langchain.vectorstores import FAISS | |
| from langchain.embeddings import HuggingFaceEmbeddings | |
| from langchain.chains import RetrievalQA | |
| from langchain.prompts import PromptTemplate | |
| from langchain.llms import HuggingFacePipeline | |
| from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline | |
| from huggingface_hub import InferenceClient | |
| # Configuration for both components | |
| EMBEDDING_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2" | |
| LLM_MODEL_NAME = "EleutherAI/gpt-neo-1.3B" | |
| CHAT_MODEL_NAME = "PygmalionAI/pygmalion-1.3b" | |
| # ------ Chat Component ------ | |
| chat_client = InferenceClient(CHAT_MODEL_NAME) | |
| def chat_respond( | |
| message: str, | |
| history: list[list[str]], | |
| system_message: str, | |
| max_tokens: int, | |
| temperature: float, | |
| top_p: float, | |
| ): | |
| messages = [{"role": "system", "content": system_message}] | |
| for human, assistant in history: | |
| messages.extend([ | |
| {"role": "user", "content": human}, | |
| {"role": "assistant", "content": assistant} | |
| ]) | |
| messages.append({"role": "user", "content": message}) | |
| response = "" | |
| for chunk in chat_client.chat_completion( | |
| messages, | |
| max_tokens=max_tokens, | |
| stream=True, | |
| temperature=temperature, | |
| top_p=top_p, | |
| ): | |
| token = chunk.choices[0].delta.content | |
| response += token | |
| yield response | |
| # ------ RAG Component ------ | |
| def load_embeddings(csv_path): | |
| df = pd.read_csv(csv_path) | |
| if 'Cleaned_Ideas' not in df.columns or 'Embeddings' not in df.columns: | |
| raise ValueError("CSV must contain 'Cleaned_Ideas' and 'Embeddings' columns") | |
| df['Embeddings'] = df['Embeddings'].apply( | |
| lambda x: np.fromstring( | |
| x.strip("[]").replace("\n", ""), | |
| sep=", ", | |
| dtype=np.float32 | |
| ) | |
| ) | |
| expected_dim = 768 | |
| valid_embeddings = df['Embeddings'].apply(lambda x: len(x) == expected_dim) | |
| if not valid_embeddings.all(): | |
| invalid_count = len(df) - valid_embeddings.sum() | |
| raise ValueError(f"{invalid_count} entries have invalid embedding dimensions") | |
| return df['Cleaned_Ideas'].tolist(), np.array(df['Embeddings'].tolist()) | |
| # Fixed here → ↑ | |
| def setup_rag_system(): | |
| # Load data and create vector store | |
| texts, embeddings = load_embeddings("ideas_with_embeddings.csv") | |
| embedding_model = HuggingFaceEmbeddings( | |
| model_name=EMBEDDING_MODEL_NAME, | |
| model_kwargs={'device': 'cpu'}, | |
| encode_kwargs={'normalize_embeddings': True} | |
| ) | |
| vector_store = FAISS.from_embeddings( | |
| text_embeddings=list(zip(texts, embeddings)), | |
| embedding=embedding_model, | |
| normalize_L2=True | |
| ) | |
| # Load LLM | |
| tokenizer = AutoTokenizer.from_pretrained(LLM_MODEL_NAME) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| LLM_MODEL_NAME, | |
| device_map="auto", | |
| torch_dtype=torch.float16, | |
| trust_remote_code=True | |
| ) | |
| pipe = pipeline( | |
| "text-generation", | |
| model=model, | |
| tokenizer=tokenizer, | |
| max_new_tokens=250, | |
| temperature=0.7, | |
| top_p=0.9, | |
| repetition_penalty=1.1, | |
| do_sample=True, | |
| return_full_text=False, | |
| pad_token_id=tokenizer.eos_token_id | |
| ) | |
| # Setup QA chain | |
| template = """<context> | |
| {context} | |
| </context> | |
| <question> | |
| {question} | |
| </question> | |
| Analyze the philosophical concept using ONLY the provided context. Follow these steps: | |
| 1. Identify key elements from the context | |
| 2. Explain relationships between concepts | |
| 3. Highlight any mentioned philosophical implications | |
| If context is insufficient, state: "This requires deeper analysis beyond current knowledge." | |
| Answer:""" | |
| qa_chain = RetrievalQA.from_chain_type( | |
| llm=HuggingFacePipeline(pipeline=pipe), | |
| chain_type="stuff", | |
| retriever=vector_store.as_retriever( | |
| search_type="similarity_score_threshold", | |
| search_kwargs={ | |
| "k": 6, | |
| "score_threshold": 0.55, | |
| "include_metadata": True | |
| } | |
| ), | |
| return_source_documents=True, | |
| chain_type_kwargs={ | |
| "prompt": PromptTemplate( | |
| template=template, | |
| input_variables=["context", "question"], | |
| template_format="f-string" | |
| ) | |
| }, | |
| verbose=True | |
| ) | |
| return qa_chain | |
| # ------ Unified Interface ------ | |
| def create_interface(): | |
| qa_chain = setup_rag_system() | |
| with gr.Blocks(title="Philosophical AI Assistant") as demo: | |
| gr.Markdown("# 🤖 AI Philosophy Assistant") | |
| gr.Markdown("Combining conversational AI with philosophical knowledge base") | |
| # Chat Tab | |
| with gr.Tab("Chat"): | |
| gr.ChatInterface( | |
| fn=chat_respond, | |
| additional_inputs=[ | |
| gr.Textbox(value="You are a philosophy expert assistant.", label="System Role"), | |
| gr.Slider(1, 2048, value=512, label="Max Tokens"), | |
| gr.Slider(0.1, 4.0, value=0.7, label="Temperature"), | |
| gr.Slider(0.1, 1.0, value=0.95, label="Top-p") | |
| ], | |
| chatbot=gr.Chatbot(height=500, avatar_images=("👤", "🤖")), | |
| textbox=gr.Textbox(placeholder="Ask me anything about philosophy...", container=False, scale=7), | |
| ) | |
| # Research Assistant Tab | |
| with gr.Tab("Research Assistant"): | |
| with gr.Row(): | |
| with gr.Column(scale=3): | |
| query_input = gr.Textbox(label="Research Query", lines=3) | |
| submit_btn = gr.Button("Analyze", variant="primary") | |
| with gr.Column(scale=2): | |
| sources_output = gr.Markdown(label="Source Context") | |
| response_output = gr.Markdown(label="Analysis Results") | |
| def process_query(query): | |
| try: | |
| result = qa_chain({"query": query}) | |
| response = result['result'].split("Answer:")[-1].strip() | |
| sources = "\n".join( | |
| f"**Source {i+1}:** {doc.page_content[:200]}..." | |
| for i, doc in enumerate(result['source_documents'][:3]) | |
| ) | |
| return response, sources | |
| except Exception as e: | |
| return f"Error: {str(e)}", "" | |
| submit_btn.click( | |
| process_query, | |
| inputs=query_input, | |
| outputs=[response_output, sources_output] | |
| ) | |
| return demo | |
| if __name__ == "__main__": | |
| demo = create_interface() | |
| demo.launch(share=True) |