Create app.py

app.py

@@ -0,0 +1,217 @@
+from functools import lru_cache
+import time, aiohttp, asyncio, json, os, multiprocessing, torch, \
+    requests, xmltodict, fitz, io
+from minivectordb.embedding_model import EmbeddingModel
+from minivectordb.vector_database import VectorDatabase
+from text_util_en_pt.cleaner import structurize_text, detect_language, Language
+import gradio as gr
+
+torch.set_num_threads(2)
+
+openrouter_key = os.environ.get("OPENROUTER_KEY")
+model = EmbeddingModel(use_quantized_onnx_model=True)
+
+def convert_xml_to_json(xml):
+    return xmltodict.parse(xml)
+
+def clean_title(title):
+    title = title.replace('\n', ' ')
+    while '  ' in title:
+        title = title.replace('  ', ' ')
+    return title
+
+@lru_cache(maxsize=500)
+def fetch_arxiv_links(query, max_results=5):
+    url = f'http://export.arxiv.org/api/query?search_query=all:{query}&start=0&max_results={max_results}'
+    response = requests.get(url)
+    json_response = convert_xml_to_json(response.text)
+
+    # Return a list of titles and links, and pdf links
+    entries = []
+    for entry in json_response['feed']['entry']:
+
+        title = entry['title']
+        id = entry['id'].split('/abs/')[-1]
+
+        link = f'http://arxiv.org/abs/{id}'
+        pdf_link = f'http://arxiv.org/pdf/{id}.pdf'
+
+        entries.append({
+            'title': clean_title(title),
+            'link': link,
+            'pdf_link': pdf_link
+        })
+    return entries
+
+def download_pdf_from_link(link):
+    # Download the file and hold it in memory
+    response = requests.get(link)
+    return io.BytesIO(response.content)
+
+@lru_cache(maxsize=100)
+def read_remote_pdf(pdf_metadata):
+    pdf_metadata = json.loads(pdf_metadata)
+
+    link = pdf_metadata['pdf_link']
+    title = pdf_metadata['title']
+
+    pdf_content = download_pdf_from_link(link)
+    pdf_file = fitz.open("pdf", pdf_content.read())
+    text_content = [page.get_text() for page in pdf_file]
+    pdf_file.close()
+    del pdf_file
+    return {'title': title, 'text': '\n'.join(text_content)}
+
+def fetch_data_from_pdfs(links):
+    links = [ json.dumps(link) for link in links ]
+    with multiprocessing.Pool(10) as pool:
+        pdf_metadata = pool.map(read_remote_pdf, links)
+    return pdf_metadata
+
+def index_and_search(query, pdf_metadata):
+    start = time.time()
+    query_embedding = model.extract_embeddings(query)
+
+    # Indexing
+    vector_db = VectorDatabase()
+
+    sentence_counter = 1
+
+    for pdf_data in pdf_metadata:
+        text = pdf_data['text']
+        title = pdf_data['title']
+
+        sentences = [ s['sentence'] for s in structurize_text(text)]
+
+        for sentence in sentences:
+            sentence_embedding = model.extract_embeddings(sentence)
+            vector_db.store_embedding(
+                sentence_counter,
+                sentence_embedding,
+                {
+                    'sentence': sentence,
+                    'title': title
+                }
+            )
+            sentence_counter += 1
+    
+    embedding_time = time.time() - start
+
+    # Retrieval
+    start = time.time()
+    search_results = vector_db.find_most_similar(query_embedding, k = 15)
+    search_metadata = search_results[2]
+    retrieval_time = time.time() - start
+
+    retrieved_contents = {}
+    for ret_cont in search_metadata:
+        title = ret_cont['title']
+        if title not in retrieved_contents:
+            retrieved_contents[title] = []
+        retrieved_contents[title].append(ret_cont['sentence'])
+    
+    retrieved_contents = {k: '\n'.join(v) for k, v in retrieved_contents.items()}
+
+    return retrieved_contents, embedding_time, retrieval_time
+
+def retrieval_pipeline(query, question):
+    start = time.time()
+    links = fetch_arxiv_links(query)
+    websearch_time = time.time() - start
+
+    start = time.time()
+    pdf_metadata = fetch_data_from_pdfs(links)
+    webcrawl_time = time.time() - start
+
+    retrieved_contents, embedding_time, retrieval_time = index_and_search(question, pdf_metadata)
+
+    return retrieved_contents, websearch_time, webcrawl_time, embedding_time, retrieval_time, links
+
+async def predict(message, history):
+    # message is in format: "Search: <query>; Question: <question>"
+    # we need to parse both parts into variables
+    message = message.split(';')
+
+    query = message[0].split(':')[-1].strip()
+    question = message[1].split(':')[-1].strip()
+
+    retrieved_contents, websearch_time, webcrawl_time, embedding_time, retrieval_time, links = retrieval_pipeline(query, question)
+
+    if detect_language(message) == Language.ptbr:
+        context = ""
+        for title, content in retrieved_contents.items():
+            context += f'Artigo "{title}"\nConteúdo:\n{content}\n\n'
+        prompt = f'{context.strip()}\n\nBaseado nos conteúdos dos artigos, responda: "{message}"\n\nPor favor, mencione a fonte da sua resposta.'
+    else:
+        context = ""
+        for title, content in retrieved_contents.items():
+            context += f'Article "{title}"\nContent:\n{content}\n\n'
+        prompt = f'{context.strip()}\n\nBased on the article\'s contents, answer: "{message}"\n\nPlease, mention the source of your answer.'
+
+    print(prompt)
+
+    url = "https://openrouter.ai/api/v1/chat/completions"
+    headers = { "Content-Type": "application/json",
+                "Authorization": f"Bearer {openrouter_key}" }
+    body = { "stream": True,
+             "models": [
+                    "mistralai/mistral-7b-instruct:free",
+                    "openchat/openchat-7b:free"
+             ],
+             "route": "fallback",
+             "max_tokens": 1024,
+             "messages": [
+                 {"role": "user", "content": prompt}
+             ] }
+
+    full_response = ""
+    async with aiohttp.ClientSession() as session:
+        async with session.post(url, headers=headers, json=body) as response:
+            buffer = ""  # A buffer to hold incomplete lines of data
+            async for chunk in response.content.iter_any():
+                buffer += chunk.decode()
+                while "\n" in buffer:  # Process as long as there are complete lines in the buffer
+                    line, buffer = buffer.split("\n", 1)
+
+                    if line.startswith("data: "):
+                        event_data = line[len("data: "):]
+                        if event_data != '[DONE]':
+                            try:
+                                current_text = json.loads(event_data)['choices'][0]['delta']['content']
+                                full_response += current_text
+                                yield full_response
+                                await asyncio.sleep(0.01)
+                            except Exception:
+                                try:
+                                    current_text = json.loads(event_data)['choices'][0]['text']
+                                    full_response += current_text
+                                    yield full_response
+                                    await asyncio.sleep(0.01)
+                                except Exception:
+                                    pass
+    
+    final_metadata_block = ""
+
+    final_metadata_block += f"Links visited:\n"
+    for link in links:
+        final_metadata_block += f"{link['title']} ({link['link']})\n"
+    final_metadata_block += f"\nWeb search time: {websearch_time:.4f} seconds\n"
+    final_metadata_block += f"\nText extraction: {webcrawl_time:.4f} seconds\n"
+    final_metadata_block += f"\nEmbedding time: {embedding_time:.4f} seconds\n"
+    final_metadata_block += f"\nRetrieval from VectorDB time: {retrieval_time:.4f} seconds"
+
+    yield f"{full_response}\n\n{final_metadata_block}"
+
+gr.ChatInterface(
+    predict,
+    title="Automated Arxiv Paper Search and Question Answering",
+    description="Provide a search term and a question to find relevant papers and answer questions about them.",
+    retry_btn=None,
+    undo_btn=None,
+    examples=[
+        'Search: RAG LLMS; Question: What are some challenges of implementing a system of RAG with LLMS ?',
+        'Search: LLM Self-Play; Question: What are the benefits of using self-play with LLMS?',
+        'Search: Brazil Tax Rate; Question: Why does Brazil has a high tax rate?',
+        'Search: Stomach medicine; Question: Can stomach medicine cause genetic mutations?'
+    ]
+).launch()