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Running on Zero
Running on Zero
| import spaces | |
| import nest_asyncio | |
| nest_asyncio.apply() | |
| import os | |
| import subprocess | |
| import gradio as gr | |
| from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings | |
| from llama_index.llms.groq import Groq | |
| from llama_index.embeddings.huggingface import HuggingFaceEmbedding | |
| from llama_index.vector_stores.qdrant import QdrantVectorStore | |
| from qdrant_client import QdrantClient | |
| from qdrant_client.models import Distance, VectorParams | |
| def zero_gpu_dummy(): | |
| pass | |
| Settings.llm = Groq(model="openai/gpt-oss-20b", api_key=os.environ.get("GROQ_API_KEY")) | |
| Settings.embed_model = HuggingFaceEmbedding( | |
| model_name="BAAI/bge-large-en-v1.5", | |
| device="cpu" | |
| ) | |
| Settings.chunk_size = 512 | |
| Settings.chunk_overlap = 50 | |
| client = QdrantClient(location=":memory:") | |
| client.create_collection( | |
| collection_name="tech_docs", | |
| vectors_config=VectorParams(size=1024, distance=Distance.COSINE), | |
| ) | |
| vector_store = QdrantVectorStore(client=client, collection_name="tech_docs") | |
| index = VectorStoreIndex.from_vector_store(vector_store=vector_store) | |
| TARGET_REPO = "https://github.com/tiangolo/fastapi.git" | |
| clone_path = "/tmp/fastapi_docs" | |
| if not os.path.exists(clone_path): | |
| print(f"Cloning {TARGET_REPO}...") | |
| subprocess.run(["git", "clone", "--depth", "1", TARGET_REPO, clone_path]) | |
| print("Indexing repository documentation...") | |
| try: | |
| documents = SimpleDirectoryReader( | |
| input_dir=clone_path, | |
| required_exts=[".md", ".mdx"], | |
| recursive=True | |
| ).load_data() | |
| for doc in documents[:200]: | |
| index.insert(doc) | |
| print("GitHub repository indexed successfully!") | |
| except Exception as e: | |
| print(f"Error indexing GitHub repo: {e}") | |
| query_engine = index.as_query_engine(streaming=False) | |
| def chat_with_docs(message, history, file_obj): | |
| global index, query_engine | |
| if file_obj is not None: | |
| try: | |
| pdf_docs = SimpleDirectoryReader(input_files=[file_obj.name]).load_data() | |
| for doc in pdf_docs: | |
| index.insert(doc) | |
| query_engine = index.as_query_engine(streaming=False) | |
| except Exception as e: | |
| return f"Error processing uploaded PDF: {str(e)}" | |
| response = query_engine.query(message) | |
| return str(response) | |
| demo = gr.ChatInterface( | |
| fn=chat_with_docs, | |
| title="📚 TechDocs-Knowledge-Engine", | |
| description="Ask technical questions about the indexed codebase or upload your own technical PDF using the file attachment icon below.", | |
| additional_inputs=[ | |
| gr.File(label="Upload Technical PDF (Optional)", file_types=[".pdf"]) | |
| ], | |
| textbox=gr.Textbox(placeholder="Ask a technical question about code or architecture...", container=False, scale=7) | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch( | |
| server_name="0.0.0.0", | |
| server_port=7860, | |
| ssr_mode=False, | |
| auth=("user", os.environ.get("ACCESS_CODE")) | |
| ) |