TxtAI MCP Server

""" RAG Quick Start Easy to use way to get started with RAG using YOUR data For a complete application see this: https://github.com/neuml/rag TxtAI has many example notebooks covering everything the framework provides Examples: https://neuml.github.io/txtai/examples Install TxtAI pip install txtai[pipeline-data] """ # pylint: disable=C0103 import os from txtai import Embeddings, RAG from txtai.pipeline import Textractor # Step 1: Collect files from local directory # # Defaults to "data". Set to whereever your files are. path = "data" files = [os.path.join(path, f) for f in os.listdir(path) if os.path.isfile(os.path.join(path, f))] # Step 2: Text Extraction / Chunking # # Using section based chunking here. More complex options available such as semantic chunking, iterative chunking etc. # Documentation: https://neuml.github.io/txtai/pipeline/data/textractor # Supports Chonkie chunking as well: https://docs.chonkie.ai/oss/chunkers/overview textractor = Textractor(backend="docling", sections=True) chunks = [] for f in files: for chunk in textractor(f): chunks.append((f, chunk)) # Step 3: Build an embeddings database # # The `path` parameter sets the vector embeddings model. Supports Hugging Face models, llama.cpp, Ollama, vLLM and more. # Documentation: https://neuml.github.io/txtai/embeddings/ embeddings = Embeddings(content=True, path="Qwen/Qwen3-Embedding-0.6B", maxlength=2048) embeddings.index(chunks) # Step 4: Create RAG pipeline # # Combines an embeddings database and an LLM. # Supports Hugging Face models, llama.cpp, Ollama, vLLM and more # Documentation: https://neuml.github.io/txtai/pipeline/text/rag # User prompt template template = """ Answer the following question using the provided context. Question: {question} Context: {context} """ rag = RAG( embeddings, "Qwen/Qwen3-0.6B", system="You are a friendly assistant", template=template, output="flatten", ) question = "Summarize the main advancements made by BERT" print(rag(question, maxlength=2048, stripthink=True))