knotaru-nlp 0.1.1

Modular NLP primitives for Knotaru — Embedding, Indexing, Chat, Memory

knotaru-nlp

Modular NLP primitives for Knotaru. Each module is independently installable and built around abstract base classes so implementations can be swapped without changing application code.

Modules

Module	Purpose	Extra
embedding	Convert text → dense vectors	[embedding]
indexing	Store and query vectors	[indexing]
chat	LLM completions	[chat]

Installation

# All modules
pip install "knotaru-nlp[all]"

# Individual modules
pip install "knotaru-nlp[embedding]"
pip install "knotaru-nlp[indexing]"
pip install "knotaru-nlp[chat]"

Quick Start

from knotaru_nlp.embedding import OpenAIEmbedder
from knotaru_nlp.indexing import MilvusIndex
from knotaru_nlp.chat import OpenAIChat

# Embedding
embedder = OpenAIEmbedder(api_key="sk-...")
vectors = await embedder.embed(["Hello world", "Knotaru rocks"])

# Indexing (Milvus test)
index = MilvusIndex(dim=1536)
await index.upsert([{"id": "1", "vector": vectors[0], "text": "Hello world"}])
results = await index.search(vectors[0], top_k=5)

# Chat
chat = OpenAIChat(api_key="sk-...")
response = await chat.complete([{"role": "user", "content": "Hello!"}])

Design Principles

• Abstract-first: Every module exports an abstract base class. Swap implementations by changing one line.
• Async-native: All I/O methods are async.
• Zero mandatory deps: Core package installs with no heavy dependencies. Add only what you use via extras.
• Typed: Full mypy --strict compliance.