krira-augment 2.1.13

Production-grade document chunking library for RAG systems - Rust-powered Python library

Krira Augment Presents Krira Chunker(beta)

High-Performance Rust Chunking Engine for RAG Pipelines

Process gigabytes of text in seconds. 40x faster than LangChain with O(1) memory usage.

---

Installation

pip install krira-augment

---

Quick Usage

from krira_augment.krira_chunker import Pipeline, PipelineConfig, SplitStrategy

config = PipelineConfig(
    chunk_size=512,
    strategy=SplitStrategy.SMART,
    clean_html=True,
    clean_unicode=True,
)

pipeline = Pipeline(config=config)

result = pipeline.process("sample.csv", output_path="output.jsonl")

print(result)
print(f"Chunks Created: {result.chunks_created}")
print(f"Execution Time: {result.execution_time:.2f}s")
print(f"Throughput: {result.mb_per_second:.2f} MB/s")
print(f"Preview: {result.preview_chunks[:3]}")

---

Performance Benchmark

Processing 42.4 million chunks in 105 seconds (51.16 MB/s).

============================================================
✅ KRIRA AUGMENT - Processing Complete
============================================================
📊 Chunks Created:  42,448,765
⏱️  Execution Time:  113.79 seconds
🚀 Throughput:      47.51 MB/s
📁 Output File:     output.jsonl
============================================================

📝 Preview (Top 3 Chunks):
------------------------------------------------------------
[1] event_time,event_type,product_id,category_id,category_code,brand,price,user_id,user_session
[2] 2019-10-01 00:00:00 UTC,view,44600062,2103807459595387724,,shiseido,35.79,541312140,72d76fde-8bb3-4e00-8c23-a032dfed738c
[3] 2019-10-01 00:00:00 UTC,view,3900821,2053013552326770905,appliances.environment.water_heater...

---

Krira-Chunker Architecture

---

Working of Krira-Chunker

---

Complete Example: Local (ChromaDB) - FREE

No API keys required. Runs entirely on your machine.

pip install sentence-transformers chromadb

from krira_augment.krira_chunker import Pipeline, PipelineConfig
from sentence_transformers import SentenceTransformer
import chromadb
import json

# Step 1: Chunk the file (Rust Core)
config = PipelineConfig(chunk_size=512, chunk_overlap=50)
pipeline = Pipeline(config=config)
result = pipeline.process("sample.csv", output_path="chunks.jsonl")

print(f"Chunks Created: {result.chunks_created}")
print(f"Execution Time: {result.execution_time:.2f}s")
print(f"Throughput: {result.mb_per_second:.2f} MB/s")
print(f"Preview: {result.preview_chunks[:3]}")

# Step 2: Embed and store (Local)
print("Loading model...")
model = SentenceTransformer('all-MiniLM-L6-v2')
client = chromadb.Client()
# Note: In new versions of Chroma, use get_or_create_collection
collection = client.get_or_create_collection("my_rag_db")

with open("chunks.jsonl", "r") as f:
    for line_num, line in enumerate(f, 1):
        chunk = json.loads(line)
        embedding = model.encode(chunk["text"])
        
        # Handle empty metadata
        meta = chunk.get("metadata")
        
        collection.add(
            ids=[f"chunk_{line_num}"],
            embeddings=[embedding.tolist()],
            metadatas=[meta] if meta else None,
            documents=[chunk["text"]]
        )
        
        if line_num % 100 == 0:
            print(f"Processed {line_num} chunks...")

print("Done! All chunks stored in ChromaDB.")

---

Cloud Integrations (OpenAI, Pinecone, Cohere)

If you have API keys, you can swap Step 2 with these integrations:

OpenAI + Pinecone

pip install openai pinecone-client

from openai import OpenAI
from pinecone import Pinecone

# API Keys
OPENAI_API_KEY = "sk-..." 
PINECONE_API_KEY = "pcone-..."
PINECONE_INDEX_NAME = "my-rag"

client = OpenAI(api_key=OPENAI_API_KEY)
pc = Pinecone(api_key=PINECONE_API_KEY)
index = pc.Index(PINECONE_INDEX_NAME)

with open("chunks.jsonl", "r") as f:
    for line_num, line in enumerate(f, 1):
        chunk = json.loads(line)
        
        response = client.embeddings.create(
            input=chunk["text"],
            model="text-embedding-3-small"
        )
        embedding = response.data[0].embedding
        
        index.upsert(vectors=[(f"chunk_{line_num}", embedding, chunk.get("metadata", {}))])
        
        if line_num % 100 == 0:
            print(f"Processed {line_num} chunks...")

OpenAI + Qdrant

pip install openai qdrant-client

from openai import OpenAI
from qdrant_client import QdrantClient
from qdrant_client.models import PointStruct

client = OpenAI(api_key="sk-...")
qdrant = QdrantClient(url="https://xyz.qdrant.io", api_key="qdrant-...")

with open("chunks.jsonl", "r") as f:
    for line_num, line in enumerate(f, 1):
        chunk = json.loads(line)
        response = client.embeddings.create(input=chunk["text"], model="text-embedding-3-small")
        embedding = response.data[0].embedding
        qdrant.upsert(collection_name="my-chunks", points=[PointStruct(id=line_num, vector=embedding, payload=chunk.get("metadata", {}))])
        
        if line_num % 100 == 0:
            print(f"Processed {line_num} chunks...")

OpenAI + Weaviate

pip install openai weaviate-client

import weaviate
import weaviate.classes as wvc
from openai import OpenAI

# Connect to Weaviate Cloud
client_w = weaviate.connect_to_wcs(
    cluster_url="https://xyz.weaviate.network",
    auth_credentials=weaviate.auth.AuthApiKey("weaviate-...")
)
client_o = OpenAI(api_key="sk-...")

# Get collection
collection = client_w.collections.get("Chunk")

with open("chunks.jsonl", "r") as f:
    for line_num, line in enumerate(f, 1):
        chunk = json.loads(line)
        response = client_o.embeddings.create(input=chunk["text"], model="text-embedding-3-small")
        embedding = response.data[0].embedding
        
        # Insert with vector
        collection.data.insert(
            properties={"text": chunk["text"], "metadata": str(chunk.get("metadata", {}))},
            vector=embedding
        )
        
        if line_num % 100 == 0:
            print(f"Processed {line_num} chunks...")

Cohere + Pinecone

pip install cohere pinecone-client

import cohere
from pinecone import Pinecone

co = cohere.Client("co-...")
pc = Pinecone(api_key="pcone-...")
index = pc.Index("my-rag")

with open("chunks.jsonl", "r") as f:
    for line_num, line in enumerate(f, 1):
        chunk = json.loads(line)
        response = co.embed(texts=[chunk["text"]], model="embed-english-v3.0")
        embedding = response.embeddings[0]
        index.upsert(vectors=[(f"chunk_{line_num}", embedding, chunk.get("metadata", {}))])
        
        if line_num % 100 == 0:
            print(f"Processed {line_num} chunks...")

Cohere + Qdrant

pip install cohere qdrant-client

import cohere
from qdrant_client import QdrantClient
from qdrant_client.models import PointStruct

co = cohere.Client("co-...")
qdrant = QdrantClient(url="https://xyz.qdrant.io", api_key="qdrant-...")

with open("chunks.jsonl", "r") as f:
    for line_num, line in enumerate(f, 1):
        chunk = json.loads(line)
        response = co.embed(texts=[chunk["text"]], model="embed-english-v3.0")
        embedding = response.embeddings[0]
        qdrant.upsert(
            collection_name="my-chunks",
            points=[PointStruct(id=line_num, vector=embedding, payload=chunk.get("metadata", {}))]
        )
        
        if line_num % 100 == 0:
            print(f"Processed {line_num} chunks...")

Hugging Face + FAISS (FREE)

pip install transformers torch faiss-cpu

from transformers import AutoTokenizer, AutoModel
import torch
import torch.nn.functional as F
import faiss
import numpy as np
import json

# Helper for Mean Pooling
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0] 
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)

print("Loading model...")
tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
model = AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
index = faiss.IndexFlatL2(384)

batch_embeddings = []
BATCH_SIZE = 64

with open("chunks.jsonl", "r") as f:
    for line_num, line in enumerate(f, 1):
        chunk = json.loads(line)
        
        # Tokenize
        encoded_input = tokenizer(chunk["text"], padding=True, truncation=True, max_length=512, return_tensors='pt')
        
        # Compute Token Embeddings
        with torch.no_grad():
            model_output = model(**encoded_input)
            
        # Pooling & Normalization
        sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
        sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
        
        batch_embeddings.append(sentence_embeddings.squeeze().numpy())
        
        if len(batch_embeddings) >= BATCH_SIZE:
            index.add(np.vstack(batch_embeddings).astype('float32'))
            batch_embeddings = []
            
        if line_num % 100 == 0:
            print(f"Processed {line_num} chunks...")

if batch_embeddings:
    index.add(np.vstack(batch_embeddings).astype('float32'))

faiss.write_index(index, "my_vectors.index")
print("Done! Vectors saved to my_vectors.index")

---

Streaming Mode (No Files)

Process chunks without saving to disk - maximum efficiency for real-time pipelines:

Complete Example: OpenAI + Pinecone (Streaming)

pip install openai pinecone-client

from krira_augment.krira_chunker import Pipeline, PipelineConfig
from openai import OpenAI
from pinecone import Pinecone

# API Keys
OPENAI_API_KEY = "sk-..."        # https://platform.openai.com/api-keys
PINECONE_API_KEY = "pcone-..."   # https://app.pinecone.io/
PINECONE_INDEX_NAME = "my-rag"

# Initialize
client = OpenAI(api_key=OPENAI_API_KEY)
pc = Pinecone(api_key=PINECONE_API_KEY)
index = pc.Index(PINECONE_INDEX_NAME)

# Configure pipeline
config = PipelineConfig(chunk_size=512, chunk_overlap=50)
pipeline = Pipeline(config=config)

# Stream and embed (no file created)
chunk_count = 0
print("Starting streaming pipeline...")

for chunk in pipeline.process_stream("data.csv"):
    chunk_count += 1
    
    # Embed
    response = client.embeddings.create(
        input=chunk["text"],
        model="text-embedding-3-small"
    )
    embedding = response.data[0].embedding
    
    # Store immediately
    index.upsert(vectors=[(
        f"chunk_{chunk_count}",
        embedding,
        chunk["metadata"]
    )])
    
    # Progress
    if chunk_count % 100 == 0:
        print(f"Processed {chunk_count} chunks...")

print(f"Done! Embedded {chunk_count} chunks. No intermediate file created.")

---

Other Streaming Integrations

Replace the embedding/storage logic with any of these:

OpenAI + Qdrant (Streaming)

pip install openai qdrant-client

from krira_augment.krira_chunker import Pipeline, PipelineConfig
from openai import OpenAI
from qdrant_client import QdrantClient
from qdrant_client.models import PointStruct

# Initialize
client = OpenAI(api_key="sk-...")
qdrant = QdrantClient(url="https://xyz.qdrant.io", api_key="qdrant-...")

# Configure and stream
config = PipelineConfig(chunk_size=512, chunk_overlap=50)
pipeline = Pipeline(config=config)

chunk_count = 0
for chunk in pipeline.process_stream("data.csv"):
    chunk_count += 1
    
    # Embed
    response = client.embeddings.create(input=chunk["text"], model="text-embedding-3-small")
    embedding = response.data[0].embedding
    
    # Store
    qdrant.upsert(
        collection_name="my-chunks",
        points=[PointStruct(id=chunk_count, vector=embedding, payload=chunk["metadata"])]
    )
    
    if chunk_count % 100 == 0:
        print(f"Processed {chunk_count} chunks...")

print(f"Done! {chunk_count} chunks embedded.")

OpenAI + Weaviate (Streaming)

pip install openai weaviate-client

from krira_augment.krira_chunker import Pipeline, PipelineConfig
from openai import OpenAI
import weaviate

# Initialize
client_o = OpenAI(api_key="sk-...")
client_w = weaviate.connect_to_wcs(
    cluster_url="https://xyz.weaviate.network",
    auth_credentials=weaviate.auth.AuthApiKey("weaviate-...")
)
collection = client_w.collections.get("Chunk")

# Configure and stream
config = PipelineConfig(chunk_size=512, chunk_overlap=50)
pipeline = Pipeline(config=config)

chunk_count = 0
for chunk in pipeline.process_stream("data.csv"):
    chunk_count += 1
    
    # Embed
    response = client_o.embeddings.create(input=chunk["text"], model="text-embedding-3-small")
    embedding = response.data[0].embedding
    
    # Store
    collection.data.insert(
        properties={"text": chunk["text"], "metadata": str(chunk["metadata"])},
        vector=embedding
    )
    
    if chunk_count % 100 == 0:
        print(f"Processed {chunk_count} chunks...")

print(f"Done! {chunk_count} chunks embedded.")

Cohere + Pinecone (Streaming)

pip install cohere pinecone-client

from krira_augment.krira_chunker import Pipeline, PipelineConfig
import cohere
from pinecone import Pinecone

# Initialize
co = cohere.Client("co-...")
pc = Pinecone(api_key="pcone-...")
index = pc.Index("my-rag")

# Configure and stream
config = PipelineConfig(chunk_size=512, chunk_overlap=50)
pipeline = Pipeline(config=config)

chunk_count = 0
for chunk in pipeline.process_stream("data.csv"):
    chunk_count += 1
    
    # Embed
    response = co.embed(texts=[chunk["text"]], model="embed-english-v3.0")
    embedding = response.embeddings[0]
    
    # Store
    index.upsert(vectors=[(f"chunk_{chunk_count}", embedding, chunk["metadata"])])
    
    if chunk_count % 100 == 0:
        print(f"Processed {chunk_count} chunks...")

print(f"Done! {chunk_count} chunks embedded.")

Cohere + Qdrant (Streaming)

pip install cohere qdrant-client

from krira_augment.krira_chunker import Pipeline, PipelineConfig
import cohere
from qdrant_client import QdrantClient
from qdrant_client.models import PointStruct

# Initialize
co = cohere.Client("co-...")
qdrant = QdrantClient(url="https://xyz.qdrant.io", api_key="qdrant-...")

# Configure and stream
config = PipelineConfig(chunk_size=512, chunk_overlap=50)
pipeline = Pipeline(config=config)

chunk_count = 0
for chunk in pipeline.process_stream("data.csv"):
    chunk_count += 1
    
    # Embed
    response = co.embed(texts=[chunk["text"]], model="embed-english-v3.0")
    embedding = response.embeddings[0]
    
    # Store
    qdrant.upsert(
        collection_name="my-chunks",
        points=[PointStruct(id=chunk_count, vector=embedding, payload=chunk["metadata"])]
    )
    
    if chunk_count % 100 == 0:
        print(f"Processed {chunk_count} chunks...")

print(f"Done! {chunk_count} chunks embedded.")

Local (Sentence Transformers) + ChromaDB (Streaming, FREE)

pip install sentence-transformers chromadb

from krira_augment.krira_chunker import Pipeline, PipelineConfig
from sentence_transformers import SentenceTransformer
import chromadb

# Initialize (no API keys needed)
model = SentenceTransformer('all-MiniLM-L6-v2')
client = chromadb.Client()
collection = client.create_collection("my_chunks")

# Configure and stream
config = PipelineConfig(chunk_size=512, chunk_overlap=50)
pipeline = Pipeline(config=config)

chunk_count = 0
for chunk in pipeline.process_stream("data.csv"):
    chunk_count += 1
    
    # Embed locally (free, runs on your machine)
    embedding = model.encode(chunk["text"])
    
    # Store locally
    collection.add(
        ids=[f"chunk_{chunk_count}"],
        embeddings=[embedding.tolist()],
        metadatas=[chunk["metadata"]],
        documents=[chunk["text"]]
    )
    
    if chunk_count % 100 == 0:
        print(f"Processed {chunk_count} chunks...")

print(f"Done! {chunk_count} chunks embedded. All local, no API costs.")

Hugging Face + FAISS (Streaming, FREE)

pip install transformers torch faiss-cpu

from krira_augment.krira_chunker import Pipeline, PipelineConfig
from transformers import AutoTokenizer, AutoModel
import torch
import torch.nn.functional as F
import faiss
import numpy as np

# Helper for Mean Pooling
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0] 
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)

# Initialize (no API keys needed)
tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
model = AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
index = faiss.IndexFlatL2(384)

# Configure and stream
config = PipelineConfig(chunk_size=512, chunk_overlap=50)
pipeline = Pipeline(config=config)

chunk_count = 0
embeddings_batch = []
BATCH_SIZE = 64

for chunk in pipeline.process_stream("data.csv"):
    chunk_count += 1
    
    # Tokenize
    inputs = tokenizer(chunk["text"], return_tensors="pt", padding=True, truncation=True, max_length=512)
    
    # Embed
    with torch.no_grad():
        outputs = model(**inputs)
        embedding = mean_pooling(outputs, inputs['attention_mask'])
        embedding = F.normalize(embedding, p=2, dim=1)
        embedding = embedding.squeeze().numpy()
    
    embeddings_batch.append(embedding)
    
    # Add to FAISS in batches
    if len(embeddings_batch) >= BATCH_SIZE:
        index.add(np.vstack(embeddings_batch).astype('float32'))
        embeddings_batch = []
        print(f"Processed {chunk_count} chunks...")

# Add remaining embeddings
if embeddings_batch:
    index.add(np.vstack(embeddings_batch).astype('float32'))

# Save index
faiss.write_index(index, "my_vectors.index")
print(f"Done! {chunk_count} chunks embedded and saved to my_vectors.index")

---

Streaming Mode Advantages

Feature	File-Based	Streaming
Disk I/O	Creates chunks.jsonl	None
Memory Usage	O(1) constant	O(1) constant
Speed	Chunking + Embedding	Overlapped (faster)
Use Case	Large files, batch processing	Real-time, no storage
Flexibility	Can re-process chunks	Single pass only

---

When to Use Streaming vs File-Based

Use Streaming When:

• You want maximum speed (no disk writes)
• You don't need to save chunks for later
• You're building real-time pipelines
• You have limited disk space

Use File-Based When:

• You want to inspect/debug chunks
• You need to re-process with different embeddings
• You want to share chunks with your team
• You're experimenting with different models

---

Error Handling (Production Ready)

from krira_augment.krira_chunker import Pipeline, PipelineConfig
from openai import OpenAI
from pinecone import Pinecone
import time

client = OpenAI(api_key="sk-...")
pc = Pinecone(api_key="pcone-...")
index = pc.Index("my-rag")

config = PipelineConfig(chunk_size=512, chunk_overlap=50)
pipeline = Pipeline(config=config)

chunk_count = 0
error_count = 0

for chunk in pipeline.process_stream("data.csv"):
    chunk_count += 1
    
    try:
        # Embed
        response = client.embeddings.create(input=chunk["text"], model="text-embedding-3-small")
        embedding = response.data[0].embedding
        
        # Store
        index.upsert(vectors=[(f"chunk_{chunk_count}", embedding, chunk["metadata"])])
        
    except Exception as e:
        error_count += 1
        print(f"Error on chunk {chunk_count}: {e}")
        
        # Retry logic
        if "rate_limit" in str(e).lower():
            print("Rate limited, waiting 60 seconds...")
            time.sleep(60)
            # Retry (add your retry logic here)
    
    if chunk_count % 100 == 0:
        print(f"Processed {chunk_count} chunks, {error_count} errors")

print(f"Done! {chunk_count} chunks processed, {error_count} errors")

---

Supported Formats

Format	Extension	Method
CSV	.csv	Direct processing
Text	.txt	Direct processing
JSONL	.jsonl	Direct processing
JSON	.json	Auto-flattening
PDF	.pdf	pdfplumber extraction
Word	.docx	python-docx extraction
Excel	.xlsx	openpyxl extraction
XML	.xml	ElementTree parsing
URLs	http://	BeautifulSoup scraping

---

Provider Comparison

Embedding	Vector Store	Cost	API Keys	Streaming Support
OpenAI	Pinecone	Paid	2	✅ Yes
OpenAI	Qdrant	Paid	2	✅ Yes
OpenAI	Weaviate	Paid	2	✅ Yes
Cohere	Pinecone	Paid	2	✅ Yes
Cohere	Qdrant	Paid	2	✅ Yes
SentenceTransformers	ChromaDB	FREE	0	✅ Yes
Hugging Face	FAISS	FREE	0	✅ Yes

---

API Keys Setup

Get your keys from:

• OpenAI: https://platform.openai.com/api-keys
• Cohere: https://dashboard.cohere.com/api-keys
• Pinecone: https://app.pinecone.io/
• Qdrant: https://cloud.qdrant.io/
• Weaviate: https://console.weaviate.cloud/

---

Development

1. Clone the repo
2. Install Maturin

   pip install maturin
   

3. Build and Install locally

   maturin develop