krira-augment 2.1.0

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

Krira Augment

High-Performance Rust Chunking Engine for RAG Pipelines

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

---

Performance Benchmarks

Dataset Size	LangChain/Pandas	Krira (Rust)	Speedup
100 MB	~45 sec	0.8 sec	56x
1 GB	~8.0 min	12.0 sec	40x
10 GB	Timeout / OOM	2.1 min	Stable

Memory stays constant (O(1)) regardless of file size.

---

Installation

pip install krira-augment

---

Complete Example: OpenAI + Pinecone

from krira_augment import Pipeline, PipelineConfig
import json
import openai
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"

# Step 1: Chunk the file
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
openai.api_key = OPENAI_API_KEY
pinecone.init(api_key=PINECONE_API_KEY)
index = pinecone.Index(PINECONE_INDEX_NAME)

with open("chunks.jsonl", "r") as f:
    for line_num, line in enumerate(f, 1):
        chunk = json.loads(line)
        
        response = openai.Embedding.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...")

print("Done! All chunks embedded and stored in Pinecone.")

---

Other Integrations

After running Step 1 (chunking), replace Step 2 with any of these integrations:

OpenAI + Qdrant

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

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 = openai.Embedding.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

import openai
import weaviate

openai.api_key = "sk-..."
client = weaviate.Client(
    url="https://xyz.weaviate.network",
    auth_client_secret=weaviate.AuthApiKey(api_key="weaviate-...")
)

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

Cohere + Pinecone

import cohere
import pinecone

co = cohere.Client("co-...")
pinecone.init(api_key="pcone-...")
index = pinecone.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

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...")

Local (Sentence Transformers) + ChromaDB (FREE)

from sentence_transformers import SentenceTransformer
import chromadb
import json

model = SentenceTransformer('all-MiniLM-L6-v2')
client = chromadb.Client()
collection = client.create_collection("my_chunks")

with open("chunks.jsonl", "r") as f:
    for line_num, line in enumerate(f, 1):
        chunk = json.loads(line)
        embedding = model.encode(chunk["text"])
        collection.add(
            ids=[f"chunk_{line_num}"],
            embeddings=[embedding.tolist()],
            metadatas=[chunk.get("metadata", {})],
            documents=[chunk["text"]]
        )
        
        if line_num % 100 == 0:
            print(f"Processed {line_num} chunks...")

Hugging Face + FAISS (FREE)

from transformers import AutoTokenizer, AutoModel
import torch
import faiss
import numpy as np
import json

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

embeddings_list = []
with open("chunks.jsonl", "r") as f:
    for line_num, line in enumerate(f, 1):
        chunk = json.loads(line)
        inputs = tokenizer(chunk["text"], return_tensors="pt", truncation=True, max_length=512)
        with torch.no_grad():
            outputs = model(**inputs)
            embedding = outputs.last_hidden_state.mean(dim=1).squeeze().numpy()
        embeddings_list.append(embedding)
        
        if line_num % 100 == 0:
            print(f"Processed {line_num} chunks...")

embeddings_array = np.array(embeddings_list).astype('float32')
index.add(embeddings_array)
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)

from krira_augment import Pipeline, PipelineConfig
import openai
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
openai.api_key = OPENAI_API_KEY
pinecone.init(api_key=PINECONE_API_KEY)
index = pinecone.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 = openai.Embedding.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)

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

# Initialize
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 = openai.Embedding.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)

from krira_augment import Pipeline, PipelineConfig
import openai
import weaviate

# Initialize
openai.api_key = "sk-..."
client = weaviate.Client(
    url="https://xyz.weaviate.network",
    auth_client_secret=weaviate.AuthApiKey(api_key="weaviate-...")
)

# 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 = openai.Embedding.create(input=chunk["text"], model="text-embedding-3-small")
    embedding = response["data"][0]["embedding"]
    
    # Store
    client.data_object.create(
        data_object={"text": chunk["text"], "metadata": chunk["metadata"]},
        class_name="Chunk",
        vector=embedding
    )
    
    if chunk_count % 100 == 0:
        print(f"Processed {chunk_count} chunks...")

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

Cohere + Pinecone (Streaming)

from krira_augment import Pipeline, PipelineConfig
import cohere
import pinecone

# Initialize
co = cohere.Client("co-...")
pinecone.init(api_key="pcone-...")
index = pinecone.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)

from krira_augment 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)

from krira_augment 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)

from krira_augment import Pipeline, PipelineConfig
from transformers import AutoTokenizer, AutoModel
import torch
import faiss
import numpy as np

# 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 = 100  # Process in batches for efficiency

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

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

# 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 import Pipeline, PipelineConfig
import openai
import pinecone
import time

openai.api_key = "sk-..."
pinecone.init(api_key="pcone-...")
index = pinecone.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 = openai.Embedding.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

   python -m build
   pip install dist/*.whl --force-reinstall
   

---

License

MIT License. (c) 2025 Krira Labs.