Building a RAG System for Turkish Documents

What is RAG and Why Does It Matter?

Retrieval-Augmented Generation (RAG) combines the power of search engines with large language models. Instead of relying solely on an LLM's training data, RAG retrieves relevant documents first, then generates answers grounded in your actual data.

For Turkish businesses, this means you can build AI systems that accurately answer questions about your internal documents -- contracts, manuals, support tickets, knowledge bases -- in Turkish.

The Turkish Language Challenge

Turkish presents unique challenges for NLP systems:

Agglutinative morphology: Words can have many suffixes (ev → evlerinizden)

Vowel harmony: Suffixes change based on vowels in the root

Case marking: Grammatical roles encoded in word endings

Limited training data: Most NLP models are English-first

Standard English-only embeddings fail miserably on Turkish text. You need multilingual models that understand Turkish morphology.

Architecture Overview

``

Documents → Parser → Chunker → Embeddings → Weaviate

↓

User Query → Embed → Vector Search → Top-K → LLM → Answer

`

Component Stack

Document Parser: Support for PDF, DOCX, XLSX, PPTX with OCR fallback

Chunker: Semantic chunking that respects Turkish sentence boundaries

Embeddings: multilingual-e5-large (best balance of quality/speed for Turkish)

Vector DB: Weaviate (hybrid search = vector + BM25 keyword)

LLM: Claude 3.5 or GPT-4 for answer generation

UI: Streamlit dashboard for document upload and Q&A

Setting Up Weaviate

Weaviate is our recommended vector database because it supports hybrid search natively:

`yaml

# docker-compose.yml

services:

weaviate:

image: semitechnologies/weaviate:1.24.0

ports:

- "8080:8080"

environment:

QUERY_DEFAULTS_LIMIT: 25

AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED: 'true'

DEFAULT_VECTORIZER_MODULE: 'text2vec-transformers'

ENABLE_MODULES: 'text2vec-transformers,reranker-transformers'

CLUSTER_HOSTNAME: 'node1'

`

Embedding Turkish Text

The key insight: use multilingual-e5-large for embeddings. It handles Turkish morphological variations much better than standard models.

`python

from sentence_transformers import SentenceTransformer

model = SentenceTransformer('intfloat/multilingual-e5-large')

# Prefix matters for e5 models

query_embedding = model.encode("query: Bebek bakım ürünleri nelerdir?")

doc_embedding = model.encode("passage: Bebek bakım ürünleri arasında...")

`

Hybrid Search: The Secret Weapon

Pure vector search sometimes misses exact keyword matches. Hybrid search combines:

Vector similarity: Semantic understanding (finds related concepts)

BM25 keyword: Exact term matching (finds specific product names)

`python

results = client.query.get("Document", ["content", "title"]) \

.with_hybrid(query="bebek şampuanı fiyat", alpha=0.7) \

.with_limit(5) \

.do()

`

The alpha parameter controls the balance: 0.7 means 70% vector, 30% keyword.

Turkish-Specific Optimizations

1. Suffix-Aware Tokenization

Turkish words need special tokenization. "evlerinizden" should relate to "ev" (house):

`python

from zeyrek import MorphAnalyzer

analyzer = MorphAnalyzer()

lemmas = analyzer.lemmatize("evlerinizden")

# Returns: ["ev"]

``

2. Stopword Handling

Turkish stopwords differ from English. Remove: "bir", "ve", "de", "da", "ile", "bu", etc.

3. Chunk Size Tuning

Turkish sentences are often longer than English. We found 512 tokens per chunk optimal (vs. 256 for English).

Production Deployment

For production, consider:

Weaviate cluster (3 nodes) for high availability

GPU inference for faster embedding generation

Redis cache for repeated queries

Monitoring with Prometheus + Grafana

Results

Our RAG system achieves:

Precision@5: 0.89 on Turkish document QA

Latency: 200ms average query time

Throughput: 100 concurrent queries

Document support: PDF, DOCX, XLSX, PPTX, TXT

Ready-Made Solution

Building a RAG system from scratch takes weeks. Our production-ready RAG System includes everything described above, plus a Streamlit dashboard, API endpoints, and deployment scripts.

Starter plan handles 1,000 documents. Professional scales to 10,000+ with cluster deployment and custom embedding fine-tuning.