RAG Implementation Guide: Build Knowledge Bases with Chinese AI Embeddings (2026)

August 13, 2026 · 22 min read

Retrieval-Augmented Generation (RAG) has become the dominant architecture for building AI applications that need to reason over private or domain-specific data. This guide shows you how to implement production-grade RAG using Chinese AI models via TokenEase — covering embedding models, vector databases, chunking strategies, and retrieval optimization.

Why RAG with Chinese AI Models?

Chinese AI providers now offer embedding and reranking models that match or exceed OpenAI's performance at a fraction of the cost:

• DeepSeek embeddings: 1024-dim vectors, strong on both English and Chinese semantic similarity
• Zhipu (GLM) embeddings: Optimized for Chinese text understanding and long-context retrieval
• BGE (BAAI): Open-source embeddings that consistently top the MTEB leaderboard
• Doubao embeddings: ByteDance's proprietary model with sub-millisecond latency

Architecture Overview

A production RAG system has four main components:

1. Document Processing — Load, parse, and chunk documents
2. Embedding Pipeline — Convert chunks to vector representations
3. Vector Store — Index and search vectors efficiently
4. Generation Layer — Retrieve context and generate answers

Step 1: Document Chunking Strategy

Chunking is the most impactful decision in RAG. Poor chunks = poor retrieval.

# Optimal chunking for different document types
import tiktoken

def chunk_document(text, chunk_size=512, overlap=50):
    """Semantic chunking with sentence boundaries"""
    tokenizer = tiktoken.get_encoding("cl100k_base")
    sentences = text.replace(". ", ".
").split("
")
    
    chunks = []
    current_chunk = []
    current_tokens = 0
    
    for sentence in sentences:
        tokens = len(tokenizer.encode(sentence))
        if current_tokens + tokens > chunk_size and current_chunk:
            chunks.append(" ".join(current_chunk))
            # Keep overlap sentences
            overlap_text = " ".join(current_chunk[-2:])
            current_chunk = [overlap_text, sentence]
            current_tokens = len(tokenizer.encode(overlap_text)) + tokens
        else:
            current_chunk.append(sentence)
            current_tokens += tokens
    
    if current_chunk:
        chunks.append(" ".join(current_chunk))
    
    return chunks

# Chunk size recommendations:
# - FAQ / Q&A: 128-256 tokens (precise matches)
# - Technical docs: 512 tokens (standard)
# - Legal contracts: 1024 tokens (context-dependent)
# - Code: 256 tokens (function-level)

Step 2: Embedding Generation via TokenEase

import requests
import json

TOKEN = "your_tokenease_api_key"
BASE_URL = "https://tokenease.io/v1"

def get_embedding(text, model="deepseek-embedding"):
    """Generate embeddings through TokenEase unified API"""
    response = requests.post(
        f"{BASE_URL}/embeddings",
        headers={"Authorization": f"Bearer {TOKEN}"},
        json={
            "model": model,
            "input": text,
            "encoding_format": "float"
        }
    )
    return response.json()["data"][0]["embedding"]

# Batch embedding for efficiency
def get_embeddings_batch(texts, model="deepseek-embedding"):
    response = requests.post(
        f"{BASE_URL}/embeddings",
        headers={"Authorization": f"Bearer {TOKEN}"},
        json={
            "model": model,
            "input": texts,
            "encoding_format": "float"
        }
    )
    return [d["embedding"] for d in response.json()["data"]]

# Example: Embed 1000 chunks
chunks = chunk_document(long_document)
embeddings = get_embeddings_batch(chunks[:100])  # Batch in 100s

Step 3: Vector Database Setup

We recommend Milvus or Chroma for production. Here's a Milvus setup:

# docker-compose.yml for Milvus
version: '3.5'
services:
  etcd:
    image: quay.io/coreos/etcd:v3.5.5
  minio:
    image: minio/minio:RELEASE.2023-03-20T20-16-18Z
  standalone:
    image: milvusdb/milvus:v2.4.0
    ports:
      - "19530:19530"
    environment:
      ETCD_ENDPOINTS: etcd:2379
      MINIO_ADDRESS: minio:9000

# Python client for vector operations
from pymilvus import connections, FieldSchema, CollectionSchema, DataType, Collection

connections.connect("default", host="localhost", port="19530")

# Define collection schema for 1024-dim DeepSeek embeddings
fields = [
    FieldSchema(name="id", dtype=DataType.INT64, is_primary=True, auto_id=True),
    FieldSchema(name="chunk_text", dtype=DataType.VARCHAR, max_length=65535),
    FieldSchema(name="source_doc", dtype=DataType.VARCHAR, max_length=512),
    FieldSchema(name="embedding", dtype=DataType.FLOAT_VECTOR, dim=1024)
]

schema = CollectionSchema(fields, "RAG Knowledge Base")
collection = Collection("kb_collection", schema)

# Create IVF_FLAT index for fast search
index_params = {
    "metric_type": "COSINE",
    "index_type": "IVF_FLAT",
    "params": {"nlist": 128}
}
collection.create_index("embedding", index_params)
collection.load()

# Insert documents
entities = [
    chunks,           # chunk_text
    ["doc1.pdf"] * len(chunks),  # source_doc
    embeddings        # embedding vectors
]
collection.insert(entities)

Step 4: Retrieval with Re-ranking

Two-stage retrieval (vector search + cross-encoder rerank) significantly improves accuracy:

def retrieve_with_rerank(query, collection, top_k=10, final_k=5):
    # Stage 1: Vector similarity search
    query_emb = get_embedding(query)
    
    search_params = {"metric_type": "COSINE", "params": {"nprobe": 16}}
    results = collection.search(
        data=[query_emb],
        anns_field="embedding",
        param=search_params,
        limit=top_k,
        output_fields=["chunk_text", "source_doc"]
    )
    
    candidates = []
    for hit in results[0]:
        candidates.append({
            "text": hit.entity.get("chunk_text"),
            "source": hit.entity.get("source_doc"),
            "score": hit.score
        })
    
    # Stage 2: Cross-encoder reranking (using GLM-4 via TokenEase)
    rerank_prompts = [
        f"Query: {query}\nDocument: {c['text']}\nRelevance (0-10):"
        for c in candidates
    ]
    
    rerank_scores = batch_score_relevance(rerank_prompts)
    
    for i, score in enumerate(rerank_scores):
        candidates[i]["rerank_score"] = score
    
    # Sort by rerank score and return top final_k
    candidates.sort(key=lambda x: x["rerank_score"], reverse=True)
    return candidates[:final_k]

def batch_score_relevance(prompts):
    """Use GLM-4 to score relevance"""
    scores = []
    for prompt in prompts:
        response = requests.post(
            f"{BASE_URL}/chat/completions",
            headers={"Authorization": f"Bearer {TOKEN}"},
            json={
                "model": "glm-4",
                "messages": [{"role": "user", "content": prompt}],
                "max_tokens": 5,
                "temperature": 0.1
            }
        )
        try:
            score = float(response.json()["choices"][0]["message"]["content"])
            scores.append(min(max(score, 0), 10))
        except:
            scores.append(5.0)
    return scores

Step 5: Generation with Retrieved Context

def generate_answer(query, retrieved_chunks):
    context = "\n\n".join([
        f"[Document {i+1}] {chunk['text']}"
        for i, chunk in enumerate(retrieved_chunks)
    ])
    
    system_prompt = """You are a helpful assistant. Answer the user's question based ONLY on the provided documents.
If the answer is not in the documents, say "I don't have enough information to answer that."
Always cite the document number(s) you used."""
    
    user_prompt = f"""Documents:
{context}

Question: {query}

Answer:"""
    
    response = requests.post(
        f"{BASE_URL}/chat/completions",
        headers={"Authorization": f"Bearer {TOKEN}"},
        json={
            "model": "deepseek-v4",
            "messages": [
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": user_prompt}
            ],
            "temperature": 0.3,
            "max_tokens": 1024
        }
    )
    
    return response.json()["choices"][0]["message"]["content"]

# Full RAG pipeline
query = "What are the refund policies for enterprise accounts?"
chunks = retrieve_with_rerank(query, collection)
answer = generate_answer(query, chunks)
print(answer)

Advanced: Hybrid Search

Combine vector similarity with keyword matching for better recall:

# BM25 + Vector hybrid search
from rank_bm25 import BM25Okapi

def hybrid_search(query, collection, documents, alpha=0.7):
    # Vector scores
    vector_results = retrieve_with_rerank(query, collection, top_k=50)
    
    # BM25 scores
    tokenized_docs = [doc.split() for doc in documents]
    bm25 = BM25Okapi(tokenized_docs)
    bm25_scores = bm25.get_scores(query.split())
    
    # Normalize and combine
    vector_scores = {r["text"]: r["score"] for r in vector_results}
    
    combined = []
    for i, doc in enumerate(documents):
        v_score = vector_scores.get(doc, 0)
        b_score = bm25_scores[i] / max(bm25_scores) if max(bm25_scores) > 0 else 0
        final = alpha * v_score + (1 - alpha) * b_score
        combined.append((doc, final))
    
    combined.sort(key=lambda x: x[1], reverse=True)
    return combined[:10]

Performance Optimization

Monitoring Your RAG System

# Key metrics to track
metrics = {
    "retrieval_latency_p99": "< 100ms",
    "retrieval_recall@5": "> 85%",
    "answer_relevance_score": "> 4.0 / 5.0",
    "hallucination_rate": "< 5%",
    "cost_per_query": "< $0.001"
}

# Simple evaluation
def evaluate_rag(test_queries, ground_truth, collection):
    correct = 0
    for query, expected in zip(test_queries, ground_truth):
        chunks = retrieve_with_rerank(query, collection, final_k=5)
        retrieved_text = " ".join([c["text"] for c in chunks])
        if expected.lower() in retrieved_text.lower():
            correct += 1
    
    recall = correct / len(test_queries)
    print(f"Recall@5: {recall:.2%}")
    return recall

Cost Analysis: Full RAG Pipeline

Processing 1 million documents through the full RAG pipeline:

• Embedding: 1B tokens @ $0.007/1M = $7,000
• Storage: 1M × 1024 dims × 4 bytes = 4GB in Milvus = $20/month
• Query: Per query = embedding ($0.00007) + LLM ($0.001) = $0.00107/query

Equivalent OpenAI pipeline: ~$15,000 embedding + $0.003/query = 3-5× more expensive.

Next Steps

Ready to build your RAG system? Get your TokenEase API key and start with our migration guide. For production deployments, consider adding query classification (route simple questions to cached answers) and user feedback loops to continuously improve retrieval quality.

Last updated: August 2026. Embedding prices and model capabilities subject to change.