Integrating Baichuan API with LangChain and LlamaIndex for Chinese Enterprise RAG

Building Retrieval-Augmented Generation (RAG) pipelines for Chinese enterprise documents presents engineering challenges that standard Western-centric pipelines often handle poorly. Dense Chinese text lacks whitespace boundaries, technical and legal contracts contain specialized clauses, and generic multilingual embedding models frequently suffer from sub-character token fragmentation that degrades retrieval recall.

Baichuan AI provides models specifically trained on massive Chinese corporate corpora—notably Baichuan4 and Baichuan3-Turbo for reasoning and text generation, alongside Baichuan-Text-Embedding (1024 dimensions) for semantic retrieval. Because Baichuan exposes an OpenAI-compatible REST API at https://api.baichuan-ai.com/v1, backend engineers can integrate Baichuan into both LangChain and LlamaIndex using native community classes or standard OpenAI-compatible wrappers.

This guide provides practical architectures, production-grade Python implementations, and chunking strategies tailored for Chinese enterprise knowledge bases.

1. Prerequisites and API Setup

Before wiring up ingestion and query pipelines, obtain your API key from the Baichuan Intelligent Developer Platform. Set your credentials in your environment:

export BAICHUAN_API_KEY="your-baichuan-api-key"

Install the required orchestration and vector store packages:

pip install langchain-community langchain-core langchain-openai \
            llama-index-core llama-index-llms-openai-like llama-index-embeddings-openai \
            chromadb pydantic

Baichuan's core endpoints adhere to standard REST patterns:

• Base URL: https://api.baichuan-ai.com/v1
• Chat Completion: POST /v1/chat/completions (Models: Baichuan4, Baichuan4-Air, Baichuan3-Turbo)
• Embeddings: POST /v1/embeddings (Model: Baichuan-Text-Embedding)

2. Text Splitting for Chinese Enterprise Documents

Standard character splitters often fracture sentences mid-phrase, destroying corporate terminology, SKU identifiers, and legal clauses. Chinese document ingestion requires a recursive splitter that prioritizes full paragraphs and Chinese sentence terminators (。, ！, ？, ；) over spaces.

Crucially, Baichuan-Text-Embedding enforces an input limit of 512 tokens. To avoid 400 Bad Request payload errors during batch ingestion, keep chunk sizes around 350–400 characters, with an overlap of 40–50 characters.

from langchain_text_splitters import RecursiveCharacterTextSplitter

def create_chinese_document_splitter(chunk_size: int = 400, chunk_overlap: int = 50):
    """
    Creates a text splitter tuned for Chinese enterprise contracts and SOPs.
    """
    return RecursiveCharacterTextSplitter(
        chunk_size=chunk_size,
        chunk_overlap=chunk_overlap,
        separators=[
            "\n\n",      # Paragraph breaks
            "\n",        # Line breaks
            "。",        # Full stop
            "！",        # Exclamation
            "？",        # Question mark
            "；",        # Semicolon (common in contract sub-clauses)
            " ",         # Whitespace
            ""           # Fallback character level
        ]
    )

3. LangChain Integration

In LangChain, you can connect Baichuan either through langchain_community classes (ChatBaichuan, BaichuanTextEmbeddings) or through langchain_openai with Baichuan's base_url. Using the OpenAI-compatible wrapper is often favored in enterprise production because it supports modern streaming, async execution, and LangChain Expression Language (LCEL) constructs cleanly.

Complete LangChain RAG Pipeline

import os
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_community.vectorstores import Chroma
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_core.output_parsers import StrOutputParser
from langchain_core.documents import Document

# 1. Initialize Baichuan LLM and Embedding Model
BAICHUAN_BASE_URL = "https://api.baichuan-ai.com/v1"
api_key = os.environ.get("BAICHUAN_API_KEY")

embeddings = OpenAIEmbeddings(
    model="Baichuan-Text-Embedding",
    openai_api_base=BAICHUAN_BASE_URL,
    openai_api_key=api_key,
    check_embedding_ctx_length=False  # Avoid OpenAI-specific token validation
)

llm = ChatOpenAI(
    model="Baichuan4",
    openai_api_base=BAICHUAN_BASE_URL,
    openai_api_key=api_key,
    temperature=0.1,  # Low temperature for strict factual retrieval
    streaming=True
)

# 2. Ingest Sample Chinese Enterprise Documents
raw_docs = [
    Document(
        page_content="第十三条 采购付款管理规范：对于超过人民币50万元的设备采购合同，供应商必须提供全额增值税专用发票后，财务部方可安排第二笔30%的进度款支付。项目初验合格前，累计付款比例不得超过合同总额的80%。",
        metadata={"source": "采购管理制度2024.pdf", "department": "Finance"}
    ),
    Document(
        page_content="第二十四条 差旅报销标准：研发部门员工赴一类城市（北京、上海、广州、深圳）出差，住宿费上限为每人每天550元，餐饮补贴为每人每天120元，按实际出差自然天数核算。",
        metadata={"source": "员工差旅规范v3.pdf", "department": "HR"}
    )
]

splitter = create_chinese_document_splitter(chunk_size=400, chunk_overlap=40)
chunked_docs = splitter.split_documents(raw_docs)

# 3. Vector Indexing with Chroma
vectorstore = Chroma.from_documents(
    documents=chunked_docs,
    embedding=embeddings,
    collection_name="enterprise_knowledge"
)
retriever = vectorstore.as_retriever(search_kwargs={"k": 2})

# 4. Construct Chinese Prompt Template
system_prompt = (
    "你是一个严谨的企业知识库助手。请严格根据以下检索到的参考文档回答用户的问题。\n"
    "如果参考文档中没有提供明确信息，请直接回答'依据现有内部制度未查到相关说明'，严禁虚构或主观臆测。\n\n"
    "【参考文档】：\n{context}"
)

prompt = ChatPromptTemplate.from_messages([
    ("system", system_prompt),
    ("human", "{question}")
])

def format_docs(docs):
    return "\n\n".join(f"[{doc.metadata.get('source', '未知')}] {doc.page_content}" for doc in docs)

# 5. Build LCEL Chain
rag_chain = (
    {"context": retriever | format_docs, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

# 6. Execute Query
if __name__ == "__main__":
    query = "50万元以上的设备采购合同，第二笔进度款支付的前提条件是什么？"
    response = rag_chain.invoke(query)
    print("Answer:\n", response)

4. LlamaIndex Integration

In LlamaIndex (v0.10+), components are modularized. Because LlamaIndex validates model names on standard OpenAI classes, backend engineers connect to third-party endpoints using OpenAILike from llama-index-llms-openai-like and OpenAIEmbedding configured with api_base.

Complete LlamaIndex Pipeline

import os
from llama_index.core import Settings, VectorStoreIndex, Document
from llama_index.core.node_parser import SentenceSplitter
from llama_index.llms.openai_like import OpenAILike
from llama_index.embeddings.openai import OpenAIEmbedding

# 1. Configure Global LlamaIndex Settings
BAICHUAN_BASE_URL = "https://api.baichuan-ai.com/v1"
api_key = os.environ.get("BAICHUAN_API_KEY")

Settings.llm = OpenAILike(
    model="Baichuan4",
    api_base=BAICHUAN_BASE_URL,
    api_key=api_key,
    is_chat_model=True,
    temperature=0.1
)

Settings.embed_model = OpenAIEmbedding(
    model="Baichuan-Text-Embedding",
    api_base=BAICHUAN_BASE_URL,
    api_key=api_key
)

# 2. Chinese-Aware Node Splitting
Settings.node_parser = SentenceSplitter(
    chunk_size=400,
    chunk_overlap=40,
    separator="。"
)

# 3. Build Index
documents = [
    Document(
        text="第七章 核心数据安全防护要求：涉及生产数据库导出操作必须提前48小时提交工单，经信息安全负责人与业务分管领导双重审批后方可在堡垒机中执行。导出文件须启用国密SM4算法加密并在3天内自动销毁。",
        metadata={"category": "SecOps", "author": "Security Committee"}
    )
]

index = VectorStoreIndex.from_documents(documents)

# 4. Create Query Engine with Tailored System Prompt
query_engine = index.as_query_engine(
    similarity_top_k=2,
    system_prompt=(
        "你代表企业信息化与安全管理委员会回答技术咨询。"
        "请严格根据检索到的规范作答，列出具体审核流程及技术加密要求。"
    )
)

if __name__ == "__main__":
    response = query_engine.query("导出生产环境数据库需要满足哪些审批与安全条件？")
    print("Response:\n", str(response))

5. Production Engineering Considerations

Deploying Baichuan in enterprise backend systems requires addressing several infrastructure and architectural requirements:

1. Hybrid Search (Dense + BM25)

Dense vector embeddings capture abstract semantics, but enterprise queries often rely on exact strings—such as project codes, legal clause citations ("第十五条"), or hardware part numbers. Combining Baichuan-Text-Embedding with a sparse keyword retriever (BM25 or Elasticsearch) via LangChain's EnsembleRetriever or LlamaIndex's hybrid search yields significantly higher recall.

2. Strict Token Truncation Before Embedding

Because Baichuan-Text-Embedding caps inputs at 512 tokens, any unexpected character burst (e.g., base64 strings in documents or OCR artifacts) will trigger an unhandled HTTP exception. Place a token-length validation filter upstream of the vectorizer to truncate or re-split oversized chunks before calling the API.

3. Rate Limiting and Resilience

Baichuan implements requests-per-minute (RPM) limits across its API tiers. In high-concurrency enterprise batch jobs (such as re-indexing entire departmental file shares), wrap API invocations with exponential backoff using tenacity or process records through an asynchronous token bucket limiter.

4. Hallucination Guardrails

To enforce compliance, system prompts should strictly instruct the model not to extrapolate beyond the provided text. For sensitive compliance audits, configure Baichuan4 with temperature=0.0 or 0.1 and verify source citations in the response metadata.

References

• 百川大模型-汇聚世界知识 创作妙笔生花-百川智能
• 百川大模型-汇聚世界知识 创作妙笔生花-百川智能
• LlamaIndex Framework, by the makers of LlamaParse | Developer Documentation
• Retrieval Augmented Generation (RAG) with Deep Agents - Docs by LangChain
• GitHub - baichuan-inc/baichuan-mcp-servers: 百川智能官方mcp server集合 · GitHub