Baichuan AI API Developer Guide: Finance & Healthcare Vertical Industry in Practice
Complete Baichuan 4 API tutorial: vertical-industry models, Baichuan-M1 reasoning, finance/healthcare applications, OpenAI-compatible calling. Includes industry knowledge base integration.
What Problem Does This Tutorial Solve?
You will master the Baichuan AI Baichuan series API:
- Baichuan 4 basic conversation and long-context
- Baichuan-M1 deep reasoning
- Finance / healthcare vertical-industry applications
- Domain-specific knowledge base integration
- OpenAI-compatible API calling
🎯 Baichuan AI was founded by Wang Xiaochuan (founder of Sogou), focusing on vertical-industry AI applications. Baichuan 4 excels in Chinese-language finance and healthcare domains.
Baichuan Model Matrix
| Model | Context | Positioning | Highlights |
|---|---|---|---|
| Baichuan 4 | 128K | Flagship general-purpose | Chinese understanding SOTA |
| Baichuan 4-Turbo | 128K | Speed-optimized | 3x speed improvement |
| Baichuan 4-Air | 32K | Lightweight | Exceptional cost-performance |
| Baichuan-M1 | 128K | Deep reasoning | Excellent math/logic |
| Baichuan-NPC | 8K | Role-playing | Gaming / virtual characters |
Step 1: Get Your API Key
# 1. Visit https://platform.baichuan-ai.com
# 2. Register → Identity verification → Create API Key
# 3. Set environment variable
export BAICHUAN_API_KEY="your-api-key-here"Step 2: Basic Conversation
from openai import OpenAI
import os
client = OpenAI(
api_key=os.getenv("BAICHUAN_API_KEY"),
base_url="https://api.baichuan-ai.com/v1",
)
def baichuan_chat(prompt: str, model: str = "Baichuan4-Turbo") -> str:
"""Basic chat"""
response = client.chat.completions.create(
model=model,
messages=[
{"role": "system", "content": "You are the Baichuan AI assistant, focused on Chinese-language scenarios."},
{"role": "user", "content": prompt},
],
temperature=0.7,
max_tokens=2048,
)
return response.choices[0].message.content
print(baichuan_chat("Summarize Baichuan AI's competitive advantages in three sentences"))Step 3: Finance Industry Applications
Baichuan has specialized optimizations for the finance domain, ideal for financial report analysis, risk assessment, and similar scenarios.
class BaichuanFinanceAdvisor:
"""Baichuan financial analysis assistant"""
SYSTEM_PROMPT = """You are a professional financial analysis assistant. Analysis requirements:
1. Use professional financial terminology
2. Reference specific data indicators (ROE, P/E ratio, gross margin, etc.)
3. Risk assessments must be quantified
4. Distinguish between factual analysis and subjective judgment"""
def __init__(self, model: str = "Baichuan4"):
self.model = model
def analyze_financial_report(self, report_text: str) -> dict:
"""Analyze a financial report"""
response = client.chat.completions.create(
model=self.model,
messages=[
{"role": "system", "content": self.SYSTEM_PROMPT},
{
"role": "user",
"content": f"""Analyze the following financial report summary and answer:
1. Overall financial health (Excellent / Good / Fair / Poor)
2. Year-over-year changes for 3 key financial indicators
3. Potential risk points (list at least 3)
4. Investment recommendation (Buy / Hold / Sell)
Financial report content:
{report_text}""",
},
],
temperature=0.3,
max_tokens=3072,
)
return {
"analysis": response.choices[0].message.content,
"tokens": response.usage.total_tokens,
}
def assess_credit_risk(self, company_profile: str) -> str:
"""Credit risk assessment"""
response = client.chat.completions.create(
model=self.model,
messages=[
{"role": "system", "content": self.SYSTEM_PROMPT},
{
"role": "user",
"content": f"""Perform a credit risk assessment for the following company.
Score each dimension (1-10) and provide rationale:
- Debt repayment capacity
- Earnings stability
- Cash flow health
- Industry risk exposure
- ESG compliance
Company information:
{company_profile}""",
},
],
temperature=0.2,
)
return response.choices[0].message.content
# Usage
advisor = BaichuanFinanceAdvisor()
# Financial report analysis
report = """
A tech company's FY2025 annual report:
- Revenue: ¥12 billion (YoY +18%)
- Net profit: ¥1.8 billion (YoY +12%)
- ROE: 15.2%
- Debt-to-asset ratio: 42%
- Operating cash flow: ¥2.2 billion
- R&D spending ratio: 15%
"""
analysis = advisor.analyze_financial_report(report)
print(analysis["analysis"])Step 4: Healthcare Industry Applications
class BaichuanMedicalAssistant:
"""Baichuan medical assistance system"""
MEDICAL_SYSTEM = """You are a medical information assistance system.
⚠️ Important Disclaimer:
- You provide reference information only and cannot replace a doctor's diagnosis
- In emergency situations, advise users to seek immediate medical attention
- When citing medical knowledge, indicate the source and evidence level"""
def __init__(self):
self.model = "Baichuan4"
def symptom_analysis(self, symptoms: str) -> str:
"""Symptom analysis (for educational reference only)"""
response = client.chat.completions.create(
model=self.model,
messages=[
{"role": "system", "content": self.MEDICAL_SYSTEM},
{
"role": "user",
"content": f"""User-reported symptoms:
{symptoms}
Please provide:
1. Possible diagnoses (sorted by probability, with evidence level for each)
2. Recommended tests / examinations
3. Lifestyle and self-care suggestions
4. Warning signs indicating when to seek immediate medical attention
⚠️ Please begin with: "This analysis is for educational reference only and cannot replace professional medical diagnosis." """,
},
],
temperature=0.2,
max_tokens=2048,
)
return response.choices[0].message.content
def drug_interaction_check(self, drugs: list[str]) -> str:
"""Drug interaction check"""
drug_list = "\n".join(f"- {d}" for d in drugs)
response = client.chat.completions.create(
model=self.model,
messages=[
{"role": "system", "content": self.MEDICAL_SYSTEM},
{
"role": "user",
"content": f"""Check for potential interactions between the following drugs:
{drug_list}
Please analyze:
1. Interaction risk between each pair of drugs
2. Any contraindicated combinations
3. Dosing schedule recommendations""",
},
],
temperature=0.1, # Drug information requires high accuracy
)
return response.choices[0].message.content
# Usage
medical = BaichuanMedicalAssistant()
symptoms = "Headache for 3 days, accompanied by nausea, body temperature 37.8°C, no cough"
print(medical.symptom_analysis(symptoms))
print("\n" + "="*50 + "\n")
drugs = ["Amoxicillin", "Ibuprofen", "Omeprazole"]
print(medical.drug_interaction_check(drugs))Step 5: Baichuan-M1 Reasoning
def baichuan_m1_reasoning(problem: str) -> dict:
"""Use Baichuan-M1 for deep reasoning"""
response = client.chat.completions.create(
model="Baichuan-M1", # Reasoning-specific model
messages=[
{
"role": "system",
"content": "You are a reasoning expert. Analyze problems step by step and show your complete reasoning chain.",
},
{"role": "user", "content": problem},
],
temperature=0.0, # Zero temperature for reasoning
max_tokens=8192,
)
return {
"reasoning": response.choices[0].message.content,
"tokens": response.usage.total_tokens,
}
# Test logic puzzle
problem = """Among four people — A, B, C, and D — one is a thief. It is known that:
- A says: I am not the thief
- B says: D is the thief
- C says: B is the thief
- D says: I am not the thief
Only one person is telling the truth. Who is the thief? Reason step by step."""
result = baichuan_m1_reasoning(problem)
print(result["reasoning"])Step 6: Industry Knowledge Base Integration
class BaichuanKnowledgeBase:
"""Baichuan + domain knowledge base — RAG pattern"""
def __init__(self, industry: str = "通用"):
self.industry = industry
self.knowledge_base = []
self.prompts = {
"金融": "You are a financial analyst. Answer based on regulations and case studies in the knowledge base.",
"医疗": "You are a medical information specialist. Answer based on medical literature in the knowledge base.",
"法律": "You are a legal advisor. Answer based on statutes and precedents in the knowledge base.",
"通用": "You are a knowledge management assistant. Answer based on the knowledge base.",
}
def add_knowledge(self, title: str, content: str, source: str = ""):
"""Add a knowledge entry"""
self.knowledge_base.append({
"title": title,
"content": content,
"source": source,
})
print(f"Knowledge base now has {len(self.knowledge_base)} entries")
def query(self, question: str, top_k: int = 3) -> str:
"""Query against the knowledge base"""
# Simple keyword-matching retrieval
scored = []
for item in self.knowledge_base:
# Calculate relevance
score = sum(
1 for word in question
if word in item["title"] + item["content"]
)
if score > 0:
scored.append((score, item))
scored.sort(key=lambda x: x[0], reverse=True)
relevant = scored[:top_k]
if not relevant:
return "No relevant information found in the knowledge base."
# Build context
context = "\n\n---\n\n".join(
f"[{item['title']}]\n{item['content']}\nSource: {item['source']}"
for _, item in relevant
)
# Call Baichuan to generate answer
response = client.chat.completions.create(
model="Baichuan4",
messages=[
{
"role": "system",
"content": self.prompts.get(self.industry, self.prompts["通用"]),
},
{
"role": "user",
"content": f"Knowledge base content:\n{context}\n\nQuestion: {question}",
},
],
temperature=0.3,
max_tokens=2048,
)
return response.choices[0].message.content
# Usage
kb = BaichuanKnowledgeBase(industry="金融")
kb.add_knowledge(
"New Delisting Rules for Listed Companies",
"The 2025 revised delisting criteria include: closing price below ¥1 for 20 consecutive trading days, material financial fraud with serious violations...",
"CSRC Announcement [2025] No. 5",
)
kb.add_knowledge(
"STAR Market Listing Review Rules",
"Key review criteria for STAR Market listing: advanced core technology, R&D spending ratio, IP completeness...",
"SSE STAR Market Department",
)
print(kb.query("What are the R&D spending requirements for STAR Market listing?"))Step 7: Multi-Model Comparison
import time
def benchmark_baichuan(prompt: str, models: list[str] = None):
"""Compare performance across different Baichuan models"""
if models is None:
models = ["Baichuan4-Turbo", "Baichuan4", "Baichuan4-Air"]
results = {}
for model in models:
print(f"\nTesting {model}...")
start = time.time()
response = client.chat.completions.create(
model=model,
messages=[{"role": "user", "content": prompt}],
temperature=0.7,
max_tokens=1024,
)
elapsed = time.time() - start
results[model] = {
"Time": f"{elapsed:.1f}s",
"Tokens": response.usage.total_tokens,
"Speed": f"{response.usage.completion_tokens / elapsed:.0f} tok/s",
"Response": response.choices[0].message.content[:150],
}
# Print comparison table
print("\nModel comparison:")
for model, data in results.items():
print(f"\n{model}:")
print(f" Time: {data['Time']}")
print(f" Speed: {data['Speed']}")
print(f" Tokens: {data['Tokens']}")
print(f" Response preview: {data['Response']}...")
return results
benchmark_baichuan("Analyze in detail the development trends of China's new energy vehicle market in 2026")Recommended Models by Use Case
| Scenario | Recommended Model | Reason |
|---|---|---|
| Financial analysis / research reports | Baichuan 4 | Optimized financial corpus |
| Medical consultation | Baichuan 4 | Rich medical knowledge |
| Daily conversation | Baichuan 4-Turbo | 3x faster |
| High concurrency / low cost | Baichuan 4-Air | Best cost-performance |
| Mathematical reasoning | Baichuan-M1 | Deep reasoning |
| Role-playing | Baichuan-NPC | Character consistency |
FAQ
Q: How do I choose between Baichuan and DeepSeek?
A: For general use, DeepSeek is more balanced; for finance/healthcare vertical domains, Baichuan is more specialized; reasoning capabilities are comparable.
Q: Does Baichuan support Function Calling?
A: Yes. Fully compatible with the OpenAI format; usage is identical to DeepSeek Function Calling.
Q: What about Baichuan’s pricing?
A: Baichuan 4-Turbo: input ¥0.004/1K tokens, output ¥0.012/1K tokens, pricing similar to DeepSeek.
Next Steps
📝 Based on the Baichuan AI Baichuan series, tested June 2026.
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