mirror of
https://github.com/MervinPraison/PraisonAI.git
synced 2026-11-07 12:47:55 -10:00
Mervin Praison
83ca07a9a9
- Remove `verbose` parameter from Agents() calls across all examples - Remove `markdown` parameter from Agent() initialization - Change `model` to `llm` in observability example - Remove unused `kind` parameter from obs.span() - Fix thinking_budget initialization to use property setter instead of constructor - Add missing `instructions` parameter to routellm_example.py Agent
131 lines
4.4 KiB
Python
131 lines
4.4 KiB
Python
"""
|
|
Basic RAG: Retrieve and Generate
|
|
|
|
This example demonstrates the fundamental RAG pattern in PraisonAI:
|
|
1. Provide context to an agent
|
|
2. Agent uses context to answer questions accurately
|
|
3. Generate grounded answers using the provided context
|
|
|
|
RAG Concept: The core retrieve-then-generate pattern that grounds LLM responses
|
|
in factual content, reducing hallucinations.
|
|
"""
|
|
|
|
from praisonaiagents import Agent
|
|
|
|
# Sample knowledge base: Technology company profiles as text strings
|
|
COMPANY_PROFILES = [
|
|
"""NovaTech Solutions specializes in cloud infrastructure and DevOps automation.
|
|
Founded in 2018, they serve over 500 enterprise clients globally.
|
|
Key products: CloudSync (infrastructure orchestration), DevPipeline (CI/CD platform).
|
|
Headquarters: Austin, Texas. Employees: 1,200.""",
|
|
|
|
"""GreenLeaf Analytics provides environmental monitoring and sustainability reporting.
|
|
Established in 2015, they work with governments and corporations on climate data.
|
|
Key products: EcoTrack (emissions monitoring), SustainReport (ESG reporting).
|
|
Headquarters: Seattle, Washington. Employees: 450.""",
|
|
|
|
"""QuantumBridge Financial offers algorithmic trading and risk management solutions.
|
|
Started in 2012, they manage over $50 billion in assets for institutional clients.
|
|
Key products: AlgoTrader Pro (trading platform), RiskShield (portfolio analytics).
|
|
Headquarters: New York City. Employees: 800.""",
|
|
|
|
"""MediCore Health develops AI-powered diagnostic tools for healthcare providers.
|
|
Founded in 2019, they partner with 200+ hospitals across North America.
|
|
Key products: DiagnosAI (medical imaging analysis), PatientFlow (care coordination).
|
|
Headquarters: Boston, Massachusetts. Employees: 350."""
|
|
]
|
|
|
|
|
|
def basic_rag_example():
|
|
"""Demonstrate basic RAG with context injection."""
|
|
|
|
# Build context from knowledge base
|
|
context = "\n\n".join(COMPANY_PROFILES)
|
|
|
|
# Create an agent with instructions
|
|
agent = Agent(
|
|
name="Company Analyst",
|
|
instructions=f"""You are a business analyst who answers questions about companies.
|
|
Use only the following company information to answer questions accurately.
|
|
If the information isn't available, say so clearly.
|
|
|
|
COMPANY DATABASE:
|
|
{context}""",
|
|
)
|
|
|
|
# Test queries
|
|
queries = [
|
|
"What products does NovaTech Solutions offer?",
|
|
"Which company focuses on environmental monitoring?",
|
|
"How many employees does QuantumBridge Financial have?",
|
|
"What is MediCore Health's main focus area?"
|
|
]
|
|
|
|
print("=" * 60)
|
|
print("BASIC RAG EXAMPLE: Company Knowledge Base")
|
|
print("=" * 60)
|
|
|
|
for query in queries:
|
|
print(f"\n📝 Query: {query}")
|
|
response = agent.chat(query)
|
|
print(f"💡 Answer: {response}")
|
|
print("-" * 40)
|
|
|
|
|
|
def rag_with_query_context():
|
|
"""Demonstrate per-query context injection for RAG."""
|
|
|
|
# Create a general agent
|
|
agent = Agent(
|
|
name="Research Assistant",
|
|
instructions="You answer questions based on the context provided in each query.",
|
|
verbose=False
|
|
)
|
|
|
|
# Simulate retrieved context (in real RAG, this comes from vector search)
|
|
retrieved_context = """
|
|
The Pacific Ocean is the largest and deepest ocean on Earth.
|
|
It covers approximately 165.25 million square kilometers.
|
|
The Mariana Trench, located in the Pacific, is the deepest known point at 10,994 meters.
|
|
The Pacific Ocean was named by explorer Ferdinand Magellan in 1520.
|
|
"""
|
|
|
|
query = "What is the deepest point in the Pacific Ocean?"
|
|
|
|
# Build prompt with context (RAG pattern)
|
|
prompt_with_context = f"""Use the following context to answer the question.
|
|
|
|
Context:
|
|
{retrieved_context}
|
|
|
|
Question: {query}
|
|
|
|
Answer based only on the context provided:"""
|
|
|
|
print("\n" + "=" * 60)
|
|
print("RAG WITH PER-QUERY CONTEXT INJECTION")
|
|
print("=" * 60)
|
|
|
|
print(f"\n📚 Context: {retrieved_context[:100]}...")
|
|
print(f"\n📝 Query: {query}")
|
|
|
|
response = agent.chat(prompt_with_context)
|
|
print(f"\n💡 Answer: {response}")
|
|
|
|
|
|
def main():
|
|
"""Run all basic RAG examples."""
|
|
print("\n🚀 PraisonAI Basic RAG Examples\n")
|
|
|
|
# Example 1: Basic RAG with knowledge base
|
|
basic_rag_example()
|
|
|
|
# Example 2: Per-query context injection
|
|
rag_with_query_context()
|
|
|
|
print("\n✅ Basic RAG examples completed!")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|