Knowledge Base

from praisonaiagents import Agent

agent = Agent(
    name="Research Assistant",
    instructions="Answer questions using the knowledge base.",
    knowledge=["research_paper.pdf", "data.txt"],
)

agent.start("What are the key findings?")

knowledge_config = {
    "vector_store": {
        "provider": "chroma",
        "config": {
            "collection_name": "knowledge_base",
            "path": ".praison",
            "distance_metric": "cosine"
        }
    },
    "embedder": {
        "provider": "openai",
        "config": {
            # Overridden by OPENAI_EMBEDDING_MODEL if set; see /docs/cli/knowledge-cli#environment-variables
            "model": "text-embedding-3-small"
        }
    }
}

from praisonaiagents import Knowledge

kb = Knowledge(config={
    "vector_store": {
        "provider": "mem0",
        "config": {"collection_name": "my_docs"},
    }
})
print(type(kb.memory).__name__)   # 'Mem0Adapter' on the fix; 'SQLiteKnowledgeAdapter' means you're on a buggy release.

WARNING praisonaiagents.knowledge.knowledge: Knowledge provider 'mem0' failed to initialize (<reason>); falling back to a different backend. Retrieval quality may be reduced.

from praisonaiagents import Agent, Knowledge

kb = Knowledge(config={
    "vector_store": {
        "provider": "mem0",
        "config": {"collection_name": "research_docs"},
    }
})

agent = Agent(
    name="Researcher",
    instructions="Answer research questions using the knowledge base.",
    knowledge=kb,
)

agent.start("What did we learn about solar output in 2024?")

knowledge_config = {
    "vector_store": {
        "provider": "chroma",
        "config": {
            "collection_name": "knowledge_base",
            "path": ".praison"
        }
    },
    "graph_store": {
        "provider": "neo4j",
        "config": {
            "url": "bolt://localhost:7687",
            "username": "neo4j",
            "password": "password"
        }
    },
    "llm": {
        "provider": "openai",
        "config": {
            "model": "gpt-4o-mini",
            "temperature": 0
        }
    },
    "reranker": {
        "enabled": True,
        "default_rerank": False
    }
}

from praisonaiagents import Agent

agent = Agent(
    name="Code Reviewer",
    instructions="Answer questions about the codebase.",
    knowledge=["src/auth.py", "src/config.yaml", "Dockerfile"],
)

agent.start("How does authentication work?")

# Add with metadata
kb.add(
    "research.pdf",
    user_id="user123",
    metadata={
        "category": "AI Research",
        "year": 2024,
        "author": "Dr. Smith"
    }
)

# Batch processing
documents = ["doc1.pdf", "doc2.txt", "doc3.md"]
for doc in documents:
    kb.add(doc, user_id="user123")

from praisonaiagents import Knowledge

kb = Knowledge()
try:
    kb.add("research_paper.pdf")
except RuntimeError as e:
    # message includes the embedding model and the underlying error, e.g.
    # "Failed to generate embedding (model=text-embedding-3-small): 403 model_not_found"
    print(f"Indexing failed: {e}")

result = adapter.add("Paris is the capital of France.")
if not result.success:
    print(result.message)   # "Failed to generate embedding (model=…): …"

# Simple search
results = kb.search("artificial intelligence", limit=5)

# User-scoped search
results = kb.search(
    query="machine learning",
    user_id="user123",
    limit=10
)

agent = Agent(
    name="Research Assistant",
    knowledge={
        "sources": ["papers/"],
        "vector_store": {
            "provider": "chroma",
            "config": {"collection_name": "research_docs"}
        }
    },
    memory=True  # Enable memory integration
)

# Knowledge is automatically searched during conversations
response = agent.start("What does the research say about transformers?")

knowledge_config = {
    "graph_store": {
        "provider": "neo4j",  # or "memgraph"
        "config": {
            "url": "bolt://localhost:7687",
            "username": "neo4j",
            "password": "password"
        }
    },
    "extract_relationships": True
}

# Find related concepts
results = kb.search_graph(
    "What concepts are related to transformers?",
    user_id="user123"
)

# Explore connections
results = kb.search_graph(
    "How is attention mechanism connected to BERT?",
    user_id="user123"
)

import asyncio
from praisonaiagents import Agent

agent = Agent(
    name="Research Assistant",
    instructions="Answer questions using the knowledge base.",
    knowledge=["research_paper.pdf"],
)

async def main():
    # Both lookups offload to worker threads — the loop stays responsive.
    await asyncio.gather(
        agent.astart("What are the key findings?"),
        agent.astart("Summarise the methodology"),
    )

asyncio.run(main())