Custom Knowledge Adapters

Register your own knowledge backend and reach it through vector_store.provider

Register a custom knowledge backend under any provider name, then use it from an Agent through vector_store.provider.

Quick Start

1. Register and use a custom backend

   Register your adapter class, then point an Agent’s Knowledge at it.

   from praisonaiagents import Agent, Knowledge
   from praisonaiagents.knowledge.adapters import register_knowledge_adapter
   
   class QdrantKnowledgeAdapter:
       def __init__(self, config=None, verbose=0):
           self.config = config
       def store(self, *args, **kwargs): ...
       def search(self, *args, **kwargs): return []
       def delete(self, *args, **kwargs): ...
   
   register_knowledge_adapter("qdrant", QdrantKnowledgeAdapter)
   
   agent = Agent(
       name="Researcher",
       instructions="Answer using the knowledge base.",
       knowledge=Knowledge(config={"vector_store": {"provider": "qdrant"}}),
   )
   
   agent.start("What did we learn about solar output in 2024?")

2. Lazy-load heavy backends with a factory

   Use a factory when the backend imports heavy dependencies you want to defer.

   from praisonaiagents.knowledge.adapters import register_knowledge_factory
   
   def make_qdrant_adapter(config=None, verbose=0):
       from my_backends.qdrant import QdrantKnowledgeAdapter
       return QdrantKnowledgeAdapter(config=config, verbose=verbose)
   
   register_knowledge_factory("qdrant", make_qdrant_adapter)

How It Works

The Agent reads vector_store.provider, looks it up in the registry, and calls your adapter — mem0-normalised results flow back to the user.

Pick the right path — a built-in adapter or a custom one.

Protocol Contract

Your adapter implements KnowledgeStoreProtocol. The minimum method set is store, search, and delete, each accepting the scope kwargs user_id, agent_id, and run_id.

from praisonaiagents import KnowledgeStoreProtocol

class QdrantKnowledgeAdapter:
    def __init__(self, config=None, verbose=0):
        self.config = config

    def store(self, content, *, user_id=None, agent_id=None, run_id=None, **kwargs):
        ...

    def search(self, query, *, user_id=None, agent_id=None, run_id=None, **kwargs):
        return []

    def delete(self, *, user_id=None, agent_id=None, run_id=None, **kwargs):
        ...

No base-class inheritance is required — any class matching the protocol works.

Registration APIs

Four public functions in praisonaiagents.knowledge.adapters manage the registry.

from praisonaiagents.knowledge.adapters import (
    has_knowledge_adapter,
    list_knowledge_adapters,
)

print(list_knowledge_adapters())      # ['chroma', 'mem0', 'mongodb', 'sqlite', ...]
print(has_knowledge_adapter("qdrant")) # True after registration

When Registration Must Run

Register before the first Agent(...) or Knowledge(...) call reads .memory. Import-time registration is fine; deferred registration must beat the cached_property that resolves the adapter.

from praisonaiagents.knowledge.adapters import register_knowledge_adapter

register_knowledge_adapter("qdrant", QdrantKnowledgeAdapter)  # first

agent = Agent(knowledge=Knowledge(config={"vector_store": {"provider": "qdrant"}}))
agent.start("...")  # resolves the adapter here

Best Practices

Related