Kapso knowledge search backends: graph and navigation

from kapso.knowledge_base.search import KnowledgeSearchFactory, KGSearchFilters

search = KnowledgeSearchFactory.create("kg_graph_search", params={
    "weaviate_collection": "KapsoKG",
    "embedding_model": "text-embedding-3-large",
    "use_llm_reranker": True,
})

result = search.search(
    query="How to fine-tune an LLM with limited GPU memory?",
    filters=KGSearchFilters(top_k=5, domains=["LLMs", "PEFT"]),
)

for item in result:
    print(f"{item.id}: {item.score:.2f}")

context = result.to_context_string(max_results=3)

import json
from kapso.knowledge_base.search import KnowledgeSearchFactory

search = KnowledgeSearchFactory.create("kg_llm_navigation")

with open("kg_data.json") as handle:
    search.index(json.load(handle))

result = search.search(
    query="Best practices for feature engineering",
    context="Working on a tabular data competition",
)

@dataclass
class KGOutput:
    query: str
    filters: Optional[KGSearchFilters]
    results: List[KGResultItem]
    total_found: int
    search_metadata: Dict[str, Any]

    is_empty: bool          # property
    top_result: Optional[KGResultItem]   # property

    def get_by_type(self, page_type: str) -> List[KGResultItem]
    def get_by_domain(self, domain: str) -> List[KGResultItem]
    def get_above_score(self, min_score: float) -> List[KGResultItem]
    def to_context_string(self, max_results=5) -> str

@dataclass
class KGSearchFilters:
    top_k: int = 10
    min_score: Optional[float] = None
    page_types: Optional[List[str]] = None
    domains: Optional[List[str]] = None
    include_content: bool = True

from kapso import Kapso

kapso = Kapso()

kapso.index_kg(
    wiki_dir="data/wikis",
    save_to="data/indexes/finetuning.index",
    force=True,
)

kapso.index_kg(
    data_path="benchmarks/mle/data/kg_data.json",
    save_to="data/indexes/kaggle.index",
    search_type="kg_llm_navigation",
)

{
  "version": "1.0",
  "created_at": "2026-01-15T10:30:00Z",
  "data_source": "data/wikis",
  "search_backend": "kg_graph_search",
  "backend_refs": {
    "weaviate_collection": "KapsoKG",
    "embedding_model": "text-embedding-3-large"
  },
  "page_count": 99
}

from kapso.knowledge_base.search.base import KnowledgeSearch
from kapso.knowledge_base.search.factory import register_knowledge_search

@register_knowledge_search("my_custom_search")
class MyCustomSearch(KnowledgeSearch):
    def index(self, data: KGIndexInput) -> None:
        ...

    def search(self, query: str, filters=None, context=None) -> KGOutput:
        ...

    def get_page(self, page_title: str) -> Optional[WikiPage]:
        ...

    def edit(self, data: KGEditInput) -> bool:
        ...