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File: /entities.py
Project: controllers
Created Date: Sunday January 18th 2026
Author: Christian Nonis <alch.infoemail@gmail.com>
-----
Last Modified: Thursday January 29th 2026 8:43:59 pm
Modified By: Christian Nonis <alch.infoemail@gmail.com>
-----
"""
from typing import List, Literal, Optional
from fastapi import HTTPException
from starlette.responses import JSONResponse
from src.core.search.entity_context import EntityContext
from src.core.search.entity_info import EventSynergyRetriever
from src.services.api.constants.requests import (
GetEntityInfoResponse,
GetEntityContextResponse,
GetEntitySibilingsResponse,
GetEntityStatusResponse,
RecommendItem,
RecommendResponse,
)
from src.core.search.entity_sibilings import EntitySinergyRetriever
from src.core.search.recommend import EntityRecommendRetriever
from src.services.data.main import data_adapter
from src.services.input.agents import embeddings_adapter
from src.services.kg_agent.main import graph_adapter, vector_store_adapter
from src.utils.vector_search import VectorSearchFacade
vector_search = VectorSearchFacade(vector_store_adapter)
async def get_entity_info(
target: str, query: str, max_depth: int = 3, brain_id: str = "default"
) -> GetEntityInfoResponse:
"""
Retrieve matching event paths for a target and query up to a specified traversal depth.
Parameters:
target (str): The entity identifier or text to locate.
query (str): The query text used to find relevant event matches.
max_depth (int): Maximum path traversal depth to consider when retrieving matches.
brain_id (str): Brain/workspace identifier to scope the retrieval.
Returns:
GetEntityInfoResponse: Contains the located target node (`target_node`) and the retrieved paths (`path`).
"""
event_synergy_retriever = EventSynergyRetriever(brain_id)
paths = event_synergy_retriever.retrieve_matches(target, query, max_depth)
return GetEntityInfoResponse(target_node=paths.target_node, path=paths)
async def get_entity_context(
target: str, context_depth: int = 3, brain_id: str = "default"
) -> GetEntityContextResponse:
"""
Retrieve contextual information for the specified entity target.
Parameters:
target (str): The entity identifier or text to retrieve context for.
context_depth (int): Maximum graph depth (number of hops) to include in the neighborhood.
brain_id (str): Identifier of the brain/workspace to query.
Returns:
GetEntityContextResponse: Response with the following fields:
target_node: The node representing the target entity.
neighborhood: Nearby nodes and relationships up to `context_depth`.
text_contexts: Relevant text excerpts or documents associated with the target and neighborhood.
natural_language_web: A natural-language representation or summary of the surrounding context.
"""
entity_context = EntityContext(target, brain_id)
target_node, neighborhood, text_contexts, natural_language_web = (
entity_context.get_context(context_depth=context_depth)
)
return GetEntityContextResponse(
target_node=target_node,
neighborhood=neighborhood,
text_contexts=text_contexts,
natural_language_web=natural_language_web,
)
async def get_entity_sibilings(
target: str,
polarity: Literal["same", "opposite"] = "same",
do: bool = False,
pa: bool = False,
ppa: bool = False,
top_k: int = 50,
labels: Optional[List[str]] = None,
brain_id: str = "default",
) -> GetEntitySibilingsResponse:
"""
Retrieve sibling entities (synergies) for a target entity.
Parameters:
polarity: "same" for matching node polarity; "opposite" for positive↔negative.
do: If True, only direct synergies are returned.
pa: If True, potential anchors are returned.
ppa: If True, seed anchors are returned.
top_k: Max synergies to return.
labels: Optional label filter for candidates.
Returns:
GetEntitySibilingsResponse: Object containing the resolved target node and its list of synergies.
"""
entity_sibilings = EntitySinergyRetriever(brain_id)
target_node, synergies, seed_nodes, potential_anchors = (
entity_sibilings.retrieve_sibilings(
target, polarity, do, pa, ppa, top_k=top_k, labels=labels
)
)
if target_node is None:
raise HTTPException(
status_code=404,
detail="No entity found matching the target.",
)
return JSONResponse(
content={
"target_node": target_node.model_dump(mode="json"),
"synergies": [synergy.model_dump(mode="json") for synergy in synergies],
**(
{"anchors": [anchor.model_dump(mode="json") for anchor in seed_nodes]}
if ppa and seed_nodes
else {}
),
**(
{
"potential_anchors": [
anchor.model_dump(mode="json") for anchor in potential_anchors
]
}
if pa and potential_anchors
else {}
),
}
)
async def get_recommendations(
target: str,
polarity: Literal["same", "opposite"] = "same",
top_k: int = 20,
labels: Optional[List[str]] = None,
include_asymmetric: bool = True,
include_multi_interest: bool = True,
diversify: bool = True,
asymmetric_direction: Literal["outbound", "inbound", "both"] = "outbound",
brain_id: str = "default",
exclude_seen: bool = False,
recency_half_life_days: Optional[float] = None,
dampen_degree: bool = False,
include_attribute_pref: bool = False,
behavior_weights: Optional[dict] = None,
) -> RecommendResponse:
retriever = EntityRecommendRetriever(brain_id)
target_node, recommendations = retriever.recommend(
target,
polarity=polarity,
top_k=top_k,
labels=labels,
include_asymmetric=include_asymmetric,
include_multi_interest=include_multi_interest,
include_attribute_pref=include_attribute_pref,
diversify=diversify,
asymmetric_direction=asymmetric_direction,
exclude_seen=exclude_seen,
recency_half_life_days=recency_half_life_days,
dampen_degree=dampen_degree,
behavior_weights=behavior_weights,
)
if target_node is None:
raise HTTPException(
status_code=404,
detail="No entity found matching the target.",
)
items = [
RecommendItem(
node=r["node"],
score=r["score"],
connected_by=r["connected_by"],
channel=r["channel"],
)
for r in recommendations
]
return JSONResponse(
content=RecommendResponse(
target_node=target_node,
recommendations=items,
).model_dump(mode="json")
)
async def get_entity_status(
target: str,
types: List[str] | None = None,
brain_id: str = "default",
) -> GetEntityStatusResponse:
"""
Retrieve status information for an entity matching the provided target text.
Parameters:
target (str): Text used to locate the entity.
types (List[str]): Optional list of node label types to filter matches; if provided, the first matching node whose labels intersect `types` is chosen.
brain_id (str): Identifier of the brain/workspace to query.
Returns:
GetEntityStatusResponse: Response containing the matched node (or `None` if not found), `exists` indicating presence, `has_relationships` indicating whether the node has neighbors, `relationships` listing neighbor tuples, and `observations` associated with the node. When no matching node is found, `exists` is `False` and `relationships` and `observations` are empty.
"""
if types is None:
types = []
target_embeddings = embeddings_adapter.embed_query(target)
target_node_vs = vector_search.search_nodes(
target_embeddings.embeddings,
brain_id=brain_id,
)
target_node = None
for target_node_v in target_node_vs:
target_node_id = target_node_v.metadata.get("uuid")
nodes = graph_adapter.get_by_uuids([target_node_id], brain_id=brain_id)
if len(nodes) == 0:
continue
target_node = nodes[0]
if len(types) > 0:
if set(target_node.labels).intersection(set(types)):
break
else:
break
if not target_node:
return GetEntityStatusResponse(
node=None,
exists=False,
has_relationships=False,
relationships=[],
observations=[],
)
rel_tuples = graph_adapter.get_neighbors([target_node], brain_id=brain_id)
observations = data_adapter.get_observations_list(
brain_id=brain_id, resource_id=target_node.uuid
)
return GetEntityStatusResponse(
node=target_node,
exists=True,
has_relationships=len(rel_tuples[target_node.uuid]) > 0,
relationships=rel_tuples[target_node.uuid],
observations=observations,
)
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