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https://github.com/HKUDS/LightRAG.git
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Update insert_custom_kg
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zrguo
parent
5385616e7e
commit
40b10e8fcf
21
README.md
21
README.md
@ -903,7 +903,8 @@ custom_kg = {
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"chunks": [
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{
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"content": "Alice and Bob are collaborating on quantum computing research.",
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"source_id": "doc-1"
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"source_id": "doc-1",
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"file_path": "test_file",
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}
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],
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"entities": [
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@ -911,19 +912,22 @@ custom_kg = {
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"entity_name": "Alice",
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"entity_type": "person",
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"description": "Alice is a researcher specializing in quantum physics.",
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"source_id": "doc-1"
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"source_id": "doc-1",
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"file_path": "test_file"
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},
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{
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"entity_name": "Bob",
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"entity_type": "person",
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"description": "Bob is a mathematician.",
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"source_id": "doc-1"
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"source_id": "doc-1",
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"file_path": "test_file"
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},
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{
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"entity_name": "Quantum Computing",
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"entity_type": "technology",
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"description": "Quantum computing utilizes quantum mechanical phenomena for computation.",
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"source_id": "doc-1"
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"source_id": "doc-1",
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"file_path": "test_file"
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}
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],
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"relationships": [
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@ -933,7 +937,8 @@ custom_kg = {
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"description": "Alice and Bob are research partners.",
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"keywords": "collaboration research",
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"weight": 1.0,
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"source_id": "doc-1"
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"source_id": "doc-1",
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"file_path": "test_file"
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},
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{
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"src_id": "Alice",
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@ -941,7 +946,8 @@ custom_kg = {
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"description": "Alice conducts research on quantum computing.",
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"keywords": "research expertise",
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"weight": 1.0,
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"source_id": "doc-1"
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"source_id": "doc-1",
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"file_path": "test_file"
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},
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{
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"src_id": "Bob",
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@ -949,7 +955,8 @@ custom_kg = {
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"description": "Bob researches quantum computing.",
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"keywords": "research application",
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"weight": 1.0,
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"source_id": "doc-1"
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"source_id": "doc-1",
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"file_path": "test_file"
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}
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]
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}
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@ -4,6 +4,7 @@ import traceback
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import asyncio
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import configparser
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import os
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import time
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import warnings
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from dataclasses import asdict, dataclass, field
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from datetime import datetime, timezone
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@ -1235,7 +1236,6 @@ class LightRAG:
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self,
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custom_kg: dict[str, Any],
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full_doc_id: str = None,
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file_path: str = "custom_kg",
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) -> None:
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update_storage = False
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try:
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@ -1245,6 +1245,7 @@ class LightRAG:
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for chunk_data in custom_kg.get("chunks", []):
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chunk_content = clean_text(chunk_data["content"])
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source_id = chunk_data["source_id"]
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file_path = chunk_data.get("file_path", "custom_kg")
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tokens = len(self.tokenizer.encode(chunk_content))
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chunk_order_index = (
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0
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@ -1261,7 +1262,7 @@ class LightRAG:
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"full_doc_id": full_doc_id
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if full_doc_id is not None
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else source_id,
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"file_path": file_path, # Add file path
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"file_path": file_path,
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"status": DocStatus.PROCESSED,
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}
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all_chunks_data[chunk_id] = chunk_entry
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@ -1282,6 +1283,7 @@ class LightRAG:
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description = entity_data.get("description", "No description provided")
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source_chunk_id = entity_data.get("source_id", "UNKNOWN")
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source_id = chunk_to_source_map.get(source_chunk_id, "UNKNOWN")
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file_path = entity_data.get("file_path", "custom_kg")
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# Log if source_id is UNKNOWN
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if source_id == "UNKNOWN":
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@ -1296,6 +1298,7 @@ class LightRAG:
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"description": description,
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"source_id": source_id,
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"file_path": file_path,
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"created_at": int(time.time()),
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}
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# Insert node data into the knowledge graph
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await self.chunk_entity_relation_graph.upsert_node(
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@ -1315,6 +1318,7 @@ class LightRAG:
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weight = relationship_data.get("weight", 1.0)
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source_chunk_id = relationship_data.get("source_id", "UNKNOWN")
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source_id = chunk_to_source_map.get(source_chunk_id, "UNKNOWN")
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file_path = relationship_data.get("file_path", "custom_kg")
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# Log if source_id is UNKNOWN
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if source_id == "UNKNOWN":
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@ -1334,6 +1338,8 @@ class LightRAG:
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"source_id": source_id,
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"description": "UNKNOWN",
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"entity_type": "UNKNOWN",
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"file_path": file_path,
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"created_at": int(time.time()),
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},
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)
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@ -1346,8 +1352,11 @@ class LightRAG:
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"description": description,
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"keywords": keywords,
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"source_id": source_id,
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"file_path": file_path,
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"created_at": int(time.time()),
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},
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)
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edge_data: dict[str, str] = {
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"src_id": src_id,
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"tgt_id": tgt_id,
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@ -1355,6 +1364,8 @@ class LightRAG:
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"keywords": keywords,
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"source_id": source_id,
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"weight": weight,
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"file_path": file_path,
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"created_at": int(time.time()),
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}
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all_relationships_data.append(edge_data)
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update_storage = True
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@ -1367,7 +1378,7 @@ class LightRAG:
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"source_id": dp["source_id"],
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"description": dp["description"],
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"entity_type": dp["entity_type"],
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"file_path": file_path, # Add file path
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"file_path": dp.get("file_path", "custom_kg"),
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}
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for dp in all_entities_data
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}
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@ -1383,7 +1394,7 @@ class LightRAG:
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"keywords": dp["keywords"],
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"description": dp["description"],
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"weight": dp["weight"],
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"file_path": file_path, # Add file path
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"file_path": dp.get("file_path", "custom_kg"),
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}
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for dp in all_relationships_data
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}
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@ -496,6 +496,7 @@ async def _merge_edges_then_upsert(
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keywords=keywords,
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source_id=source_id,
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file_path=file_path,
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created_at=int(time.time()),
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)
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return edge_data
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@ -1,5 +1,6 @@
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from __future__ import annotations
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import time
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import asyncio
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from typing import Any, cast
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@ -479,7 +480,9 @@ async def acreate_entity(
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"entity_id": entity_name,
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"entity_type": entity_data.get("entity_type", "UNKNOWN"),
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"description": entity_data.get("description", ""),
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"source_id": entity_data.get("source_id", "manual"),
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"source_id": entity_data.get("source_id", "manual_creation"),
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"file_path": entity_data.get("file_path", "manual_creation"),
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"created_at": int(time.time()),
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}
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# Add entity to knowledge graph
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@ -575,8 +578,10 @@ async def acreate_relation(
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edge_data = {
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"description": relation_data.get("description", ""),
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"keywords": relation_data.get("keywords", ""),
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"source_id": relation_data.get("source_id", "manual"),
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"source_id": relation_data.get("source_id", "manual_creation"),
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"weight": float(relation_data.get("weight", 1.0)),
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"file_path": relation_data.get("file_path", "manual_creation"),
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"created_at": int(time.time()),
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}
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# Add relation to knowledge graph
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