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import logging
import threading
from concurrent.futures import ThreadPoolExecutor, as_completed
from typing import List
from ..models import Document
from .neo4j_client import Neo4jClient
from .entity_extractor import EntityExtractor
from .relationship_extractor import RelationshipExtractor
from .entity_resolver import EntityResolver
from .vector_retriever import VectorRetriever
logger = logging.getLogger(__name__)
class GraphBuilder:
def __init__(self):
logger.info("Initializing GraphBuilder orchestrator service.")
self.neo4j_client = Neo4jClient()
self.entity_extractor = EntityExtractor()
self.relationship_extractor = RelationshipExtractor()
self.entity_resolver = EntityResolver()
self.vector_retriever = VectorRetriever()
def _update_progress(self, doc, step: str, progress: int):
"""Update document processing progress for frontend polling."""
doc.processing_step = step
doc.processing_progress = progress
doc.save(update_fields=['processing_step', 'processing_progress'])
def _batch_create_entities(self, entities: List[dict], user_id: str, document_id: str):
"""Batch create entities using UNWIND to reduce N+1 queries."""
if not entities:
return
batch_size = 50
for i in range(0, len(entities), batch_size):
batch = entities[i:i + batch_size]
query = (
"UNWIND $entities AS ent "
"MERGE (e:Entity {name: ent.name, user_id: $user_id}) "
"ON CREATE SET e.type = ent.type, e.description = ent.description, "
" e.source_doc = ent.source_doc, e.source_doc_id = $document_id, e.page = ent.page, e.created_at = timestamp() "
"ON MATCH SET e.description = coalesce(e.description, ent.description), e.source_doc_id = $document_id, e.source_doc = ent.source_doc"
)
params = {
"entities": [
{
"name": e["name"].strip(),
"type": e["type"].strip(),
"description": e["description"].strip(),
"source_doc": e.get("source_doc", ""),
"page": e.get("page", 0)
}
for e in batch
],
"user_id": str(user_id),
"document_id": str(document_id)
}
self.neo4j_client.execute_query(query, params)
def _batch_create_relationships(self, relationships: List[dict], user_id: str, document_id: str):
"""Batch create relationships grouped by type using UNWIND."""
if not relationships:
return
from .neo4j_client import VALID_RELATIONSHIP_TYPES
# Group relationships by type
grouped: dict[str, list] = {}
for rel in relationships:
rel_type = rel["relationship_type"].upper().strip()
if rel_type not in VALID_RELATIONSHIP_TYPES:
rel_type = "RELATED_TO"
grouped.setdefault(rel_type, []).append(rel)
for rel_type, rels in grouped.items():
batch_size = 50
for i in range(0, len(rels), batch_size):
batch = rels[i:i + batch_size]
query = (
"UNWIND $rels AS rel "
"MATCH (source:Entity {name: rel.source, user_id: $user_id}) "
"MATCH (target:Entity {name: rel.target, user_id: $user_id}) "
f"MERGE (source)-[r:{rel_type}]->(target) "
"ON CREATE SET r.description = rel.description, r.confidence = rel.confidence, "
" r.source_doc = rel.source_doc, r.source_doc_id = $document_id, r.page = rel.page, r.created_at = timestamp() "
"ON MATCH SET r.source_doc_id = $document_id, r.source_doc = rel.source_doc "
"RETURN r LIMIT 1"
)
params = {
"rels": [
{
"source": rel["source_entity"].strip(),
"target": rel["target_entity"].strip(),
"description": rel["description"].strip(),
"confidence": float(rel["confidence"]),
"source_doc": rel.get("source_doc", ""),
"page": rel.get("page", 0)
}
for rel in batch
],
"user_id": str(user_id),
"document_id": str(document_id)
}
self.neo4j_client.execute_query(query, params)
def process_document(self, document_id, user_id):
"""
Orchestrates the entire GraphRAG ingestion pipeline.
Reads file, extracts entities & relationships, resolves duplicates, and writes to Neo4j.
Updates processing_progress and processing_step at each stage for frontend polling.
"""
try:
doc = Document.objects.get(id=document_id)
except Document.DoesNotExist:
logger.error("Document with ID %s does not exist. Ingestion aborted.", document_id)
return
logger.info("Beginning background graph building for Document: %s (User ID: %s)", doc.name, user_id)
# 1. Update status to PROCESSING
doc.status = Document.Status.PROCESSING
doc.save()
try:
filepath = doc.file.path
if not os.path.exists(filepath):
raise FileNotFoundError(f"File not found on disk: {filepath}")
# 2. Parse file into sections/pages
self._update_progress(doc, "Parsing document...", 5)
sections = self._parse_file_to_sections(filepath)
logger.info("Parsed document into %d sections for analysis.", len(sections))
# 2b. Index document text in ChromaDB vector store
self._update_progress(doc, "Indexing vectors in ChromaDB...", 15)
full_text = "\n\n".join([sec["text"] for sec in sections])
logger.info("Indexing document text in ChromaDB (Doc: %s, User: %s)...", doc.name, user_id)
self.vector_retriever.index_document(
text_content=full_text,
doc_name=doc.name,
user_id=user_id
)
all_entities = []
all_relationships = []
all_entities_lock = threading.Lock()
all_relationships_lock = threading.Lock()
total_sections = len(sections)
def process_section(sec: dict) -> tuple[List[dict], List[dict]]:
"""Process a single section: extract entities and relationships."""
import time
text = sec["text"]
page = sec["page"]
time.sleep(2)
ents = self.entity_extractor.extract_entities(text)
for e in ents:
e["page"] = page
e["source_doc"] = doc.name
time.sleep(2)
rels = self.relationship_extractor.extract_relationships(text)
for r in rels:
r["page"] = page
r["source_doc"] = doc.name
return ents, rels
# 3. Perform Entity and Relationship Extraction per section (parallel)
completed = 0
failed_count = 0
last_error = None
with ThreadPoolExecutor(max_workers=1) as executor:
futures = {executor.submit(process_section, sec): sec for sec in sections}
for future in as_completed(futures):
try:
ents, rels = future.result()
with all_entities_lock:
all_entities.extend(ents)
with all_relationships_lock:
all_relationships.extend(rels)
except Exception as e:
logger.error("Section processing failed: %s", str(e))
failed_count += 1
last_error = e
completed += 1
extraction_progress = 20 + int(55 * (completed / total_sections)) if total_sections > 0 else 20
self._update_progress(doc, f"Extracting entities... ({completed}/{total_sections})", extraction_progress)
if failed_count == total_sections and total_sections > 0:
raise RuntimeError(f"All sections failed to process. Last error: {last_error}")
# 4. Run entity resolution (deduplicate entities and rewrite relationships)
self._update_progress(doc, "Resolving duplicates...", 80)
resolved_ents, rewritten_rels = self.entity_resolver.resolve_entities(
all_entities, all_relationships
)
# 5. Batch store resolved nodes inside Neo4j
self._update_progress(doc, "Building knowledge graph...", 85)
logger.info("Writing %d resolved entities to Neo4j...", len(resolved_ents))
self._batch_create_entities(resolved_ents, user_id, document_id)
# 6. Batch store rewritten edges inside Neo4j
self._update_progress(doc, "Writing relationships...", 92)
logger.info("Writing %d rewritten relationships to Neo4j...", len(rewritten_rels))
self._batch_create_relationships(rewritten_rels, user_id, document_id)
# 7. Update status to COMPLETED and record counts
doc.entity_count = len(resolved_ents)
doc.relationship_count = len(rewritten_rels)
doc.status = Document.Status.COMPLETED
doc.error_message = None
doc.processing_progress = 100
doc.processing_step = "Complete"
doc.save()
logger.info("Successfully finished building knowledge graph for Document: %s", doc.name)
except Exception as e:
logger.error("Failed to process document: %s. Error: %s", doc.name, str(e), exc_info=True)
doc.status = Document.Status.FAILED
doc.error_message = str(e)
doc.processing_step = f"Failed: {str(e)[:100]}"
doc.save()
def delete_document_data(self, document_id, user_id):
"""
Cleans up and deletes associated Neo4j node/edge elements for a deleted document.
Attempts both graph and vector cleanup independently to avoid orphaned data.
"""
try:
doc = Document.objects.get(id=document_id)
logger.info("Triggering graph wipe for Document: %s (User ID: %s)", doc.name, user_id)
# Attempt both cleanups independently
neo4j_ok = True
vector_ok = True
try:
self.neo4j_client.delete_document_nodes(doc.id, user_id)
except Exception as e:
logger.error("Neo4j cleanup failed for Document: %s. Error: %s", doc.name, str(e))
neo4j_ok = False
try:
self.vector_retriever.delete_document_vectors(doc.name, user_id)
except Exception as e:
logger.error("ChromaDB cleanup failed for Document: %s. Error: %s", doc.name, str(e))
vector_ok = False
if neo4j_ok and vector_ok:
logger.info("Finished Graph cleanup for Document: %s", doc.name)
else:
logger.warning("Partial cleanup for Document: %s (Neo4j: %s, Vector: %s)",
doc.name, "OK" if neo4j_ok else "FAIL", "OK" if vector_ok else "FAIL")
except Document.DoesNotExist:
logger.error("Document with ID %s does not exist. Cleanup aborted.", document_id)
except Exception as e:
logger.error("Failed to clean up graph data for Document ID: %s. Error: %s",
document_id, str(e), exc_info=True)
def _parse_file_to_sections(self, filepath: str) -> List[dict]:
"""
Loads document file and splits content into page/paragraph sections.
"""
ext = filepath.split(".")[-1].lower()
sections = []
if ext == "pdf":
import pypdf
reader = pypdf.PdfReader(filepath)
for idx, page in enumerate(reader.pages):
text = page.extract_text()
if text and text.strip():
sections.append({
"text": text.strip(),
"page": idx + 1
})
elif ext in ["docx", "doc"]:
import docx
doc = docx.Document(filepath)
current_chunk = []
current_len = 0
section_idx = 1
for p in doc.paragraphs:
txt = p.text.strip() if p.text else ""
if txt:
current_chunk.append(txt)
current_len += len(txt)
if current_len >= 1500:
sections.append({
"text": "\n".join(current_chunk),
"page": section_idx
})
current_chunk = []
current_len = 0
section_idx += 1
if current_chunk:
sections.append({
"text": "\n".join(current_chunk),
"page": section_idx
})
else:
# Default fallback for TXT, Markdown, etc.
with open(filepath, "r", encoding="utf-8", errors="ignore") as f:
content = f.read()
# Split by double newlines
paragraphs = [p.strip() for p in content.split("\n\n") if p.strip()]
current_chunk = []
current_len = 0
section_idx = 1
for p in paragraphs:
current_chunk.append(p)
current_len += len(p)
if current_len >= 1500:
sections.append({
"text": "\n\n".join(current_chunk),
"page": section_idx
})
current_chunk = []
current_len = 0
section_idx += 1
if current_chunk:
sections.append({
"text": "\n\n".join(current_chunk),
"page": section_idx
})
return sections
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