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Fix: meta data filter with AND logic operations. (#9687)

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### What problem does this PR solve?

Close #9648

### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)
This commit is contained in:
Kevin Hu
2025-08-25 18:29:24 +08:00
committed by GitHub
parent d0fa66f4d5
commit 8d8a5f73b6
7 changed files with 41 additions and 20 deletions
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7
rag/svr/task_executor.py
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@ -21,7 +21,7 @@ import sys
import threading
import time
from api.utils.api_utils import timeout, is_strong_enough
from api.utils.api_utils import timeout
from api.utils.log_utils import init_root_logger, get_project_base_directory
from graphrag.general.index import run_graphrag
from graphrag.utils import get_llm_cache, set_llm_cache, get_tags_from_cache, set_tags_to_cache
@ -478,8 +478,6 @@ async def embedding(docs, mdl, parser_config=None, callback=None):
@timeout(3600)
async def run_raptor(row, chat_mdl, embd_mdl, vector_size, callback=None):
# Pressure test for GraphRAG task
await is_strong_enough(chat_mdl, embd_mdl)
chunks = []
vctr_nm = "q_%d_vec"%vector_size
for d in settings.retrievaler.chunk_list(row["doc_id"], row["tenant_id"], [str(row["kb_id"])],
@ -553,7 +551,6 @@ async def do_handle_task(task):
try:
# bind embedding model
embedding_model = LLMBundle(task_tenant_id, LLMType.EMBEDDING, llm_name=task_embedding_id, lang=task_language)
await is_strong_enough(None, embedding_model)
vts, _ = embedding_model.encode(["ok"])
vector_size = len(vts[0])
except Exception as e:
@ -568,7 +565,6 @@ async def do_handle_task(task):
if task.get("task_type", "") == "raptor":
# bind LLM for raptor
chat_model = LLMBundle(task_tenant_id, LLMType.CHAT, llm_name=task_llm_id, lang=task_language)
await is_strong_enough(chat_model, None)
# run RAPTOR
async with kg_limiter:
chunks, token_count = await run_raptor(task, chat_model, embedding_model, vector_size, progress_callback)
@ -580,7 +576,6 @@ async def do_handle_task(task):
graphrag_conf = task["kb_parser_config"].get("graphrag", {})
start_ts = timer()
chat_model = LLMBundle(task_tenant_id, LLMType.CHAT, llm_name=task_llm_id, lang=task_language)
await is_strong_enough(chat_model, None)
with_resolution = graphrag_conf.get("resolution", False)
with_community = graphrag_conf.get("community", False)
async with kg_limiter: