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Merge branch 'release/v0.2.3' into develop

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# Conflicts:
#	api/app/core/agent/langchain_agent.py
#	api/app/core/memory/agent/langgraph_graph/write_graph.py
#	api/app/repositories/neo4j/graph_saver.py
#	api/app/services/draft_run_service.py
This commit is contained in:
Mark
2026-02-06 14:48:50 +08:00
parent be29666916 52260f469a
commit 7b72bf0cd0
45 changed files with 973 additions and 850 deletions
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40
api/app/core/rag/nlp/search.py
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@@ -28,7 +28,9 @@ from app.core.rag.common.float_utils import get_float
from app.core.rag.common.constants import PAGERANK_FLD, TAG_FLD
from app.core.rag.llm.chat_model import Base
from app.core.rag.llm.embedding_model import OpenAIEmbed
import logging
logger = logging.getLogger(__name__)
def knowledge_retrieval(
query: str,
@@ -62,7 +64,15 @@ def knowledge_retrieval(
merge_strategy = config.get("merge_strategy", "weight")
reranker_id = config.get("reranker_id")
reranker_top_k = config.get("reranker_top_k", 1024)
use_graph = config.get("use_graph", "false").lower() == "true"
# use_graph = config.get("use_graph", "false").lower() == "true"
use_graph_value = config.get("use_graph", False)
if isinstance(use_graph_value, bool):
use_graph = use_graph_value
elif isinstance(use_graph_value, str):
use_graph = use_graph_value.lower() in ("true", "1", "yes")
else:
use_graph = False
file_names_filter = []
if user_ids:
@@ -159,13 +169,29 @@ def knowledge_retrieval(
# Use the specified reranker for re-ranking
if reranker_id:
return rerank(db=db, reranker_id=reranker_id, query=query, docs=all_results, top_k=reranker_top_k)
# use graph
try:
return rerank(db=db, reranker_id=reranker_id, query=query, docs=all_results, top_k=reranker_top_k)
except Exception as rerank_error:
# If reranker fails, log warning and continue with original results
logger.warning(
"Reranker failed, falling back to original results",
extra={
"reranker_id": reranker_id,
"query": query,
"doc_count": len(all_results),
"error": str(rerank_error),
},
)
if use_graph:
from app.core.rag.common.settings import kg_retriever
doc = kg_retriever.retrieval(question=query, workspace_ids=workspace_ids, kb_ids=kb_ids, emb_mdl=embedding_model, llm=chat_model)
if doc:
all_results.insert(0, doc)
try:
from app.core.rag.common.settings import kg_retriever
doc = kg_retriever.retrieval(question=query, workspace_ids=workspace_ids, kb_ids=kb_ids, emb_mdl=embedding_model, llm=chat_model)
if doc:
all_results.insert(0, doc)
except Exception as graph_error:
print(f"Failed to retrieve from knowledge graph: {str(graph_error)}")
return all_results
except Exception as e: