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GML-1980 enhance community search results for non-cross-domain questions #5

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Merged
chengbiao-jin merged 2 commits into from
Aug 1, 2025
Merged

GML-1980 enhance community search results for non-cross-domain questions #5

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c3b7b68
GML-1980 enhance community search
chengbiao-jin Aug 1, 2025
29743a8
GML-1980 enhance community search
chengbiao-jin Aug 1, 2025
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4 changes: 4 additions & 0 deletions common/gsql/supportai/retrievers/GraphRAG_Community_Vector_Search.gsql
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Original file line number Diff line number Diff line change
Expand Up @@ -37,6 +37,10 @@ CREATE OR REPLACE DISTRIBUTED QUERY GraphRAG_Community_Vector_Search(LIST<FLOAT>
ACCUM s.@children += c
POST-ACCUM @@verbose_info += ("community_level_"+to_string(i-1) -> s.@children);
END;
start_chunks = vectorSearch({DocumentChunk.embedding}, query_vector, top_k);
extra_selected_comms = SELECT m FROM start_chunks:dc -(CONTAINS_ENTITY>)- Entity:v -(RESOLVES_TO>)- ResolvedEntity:r -(IN_COMMUNITY>)- Community:m;
selected_comms = selected_comms UNION extra_selected_comms;

IF with_doc THEN
related_chunks = SELECT c FROM Content:c -(<HAS_CONTENT)- Document:d -(HAS_CHILD>)- DocumentChunk:dc -(CONTAINS_ENTITY>)- Entity:v -(RESOLVES_TO>)- ResolvedEntity:r -(IN_COMMUNITY>)- selected_comms:m
ACCUM m.@context += c.text, m.@children += d
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2 changes: 2 additions & 0 deletions graphrag/app/routers/supportai.py
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Expand Up @@ -238,6 +238,8 @@ def search(
query.method_params["with_doc"],
query.method_params["verbose"],
)
else:
raise Exception(f"Method {query.method} not implemented")
return res


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16 changes: 16 additions & 0 deletions graphrag/app/supportai/retrievers/CommunityRetriever.py
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Expand Up @@ -62,6 +62,22 @@ def search(self, question, community_level: int, top_k: int = 5, similarity_thre
},
usePost=True
)

# Include similarity search results
if with_chunk or with_doc:
start_set = self._generate_start_set(questions, ["DocumentChunk"], top_k, similarity_threshold)

self._check_query_install("Content_Similarity_Search")
resp = self.conn.runInstalledQuery(
"Content_Similarity_Search",
params = {
"json_list_vts": str(start_set),
"v_type": "DocumentChunk",
"verbose": verbose,
},
usePost=True
)
res[0]["final_retrieval"]["Similarity_Context"] = [resp[0]["final_retrieval"][x] for x in resp[0]["final_retrieval"]]
else:
query_vector = self._generate_embedding(question)

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