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Remove multi search #2093

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Merged
natoverse merged 10 commits into from
Oct 11, 2025
Merged

Remove multi search #2093

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8574a19
Remove multi-search from CLI
natoverse Oct 10, 2025
9f98fca
Remove multi-search from API
natoverse Oct 10, 2025
9ca2b47
Flatten vector_store config
natoverse Oct 10, 2025
c64fb2f
Push hydrated vector store down to embed_text
natoverse Oct 10, 2025
94bbeb6
Remove outputs from config
natoverse Oct 10, 2025
2d9ea5a
Remove multi-search notebook/docs
natoverse Oct 10, 2025
4b202f2
Add missing response_type in basic search API
natoverse Oct 10, 2025
063c018
Fix basic search context and id mapping
natoverse Oct 10, 2025
2ffdc60
Fix v1 migration notebook
natoverse Oct 10, 2025
eb8e63f
Fix query entity search tests
natoverse Oct 10, 2025
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21 changes: 15 additions & 6 deletions docs/examples_notebooks/index_migration_to_v1.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -204,21 +204,29 @@
"source": [
"from graphrag.cache.factory import CacheFactory\n",
"from graphrag.callbacks.noop_workflow_callbacks import NoopWorkflowCallbacks\n",
"from graphrag.config.get_vector_store_settings import get_vector_store_settings\n",
"from graphrag.index.workflows.generate_text_embeddings import generate_text_embeddings\n",
"from graphrag.language_model.manager import ModelManager\n",
"from graphrag.tokenizer.get_tokenizer import get_tokenizer\n",
"\n",
"# We only need to re-run the embeddings workflow, to ensure that embeddings for all required search fields are in place\n",
"# We'll construct the context and run this function flow directly to avoid everything else\n",
"\n",
"\n",
"vector_store_config = get_vector_store_settings(config)\n",
"model_config = config.get_language_model_config(config.embed_text.model_id)\n",
"callbacks = NoopWorkflowCallbacks()\n",
"cache_config = config.cache.model_dump() # type: ignore\n",
"cache = CacheFactory().create_cache(\n",
" cache_type=cache_config[\"type\"], # type: ignore\n",
" **cache_config,\n",
")\n",
"model = ModelManager().get_or_create_embedding_model(\n",
" name=\"text_embedding\",\n",
" model_type=model_config.type,\n",
" config=model_config,\n",
" callbacks=callbacks,\n",
" cache=cache,\n",
")\n",
"\n",
"tokenizer = get_tokenizer(model_config)\n",
"\n",
"await generate_text_embeddings(\n",
" documents=None,\n",
Expand All @@ -227,11 +235,12 @@
" entities=final_entities,\n",
" community_reports=final_community_reports,\n",
" callbacks=callbacks,\n",
" cache=cache,\n",
" model_config=model_config,\n",
" model=model,\n",
" tokenizer=tokenizer,\n",
" batch_size=config.embed_text.batch_size,\n",
" batch_max_tokens=config.embed_text.batch_max_tokens,\n",
" vector_store_config=vector_store_config,\n",
" num_threads=model_config.concurrent_requests,\n",
" vector_store_config=config.vector_store,\n",
" embedded_fields=config.embed_text.names,\n",
")"
]
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