Nuclear6
commented
1 month ago It should be that something went wrong in your index phase. You can look at the logs in the index phase.
Closed prasantpoudel closed 1 month ago
Nuclear6
commented
1 month ago It should be that something went wrong in your index phase. You can look at the logs in the index phase.
sebnapi
commented
1 month ago Yes this is due to your locally run embedding model, not returning the weights in a correct format. OpenAI uses internally base64 encoded floats, and most other models will return floats as numbers.
I've hacked the encoding_format into this piece of code to make local search work:
def map_query_to_entities(
query: str,
text_embedding_vectorstore: BaseVectorStore,
text_embedder: BaseTextEmbedding,
all_entities: list[Entity],
embedding_vectorstore_key: str = EntityVectorStoreKey.ID,
include_entity_names: list[str] | None = None,
exclude_entity_names: list[str] | None = None,
k: int = 10,
oversample_scaler: int = 2,
) -> list[Entity]:
"""Extract entities that match a given query using semantic similarity of text embeddings of query and entity descriptions."""
if include_entity_names is None:
include_entity_names = []
if exclude_entity_names is None:
exclude_entity_names = []
matched_entities = []
if query != "":
# get entities with highest semantic similarity to query
# oversample to account for excluded entities
search_results = text_embedding_vectorstore.similarity_search_by_text(
text=query,
text_embedder=lambda t: text_embedder.embed(t, encoding_format="float"), # added to make embedding api work, openai uses base64 by default
k=k * oversample_scaler,
)
for result in search_results:
matched = get_entity_by_key(
entities=all_entities,
key=embedding_vectorstore_key,
value=result.document.id,
)
if matched:
matched_entities.append(matched)
else:
all_entities.sort(key=lambda x: x.rank if x.rank else 0, reverse=True)
matched_entities = all_entities[:k]
# filter out excluded entities
if exclude_entity_names:
matched_entities = [
entity
for entity in matched_entities
if entity.title not in exclude_entity_names
]
# add entities in the include_entity list
included_entities = []
for entity_name in include_entity_names:
included_entities.extend(get_entity_by_name(all_entities, entity_name))
return included_entities + matched_entities
Anthonyfhd
commented
1 month ago 是的，这是由于您本地运行的嵌入模型未以正确的格式返回权重。OpenAI 使用内部 base64 编码的浮点数，而大多数其他模型将以数字形式返回浮点数。
我把 encoding_format 修改成了这段代码，以使本地搜索能够正常工作：
def map_query_to_entities( query: str, text_embedding_vectorstore: BaseVectorStore, text_embedder: BaseTextEmbedding, all_entities: list[Entity], embedding_vectorstore_key: str = EntityVectorStoreKey.ID, include_entity_names: list[str] | None = None, exclude_entity_names: list[str] | None = None, k: int = 10, oversample_scaler: int = 2, ) -> list[Entity]: """Extract entities that match a given query using semantic similarity of text embeddings of query and entity descriptions.""" if include_entity_names is None: include_entity_names = [] if exclude_entity_names is None: exclude_entity_names = [] matched_entities = [] if query != "": # get entities with highest semantic similarity to query # oversample to account for excluded entities search_results = text_embedding_vectorstore.similarity_search_by_text( text=query, text_embedder=lambda t: text_embedder.embed(t, encoding_format="float"), # added to make embedding api work, openai uses base64 by default k=k * oversample_scaler, ) for result in search_results: matched = get_entity_by_key( entities=all_entities, key=embedding_vectorstore_key, value=result.document.id, ) if matched: matched_entities.append(matched) else: all_entities.sort(key=lambda x: x.rank if x.rank else 0, reverse=True) matched_entities = all_entities[:k] # filter out excluded entities if exclude_entity_names: matched_entities = [ entity for entity in matched_entities if entity.title not in exclude_entity_names ] # add entities in the include_entity list included_entities = [] for entity_name in include_entity_names: included_entities.extend(get_entity_by_name(all_entities, entity_name)) return included_entities + matched_entities
这好像改了还是不工作
Johnny0512
commented
1 month ago It's because you're using local model. If you are using Ollama (just like me), you might see this answer: https://github.com/microsoft/graphrag/issues/345#issuecomment-2212471697 Works perfectly for me...
natoverse
commented
1 month ago Consolidating alternate model issues here: https://github.com/microsoft/graphrag/issues/657
AymaneHan1
commented
5 hours ago Hello, currently running through the same problem, I am using an azure openai instance
text_embedder = OpenAIEmbedding(
api_key=api_key,
deployment_name="ada-small-emb-graphrag",
model="text-embedding-ada-002",
api_base="https://xxx-oai.openai.azure.com/",
)
text_embedder.embed("hello world")This returns the error
ZeroDivisionError: Weights sum to zero, can't be normalized
I have added the float encoding in teh source code but still do not work
text_embedder=lambda t: text_embedder.embed(t, encoding_format="float")
Any ideas why is still not working? Thanks
Describe the issue
When I run the query using local scope I got the error of ZeroDivisionError: Weights sum to zero, can't be normalized. But for the Global scope it worked correctly. If any one have the Idea please give the solution.
Steps to reproduce
No response
GraphRAG Config Used
No response
Logs and screenshots
No response
Additional Information