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feat(workflow): implement a workflow node for knowledge base retrieval

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This commit is contained in:
mengyonghao
2025-12-24 12:10:52 +08:00
parent d423e80ddb
commit 8c4d31e4d5
10 changed files with 179 additions and 40 deletions
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29
api/app/core/rag/vdb/elasticsearch/elasticsearch_vector.py
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@@ -740,8 +740,9 @@ class ElasticSearchVector(BaseVector):
self._client.indices.create(index=self._collection_name, body=index_mapping)
class ElasticSearchVectorFactory(ABC):
def init_vector(self, knowledge: Knowledge) -> ElasticSearchVector:
class ElasticSearchVectorFactory:
@staticmethod
def init_vector(knowledge: Knowledge) -> ElasticSearchVector:
collection_name = f"Vector_index_{knowledge.id}_Node"
# Use regular Elasticsearch with config values
@@ -763,17 +764,17 @@ class ElasticSearchVectorFactory(ABC):
}
)
if knowledge.embedding and knowledge.reranker:
return ElasticSearchVector(
index_name=collection_name,
config=ElasticSearchConfig(**config_dict),
embedding_config=knowledge.embedding.api_keys[0],
reranker_config=knowledge.reranker.api_keys[0]
)
else:
if knowledge.embedding is None:
raise ValueError(f"embedding_id config error: {str(knowledge.embedding_id)}")
if knowledge.reranker is None:
raise ValueError(f"reranker_id config error: {str(knowledge.reranker_id)}")
if knowledge.embedding is None:
raise ValueError(f"embedding_id config error: {str(knowledge.embedding_id)}")
if knowledge.reranker is None:
raise ValueError(f"reranker_id config error: {str(knowledge.reranker_id)}")
return ElasticSearchVector(
index_name=collection_name,
config=ElasticSearchConfig(**config_dict),
embedding_config=knowledge.embedding.api_keys[0],
reranker_config=knowledge.reranker.api_keys[0]
)