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feat: migrate store to source_id
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@@ -9,9 +9,9 @@ import elasticsearch
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class ElasticsearchStore(BaseVectorStore):
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_es_connection = None # Class attribute to hold the Elasticsearch connection
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def __init__(self, path, embeddings_key, index_name=settings.ELASTIC_INDEX):
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def __init__(self, source_id, embeddings_key, index_name=settings.ELASTIC_INDEX):
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super().__init__()
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self.path = path.replace("application/indexes/", "").rstrip("/")
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self.source_id = source_id.replace("application/indexes/", "").rstrip("/")
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self.embeddings_key = embeddings_key
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self.index_name = index_name
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@@ -81,7 +81,7 @@ class ElasticsearchStore(BaseVectorStore):
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embeddings = self._get_embeddings(settings.EMBEDDINGS_NAME, self.embeddings_key)
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vector = embeddings.embed_query(question)
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knn = {
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"filter": [{"match": {"metadata.store.keyword": self.path}}],
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"filter": [{"match": {"metadata.source_id.keyword": self.source_id}}],
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"field": "vector",
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"k": k,
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"num_candidates": 100,
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@@ -100,7 +100,7 @@ class ElasticsearchStore(BaseVectorStore):
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}
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}
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],
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"filter": [{"match": {"metadata.store.keyword": self.path}}],
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"filter": [{"match": {"metadata.source_id.keyword": self.source_id}}],
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}
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},
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"rank": {"rrf": {}},
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@@ -209,4 +209,4 @@ class ElasticsearchStore(BaseVectorStore):
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def delete_index(self):
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self._es_connection.delete_by_query(index=self.index_name, query={"match": {
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"metadata.store.keyword": self.path}},)
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"metadata.source_id.keyword": self.source_id}},)
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