renamed embed_query to get_embedding

align_data/common/utils.py

@@ -79,7 +79,6 @@ def compute_openai_embeddings(non_flagged_texts, engine, **kwargs):
     data = openai.Embedding.create(input=non_flagged_texts, engine=engine, **kwargs).data
     return [d["embedding"] for d in data]
 
-from openai.embeddings_utils import get_embeddings
 def get_embeddings(
     texts: List[str], 
     embed_all: bool = False,
@@ -126,8 +125,8 @@ def get_embeddings(
     return final_embeddings
 
 
-def embed_query(query: str, engine=OPENAI_EMBEDDINGS_MODEL, **kwargs) -> List[float]:
-    return get_embeddings([query], engine=engine, **kwargs)[0]
+def get_embedding(text: str, **kwargs) -> List[float]:
+    return get_embeddings(texts=[text], **kwargs)[0]
 
 
 def get_recursive_type(obj):

align_data/pinecone/pinecone_db_handler.py

@@ -4,7 +4,7 @@
 
 import pinecone
 
-from align_data.common.utils import get_embeddings, embed_query
+from align_data.common.utils import get_embedding
 from align_data.pinecone.pinecone_models import PineconeEntry, PineconeMatch, PineconeMetadata
 from align_data.settings import (
     PINECONE_INDEX_NAME,
@@ -82,7 +82,7 @@ def query_text(
             include_values: bool = False, include_metadata: bool = True, **kwargs
             ) -> List[PineconeMatch]:
 
-        query_vector = embed_query(query)
+        query_vector = get_embedding(query)
         return self.query_vector(
             query=query_vector, top_k=top_k, 
             include_values=include_values, include_metadata=include_metadata, **kwargs

align_data/pinecone/pinecone_models.py

@@ -23,7 +23,7 @@ class PineconeEntry(BaseModel):
     source: str
     title: str
     url: str
-    date_published: int
+    date_published: float
     authors: List[str]
     text_chunks: List[str]
     embeddings: List[List[float]]