Feature/change api endpoint

utils/automate_api.py

@@ -2,23 +2,17 @@
     import os
     from openapi_to_sdk.sdk_automation import PythonSDKBuilder
 
-    url="https://vectorai-development-api.azurewebsites.net"
-    url="https://vectorai-development-api.azurewebsites.net"
-    # url = "https://api.vctr.ai"
+    url = "https://vectorai-production-api.azurewebsites.net"
     sdk = PythonSDKBuilder(
         url=url,
-        # url="https://vectorai-development-api.azurewebsites.net",
-        # url='https://vecdb-aueast-api.azurewebsites.net',
         inherited_properties=['username', 'api_key', 'url'],
         decorators=[
             'retry()',
             "return_curl_or_response('json')"],
         override_param_defaults=dict(
             min_score=None,
             cursor=None,
-            # url='https://vecdb-aueast-api.azurewebsites.net',
             url=url,
-            # sort=False,
             sort_by_created_at_date=False,
         ),
         internal_functions=[

vectorai/api/api.py

@@ -1129,7 +1129,7 @@ def bulk_encode(self, encoders, documents, **kwargs):
 
 	@retry()
 	@return_curl_or_response('json')
-	def predict_knn_regression(self, collection_name, vector, search_field, target_field, impute_value, k=5, weighting=True, predict_operation="mean", **kwargs):
+	def predict_knn_regression(self, collection_name, vector, search_field, target_field, impute_value, k=5, weighting=True, predict_operation="mean", include_search_results=True, **kwargs):
 		"""Predict KNN regression.
 Predict with KNN regression using normal search.
 
@@ -1145,6 +1145,7 @@ def predict_knn_regression(self, collection_name, vector, search_field, target_f
 weighting: weighting
 impute_value: What value to fill if target field is missing.
 predict_operation: How to predict using the vectors.
+include_search_results: If True, returns the results as well.
 
 """
 		return requests.post(
@@ -1160,6 +1161,7 @@ def predict_knn_regression(self, collection_name, vector, search_field, target_f
 				weighting=weighting, 
 				impute_value=impute_value, 
 				predict_operation=predict_operation, 
+				include_search_results=include_search_results, 
 				))
 
 	@retry()
@@ -1241,6 +1243,10 @@ def filters(self, collection_name, filters=[], page=1, page_size=20, asc=False,
 These are the available conditions:
 
     "==", "!=", ">=", ">", "<", "<="
+
+If you are looking to combine your filters with multiple ORs, simply add the following inside the query
+`{"strict":"must_or"}`. 
+
 
 Args
 ========
@@ -2121,7 +2127,7 @@ def text_chunking(self, collection_name, text_field, chunk_field, insert_results
 
 	@retry()
 	@return_curl_or_response('json')
-	def text_chunking_encoder(self, collection_name, text_field, chunk_field, insert_results_to_seperate_collection_name, encoder_task="text", refresh=True, store_to_pipeline=True, **kwargs):
+	def text_chunking_encoder(self, collection_name, text_field, chunk_field, insert_results_to_seperate_collection_name, encoder_task="text", refresh=True, store_to_pipeline=True, alias="", **kwargs):
 		"""Chunk a text field and encode the chunks
 Split text into separate sentences. Encode each sentence to create chunkvectors.
 These are stored as \_chunkvector\_. The chunk field created is `field` + \_chunk\_.
@@ -2137,6 +2143,7 @@ def text_chunking_encoder(self, collection_name, text_field, chunk_field, insert
 refresh: Whether to refresh the whole collection and re-encode all to vectors
 insert_results_to_seperate_collection_name: If specified the chunks will be inserted into a seperate collection. Default is None which means no seperate collection.
 store_to_pipeline: Whether to store the encoder to the chunking pipeline
+alias: If alias is present, it will create change the name of the created vector field into field_{alias}_chunkvector_
 
 """
 		return requests.post(
@@ -2151,6 +2158,7 @@ def text_chunking_encoder(self, collection_name, text_field, chunk_field, insert
 				refresh=refresh, 
 				insert_results_to_seperate_collection_name=insert_results_to_seperate_collection_name, 
 				store_to_pipeline=store_to_pipeline, 
+				alias=alias, 
 				))
 
 	@retry()
@@ -2178,6 +2186,31 @@ def process_pdf(self, collection_name, file_url, filename, **kwargs):
 				filename=filename, 
 				))
 
+	@retry()
+	@return_curl_or_response('json')
+	def bulk_process_pdf(self, collection_name, file_urls, filenames, **kwargs):
+		"""Process multiple pdfs
+Insert multiple PDFs into Vector AI:
+
+Args
+========
+username: Username
+api_key: Api Key, you can request it from request_api_key
+collection_name: What collection to insert the PDF into
+file_urls: The file url blobs
+filenames: The name of the PDF files
+
+"""
+		return requests.post(
+			url=self.url+'/collection/job/bulk_process_pdf',
+			json=dict(
+				username=self.username,
+				api_key=self.api_key,
+				collection_name=collection_name, 
+				file_urls=file_urls, 
+				filenames=filenames, 
+				))
+
 	@retry()
 	@return_curl_or_response('json')
 	def process_doc(self, collection_name, file_url, filename, **kwargs):
@@ -2203,6 +2236,31 @@ def process_doc(self, collection_name, file_url, filename, **kwargs):
 				filename=filename, 
 				))
 
+	@retry()
+	@return_curl_or_response('json')
+	def bulk_process_doc(self, collection_name, file_urls, filenames, **kwargs):
+		"""Process multiple doc or docx files
+Insert multiple word docs into Vector AI
+
+Args
+========
+username: Username
+api_key: Api Key, you can request it from request_api_key
+collection_name: What collection to insert the word doc into
+file_urls: The file url blobs
+filenames: The name of the Doc or DocX files
+
+"""
+		return requests.post(
+			url=self.url+'/collection/job/bulk_process_doc',
+			json=dict(
+				username=self.username,
+				api_key=self.api_key,
+				collection_name=collection_name, 
+				file_urls=file_urls, 
+				filenames=filenames, 
+				))
+
 	@retry()
 	@return_curl_or_response('json')
 	def copy_collection_from_another_user(self, collection_name, source_collection_name, source_username, source_api_key, **kwargs):
@@ -4123,82 +4181,3 @@ def tag_documents_from_hub(self, tag_collection_name, vector_field, hub_username
 				hub_api_key=hub_api_key, 
 				))
 
-	@retry()
-	@return_curl_or_response('json')
-	def rank_comparator(self, ranked_list_1, ranked_list_2, **kwargs):
-		"""Compare ranks between 2 results list.
-Compare the ranks between 2 results list in VecDB
-
-Args
-========
-username: Username
-api_key: Api Key, you can request it from request_api_key
-ranked_list_1: First ranked List
-ranked_list_2: Second ranked list
-
-"""
-		return requests.post(
-			url=self.url+'/experimentation/rank_comparator',
-			json=dict(
-				username=self.username,
-				api_key=self.api_key,
-				ranked_list_1=ranked_list_1, 
-				ranked_list_2=ranked_list_2, 
-				))
-
-	@retry()
-	@return_curl_or_response('json')
-	def bias_indicator(self, anchor_documents, documents, metadata_field, vector_field, **kwargs):
-		"""Compare bias of documents against anchor documents
-Compare bias of documents against anchor documents
-
-Args
-========
-username: Username
-api_key: Api Key, you can request it from request_api_key
-anchor_documents: Anchor documents to compare other documents against.
-documents: Documents to compare against the anchor documents
-metadata_field: Field from which the vector was derived
-vector_field: Vector field to compare against
-
-"""
-		return requests.post(
-			url=self.url+'/experimentation/bias_indicator',
-			json=dict(
-				username=self.username,
-				api_key=self.api_key,
-				anchor_documents=anchor_documents, 
-				documents=documents, 
-				metadata_field=metadata_field, 
-				vector_field=vector_field, 
-				))
-
-	@retry()
-	@return_curl_or_response('json')
-	def cluster_comparator(self, collection_name, cluster_field, cluster_value, vector_field, alias, **kwargs):
-		"""Compare clusters
-Compare the clusters for cluster comparator
-
-Args
-========
-username: Username
-api_key: Api Key, you can request it from request_api_key
-collection_name: the name of the collection
-cluster_field: the cluster field
-cluster_value: the cluster values by which to compare on
-vector_field: The vector field that has been clustered
-alias: The alias of the vector field
-
-"""
-		return requests.post(
-			url=self.url+'/experimentation/cluster_comparator',
-			json=dict(
-				username=self.username,
-				api_key=self.api_key,
-				collection_name=collection_name, 
-				cluster_field=cluster_field, 
-				cluster_value=cluster_value, 
-				vector_field=vector_field, 
-				alias=alias, 
-				))
-

vectorai/client.py

@@ -31,9 +31,7 @@ class ViClient(ViWriteClient, ViAnalyticsClient):
     def __init__(self,
         username: str=None,
         api_key: str=None,
-        # Old API URL: https://vecdb-aueast-api.azurewebsites.net
-        # url: str="https://vectorai-development-api-vectorai-test-api.azurewebsites.net/",
-        url: str="https://vectorai-development-api.azurewebsites.net",
+        url: str ="https://vectorai-production-api.azurewebsites.net",
         analytics_url="https://vector-analytics.vctr.ai", verbose: bool = True) -> None:
         super().__init__(username, api_key, url)
         if username is None: