example: switch from storages to targets

examples/amazon_s3_embedding/main.py

@@ -61,7 +61,7 @@ def amazon_s3_text_embedding_flow(
 
     doc_embeddings.export(
         "doc_embeddings",
-        cocoindex.storages.Postgres(),
+        cocoindex.targets.Postgres(),
         primary_key_fields=["filename", "location"],
         vector_indexes=[
             cocoindex.VectorIndexDef(
@@ -74,7 +74,7 @@ def amazon_s3_text_embedding_flow(
 
 def search(pool: ConnectionPool, query: str, top_k: int = 5):
     # Get the table name, for the export target in the amazon_s3_text_embedding_flow above.
-    table_name = cocoindex.utils.get_target_storage_default_name(
+    table_name = cocoindex.utils.get_target_default_name(
         amazon_s3_text_embedding_flow, "doc_embeddings"
     )
     # Evaluate the transform flow defined above with the input query, to get the embedding.

examples/amazon_s3_embedding/pyproject.toml

@@ -3,7 +3,7 @@ name = "amazon-s3-text-embedding"
 version = "0.1.0"
 description = "Simple example for cocoindex: build embedding index based on Amazon S3 files."
 requires-python = ">=3.11"
-dependencies = ["cocoindex>=0.1.42", "python-dotenv>=1.0.1"]
+dependencies = ["cocoindex>=0.1.52", "python-dotenv>=1.0.1"]
 
 [tool.setuptools]
 packages = []

examples/code_embedding/main.py

@@ -60,7 +60,7 @@ def code_embedding_flow(
 
     code_embeddings.export(
         "code_embeddings",
-        cocoindex.storages.Postgres(),
+        cocoindex.targets.Postgres(),
         primary_key_fields=["filename", "location"],
         vector_indexes=[
             cocoindex.VectorIndexDef(
@@ -73,7 +73,7 @@ def code_embedding_flow(
 
 def search(pool: ConnectionPool, query: str, top_k: int = 5):
     # Get the table name, for the export target in the code_embedding_flow above.
-    table_name = cocoindex.utils.get_target_storage_default_name(
+    table_name = cocoindex.utils.get_target_default_name(
         code_embedding_flow, "code_embeddings"
     )
     # Evaluate the transform flow defined above with the input query, to get the embedding.

examples/code_embedding/pyproject.toml

@@ -3,7 +3,7 @@ name = "code-embedding"
 version = "0.1.0"
 description = "Simple example for cocoindex: build embedding index based on source code."
 requires-python = ">=3.11"
-dependencies = ["cocoindex>=0.1.42", "python-dotenv>=1.0.1"]
+dependencies = ["cocoindex>=0.1.52", "python-dotenv>=1.0.1"]
 
 [tool.setuptools]
 packages = []

examples/docs_to_knowledge_graph/README.md

@@ -14,12 +14,12 @@ Please drop [Cocoindex on Github](https://github.com/cocoindex-io/cocoindex) a s
 
 ## Prerequisite
 *   [Install Postgres](https://cocoindex.io/docs/getting_started/installation#-install-postgres) if you don't have one.
-*   Install [Neo4j](https://cocoindex.io/docs/ops/storages#neo4j-dev-instance) or [Kuzu](https://cocoindex.io/docs/ops/storages#kuzu-dev-instance) if you don't have one.
+*   Install [Neo4j](https://cocoindex.io/docs/ops/targets#neo4j-dev-instance) or [Kuzu](https://cocoindex.io/docs/ops/targets#kuzu-dev-instance) if you don't have one.
     *   The example uses Neo4j by default for now. If you want to use Kuzu, find out the "SELECT ONE GRAPH DATABASE TO USE" section and switch the active branch.
 *   [Configure your OpenAI API key](https://cocoindex.io/docs/ai/llm#openai).
 
 ## Documentation
-You can read the official CocoIndex Documentation for Property Graph Targets [here](https://cocoindex.io/docs/ops/storages#property-graph-targets).
+You can read the official CocoIndex Documentation for Property Graph Targets [here](https://cocoindex.io/docs/ops/targets#property-graph-targets).
 
 ## Run
 
@@ -48,7 +48,7 @@ cocoindex update main.py
 After the knowledge graph is built, you can explore the knowledge graph.
 
 * If you're using Neo4j, you can open the explorer at [http://localhost:7474](http://localhost:7474), with username `neo4j` and password `cocoindex`.
-* If you're using Kuzu, you can start a Kuzu explorer locally. See [Kuzu dev instance](https://cocoindex.io/docs/ops/storages#kuzu-dev-instance) for more details.
+* If you're using Kuzu, you can start a Kuzu explorer locally. See [Kuzu dev instance](https://cocoindex.io/docs/ops/targets#kuzu-dev-instance) for more details.
 
 You can run the following Cypher query to get all relationships:
 

examples/docs_to_knowledge_graph/main.py

@@ -7,15 +7,15 @@
 
 neo4j_conn_spec = cocoindex.add_auth_entry(
     "Neo4jConnection",
-    cocoindex.storages.Neo4jConnection(
+    cocoindex.targets.Neo4jConnection(
         uri="bolt://localhost:7687",
         user="neo4j",
         password="cocoindex",
     ),
 )
 kuzu_conn_spec = cocoindex.add_auth_entry(
     "KuzuConnection",
-    cocoindex.storages.KuzuConnection(
+    cocoindex.targets.KuzuConnection(
         api_server_url="http://localhost:8123",
     ),
 )
@@ -25,15 +25,15 @@
 # Please make sure only one branch is live and others are commented out.
 
 # Use Neo4j
-GraphDbSpec = cocoindex.storages.Neo4j
-GraphDbConnection = cocoindex.storages.Neo4jConnection
-GraphDbDeclaration = cocoindex.storages.Neo4jDeclaration
+GraphDbSpec = cocoindex.targets.Neo4j
+GraphDbConnection = cocoindex.targets.Neo4jConnection
+GraphDbDeclaration = cocoindex.targets.Neo4jDeclaration
 conn_spec = neo4j_conn_spec
 
 # Use Kuzu
-#  GraphDbSpec = cocoindex.storages.Kuzu
-#  GraphDbConnection = cocoindex.storages.KuzuConnection
-#  GraphDbDeclaration = cocoindex.storages.KuzuDeclaration
+#  GraphDbSpec = cocoindex.targets.Kuzu
+#  GraphDbConnection = cocoindex.targets.KuzuConnection
+#  GraphDbDeclaration = cocoindex.targets.KuzuDeclaration
 #  conn_spec = kuzu_conn_spec
 
 
@@ -134,7 +134,7 @@ def docs_to_kg_flow(
     document_node.export(
         "document_node",
         GraphDbSpec(
-            connection=conn_spec, mapping=cocoindex.storages.Nodes(label="Document")
+            connection=conn_spec, mapping=cocoindex.targets.Nodes(label="Document")
         ),
         primary_key_fields=["filename"],
     )
@@ -150,20 +150,20 @@ def docs_to_kg_flow(
         "entity_relationship",
         GraphDbSpec(
             connection=conn_spec,
-            mapping=cocoindex.storages.Relationships(
+            mapping=cocoindex.targets.Relationships(
                 rel_type="RELATIONSHIP",
-                source=cocoindex.storages.NodeFromFields(
+                source=cocoindex.targets.NodeFromFields(
                     label="Entity",
                     fields=[
-                        cocoindex.storages.TargetFieldMapping(
+                        cocoindex.targets.TargetFieldMapping(
                             source="subject", target="value"
                         ),
                     ],
                 ),
-                target=cocoindex.storages.NodeFromFields(
+                target=cocoindex.targets.NodeFromFields(
                     label="Entity",
                     fields=[
-                        cocoindex.storages.TargetFieldMapping(
+                        cocoindex.targets.TargetFieldMapping(
                             source="object", target="value"
                         ),
                     ],
@@ -176,16 +176,16 @@ def docs_to_kg_flow(
         "entity_mention",
         GraphDbSpec(
             connection=conn_spec,
-            mapping=cocoindex.storages.Relationships(
+            mapping=cocoindex.targets.Relationships(
                 rel_type="MENTION",
-                source=cocoindex.storages.NodeFromFields(
+                source=cocoindex.targets.NodeFromFields(
                     label="Document",
-                    fields=[cocoindex.storages.TargetFieldMapping("filename")],
+                    fields=[cocoindex.targets.TargetFieldMapping("filename")],
                 ),
-                target=cocoindex.storages.NodeFromFields(
+                target=cocoindex.targets.NodeFromFields(
                     label="Entity",
                     fields=[
-                        cocoindex.storages.TargetFieldMapping(
+                        cocoindex.targets.TargetFieldMapping(
                             source="entity", target="value"
                         )
                     ],

examples/docs_to_knowledge_graph/pyproject.toml

@@ -3,7 +3,7 @@ name = "manuals-to-kg"
 version = "0.1.0"
 description = "Simple example for cocoindex: extract triples from files and build knowledge graph."
 requires-python = ">=3.11"
-dependencies = ["cocoindex>=0.1.42"]
+dependencies = ["cocoindex>=0.1.52"]
 
 [tool.setuptools]
 packages = []

examples/fastapi_server_docker/main.py

@@ -54,7 +54,7 @@ def markdown_embedding_flow(
 
     doc_embeddings.export(
         "doc_embeddings",
-        cocoindex.storages.Postgres(),
+        cocoindex.targets.Postgres(),
         primary_key_fields=["filename", "location"],
         vector_indexes=[
             cocoindex.VectorIndexDef(
@@ -67,7 +67,7 @@ def markdown_embedding_flow(
 
 def search(pool: ConnectionPool, query: str, top_k: int = 5):
     # Get the table name, for the export target in the text_embedding_flow above.
-    table_name = cocoindex.utils.get_target_storage_default_name(
+    table_name = cocoindex.utils.get_target_default_name(
         markdown_embedding_flow, "doc_embeddings"
     )
     # Evaluate the transform flow defined above with the input query, to get the embedding.

examples/fastapi_server_docker/requirements.txt

@@ -1,4 +1,4 @@
-cocoindex>=0.1.42
+cocoindex>=0.1.52
 python-dotenv>=1.0.1
 fastapi==0.115.12
 fastapi-cli==0.0.7

examples/gdrive_text_embedding/main.py

@@ -60,7 +60,7 @@ def gdrive_text_embedding_flow(
 
     doc_embeddings.export(
         "doc_embeddings",
-        cocoindex.storages.Postgres(),
+        cocoindex.targets.Postgres(),
         primary_key_fields=["filename", "location"],
         vector_indexes=[
             cocoindex.VectorIndexDef(
@@ -73,7 +73,7 @@ def gdrive_text_embedding_flow(
 
 def search(pool: ConnectionPool, query: str, top_k: int = 5):
     # Get the table name, for the export target in the gdrive_text_embedding_flow above.
-    table_name = cocoindex.utils.get_target_storage_default_name(
+    table_name = cocoindex.utils.get_target_default_name(
         gdrive_text_embedding_flow, "doc_embeddings"
     )
     # Evaluate the transform flow defined above with the input query, to get the embedding.

examples/gdrive_text_embedding/pyproject.toml

@@ -3,7 +3,7 @@ name = "gdrive-text-embedding"
 version = "0.1.0"
 description = "Simple example for cocoindex: build embedding index based on Google Drive files."
 requires-python = ">=3.11"
-dependencies = ["cocoindex>=0.1.42", "python-dotenv>=1.0.1"]
+dependencies = ["cocoindex>=0.1.52", "python-dotenv>=1.0.1"]
 
 [tool.setuptools]
 packages = []

examples/image_search/main.py

@@ -81,7 +81,7 @@ def image_object_embedding_flow(
 
     img_embeddings.export(
         "img_embeddings",
-        cocoindex.storages.Qdrant(collection_name=QDRANT_COLLECTION),
+        cocoindex.targets.Qdrant(collection_name=QDRANT_COLLECTION),
         primary_key_fields=["id"],
     )
 

examples/image_search/pyproject.toml

@@ -4,7 +4,7 @@ version = "0.1.0"
 description = "Simple example for cocoindex: build embedding index based on images."
 requires-python = ">=3.11"
 dependencies = [
-    "cocoindex>=0.1.42",
+    "cocoindex>=0.1.52",
     "python-dotenv>=1.0.1",
     "fastapi>=0.100.0",
     "torch>=2.0.0",

examples/image_search/requirements.txt

@@ -1,4 +1,4 @@
-cocoindex>=0.1.42
+cocoindex>=0.1.52
 python-dotenv>=1.0.1
 requests>=2.31.0
 uvicorn>=0.29.0

examples/manuals_llm_extraction/main.py

@@ -131,6 +131,6 @@ def manual_extraction_flow(
 
     modules_index.export(
         "modules",
-        cocoindex.storages.Postgres(table_name="modules_info"),
+        cocoindex.targets.Postgres(table_name="modules_info"),
         primary_key_fields=["filename"],
     )

examples/manuals_llm_extraction/pyproject.toml

@@ -3,7 +3,7 @@ name = "manuals-llm-extraction"
 version = "0.1.0"
 description = "Simple example for cocoindex: extract structured information from a Markdown file using LLM."
 requires-python = ">=3.11"
-dependencies = ["cocoindex>=0.1.42", "marker-pdf>=1.5.2"]
+dependencies = ["cocoindex>=0.1.52", "marker-pdf>=1.5.2"]
 
 [tool.setuptools]
 packages = []

examples/pdf_embedding/main.py

@@ -86,7 +86,7 @@ def pdf_embedding_flow(
 
     pdf_embeddings.export(
         "pdf_embeddings",
-        cocoindex.storages.Postgres(),
+        cocoindex.targets.Postgres(),
         primary_key_fields=["id"],
         vector_indexes=[
             cocoindex.VectorIndexDef(
@@ -99,7 +99,7 @@ def pdf_embedding_flow(
 
 def search(pool: ConnectionPool, query: str, top_k: int = 5):
     # Get the table name, for the export target in the pdf_embedding_flow above.
-    table_name = cocoindex.utils.get_target_storage_default_name(
+    table_name = cocoindex.utils.get_target_default_name(
         pdf_embedding_flow, "pdf_embeddings"
     )
     # Evaluate the transform flow defined above with the input query, to get the embedding.

examples/pdf_embedding/pyproject.toml

@@ -4,7 +4,7 @@ version = "0.1.0"
 description = "Simple example for cocoindex: build embedding index based on local PDF files."
 requires-python = ">=3.11"
 dependencies = [
-    "cocoindex>=0.1.42",
+    "cocoindex>=0.1.52",
     "python-dotenv>=1.0.1",
     "marker-pdf>=1.5.2",
 ]

examples/product_recommendation/README.md

@@ -9,12 +9,12 @@ Please drop [CocoIndex on Github](https://github.com/cocoindex-io/cocoindex) a s
 
 ## Prerequisite
 *   [Install Postgres](https://cocoindex.io/docs/getting_started/installation#-install-postgres) if you don't have one.
-*   Install [Neo4j](https://cocoindex.io/docs/ops/storages#neo4j-dev-instance) or [Kuzu](https://cocoindex.io/docs/ops/storages#kuzu-dev-instance) if you don't have one.
+*   Install [Neo4j](https://cocoindex.io/docs/ops/targets#neo4j-dev-instance) or [Kuzu](https://cocoindex.io/docs/ops/targets#kuzu-dev-instance) if you don't have one.
     *   The example uses Neo4j by default for now. If you want to use Kuzu, find out the "SELECT ONE GRAPH DATABASE TO USE" section and switch the active branch.
 *   [Configure your OpenAI API key](https://cocoindex.io/docs/ai/llm#openai).
 
 ## Documentation
-You can read the official CocoIndex Documentation for Property Graph Targets [here](https://cocoindex.io/docs/ops/storages#property-graph-targets).
+You can read the official CocoIndex Documentation for Property Graph Targets [here](https://cocoindex.io/docs/ops/targets#property-graph-targets).
 
 ## Run
 
@@ -43,7 +43,7 @@ cocoindex update main.py
 After the knowledge graph is built, you can explore the knowledge graph.
 
 * If you're using Neo4j, you can open the explorer at [http://localhost:7474](http://localhost:7474), with username `neo4j` and password `cocoindex`.
-* If you're using Kuzu, you can start a Kuzu explorer locally. See [Kuzu dev instance](https://cocoindex.io/docs/ops/storages#kuzu-dev-instance) for more details.
+* If you're using Kuzu, you can start a Kuzu explorer locally. See [Kuzu dev instance](https://cocoindex.io/docs/ops/targets#kuzu-dev-instance) for more details.
 
 You can run the following Cypher query to get all relationships: