20241008 updates

_includes/code/connections/connect-python-v3.py

@@ -157,4 +157,4 @@
 
 # Use OIDC credentials to authenticate
 client = weaviate.Client("http://localhost:8080", auth_client_secret=resource_owner_config)
-# END OIDCConnect
+# END OIDCConnect

_includes/code/embedded.instantiate.custom.mdx

@@ -4,6 +4,7 @@ import FilteredTextBlock from '@site/src/components/Documentation/FilteredTextBl
 import PyCode from '!!raw-loader!/_includes/code/install/embedded.py';
 import PyCodeV3 from '!!raw-loader!/_includes/code/install/embedded-v3.py';
 import TsCodeV2 from '!!raw-loader!/_includes/code/install/embedded-v2.ts';
+import TsCodeV2CustomModules from '!!raw-loader!/_includes/code/install/embedded-v2-custommodules.ts';
 
 <Tabs groupId="languages">
     <TabItem value="py" label="Python Client v4">
@@ -24,10 +25,10 @@ import TsCodeV2 from '!!raw-loader!/_includes/code/install/embedded-v2.ts';
     </TabItem>
     <TabItem value="js" label="JS/TS Client v2">
         <FilteredTextBlock
-          text={TsCodeV2}
+          text={TsCodeV2CustomModules}
           startMarker="// START CustomModules"
           endMarker="// END CustomModules"
           language="js"
         />
     </TabItem>
-</Tabs>
+</Tabs>

_includes/code/embedded.instantiate.module.mdx

@@ -4,6 +4,7 @@ import FilteredTextBlock from '@site/src/components/Documentation/FilteredTextBl
 import PyCode from '!!raw-loader!/_includes/code/install/embedded.py';
 import PyCodeV3 from '!!raw-loader!/_includes/code/install/embedded-v3.py';
 import TsCodeV2 from '!!raw-loader!/_includes/code/install/embedded-v2.ts';
+import TsCodeV2CustomModules from '!!raw-loader!/_includes/code/install/embedded-v2-custommodules.ts';
 
 <Tabs groupId="languages">
     <TabItem value="py" label="Python Client v4">
@@ -24,7 +25,7 @@ import TsCodeV2 from '!!raw-loader!/_includes/code/install/embedded-v2.ts';
     </TabItem>
     <TabItem value="js" label="JS/TS Client v2">
         <FilteredTextBlock
-          text={TsCodeV2}
+          text={TsCodeV2CustomModules}
           startMarker="// START CustomModules"
           endMarker="// END CustomModules"
           language="js"

_includes/code/install/embedded-v2-custommodules.ts

@@ -0,0 +1,17 @@
+// START CustomModules
+import weaviate, { EmbeddedOptions } from 'weaviate-ts-embedded';
+
+const client = weaviate.client(
+  new EmbeddedOptions({
+    env: {
+      ENABLE_MODULES: "backup-s3,text2vec-openai,text2vec-cohere,text2vec-huggingface,ref2vec-centroid,generative-openai,qna-openai",
+    },
+  })
+);
+
+await client.embedded.start();
+
+// Add your client code here.
+
+await client.embedded.stop();
+// END CustomModules

_includes/code/install/embedded-v2.ts

@@ -1,34 +1,12 @@
-// NOT TESTED TO RUN AS AN END TO END SCRIPT
-
 // START SimpleInstance
 import weaviate, { EmbeddedOptions } from 'weaviate-ts-embedded';
 
-  const client = weaviate.client(new EmbeddedOptions());
+const client = weaviate.client(new EmbeddedOptions());
 
-  await client.embedded.start();
+await client.embedded.start();
 
-  const response = await client.data
-    .creator()
-    .withClassName('Wine')
-    .withProperties({
-      name: 'Chardonnay',
-      description: 'Goes with fish',
-    })
-    .do();
+// Add your client code here.
 
-  console.log(JSON.stringify(response, null, 2));
+await client.embedded.stop();
 // END SimpleInstance
 
-
-
-// START CustomModules
-import weaviate, { EmbeddedOptions } from 'weaviate-ts-embedded';
-
-const client = weaviate.client(
-  new EmbeddedOptions({
-    env: {
-      ENABLE_MODULES: "backup-s3,text2vec-openai,text2vec-cohere,text2vec-huggingface,ref2vec-centroid,generative-openai,qna-openai",
-    },
-  })
-);
-// END CustomModules

_includes/code/install/embedded-v3.py

@@ -1,32 +1,29 @@
-#NOT TESTED AS A RUNNING END TO END SCRIPT
-
 # START SimpleInstance
 import weaviate
 from weaviate.embedded import EmbeddedOptions
 
 client = weaviate.Client(
-embedded_options=EmbeddedOptions()
+    embedded_options=EmbeddedOptions()
 )
 
 data_obj = {
-"name": "Chardonnay",
-"description": "Goes with fish"
+    "name": "Chardonnay",
+    "description": "Goes with fish"
 }
 
 client.data_object.create(data_obj, "Wine")
 # END SimpleInstance
-client.close()
+
 
 # START CustomModules
 import weaviate
 from weaviate.embedded import EmbeddedOptions
 
 client = weaviate.Client(
-  embedded_options=EmbeddedOptions(
-      additional_env_vars={
-      "ENABLE_MODULES":
-      "backup-s3,text2vec-openai,text2vec-cohere,text2vec-huggingface,ref2vec-centroid,generative-openai,qna-openai"}
-  )
+    embedded_options=EmbeddedOptions(
+        additional_env_vars={
+        "ENABLE_MODULES":
+        "backup-s3,text2vec-openai,text2vec-cohere,text2vec-huggingface,ref2vec-centroid,generative-openai,qna-openai"}
+    )
 )
 # END CustomModules
-client.close()

_includes/code/install/embedded.py

@@ -5,7 +5,7 @@
 import os
 
 client = weaviate.connect_to_embedded(
-    version=weaviate_version,  # e.g. version="1.23.10"
+    version=weaviate_version,  # e.g. version="1.26.5"
     headers={
         "X-OpenAI-Api-Key": os.getenv("OPENAI_APIKEY")  # Replace with your API key
     },

developers/weaviate/concepts/search/index.md

@@ -17,8 +17,8 @@ The following table illustrates the search process in Weaviate. Around the core
 
 | Step | Description | Optional |
 |------|-------------|----------|
-| 1. [Retrieval](#retrieval) | <strong>[Filter](#retrieval-filter):</strong> Narrow result sets based on criteria<br/><strong>[Search](#retrieval-search):</strong> Find the most relevant entries, using one of [keyword](#keyword-search), [vector](#vector-search) or [hybrid](#hybrid-search) search types<br/> | Required |
-| 2. [Reranking](#reranking) | Reorder results using a different (e.g. more complex) model | Optional |
+| 1. [Retrieval](#retrieval-filter) | <strong>[Filter](#retrieval-filter):</strong> Narrow result sets based on criteria<br/><strong>[Search](#retrieval-search):</strong> Find the most relevant entries, using one of [keyword](#keyword-search), [vector](#vector-search) or [hybrid](#hybrid-search) search types<br/> | Required |
+| 2. [Reranking](#rerank) | Reorder results using a different (e.g. more complex) model | Optional |
 | 3. [Generative](#generative-search--rag) | Send retrieved data and a prompt to a generative AI model. Also called retrieval augmented generation, or RAG. | Optional |
 
 Here is a brief overview of each step: