automate contributors in relnotes

.github/workflows/github_release.yml

@@ -8,6 +8,7 @@ on:
       # Ignore release versions tagged with -rc0 suffix
       - "!v2.[0-9]+.[0-9]-rc0"
 
+  pull_request:
 jobs:
   generate-notes:
     runs-on: ubuntu-latest
@@ -59,16 +60,36 @@ jobs:
           reno report --no-show-source --ignore-cache --version v${{ steps.version.outputs.current_release }} -o relnotes.rst
 
       - name: Convert to Markdown
-        uses: docker://pandoc/core:3.1
+        uses: docker://pandoc/core:3.8
         with:
-          args: "--from rst --to markdown_github --no-highlight relnotes.rst -o relnotes.md --wrap=none"
+          args: "--from rst --to gfm --no-highlight relnotes.rst -o relnotes.md --wrap=none"
+
+      - name: Add contributor list
+        # only for minor releases and minor release candidates (not bugfix releases)
+        if: endsWith(steps.version.outputs.current_release, '.0')
+        env:
+          GH_TOKEN: ${{ github.token }}
+          START: v${{ steps.version.outputs.current_release }}-rc0
+          END: "v2.20.x"
+          # END: ${{ github.ref_name }}
+        run: |
+          CONTRIBUTORS=$(gh api "repos/deepset-ai/haystack/compare/$START...$END" \
+            --jq '[.commits[].author.login] | map(select(. != null)) | unique | map("@\(.)") | join(", ")')
+
+          cat relnotes.md > enhanced_relnotes.md
+          {
+            echo ""
+            echo "## 💙 Big thank you to everyone who contributed to this release!"
+            echo ""
+            echo "$CONTRIBUTORS"
+          } >> enhanced_relnotes.md
 
       - name: Debug
         run: |
-          cat relnotes.md
+          cat enhanced_relnotes.md
 
-      - uses: ncipollo/release-action@v1
-        with:
-          bodyFile: "relnotes.md"
-          prerelease: ${{ steps.version.outputs.current_pre_release }}
-          allowUpdates: true
+      # - uses: ncipollo/release-action@v1
+      #   with:
+      #     bodyFile: "relnotes.md"
+      #     prerelease: ${{ steps.version.outputs.current_pre_release != '' }}
+      #     allowUpdates: true

.github/workflows/readme_sync.yml

@@ -4,13 +4,14 @@ on:
   pull_request:
     paths:
       - "docs/pydoc/**"
-  push:
-    branches:
-      - main
-      # release branches have the form v1.9.x
-      - "v[0-9]+.[0-9]+.x"
-      # Exclude 1.x release branches, there's another workflow handling those
-      - "!v1.[0-9]+.x"
+  # TODO: remove this workflow once migration to Docusaurus is complete
+  # push:
+  #   branches:
+  #     - main
+  #     # release branches have the form v1.9.x
+  #     - "v[0-9]+.[0-9]+.x"
+  #     # Exclude 1.x release branches, there's another workflow handling those
+  #     - "!v1.[0-9]+.x"
 
 env:
   HATCH_VERSION: "1.14.2"

VERSION.txt

@@ -1 +1 @@
-2.20.0-rc0
+2.21.0-rc0

docs-website/docusaurus.config.js

@@ -48,7 +48,7 @@ const config = {
           beforeDefaultRemarkPlugins: [require('./src/remark/versionedReferenceLinks')],
           versions: {
             current: {
-              label: '2.20-unstable',
+              label: '2.21-unstable',
               path: 'next',
               banner: 'unreleased',
             },
@@ -88,7 +88,7 @@ const config = {
         exclude: ['**/_templates/**'],
         versions: {
           current: {
-            label: '2.20-unstable',
+            label: '2.21-unstable',
             path: 'next',
             banner: 'unreleased',
           },

docs-website/reference/integrations-api/watsonx.md

@@ -18,11 +18,8 @@ Enables text completions using IBM's watsonx.ai foundation models.
 This component extends WatsonxChatGenerator to provide the standard Generator interface that works with prompt
 strings instead of ChatMessage objects.
 
-The generator works with IBM's foundation models including:
-- granite-13b-chat-v2
-- llama-2-70b-chat
-- llama-3-70b-instruct
-- Other watsonx.ai chat models
+The generator works with IBM's foundation models that are listed
+[here](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-models.html?context=wx&audience=wdp).
 
 You can customize the generation behavior by passing parameters to the watsonx.ai API through the
 `generation_kwargs` argument. These parameters are passed directly to the watsonx.ai inference endpoint.
@@ -73,7 +70,7 @@ Output:
 ```python
 def __init__(*,
              api_key: Secret = Secret.from_env_var("WATSONX_API_KEY"),
-             model: str = "ibm/granite-3-2b-instruct",
+             model: str = "ibm/granite-3-3-8b-instruct",
              project_id: Secret = Secret.from_env_var("WATSONX_PROJECT_ID"),
              api_base_url: str = "https://us-south.ml.cloud.ibm.com",
              system_prompt: str | None = None,
@@ -233,14 +230,8 @@ This component interacts with IBM's watsonx.ai platform to generate chat respons
 models. It supports the [ChatMessage](https://docs.haystack.deepset.ai/docs/chatmessage) format for both input
 and output, including multimodal inputs with text and images.
 
-The generator works with IBM's foundation models including:
-- granite-13b-chat-v2
-- llama-2-70b-chat
-- llama-3-70b-instruct
-- llama-3-2-11b-vision-instruct (multimodal)
-- llama-3-2-90b-vision-instruct (multimodal)
-- pixtral-12b (multimodal)
-- Other watsonx.ai chat models
+The generator works with IBM's foundation models that are listed
+[here](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-models.html?context=wx&audience=wdp).
 
 You can customize the generation behavior by passing parameters to the watsonx.ai API through the
 `generation_kwargs` argument. These parameters are passed directly to the watsonx.ai inference endpoint.
@@ -294,7 +285,7 @@ print(response)
 ```python
 def __init__(*,
              api_key: Secret = Secret.from_env_var("WATSONX_API_KEY"),
-             model: str = "ibm/granite-3-2b-instruct",
+             model: str = "ibm/granite-3-3-8b-instruct",
              project_id: Secret = Secret.from_env_var("WATSONX_PROJECT_ID"),
              api_base_url: str = "https://us-south.ml.cloud.ibm.com",
              generation_kwargs: dict[str, Any] | None = None,
@@ -454,7 +445,7 @@ documents = [
 ]
 
 document_embedder = WatsonxDocumentEmbedder(
-    model="ibm/slate-30m-english-rtrvr",
+    model="ibm/slate-30m-english-rtrvr-v2",
     api_key=Secret.from_env_var("WATSONX_API_KEY"),
     api_base_url="https://us-south.ml.cloud.ibm.com",
     project_id=Secret.from_env_var("WATSONX_PROJECT_ID"),
@@ -472,7 +463,7 @@ print(result["documents"][0].embedding)
 
 ```python
 def __init__(*,
-             model: str = "ibm/slate-30m-english-rtrvr",
+             model: str = "ibm/slate-30m-english-rtrvr-v2",
              api_key: Secret = Secret.from_env_var("WATSONX_API_KEY"),
              api_base_url: str = "https://us-south.ml.cloud.ibm.com",
              project_id: Secret = Secret.from_env_var("WATSONX_PROJECT_ID"),
@@ -492,7 +483,7 @@ Creates a WatsonxDocumentEmbedder component.
 **Arguments**:
 
 - `model`: The name of the model to use for calculating embeddings.
-Default is "ibm/slate-30m-english-rtrvr".
+Default is "ibm/slate-30m-english-rtrvr-v2".
 - `api_key`: The WATSONX API key. Can be set via environment variable WATSONX_API_KEY.
 - `api_base_url`: The WATSONX URL for the watsonx.ai service.
 Default is "https://us-south.ml.cloud.ibm.com".
@@ -582,7 +573,7 @@ from haystack_integrations.components.embedders.watsonx.text_embedder import Wat
 text_to_embed = "I love pizza!"
 
 text_embedder = WatsonxTextEmbedder(
-    model="ibm/slate-30m-english-rtrvr",
+    model="ibm/slate-30m-english-rtrvr-v2",
     api_key=Secret.from_env_var("WATSONX_API_KEY"),
     api_base_url="https://us-south.ml.cloud.ibm.com",
     project_id=Secret.from_env_var("WATSONX_PROJECT_ID"),
@@ -591,7 +582,7 @@ text_embedder = WatsonxTextEmbedder(
 print(text_embedder.run(text_to_embed))
 
 # {'embedding': [0.017020374536514282, -0.023255806416273117, ...],
-#  'meta': {'model': 'ibm/slate-30m-english-rtrvr',
+#  'meta': {'model': 'ibm/slate-30m-english-rtrvr-v2',
 #           'truncated_input_tokens': 3}}
 ```
 
@@ -601,7 +592,7 @@ print(text_embedder.run(text_to_embed))
 
 ```python
 def __init__(*,
-             model: str = "ibm/slate-30m-english-rtrvr",
+             model: str = "ibm/slate-30m-english-rtrvr-v2",
              api_key: Secret = Secret.from_env_var("WATSONX_API_KEY"),
              api_base_url: str = "https://us-south.ml.cloud.ibm.com",
              project_id: Secret = Secret.from_env_var("WATSONX_PROJECT_ID"),
@@ -617,7 +608,7 @@ Creates an WatsonxTextEmbedder component.
 **Arguments**:
 
 - `model`: The name of the IBM watsonx model to use for calculating embeddings.
-Default is "ibm/slate-30m-english-rtrvr".
+Default is "ibm/slate-30m-english-rtrvr-v2".
 - `api_key`: The WATSONX API key. Can be set via environment variable WATSONX_API_KEY.
 - `api_base_url`: The WATSONX URL for the watsonx.ai service.
 Default is "https://us-south.ml.cloud.ibm.com".
@@ -683,3 +674,4 @@ Embeds a single string.
 A dictionary with:
 - 'embedding': The embedding of the input text
 - 'meta': Information about the model usage
+

docs-website/reference_versioned_docs/version-2.17/integrations-api/watsonx.md

@@ -18,11 +18,8 @@ Enables text completions using IBM's watsonx.ai foundation models.
 This component extends WatsonxChatGenerator to provide the standard Generator interface that works with prompt
 strings instead of ChatMessage objects.
 
-The generator works with IBM's foundation models including:
-- granite-13b-chat-v2
-- llama-2-70b-chat
-- llama-3-70b-instruct
-- Other watsonx.ai chat models
+The generator works with IBM's foundation models that are listed
+[here](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-models.html?context=wx&audience=wdp).
 
 You can customize the generation behavior by passing parameters to the watsonx.ai API through the
 `generation_kwargs` argument. These parameters are passed directly to the watsonx.ai inference endpoint.
@@ -73,7 +70,7 @@ Output:
 ```python
 def __init__(*,
              api_key: Secret = Secret.from_env_var("WATSONX_API_KEY"),
-             model: str = "ibm/granite-3-2b-instruct",
+             model: str = "ibm/granite-3-3-8b-instruct",
              project_id: Secret = Secret.from_env_var("WATSONX_PROJECT_ID"),
              api_base_url: str = "https://us-south.ml.cloud.ibm.com",
              system_prompt: str | None = None,
@@ -233,14 +230,8 @@ This component interacts with IBM's watsonx.ai platform to generate chat respons
 models. It supports the [ChatMessage](https://docs.haystack.deepset.ai/docs/chatmessage) format for both input
 and output, including multimodal inputs with text and images.
 
-The generator works with IBM's foundation models including:
-- granite-13b-chat-v2
-- llama-2-70b-chat
-- llama-3-70b-instruct
-- llama-3-2-11b-vision-instruct (multimodal)
-- llama-3-2-90b-vision-instruct (multimodal)
-- pixtral-12b (multimodal)
-- Other watsonx.ai chat models
+The generator works with IBM's foundation models that are listed
+[here](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-models.html?context=wx&audience=wdp).
 
 You can customize the generation behavior by passing parameters to the watsonx.ai API through the
 `generation_kwargs` argument. These parameters are passed directly to the watsonx.ai inference endpoint.
@@ -294,7 +285,7 @@ print(response)
 ```python
 def __init__(*,
              api_key: Secret = Secret.from_env_var("WATSONX_API_KEY"),
-             model: str = "ibm/granite-3-2b-instruct",
+             model: str = "ibm/granite-3-3-8b-instruct",
              project_id: Secret = Secret.from_env_var("WATSONX_PROJECT_ID"),
              api_base_url: str = "https://us-south.ml.cloud.ibm.com",
              generation_kwargs: dict[str, Any] | None = None,
@@ -454,7 +445,7 @@ documents = [
 ]
 
 document_embedder = WatsonxDocumentEmbedder(
-    model="ibm/slate-30m-english-rtrvr",
+    model="ibm/slate-30m-english-rtrvr-v2",
     api_key=Secret.from_env_var("WATSONX_API_KEY"),
     api_base_url="https://us-south.ml.cloud.ibm.com",
     project_id=Secret.from_env_var("WATSONX_PROJECT_ID"),
@@ -472,7 +463,7 @@ print(result["documents"][0].embedding)
 
 ```python
 def __init__(*,
-             model: str = "ibm/slate-30m-english-rtrvr",
+             model: str = "ibm/slate-30m-english-rtrvr-v2",
              api_key: Secret = Secret.from_env_var("WATSONX_API_KEY"),
              api_base_url: str = "https://us-south.ml.cloud.ibm.com",
              project_id: Secret = Secret.from_env_var("WATSONX_PROJECT_ID"),
@@ -492,7 +483,7 @@ Creates a WatsonxDocumentEmbedder component.
 **Arguments**:
 
 - `model`: The name of the model to use for calculating embeddings.
-Default is "ibm/slate-30m-english-rtrvr".
+Default is "ibm/slate-30m-english-rtrvr-v2".
 - `api_key`: The WATSONX API key. Can be set via environment variable WATSONX_API_KEY.
 - `api_base_url`: The WATSONX URL for the watsonx.ai service.
 Default is "https://us-south.ml.cloud.ibm.com".
@@ -582,7 +573,7 @@ from haystack_integrations.components.embedders.watsonx.text_embedder import Wat
 text_to_embed = "I love pizza!"
 
 text_embedder = WatsonxTextEmbedder(
-    model="ibm/slate-30m-english-rtrvr",
+    model="ibm/slate-30m-english-rtrvr-v2",
     api_key=Secret.from_env_var("WATSONX_API_KEY"),
     api_base_url="https://us-south.ml.cloud.ibm.com",
     project_id=Secret.from_env_var("WATSONX_PROJECT_ID"),
@@ -591,7 +582,7 @@ text_embedder = WatsonxTextEmbedder(
 print(text_embedder.run(text_to_embed))
 
 # {'embedding': [0.017020374536514282, -0.023255806416273117, ...],
-#  'meta': {'model': 'ibm/slate-30m-english-rtrvr',
+#  'meta': {'model': 'ibm/slate-30m-english-rtrvr-v2',
 #           'truncated_input_tokens': 3}}
 ```
 
@@ -601,7 +592,7 @@ print(text_embedder.run(text_to_embed))
 
 ```python
 def __init__(*,
-             model: str = "ibm/slate-30m-english-rtrvr",
+             model: str = "ibm/slate-30m-english-rtrvr-v2",
              api_key: Secret = Secret.from_env_var("WATSONX_API_KEY"),
              api_base_url: str = "https://us-south.ml.cloud.ibm.com",
              project_id: Secret = Secret.from_env_var("WATSONX_PROJECT_ID"),
@@ -617,7 +608,7 @@ Creates an WatsonxTextEmbedder component.
 **Arguments**:
 
 - `model`: The name of the IBM watsonx model to use for calculating embeddings.
-Default is "ibm/slate-30m-english-rtrvr".
+Default is "ibm/slate-30m-english-rtrvr-v2".
 - `api_key`: The WATSONX API key. Can be set via environment variable WATSONX_API_KEY.
 - `api_base_url`: The WATSONX URL for the watsonx.ai service.
 Default is "https://us-south.ml.cloud.ibm.com".
@@ -683,3 +674,4 @@ Embeds a single string.
 A dictionary with:
 - 'embedding': The embedding of the input text
 - 'meta': Information about the model usage
+