diff --git a/.github/workflows/deploy-dev.yml b/.github/workflows/deploy-dev.yml
new file mode 100644
index 0000000..c1f2009
--- /dev/null
+++ b/.github/workflows/deploy-dev.yml
@@ -0,0 +1,85 @@
+name: Deploy to Dev Workspace
+
+on:
+  push:
+    branches:
+      - dev
+
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v3
+
+      - name: Install jq and yq
+        run: |
+          sudo apt-get update
+          sudo apt-get install -y jq
+          sudo wget https://github.com/mikefarah/yq/releases/latest/download/yq_linux_amd64 -O /usr/bin/yq
+          sudo chmod +x /usr/bin/yq
+
+      - name: Create or Update Pipelines
+        env:
+          DEEPSET_CLOUD_API_KEY: ${{ secrets.DEEPSET_CLOUD_API_KEY }}
+          DEEPSET_CLOUD_WORKSPACE_NAME: "your-dev-workspace-name"
+        run: |
+          for pipeline_dir in pipelines/*/; do
+            pipeline_name=$(basename "$pipeline_dir")
+            indexing_yaml="${pipeline_dir}indexing.yaml"
+            query_yaml="${pipeline_dir}query.yaml"
+            
+            if [[ ! -f "$indexing_yaml" || ! -f "$query_yaml" ]]; then
+              echo "Error: Both indexing.yaml and query.yaml must exist in $pipeline_dir"
+              exit 1
+            fi
+            
+            indexing_content=$(yq eval -o=json "$indexing_yaml")
+            query_content=$(yq eval -o=json "$query_yaml")
+            
+            payload=$(jq -n \
+              --arg name "$pipeline_name" \
+              --arg indexing "$indexing_content" \
+              --arg query "$query_content" \
+              '{
+                "name": $name,
+                "yaml": null,
+                "indexing_yaml": $indexing,
+                "query_yaml": $query,
+                "deepset_cloud_version": "v2"
+              }')
+            
+            response=$(curl --silent --show-error --fail \
+              --request POST \
+              --url "https://api.cloud.deepset.ai/api/v1/workspaces/${DEEPSET_CLOUD_WORKSPACE_NAME}/pipelines" \
+              --header "Authorization: Bearer ${DEEPSET_CLOUD_API_KEY}" \
+              --header 'Content-Type: application/json' \
+              --data "$payload")
+            
+            echo "Pipeline creation/update response for $pipeline_name: $response"
+            
+            if [[ $response == *"error"* ]]; then
+              echo "Pipeline creation/update failed for $pipeline_name"
+              exit 1
+            fi
+          done
+
+      - name: Validate Pipelines
+        env:
+          DEEPSET_CLOUD_API_KEY: ${{ secrets.DEEPSET_CLOUD_API_KEY }}
+          DEEPSET_CLOUD_WORKSPACE_NAME: "your-dev-workspace-name"
+        run: |
+          validation_response=$(curl --silent --show-error --fail \
+            --request POST \
+            --url "https://api.cloud.deepset.ai/api/v1/workspaces/${DEEPSET_CLOUD_WORKSPACE_NAME}/pipeline_validations" \
+            --header "Authorization: Bearer ${DEEPSET_CLOUD_API_KEY}" \
+            --header 'content-type: application/json' \
+            --data '{"deepset_cloud_version": "v2"}')
+          
+          echo "Validation response: $validation_response"
+          
+          if [[ $validation_response == *"error"* ]]; then
+            echo "Pipeline validation failed"
+            exit 1
+          fi
diff --git a/.github/workflows/deploy-prod.yml b/.github/workflows/deploy-prod.yml
new file mode 100644
index 0000000..802a8dc
--- /dev/null
+++ b/.github/workflows/deploy-prod.yml
@@ -0,0 +1,74 @@
+name: Deploy to Prod Workspace
+
+on:
+  push:
+    branches:
+      - main
+  workflow_dispatch:
+
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v3
+
+      - name: Install jq
+        run: sudo apt-get install jq
+
+      - name: Create or Update Pipelines
+        env:
+          DEEPSET_CLOUD_API_KEY: ${{ secrets.DEEPSET_CLOUD_API_KEY }}
+          DEEPSET_CLOUD_WORKSPACE_NAME: "your-prod-workspace-name"
+        run: |
+          for pipeline in pipelines/*.yaml; do
+            pipeline_content=$(cat "$pipeline" | jq -sR .)
+            response=$(curl --silent --show-error --fail \
+              --request POST \
+              --url "https://api.cloud.deepset.ai/api/v1/workspaces/${DEEPSET_CLOUD_WORKSPACE_NAME}/pipelines?dry_run=false" \
+              --header "Authorization: Bearer ${DEEPSET_CLOUD_API_KEY}" \
+              --header 'Content-Type: application/json' \
+              --data "{
+                \"yaml_content\": ${pipeline_content},
+                \"deepset_cloud_version\": \"v2\"
+              }")
+            
+            echo "Pipeline creation/update response for $pipeline: $response"
+            
+            if [[ $response == *"error"* ]]; then
+              echo "Pipeline creation/update failed for $pipeline"
+              exit 1
+            fi
+          done
+
+      - name: Validate Pipelines
+        env:
+          DEEPSET_CLOUD_API_KEY: ${{ secrets.DEEPSET_CLOUD_API_KEY }}
+          DEEPSET_CLOUD_WORKSPACE_NAME: "your-prod-workspace-name"
+        run: |
+          validation_response=$(curl --silent --show-error --fail \
+            --request POST \
+            --url "https://api.cloud.deepset.ai/api/v1/workspaces/${DEEPSET_CLOUD_WORKSPACE_NAME}/pipeline_validations" \
+            --header "Authorization: Bearer ${DEEPSET_CLOUD_API_KEY}" \
+            --header 'content-type: application/json' \
+            --data '{"deepset_cloud_version": "v2"}')
+          
+          echo "Validation response: $validation_response"
+          
+          if [[ $validation_response == *"error"* ]]; then
+            echo "Pipeline validation failed"
+            exit 1
+          fi
+
+      - name: Deploy Pipelines to Prod Workspace
+        env:
+          DEEPSET_CLOUD_API_KEY: ${{ secrets.DEEPSET_CLOUD_API_KEY }}
+          DEEPSET_CLOUD_WORKSPACE_NAME: "your-prod-workspace-name"
+        run: |
+          for pipeline in pipelines/*.yaml; do
+            deepset-cloud pipelines upload \
+              --api-key "$DEEPSET_CLOUD_API_KEY" \
+              --workspace-name "$DEEPSET_CLOUD_WORKSPACE_NAME" \
+              --file "$pipeline"
+          done
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..b7a9cf1
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,49 @@
+# Python-related files
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Virtual environments
+venv/
+env/
+ENV/
+
+# IDEs and editors
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# Operating system files
+.DS_Store
+Thumbs.db
+
+# Logs
+*.log
+
+# Local configuration files
+*.env
+config.local.yaml
+
+# Temporary files
+*.tmp
+*.bak
+*.swp
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..bcc5dba
--- /dev/null
+++ b/README.md
@@ -0,0 +1,221 @@
+# CI/CD Template with GitHub Actions for deepset Cloud
+
+This repository serves as a template for using **GitHub Actions** as a CI/CD pipeline to manage and deploy pipelines to **deepset Cloud**. It demonstrates how to work with two separate workspaces: **dev** and **prod**.
+
+## Table of Contents
+
+- [Prerequisites](#prerequisites)
+- [Repository Structure](#repository-structure)
+- [Setup Instructions](#setup-instructions)
+- [Usage](#usage)
+  - [Branching Strategy](#branching-strategy)
+  - [Adding or Updating Pipelines](#adding-or-updating-pipelines)
+  - [Triggering Deployments](#triggering-deployments)
+  - [Rollback Procedures](#rollback-procedures)
+- [Security Considerations](#security-considerations)
+- [Extending the Template](#extending-the-template)
+- [Support and Contributions](#support-and-contributions)
+
+---
+
+## Prerequisites
+
+Before using this template, ensure you have the following:
+
+- A **deepset Cloud** account with access to your **dev** and **prod** workspaces.
+- A **GitHub** account with permissions to create repositories and set up GitHub Actions.
+- Basic understanding of **Git** and GitHub workflows.
+
+## Repository Structure
+
+```plaintext
+your-repo/
+├── .github/
+│   └── workflows/
+│       ├── deploy-dev.yml
+│       └── deploy-prod.yml
+├── pipelines/
+│   ├── pipeline1.yaml
+│   └── pipeline2.yaml
+└── README.md
+```
+
+- **`.github/workflows/`**: Contains GitHub Actions workflow files for CI/CD.
+- **`pipelines/`**: Stores your pipeline configuration files.
+- **`README.md`**: Provides instructions and information about the repository.
+
+## Setup Instructions
+
+1. **Clone the Repository**:
+
+   ```bash
+   git clone https://github.com/your-username/your-repo.git
+   cd your-repo
+   ```
+
+2. **Set Up GitHub Secrets**:
+
+   - Navigate to your GitHub repository.
+   - Go to **Settings** > **Secrets and variables** > **Actions**.
+   - Click **"New repository secret"** and add the following secret:
+
+     - `DEEPSET_CLOUD_API_KEY`: Your deepset Cloud API key.
+
+     **Note**: The same API key is used for both dev and prod environments.
+
+3. **Configure Workspace Names**:
+
+   - Update the workspace names in the workflow files:
+
+     - In `.github/workflows/deploy-dev.yml`, replace `"your-dev-workspace-name"` with your actual **dev** workspace name.
+     - In `.github/workflows/deploy-prod.yml`, replace `"your-prod-workspace-name"` with your actual **prod** workspace name.
+
+4. **Install Dependencies (Optional)**:
+
+   - If you plan to run the deepset Cloud CLI locally, install it with:
+
+     ```bash
+     pip install deepset-cloud
+     ```
+
+## Usage
+
+### Branching Strategy
+
+This template uses a simple branching strategy to manage deployments to different environments:
+
+- **Development Branch (`dev`)**:
+
+  - Used for integrating and testing new changes.
+  - Automatically deploys to the **dev** workspace upon push.
+
+- **Production Branch (`main`)**:
+
+  - Contains stable and reviewed code.
+  - Deployment to the **prod** workspace is triggered on push or manually via GitHub Actions.
+
+### Adding or Updating Pipelines
+
+1. **Add or Modify Pipeline Files**:
+
+   - Pipeline configuration files are located in the `pipelines/` directory.
+   - Create new pipeline YAML files or modify existing ones.
+
+   **Example**:
+
+   ```yaml:pipelines/pipeline1.yaml
+   version: "1.0"
+   components:
+     - name: Retriever
+       type: BM25Retriever
+       params:
+         document_store: MyDocumentStore
+
+     - name: Reader
+       type: TransformersReader
+       params:
+         model_name_or_path: deepset/roberta-base-squad2
+
+   pipelines:
+     - name: question-answering
+       nodes:
+         - name: Retriever
+           inputs: [Query]
+         - name: Reader
+           inputs: [Retriever]
+   ```
+
+2. **Commit Changes**:
+
+   ```bash
+   git add pipelines/your-pipeline.yaml
+   git commit -m "Add new pipeline"
+   ```
+
+3. **Push to the Appropriate Branch**:
+
+   - For development:
+
+     ```bash
+     git push origin dev
+     ```
+
+   - For production:
+
+     ```bash
+     git push origin main
+     ```
+
+### Triggering Deployments
+
+- **Automatic Deployment**:
+
+  - **Dev Workspace**: Pushing to the `dev` branch triggers the `deploy-dev.yml` workflow.
+  - **Prod Workspace**: Pushing to the `main` branch triggers the `deploy-prod.yml` workflow.
+
+- **Manual Deployment**:
+
+  - For production, you can manually trigger the workflow:
+
+    1. Go to the **Actions** tab in your GitHub repository.
+    2. Select **"Deploy to Prod Workspace"** workflow.
+    3. Click **"Run workflow"**.
+
+### Rollback Procedures
+
+If you need to revert to a previous pipeline version:
+
+1. **Revert Changes in Git**:
+
+   ```bash
+   git revert <commit-hash>
+   git push origin dev  # or main, depending on the branch
+   ```
+
+2. **Deployment**:
+
+   - The GitHub Actions workflow will redeploy the pipelines based on the reverted code.
+
+## Security Considerations
+
+- **API Keys**:
+
+  - Store sensitive information like API keys in **GitHub Secrets**.
+  - **Do not** commit sensitive data to the repository.
+
+- **Workspace Names**:
+
+  - Workspace names are specified in the workflow files. Ensure they are correct to avoid deploying to unintended workspaces.
+
+- **Secret Rotation**:
+
+  - Regularly rotate your API keys and update the GitHub Secrets accordingly.
+
+## Extending the Template
+
+- **Add More Environments**:
+
+  - To add environments like staging, copy one of the existing workflows and modify the workspace name and trigger conditions.
+
+- **Validation and Testing**:
+
+  - Incorporate pipeline validation steps before deployment.
+
+    ```yaml
+    - name: Validate Pipelines
+      run: |
+        for pipeline in pipelines/*.yaml; do
+          deepset-cloud pipelines validate --file "$pipeline"
+        done
+    ```
+
+- **Notifications**:
+
+  - Add steps to send deployment notifications via email, Slack, etc.
+
+## Support and Contributions
+
+- **Issues**: If you encounter problems, please open an issue.
+- **Contributions**: Contributions are welcome! Feel free to fork the repository and submit a pull request.
+
+---
diff --git a/pipelines/rag-qa-gpt4/indexing.yaml b/pipelines/rag-qa-gpt4/indexing.yaml
new file mode 100644
index 0000000..ddd9fcc
--- /dev/null
+++ b/pipelines/rag-qa-gpt4/indexing.yaml
@@ -0,0 +1,135 @@
+# If you need help with the YAML format, have a look at https://docs.cloud.deepset.ai/v2.0/docs/create-a-pipeline#create-a-pipeline-using-pipeline-editor.
+# This section defines components that you want to use in your pipelines. Each component must have a name and a type. You can also set the component's parameters here.
+# The name is up to you, you can give your component a friendly name. You then use components' names when specifying the connections in the pipeline.
+# Type is the class path of the component. You can check the type on the component's documentation page.
+components:
+  file_classifier:
+    type: haystack.components.routers.file_type_router.FileTypeRouter
+    init_parameters:
+      mime_types:
+      - text/plain
+      - application/pdf
+      - text/markdown
+      - text/html
+      - application/vnd.openxmlformats-officedocument.wordprocessingml.document
+      - application/vnd.openxmlformats-officedocument.presentationml.presentation
+      - application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
+
+  text_converter:
+    type: haystack.components.converters.txt.TextFileToDocument
+    init_parameters:
+      encoding: utf-8
+
+  pdf_converter:
+    type: haystack.components.converters.pypdf.PyPDFToDocument
+    init_parameters:
+      converter:
+        type: haystack.components.converters.pypdf.DefaultConverter
+
+  markdown_converter:
+    type: haystack.components.converters.markdown.MarkdownToDocument
+    init_parameters:
+      table_to_single_line: false
+
+  html_converter:
+    type: haystack.components.converters.html.HTMLToDocument
+    init_parameters:
+      # A dictionary of keyword arguments to customize how you want to extract content from your HTML files.
+      # For the full list of available arguments, see
+      # the [Trafilatura documentation](https://trafilatura.readthedocs.io/en/latest/corefunctions.html#extract).
+      extraction_kwargs:
+        output_format: txt  # Extract text from HTML. You can also also choose "markdown"
+        target_language: null  # You can define a language (using the ISO 639-1 format) to discard documents that don't match that language.
+        include_tables: true  # If true, includes tables in the output
+        include_links: false  # If true, keeps links along with their targets
+
+  docx_converter:
+    type: haystack.components.converters.docx.DOCXToDocument
+    init_parameters: {}
+
+  pptx_converter:
+    type: haystack.components.converters.pptx.PPTXToDocument
+    init_parameters: {}
+
+  xlsx_converter:
+    type: deepset_cloud_custom_nodes.converters.xlsx.XLSXToDocument
+    init_parameters: {}
+
+  joiner:
+    type: haystack.components.joiners.document_joiner.DocumentJoiner
+    init_parameters:
+      join_mode: concatenate
+      sort_by_score: false
+
+  joiner_xlsx:  # merge split documents with non-split xlsx documents
+    type: haystack.components.joiners.document_joiner.DocumentJoiner
+    init_parameters:
+      join_mode: concatenate
+      sort_by_score: false
+
+  splitter:
+    type: deepset_cloud_custom_nodes.preprocessors.document_splitter.DeepsetDocumentSplitter
+    init_parameters:
+      split_by: word
+      split_length: 250
+      split_overlap: 30
+      respect_sentence_boundary: True
+      language: en
+
+  document_embedder:
+    type: haystack.components.embedders.sentence_transformers_document_embedder.SentenceTransformersDocumentEmbedder
+    init_parameters:
+      model: "intfloat/e5-base-v2"
+
+  writer:
+    type: haystack.components.writers.document_writer.DocumentWriter
+    init_parameters:
+      document_store:
+        type: haystack_integrations.document_stores.opensearch.document_store.OpenSearchDocumentStore
+        init_parameters:
+          embedding_dim: 768
+          similarity: cosine
+      policy: OVERWRITE
+
+connections:  # Defines how the components are connected
+- sender: file_classifier.text/plain
+  receiver: text_converter.sources
+- sender: file_classifier.application/pdf
+  receiver: pdf_converter.sources
+- sender: file_classifier.text/markdown
+  receiver: markdown_converter.sources
+- sender: file_classifier.text/html
+  receiver: html_converter.sources
+- sender: file_classifier.application/vnd.openxmlformats-officedocument.wordprocessingml.document
+  receiver: docx_converter.sources
+- sender: file_classifier.application/vnd.openxmlformats-officedocument.presentationml.presentation
+  receiver: pptx_converter.sources
+- sender: file_classifier.application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
+  receiver: xlsx_converter.sources
+- sender: text_converter.documents
+  receiver: joiner.documents
+- sender: pdf_converter.documents
+  receiver: joiner.documents
+- sender: markdown_converter.documents
+  receiver: joiner.documents
+- sender: html_converter.documents
+  receiver: joiner.documents
+- sender: docx_converter.documents
+  receiver: joiner.documents
+- sender: pptx_converter.documents
+  receiver: joiner.documents
+- sender: joiner.documents
+  receiver: splitter.documents
+- sender: splitter.documents
+  receiver: joiner_xlsx.documents
+- sender: xlsx_converter.documents
+  receiver: joiner_xlsx.documents
+- sender: joiner_xlsx.documents
+  receiver: document_embedder.documents
+- sender: document_embedder.documents
+  receiver: writer.documents
+
+max_loops_allowed: 100
+
+inputs:  # Define the inputs for your pipeline
+  files: "file_classifier.sources"  # This component will receive the files to index as input
diff --git a/pipelines/rag-qa-gpt4/query.yaml b/pipelines/rag-qa-gpt4/query.yaml
new file mode 100644
index 0000000..c181214
--- /dev/null
+++ b/pipelines/rag-qa-gpt4/query.yaml
@@ -0,0 +1,132 @@
+# If you need help with the YAML format, have a look at https://docs.cloud.deepset.ai/v2.0/docs/create-a-pipeline#create-a-pipeline-using-pipeline-editor.
+# This section defines components that you want to use in your pipelines. Each component must have a name and a type. You can also set the component's parameters here.
+# The name is up to you, you can give your component a friendly name. You then use components' names when specifying the connections in the pipeline.
+# Type is the class path of the component. You can check the type on the component's documentation page.
+components:
+  bm25_retriever: # Selects the most similar documents from the document store
+    type: haystack_integrations.components.retrievers.opensearch.bm25_retriever.OpenSearchBM25Retriever
+    init_parameters:
+      document_store:
+        type: haystack_integrations.document_stores.opensearch.document_store.OpenSearchDocumentStore
+        init_parameters:
+          use_ssl: True
+          verify_certs: False
+          hosts:
+            - ${OPENSEARCH_HOST}
+          http_auth:
+            - "${OPENSEARCH_USER}"
+            - "${OPENSEARCH_PASSWORD}"
+          embedding_dim: 768
+          similarity: cosine
+      top_k: 20 # The number of results to return
+
+  query_embedder:
+    type: haystack.components.embedders.sentence_transformers_text_embedder.SentenceTransformersTextEmbedder
+    init_parameters:
+      model: "intfloat/e5-base-v2"
+
+  embedding_retriever: # Selects the most similar documents from the document store
+    type: haystack_integrations.components.retrievers.opensearch.embedding_retriever.OpenSearchEmbeddingRetriever
+    init_parameters:
+      document_store:
+        type: haystack_integrations.document_stores.opensearch.document_store.OpenSearchDocumentStore
+        init_parameters:
+          use_ssl: True
+          verify_certs: False
+          hosts:
+            - ${OPENSEARCH_HOST}
+          http_auth:
+            - "${OPENSEARCH_USER}"
+            - "${OPENSEARCH_PASSWORD}"
+          embedding_dim: 768
+          similarity: cosine
+      top_k: 20 # The number of results to return
+
+  document_joiner:
+    type: haystack.components.joiners.document_joiner.DocumentJoiner
+    init_parameters:
+      join_mode: concatenate
+
+  ranker:
+    type: haystack.components.rankers.transformers_similarity.TransformersSimilarityRanker
+    init_parameters:
+      model: "intfloat/simlm-msmarco-reranker"
+      top_k: 8
+      model_kwargs:
+        torch_dtype: "torch.float16"
+
+  prompt_builder:
+    type: haystack.components.builders.prompt_builder.PromptBuilder
+    init_parameters:
+      template: |-
+        You are a technical expert.
+        You answer questions truthfully based on provided documents.
+        If the answer exists in several documents, summarize them.
+        Ignore documents that don't contain the answer to the question.
+        Only answer based on the documents provided. Don't make things up.
+        If no information related to the question can be found in the document, say so.
+        Always use references in the form [NUMBER OF DOCUMENT] when using information from a document, e.g. [3] for Document[3].
+        Never name the documents, only enter a number in square brackets as a reference.
+        The reference must only refer to the number that comes in square brackets after the document.
+        Otherwise, do not use brackets in your answer and reference ONLY the number of the document without mentioning the word document.
+        These are the documents:
+        {% for document in documents %}
+        Document[{{ loop.index }}]:
+        {{ document.content }}
+        {% endfor %}
+
+        Question: {{ question }}
+        Answer:
+
+  llm:
+    type: haystack.components.generators.openai.OpenAIGenerator
+    init_parameters:
+      api_key: {"type": "env_var", "env_vars": ["OPENAI_API_KEY"], "strict": False}
+      model: "gpt-4-turbo"
+      generation_kwargs:
+        max_tokens: 650
+        temperature: 0.0
+        seed: 0
+
+  answer_builder:
+    type: deepset_cloud_custom_nodes.augmenters.deepset_answer_builder.DeepsetAnswerBuilder
+    init_parameters:
+      reference_pattern: acm
+
+connections:  # Defines how the components are connected
+- sender: bm25_retriever.documents
+  receiver: document_joiner.documents
+- sender: query_embedder.embedding
+  receiver: embedding_retriever.query_embedding
+- sender: embedding_retriever.documents
+  receiver: document_joiner.documents
+- sender: document_joiner.documents
+  receiver: ranker.documents
+- sender: ranker.documents
+  receiver: prompt_builder.documents
+- sender: ranker.documents
+  receiver: answer_builder.documents
+- sender: prompt_builder.prompt
+  receiver: llm.prompt
+- sender: prompt_builder.prompt
+  receiver: answer_builder.prompt
+- sender: llm.replies
+  receiver: answer_builder.replies
+
+max_loops_allowed: 100
+
+inputs:  # Define the inputs for your pipeline
+  query:  # These components will receive the query as input
+  - "bm25_retriever.query"
+  - "query_embedder.text"
+  - "ranker.query"
+  - "prompt_builder.question"
+  - "answer_builder.query"
+
+  filters:  # These components will receive a potential query filter as input
+  - "bm25_retriever.filters"
+  - "embedding_retriever.filters"
+
+outputs:  # Defines the output of your pipeline
+  documents: "ranker.documents"  # The output of the pipeline is the retrieved documents
+  answers: "answer_builder.answers"  # The output of the pipeline is the generated answers