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Merge pull request #810 from Pipelex/release/v0.23.5
Release v0.23.5
2 parents a141c76 + a3ef6c6 commit 9bb186e

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‎.badges/tests.json‎

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{
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"schemaVersion": 1,
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"label": "tests",
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"message": "4046",
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"message": "4053",
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"color": "blue",
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"cacheSeconds": 300
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}

‎.pipelex-dev/test_profiles.toml‎

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openai = ["gpt-image-1", "gpt-image-1-mini", "gpt-image-1.5"]
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# --- Google Models ---
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google = ["nano-banana", "nano-banana-pro"]
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google = ["nano-banana", "nano-banana-pro", "nano-banana-2"]
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# --- Qwen Models ---
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qwen = ["qwen-image"]
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[profiles.full]
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description = "All available models"
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include_all = true
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################################################################################
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# All Configs GW Profile - One model per Portkey config and model type
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################################################################################
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[profiles.all_configs_gw]
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description = "One model per Portkey config (auto-generated)"
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backends = ["pipelex_gateway"]
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llm_models = [
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"gpt-4o-mini",
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"gpt-oss-20b",
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"claude-4-sonnet",
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"deepseek-v3.2",
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"gemini-2.5-pro",
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"grok-3",
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]
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img_gen_models = [
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"nano-banana-2",
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"gpt-image-1",
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"nano-banana",
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]
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extract_models = [
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"azure-document-intelligence",
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"deepseek-ocr",
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]
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search_models = [
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"linkup-standard",
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]

‎.pipelex/inference/backends/bedrock.toml‎

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# TODO: find out the actual cost per million tokens for nova on bedrock
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costs = { input = 3.0, output = 15.0 }
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# --- Claude LLMs --------------------------------------------------------------
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["claude-3.7-sonnet"]
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sdk = "bedrock_anthropic"
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model_id = "us.anthropic.claude-3-7-sonnet-20250219-v1:0"
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# --- DeepSeek Models ----------------------------------------------------------
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["deepseek-v3.1"]
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model_id = "deepseek.v3-v1:0"
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inputs = ["text"]
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outputs = ["text"]
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costs = { input = 0, output = 0 }
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max_tokens = 8192
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inputs = ["text", "images", "pdf"]
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outputs = ["text", "structured"]
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max_prompt_images = 100
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costs = { input = 3.0, output = 15.0 }
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thinking_mode = "manual"
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# --- Claude LLMs --------------------------------------------------------------
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[claude-4-sonnet]
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sdk = "bedrock_anthropic"
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model_id = "us.anthropic.claude-sonnet-4-20250514-v1:0"

‎.pipelex/inference/backends/google.toml‎

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inputs = ["text"]
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outputs = ["image"]
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thinking_mode = "none"
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costs = { input = 0.0, output = 0.039 }
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costs = { input = 0.30, output = 30 }
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[nano-banana-pro]
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model_type = "img_gen"
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model_id = "gemini-3-pro-image-preview"
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inputs = ["text"]
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outputs = ["image"]
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thinking_mode = "none"
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costs = { input = 0.0, output = 0.039 }
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costs = { input = 2.0, output = 120 }
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[nano-banana-2]
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model_type = "img_gen"
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model_id = "gemini-3.1-flash-image-preview"
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inputs = ["text"]
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outputs = ["image"]
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thinking_mode = "none"
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costs = { input = 0.50, output = 60 }

‎.pipelex/inference/backends/pipelex_gateway_models.md‎

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</thead>
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<tbody>
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<tr>
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<td>claude-3.7-sonnet</td>
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<td style="text-align:center;background-color:rgba(33,150,243,0.15)">✅</td>
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<td style="text-align:center;background-color:rgba(33,150,243,0.15)">✅</td>
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<td style="text-align:center;background-color:rgba(33,150,243,0.15)">✅</td>
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<td style="text-align:center;background-color:rgba(76,175,80,0.15)">✅</td>
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<td style="text-align:center;background-color:rgba(76,175,80,0.15)">✅</td>
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</tr>
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<tr>
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<td>claude-4-opus</td>
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<td style="text-align:center;background-color:rgba(33,150,243,0.15)">✅</td>
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<td style="text-align:center;background-color:rgba(33,150,243,0.15)">✅</td>
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<td style="text-align:center;background-color:rgba(76,175,80,0.15)">✅</td>
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</tr>
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<tr>
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<td>deepseek-v3.1</td>
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<td style="text-align:center;background-color:rgba(33,150,243,0.15)">✅</td>
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<td style="text-align:center;background-color:rgba(33,150,243,0.15)">❌</td>
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<td style="text-align:center;background-color:rgba(33,150,243,0.15)">❌</td>
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<td style="text-align:center;background-color:rgba(76,175,80,0.15)">✅</td>
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<td style="text-align:center;background-color:rgba(76,175,80,0.15)">✅</td>
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</tr>
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<tr>
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<td>deepseek-v3.2</td>
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<td style="text-align:center;background-color:rgba(33,150,243,0.15)">✅</td>
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<td style="text-align:center;background-color:rgba(33,150,243,0.15)">❌</td>
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<td style="text-align:center;background-color:rgba(76,175,80,0.15)">✅</td>
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</tr>
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<tr>
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<td>deepseek-v3.2-speciale</td>
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<td style="text-align:center;background-color:rgba(33,150,243,0.15)">✅</td>
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<td style="text-align:center;background-color:rgba(33,150,243,0.15)">❌</td>
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<td style="text-align:center;background-color:rgba(33,150,243,0.15)">❌</td>
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<td style="text-align:center;background-color:rgba(76,175,80,0.15)">✅</td>
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<td style="text-align:center;background-color:rgba(76,175,80,0.15)">✅</td>
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</tr>
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<tr>
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<td>gemini-2.5-flash</td>
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<td style="text-align:center;background-color:rgba(33,150,243,0.15)">✅</td>
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<td style="text-align:center;background-color:rgba(33,150,243,0.15)">✅</td>
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</thead>
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<td>flux-2-pro</td>
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<td style="text-align:center;background-color:rgba(33,150,243,0.15)">✅</td>
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<td style="text-align:center;background-color:rgba(33,150,243,0.15)">✅</td>
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<td style="text-align:center;background-color:rgba(76,175,80,0.15)">✅</td>
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</tr>
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<tr>
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<td>gpt-image-1</td>
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<td style="text-align:center;background-color:rgba(33,150,243,0.15)">✅</td>
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<td style="text-align:center;background-color:rgba(33,150,243,0.15)">✅</td>
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> **AUTO-GENERATED FILE** - Do not edit manually.
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> Last updated: 2026-03-21T13:07:30Z
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> Last updated: 2026-04-04T17:05:38Z
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>
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> Run `pipelex-dev update-gateway-models` or `make ugm` to regenerate.

‎.pipelex/inference/backends/pipelex_gateway_models_plain.md‎

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## Language Models (LLM)
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- **claude-3.7-sonnet**
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- inputs: text, images, pdf
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- outputs: text, structured
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- **claude-4-opus**
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- inputs: text, images, pdf
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- outputs: text, structured
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- **claude-4.6-opus**
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- inputs: text, images, pdf
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- outputs: text, structured
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- **deepseek-v3.1**
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- inputs: text
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- outputs: text, structured
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- **deepseek-v3.2**
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- inputs: text
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- outputs: text, structured
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- **deepseek-v3.2-speciale**
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- inputs: text
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- outputs: text, structured
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- **gemini-2.5-flash**
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- inputs: text, images, pdf
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- outputs: text, structured
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## Image Generation Models
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- **flux-2-pro**
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- inputs: text, images
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- outputs: image
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- **gpt-image-1**
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- inputs: text, images
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- outputs: image
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> **AUTO-GENERATED FILE** - Do not edit manually.
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> Last updated: 2026-03-21T13:07:30Z
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> Last updated: 2026-04-04T17:05:38Z
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>
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> Run `pipelex-dev update-gateway-models` or `make ugm` to regenerate.

‎.pipelex/inference/deck/2_img_gen_deck.toml‎

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[img_gen.aliases]
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best-gpt = "gpt-image-1.5"
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best-gemini = "nano-banana-pro"
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best-blackforestlabs = "flux-2-pro"
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best-gemini = "nano-banana-2"
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default-general = "flux-2-pro"
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default-premium = "nano-banana-pro"
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default-general = "nano-banana"
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default-premium = "nano-banana-2"
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default-small = "gpt-image-1-mini"
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####################################################################################################

‎CHANGELOG.md‎

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# Changelog
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## [v0.23.5] - 2026-04-04
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### Added
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- **Gateway config**: Introduced `GatewayConfig` to bundle gateway model specs with AWS region, propagating it through the backend library so bedrock backends use the correct region.
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- **Config coverage tests**: Integration tests that validate one model per Portkey config for each model type (LLM, image gen, extract, search), with `all_configs_gw` test profile and `make ticc` target.
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- **nano-banana-2 model**: Added `gemini-3.1-flash-image-preview` as `nano-banana-2` with updated Google image gen costs.
10+
- **DeepSeek models on bedrock**: Added DeepSeek models to the bedrock backend configuration.
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### Changed
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- **Image gen deck aliases**: Updated aliases to nano-banana model variants and removed `flux-2-pro`.
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- **Remote config**: Bumped to v08.
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- **Gateway model docs**: Regenerated, removing retired models (claude-3.7-sonnet, deepseek-v3.1, deepseek-v3.2-speciale, flux-2-pro).
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### Fixed
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- **deepseek-v3.1 structured output**: Removed unsupported `structured` output capability from the bedrock deepseek-v3.1 model spec — the `bedrock_aioboto3` worker does not implement object generation, so structured calls would fail at runtime.
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## [v0.23.4] - 2026-04-02
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### Changed

‎Makefile‎

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make tb - Shorthand -> `make test-with-prints TEST=test_boot`
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make test-inference - Run unit tests only for inference (with prints)
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make ti - Shorthand -> test-inference
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make ticc - Shorthand -> test config coverage (all Portkey configs)
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make tip - Shorthand -> test-inference-with-prints (parallelized inference tests)
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make test-llm - Run unit tests only for llm (with prints)
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make tl - Shorthand -> test-llm
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format lint ruff-format ruff-lint pyright mypy pylint plxt plxt-format plxt-lint \
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rules up-kit-configs ukc check-config-sync ccs check-rules check-urls cu insert-skeleton \
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cleanderived cleanenv cleanall \
166-
test test-xdist t test-quiet tq test-with-prints tp test-inference ti \
167+
test test-xdist t test-quiet tq test-with-prints tp test-inference ti ticc \
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test-llm tl test-img-gen tg test-extract te codex-tests gha-tests \
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run-all-tests run-manual-trigger-gha-tests run-gha_disabled-tests \
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validate v check c cc agent-check agent-test \
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ti: test-inference-fast
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@echo "> done: ti-fast = test-inference-fast"
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ticc: env
551+
$(call PRINT_TITLE,"Config coverage inference testing")
552+
@$(VENV_PIPELEX_DEV) preprocess-test-models --generate-fixtures --profile all_configs_gw --quiet
553+
$(VENV_PYTEST) -n auto --pipe-run-mode live -m "inference" -s -rfE -k "TestConfigCoverage" $(if $(filter 1,$(VERBOSE)),-v,$(if $(filter 2,$(VERBOSE)),-vv,$(if $(filter 3,$(VERBOSE)),-vvv,)))
554+
@echo "> done: ticc = test-inference config coverage (all Portkey configs)"
555+
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ti-dry: env
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$(call PRINT_TITLE,"Unit testing")
551558
@if [ -n "$(TEST)" ]; then \

‎pipelex/builder/operations/pipe_ops.py‎

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# Aliases that agents may use instead of "pipe_code". First found is promoted when canonical key is absent; extras are dropped.
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_PIPE_CODE_ALIASES = ("pipe", "the_pipe_code", "code", "name", "pipe_name", "pipe_ref")
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# Alias that agents may use instead of "output".
32-
_OUTPUT_ALIAS = "output_concept"
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# Aliases that agents may use instead of "output". First found is promoted when canonical key is absent; extras are dropped.
32+
_OUTPUT_ALIASES = ("output_concept", "output_type")
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def _normalize_sub_pipe_dict(data: dict[str, Any]) -> None:
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spec_data.pop("expression")
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# Accept "output_concept" as an alias for "output".
114-
# When both "output" and the alias coexist, try the alias value first (agents often put
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# Accept output aliases (e.g. "output_concept", "output_type") for "output".
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# When both "output" and an alias coexist, try the alias value first (agents often put
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# the correct concept name in the alias), falling back to the original "output" value.
116116
output_fallback: Any | None = None
117-
if _OUTPUT_ALIAS in spec_data:
118-
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for output_alias in _OUTPUT_ALIASES:
118+
if output_alias not in spec_data:
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continue
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alias_value = spec_data.pop(output_alias)
119121
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120122
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121123
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122124
output_fallback = spec_data["output"]
123125
spec_data["output"] = alias_value
126+
# First alias wins — drop any remaining aliases without using them.
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for remaining_alias in _OUTPUT_ALIASES:
128+
spec_data.pop(remaining_alias, None)
129+
break
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# Accept output as dict → extract the concept string
126132
# Agents sometimes structure the output like inputs (as a dict).
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# Handle {"type": "ConceptName"} and single-item dicts like {"result": "Text"}.
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if "output" in spec_data and isinstance(spec_data["output"], dict):
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output_dict: dict[str, Any] = spec_data["output"]
130-
if "type" in output_dict:
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if "concept_ref" in output_dict:
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spec_data["output"] = output_dict["concept_ref"]
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elif "type" in output_dict:
131139
spec_data["output"] = output_dict["type"]
132140
elif len(output_dict) == 1:
133141
spec_data["output"] = next(iter(output_dict.values()))

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