Update LLM deck defaults, fix object list e2e test, add bedrock token auth changelog

Bump best-claude to opus-4.7, swap small-vision/creative to gemini-3.0-flash-preview,
add cheap presets for writing/retrieval/engineering, fix bedrock_access_variant default
back to aws_access, and reduce moodboard prompt test from 5 to 3 items to match the
updated .mthds fixture.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

Author: lchoquel

CHANGELOG.md

@@ -9,6 +9,7 @@
 - **`TEMPERATURE_UNSUPPORTED` constraint**: New listed constraint for models that reject sampling parameters entirely, checked in both Anthropic and OpenAI completions workers
 - **claude-4.6-sonnet model**: Registered on anthropic, bedrock, and gateway backends
 - **LLM deck cheap presets**: Added cheap variants for writing-factual, retrieval, and engineering-code presets, with retrieval tiers from `gemini-2.5-flash-lite` to `claude-4.7-opus`
+- **Bedrock bearer token authentication**: New `bedrock_access_variant` config option supports `"bedrock_token"` auth using `AWS_BEARER_TOKEN_BEDROCK` env var, alongside the existing `"aws_access"` method (default)
 
 ### Changed
 

pipelex/kit/configs/inference/deck/1_llm_deck.toml

@@ -28,7 +28,7 @@ for_object = "@default-general"
 
 [llm.aliases]
 best-gpt = "gpt-5.2"
-best-claude = "claude-4.6-opus"
+best-claude = "claude-4.7-opus"
 best-gemini = "gemini-3.0-pro"
 best-mistral = "mistral-large"
 
@@ -41,8 +41,8 @@ default-large-context-code = "gemini-3.0-pro"
 default-large-context-text = "gemini-2.5-flash"
 default-small = "gpt-4o-mini"
 default-small-structured = "gpt-4o-mini"
-default-small-vision = "gemini-2.5-flash-lite"
-default-small-creative = "gemini-2.5-flash-lite"
+default-small-vision = "gemini-3.0-flash-preview"
+default-small-creative = "gemini-3.0-flash-preview"
 
 ####################################################################################################
 # LLM Presets
@@ -52,34 +52,36 @@ default-small-creative = "gemini-2.5-flash-lite"
 
 # Writing
 writing-factual = { model = "@default-premium", temperature = 0.1, description = "Factual writing with high accuracy" }
+writing-factual-cheap = { model = "@default-small", temperature = 0.1, description = "Cheap factual writing with high accuracy" }
 writing-creative = { model = "@default-premium", temperature = 0.9, description = "Creative writing with high variability" }
+writing-creative-cheap = { model = "@default-small-creative", temperature = 0.9, description = "Cheap creative writing with high variability" }
 
 # Retrieval
 retrieval = { model = "@default-large-context-text", temperature = 0.1, description = "Data retrieval from large text corpora" }
+retrieval-cheap = { model = "gemini-2.5-flash-lite", temperature = 0.1, description = "Cheap data retrieval from large text corpora" }
+retrieval-premium = { model = "claude-4.7-opus", temperature = 0.1, description = "Premium data retrieval with highest accuracy" }
 
 # Engineering
 engineering-structured = { model = "@default-premium-structured", temperature = 0.2, description = "Structured engineering output (JSON, schemas)" }
 engineering-code = { model = "@default-premium", temperature = 0.1, description = "Code generation and analysis" }
+engineering-code-cheap = { model = "claude-4.6-sonnet", temperature = 0.1, description = "Cheap code generation and analysis" }
+engineering-code-cheaper = { model = "claude-4.5-haiku", temperature = 0.1, description = "Cheapest code generation and analysis" }
 engineering-codebase-analysis = { model = "@best-gemini", temperature = 0.1, description = "Large codebase analysis" }
 
 # Vision
-vision = { model = "@default-premium-vision", temperature = 0.5, description = "Vision language model for understanding images" }
-vision-cheap = { model = "@default-small-vision", temperature = 0.5, description = "Budget vision model for simple image tasks" }
+vision = { model = "@default-premium-vision", temperature = 0.5, description = "Understanding and interpreting images" }
+vision-cheap = { model = "@default-small-vision", temperature = 0.5, description = "Cheap image understanding for simple tasks" }
 vision-diagram = { model = "@default-premium-vision", temperature = 0.3, description = "Diagram and chart interpretation" }
 vision-table = { model = "@default-premium-vision", temperature = 0.3, description = "Table extraction from images" }
 
 # Image generation prompting
 img-gen-prompting = { model = "@default-premium", temperature = 0.5, description = "Crafting image generation prompts" }
-img-gen-prompting-cheap = { model = "@default-small", temperature = 0.5, description = "Budget image prompt generation" }
+img-gen-prompting-cheap = { model = "@default-small-creative", temperature = 0.5, description = "Cheap image prompt generation" }
 
 # Reasoning
 deep-analysis = { model = "@default-premium", temperature = 0.1, reasoning_effort = "high", description = "Deep reasoning and analysis" }
 quick-reasoning = { model = "@default-premium", temperature = 0.3, reasoning_effort = "low", description = "Quick reasoning for simple tasks" }
 
-# Builder (isolated presets for the pipeline builder)
-pipe-builder-engineering = { model = "claude-4.6-opus", temperature = 0.2, description = "Builder: structured engineering output" }
-pipe-builder-img-gen-prompting = { model = "claude-4.5-sonnet", temperature = 0.7, description = "Builder: crafting image generation prompts" }
-
 # Testing
 testing-text = { model = "@default-small", temperature = 0.5, description = "Testing preset for text generation" }
 testing-structured = { model = "@default-small-structured", temperature = 0.1, description = "Testing preset for structured output" }

pipelex/pipelex.toml

@@ -117,7 +117,7 @@ keywords_to_hilight = []
 
 [pipelex.aws_config]
 api_key_method = "env"
-bedrock_access_variant = "bedrock_token"
+bedrock_access_variant = "aws_access"
 
 ####################################################################################################
 # Cogt inference config

tests/e2e/pipelex/pipes/pipe_operators/pipe_llm/pipe_llm_object_list.mthds

@@ -14,13 +14,12 @@ inputs = { inspiration = "Text" }
 output = "MoodboardPrompt[]"
 model = "@default-small-creative"
 prompt = """
-You are a fashion art director creating a moodboard. Given the following fashion inspiration, generate exactly 5 distinct image generation prompts. Each prompt should capture a different visual facet of the aesthetic:
+You are a fashion art director creating a moodboard. Given the following fashion inspiration, generate exactly 3 distinct image generation prompts.
+Each prompt should capture a different visual facet of the aesthetic:
 
 1. A hero outfit or look
 2. A texture or fabric close-up
 3. A color palette scene
-4. A setting or environment that evokes the mood
-5. An accessory or detail shot
 
 Fashion inspiration:
 @inspiration

tests/e2e/pipelex/pipes/pipe_operators/pipe_llm/test_pipe_llm_object_list.py

@@ -11,7 +11,7 @@
 @pytest.mark.llm
 @pytest.mark.inference
 @pytest.mark.dry_runnable
-@pytest.mark.asyncio(loop_scope="class")
+@pytest.mark.asyncio
 class TestPipeLLMObjectList:
     async def test_craft_prompts(self, pipe_run_mode: PipeRunMode) -> None:
         """Test a PipeLLM pipe that generates a list of structured MoodboardPrompt objects."""
@@ -29,6 +29,6 @@ async def test_craft_prompts(self, pipe_run_mode: PipeRunMode) -> None:
         assert pipeline_response.pipe_output.main_stuff is not None
 
         items = pipeline_response.pipe_output.main_stuff_as_list(item_type=TextContent)
-        assert len(items) == 5
+        assert len(items) == 3
 
         pretty_print(items, title="Moodboard prompts")