refactor(backend): Centralize Block Cost into a Single File

autogpt_platform/backend/backend/blocks/llm.py

@@ -58,7 +58,6 @@ def AICredentialsField() -> AICredentials:
 class ModelMetadata(NamedTuple):
     provider: str
     context_window: int
-    cost_factor: int
 
 
 class LlmModelMeta(EnumMeta):
@@ -117,31 +116,27 @@ def provider(self) -> str:
     def context_window(self) -> int:
         return self.metadata.context_window
 
-    @property
-    def cost_factor(self) -> int:
-        return self.metadata.cost_factor
-
 
 MODEL_METADATA = {
-    LlmModel.O1_PREVIEW: ModelMetadata("openai", 32000, cost_factor=16),
-    LlmModel.O1_MINI: ModelMetadata("openai", 62000, cost_factor=4),
-    LlmModel.GPT4O_MINI: ModelMetadata("openai", 128000, cost_factor=1),
-    LlmModel.GPT4O: ModelMetadata("openai", 128000, cost_factor=3),
-    LlmModel.GPT4_TURBO: ModelMetadata("openai", 128000, cost_factor=10),
-    LlmModel.GPT3_5_TURBO: ModelMetadata("openai", 16385, cost_factor=1),
-    LlmModel.CLAUDE_3_5_SONNET: ModelMetadata("anthropic", 200000, cost_factor=4),
-    LlmModel.CLAUDE_3_HAIKU: ModelMetadata("anthropic", 200000, cost_factor=1),
-    LlmModel.LLAMA3_8B: ModelMetadata("groq", 8192, cost_factor=1),
-    LlmModel.LLAMA3_70B: ModelMetadata("groq", 8192, cost_factor=1),
-    LlmModel.MIXTRAL_8X7B: ModelMetadata("groq", 32768, cost_factor=1),
-    LlmModel.GEMMA_7B: ModelMetadata("groq", 8192, cost_factor=1),
-    LlmModel.GEMMA2_9B: ModelMetadata("groq", 8192, cost_factor=1),
-    LlmModel.LLAMA3_1_405B: ModelMetadata("groq", 8192, cost_factor=1),
+    LlmModel.O1_PREVIEW: ModelMetadata("openai", 32000),
+    LlmModel.O1_MINI: ModelMetadata("openai", 62000),
+    LlmModel.GPT4O_MINI: ModelMetadata("openai", 128000),
+    LlmModel.GPT4O: ModelMetadata("openai", 128000),
+    LlmModel.GPT4_TURBO: ModelMetadata("openai", 128000),
+    LlmModel.GPT3_5_TURBO: ModelMetadata("openai", 16385),
+    LlmModel.CLAUDE_3_5_SONNET: ModelMetadata("anthropic", 200000),
+    LlmModel.CLAUDE_3_HAIKU: ModelMetadata("anthropic", 200000),
+    LlmModel.LLAMA3_8B: ModelMetadata("groq", 8192),
+    LlmModel.LLAMA3_70B: ModelMetadata("groq", 8192),
+    LlmModel.MIXTRAL_8X7B: ModelMetadata("groq", 32768),
+    LlmModel.GEMMA_7B: ModelMetadata("groq", 8192),
+    LlmModel.GEMMA2_9B: ModelMetadata("groq", 8192),
+    LlmModel.LLAMA3_1_405B: ModelMetadata("groq", 8192),
     # Limited to 16k during preview
-    LlmModel.LLAMA3_1_70B: ModelMetadata("groq", 131072, cost_factor=1),
-    LlmModel.LLAMA3_1_8B: ModelMetadata("groq", 131072, cost_factor=1),
-    LlmModel.OLLAMA_LLAMA3_8B: ModelMetadata("ollama", 8192, cost_factor=1),
-    LlmModel.OLLAMA_LLAMA3_405B: ModelMetadata("ollama", 8192, cost_factor=1),
+    LlmModel.LLAMA3_1_70B: ModelMetadata("groq", 131072),
+    LlmModel.LLAMA3_1_8B: ModelMetadata("groq", 131072),
+    LlmModel.OLLAMA_LLAMA3_8B: ModelMetadata("ollama", 8192),
+    LlmModel.OLLAMA_LLAMA3_405B: ModelMetadata("ollama", 8192),
 }
 
 for model in LlmModel:

autogpt_platform/backend/backend/data/block_cost_config.py

@@ -0,0 +1,195 @@
+from typing import Type
+
+from autogpt_libs.supabase_integration_credentials_store.store import (
+    anthropic_credentials,
+    did_credentials,
+    groq_credentials,
+    ideogram_credentials,
+    jina_credentials,
+    openai_credentials,
+    replicate_credentials,
+    revid_credentials,
+)
+
+from backend.blocks.ai_shortform_video_block import AIShortformVideoCreatorBlock
+from backend.blocks.ideogram import IdeogramModelBlock
+from backend.blocks.jina.search import SearchTheWebBlock
+from backend.blocks.llm import (
+    MODEL_METADATA,
+    AIConversationBlock,
+    AIStructuredResponseGeneratorBlock,
+    AITextGeneratorBlock,
+    AITextSummarizerBlock,
+    LlmModel,
+)
+from backend.blocks.replicate_flux_advanced import ReplicateFluxAdvancedModelBlock
+from backend.blocks.search import ExtractWebsiteContentBlock
+from backend.blocks.talking_head import CreateTalkingAvatarVideoBlock
+from backend.data.block import Block
+from backend.data.cost import BlockCost, BlockCostType
+
+# =============== Configure the cost for each LLM Model call =============== #
+
+MODEL_COST: dict[LlmModel, int] = {
+    LlmModel.O1_PREVIEW: 16,
+    LlmModel.O1_MINI: 4,
+    LlmModel.GPT4O_MINI: 1,
+    LlmModel.GPT4O: 3,
+    LlmModel.GPT4_TURBO: 10,
+    LlmModel.GPT3_5_TURBO: 1,
+    LlmModel.CLAUDE_3_5_SONNET: 4,
+    LlmModel.CLAUDE_3_HAIKU: 1,
+    LlmModel.LLAMA3_8B: 1,
+    LlmModel.LLAMA3_70B: 1,
+    LlmModel.MIXTRAL_8X7B: 1,
+    LlmModel.GEMMA_7B: 1,
+    LlmModel.GEMMA2_9B: 1,
+    LlmModel.LLAMA3_1_405B: 1,
+    # Limited to 16k during preview
+    LlmModel.LLAMA3_1_70B: 1,
+    LlmModel.LLAMA3_1_8B: 1,
+    LlmModel.OLLAMA_LLAMA3_8B: 1,
+    LlmModel.OLLAMA_LLAMA3_405B: 1,
+}
+
+for model in LlmModel:
+    if model not in MODEL_COST:
+        raise ValueError(f"Missing MODEL_COST for model: {model}")
+
+
+LLM_COST = (
+    [
+        BlockCost(
+            cost_type=BlockCostType.RUN,
+            cost_filter={
+                "model": model,
+                "api_key": None,  # Running LLM with user own API key is free.
+            },
+            cost_amount=cost,
+        )
+        for model, cost in MODEL_COST.items()
+    ]
+    + [
+        BlockCost(
+            cost_type=BlockCostType.RUN,
+            cost_filter={
+                "model": model,
+                "credentials": {
+                    "id": anthropic_credentials.id,
+                    "provider": anthropic_credentials.provider,
+                    "type": anthropic_credentials.type,
+                },
+            },
+            cost_amount=cost,
+        )
+        for model, cost in MODEL_COST.items()
+        if MODEL_METADATA[model].provider == "anthropic"
+    ]
+    + [
+        BlockCost(
+            cost_type=BlockCostType.RUN,
+            cost_filter={
+                "model": model,
+                "credentials": {
+                    "id": openai_credentials.id,
+                    "provider": openai_credentials.provider,
+                    "type": openai_credentials.type,
+                },
+            },
+            cost_amount=cost,
+        )
+        for model, cost in MODEL_COST.items()
+        if MODEL_METADATA[model].provider == "openai"
+    ]
+    + [
+        BlockCost(
+            cost_type=BlockCostType.RUN,
+            cost_filter={
+                "model": model,
+                "credentials": {"id": groq_credentials.id},
+            },
+            cost_amount=cost,
+        )
+        for model, cost in MODEL_COST.items()
+        if MODEL_METADATA[model].provider == "groq"
+    ]
+    + [
+        BlockCost(
+            # Default cost is running LlmModel.GPT4O.
+            cost_amount=MODEL_COST[LlmModel.GPT4O],
+            cost_filter={"api_key": None},
+        ),
+    ]
+)
+
+# =============== This is the exhaustive list of cost for each Block =============== #
+
+BLOCK_COSTS: dict[Type[Block], list[BlockCost]] = {
+    AIConversationBlock: LLM_COST,
+    AITextGeneratorBlock: LLM_COST,
+    AIStructuredResponseGeneratorBlock: LLM_COST,
+    AITextSummarizerBlock: LLM_COST,
+    CreateTalkingAvatarVideoBlock: [
+        BlockCost(
+            cost_amount=15,
+            cost_filter={
+                "credentials": {
+                    "id": did_credentials.id,
+                    "provider": did_credentials.provider,
+                    "type": did_credentials.type,
+                }
+            },
+        )
+    ],
+    SearchTheWebBlock: [
+        BlockCost(
+            cost_amount=1,
+            cost_filter={
+                "credentials": {
+                    "id": jina_credentials.id,
+                    "provider": jina_credentials.provider,
+                    "type": jina_credentials.type,
+                }
+            },
+        )
+    ],
+    ExtractWebsiteContentBlock: [
+        BlockCost(cost_amount=1, cost_filter={"raw_content": False})
+    ],
+    IdeogramModelBlock: [
+        BlockCost(
+            cost_amount=1,
+            cost_filter={
+                "credentials": {
+                    "id": ideogram_credentials.id,
+                    "provider": ideogram_credentials.provider,
+                    "type": ideogram_credentials.type,
+                }
+            },
+        )
+    ],
+    AIShortformVideoCreatorBlock: [
+        BlockCost(
+            cost_amount=10,
+            cost_filter={
+                "credentials": {
+                    "id": revid_credentials.id,
+                    "provider": revid_credentials.provider,
+                    "type": revid_credentials.type,
+                }
+            },
+        )
+    ],
+    ReplicateFluxAdvancedModelBlock: [
+        BlockCost(
+            cost_amount=10,
+            cost_filter={
+                "credentials": {
+                    "id": replicate_credentials.id,
+                    "provider": replicate_credentials.provider,
+                    "type": replicate_credentials.type,
+                }
+            },
+        )
+    ],
+}

autogpt_platform/backend/backend/data/cost.py

@@ -0,0 +1,32 @@
+from enum import Enum
+from typing import Any, Optional
+
+from pydantic import BaseModel
+
+from backend.data.block import BlockInput
+
+
+class BlockCostType(str, Enum):
+    RUN = "run"  # cost X credits per run
+    BYTE = "byte"  # cost X credits per byte
+    SECOND = "second"  # cost X credits per second
+
+
+class BlockCost(BaseModel):
+    cost_amount: int
+    cost_filter: BlockInput
+    cost_type: BlockCostType
+
+    def __init__(
+        self,
+        cost_amount: int,
+        cost_type: BlockCostType = BlockCostType.RUN,
+        cost_filter: Optional[BlockInput] = None,
+        **data: Any,
+    ) -> None:
+        super().__init__(
+            cost_amount=cost_amount,
+            cost_filter=cost_filter or {},
+            cost_type=cost_type,
+            **data,
+        )