Merge branch 'dev' into toran/open-2000-implement-unified-ai-image-ge…

…neration-block

SECURITY.md

@@ -0,0 +1,47 @@
+# Security Policy
+
+## Reporting Security Issues
+
+We take the security of our project seriously. If you believe you have found a security vulnerability, please report it to us privately. **Please do not report security vulnerabilities through public GitHub issues, discussions, or pull requests.**
+
+> **Important Note**: Any code within the `classic/` folder is considered legacy, unsupported, and out of scope for security reports. We will not address security vulnerabilities in this deprecated code.
+
+Instead, please report them via:
+- [GitHub Security Advisory](https://github.com/Significant-Gravitas/AutoGPT/security/advisories/new)
+- [Huntr.dev](https://huntr.com/repos/significant-gravitas/autogpt) - where you may be eligible for a bounty
+
+### Reporting Process
+1. **Submit Report**: Use one of the above channels to submit your report
+2. **Response Time**: Our team will acknowledge receipt of your report within 14 business days.
+3. **Collaboration**: We will collaborate with you to understand and validate the issue
+4. **Resolution**: We will work on a fix and coordinate the release process
+
+### Disclosure Policy
+- Please provide detailed reports with reproducible steps
+- Include the version/commit hash where you discovered the vulnerability
+- Allow us a 90-day security fix window before any public disclosure
+- Share any potential mitigations or workarounds if known
+
+## Supported Versions
+Only the following versions are eligible for security updates:
+
+| Version | Supported |
+|---------|-----------|
+| Latest release on master branch | ✅ |
+| Development commits (pre-master) | ✅ |
+| Classic folder (deprecated) | ❌ |
+| All other versions | ❌ |
+
+## Security Best Practices
+When using this project:
+1. Always use the latest stable version
+2. Review security advisories before updating
+3. Follow our security documentation and guidelines
+4. Keep your dependencies up to date
+5. Do not use code from the `classic/` folder as it is deprecated and unsupported
+
+## Past Security Advisories
+For a list of past security advisories, please visit our [Security Advisory Page](https://github.com/Significant-Gravitas/AutoGPT/security/advisories) and [Huntr Disclosures Page](https://huntr.com/repos/significant-gravitas/autogpt).
+
+---
+Last updated: November 2024

autogpt_platform/autogpt_libs/autogpt_libs/supabase_integration_credentials_store/store.py

@@ -46,21 +46,21 @@
 )
 openai_credentials = APIKeyCredentials(
     id="53c25cb8-e3ee-465c-a4d1-e75a4c899c2a",
-    provider="llm",
+    provider="openai",
     api_key=SecretStr(settings.secrets.openai_api_key),
     title="Use Credits for OpenAI",
     expires_at=None,
 )
 anthropic_credentials = APIKeyCredentials(
     id="24e5d942-d9e3-4798-8151-90143ee55629",
-    provider="llm",
+    provider="anthropic",
     api_key=SecretStr(settings.secrets.anthropic_api_key),
     title="Use Credits for Anthropic",
     expires_at=None,
 )
 groq_credentials = APIKeyCredentials(
     id="4ec22295-8f97-4dd1-b42b-2c6957a02545",
-    provider="llm",
+    provider="groq",
     api_key=SecretStr(settings.secrets.groq_api_key),
     title="Use Credits for Groq",
     expires_at=None,
@@ -79,6 +79,13 @@
     title="Use Credits for Jina",
     expires_at=None,
 )
+unreal_credentials = APIKeyCredentials(#
+    id="66f20754-1b81-48e4-91d0-f4f0dd82145f",
+    provider="unreal",
+    api_key=SecretStr(settings.secrets.unreal_speech_api_key),
+    title="Use Credits for Unreal",
+    expires_at=None,
+)
 
 
 DEFAULT_CREDENTIALS = [
@@ -90,6 +97,7 @@
     groq_credentials,
     did_credentials,
     jina_credentials,
+    unreal_credentials,
 ]
 
 
@@ -135,6 +143,8 @@ def get_all_creds(self, user_id: str) -> list[Credentials]:
             all_credentials.append(did_credentials)
         if settings.secrets.jina_api_key:
             all_credentials.append(jina_credentials)
+        if settings.secrets.unreal_speech_api_key:
+            all_credentials.append(unreal_credentials)
         return all_credentials
 
     def get_creds_by_id(self, user_id: str, credentials_id: str) -> Credentials | None:

autogpt_platform/backend/backend/blocks/llm.py

@@ -30,11 +30,12 @@
 #     "ollama": BlockSecret(value=""),
 # }
 
-AICredentials = CredentialsMetaInput[Literal["llm"], Literal["api_key"]]
+LLMProviderName = Literal["anthropic", "groq", "openai", "ollama"]
+AICredentials = CredentialsMetaInput[LLMProviderName, Literal["api_key"]]
 
 TEST_CREDENTIALS = APIKeyCredentials(
     id="ed55ac19-356e-4243-a6cb-bc599e9b716f",
-    provider="llm",
+    provider="openai",
     api_key=SecretStr("mock-openai-api-key"),
     title="Mock OpenAI API key",
     expires_at=None,
@@ -50,15 +51,18 @@
 def AICredentialsField() -> AICredentials:
     return CredentialsField(
         description="API key for the LLM provider.",
-        provider="llm",
+        provider=["anthropic", "groq", "openai", "ollama"],
         supported_credential_types={"api_key"},
+        discriminator="model",
+        discriminator_mapping={
+            model.value: model.metadata.provider for model in LlmModel
+        },
     )
 
 
 class ModelMetadata(NamedTuple):
     provider: str
     context_window: int
-    cost_factor: int
 
 
 class LlmModelMeta(EnumMeta):
@@ -117,31 +121,27 @@ 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/blocks/youtube.py

@@ -62,7 +62,22 @@ def extract_video_id(url: str) -> str:
 
     @staticmethod
     def get_transcript(video_id: str):
-        return YouTubeTranscriptApi.get_transcript(video_id)
+        try:
+            transcript_list = YouTubeTranscriptApi.list_transcripts(video_id)
+
+            if not transcript_list:
+                raise ValueError(f"No transcripts found for the video: {video_id}")
+
+            for transcript in transcript_list:
+                first_transcript = transcript_list.find_transcript(
+                    [transcript.language_code]
+                )
+                return YouTubeTranscriptApi.get_transcript(
+                    video_id, languages=[first_transcript.language_code]
+                )
+
+        except Exception:
+            raise ValueError(f"No transcripts found for the video: {video_id}")
 
     def run(self, input_data: Input, **kwargs) -> BlockOutput:
         video_id = self.extract_video_id(input_data.youtube_url)