Add codestyle checks

.github/workflows/code-style-check.yml

@@ -0,0 +1,24 @@
+name: Code Style Check
+
+on:
+  pull_request:
+    branches:
+      - main
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out repository
+        uses: actions/checkout@v2
+
+      - name: Set up Python
+        uses: actions/setup-python@v2
+        with:
+          python-version: 3.8
+
+      - name: Install Python dependencies
+        run: pip install black nbqa
+
+      - name: Run Black on Python files
+        run: black --check $(find . -type f -name "*.py")

.github/workflows/operation-test-with-jupyter.yml

@@ -44,8 +44,7 @@ jobs:
       digest: ${{ steps.dockerBuild.outputs.digest }}
     services:
       service-under-test:
-        image: |
-          echo "${{ inputs.image }}" | tr '[:upper:]' '[:lower:]'
+        image: ${{ inputs.image }}
         ports:
           - ${{ inputs.port-mapping || format('{0}:{1}', inputs.port, inputs.port) }}
 

.github/workflows/publish-docker-dev.yml

@@ -45,7 +45,7 @@ jobs:
     uses: josh-xt/AGiXT/.github/workflows/operation-test-with-jupyter.yml@main
     with:
       notebook: tests/tests.ipynb
-      image: ghcr.io/${{ github.repository }}:${{ github.sha }}
+      image: ghcr.io/josh-xt/agixt:${{ github.sha }}
       port: "7437"
       additional-python-dependencies: openai requests
     needs: build-agixt

agixt/Chains.py

@@ -67,13 +67,15 @@ async def run_chain_step(
                         chain_name=args["chain"],
                         user_input=args["input"],
                         agent_name=agent_name,
-                        all_responses=args["all_responses"]
-                        if "all_responses" in args
-                        else False,
+                        all_responses=(
+                            args["all_responses"] if "all_responses" in args else False
+                        ),
                         from_step=args["from_step"] if "from_step" in args else 1,
-                        chain_args=args["chain_args"]
-                        if "chain_args" in args
-                        else {"conversation_name": args["conversation_name"]},
+                        chain_args=(
+                            args["chain_args"]
+                            if "chain_args" in args
+                            else {"conversation_name": args["conversation_name"]}
+                        ),
                     )
         if result:
             if isinstance(result, dict) and "response" in result:
@@ -102,9 +104,11 @@ async def run_chain(
             role="USER",
             message=user_input,
             agent_name=agent_override if agent_override != "" else "AGiXT",
-            conversation_name=f"Chain Execution History: {chain_name}"
-            if "conversation_name" not in chain_args
-            else chain_args["conversation_name"],
+            conversation_name=(
+                f"Chain Execution History: {chain_name}"
+                if "conversation_name" not in chain_args
+                else chain_args["conversation_name"]
+            ),
             user=self.user,
         )
         logging.info(f"Running chain '{chain_name}'")
@@ -155,9 +159,11 @@ async def run_chain(
                 role=agent_override if agent_override != "" else "AGiXT",
                 message=last_response,
                 agent_name=agent_override if agent_override != "" else "AGiXT",
-                conversation_name=f"Chain Execution History: {chain_name}"
-                if "conversation_name" not in chain_args
-                else chain_args["conversation_name"],
+                conversation_name=(
+                    f"Chain Execution History: {chain_name}"
+                    if "conversation_name" not in chain_args
+                    else chain_args["conversation_name"]
+                ),
                 user=self.user,
             )
             return last_response

agixt/Embedding.py

@@ -105,15 +105,21 @@ def get_embedder_settings(self):
                     "API_URI",
                 ],
                 "embed": OpenAIEmbeddingFunction(
-                    model_name=self.agent_settings["AI_MODEL"]
-                    if "AI_MODEL" in self.agent_settings
-                    else "zephyr-7b-beta",
-                    api_key=self.agent_settings["LOCAL_LLM_API_KEY"]
-                    if "LOCAL_LLM_API_KEY" in self.agent_settings
-                    else None,
-                    api_base=self.agent_settings["API_URI"]
-                    if "API_URI" in self.agent_settings
-                    else "http://localhost:8091/v1",
+                    model_name=(
+                        self.agent_settings["AI_MODEL"]
+                        if "AI_MODEL" in self.agent_settings
+                        else "zephyr-7b-beta"
+                    ),
+                    api_key=(
+                        self.agent_settings["LOCAL_LLM_API_KEY"]
+                        if "LOCAL_LLM_API_KEY" in self.agent_settings
+                        else None
+                    ),
+                    api_base=(
+                        self.agent_settings["API_URI"]
+                        if "API_URI" in self.agent_settings
+                        else "http://localhost:8091/v1"
+                    ),
                 ),
             },
             "azure": {
@@ -123,42 +129,52 @@ def get_embedder_settings(self):
                     "AZURE_DEPLOYMENT_NAME",
                     "AZURE_OPENAI_ENDPOINT",
                 ],
-                "embed": OpenAIEmbeddingFunction(
-                    api_key=self.agent_settings["AZURE_API_KEY"],
-                    organization_id=self.agent_settings["AZURE_DEPLOYMENT_NAME"],
-                    api_base=self.agent_settings["AZURE_OPENAI_ENDPOINT"],
-                    api_type="azure",
-                )
-                if "AZURE_API_KEY" in self.agent_settings
-                and "AZURE_DEPLOYMENT_NAME" in self.agent_settings
-                and "AZURE_OPENAI_ENDPOINT" in self.agent_settings
-                else self.default_embedder,
+                "embed": (
+                    OpenAIEmbeddingFunction(
+                        api_key=self.agent_settings["AZURE_API_KEY"],
+                        organization_id=self.agent_settings["AZURE_DEPLOYMENT_NAME"],
+                        api_base=self.agent_settings["AZURE_OPENAI_ENDPOINT"],
+                        api_type="azure",
+                    )
+                    if "AZURE_API_KEY" in self.agent_settings
+                    and "AZURE_DEPLOYMENT_NAME" in self.agent_settings
+                    and "AZURE_OPENAI_ENDPOINT" in self.agent_settings
+                    else self.default_embedder
+                ),
             },
             "openai": {
                 "chunk_size": 1000,
                 "params": ["OPENAI_API_KEY", "API_URI"],
-                "embed": OpenAIEmbeddingFunction(
-                    api_key=self.agent_settings["OPENAI_API_KEY"],
-                    model_name="text-embedding-ada-002"
-                    if api_base == "https://api.openai.com/v1"
-                    else self.agent_settings["AI_MODEL"]
-                    if "AI_MODEL" in self.agent_settings
-                    else "Mistral-7B-OpenOrca",
-                    api_base=api_base,
-                )
-                if "OPENAI_API_KEY" in self.agent_settings
-                else self.default_embedder,
+                "embed": (
+                    OpenAIEmbeddingFunction(
+                        api_key=self.agent_settings["OPENAI_API_KEY"],
+                        model_name=(
+                            "text-embedding-ada-002"
+                            if api_base == "https://api.openai.com/v1"
+                            else (
+                                self.agent_settings["AI_MODEL"]
+                                if "AI_MODEL" in self.agent_settings
+                                else "Mistral-7B-OpenOrca"
+                            )
+                        ),
+                        api_base=api_base,
+                    )
+                    if "OPENAI_API_KEY" in self.agent_settings
+                    else self.default_embedder
+                ),
             },
             "google_vertex": {
                 "chunk_size": 1000,
                 "params": ["GOOGLE_API_KEY", "GOOGLE_PROJECT_ID"],
-                "embed": GoogleVertexEmbeddingFunction(
-                    api_key=self.agent_settings["GOOGLE_API_KEY"],
-                    project_id=self.agent_settings["GOOGLE_PROJECT_ID"],
-                )
-                if "GOOGLE_PROJECT_ID" in self.agent_settings
-                and "GOOGLE_API_KEY" in self.agent_settings
-                else self.default_embedder,
+                "embed": (
+                    GoogleVertexEmbeddingFunction(
+                        api_key=self.agent_settings["GOOGLE_API_KEY"],
+                        project_id=self.agent_settings["GOOGLE_PROJECT_ID"],
+                    )
+                    if "GOOGLE_PROJECT_ID" in self.agent_settings
+                    and "GOOGLE_API_KEY" in self.agent_settings
+                    else self.default_embedder
+                ),
             },
         }
         return embedder_settings

agixt/Interactions.py

@@ -42,11 +42,11 @@ def __init__(
                 agent_name=self.agent_name,
                 agent_config=self.agent.AGENT_CONFIG,
                 ApiClient=ApiClient,
-                searxng_instance_url=self.agent.AGENT_CONFIG["settings"][
-                    "SEARXNG_INSTANCE_URL"
-                ]
-                if "SEARXNG_INSTANCE_URL" in self.agent.AGENT_CONFIG["settings"]
-                else "",
+                searxng_instance_url=(
+                    self.agent.AGENT_CONFIG["settings"]["SEARXNG_INSTANCE_URL"]
+                    if "SEARXNG_INSTANCE_URL" in self.agent.AGENT_CONFIG["settings"]
+                    else ""
+                ),
             )
         else:
             self.agent_name = ""
@@ -403,9 +403,9 @@ async def run(
                 user=self.user,
             )
             command_output = await ext.execute_command(
-                command_name="Transcribe M4A Audio"
-                if is_m4a_audio
-                else "Transcribe WAV Audio",
+                command_name=(
+                    "Transcribe M4A Audio" if is_m4a_audio else "Transcribe WAV Audio"
+                ),
                 command_args={"base64_audio": user_input},
             )
             user_input = command_output

agixt/Memories.py

@@ -119,9 +119,11 @@ def get_chroma_client():
             return chromadb.HttpClient(
                 host=chroma_host,
                 port=os.environ.get("CHROMA_PORT", "8000"),
-                ssl=False
-                if os.environ.get("CHROMA_SSL", "false").lower() != "true"
-                else True,
+                ssl=(
+                    False
+                    if os.environ.get("CHROMA_SSL", "false").lower() != "true"
+                    else True
+                ),
                 headers=chroma_headers,
                 settings=chroma_settings,
             )

agixt/Providers.py

@@ -41,9 +41,11 @@ def get_provider_options(provider_name):
             provider_class = getattr(module, f"{provider_name.capitalize()}Provider")
             signature = inspect.signature(provider_class.__init__)
             options = {
-                name: param.default
-                if param.default is not inspect.Parameter.empty
-                else None
+                name: (
+                    param.default
+                    if param.default is not inspect.Parameter.empty
+                    else None
+                )
                 for name, param in signature.parameters.items()
                 if name != "self" and name != "kwargs"
             }

agixt/endpoints/Memory.py

@@ -297,9 +297,9 @@ async def agent_reader(
             agent_name=agent_name,
             agent_config=agent_config,
             collection_number=collection_number,
-            use_agent_settings=data["use_agent_settings"]
-            if "use_agent_settings" in data
-            else False,
+            use_agent_settings=(
+                data["use_agent_settings"] if "use_agent_settings" in data else False
+            ),
             ApiClient=ApiClient,
             user=user,
         ).write_github_repository_to_memory(

agixt/extensions/stable_diffusion.py

@@ -84,9 +84,11 @@ async def generate_image(
             )
             generation_settings = {
                 "prompt": prompt,
-                "negative_prompt": negative_prompt
-                if negative_prompt
-                else "out of frame,lowres,text,error,cropped,worst quality,low quality,jpeg artifacts,ugly,duplicate,morbid,mutilated,out of frame,extra fingers,mutated hands,poorly drawn hands,poorly drawn face,mutation,deformed,blurry,dehydrated,bad anatomy,bad proportions,extra limbs,cloned face,disfigured,gross proportions,malformed limbs,missing arms,missing legs,extra arms,extra legs,fused fingers,too many fingers,long neck,username,watermark,signature",
+                "negative_prompt": (
+                    negative_prompt
+                    if negative_prompt
+                    else "out of frame,lowres,text,error,cropped,worst quality,low quality,jpeg artifacts,ugly,duplicate,morbid,mutilated,out of frame,extra fingers,mutated hands,poorly drawn hands,poorly drawn face,mutation,deformed,blurry,dehydrated,bad anatomy,bad proportions,extra limbs,cloned face,disfigured,gross proportions,malformed limbs,missing arms,missing legs,extra arms,extra legs,fused fingers,too many fingers,long neck,username,watermark,signature"
+                ),
                 "batch_size": batch_size if batch_size else 1,
                 "cfg_scale": cfg_scale if cfg_scale else 7,
                 "denoising_strength": denoising_strength if denoising_strength else 0,

agixt/providers/gemini.py

@@ -5,11 +5,22 @@
 except ImportError:
     import sys
     import subprocess
-    subprocess.check_call([sys.executable, "-m", "pip", "install", "google.generativeai"])
+
+    subprocess.check_call(
+        [sys.executable, "-m", "pip", "install", "google.generativeai"]
+    )
     import google.generativeai as genai  # Import again after installation
 
+
 class GeminiProvider:
-    def __init__(self, GOOGLE_API_KEY: str, AI_MODEL: str = "gemini-pro", MAX_TOKENS: int = 4000, AI_TEMPERATURE: float = 0.7, **kwargs):
+    def __init__(
+        self,
+        GOOGLE_API_KEY: str,
+        AI_MODEL: str = "gemini-pro",
+        MAX_TOKENS: int = 4000,
+        AI_TEMPERATURE: float = 0.7,
+        **kwargs,
+    ):
         """
         Initialize the GeminiProvider with required parameters.
 
@@ -33,7 +44,9 @@ def __init__(self, GOOGLE_API_KEY: str, AI_MODEL: str = "gemini-pro", MAX_TOKENS
             print(f"Error setting up Gemini model: {e}")
 
         # Set default generation config
-        self.generation_config = genai.types.GenerationConfig(max_output_tokens=self.MAX_TOKENS, temperature=self.AI_TEMPERATURE)
+        self.generation_config = genai.types.GenerationConfig(
+            max_output_tokens=self.MAX_TOKENS, temperature=self.AI_TEMPERATURE
+        )
 
     async def inference(self, prompt, tokens: int = 0):
         """
@@ -49,17 +62,23 @@ async def inference(self, prompt, tokens: int = 0):
         try:
             # Adjust based on Gemini API
             new_max_tokens = int(self.MAX_TOKENS) - tokens
-            generation_config = genai.types.GenerationConfig(max_output_tokens=new_max_tokens, temperature=float(self.AI_TEMPERATURE))
+            generation_config = genai.types.GenerationConfig(
+                max_output_tokens=new_max_tokens, temperature=float(self.AI_TEMPERATURE)
+            )
 
-            response = await asyncio.to_thread(self.model.generate_content,
-                                               contents=prompt,
-                                               generation_config=generation_config)
+            response = await asyncio.to_thread(
+                self.model.generate_content,
+                contents=prompt,
+                generation_config=generation_config,
+            )
 
             # Extract the generated text from the response
             if response.parts:
-                generated_text = ''.join(part.text for part in response.parts)
+                generated_text = "".join(part.text for part in response.parts)
             else:
-                generated_text = ''.join(part.text for part in response.candidates[0].content.parts)
+                generated_text = "".join(
+                    part.text for part in response.candidates[0].content.parts
+                )
 
             return generated_text
         except Exception as e: