Feature/nestbot ai assistant

.gitignore

@@ -43,4 +43,4 @@ frontend/yarn-error.log*
 logs
 node_modules/
 TODO
-venv/
+venv/

backend/Makefile

@@ -56,6 +56,7 @@ django-shell:
 dump-data:
 	@echo "Dumping Nest data"
 	@CMD="python manage.py dumpdata \
+		core \
 		github \
 		owasp \
 		slack.Conversation \

backend/apps/ai/agents/chapter/agent.py

@@ -7,7 +7,7 @@
 from apps.ai.template_loader import env
 
 
-def create_chapter_agent() -> Agent:
+def create_chapter_agent(allow_delegation: bool = False) -> Agent:
     """Create Chapter Expert Agent.
 
     Returns:
@@ -25,6 +25,6 @@ def create_chapter_agent() -> Agent:
         tools=[search_chapters],
         llm=get_llm(),
         verbose=True,
-        allow_delegation=False,
+        allow_delegation=allow_delegation,
         memory=False,
     )

backend/apps/ai/agents/community/agent.py

@@ -10,9 +10,12 @@
 from apps.ai.template_loader import env
 
 
-def create_community_agent() -> Agent:
+def create_community_agent(allow_delegation: bool = False) -> Agent:
     """Create Community Expert Agent.
 
+    Args:
+        allow_delegation (bool): Whether the agent can delegate tasks. Defaults to False.
+
     Returns:
         Agent: Community Expert Agent configured with community tools
 
@@ -33,6 +36,6 @@ def create_community_agent() -> Agent:
         ],
         llm=get_llm(),
         verbose=True,
-        allow_delegation=False,
+        allow_delegation=allow_delegation,
         memory=False,
     )

backend/apps/ai/agents/contribution/agent.py

@@ -11,9 +11,12 @@
 from apps.ai.template_loader import env
 
 
-def create_contribution_agent() -> Agent:
+def create_contribution_agent(allow_delegation: bool = False) -> Agent:
     """Create Contribution Expert Agent.
 
+    Args:
+        allow_delegation (bool): Whether the agent can delegate tasks. Defaults to False.
+
     Returns:
         Agent: Contribution Expert Agent configured with contribution and GSoC tools
 
@@ -34,6 +37,6 @@ def create_contribution_agent() -> Agent:
         ],
         llm=get_llm(),
         verbose=True,
-        allow_delegation=False,
+        allow_delegation=allow_delegation,
         memory=False,
     )

backend/apps/ai/agents/project/agent.py

@@ -14,9 +14,12 @@
 from apps.ai.template_loader import env
 
 
-def create_project_agent() -> Agent:
+def create_project_agent(allow_delegation: bool = False) -> Agent:
     """Create Project Expert Agent.
 
+    Args:
+        allow_delegation (bool): Whether the agent can delegate tasks. Defaults to False.
+
     Returns:
         Agent: Project Expert Agent configured with project tools
 
@@ -39,6 +42,6 @@ def create_project_agent() -> Agent:
         ],
         llm=get_llm(),
         verbose=True,
-        allow_delegation=False,
+        allow_delegation=allow_delegation,
         memory=False,
     )

backend/apps/ai/agents/rag/agent.py

@@ -7,9 +7,12 @@
 from apps.ai.template_loader import env
 
 
-def create_rag_agent() -> Agent:
+def create_rag_agent(allow_delegation: bool = False) -> Agent:
     """Create RAG Agent.
 
+    Args:
+        allow_delegation (bool): Whether the agent can delegate tasks. Defaults to False.
+
     Returns:
         Agent: RAG Agent configured with semantic search tools
 
@@ -25,6 +28,6 @@ def create_rag_agent() -> Agent:
         tools=[semantic_search],
         llm=get_llm(),
         verbose=True,
-        allow_delegation=False,
+        allow_delegation=allow_delegation,
         memory=False,
     )

backend/apps/ai/common/llm_config.py

@@ -2,32 +2,46 @@
 
 from __future__ import annotations
 
-import os
+import logging
 
 from crewai import LLM
+from django.conf import settings
+
+logger = logging.getLogger(__name__)
 
 
 def get_llm() -> LLM:
     """Get configured LLM instance.
 
     Returns:
-        LLM: Configured LLM instance with gpt-4.1-mini as default model.
+        LLM: Configured LLM instance based on settings.
 
     """
-    provider = os.getenv("LLM_PROVIDER", "openai")
+    provider = settings.LLM_PROVIDER
 
     if provider == "openai":
         return LLM(
-            model=os.getenv("OPENAI_MODEL_NAME", "gpt-4.1-mini"),
-            api_key=os.getenv("DJANGO_OPEN_AI_SECRET_KEY"),
+            model=settings.OPENAI_MODEL_NAME,
+            api_key=settings.OPEN_AI_SECRET_KEY,
             temperature=0.1,
         )
-    if provider == "anthropic":
+    if provider == "google":
         return LLM(
-            model=os.getenv("ANTHROPIC_MODEL_NAME", "claude-3-5-sonnet-20241022"),
-            api_key=os.getenv("ANTHROPIC_API_KEY"),
+            model=settings.GOOGLE_MODEL_NAME,
+            base_url="https://generativelanguage.googleapis.com/v1beta/openai/",
+            api_key=settings.GOOGLE_API_KEY,
             temperature=0.1,
         )
 
-    error_msg = f"Unsupported LLM provider: {provider}"
-    raise ValueError(error_msg)
+    # Fallback to OpenAI if provider not recognized or not specified
+    if provider and provider not in ("openai", "google"):
+        logger.warning(
+            "Unrecognized LLM_PROVIDER '%s'. Falling back to OpenAI. "
+            "Supported providers: 'openai', 'google'",
+            provider,
+        )
+    return LLM(
+        model=settings.OPENAI_MODEL_NAME,
+        api_key=settings.OPEN_AI_SECRET_KEY,
+        temperature=0.1,
+    )

backend/apps/ai/embeddings/factory.py

@@ -1,18 +1,24 @@
 """Factory function to get the configured embedder."""
 
+from django.conf import settings
+
 from apps.ai.embeddings.base import Embedder
+from apps.ai.embeddings.google import GoogleEmbedder
 from apps.ai.embeddings.openai import OpenAIEmbedder
 
 
 def get_embedder() -> Embedder:
     """Get the configured embedder.
 
-    Currently returns OpenAI embedder, but can be extended to support
+    Currently returns OpenAI and Google embedder, but can be extended to support
     other providers (e.g., Anthropic, Cohere, etc.).
 
     Returns:
         Embedder instance configured for the current provider.
 
     """
-    # Currently OpenAI, but can be extended to support other providers
+    # Currently OpenAI and Google, but can be extended to support other providers
+    if settings.LLM_PROVIDER == "google":
+        return GoogleEmbedder()
+
     return OpenAIEmbedder()

backend/apps/ai/embeddings/google.py

@@ -0,0 +1,79 @@
+"""Google implementation of embedder."""
+
+from __future__ import annotations
+
+import requests
+from django.conf import settings
+
+from apps.ai.embeddings.base import Embedder
+
+
+class GoogleEmbedder(Embedder):
+    """Google implementation of embedder using OpenAI compatible endpoint."""
+
+    def __init__(self, model: str = "text-embedding-004") -> None:
+        """Initialize Google embedder.
+
+        Args:
+            model: The Google embedding model to use.
+
+        """
+        self.api_key = settings.GOOGLE_API_KEY
+        self.model = model
+        self.endpoint = "https://generativelanguage.googleapis.com/v1beta/openai/embeddings"
+        self._dimensions = 768  # text-embedding-004 dimensions
+
+    def embed_query(self, text: str) -> list[float]:
+        """Generate embedding for a query string.
+
+        Args:
+            text: The query text to embed.
+
+        Returns:
+            List of floats representing the embedding vector.
+
+        """
+        response = requests.post(
+            self.endpoint,
+            headers={"Authorization": f"Bearer {self.api_key}"},
+            json={
+                "input": text,
+                "model": self.model,
+            },
+            timeout=30,
+        )
+        response.raise_for_status()
+        data = response.json()
+        return data["data"][0]["embedding"]
+
+    def embed_documents(self, texts: list[str]) -> list[list[float]]:
+        """Generate embeddings for multiple documents.
+
+        Args:
+            texts: List of document texts to embed.
+
+        Returns:
+            List of embedding vectors, one per document.
+
+        """
+        response = requests.post(
+            self.endpoint,
+            headers={"Authorization": f"Bearer {self.api_key}"},
+            json={
+                "input": texts,
+                "model": self.model,
+            },
+            timeout=60,
+        )
+        response.raise_for_status()
+        data = response.json()
+        return [item["embedding"] for item in data["data"]]
+
+    def get_dimensions(self) -> int:
+        """Get the dimension of embeddings produced by this embedder.
+
+        Returns:
+            Integer representing the embedding dimension.
+
+        """
+        return self._dimensions