Some updates to local embedding models and fixing a couple small issues

iterations/v3-mcp-support/.env.example

@@ -4,16 +4,17 @@
 # OpenRouter: https://openrouter.ai/api/v1
 BASE_URL=
 
-# Get your Open AI API Key by following these instructions -
-# https://help.openai.com/en/articles/4936850-where-do-i-find-my-openai-api-key
-# Even if using OpenRouter/Ollama, you still need to set this for the embedding model.
-# Future versions of Archon will be more flexible with this.
-OPENAI_API_KEY=
-
 # For OpenAI: https://help.openai.com/en/articles/4936850-where-do-i-find-my-openai-api-key
 # For OpenRouter: https://openrouter.ai/keys
+# For Ollama, no need to set this unless you specifically configured an API key
 LLM_API_KEY=
 
+# Get your Open AI API Key by following these instructions -
+# https://help.openai.com/en/articles/4936850-where-do-i-find-my-openai-api-key
+# Even if using OpenRouter, you still need to set this for the embedding model.
+# No need to set this if using Ollama.
+OPENAI_API_KEY=
+
 # For the Supabase version (sample_supabase_agent.py), set your Supabase URL and Service Key.
 # Get your SUPABASE_URL from the API section of your Supabase project settings -
 # https://supabase.com/dashboard/project/<your project ID>/settings/api
@@ -34,6 +35,7 @@ REASONER_MODEL=
 # Example: qwen2.5:14b-instruct-8k
 PRIMARY_MODEL=
 
-# Embedding model you want to use (nomic-embed-text:latest, text-embedding-3-small)
-# Example: nomic-embed-text:latest
+# Embedding model you want to use
+# Example for Ollama: nomic-embed-text
+# Example for OpenAI: text-embedding-3-small
 EMBEDDING_MODEL=

iterations/v3-mcp-support/README.md

@@ -8,8 +8,6 @@ The core remains an intelligent documentation crawler and RAG (Retrieval-Augment
 
 This version supports both local LLMs with Ollama and cloud-based LLMs through OpenAI/OpenRouter.
 
-Note: We still rely on OpenAI for embeddings, but future versions will add alternatives. I wanted to do this for v3 but MCP support tool a LOT of work to implement.
-
 ## Features
 
 - MCP server support for AI IDE integration
@@ -96,8 +94,9 @@ Be sure to restart your MCP server after finishing all steps.
    OPENAI_API_KEY=your_openai_api_key
    SUPABASE_URL=your_supabase_url
    SUPABASE_SERVICE_KEY=your_supabase_service_key
-   PRIMARY_MODEL=gpt-4o-mini  # or your preferred OpenAI model for main agent
-   REASONER_MODEL=o3-mini     # or your preferred OpenAI model for reasoning
+   PRIMARY_MODEL=your_main_coding_llm
+   REASONER_MODEL=your_reasoning_llm
+   EMBEDDING_MODEL=your_embedding_model
    ```
 
 ## Usage
@@ -111,6 +110,8 @@ Execute the SQL commands in `utils/site_pages.sql` to:
 
 In Supabase, do this by going to the "SQL Editor" tab and pasting in the SQL into the editor there. Then click "Run".
 
+If using Ollama with the nomic-embed-text embedding model or another with 786 dimensions, either update site_pages.sql so that the dimensions are 768 instead of 1536 or use `utils/ollama_site_pages.sql`
+
 ### Crawl Documentation
 
 To crawl and store documentation in the vector database:
@@ -162,7 +163,7 @@ CREATE TABLE site_pages (
     summary TEXT,
     content TEXT,
     metadata JSONB,
-    embedding VECTOR(1536)
+    embedding VECTOR(1536) -- Adjust dimensions as necessary (i.e. 768 for nomic-embed-text)
 );
 ```
 

iterations/v3-mcp-support/archon/crawl_pydantic_ai_docs.py

@@ -244,7 +244,20 @@ def get_pydantic_ai_docs_urls() -> List[str]:
         print(f"Error fetching sitemap: {e}")
         return []
 
+async def clear_existing_records():
+    """Clear all existing records with source='pydantic_ai_docs' from the site_pages table."""
+    try:
+        result = supabase.table("site_pages").delete().eq("metadata->>source", "pydantic_ai_docs").execute()
+        print("Cleared existing pydantic_ai_docs records from site_pages")
+        return result
+    except Exception as e:
+        print(f"Error clearing existing records: {e}")
+        return None
+
 async def main():
+    # Clear existing records first
+    await clear_existing_records()
+    
     # Get URLs from Pydantic AI docs
     urls = get_pydantic_ai_docs_urls()
     if not urls:

mcp_server.py

@@ -45,7 +45,7 @@ def _make_request(thread_id: str, user_input: str, config: dict) -> str:
         json={
             "message": user_input,
             "thread_id": thread_id,
-        "is_first_message": not active_threads[thread_id],
+            "is_first_message": not active_threads[thread_id],
             "config": config
     }
     )