feat: support any OpenAI-compatible LLM provider (OpenRouter, Groq, etc.)

executor.py:
- Constructor accepts api_key, base_url, model params
- Reads LLM_API_KEY, LLM_BASE_URL, LLM_MODEL env vars
- Falls back to OPENAI_API_KEY for backwards compat
- ChatOpenAI works with any OpenAI-compatible endpoint

chroma.py:
- Configurable embedding model, key, base_url via env vars
- EMBEDDING_MODEL, LLM_API_KEY, LLM_BASE_URL
- Falls back to OPENAI_API_KEY

Example .env for OpenRouter:
  LLM_API_KEY=sk-or-v1-xxx
  LLM_BASE_URL=https://openrouter.ai/api/v1
  LLM_MODEL=anthropic/claude-sonnet-4
  EMBEDDING_MODEL=openai/text-embedding-3-small

Author: TheophilusChinomona

agents/application/executor.py

@@ -31,16 +31,38 @@ def retain_keys(data, keys_to_retain):
         return data
 
 class Executor:
-    def __init__(self, default_model='gpt-3.5-turbo-16k') -> None:
+    def __init__(
+        self,
+        default_model: str = None,
+        api_key: str = None,
+        base_url: str = None,
+    ) -> None:
         load_dotenv()
-        max_token_model = {'gpt-3.5-turbo-16k':15000, 'gpt-4-1106-preview':95000}
-        self.token_limit = max_token_model.get(default_model)
         self.prompter = Prompter()
-        self.openai_api_key = os.getenv("OPENAI_API_KEY")
-        self.llm = ChatOpenAI(
-            model=default_model, #gpt-3.5-turbo"
-            temperature=0,
-        )
+
+        # Support any OpenAI-compatible provider via env vars or args
+        self.api_key = api_key or os.getenv("OPENAI_API_KEY") or os.getenv("LLM_API_KEY")
+        self.base_url = base_url or os.getenv("LLM_BASE_URL")
+        self.model = default_model or os.getenv("LLM_MODEL", "gpt-3.5-turbo-16k")
+
+        llm_kwargs = {
+            "model": self.model,
+            "temperature": 0,
+            "api_key": self.api_key,
+        }
+        if self.base_url:
+            llm_kwargs["base_url"] = self.base_url
+
+        self.llm = ChatOpenAI(**llm_kwargs)
+
+        max_token_model = {
+            "gpt-3.5-turbo-16k": 15000,
+            "gpt-4-1106-preview": 95000,
+            "gpt-4o-mini": 125000,
+            "gpt-4o": 125000,
+        }
+        self.token_limit = max_token_model.get(self.model, 15000)
+
         self.gamma = Gamma()
         self.chroma = Chroma()
         self.polymarket = Polymarket()

agents/connectors/chroma.py

@@ -18,6 +18,19 @@ def __init__(self, local_db_directory=None, embedding_function=None) -> None:
         self.gamma_client = GammaMarketClient()
         self.local_db_directory = local_db_directory
         self.embedding_function = embedding_function
+        # Embedding config — supports any OpenAI-compatible provider
+        self.embedding_model = os.getenv("EMBEDDING_MODEL", "text-embedding-3-small")
+        self.embedding_api_key = os.getenv("OPENAI_API_KEY") or os.getenv("LLM_API_KEY")
+        self.embedding_base_url = os.getenv("LLM_BASE_URL")
+
+    def _get_embedding_function(self):
+        kwargs = {
+            "model": self.embedding_model,
+            "api_key": self.embedding_api_key,
+        }
+        if self.embedding_base_url:
+            kwargs["base_url"] = self.embedding_base_url
+        return OpenAIEmbeddings(**kwargs)
 
     def load_json_from_local(
         self, json_file_path=None, vector_db_directory="./local_db"
@@ -27,7 +40,7 @@ def load_json_from_local(
         )
         loaded_docs = loader.load()
 
-        embedding_function = OpenAIEmbeddings(model="text-embedding-3-small")
+        embedding_function = self._get_embedding_function()
         Chroma.from_documents(
             loaded_docs, embedding_function, persist_directory=vector_db_directory
         )
@@ -50,7 +63,7 @@ def create_local_markets_rag(self, local_directory="./local_db") -> None:
     def query_local_markets_rag(
         self, local_directory=None, query=None
     ) -> "list[tuple]":
-        embedding_function = OpenAIEmbeddings(model="text-embedding-3-small")
+        embedding_function = self._get_embedding_function()
         local_db = Chroma(
             persist_directory=local_directory, embedding_function=embedding_function
         )
@@ -83,7 +96,7 @@ def metadata_func(record: dict, metadata: dict) -> dict:
             metadata_func=metadata_func,
         )
         loaded_docs = loader.load()
-        embedding_function = OpenAIEmbeddings(model="text-embedding-3-small")
+        embedding_function = self._get_embedding_function()
         vector_db_directory = f"{local_events_directory}/chroma"
         local_db = Chroma.from_documents(
             loaded_docs, embedding_function, persist_directory=vector_db_directory
@@ -120,7 +133,7 @@ def metadata_func(record: dict, metadata: dict) -> dict:
             metadata_func=metadata_func,
         )
         loaded_docs = loader.load()
-        embedding_function = OpenAIEmbeddings(model="text-embedding-3-small")
+        embedding_function = self._get_embedding_function()
         vector_db_directory = f"{local_events_directory}/chroma"
         local_db = Chroma.from_documents(
             loaded_docs, embedding_function, persist_directory=vector_db_directory