Handle the case of LLM configuration missing from environment

Author: denkv

learn2rag/pipeline/generate.py

@@ -15,6 +15,7 @@
 """
 
 def generate(query: str, search_results: list[ScoredPoint], opt_config: dict[str, Any]) -> Any:
+    assert llm is not None
     if hasattr(search_results, "points"):
         search_results = search_results.points
     context = "\n\n".join([context_template.format(source=result.payload['path'], content=result.payload['content']) for result in search_results]) # type: ignore[index]
@@ -28,6 +29,7 @@ def generate(query: str, search_results: list[ScoredPoint], opt_config: dict[str
 
 def generate_stream(query: str, search_results: list[ScoredPoint], opt_config: dict[str, Any], request_id: str | None=None) -> Generator[str, None, None]:
     profilingLogger.info('start', extra={'activity': 'generate', 'request_id': request_id})
+    assert llm is not None
 
     if hasattr(search_results, "points"):
         search_results = search_results.points

learn2rag/pipeline/llm.py

@@ -54,16 +54,20 @@ def __init__(self, *, url: str, token: str | None, model: str, proxy: str | None
         )
 
 
-default_llm = OpenAIClient
-llm_id = os.environ.get('LLM_API_TYPE', default_llm.ID)
-logger.debug('Using LLM: %s', llm_id)
-
-llm_kwargs = {
-    'url': os.environ.get('LLM_API_URL'),
-    'token': os.environ.get('LLM_API_TOKEN') or None,
-    'model': os.environ.get('LLM_API_MODEL'),
-    'proxy': os.environ.get('LLM_API_PROXY') or None,
-}
-logger.debug('Using LLM args: %s', llm_kwargs)
-
-llm = llms[llm_id](**llm_kwargs).chat_model
+def chat_model_from_env() -> BaseChatModel:
+    default_llm = OpenAIClient
+    llm_id = os.environ.get('LLM_API_TYPE', default_llm.ID)
+    logger.debug('Using LLM: %s', llm_id)
+    llm_kwargs = {
+        'url': os.environ.get('LLM_API_URL'),
+        'token': os.environ.get('LLM_API_TOKEN') or None,
+        'model': os.environ.get('LLM_API_MODEL'),
+        'proxy': os.environ.get('LLM_API_PROXY') or None,
+    }
+    logger.debug('Using LLM args: %s', llm_kwargs)
+    return llms[llm_id](**llm_kwargs).chat_model
+
+
+llm = chat_model_from_env() if 'LLM_API_TYPE' in os.environ else None
+if not llm:
+    logger.warning('LLM is not configured')