fix: handle /v1-suffixed BASE_URL and improve error reporting for OpenAI-compat providers

README.md

@@ -86,7 +86,7 @@ This is a tutorial project of [Pocket Flow](https://github.com/The-Pocket/Pocket
    pip install -r requirements.txt
    ```
 
-4. Set up LLM in [`utils/call_llm.py`](./utils/call_llm.py) by providing credentials. To do so, you can put the values in a `.env` file. By default, you can use the AI Studio key with this client for Gemini Pro 2.5 by setting the `GEMINI_API_KEY` environment variable. If you want to use another LLM, you can set the `LLM_PROVIDER` environment variable (e.g. `XAI`), and then set the model, url, and API key (e.g. `XAI_MODEL`, `XAI_URL`,`XAI_API_KEY`). If using Ollama, the url is `http://localhost:11434/` and the API key can be omitted.
+4. Set up LLM in [`utils/call_llm.py`](./utils/call_llm.py) by providing credentials. To do so, you can put the values in a `.env` file. By default, you can use the AI Studio key with this client for Gemini Pro 2.5 by setting the `GEMINI_API_KEY` environment variable. If you want to use another LLM, you can set the `LLM_PROVIDER` environment variable (e.g. `XAI`), and then set the model, base URL, and API key (e.g. `XAI_MODEL`, `XAI_BASE_URL`, `XAI_API_KEY`). The base URL can include or omit the `/v1` suffix — both `https://api.x.ai` and `https://api.x.ai/v1` work. If using Ollama, set `OLLAMA_BASE_URL=http://localhost:11434` and the API key can be omitted. For OpenRouter, set `XAI_BASE_URL=https://openrouter.ai/api/v1`.
    You can use your own models. We highly recommend the latest models with thinking capabilities (Claude 3.7 with thinking, O1). You can verify that it is correctly set up by running:
    ```bash
    python utils/call_llm.py

utils/call_llm.py

@@ -57,9 +57,11 @@ def _call_llm_provider(prompt: str) -> str:
     Environment variables:
     - LLM_PROVIDER: "OLLAMA" or "XAI"
     - <provider>_MODEL: Model name (e.g., OLLAMA_MODEL, XAI_MODEL)
-    - <provider>_BASE_URL: Base URL without endpoint (e.g., OLLAMA_BASE_URL, XAI_BASE_URL)
+    - <provider>_BASE_URL: Base URL of the API. May include or omit a trailing /v1
+      (e.g., OLLAMA_BASE_URL=http://localhost:11434 or http://localhost:11434/v1,
+             XAI_BASE_URL=https://api.x.ai/v1 or https://openrouter.ai/api/v1)
     - <provider>_API_KEY: API key (e.g., OLLAMA_API_KEY, XAI_API_KEY; optional for providers that don't require it)
-    The endpoint /v1/chat/completions will be appended to the base URL.
+    The /chat/completions endpoint is appended; /v1 is added only if not already present in the URL.
     """
     logger.info(f"PROMPT: {prompt}") # log the prompt
 
@@ -84,8 +86,13 @@ def _call_llm_provider(prompt: str) -> str:
     if not base_url:
         raise ValueError(f"{base_url_var} environment variable is required")
 
-    # Append the endpoint to the base URL
-    url = f"{base_url.rstrip('/')}/v1/chat/completions"
+    # Build the chat completions URL. Avoid duplicating /v1 when the caller
+    # already includes it in BASE_URL (e.g. https://openrouter.ai/api/v1).
+    clean_base = base_url.rstrip('/')
+    if clean_base.endswith('/v1'):
+        url = f"{clean_base}/chat/completions"
+    else:
+        url = f"{clean_base}/v1/chat/completions"
 
     # Configure headers and payload based on provider
     headers = {
@@ -100,29 +107,39 @@ def _call_llm_provider(prompt: str) -> str:
         "temperature": 0.7,
     }
 
+    response_json = None
     try:
         response = requests.post(url, headers=headers, json=payload)
-        response_json = response.json() # Log the response
-        logger.info("RESPONSE:\n%s", json.dumps(response_json, indent=2))
-        #logger.info(f"RESPONSE: {response.json()}")
+        # Parse JSON first so we can log it and include error details on failure
+        try:
+            response_json = response.json()
+            logger.info("RESPONSE:\n%s", json.dumps(response_json, indent=2))
+        except (ValueError, requests.exceptions.JSONDecodeError):
+            logger.warning(
+                "Non-JSON response from %s (HTTP %s): %s",
+                provider, response.status_code, response.text[:500]
+            )
         response.raise_for_status()
-        return response.json()["choices"][0]["message"]["content"]
+        if response_json is None:
+            raise Exception(
+                f"Empty or non-JSON response from {provider} (HTTP {response.status_code}). "
+                f"Verify that {base_url_var} points to a valid OpenAI-compatible endpoint."
+            )
+        return response_json["choices"][0]["message"]["content"]
     except requests.exceptions.HTTPError as e:
         error_message = f"HTTP error occurred: {e}"
-        try:
-            error_details = response.json().get("error", "No additional details")
+        if response_json is not None:
+            error_details = response_json.get("error", "No additional details")
             error_message += f" (Details: {error_details})"
-        except:
-            pass
+        elif response.text:
+            error_message += f" (Response: {response.text[:200]})"
         raise Exception(error_message)
     except requests.exceptions.ConnectionError:
         raise Exception(f"Failed to connect to {provider} API. Check your network connection.")
     except requests.exceptions.Timeout:
         raise Exception(f"Request to {provider} API timed out.")
     except requests.exceptions.RequestException as e:
         raise Exception(f"An error occurred while making the request to {provider}: {e}")
-    except ValueError:
-        raise Exception(f"Failed to parse response as JSON from {provider}. The server might have returned an invalid response.")
 
 # By default, we Google Gemini 2.5 pro, as it shows great performance for code understanding
 def call_llm(prompt: str, use_cache: bool = True) -> str: