degbu embeddings

app.py

@@ -14,7 +14,7 @@ from llama_index.core import (
     Settings,
 )
 
-os.environ["OPENAI_API_KEY"] = "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"
+# os.environ["OPENAI_API_KEY"] = "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"
 
 from llama_parse import LlamaParse
 
@@ -38,7 +38,7 @@ def main():
         provider = st.selectbox(
             label="Select LLM Provider",
             options=['google', 'huggingface', 'mistralai', 'openai'],
-            index=0
+            index=3
         )
 
         # Select LLM
@@ -83,15 +83,14 @@ def main():
                 Settings.llm = OpenAI(
                     model=llm_name, 
                     temperature=temperature,
-                    max_tokens=max_tokens,
-                    api_key=os.environ.get("OPENAI_API_KEY")
+                    max_tokens=max_output_tokens
                 )
                 # Global tokenization needs to be consistent with LLM
                 # https://docs.llamaindex.ai/en/stable/module_guides/models/llms/
                 Settings.tokenizer = tiktoken.encoding_for_model(llm_name).encode
                 Settings.num_output = max_tokens
                 Settings.context_window = 4096 # max possible
-                Settings.embed_model = OpenAIEmbedding(api_key=os.environ.get("OPENAI_API_KEY"))
+                Settings.embed_model = OpenAIEmbedding()
             elif provider == 'huggingface':
                 os.environ['HFTOKEN'] = str(llm_token)