added langchain open ai

app.py

@@ -7,6 +7,9 @@ import wave
 import uuid
 from nemoguardrails import LLMRails, RailsConfig
 from GoogleTTS import GoogleTTS
+from langchain_openai import ChatOpenAI
+
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
 #tts
 #import torchaudio
@@ -51,9 +54,9 @@ def combine_audio_files(audio_files):
     return outfile
     
 #client
-client = Groq(
-    api_key=os.getenv("GROQ_API_KEY"),
-)
+# client = Groq(
+#     api_key=os.getenv("GROQ_API_KEY"),
+# )
 
 llm = Llama.from_pretrained(
     repo_id="amir22010/fine_tuned_product_marketing_email_gemma_2_9b_q4_k_m", #custom fine tuned model
@@ -76,7 +79,7 @@ marketing_email_prompt = """Below is a product and description, please write a m
 ### Marketing Email:
 {}"""
 
-def greet(product,description):
+async def greet(product,description):
     user_reques = marketing_email_prompt.format(
     product, # product
     description, # description
@@ -88,9 +91,10 @@ def greet(product,description):
     #nemo guard
     config = RailsConfig.from_path("guard")
     #config = RailsConfig.from_content(yaml_content=YAML_CONFIG)
+    client = ChatOpenAI(openai_api_key=os.getenv("GROQ_API_KEY"), model_name="llama-3.2-11b-text-preview")
     app = LLMRails(config=config, llm=client)
     options = {"output_vars": ["triggered_input_rail", "triggered_output_rail"]}
-    output = app.generate(messages=messages, options=options)
+    output = await app.generate_async(messages=messages, options=options)
     print(output)
     warning_message = output.output_data["triggered_input_rail"] or output.output_data["triggered_output_rail"]
     if warning_message:

requirements.txt

@@ -4,4 +4,5 @@ llama-cpp-python
 groq
 transformers
 nemoguardrails==0.9.1.1
-google-tts
+google-tts
+langchain-openai