Moving azureopenai embedding model name to env var

docker-compose.yml

@@ -4,7 +4,7 @@ services:
     image: assafelovic/gpt-researcher
     build: ./
     environment: 
-      AZURE_OPENAI_API_KEY: ${OPENAI_API_KEY}
+      OPENAI_API_KEY: ${OPENAI_API_KEY}
       TAVILY_API_KEY: ${TAVILY_API_KEY}
     ports:
       - 8000:8000

gpt_researcher/config/config.py

@@ -11,7 +11,6 @@ class Config:
         self.config_file = config_file if config_file else os.getenv('CONFIG_FILE')
         self.retriever = os.getenv('SEARCH_RETRIEVER', "BingSearch")
         self.embedding_provider = os.getenv('EMBEDDING_PROVIDER', 'azureopenai')
-        self.azure_embedding_model = os.getenv('AZURE_EMBEDDING_MODEL', "ada2")
         self.llm_provider = os.getenv('LLM_PROVIDER', "azureopenai")
         self.fast_llm_model = os.getenv('FAST_LLM_MODEL', "gpt-3.5-turbo-16k")
         self.smart_llm_model = os.getenv('SMART_LLM_MODEL', "gpt4")

gpt_researcher/memory/embeddings.py

@@ -1,5 +1,5 @@
 from langchain_community.vectorstores import FAISS
-from gpt_researcher.config import Config
+import os
 
 
 class Memory:
@@ -15,7 +15,7 @@ class Memory:
                 _embeddings = OpenAIEmbeddings()
             case "azureopenai":
                 from langchain_openai import AzureOpenAIEmbeddings
-                _embeddings = AzureOpenAIEmbeddings(deployment=Config().azure_embedding_model, chunk_size=16)
+                _embeddings = AzureOpenAIEmbeddings(deployment=os.environ["AZURE_EMBEDDING_MODEL"], chunk_size=16)
             case "huggingface":
                 from langchain.embeddings import HuggingFaceEmbeddings
                 _embeddings = HuggingFaceEmbeddings()