Switched to OpenAI instead of Azure OAI

app.py

@@ -90,7 +90,7 @@ def parse_output_llm_with_sources(output):
 
 # Create vectorstore and retriever
 vectorstore = get_pinecone_vectorstore(embeddings_function)
-llm = get_llm(max_tokens = 1024,temperature = 0.0)
+llm = get_llm(provider="openai",max_tokens = 1024,temperature = 0.0)
 
 
 def make_pairs(lst):

climateqa/engine/llm.py

@@ -1,26 +0,0 @@
-from langchain_community.chat_models import AzureChatOpenAI
-import os
-# LOAD ENVIRONMENT VARIABLES
-try:
-    from dotenv import load_dotenv
-    load_dotenv()
-except:
-    pass
-
-
-def get_llm(max_tokens = 1024,temperature = 0.0,verbose = True,streaming = False, **kwargs):
-
-    llm = AzureChatOpenAI(
-        openai_api_base=os.environ["AZURE_OPENAI_API_BASE_URL"],
-        openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],
-        deployment_name=os.environ["AZURE_OPENAI_API_DEPLOYMENT_NAME"],
-        openai_api_key=os.environ["AZURE_OPENAI_API_KEY"],
-        openai_api_type = "azure",
-        max_tokens = max_tokens,
-        temperature = temperature,
-        request_timeout = 60,
-        verbose = verbose,
-        streaming = streaming,
-        **kwargs,
-    )
-    return llm

climateqa/engine/llm/__init__.py

@@ -0,0 +1,15 @@
+from climateqa.engine.llm.openai import get_llm as get_openai_llm
+from climateqa.engine.llm.azure import get_llm as get_azure_llm
+
+
+def get_llm(provider="openai",**kwargs):
+
+    if provider == "openai":
+        return get_openai_llm(**kwargs)
+    elif provider == "azure":
+        return get_azure_llm(**kwargs)
+    else:
+        raise ValueError(f"Unknown provider: {provider}")
+    
+    
+

climateqa/engine/llm/azure.py

@@ -0,0 +1,99 @@
+import os
+import time
+
+from langchain_openai import AzureChatOpenAI
+from msal import ConfidentialClientApplication
+
+DEFAULT_TOKEN_UPDATE_FREQUENCY = 3300  # Default token duration is 1 hour (3600 s.)
+
+# LOAD ENVIRONMENT VARIABLES
+try:
+    from dotenv import load_dotenv
+    load_dotenv()
+except Exception:
+    pass
+
+
+client_id = os.environ.get("AZURE_CLIENT_ID", None)
+client_credential = os.environ.get("AZURE_CLIENT_CREDENTIAL", None)
+tenant_name = os.environ.get("AZURE_TENANT_NAME", None)
+scopes = [os.environ.get("AZURE_SCOPE", None)]
+
+azure_ad_token_frequency = int(
+    os.environ.get("TOKEN_UPDATE_FREQUENCY", DEFAULT_TOKEN_UPDATE_FREQUENCY)
+)
+azure_ad_token = None
+azure_ad_token_timestamp = 0.0
+
+
+def _get_azure_ad_token():
+    global azure_ad_token
+    global azure_ad_token_timestamp
+    now = time.time()
+
+    # Return current token if not outdated:
+    if (azure_ad_token is not None) and (
+        azure_ad_token_timestamp + azure_ad_token_frequency > now
+    ):
+        print("Using current token (not expired)...")
+        return azure_ad_token
+
+    # Else, generate a new token:
+    print("Generating new token...")
+    app = ConfidentialClientApplication(
+        client_id=client_id,
+        client_credential=client_credential,
+        authority=f"https://login.microsoftonline.com/{tenant_name}",
+    )
+    result = app.acquire_token_for_client(scopes=scopes)
+    if "access_token" not in result:
+        raise ValueError("No access token in result")
+
+    if result["access_token"] != azure_ad_token:
+        print("New token received.")
+        azure_ad_token = result["access_token"]
+        azure_ad_token_timestamp = now
+    else:
+        print("Same token received.")
+
+    return azure_ad_token
+
+
+def get_llm(
+    max_tokens: int = 1024,
+    temperature: float = 0.0,
+    verbose: bool = True,
+    streaming: bool = False,
+    **kwargs,
+) -> AzureChatOpenAI:
+    auth_dict = dict(openai_api_type="azure")
+    # Note: OPENAI_API_VERSION is automatically taken from environment variables.
+
+    # First option: provide AZURE_OPENAI_API_BASE_URL, OPENAI_API_VERSION, AZURE_CLIENT_ID,
+    # AZURE_CLIENT_CREDENTIAL, AZURE_TENANT_NAME & AZURE_SCOPE:
+    if (
+        (client_id is not None)
+        and (client_credential is not None)
+        and (tenant_name is not None)
+    ):
+        print("Using Azure AD token")
+        auth_dict["openai_api_base"] = os.environ["AZURE_OPENAI_API_BASE_URL"]
+        auth_dict["azure_ad_token_provider"] = _get_azure_ad_token
+
+    # Second option: provide AZURE_OPENAI_ENDPOINT, AZURE_OPENAI_API_DEPLOYMENT_NAME,
+    # OPENAI_API_VERSION & AZURE_OPENAI_API_KEY:
+    else:
+        print("Using AZURE_OPENAI_API_DEPLOYMENT_NAME and AZURE_OPENAI_API_KEY")
+        auth_dict["deployment_name"] = os.environ["AZURE_OPENAI_API_DEPLOYMENT_NAME"]
+        # Note: AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_API_KEY are automatically taken
+        # from environment variable.
+
+    llm = AzureChatOpenAI(
+        **auth_dict,
+        max_tokens=max_tokens,
+        temperature=temperature,
+        verbose=verbose,
+        streaming=streaming,
+        **kwargs,
+    )
+    return llm

climateqa/engine/llm/openai.py

@@ -0,0 +1,22 @@
+from langchain_openai import ChatOpenAI
+import os
+
+try:
+    from dotenv import load_dotenv
+    load_dotenv()
+except Exception:
+    pass
+
+def get_llm(model="gpt-3.5-turbo-0125",max_tokens=1024, temperature=0.0, streaming=True,timeout=30, **kwargs):
+    
+    llm = ChatOpenAI(
+        model=model,
+        api_key=os.environ.get("THEO_API_KEY", None),
+        max_tokens = max_tokens,
+        streaming = streaming,
+        temperature=temperature,
+        timeout = timeout,
+        **kwargs,
+    )
+
+    return llm

requirements.txt

@@ -4,6 +4,8 @@ azure-storage-file-share==12.11.1
 azure-storage-blob
 python-dotenv==1.0.0
 langchain==0.1.4
+langchain_openai==0.0.6
 pinecone-client==3.0.2
 sentence-transformers
-huggingface-hub
+huggingface-hub
+msal