Added llm model variable

application/app.py

@@ -28,21 +28,17 @@ from werkzeug.utils import secure_filename
 
 from error import bad_request
 from worker import ingest_worker
+from core.settings import settings
 import celeryconfig
 
 # os.environ["LANGCHAIN_HANDLER"] = "langchain"
 
-if os.getenv("LLM_NAME") is not None:
-    llm_choice = os.getenv("LLM_NAME")
-else:
-    llm_choice = "openai_chat"
-
 if os.getenv("EMBEDDINGS_NAME") is not None:
     embeddings_choice = os.getenv("EMBEDDINGS_NAME")
 else:
     embeddings_choice = "openai_text-embedding-ada-002"
 
-if llm_choice == "manifest":
+if settings.LLM_NAME == "manifest":
     from manifest import Manifest
     from langchain.llms.manifest import ManifestWrapper
 
@@ -122,7 +118,7 @@ def ingest(self, directory, formats, name_job, filename, user):
 
 @app.route("/")
 def home():
-    return render_template("index.html", api_key_set=api_key_set, llm_choice=llm_choice,
+    return render_template("index.html", api_key_set=api_key_set, llm_choice=settings.LLM_NAME,
                            embeddings_choice=embeddings_choice)
 
 
@@ -182,7 +178,7 @@ def api_answer():
 
         q_prompt = PromptTemplate(input_variables=["context", "question"], template=template_quest,
                                   template_format="jinja2")
-        if llm_choice == "openai_chat":
+        if settings.LLM_NAME == "openai_chat":
             # llm = ChatOpenAI(openai_api_key=api_key, model_name="gpt-4")
             llm = ChatOpenAI(openai_api_key=api_key)
             messages_combine = [
@@ -195,16 +191,16 @@ def api_answer():
                 HumanMessagePromptTemplate.from_template("{question}")
             ]
             p_chat_reduce = ChatPromptTemplate.from_messages(messages_reduce)
-        elif llm_choice == "openai":
+        elif settings.LLM_NAME == "openai":
             llm = OpenAI(openai_api_key=api_key, temperature=0)
-        elif llm_choice == "manifest":
+        elif settings.LLM_NAME == "manifest":
             llm = ManifestWrapper(client=manifest, llm_kwargs={"temperature": 0.001, "max_tokens": 2048})
-        elif llm_choice == "huggingface":
+        elif settings.LLM_NAME == "huggingface":
             llm = HuggingFaceHub(repo_id="bigscience/bloom", huggingfacehub_api_token=api_key)
-        elif llm_choice == "cohere":
+        elif settings.LLM_NAME == "cohere":
             llm = Cohere(model="command-xlarge-nightly", cohere_api_key=api_key)
 
-        if llm_choice == "openai_chat":
+        if settings.LLM_NAME == "openai_chat":
             question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)
             doc_chain = load_qa_chain(llm, chain_type="map_reduce", combine_prompt=p_chat_combine)
             chain = ConversationalRetrievalChain(

application/core/settings.py

@@ -3,6 +3,7 @@ from pathlib import Path
 
 
 class Settings(BaseSettings):
+    LLM_NAME: str = "openai_chat"
     openai_token: str