Merge branch 'main' into featue/elasticsearch

application/api/answer/routes.py

@@ -13,8 +13,8 @@ from transformers import GPT2TokenizerFast
 
 
 from application.core.settings import settings
-from application.llm.openai import OpenAILLM, AzureOpenAILLM
 from application.vectorstore.vector_creator import VectorCreator
+from application.llm.llm_creator import LLMCreator
 from application.error import bad_request
 
 
@@ -128,16 +128,8 @@ def is_azure_configured():
 
 
 def complete_stream(question, docsearch, chat_history, api_key, conversation_id):
-    if is_azure_configured():
-        llm = AzureOpenAILLM(
-            openai_api_key=api_key,
-            openai_api_base=settings.OPENAI_API_BASE,
-            openai_api_version=settings.OPENAI_API_VERSION,
-            deployment_name=settings.AZURE_DEPLOYMENT_NAME,
-        )
-    else:
-        logger.debug("plain OpenAI")
-        llm = OpenAILLM(api_key=api_key)
+    llm = LLMCreator.create_llm(settings.LLM_NAME, api_key=api_key)
+    
 
     docs = docsearch.search(question, k=2)
     # join all page_content together with a newline
@@ -270,16 +262,8 @@ def api_answer():
         # Note if you have used other embeddings than OpenAI, you need to change the embeddings
         docsearch = VectorCreator.create_vectorstore(settings.VECTOR_STORE, vectorstore, embeddings_key)
 
-        if is_azure_configured():
-            llm = AzureOpenAILLM(
-                openai_api_key=api_key,
-                openai_api_base=settings.OPENAI_API_BASE,
-                openai_api_version=settings.OPENAI_API_VERSION,
-                deployment_name=settings.AZURE_DEPLOYMENT_NAME,
-            )
-        else:
-            logger.debug("plain OpenAI")
-            llm = OpenAILLM(api_key=api_key)
+
+        llm = LLMCreator.create_llm(settings.LLM_NAME, api_key=api_key)
 
 
 

application/core/settings.py

@@ -4,7 +4,7 @@ from pydantic import BaseSettings
 
 
 class Settings(BaseSettings):
-    LLM_NAME: str = "openai_chat"
+    LLM_NAME: str = "openai"
     EMBEDDINGS_NAME: str = "openai_text-embedding-ada-002"
     CELERY_BROKER_URL: str = "redis://localhost:6379/0"
     CELERY_RESULT_BACKEND: str = "redis://localhost:6379/1"

application/llm/llm_creator.py

@@ -0,0 +1,20 @@
+from application.llm.openai import OpenAILLM, AzureOpenAILLM
+from application.llm.sagemaker import SagemakerAPILLM
+from application.llm.huggingface import HuggingFaceLLM
+
+
+
+class LLMCreator:
+    llms = {
+        'openai': OpenAILLM,
+        'azure_openai': AzureOpenAILLM,
+        'sagemaker': SagemakerAPILLM,
+        'huggingface': HuggingFaceLLM
+    }
+
+    @classmethod
+    def create_llm(cls, type, *args, **kwargs):
+        llm_class = cls.llms.get(type.lower())
+        if not llm_class:
+            raise ValueError(f"No LLM class found for type {type}")
+        return llm_class(*args, **kwargs)

application/llm/openai.py

@@ -1,4 +1,5 @@
 from application.llm.base import BaseLLM
+from application.core.settings import settings
 
 class OpenAILLM(BaseLLM):
 
@@ -44,9 +45,9 @@ class AzureOpenAILLM(OpenAILLM):
 
     def __init__(self, openai_api_key, openai_api_base, openai_api_version, deployment_name):
         super().__init__(openai_api_key)
-        self.api_base = openai_api_base
-        self.api_version = openai_api_version
-        self.deployment_name = deployment_name
+        self.api_base = settings.OPENAI_API_BASE,
+        self.api_version = settings.OPENAI_API_VERSION,
+        self.deployment_name = settings.AZURE_DEPLOYMENT_NAME,
 
     def _get_openai(self):
         openai = super()._get_openai()

application/llm/sagemaker.py

@@ -0,0 +1,27 @@
+from application.llm.base import BaseLLM
+from application.core.settings import settings
+import requests
+import json
+
+class SagemakerAPILLM(BaseLLM):
+
+    def __init__(self, *args, **kwargs):
+        self.url = settings.SAGEMAKER_API_URL
+
+    def gen(self, model, engine, messages, stream=False, **kwargs):
+        context = messages[0]['content']
+        user_question = messages[-1]['content']
+        prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"
+
+        response = requests.post(
+                    url=self.url,
+                    headers={
+                        "Content-Type": "application/json; charset=utf-8",
+                    },
+                    data=json.dumps({"input": prompt})
+        )
+
+        return response.json()['answer']
+
+    def gen_stream(self, model, engine, messages, stream=True, **kwargs):
+        raise NotImplementedError("Sagemaker does not support streaming")

application/requirements.txt

@@ -68,6 +68,7 @@ pyasn1==0.4.8
 pycares==4.3.0
 pycparser==2.21
 pycryptodomex==3.17
+pycryptodome==3.19.0
 pydantic==1.10.5
 PyJWT==2.6.0
 pymongo==4.3.3