Merge branch 'main' of https://github.com/manishMadan2882/docsgpt

application/core/settings.py

@@ -39,6 +39,9 @@ class Settings(BaseSettings):
     SAGEMAKER_ACCESS_KEY: Optional[str] = None # SageMaker access key
     SAGEMAKER_SECRET_KEY: Optional[str] = None # SageMaker secret key
 
+    # prem ai project id    
+    PREMAI_PROJECT_ID: Optional[str] = None
+
 
 path = Path(__file__).parent.parent.absolute()
 settings = Settings(_env_file=path.joinpath(".env"), _env_file_encoding="utf-8")

application/llm/docsgpt_provider.py

@@ -20,7 +20,7 @@ class DocsGPTAPILLM(BaseLLM):
                 "max_new_tokens": 30
             }
         )
-        response_clean = response.json()['a'].split("###")[0]
+        response_clean = response.json()['a'].replace("###", "")
 
         return response_clean
 

application/llm/llm_creator.py

@@ -4,6 +4,7 @@ from application.llm.huggingface import HuggingFaceLLM
 from application.llm.llama_cpp import LlamaCpp
 from application.llm.anthropic import AnthropicLLM
 from application.llm.docsgpt_provider import DocsGPTAPILLM
+from application.llm.premai import PremAILLM
 
 
 
@@ -15,7 +16,8 @@ class LLMCreator:
         'huggingface': HuggingFaceLLM,
         'llama.cpp': LlamaCpp,
         'anthropic': AnthropicLLM,
-        'docsgpt': DocsGPTAPILLM
+        'docsgpt': DocsGPTAPILLM,
+        'premai': PremAILLM,
     }
 
     @classmethod

application/llm/premai.py

@@ -0,0 +1,33 @@
+from application.llm.base import BaseLLM
+from application.core.settings import settings
+
+class PremAILLM(BaseLLM):
+
+    def __init__(self, api_key):
+        from premai import Prem
+        
+        self.client = Prem(
+            api_key=api_key
+        )
+        self.api_key = api_key
+        self.project_id = settings.PREMAI_PROJECT_ID
+
+    def gen(self, model, engine, messages, stream=False, **kwargs):
+        response = self.client.chat.completions.create(model=model,
+            project_id=self.project_id,
+            messages=messages,
+            stream=stream,
+            **kwargs)
+
+        return response.choices[0].message["content"]
+
+    def gen_stream(self, model, engine, messages, stream=True, **kwargs):
+        response = self.client.chat.completions.create(model=model,
+            project_id=self.project_id,
+            messages=messages,
+            stream=stream,
+            **kwargs)
+
+        for line in response:
+            if line.choices[0].delta["content"] is not None:
+                yield line.choices[0].delta["content"]