Merge branch 'main' into prompt-engine-api

assets/config_custom_chromadb.json

@@ -7,7 +7,7 @@
     },
     "vectordb": {
         "name": "chromadb",
-        "class_name": "llm_stack",
+        "class_name": "genai_stack",
         "embedding": {
             "name": "HuggingFaceEmbeddings",
             "fields": {

assets/etl.json

@@ -8,6 +8,6 @@
     },
     "vectordb": {
         "name": "chromadb",
-        "class_name": "llm_stack"
+        "class_name": "genai_stack"
     }
 }

genai_stack/__init__.py

@@ -1,8 +1,8 @@
 """Top-level package for genai_stack."""
 
-__author__ = """AIM by DPhi"""
+__author__ = """AI Planet Tech Team"""
 __email__ = "[email protected]"
-__version__ = "0.2.2"
+__version__ = "0.2.3"
 
 import os
 

genai_stack/constant.py

@@ -7,3 +7,4 @@
 VECTORDB = "/vectordb"
 ETL = "/etl"
 PROMPT_ENGINE = "/prompt-engine"
+MODEL = "/model"

genai_stack/etl/run.py

@@ -1,17 +1,17 @@
-from genai_stack.constants.etl.etl import PREBUILT_ETL_LOADERS, ETL_MODULE
+from genai_stack.constants.etl.etl import AVAILABLE_ETL_LOADERS, ETL_MODULE
 from genai_stack.utils.importing import import_class
 
 from genai_stack.core import ConfigLoader
 
 
 def list_etl_loaders():
-    return PREBUILT_ETL_LOADERS.keys()
+    return AVAILABLE_ETL_LOADERS.keys()
 
 
 def run_etl_loader(config_file: str, vectordb):
     config_cls = ConfigLoader(name="EtlLoader", config=config_file)
     etl_cls = import_class(
-        f"{ETL_MODULE}.{PREBUILT_ETL_LOADERS.get(config_cls.config.get('etl'))}".replace(
+        f"{ETL_MODULE}.{AVAILABLE_ETL_LOADERS.get(config_cls.config.get('etl'))}".replace(
             "/",
             ".",
         )

genai_stack/genai_server/models/model_models.py

@@ -0,0 +1,13 @@
+from pydantic import BaseModel
+
+
+class ModelBaseModel(BaseModel):
+    pass
+
+
+class ModelRequestModel(ModelBaseModel):
+    prompt: str
+
+
+class ModelResponseModel(ModelBaseModel):
+    output: str

genai_stack/genai_server/routers/model_routes.py

@@ -0,0 +1,15 @@
+from fastapi import APIRouter
+
+from genai_stack.constant import API, MODEL
+from genai_stack.genai_server.settings.settings import settings
+from genai_stack.genai_server.services.model_service import ModelService
+from genai_stack.genai_server.models.model_models import ModelResponseModel, ModelRequestModel
+
+service = ModelService(store=settings.STORE)
+
+router = APIRouter(prefix=API + MODEL, tags=["model"])
+
+
+@router.post("/predict")
+def predict(data: ModelRequestModel) -> ModelResponseModel:
+    return service.predict(data=data)

genai_stack/genai_server/server.py

@@ -1,6 +1,12 @@
 from fastapi import FastAPI
-
-from genai_stack.genai_server.routers import session_routes, retriever_routes, vectordb_routes, etl_routes, prompt_engine_routes
+from genai_stack.genai_server.routers import (
+    session_routes,
+    retriever_routes,
+    vectordb_routes,
+    etl_routes,
+    prompt_engine_routes,
+    model_routes,
+)
 
 
 def get_genai_server_app():
@@ -21,5 +27,6 @@ def get_genai_server_app():
     app.include_router(vectordb_routes.router)
     app.include_router(etl_routes.router)
     app.include_router(prompt_engine_routes.router)
+    app.include_router(model_routes.router)
 
     return app

genai_stack/genai_server/services/model_service.py

@@ -0,0 +1,13 @@
+from genai_stack.genai_platform.services.base_service import BaseService
+from genai_stack.genai_server.models.model_models import ModelRequestModel, ModelResponseModel
+from genai_stack.genai_server.utils import get_current_stack
+from genai_stack.genai_server.settings.config import stack_config
+
+
+class ModelService(BaseService):
+    def predict(self, data: ModelRequestModel) -> ModelResponseModel:
+        stack = get_current_stack(config=stack_config)
+        response = stack.model.predict(data.prompt)
+        return ModelResponseModel(
+            output=response["output"],
+        )

genai_stack/model/__init__.py

@@ -1,4 +1,3 @@
-from .server import HttpServer
 from .base import BaseModel
 from .gpt3_5 import OpenAIGpt35Model
 from .run import list_supported_models, get_model_class, AVAILABLE_MODEL_MAPS, run_custom_model

genai_stack/model/gpt3_5.py

@@ -7,38 +7,68 @@
 
 class OpenAIGpt35Parameters(BaseModelConfigModel):
     model_name: str = Field(default="gpt-3.5-turbo-16k", alias="model")
-    """Model name to use."""
+    """
+    Model name to use.
+
+    """
     temperature: float = 0
-    """What sampling temperature to use."""
+    """
+    What sampling temperature to use.
+    
+    """
     model_kwargs: Dict[str, Any] = Field(default_factory=dict)
-    """Holds any model parameters valid for `create` call not explicitly specified."""
+    """
+    Holds any model parameters valid for `create` call not explicitly specified.
+
+    """
     openai_api_key: str
-    """Base URL path for API requests, 
-    leave blank if not using a proxy or service emulator."""
+    """
+    Base URL path for API requests, 
+    leave blank if not using a proxy or service emulator.
+
+    """
     openai_api_base: Optional[str] = None
     openai_organization: Optional[str] = None
     # to support explicit proxy for OpenAI
     openai_proxy: Optional[str] = None
     request_timeout: Optional[Union[float, Tuple[float, float]]] = None
-    """Timeout for requests to OpenAI completion API. Default is 600 seconds."""
+    """
+    Timeout for requests to OpenAI completion API. Default is 600 seconds.
+
+    """
     max_retries: int = 6
-    """Maximum number of retries to make when generating."""
+    """
+    Maximum number of retries to make when generating.
+
+    """
     streaming: bool = False
-    """Whether to stream the results or not."""
+    """
+    Whether to stream the results or not.
+
+    """
     n: int = 1
-    """Number of chat completions to generate for each prompt."""
+    """
+    Number of chat completions to generate for each prompt.
+    
+    """
     max_tokens: Optional[int] = None
-    """Maximum number of tokens to generate."""
+    """
+    Maximum number of tokens to generate.
+    
+    """
     tiktoken_model_name: Optional[str] = None
-    """The model name to pass to tiktoken when using this class. 
+    """
+    The model name to pass to tiktoken when using this class. 
     Tiktoken is used to count the number of tokens in documents to constrain 
     them to be under a certain limit. By default, when set to None, this will 
     be the same as the embedding model name. However, there are some cases 
     where you may want to use this Embedding class with a model name not 
     supported by tiktoken. This can include when using Azure embeddings or 
     when using one of the many model providers that expose an OpenAI-like 
     API but with different models. In those cases, in order to avoid erroring 
-    when tiktoken is called, you can specify a model name to use here."""
+    when tiktoken is called, you can specify a model name to use here.
+    
+    """
 
 
 class OpenAIGpt35ModelConfigModel(BaseModelConfigModel):

genai_stack/model/hf.py

@@ -1,6 +1,4 @@
 from typing import Optional, Dict
-
-import torch
 from langchain.llms import HuggingFacePipeline
 
 from genai_stack.model.base import BaseModel, BaseModelConfig, BaseModelConfigModel
@@ -31,18 +29,14 @@ class HuggingFaceModel(BaseModel):
     def _post_init(self, *args, **kwargs):
         self.model = self.load()
 
-    def get_device(self):
-        return torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
-
     def load(self):
         model = HuggingFacePipeline.from_model_id(
-            model_id=self.config.model,
-            task=self.config.task,
-            model_kwargs=self.config.model_kwargs,
-            device=self.get_device(),
+            model_id=self.config.model, task=self.config.task, model_kwargs=self.config.model_kwargs
         )
         return model
 
     def predict(self, prompt: str):
         response = self.model(prompt)
-        return {"output": response[0]["generated_text"]}
+        # Note: Huggingface model response format is different for different model
+        # so user should extract the info which is required.
+        return {"output": response}

genai_stack/retriever/base.py

@@ -6,6 +6,7 @@ class BaseRetrieverConfigModel(BaseModel):
     """
     Data Model for the configs
     """
+
     pass
 
 
@@ -16,55 +17,26 @@ class BaseRetrieverConfig(StackComponentConfig):
 class BaseRetriever(StackComponent):
     config_class = BaseRetrieverConfig
 
-    def get_prompt(self, query:str):
+    def get_prompt(self, query: str):
         """
         This method returns the prompt template from the prompt engine component
         """
         return self.mediator.get_prompt_template(query)
 
-    def retrieve(self, query:str) -> dict:
+    def retrieve(self, query: str) -> dict:
         """
         This method returns the model response for the prompt template.
         """
         raise NotImplementedError()
-    
-    def get_context(self, query:str):
+
+    def get_context(self, query: str):
         """
         This method returns the relevant documents returned by the similarity search from a vectordb based on the query
         """
         raise NotImplementedError()
-    
+
     def get_chat_history(self) -> str:
         """
         This method returns the chat conversation history
         """
         return self.mediator.get_chat_history()
-
-
-# from typing import Any
-
-# from genai_stack.core import BaseComponent
-# from genai_stack.constants.retriever import RETRIEVER_CONFIG_KEY
-# from genai_stack.vectordb.base import BaseVectordb
-
-# class BaseRetriever(BaseComponent):
-#     module_name = "BaseRetriever"
-#     config_key = RETRIEVER_CONFIG_KEY
-
-#     def __init__(self, config: str, vectordb: BaseVectordb = None):
-#         super().__init__(self.module_name, config)
-#         self.parse_config(self.config_key, self.required_fields)
-#         self.vectordb = vectordb
-
-#     def retrieve(self, query: Any):
-#         raise NotImplementedError()
-
-#     def get_langchain_retriever(self):
-#         return self.vectordb.get_langchain_client().as_retriever()
-
-#     def get_langchain_memory_retriever(self):
-#         return self.vectordb.get_langchain_memory_client().as_retriever()
-
-#     @classmethod
-#     def from_config(cls, config):
-#         raise NotImplementedError