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refactor get prompt service
1 parent 53a995f commit 5aa7ff4

2 files changed

Lines changed: 171 additions & 31 deletions

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‎dummy.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "initial_id",
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"metadata": {
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"collapsed": true,
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"ExecuteTime": {
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"end_time": "2023-10-05T13:43:29.749167090Z",
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"start_time": "2023-10-05T13:43:24.752225360Z"
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}
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},
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"outputs": [],
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"source": [
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"from genai_stack.stack.stack import Stack\n",
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"from genai_stack.model import HuggingFaceModel\n",
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"from genai_stack.etl.langchain import LangchainETL\n",
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"from genai_stack.embedding.langchain import LangchainEmbedding\n",
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"from genai_stack.vectordb.chromadb import ChromaDB\n",
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"from genai_stack.prompt_engine.engine import PromptEngine\n",
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"from genai_stack.retriever.langchain import LangChainRetriever\n",
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"from genai_stack.memory.langchain import ConversationBufferMemory"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"outputs": [],
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"source": [
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"etl = LangchainETL.from_kwargs(\n",
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" name=\"WebBaseLoader\",\n",
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" fields={\"web_path\": [\n",
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" \"https://aiplanet.com\",\n",
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" ]\n",
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" }\n",
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")"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2023-10-05T13:43:29.752762828Z",
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"start_time": "2023-10-05T13:43:29.749885841Z"
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}
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},
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"id": "58197b4e9c357f27"
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"outputs": [],
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"source": [
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"llm = HuggingFaceModel.from_kwargs(model=\"skt/ko-gpt-trinity-1.2B-v0.5\")\n",
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"config = {\n",
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" \"model_name\": \"sentence-transformers/all-mpnet-base-v2\",\n",
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" \"model_kwargs\": {\"device\": \"cpu\"},\n",
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" \"encode_kwargs\": {\"normalize_embeddings\": False},\n",
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"}\n",
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"embedding = LangchainEmbedding.from_kwargs(name=\"HuggingFaceEmbeddings\", fields=config)\n",
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"chromadb = ChromaDB.from_kwargs()"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2023-10-05T13:44:37.250590Z",
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"start_time": "2023-10-05T13:44:37.223926726Z"
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}
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},
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"id": "42fa2dcd7085ac80"
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"outputs": [
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{
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"ename": "NameError",
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"evalue": "name 'PromptEngine' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
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"\u001B[0;31mNameError\u001B[0m Traceback (most recent call last)",
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"Cell \u001B[0;32mIn[1], line 1\u001B[0m\n\u001B[0;32m----> 1\u001B[0m prompt_engine \u001B[38;5;241m=\u001B[39m \u001B[43mPromptEngine\u001B[49m\u001B[38;5;241m.\u001B[39mfrom_kwargs(should_validate\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m)\n\u001B[1;32m 2\u001B[0m retriever \u001B[38;5;241m=\u001B[39m LangChainRetriever\u001B[38;5;241m.\u001B[39mfrom_kwargs()\n\u001B[1;32m 3\u001B[0m memory \u001B[38;5;241m=\u001B[39m ConversationBufferMemory\u001B[38;5;241m.\u001B[39mfrom_kwargs()\n",
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"\u001B[0;31mNameError\u001B[0m: name 'PromptEngine' is not defined"
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]
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}
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],
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"source": [
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"prompt_engine = PromptEngine.from_kwargs(should_validate=False)\n",
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"retriever = LangChainRetriever.from_kwargs()\n",
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"memory = ConversationBufferMemory.from_kwargs()\n",
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"Stack(\n",
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" etl=etl,\n",
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" embedding=embedding,\n",
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" vectordb=chromadb,\n",
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" model=llm,\n",
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" prompt_engine=prompt_engine,\n",
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" retriever=retriever,\n",
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" memory=memory\n",
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")\n"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2023-10-05T13:45:48.582398487Z",
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"start_time": "2023-10-05T13:45:48.417937857Z"
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}
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},
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"id": "81e991b63b233e6d"
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"prompt1 = \"Why choose models from AI Marketplace?\""
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"start_time": "2023-10-05T13:43:49.783528419Z"
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}
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},
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"id": "2e815c9b106810f0"
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"start_time": "2023-10-05T13:43:49.786045019Z"
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}
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},
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"id": "80d05b20a6921f73"
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [],
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"metadata": {
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"collapsed": false
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},
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"id": "8226f4204a117563"
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎genai_stack/genai_server/services/prompt_engine_service.py‎

Lines changed: 1 addition & 31 deletions
Original file line numberDiff line numberDiff line change
@@ -21,37 +21,7 @@ def get_prompt(self, data: PromptEngineGetRequestModel) -> PromptEngineGetRespon
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stack_session = session.get(StackSessionSchema, data.session_id)
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if stack_session is None:
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raise HTTPException(status_code=404, detail=f"Session {data.session_id} not found")
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prompt_session = (
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session.query(PromptSchema)
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.filter_by(stack_session=data.session_id, type=data.type.value)
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.first()
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)
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if prompt_session is not None:
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template = prompt_session.template
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prompt_type_map = {
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PromptTypeEnum.SIMPLE_CHAT_PROMPT.value: "simple_chat_prompt_template",
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PromptTypeEnum.CONTEXTUAL_CHAT_PROMPT.value: "contextual_chat_prompt_template",
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PromptTypeEnum.CONTEXTUAL_QA_PROMPT.value: "contextual_qa_prompt_template",
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}
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input_variables = ["context", "history", "query"]
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if data.type == PromptTypeEnum.SIMPLE_CHAT_PROMPT:
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input_variables.remove("context")
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elif data.type == PromptTypeEnum.CONTEXTUAL_QA_PROMPT:
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input_variables.remove("history")
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prompt = PromptTemplate(template=template, input_variables=input_variables)
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stack = get_current_stack(
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engine=session,
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config=stack_config,
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session=stack_session,
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overide_config={
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"prompt_engine": {
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"should_validate": data.should_validate,
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prompt_type_map[data.type.value]: prompt
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}
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}
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)
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else:
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stack = get_current_stack(config=stack_config, engine=session, session=stack_session)
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stack = get_current_stack(config=stack_config, engine=session, session=stack_session)
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prompt = stack.prompt_engine.get_prompt_template(promptType=data.type, query=data.query)
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return PromptEngineGetResponseModel(
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template=prompt.template,

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