Add megaservice definition without microservice wrappers (#700)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

ChatQnA/docker/Dockerfile_no_wrapper

@@ -0,0 +1,34 @@
+
+
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+FROM python:3.11-slim
+
+RUN apt-get update -y && apt-get install -y --no-install-recommends --fix-missing \
+ libgl1-mesa-glx \
+ libjemalloc-dev \
+ vim \
+ git
+
+RUN useradd -m -s /bin/bash user && \
+ mkdir -p /home/user && \
+ chown -R user /home/user/
+
+WORKDIR /home/user/
+RUN git clone https://github.com/opea-project/GenAIComps.git
+
+WORKDIR /home/user/GenAIComps
+RUN pip install --no-cache-dir --upgrade pip && \
+ pip install --no-cache-dir -r /home/user/GenAIComps/requirements.txt && \
+ pip install --no-cache-dir langchain_core
+
+COPY ./chatqna_no_wrapper.py /home/user/chatqna_no_wrapper.py
+
+ENV PYTHONPATH=$PYTHONPATH:/home/user/GenAIComps
+
+USER user
+
+WORKDIR /home/user
+
+ENTRYPOINT ["python", "chatqna_no_wrapper.py"]

ChatQnA/docker/chatqna_no_wrapper.py

@@ -0,0 +1,265 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import json
+import os
+import re
+
+from comps import ChatQnAGateway, MicroService, ServiceOrchestrator, ServiceType
+from langchain_core.prompts import PromptTemplate
+
+
+class ChatTemplate:
+ @staticmethod
+ def generate_rag_prompt(question, documents):
+ context_str = "\n".join(documents)
+ if context_str and len(re.findall("[\u4E00-\u9FFF]", context_str)) / len(context_str) >= 0.3:
+ # chinese context
+ template = """
+### 你将扮演一个乐于助人、尊重他人并诚实的助手，你的目标是帮助用户解答问题。有效地利用来自本地知识库的搜索结果。确保你的回答中只包含相关信息。如果你不确定问题的答案，请避免分享不准确的信息。
+### 搜索结果：{context}
+### 问题：{question}
+### 回答：
+"""
+ else:
+ template = """
+### You are a helpful, respectful and honest assistant to help the user with questions. \
+Please refer to the search results obtained from the local knowledge base. \
+But be careful to not incorporate the information that you think is not relevant to the question. \
+If you don't know the answer to a question, please don't share false information. \n
+### Search results: {context} \n
+### Question: {question} \n
+### Answer:
+"""
+ return template.format(context=context_str, question=question)
+
+
+MEGA_SERVICE_HOST_IP = os.getenv("MEGA_SERVICE_HOST_IP", "0.0.0.0")
+MEGA_SERVICE_PORT = int(os.getenv("MEGA_SERVICE_PORT", 8888))
+# EMBEDDING_SERVICE_HOST_IP = os.getenv("EMBEDDING_SERVICE_HOST_IP", "0.0.0.0")
+# EMBEDDING_SERVICE_PORT = int(os.getenv("EMBEDDING_SERVICE_PORT", 6000))
+# RETRIEVER_SERVICE_HOST_IP = os.getenv("RETRIEVER_SERVICE_HOST_IP", "0.0.0.0")
+# RETRIEVER_SERVICE_PORT = int(os.getenv("RETRIEVER_SERVICE_PORT", 7000))
+# RERANK_SERVICE_HOST_IP = os.getenv("RERANK_SERVICE_HOST_IP", "0.0.0.0")
+# RERANK_SERVICE_PORT = int(os.getenv("RERANK_SERVICE_PORT", 8000))
+# LLM_SERVICE_HOST_IP = os.getenv("LLM_SERVICE_HOST_IP", "0.0.0.0")
+# LLM_SERVICE_PORT = int(os.getenv("LLM_SERVICE_PORT", 9000))
+EMBEDDING_SERVER_HOST_IP = os.getenv("EMBEDDING_SERVER_HOST_IP", "0.0.0.0")
+EMBEDDING_SERVER_PORT = int(os.getenv("EMBEDDING_SERVER_PORT", 6006))
+RETRIEVER_SERVICE_HOST_IP = os.getenv("RETRIEVER_SERVICE_HOST_IP", "0.0.0.0")
+RETRIEVER_SERVICE_PORT = int(os.getenv("RETRIEVER_SERVICE_PORT", 7000))
+RERANK_SERVER_HOST_IP = os.getenv("RERANK_SERVER_HOST_IP", "0.0.0.0")
+RERANK_SERVER_PORT = int(os.getenv("RERANK_SERVER_PORT", 8808))
+LLM_SERVER_HOST_IP = os.getenv("LLM_SERVER_HOST_IP", "0.0.0.0")
+LLM_SERVER_PORT = int(os.getenv("LLM_SERVER_PORT", 9009))
+
+
+def align_inputs(self, inputs, cur_node, runtime_graph, llm_parameters_dict, **kwargs):
+ if self.services[cur_node].service_type == ServiceType.EMBEDDING:
+ inputs["inputs"] = inputs["text"]
+ del inputs["text"]
+ elif self.services[cur_node].service_type == ServiceType.RETRIEVER:
+ # prepare the retriever params
+ retriever_parameters = kwargs.get("retriever_parameters", None)
+ if retriever_parameters:
+ inputs.update(retriever_parameters.dict())
+ elif self.services[cur_node].service_type == ServiceType.LLM:
+ # convert TGI/vLLM to unified OpenAI /v1/chat/completions format
+ next_inputs = {}
+ next_inputs["model"] = "tgi" # specifically clarify the fake model to make the format unified
+ next_inputs["messages"] = [{"role": "user", "content": inputs["inputs"]}]
+ next_inputs["max_tokens"] = llm_parameters_dict["max_new_tokens"]
+ next_inputs["top_p"] = llm_parameters_dict["top_p"]
+ next_inputs["stream"] = inputs["streaming"]
+ next_inputs["frequency_penalty"] = inputs["repetition_penalty"]
+ next_inputs["temperature"] = inputs["temperature"]
+ inputs = next_inputs
+
+ return inputs
+
+
+def align_outputs(self, data, cur_node, inputs, runtime_graph, llm_parameters_dict, **kwargs):
+ next_data = {}
+ if self.services[cur_node].service_type == ServiceType.EMBEDDING:
+ assert isinstance(data, list)
+ next_data = {"text": inputs["inputs"], "embedding": data[0]}
+ elif self.services[cur_node].service_type == ServiceType.RETRIEVER:
+
+ docs = [doc["text"] for doc in data["retrieved_docs"]]
+
+ with_rerank = runtime_graph.downstream(cur_node)[0].startswith("rerank")
+ if with_rerank and docs:
+ # forward to rerank
+ # prepare inputs for rerank
+ next_data["query"] = data["initial_query"]
+ next_data["texts"] = [doc["text"] for doc in data["retrieved_docs"]]
+ else:
+ # forward to llm
+ if not docs:
+ # delete the rerank from retriever -> rerank -> llm
+ for ds in reversed(runtime_graph.downstream(cur_node)):
+ for nds in runtime_graph.downstream(ds):
+ runtime_graph.add_edge(cur_node, nds)
+ runtime_graph.delete_node_if_exists(ds)
+
+ # handle template
+ # if user provides template, then format the prompt with it
+ # otherwise, use the default template
+ prompt = data["initial_query"]
+ chat_template = llm_parameters_dict["chat_template"]
+ if chat_template:
+ prompt_template = PromptTemplate.from_template(chat_template)
+ input_variables = prompt_template.input_variables
+ if sorted(input_variables) == ["context", "question"]:
+ prompt = prompt_template.format(question=data["initial_query"], context="\n".join(docs))
+ elif input_variables == ["question"]:
+ prompt = prompt_template.format(question=data["initial_query"])
+ else:
+ print(f"{prompt_template} not used, we only support 2 input variables ['question', 'context']")
+ prompt = ChatTemplate.generate_rag_prompt(data["initial_query"], docs)
+ else:
+ prompt = ChatTemplate.generate_rag_prompt(data["initial_query"], docs)
+
+ next_data["inputs"] = prompt
+
+ elif self.services[cur_node].service_type == ServiceType.RERANK:
+ # rerank the inputs with the scores
+ reranker_parameters = kwargs.get("reranker_parameters", None)
+ top_n = reranker_parameters.top_n if reranker_parameters else 1
+ docs = inputs["texts"]
+ reranked_docs = []
+ for best_response in data[:top_n]:
+ reranked_docs.append(docs[best_response["index"]])
+
+ # handle template
+ # if user provides template, then format the prompt with it
+ # otherwise, use the default template
+ prompt = inputs["query"]
+ chat_template = llm_parameters_dict["chat_template"]
+ if chat_template:
+ prompt_template = PromptTemplate.from_template(chat_template)
+ input_variables = prompt_template.input_variables
+ if sorted(input_variables) == ["context", "question"]:
+ prompt = prompt_template.format(question=prompt, context="\n".join(docs))
+ elif input_variables == ["question"]:
+ prompt = prompt_template.format(question=prompt)
+ else:
+ print(f"{prompt_template} not used, we only support 2 input variables ['question', 'context']")
+ prompt = ChatTemplate.generate_rag_prompt(prompt, docs)
+ else:
+ prompt = ChatTemplate.generate_rag_prompt(prompt, docs)
+
+ next_data["inputs"] = prompt
+
+ return next_data
+
+
+def align_generator(self, gen, **kwargs):
+ # openai reaponse format
+ # b'data:{"id":"","object":"text_completion","created":1725530204,"model":"meta-llama/Meta-Llama-3-8B-Instruct","system_fingerprint":"2.0.1-native","choices":[{"index":0,"delta":{"role":"assistant","content":"?"},"logprobs":null,"finish_reason":null}]}\n\n'
+ for line in gen:
+ line = line.decode("utf-8")
+ start = line.find("{")
+ end = line.rfind("}") + 1
+
+ json_str = line[start:end]
+ try:
+ # sometimes yield empty chunk, do a fallback here
+ json_data = json.loads(json_str)
+ if json_data["choices"][0]["finish_reason"] != "eos_token":
+ yield f"data: {repr(json_data['choices'][0]['delta']['content'].encode('utf-8'))}\n\n"
+ except Exception as e:
+ yield f"data: {repr(json_str.encode('utf-8'))}\n\n"
+ yield "data: [DONE]\n\n"
+
+
+class ChatQnAService:
+ def __init__(self, host="0.0.0.0", port=8000):
+ self.host = host
+ self.port = port
+ ServiceOrchestrator.align_inputs = align_inputs
+ ServiceOrchestrator.align_outputs = align_outputs
+ ServiceOrchestrator.align_generator = align_generator
+ self.megaservice = ServiceOrchestrator()
+
+ def add_remote_service(self):
+
+ embedding = MicroService(
+ name="embedding",
+ host=EMBEDDING_SERVER_HOST_IP,
+ port=EMBEDDING_SERVER_PORT,
+ endpoint="/embed",
+ use_remote_service=True,
+ service_type=ServiceType.EMBEDDING,
+ )
+
+ retriever = MicroService(
+ name="retriever",
+ host=RETRIEVER_SERVICE_HOST_IP,
+ port=RETRIEVER_SERVICE_PORT,
+ endpoint="/v1/retrieval",
+ use_remote_service=True,
+ service_type=ServiceType.RETRIEVER,
+ )
+
+ rerank = MicroService(
+ name="rerank",
+ host=RERANK_SERVER_HOST_IP,
+ port=RERANK_SERVER_PORT,
+ endpoint="/rerank",
+ use_remote_service=True,
+ service_type=ServiceType.RERANK,
+ )
+
+ llm = MicroService(
+ name="llm",
+ host=LLM_SERVER_HOST_IP,
+ port=LLM_SERVER_PORT,
+ endpoint="/v1/chat/completions",
+ use_remote_service=True,
+ service_type=ServiceType.LLM,
+ )
+ self.megaservice.add(embedding).add(retriever).add(rerank).add(llm)
+ self.megaservice.flow_to(embedding, retriever)
+ self.megaservice.flow_to(retriever, rerank)
+ self.megaservice.flow_to(rerank, llm)
+ self.gateway = ChatQnAGateway(megaservice=self.megaservice, host="0.0.0.0", port=self.port)
+
+ def add_remote_service_without_rerank(self):
+
+ embedding = MicroService(
+ name="embedding",
+ host=EMBEDDING_SERVER_HOST_IP,
+ port=EMBEDDING_SERVER_PORT,
+ endpoint="/embed",
+ use_remote_service=True,
+ service_type=ServiceType.EMBEDDING,
+ )
+
+ retriever = MicroService(
+ name="retriever",
+ host=RETRIEVER_SERVICE_HOST_IP,
+ port=RETRIEVER_SERVICE_PORT,
+ endpoint="/v1/retrieval",
+ use_remote_service=True,
+ service_type=ServiceType.RETRIEVER,
+ )
+
+ llm = MicroService(
+ name="llm",
+ host=LLM_SERVER_HOST_IP,
+ port=LLM_SERVER_PORT,
+ endpoint="/v1/chat/completions",
+ use_remote_service=True,
+ service_type=ServiceType.LLM,
+ )
+ self.megaservice.add(embedding).add(retriever).add(llm)
+ self.megaservice.flow_to(embedding, retriever)
+ self.megaservice.flow_to(retriever, llm)
+ self.gateway = ChatQnAGateway(megaservice=self.megaservice, host="0.0.0.0", port=self.port)
+
+
+if __name__ == "__main__":
+ chatqna = ChatQnAService(host=MEGA_SERVICE_HOST_IP, port=MEGA_SERVICE_PORT)
+ chatqna.add_remote_service()
+ # chatqna.add_remote_service_without_rerank()

ChatQnA/docker/docker_build_compose.yaml

@@ -20,6 +20,11 @@ services:
  dockerfile: ./Dockerfile_without_rerank
  extends: chatqna
  image: ${REGISTRY:-opea}/chatqna-without-rerank:${TAG:-latest}
+ chatqna-no-wrapper:
+ build:
+ dockerfile: ./Dockerfile_no_wrapper
+ extends: chatqna
+ image: ${REGISTRY:-opea}/chatqna-no-wrapper:${TAG:-latest}
  chatqna-ui:
  build:
  context: ui