Add megaservice definition without microservice wrappers (opea-projec…

…t#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