414 lines
17 KiB
Python
414 lines
17 KiB
Python
# Copyright (C) 2024 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import argparse
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import json
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import os
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import re
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from comps import MegaServiceEndpoint, MicroService, ServiceOrchestrator, ServiceRoleType, ServiceType
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from comps.cores.mega.utils import handle_message
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from comps.cores.proto.api_protocol import (
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ChatCompletionRequest,
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ChatCompletionResponse,
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ChatCompletionResponseChoice,
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ChatMessage,
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UsageInfo,
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)
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from comps.cores.proto.docarray import LLMParams, RerankerParms, RetrieverParms
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from fastapi import Request
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from fastapi.responses import StreamingResponse
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from langchain_core.prompts import PromptTemplate
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class ChatTemplate:
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@staticmethod
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def generate_rag_prompt(question, documents):
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context_str = "\n".join(documents)
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if context_str and len(re.findall("[\u4E00-\u9FFF]", context_str)) / len(context_str) >= 0.3:
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# chinese context
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template = """
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### 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。
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### 搜索结果：{context}
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### 问题：{question}
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### 回答：
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"""
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else:
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template = """
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### You are a helpful, respectful and honest assistant to help the user with questions. \
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Please refer to the search results obtained from the local knowledge base. \
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But be careful to not incorporate the information that you think is not relevant to the question. \
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If you don't know the answer to a question, please don't share false information. \n
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### Search results: {context} \n
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### Question: {question} \n
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### Answer:
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"""
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return template.format(context=context_str, question=question)
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MEGA_SERVICE_PORT = int(os.getenv("MEGA_SERVICE_PORT", 8888))
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GUARDRAIL_SERVICE_HOST_IP = os.getenv("GUARDRAIL_SERVICE_HOST_IP", "0.0.0.0")
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GUARDRAIL_SERVICE_PORT = int(os.getenv("GUARDRAIL_SERVICE_PORT", 80))
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EMBEDDING_SERVER_HOST_IP = os.getenv("EMBEDDING_SERVER_HOST_IP", "0.0.0.0")
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EMBEDDING_SERVER_PORT = int(os.getenv("EMBEDDING_SERVER_PORT", 80))
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RETRIEVER_SERVICE_HOST_IP = os.getenv("RETRIEVER_SERVICE_HOST_IP", "0.0.0.0")
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RETRIEVER_SERVICE_PORT = int(os.getenv("RETRIEVER_SERVICE_PORT", 7000))
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RERANK_SERVER_HOST_IP = os.getenv("RERANK_SERVER_HOST_IP", "0.0.0.0")
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RERANK_SERVER_PORT = int(os.getenv("RERANK_SERVER_PORT", 80))
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LLM_SERVER_HOST_IP = os.getenv("LLM_SERVER_HOST_IP", "0.0.0.0")
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LLM_SERVER_PORT = int(os.getenv("LLM_SERVER_PORT", 80))
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LLM_MODEL = os.getenv("LLM_MODEL", "meta-llama/Meta-Llama-3-8B-Instruct")
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def align_inputs(self, inputs, cur_node, runtime_graph, llm_parameters_dict, **kwargs):
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if self.services[cur_node].service_type == ServiceType.EMBEDDING:
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inputs["inputs"] = inputs["text"]
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del inputs["text"]
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elif self.services[cur_node].service_type == ServiceType.RETRIEVER:
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# prepare the retriever params
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retriever_parameters = kwargs.get("retriever_parameters", None)
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if retriever_parameters:
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inputs.update(retriever_parameters.dict())
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elif self.services[cur_node].service_type == ServiceType.LLM:
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# convert TGI/vLLM to unified OpenAI /v1/chat/completions format
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next_inputs = {}
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next_inputs["model"] = LLM_MODEL
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next_inputs["messages"] = [{"role": "user", "content": inputs["inputs"]}]
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next_inputs["max_tokens"] = llm_parameters_dict["max_tokens"]
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next_inputs["top_p"] = llm_parameters_dict["top_p"]
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next_inputs["stream"] = inputs["stream"]
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next_inputs["frequency_penalty"] = inputs["frequency_penalty"]
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# next_inputs["presence_penalty"] = inputs["presence_penalty"]
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# next_inputs["repetition_penalty"] = inputs["repetition_penalty"]
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next_inputs["temperature"] = inputs["temperature"]
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inputs = next_inputs
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return inputs
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def align_outputs(self, data, cur_node, inputs, runtime_graph, llm_parameters_dict, **kwargs):
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next_data = {}
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if self.services[cur_node].service_type == ServiceType.EMBEDDING:
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assert isinstance(data, list)
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next_data = {"text": inputs["inputs"], "embedding": data[0]}
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elif self.services[cur_node].service_type == ServiceType.RETRIEVER:
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docs = [doc["text"] for doc in data["retrieved_docs"]]
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with_rerank = runtime_graph.downstream(cur_node)[0].startswith("rerank")
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if with_rerank and docs:
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# forward to rerank
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# prepare inputs for rerank
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next_data["query"] = data["initial_query"]
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next_data["texts"] = [doc["text"] for doc in data["retrieved_docs"]]
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else:
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# forward to llm
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if not docs and with_rerank:
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# delete the rerank from retriever -> rerank -> llm
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for ds in reversed(runtime_graph.downstream(cur_node)):
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for nds in runtime_graph.downstream(ds):
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runtime_graph.add_edge(cur_node, nds)
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runtime_graph.delete_node_if_exists(ds)
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# handle template
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# if user provides template, then format the prompt with it
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# otherwise, use the default template
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prompt = data["initial_query"]
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chat_template = llm_parameters_dict["chat_template"]
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if chat_template:
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prompt_template = PromptTemplate.from_template(chat_template)
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input_variables = prompt_template.input_variables
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if sorted(input_variables) == ["context", "question"]:
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prompt = prompt_template.format(question=data["initial_query"], context="\n".join(docs))
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elif input_variables == ["question"]:
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prompt = prompt_template.format(question=data["initial_query"])
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else:
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print(f"{prompt_template} not used, we only support 2 input variables ['question', 'context']")
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prompt = ChatTemplate.generate_rag_prompt(data["initial_query"], docs)
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else:
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prompt = ChatTemplate.generate_rag_prompt(data["initial_query"], docs)
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next_data["inputs"] = prompt
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elif self.services[cur_node].service_type == ServiceType.RERANK:
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# rerank the inputs with the scores
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reranker_parameters = kwargs.get("reranker_parameters", None)
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top_n = reranker_parameters.top_n if reranker_parameters else 1
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docs = inputs["texts"]
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reranked_docs = []
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for best_response in data[:top_n]:
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reranked_docs.append(docs[best_response["index"]])
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# handle template
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# if user provides template, then format the prompt with it
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# otherwise, use the default template
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prompt = inputs["query"]
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chat_template = llm_parameters_dict["chat_template"]
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if chat_template:
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prompt_template = PromptTemplate.from_template(chat_template)
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input_variables = prompt_template.input_variables
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if sorted(input_variables) == ["context", "question"]:
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prompt = prompt_template.format(question=prompt, context="\n".join(reranked_docs))
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elif input_variables == ["question"]:
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prompt = prompt_template.format(question=prompt)
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else:
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print(f"{prompt_template} not used, we only support 2 input variables ['question', 'context']")
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prompt = ChatTemplate.generate_rag_prompt(prompt, reranked_docs)
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else:
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prompt = ChatTemplate.generate_rag_prompt(prompt, reranked_docs)
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next_data["inputs"] = prompt
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elif self.services[cur_node].service_type == ServiceType.LLM and not llm_parameters_dict["stream"]:
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next_data["text"] = data["choices"][0]["message"]["content"]
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else:
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next_data = data
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return next_data
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def align_generator(self, gen, **kwargs):
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# OpenAI response format
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# 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'
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for line in gen:
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line = line.decode("utf-8")
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start = line.find("{")
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end = line.rfind("}") + 1
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json_str = line[start:end]
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try:
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# sometimes yield empty chunk, do a fallback here
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json_data = json.loads(json_str)
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if (
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json_data["choices"][0]["finish_reason"] != "eos_token"
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and "content" in json_data["choices"][0]["delta"]
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):
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yield f"data: {repr(json_data['choices'][0]['delta']['content'].encode('utf-8'))}\n\n"
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except Exception as e:
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yield f"data: {repr(json_str.encode('utf-8'))}\n\n"
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yield "data: [DONE]\n\n"
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class ChatQnAService:
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def __init__(self, host="0.0.0.0", port=8000):
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self.host = host
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self.port = port
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ServiceOrchestrator.align_inputs = align_inputs
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ServiceOrchestrator.align_outputs = align_outputs
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ServiceOrchestrator.align_generator = align_generator
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self.megaservice = ServiceOrchestrator()
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self.endpoint = str(MegaServiceEndpoint.CHAT_QNA)
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def add_remote_service(self):
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embedding = MicroService(
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name="embedding",
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host=EMBEDDING_SERVER_HOST_IP,
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port=EMBEDDING_SERVER_PORT,
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endpoint="/embed",
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use_remote_service=True,
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service_type=ServiceType.EMBEDDING,
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)
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retriever = MicroService(
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name="retriever",
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host=RETRIEVER_SERVICE_HOST_IP,
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port=RETRIEVER_SERVICE_PORT,
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endpoint="/v1/retrieval",
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use_remote_service=True,
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service_type=ServiceType.RETRIEVER,
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)
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rerank = MicroService(
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name="rerank",
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host=RERANK_SERVER_HOST_IP,
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port=RERANK_SERVER_PORT,
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endpoint="/rerank",
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use_remote_service=True,
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service_type=ServiceType.RERANK,
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)
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llm = MicroService(
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name="llm",
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host=LLM_SERVER_HOST_IP,
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port=LLM_SERVER_PORT,
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endpoint="/v1/chat/completions",
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use_remote_service=True,
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service_type=ServiceType.LLM,
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)
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self.megaservice.add(embedding).add(retriever).add(rerank).add(llm)
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self.megaservice.flow_to(embedding, retriever)
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self.megaservice.flow_to(retriever, rerank)
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self.megaservice.flow_to(rerank, llm)
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def add_remote_service_without_rerank(self):
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embedding = MicroService(
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name="embedding",
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host=EMBEDDING_SERVER_HOST_IP,
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port=EMBEDDING_SERVER_PORT,
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endpoint="/embed",
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use_remote_service=True,
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service_type=ServiceType.EMBEDDING,
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)
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retriever = MicroService(
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name="retriever",
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host=RETRIEVER_SERVICE_HOST_IP,
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port=RETRIEVER_SERVICE_PORT,
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endpoint="/v1/retrieval",
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use_remote_service=True,
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service_type=ServiceType.RETRIEVER,
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)
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llm = MicroService(
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name="llm",
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host=LLM_SERVER_HOST_IP,
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port=LLM_SERVER_PORT,
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endpoint="/v1/chat/completions",
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use_remote_service=True,
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service_type=ServiceType.LLM,
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)
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self.megaservice.add(embedding).add(retriever).add(llm)
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self.megaservice.flow_to(embedding, retriever)
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self.megaservice.flow_to(retriever, llm)
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def add_remote_service_with_guardrails(self):
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guardrail_in = MicroService(
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name="guardrail_in",
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host=GUARDRAIL_SERVICE_HOST_IP,
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port=GUARDRAIL_SERVICE_PORT,
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endpoint="/v1/guardrails",
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use_remote_service=True,
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service_type=ServiceType.GUARDRAIL,
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)
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embedding = MicroService(
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name="embedding",
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host=EMBEDDING_SERVER_HOST_IP,
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port=EMBEDDING_SERVER_PORT,
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endpoint="/embed",
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use_remote_service=True,
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service_type=ServiceType.EMBEDDING,
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)
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retriever = MicroService(
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name="retriever",
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host=RETRIEVER_SERVICE_HOST_IP,
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port=RETRIEVER_SERVICE_PORT,
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endpoint="/v1/retrieval",
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use_remote_service=True,
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service_type=ServiceType.RETRIEVER,
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)
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rerank = MicroService(
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name="rerank",
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host=RERANK_SERVER_HOST_IP,
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port=RERANK_SERVER_PORT,
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endpoint="/rerank",
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use_remote_service=True,
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service_type=ServiceType.RERANK,
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)
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llm = MicroService(
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name="llm",
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host=LLM_SERVER_HOST_IP,
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port=LLM_SERVER_PORT,
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endpoint="/v1/chat/completions",
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use_remote_service=True,
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service_type=ServiceType.LLM,
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)
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# guardrail_out = MicroService(
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# name="guardrail_out",
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# host=GUARDRAIL_SERVICE_HOST_IP,
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# port=GUARDRAIL_SERVICE_PORT,
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# endpoint="/v1/guardrails",
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# use_remote_service=True,
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# service_type=ServiceType.GUARDRAIL,
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# )
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# self.megaservice.add(guardrail_in).add(embedding).add(retriever).add(rerank).add(llm).add(guardrail_out)
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self.megaservice.add(guardrail_in).add(embedding).add(retriever).add(rerank).add(llm)
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self.megaservice.flow_to(guardrail_in, embedding)
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self.megaservice.flow_to(embedding, retriever)
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self.megaservice.flow_to(retriever, rerank)
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self.megaservice.flow_to(rerank, llm)
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# self.megaservice.flow_to(llm, guardrail_out)
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async def handle_request(self, request: Request):
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data = await request.json()
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stream_opt = data.get("stream", True)
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chat_request = ChatCompletionRequest.parse_obj(data)
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prompt = handle_message(chat_request.messages)
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parameters = LLMParams(
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max_tokens=chat_request.max_tokens if chat_request.max_tokens else 1024,
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top_k=chat_request.top_k if chat_request.top_k else 10,
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top_p=chat_request.top_p if chat_request.top_p else 0.95,
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temperature=chat_request.temperature if chat_request.temperature else 0.01,
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frequency_penalty=chat_request.frequency_penalty if chat_request.frequency_penalty else 0.0,
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presence_penalty=chat_request.presence_penalty if chat_request.presence_penalty else 0.0,
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repetition_penalty=chat_request.repetition_penalty if chat_request.repetition_penalty else 1.03,
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stream=stream_opt,
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chat_template=chat_request.chat_template if chat_request.chat_template else None,
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)
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retriever_parameters = RetrieverParms(
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search_type=chat_request.search_type if chat_request.search_type else "similarity",
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k=chat_request.k if chat_request.k else 4,
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distance_threshold=chat_request.distance_threshold if chat_request.distance_threshold else None,
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fetch_k=chat_request.fetch_k if chat_request.fetch_k else 20,
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lambda_mult=chat_request.lambda_mult if chat_request.lambda_mult else 0.5,
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score_threshold=chat_request.score_threshold if chat_request.score_threshold else 0.2,
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)
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reranker_parameters = RerankerParms(
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top_n=chat_request.top_n if chat_request.top_n else 1,
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)
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result_dict, runtime_graph = await self.megaservice.schedule(
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initial_inputs={"text": prompt},
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llm_parameters=parameters,
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retriever_parameters=retriever_parameters,
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reranker_parameters=reranker_parameters,
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)
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for node, response in result_dict.items():
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if isinstance(response, StreamingResponse):
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return response
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last_node = runtime_graph.all_leaves()[-1]
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response = result_dict[last_node]["text"]
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choices = []
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usage = UsageInfo()
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choices.append(
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ChatCompletionResponseChoice(
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index=0,
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message=ChatMessage(role="assistant", content=response),
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finish_reason="stop",
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)
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)
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return ChatCompletionResponse(model="chatqna", choices=choices, usage=usage)
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def start(self):
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self.service = MicroService(
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self.__class__.__name__,
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service_role=ServiceRoleType.MEGASERVICE,
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host=self.host,
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port=self.port,
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endpoint=self.endpoint,
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input_datatype=ChatCompletionRequest,
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output_datatype=ChatCompletionResponse,
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)
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self.service.add_route(self.endpoint, self.handle_request, methods=["POST"])
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self.service.start()
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--without-rerank", action="store_true")
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parser.add_argument("--with-guardrails", action="store_true")
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args = parser.parse_args()
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chatqna = ChatQnAService(port=MEGA_SERVICE_PORT)
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if args.without_rerank:
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chatqna.add_remote_service_without_rerank()
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elif args.with_guardrails:
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chatqna.add_remote_service_with_guardrails()
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else:
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chatqna.add_remote_service()
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chatqna.start()
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