Graph rag (#1007)
Signed-off-by: Rita Brugarolas <rita.brugarolas.brufau@intel.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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rbrugaro
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GraphRAG/graphrag.py
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GraphRAG/graphrag.py
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# 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 GraphragGateway, MicroService, ServiceOrchestrator, ServiceType
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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 combine the following intermediate answers into a final, conscise and coherent response. \
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refer to the search results obtained from the local knowledge base. \
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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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### Intermediate answers: {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_HOST_IP = os.getenv("MEGA_SERVICE_HOST_IP", "0.0.0.0")
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MEGA_SERVICE_PORT = int(os.getenv("MEGA_SERVICE_PORT", 8888))
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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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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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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.RETRIEVER:
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print("make no changes for retriever inputs. AlreadyCheckCompletionRequest")
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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"] = "tgi" # specifically clarify the fake model to make the format unified
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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["streaming"]
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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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print("inputs after align:\n", 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.RETRIEVER:
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docs = [doc["text"] for doc in data["retrieved_docs"]]
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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.messages[0]["content"]
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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(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, docs)
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else:
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print("no rerank no chat template")
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prompt = ChatTemplate.generate_rag_prompt(prompt, docs)
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next_data["inputs"] = prompt
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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 reaponse 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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print("generator in align generator:\n", gen)
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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 json_data["choices"][0]["finish_reason"] != "eos_token":
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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 GraphRAGService:
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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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def add_remote_service(self):
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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(retriever).add(llm)
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self.megaservice.flow_to(retriever, llm)
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self.gateway = GraphragGateway(megaservice=self.megaservice, host="0.0.0.0", port=self.port)
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if __name__ == "__main__":
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graphrag = GraphRAGService(host=MEGA_SERVICE_HOST_IP, port=MEGA_SERVICE_PORT)
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graphrag.add_remote_service()
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