imbajin commented on code in PR #77: URL: https://github.com/apache/incubator-hugegraph-ai/pull/77#discussion_r1751251994
########## hugegraph-llm/src/hugegraph_llm/config/config_data.py: ########## @@ -0,0 +1,176 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License.\ + + +import os +from dataclasses import dataclass +from typing import Literal, Optional + + +@dataclass +class ConfigData: + """LLM settings""" + + # env_path: Optional[str] = ".env" + llm_type: Literal["openai", "ollama", "qianfan_wenxin", "zhipu"] = "openai" + embedding_type: Optional[Literal["openai", "ollama", "qianfan_wenxin", "zhipu"]] = "openai" + reranker_type: Optional[Literal["cohere", "siliconflow"]] = None + # 1. OpenAI settings + openai_api_base: Optional[str] = os.environ.get("OPENAI_BASE_URL", "https://api.openai.com/v1";) + openai_api_key: Optional[str] = os.environ.get("OPENAI_API_KEY") + openai_language_model: Optional[str] = "gpt-4o-mini" + openai_embedding_model: Optional[str] = "text-embedding-3-small" + openai_max_tokens: int = 4096 + # 2. Rerank settings + cohere_base_url: Optional[str] = os.environ.get("CO_API_URL", "https://api.cohere.com/v1/rerank";) + reranker_api_key: Optional[str] = None + reranker_model: Optional[str] = None + # 3. Ollama settings + ollama_host: Optional[str] = "127.0.0.1" + ollama_port: Optional[int] = 11434 + ollama_language_model: Optional[str] = None + ollama_embedding_model: Optional[str] = None + # 4. QianFan/WenXin settings + qianfan_api_key: Optional[str] = None + qianfan_secret_key: Optional[str] = None + qianfan_access_token: Optional[str] = None + # 4.1 URL settings + qianfan_url_prefix: Optional[str] = "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop"; + qianfan_chat_url: Optional[str] = qianfan_url_prefix + "/chat/" + qianfan_language_model: Optional[str] = "ERNIE-4.0-Turbo-8K" + qianfan_embed_url: Optional[str] = qianfan_url_prefix + "/embeddings/" + # refer https://cloud.baidu.com/doc/WENXINWORKSHOP/s/alj562vvu to get more details + qianfan_embedding_model: Optional[str] = "embedding-v1" + # TODO: To be confirmed, whether to configure + # 5. ZhiPu(GLM) settings + zhipu_api_key: Optional[str] = None + zhipu_language_model: Optional[str] = "glm-4" + zhipu_embedding_model: Optional[str] = "embedding-2" + + """HugeGraph settings""" + graph_ip: Optional[str] = "127.0.0.1" + graph_port: Optional[str] = "8080" + graph_name: Optional[str] = "hugegraph" + graph_user: Optional[str] = "admin" + graph_pwd: Optional[str] = "xxx" + graph_space: Optional[str] = None + + +# Additional static content like PromptConfig +class PromptData: + + custom_related_information = """just for test +hello!""" + + question = """Tell me about Sarah.""" + + # Data is detached from hugegraph-llm/src/hugegraph_llm/operators/llm_op/property_graph_extract.py + schema_example_prompt = """## Main Task +Given the following graph schema and a piece of text, your task is to analyze the text and extract information that fits into the schema's structure, formatting the information into vertices and edges as specified. +## Basic Rules +### Schema Format +Graph Schema: +- Vertices: [List of vertex labels and their properties] +- Edges: [List of edge labels, their source and target vertex labels, and properties] +### Content Rule +Please read the provided text carefully and identify any information that corresponds to the vertices and edges defined in the schema. For each piece of information that matches a vertex or edge, format it according to the following JSON structures: +#### Vertex Format: +{"id":"vertexLabelID:entityName","label":"vertexLabel","type":"vertex","properties":{"propertyName":"propertyValue", ...}} +#### Edge Format: +{"label":"edgeLabel","type":"edge","outV":"sourceVertexId","outVLabel":"sourceVertexLabel","inV":"targetVertexId","inVLabel":"targetVertexLabel","properties":{"propertyName":"propertyValue",...}} +Also follow the rules: +1. Don't extract property fields that do not exist in the given schema +2. Ensure the extracted property is in the same type as the schema (like 'age' should be a number) +3. If there are multiple primary keys, the strategy for generating VID is: vertexlabelID:pk1!pk2!pk3 (pk means primary key, and '!' is the separator) Review Comment: read the config/str from `config_prompt.yaml` directly? (seems no need to store them again) -- This is an automated message from the Apache Git Service. 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