这里再补充下,在 Llama Index 中还有额外的 generate_query 的方法,它主要实现返回 Cypher 而不做查询的功能,这样你就能获得对应的查询语句,而不是查询结果。 RAG 搜索增强 前文也有做简单的 RAG 介绍,这里再补充下额外的点。 Background,RAG Paradigm 一般来说,我们使用 RAG 时,会对文档进行 Embedding(对应上...
在LangChain 中引入 NebulaGraph,再连接上你的 NebulaGraph 服务,实例化 NebulaGraphQAChain,再借助一行chain.run()函数,就能实现你的需求。类似的,在 Llama Index 有相同的代码实现: ## Llama Index# Doc: https://gpt-index.readthedocs.io/en/latest/examples/query_engine/knowledge_graph_query_engine.htmlfr...
Your task is to identify the entities and relations requested with the user prompt, from a given product specification. You must generate the output in a JSON containing a list with JOSN objects having the following keys: "head", "head_type", "relation", "tail", and "tail_type". The "...
kg_index=KnowledgeGraphIndex.from_documents(documents,storage_context=storage_context,max_triplets_per_chunk=10,service_context=service_context,space_name=space_name,edge_types=edge_types,rel_prop_names=rel_prop_names,tags=tags,include_Embeddings=True,) 抽取完之后的数据,可以进行图谱可视化展示或者是用...
作者还为LLM设计了两种类型的prompt范式:第一种是为了获取与问题最相关的数据信息,"Here are [Y]. Which [X] are most relevant to answer the question [Q]";第二种是为了得到目标结果,"Based on [Y], please generate [Z] for the question [Q]"。 以下是对于三种推理任务在框架中的运行示例: 补充一...
其实,不只有 Knowledge Graph 这一个图的应用场景。 简单来说,假如你有海量的图关联场景,你用非图的数据库写查询语句(像是上图 SQL 部分)。虽然理论上 SQL 是可以实现多跳的查询,或是查询是两点之间任意的路径,但往往这个查询语言不好写,并且响应速度满足不了业务需求。简单来说,非常痛苦。
retriever.knowledge_graph_retriever import generate_graph_info load_dotenv() app = Flask(__name__) @app.route('/') def index(): return render_template('index.html') @app.route('/update_graph', methods=['POST']) def update_graph(): raw_text = request.json.get('text', '') try:...
Instead of training the LLM, you can use the LLM to generate the queries to get the answers directly from your Content Knowledge Graph. This approach of generating answers through the LLM is less complicated, less expensive and more scalable. All you need is a Content Knowledge Graph and a ...
Example of Prompt Used to Generate Graph Here is an example of the prompt template, with place holders, used to generate related entities from a given source entity. This is applied recursively to create a knowledge graph, merging duplicated nodes as required. ...
2. Using LLMs to Convert Unstructured Data to Knowledge Graphs Noah demonstrates how LLMs can be used for entity extraction, semantic relationship recognition, and context inference to generate interconnected knowledge graphs. This is a great inspiration to harness LLMs for your uses of unstructured...