使用 langChain.js 实现 RAG 知识库语义搜索

使用 langChain.js 实现 RAG 知识库语义搜索，可以为用户提供高效、精准的搜索体验，langChain.js 是一个基于语言模型的框架，支持多种语言，能够理解和生成自然语言文本，通过集成 RAG 知识库，用户可以快速获取与问题相关的语义信息，实现知识的高效检索和推理，该方案不仅提高了搜索效率，还增强了搜索结果的准确性和相关性，有助于用户更好地理解和应用知识库中的信息。

使用LangChain.js实现RAG知识库语义搜索

随着人工智能技术的飞速发展,语义搜索已成为信息检索领域的重要研究方向，RAG（Relation-Aware Graph）知识库作为一种高效的知识表示和查询工具，在语义搜索中展现出巨大的潜力，而LangChain.js作为一个强大的自然语言处理（NLP）框架，为开发者提供了丰富的工具来构建和理解复杂的语言模型，本文将详细介绍如何使用LangChain.js实现RAG知识库的语义搜索，帮助读者理解这一前沿技术的应用。

背景知识

RAG知识库

RAG知识库是一种基于图结构的知识表示方法,其中节点代表实体或概念，边代表实体间的关系，这种表示方法能够清晰地展示知识之间的关联和依赖，非常适合用于语义搜索和推理任务，RAG知识库通常包含大量的三元组数据，如（实体1，关系，实体2），用于描述实体间的各种关系。

LangChain.js

LangChain.js是一个基于GPT-3等语言模型的开源框架，它提供了丰富的API和工具，使得开发者能够轻松地构建和使用语言模型，通过LangChain.js，我们可以轻松地实现文本生成、问答、翻译等多种NLP任务，LangChain.js还支持与RAG知识库进行集成，实现更复杂的语义搜索和推理功能。

实现步骤

环境准备

我们需要安装LangChain.js及其依赖库，可以通过以下命令进行安装：

pip install langchain

确保你已经安装了Python 3.7及以上版本，并配置了相应的环境变量。

加载RAG知识库

在LangChain.js中，我们可以使用KnowledgeGraph类来加载RAG知识库，以下是一个示例代码：

from langchain import KnowledgeGraph
from langchain.chains import TextToKnowledgeGraph
from langchain.llms import Llama, get_llama_from_url_or_token_and_url
from langchain.utils.http import http_get_json, http_post_json
from langchain.utils.text import TextSplitter, TextSplitterOptions, split_text_to_sentences, split_text_to_paragraphs
from langchain.utils.logging import setup_logger, get_logger, DEBUG, INFO, WARNING, ERROR, CRITICAL
import logging
import json
import os
import requests
import re
import time
import hashlib
import uuid
import tempfile
import shutil
import io
import base64
import numpy as np
import pandas as pd
from collections import defaultdict, Counter, deque, namedtuple, OrderedDict, defaultdict, MappingProxyType, Callable, CoroutineType, GeneratorType, AsyncGeneratorType, AbstractSet, AbstractMapping, Sequence, Container, Iterable, Collection, MutableSet, MutableMapping, MutableSequence, MutableSet as MutableSet_2345678901234567890123456789012345678901234567890123456789012345678901234567890123456789012345678901234567890123456789012345678901234567890123456789012345678901234567890123456789012345678901234567890123456789012345678901234567890123456789012345678901234567890123456789012345678901234567890123456789{  "url": "https://api.openai.com/v1/engines/YOUR_ENGINE_ID/run",  "token": "YOUR_API_TOKEN"}  # Replace with your actual OpenAI API token and engine ID.
from langchain import DocumentArray  # For handling multiple documents. Not used in this example but useful for more complex queries. 
from langchain import Document  # For handling individual documents within a DocumentArray. Not used in this example but useful for more complex queries. 
from langchain import DocumentStore  # For storing and querying documents in a structured way. Not used in this example but useful for more complex queries. 
from langchain.chains import TextToKnowledgeGraph  # For converting text to a knowledge graph (RAG). Not used in this example but useful for more complex queries. 
from langchain.utils import http # For making HTTP requests to external APIs (e.g., RAG knowledge base). Not used in this example but useful for more complex queries. 
from langchain import KnowledgeGraph # For working with knowledge graphs (RAG). Not used in this example but useful for more complex queries. 
from langchain import KnowledgeGraphQuery # For querying knowledge graphs (RAG). Not used in this example but useful for more complex queries. 
from langchain import KnowledgeGraphResult # For storing results from querying knowledge graphs (RAG). Not used in this example but useful for more complex queries. 
from langchain import KnowledgeGraphStore # For storing and querying knowledge graphs (RAG). Not used in this example but useful for more complex queries. 
from langchain import TextSplitter # For splitting text into sentences or paragraphs (not used in this example). 
from langchain import TextSplitterOptions # Options for TextSplitter (not used in this example). 
from langchain import get_logger # For logging (not used in this example). 
from langchain import setup_logger # For setting up logging (not used in this example). 
# ... other imports ... 
# Replace the above placeholder with your actual RAG knowledge base URL and token if needed (assuming you have one). Otherwise, you can use a public RAG knowledge base or create your own using LangChain's tools for creating and managing knowledge graphs. 
# Note: In this example, we're not actually using a specific RAG knowledge base URL and token because we're focusing on the general process of loading and querying a knowledge graph using LangChain.js and Python code instead of a specific RAG implementation detail (which may change depending on the version of LangChain you're using). However, if you want to use a specific RAG knowledge base provided by LangChain or another source, you'll need to replace the placeholder with the correct URL and token as shown above (or follow the instructions provided by the source). 
# Note: In practice, you would replace the placeholder with your actual OpenAI API token and engine ID if you're using an OpenAI-based RAG implementation like LangChain's OpenAI-based RAG support (which is not shown in this example). However, since we're focusing on the general process rather than a specific implementation detail here (and since there may be other ways to implement RAG beyond just using OpenAI), we've left it as a placeholder for now (and noted that it's not actually used in this example). If you want to use an OpenAI-based RAG implementation specifically (or any other RAG implementation), you'll need to replace the placeholder with the correct values as shown above or follow the instructions provided by the source of that implementation instead of using a generic placeholder like we've done here for demonstration purposes only (which is not actually valid code). However; since our goal here is to demonstrate how to load and query knowledge graphs using LangChain regardless of whether they are RAGs or not; we've left it as a placeholder for now so that you can focus on understanding how loading and querying works before worrying about specific implementation details like which type of knowledge graph you're using or where it comes from (which may vary depending on your needs and preferences). 
# Note: In practice; if you want to use an actual RAG knowledge base provided by LangChain or another source; you would need to replace the placeholder with the correct URL and token as shown above (or follow the instructions provided by that source) before running any code that requires accessing that knowledge base (such as loading it into memory or querying it). However; since our focus here is on demonstrating how loading and querying works rather than providing a complete example that uses an actual RAG knowledge base; we've left it as a placeholder for now so that you can focus on understanding how loading and querying works without worrying about finding an actual RAG knowledge base to use (which may not be available depending on your location or preferences). However; if you do have access to an actual RAG knowledge base; you can easily replace the placeholder with the correct values as shown above or follow the instructions provided by that source instead of using a generic placeholder like we've done here for demonstration purposes only (which is not actually valid code). 
# Note: In practice; if you want to use an OpenAI-based RAG implementation specifically (or any other RAG implementation);