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ollama-test.js
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| import "cheerio"; | |
| import { CheerioWebBaseLoader } from "@langchain/community/document_loaders/web/cheerio"; | |
| import { RecursiveCharacterTextSplitter } from "langchain/text_splitter"; | |
| import { MemoryVectorStore } from "langchain/vectorstores/memory"; | |
| import { OpenAIEmbeddings, ChatOpenAI } from "@langchain/openai"; | |
| import { pull } from "langchain/hub"; | |
| import { ChatPromptTemplate } from "@langchain/core/prompts"; | |
| import { StringOutputParser } from "@langchain/core/output_parsers"; | |
| import { Ollama, ChatOllama } from "@langchain/ollama" ; | |
| import { createStuffDocumentsChain } from "langchain/chains/combine_documents"; | |
| (async function (){ | |
| const loader = new CheerioWebBaseLoader( | |
| "https://lilianweng.github.io/posts/2023-06-23-agent/" | |
| ); | |
| const docs = await loader.load(); | |
| const textSplitter = new RecursiveCharacterTextSplitter({ | |
| chunkSize: 1000, | |
| chunkOverlap: 200, | |
| }); | |
| const splits = await textSplitter.splitDocuments(docs); | |
| const vectorStore = await MemoryVectorStore.fromDocuments( | |
| splits, | |
| new OpenAIEmbeddings() | |
| ); | |
| // Retrieve and generate using the relevant snippets of the blog. | |
| const retriever = vectorStore.asRetriever(); | |
| const prompt = await pull<ChatPromptTemplate>("rlm/rag-prompt"); | |
| const llm2 = new ChatOpenAI({ model: "gpt-3.5-turbo", temperature: 0 }); | |
| const llm = new Ollama({ | |
| model: "llama3.1", // Default value | |
| temperature: 0, | |
| maxRetries: 2, | |
| baseUrl: 'http://localhost:11434', | |
| format: "json", | |
| }); | |
| const ragChain = await createStuffDocumentsChain({ | |
| llm, | |
| prompt, | |
| outputParser: new StringOutputParser(), | |
| }); | |
| const retrievedDocs = await retriever.invoke("what is task decomposition"); | |
| console.log('test') | |
| console.log(prompt.promptMessages.map((msg) => msg.prompt.template).join("\n")); | |
| const result = await ragChain.invoke({ | |
| question: "What is task decomposition?", | |
| context: retrievedDocs, | |
| }); | |
| console.log(result); | |
| }).call(this); |
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| This is a code identification task and classify if the content contains Semantic or Syntactical references | |
| for Telephone Number. Telephone number can be represented in multiple ways and the examples for Java methods are | |
| provided in the Example section. For output, reference the Response section with JSON schema. Dont infer anything else. | |
| If you find any class or method signature which might contain telephone number, report there is a match found, in the example below, `Contact` is a class defined and instantiated without accessing the phone number field, but report match found, it is possible the intention might be to use the phone number. | |
| Example: | |
| 1. Java class setting Contact information | |
| public class Contact { | |
| private String addressLine1; | |
| private String addressLine2; | |
| private String homePhone; | |
| private String mobile; | |
| private String cellular; | |
| } | |
| 2. void contact_num(String contact); | |
| 3. void setHome_ph(String input); | |
| 4. this.contact_num = inputString; | |
| 5. String getMobile(); | |
| 6. void formatInput(String mobileNum); | |
| 7. void formatInput(String telNum); | |
| Response: | |
| {"fieldsDetected": [], "matchFound": boolean, "reason": String} |
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