- Python
- TypeScript
Custom Retriever
You can wrap the Larkup Python SDK into a custom retriever, allowing you to use it directly in your LangChain chains and agents.from typing import List
from langchain_core.retrievers import BaseRetriever
from langchain_core.documents import Document
from pydantic import Field
from larkup import LarkupClient, LarkupClientOptions
class LarkupRetriever(BaseRetriever):
client: LarkupClient = Field(
default_factory=lambda: LarkupClient(
LarkupClientOptions(base_url="http://localhost:8080", api_key="key")
)
)
def _get_relevant_documents(self, query: str, *, run_manager=None) -> List[Document]:
results = self.client.query(query, top_k=5)
# Convert hits to LangChain Documents
return [
Document(page_content=hit.text, metadata={"score": hit.score})
for hit in results.hits
]
# Usage:
# retriever = LarkupRetriever()
# docs = retriever.invoke("What is Larkup?")
OpenAI Compatible API
If you are running the generated RAG server, you can connect directly using LangChain’s OpenAI integration:from langchain_openai import ChatOpenAI
llm = ChatOpenAI(
openai_api_base="http://localhost:8080/v1",
openai_api_key="not-needed-for-local",
model_name="rag-model"
)
response = llm.invoke("What is Larkup?")
print(response.content)
Custom Retriever
You can wrap the Larkup TypeScript SDK into a custom retriever for LangChain.js to use within your chains or agents.import { BaseRetriever } from "@langchain/core/retrievers";
import { Document } from "@langchain/core/documents";
import { LarkupClient } from "@larkup/sdk";
export class LarkupRetriever extends BaseRetriever {
lc_namespace = ["langchain", "retrievers"];
client: LarkupClient;
constructor() {
super();
this.client = new LarkupClient();
}
async _getRelevantDocuments(query: string): Promise<Document[]> {
const results = await this.client.query(query, 5);
// Convert hits to LangChain Documents
return results.hits.map(
(hit) => new Document({ pageContent: hit.text, metadata: { score: hit.score } })
);
}
}
// Usage:
// const retriever = new LarkupRetriever();
// const docs = await retriever.invoke("What is Larkup?");
OpenAI Compatible API
If you are running the generated RAG server, you can connect directly using LangChain.js’s OpenAI integration:import { ChatOpenAI } from "@langchain/openai";
const llm = new ChatOpenAI({
configuration: {
baseURL: "http://localhost:8080/v1",
},
openAIApiKey: "not-needed-for-local",
modelName: "rag-model"
});
const response = await llm.invoke("What is Larkup?");
console.log(response.content);