Integrations Overview

import Anthropic from '@anthropic-ai/sdk';
import Raily from '@raily/sdk';

const anthropic = new Anthropic();
const raily = new Raily({ apiKey: process.env.RAILY_API_KEY });

async function askClaudeWithContent(question, contentId) {
  const access = await raily.access.check({
    contentId,
    requesterId: "my_claude_app",
    context: { purpose: "inference" }
  });

  if (!access.allowed) {
    return `Unable to access content: ${access.reason}`;
  }

  const contentResponse = await fetch(access.contentUrl);
  const content = await contentResponse.text();

  const message = await anthropic.messages.create({
    model: "claude-3-opus-20240229",
    max_tokens: 1024,
    messages: [
      {
        role: "user",
        content: `Based on this content:\n\n${content}\n\nAnswer: ${question}`
      }
    ]
  });

  return message.content[0].text;
}

from langchain.llms import OpenAI
from langchain.chains import RetrievalQA
from raily import Raily

raily = Raily(api_key=os.environ["RAILY_API_KEY"])

class RailyRetriever:
    def __init__(self, content_ids):
        self.content_ids = content_ids

    def get_relevant_documents(self, query):
        documents = []
        for content_id in self.content_ids:
            access = raily.access.check(
                content_id=content_id,
                requester_id="langchain_app",
                context={"purpose": "rag"}
            )
            if access.allowed:
                content = requests.get(access.content_url).text
                documents.append(Document(page_content=content))
        return documents

# Use in a chain
retriever = RailyRetriever(["cnt_abc", "cnt_def"])
qa = RetrievalQA.from_chain_type(
    llm=OpenAI(),
    retriever=retriever
)