LangChain

import os
from honeyhive import HoneyHiveTracer
from openinference.instrumentation.langchain import LangChainInstrumentor
from langchain.agents import create_agent
from langchain_core.tools import tool

tracer = HoneyHiveTracer.init(
    api_key=os.environ["HH_API_KEY"],
    project=os.environ["HH_PROJECT"],
)
LangChainInstrumentor().instrument(tracer_provider=tracer.provider)

@tool
def calculator(expression: str) -> str:
    """Evaluate a basic arithmetic expression."""
    return str(eval(expression, {"__builtins__": {}}, {}))

@tool
def policy_lookup(topic: str) -> str:
    """Look up company policy on a topic."""
    policies = {
        "soc2": "SOC 2 covers security, availability, processing integrity, confidentiality, and privacy.",
        "retention": "Default retention is 30 days unless compliance requires longer.",
    }
    return policies.get(topic.lower(), "No policy found.")

agent = create_agent(
    model="openai:gpt-4o-mini",
    tools=[calculator, policy_lookup],
    system_prompt="You are a support assistant. Use tools when needed.",
)

result = agent.invoke(
    {"messages": [{"role": "user", "content": "What is 17 * 3 + 5? Also summarize our SOC2 policy."}]}
)
print(result["messages"][-1].content)

import * as fs from 'fs';
import { OpenAI } from "@langchain/openai";
import { TextLoader } from 'langchain/document_loaders/fs/text';
import { RecursiveCharacterTextSplitter } from 'langchain/text_splitter';
import { OpenAIEmbeddings } from "@langchain/openai";
import { FaissStore } from "@langchain/community/vectorstores/faiss";
import { RetrievalQAChain } from 'langchain/chains';
import { HoneyHiveLangChainTracer } from 'honeyhive';

async function runQA(): Promise<void> {
  const tracer = new HoneyHiveLangChainTracer({
    project: process.env.HH_PROJECT,
    sessionName: process.env.HH_SESSION_NAME,
    apiKey: process.env.HH_API_KEY,
  });

  const tracerConfig = {
    callbacks: [tracer],
  };

  // Load the document with tracing
  const loader = new TextLoader('state_of_the_union.txt', tracerConfig);
  const documents = await loader.load();

  // Split the document into chunks with tracing
  const textSplitter = new RecursiveCharacterTextSplitter({
    chunkSize: 1000,
    chunkOverlap: 200,
    ...tracerConfig,
  });
  const docs = await textSplitter.splitDocuments(documents);

  // Create embeddings with tracing
  const embeddings = new OpenAIEmbeddings(tracerConfig);

  // Create a FAISS vector store from the documents with tracing
  const vectorStore = await FaissStore.fromDocuments(docs, embeddings, tracerConfig);

  // Create a retriever interface with tracing
  const retriever = vectorStore.asRetriever(tracerConfig);

  // Initialize the OpenAI LLM with tracing
  const llm = new OpenAI({
    temperature: 0,
    ...tracerConfig,
  });

  // Create a RetrievalQA chain with tracing
  const qaChain = RetrievalQAChain.fromLLM(llm, retriever, tracerConfig);

  // Ask a question
  const query = "What did the president say about Ketanji Brown Jackson?";
  const res = await qaChain.call({ query, ...tracerConfig });

  console.log(res.text);
}

runQA().catch(console.error);