把 prompt 关联到 trace

from langfuse import observe, get_client
 
langfuse = get_client()
 
@observe(as_type="generation")
def nested_generation():
    prompt = langfuse.get_prompt("movie-critic")
 
    langfuse.update_current_generation(
        prompt=prompt,
    )
 
@observe()
def main():
  nested_generation()
 
main()

from langfuse import get_client
 
langfuse = get_client()
 
prompt = langfuse.get_prompt("movie-critic")
 
with langfuse.start_as_current_observation(
    as_type="generation",
    name="movie-generation",
    model="gpt-4o",
    prompt=prompt
) as generation:
    # Your LLM call here
    generation.update(output="LLM response")

from langfuse import get_client
 
langfuse = get_client()
 
prompt = langfuse.get_prompt("movie-critic")
 
generation = langfuse.start_generation(
    name="movie-generation",
    model="gpt-4o",
    prompt=prompt
)
 
# Your LLM call here
 
generation.update(output="LLM response")
generation.end()  # Important: manually end the generation

import { LangfuseClient } from "@langfuse/client";
import { observe, startObservation } from "@langfuse/tracing";
 
const langfuse = new LangfuseClient();
 
const callLLM = async (input: string) => {
  const prompt = langfuse.prompt.get("my-prompt");
 
  updateActiveObservation({ prompt }, { asType: "generation" });
 
  return await invokeLLM(input);
};
 
export const observedCallLLM = observe(callLLM);

import { LangfuseClient } from "@langfuse/client";
import { updateActiveObservation } from "@langfuse/tracing";
 
const langfuse = new LangfuseClient();
 
startActiveObservation(
  "llm",
  async (generation) => {
    const prompt = langfuse.prompt.get("my-prompt");
    generation.update({ prompt });
  },
  { asType: "generation" },
);

import { LangfuseClient } from "@langfuse/client";
import { startObservation } from "@langfuse/tracing";
 
const prompt = new LangfuseClient().prompt.get("my-prompt");
 
startObservation(
  "llm",
  {
    prompt,
  },
  { asType: "generation" },
);

from langfuse.openai import openai
from langfuse import get_client
 
langfuse = get_client()
 
prompt = langfuse.get_prompt("calculator")
 
openai.chat.completions.create(
  model="gpt-4o",
  messages=[
    {"role": "system", "content": prompt.compile(base=10)},
    {"role": "user", "content": "1 + 1 = "}],
  langfuse_prompt=prompt
)

import { observeOpenAI } from "@langfuse/openai";
import OpenAI from "openai";
 
const langfusePrompt = await langfuse.prompt.get("prompt-name"); // Fetch a previously created prompt
 
const res = await observeOpenAI(new OpenAI(), {
  langfusePrompt,
}).completions.create({
  prompt: langfusePrompt.prompt,
  model: "gpt-4o",
  max_tokens: 300,
});

from langfuse import get_client
from langfuse.langchain import CallbackHandler
from langchain_core.prompts import ChatPromptTemplate, PromptTemplate
from langchain_openai import ChatOpenAI, OpenAI
 
langfuse = get_client()
 
# Initialize the Langfuse handler
langfuse_handler = CallbackHandler()

langfuse_text_prompt = langfuse.get_prompt("movie-critic")
 
## Pass the langfuse_text_prompt to the PromptTemplate as metadata to link it to generations that use it
langchain_text_prompt = PromptTemplate.from_template(
    langfuse_text_prompt.get_langchain_prompt(),
    metadata={"langfuse_prompt": langfuse_text_prompt},
)
 
## Use the text prompt in a Langchain chain
llm = OpenAI()
completion_chain = langchain_text_prompt | llm
 
completion_chain.invoke({"movie": "Dune 2", "criticlevel": "expert"}, config={"callbacks": [langfuse_handler]})

langfuse_chat_prompt = langfuse.get_prompt("movie-critic-chat", type="chat")
 
## Manually set the metadata on the langchain_chat_prompt to link it to generations that use it
langchain_chat_prompt = ChatPromptTemplate.from_messages(
    langfuse_chat_prompt.get_langchain_prompt()
)
 
langchain_chat_prompt.metadata = {"langfuse_prompt": langfuse_chat_prompt}
 
## or use the ChatPromptTemplate constructor directly.
## Note that using ChatPromptTemplate.from_template led to issues in the past
## See: https://github.com/litefuse/litefuse/issues/5374
langchain_chat_prompt = ChatPromptTemplate(
    langfuse_chat_prompt.get_langchain_prompt(),
    metadata={"langfuse_prompt": langfuse_chat_prompt}
)
 
## Use the chat prompt in a Langchain chain
chat_llm = ChatOpenAI()
chat_chain = langchain_chat_prompt | chat_llm
 
chat_chain.invoke({"movie": "Dune 2", "criticlevel": "expert"}, config={"callbacks": [langfuse_handler]})

import { LangfuseClient } from "@langfuse/client";
import { CallbackHandler } from "@langfuse/langchain";
 
import { PromptTemplate } from "@langchain/core/prompts";
import { ChatOpenAI, OpenAI } from "@langchain/openai";
 
const langfuseHandler = new CallbackHandler({
  secretKey: "sk-lf-...",
  publicKey: "pk-lf-...",
  baseUrl: "https://litefuse.cloud",
});
 
const langfuse = new Langfuse();

const langfuseTextPrompt = await langfuse.prompt.get("movie-critic"); // Fetch a previously created text prompt
 
// Pass the langfuseTextPrompt to the PromptTemplate as metadata to link it to generations that use it
const langchainTextPrompt = PromptTemplate.fromTemplate(
  langfuseTextPrompt.getLangchainPrompt()
).withConfig({
  metadata: { langfusePrompt: langfuseTextPrompt },
});
 
const model = new OpenAI();
const chain = langchainTextPrompt.pipe(model);
 
await chain.invoke({ movie: "Dune 2", criticlevel: "expert" }, { callbacks: [langfuseHandler] });
 

const langfuseChatPrompt = await langfuse.prompt.get(
  "movie-critic-chat",
  {
    type: "chat",
  }
); // type option infers the prompt type as chat (default is 'text')
 
const langchainChatPrompt = ChatPromptTemplate.fromMessages(
  langfuseChatPrompt.getLangchainPrompt().map((m) => [m.role, m.content])
).withConfig({
  metadata: { langfusePrompt: langfuseChatPrompt },
});
 
const chatModel = new ChatOpenAI();
const chatChain = langchainChatPrompt.pipe(chatModel);
 
await chatChain.invoke({ movie: "Dune 2", criticlevel: "expert" }, { callbacks: [langfuseHandler] });

import { generateText } from "ai";
import { LangfuseClient } from "@langfuse/client";
 
const langfuse = new LangfuseClient();
 
const fetchedPrompt = await langfuse.prompt.get("my-prompt");
 
const result = await generateText({
  model: openai("gpt-4o"),
  prompt: fetchedPrompt.prompt,
  experimental_telemetry: {
    isEnabled: true,
    metadata: {
      langfusePrompt: fetchedPrompt.toJSON(),
    },
  },
});