Upgrade Paths: Langchain Integration
This doc is a collection of upgrade paths for different versions of the integration. If you want to add the integration to your project, you should start with the latest version and follow the integration guide.
Lanfuse and Langchain are under active development. Thus, we are constantly improving the intgration. This means that we sometimes need to make breaking changes to our APIs or need to react to breaking changes in Langchain. We try to keep these to a minimum and to provide clear upgrade paths when we do make them.
Python
JS/TS
Python
From v1.x.x to v2.x.x
The CallbackHandler can be used in multiple invocations of a Langchain chain as shown below.
from langfuse.callback import CallbackHandler
langfuse_handler = CallbackHandler(PUBLIC_KEY, SECRET_KEY)
# Setup Langchain
from langchain.chains import LLMChain
...
chain = LLMChain(llm=llm, prompt=prompt, callbacks=[langfuse_handler])
# Add Langfuse handler as callback
chain.run(input="<first_user_input>", callbacks=[langfuse_handler])
chain.run(input="<second_user_input>", callbacks=[langfuse_handler])
So far, invoking the chain multiple times would group the observations in one trace.
TRACE
|
|-- SPAN: Retrieval
| |
| |-- SPAN: LLM Chain
| | |
| | |-- GENERATION: ChatOpenAi
|-- SPAN: Retrieval
| |
| |-- SPAN: LLM Chain
| | |
| | |-- GENERATION: ChatOpenAiWe changed this, so that each invocation will end up on its own trace. This allows us to derive the user inputs and outputs to Langchain applications.
TRACE_1
|
|-- SPAN: Retrieval
| |
| |-- SPAN: LLM Chain
| | |
| | |-- GENERATION: ChatOpenAi
TRACE_2
|
|-- SPAN: Retrieval
| |
| |-- SPAN: LLM Chain
| | |
| | |-- GENERATION: ChatOpenAiIf you still want to group multiple invocations on one trace, you can use the Langfuse SDK combined with the Langchain integration (more details).
from langfuse import Langfuse
langfuse = Langfuse()
# Get Langchain handler for a trace
trace = langfuse.trace()
langfuse_handler = trace.get_langchain_handler()
# langfuse_handler will use the trace for all invocationsJS/TS
From v2.x.x to v3.x.x
Requires langchain ^0.1.10 (opens in a new tab). Langchain released a new stable version of the Callback Handler interface and this version of the Langfuse SDK implements it. Older versions are no longer supported.
From v1.x.x to v2.x.x
The CallbackHandler can be used in multiple invocations of a Langchain chain as shown below.
import { CallbackHandler } from "langfuse-langchain";
// create a handler
const langfuseHandler = new CallbackHandler({
publicKey: LANGFUSE_PUBLIC_KEY,
secretKey: LANGFUSE_SECRET_KEY,
});
import { LLMChain } from "langchain/chains";
// create a chain
const chain = new LLMChain({
llm: model,
prompt,
callbacks: [langfuseHandler],
});
// execute the chain
await chain.call(
{ product: "<user_input_one>" },
{ callbacks: [langfuseHandler] }
);
await chain.call(
{ product: "<user_input_two>" },
{ callbacks: [langfuseHandler] }
);So far, invoking the chain multiple times would group the observations in one trace.
TRACE
|
|-- SPAN: Retrieval
| |
| |-- SPAN: LLM Chain
| | |
| | |-- GENERATION: ChatOpenAi
|-- SPAN: Retrieval
| |
| |-- SPAN: LLM Chain
| | |
| | |-- GENERATION: ChatOpenAiWe changed this, so that each invocation will end up on its own trace. This is a more sensible default setting for most users.
TRACE_1
|
|-- SPAN: Retrieval
| |
| |-- SPAN: LLM Chain
| | |
| | |-- GENERATION: ChatOpenAi
TRACE_2
|
|-- SPAN: Retrieval
| |
| |-- SPAN: LLM Chain
| | |
| | |-- GENERATION: ChatOpenAiIf you still want to group multiple invocations on one trace, you can use the Langfuse SDK combined with the Langchain integration (more details).
const trace = langfuse.trace({ id: "special-id" });
// CallbackHandler will use the trace with the id "special-id" for all invocations
const langfuseHandler = new CallbackHandler({ root: trace });