OpenInference LangChain Instrumentation
Project description
OpenInference LangChain Instrumentation
Python auto-instrumentation library for LangChain.
These traces are fully OpenTelemetry compatible and can be sent to an OpenTelemetry collector for viewing, such as arize-phoenix
.
Installation
pip install openinference-instrumentation-langchain
Quickstart
Install packages needed for this demonstration.
pip install openinference-instrumentation-langchain langchain arize-phoenix opentelemetry-sdk opentelemetry-exporter-otlp
Start the Phoenix app in the background as a collector. By default, it listens on http://localhost:6006
. You can visit the app via a browser at the same address.
The Phoenix app does not send data over the internet. It only operates locally on your machine.
python -m phoenix.server.main serve
The following Python code sets up the LangChainInstrumentor
to trace langchain
and send the traces to Phoenix at the endpoint shown below.
from langchain.chains import LLMChain
from langchain_core.prompts import PromptTemplate
from langchain_openai import OpenAI
from openinference.instrumentation.langchain import LangChainInstrumentor
from opentelemetry import trace as trace_api
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk import trace as trace_sdk
from opentelemetry.sdk.trace.export import ConsoleSpanExporter, SimpleSpanProcessor
endpoint = "http://127.0.0.1:6006/v1/traces"
tracer_provider = trace_sdk.TracerProvider()
trace_api.set_tracer_provider(tracer_provider)
tracer_provider.add_span_processor(SimpleSpanProcessor(OTLPSpanExporter(endpoint)))
tracer_provider.add_span_processor(SimpleSpanProcessor(ConsoleSpanExporter()))
LangChainInstrumentor().instrument()
To demonstrate langchain
tracing, we'll make a simple chain to tell a joke. First, configure your OpenAI credentials.
import os
os.environ["OPENAI_API_KEY"] = "<your openai key>"
Now we can create a chain and run it.
prompt_template = "Tell me a {adjective} joke"
prompt = PromptTemplate(input_variables=["adjective"], template=prompt_template)
llm = LLMChain(llm=OpenAI(), prompt=prompt, metadata={"category": "jokes"})
completion = llm.predict(adjective="funny", metadata={"variant": "funny"})
print(completion)
Visit the Phoenix app at http://localhost:6006
to see the traces.
More Info
More details about tracing with OpenInference and Phoenix can be found in the Phoenix documentation.
For AI/ML observability solutions in production, including a cloud-based trace collector, visit Arize.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for openinference_instrumentation_langchain-0.1.23.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | f856676814ef1dbe4b476102972648252ec396c8e494deb5442e522ccc6075ff |
|
MD5 | 5dab8ee37d7d237263af712109c17af7 |
|
BLAKE2b-256 | 0593bbc2e3423ca56b8ab41fc64a08720d44bff7f362cdc933539b5e20665c74 |
Hashes for openinference_instrumentation_langchain-0.1.23-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 59412d8120f01d633dd816db6f1562358f1431368222b0fde210f01ea5013914 |
|
MD5 | 49d8a41e1686a57f8547f5531d447cbd |
|
BLAKE2b-256 | cbe2213a0ef06c2437010fad9220fab3c0fbcbfe8cb51938a40a8a9fe983c147 |