Log10

This page covers how to use the Log10 within LangChain.

What is Log10?

Log10 is an open-source proxiless LLM data management and application development platform that lets you log, debug and tag your Langchain calls.

Quick start

1. Create your free account at log10.io
2. Add your LOG10_TOKEN and LOG10_ORG_ID from the Settings and Organization tabs respectively as environment variables.
3. Also add LOG10_URL=https://log10.io and your usual LLM API key: for e.g. OPENAI_API_KEY or ANTHROPIC_API_KEY to your environment

How to enable Log10 data management for Langchain

Integration with log10 is a simple one-line log10_callback integration as shown below:

from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage

from log10.langchain import Log10Callback
from log10.llm import Log10Config

log10_callback = Log10Callback(log10_config=Log10Config())

messages = [
    HumanMessage(content="You are a ping pong machine"),
    HumanMessage(content="Ping?"),
]

llm = ChatOpenAI(model="gpt-3.5-turbo", callbacks=[log10_callback])

API Reference:ChatOpenAI | HumanMessage

Log10 + Langchain + Logs docs

More details + screenshots including instructions for self-hosting logs

How to use tags with Log10

from langchain_openai import OpenAI
from langchain_community.chat_models import ChatAnthropic
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage

from log10.langchain import Log10Callback
from log10.llm import Log10Config

log10_callback = Log10Callback(log10_config=Log10Config())

messages = [
    HumanMessage(content="You are a ping pong machine"),
    HumanMessage(content="Ping?"),
]

llm = ChatOpenAI(model="gpt-3.5-turbo", callbacks=[log10_callback], temperature=0.5, tags=["test"])
completion = llm.predict_messages(messages, tags=["foobar"])
print(completion)

llm = ChatAnthropic(model="claude-2", callbacks=[log10_callback], temperature=0.7, tags=["baz"])
llm.predict_messages(messages)
print(completion)

llm = OpenAI(model_name="gpt-3.5-turbo-instruct", callbacks=[log10_callback], temperature=0.5)
completion = llm.predict("You are a ping pong machine.\nPing?\n")
print(completion)

API Reference:OpenAI | ChatAnthropic | ChatOpenAI | HumanMessage

You can also intermix direct OpenAI calls and Langchain LLM calls:

import os
from log10.load import log10, log10_session
import openai
from langchain_openai import OpenAI

log10(openai)

with log10_session(tags=["foo", "bar"]):
    # Log a direct OpenAI call
    response = openai.Completion.create(
        model="text-ada-001",
        prompt="Where is the Eiffel Tower?",
        temperature=0,
        max_tokens=1024,
        top_p=1,
        frequency_penalty=0,
        presence_penalty=0,
    )
    print(response)

    # Log a call via Langchain
    llm = OpenAI(model_name="text-ada-001", temperature=0.5)
    response = llm.predict("You are a ping pong machine.\nPing?\n")
    print(response)

API Reference:OpenAI

How to debug Langchain calls

Example of debugging

More Langchain examples