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- Description: Instruction for integration with Log10: an [open source](https://github.com/log10-io/log10) proxiless LLM data management and application development platform that lets you log, debug and tag your Langchain calls - Tag maintainer: @baskaryan - Twitter handle: @log10io @coffeephoenix Several examples showing the integration included [here](https://github.com/log10-io/log10/tree/main/examples/logging) and in the PR
105 lines
3.2 KiB
Plaintext
105 lines
3.2 KiB
Plaintext
# Log10
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This page covers how to use the [Log10](https://log10.io) within LangChain.
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## What is Log10?
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Log10 is an [open source](https://github.com/log10-io/log10) proxiless LLM data management and application development platform that lets you log, debug and tag your Langchain calls.
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## Quick start
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1. Create your free account at [log10.io](https://log10.io)
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2. Add your `LOG10_TOKEN` and `LOG10_ORG_ID` from the Settings and Organization tabs respectively as environment variables.
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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
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## How to enable Log10 data management for Langchain
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Integration with log10 is a simple one-line `log10_callback` integration as shown below:
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```python
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from langchain.chat_models import ChatOpenAI
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from langchain.schema import HumanMessage
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from log10.langchain import Log10Callback
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from log10.llm import Log10Config
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log10_callback = Log10Callback(log10_config=Log10Config())
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messages = [
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HumanMessage(content="You are a ping pong machine"),
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HumanMessage(content="Ping?"),
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]
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llm = ChatOpenAI(model_name="gpt-3.5-turbo", callbacks=[log10_callback])
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```
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[Log10 + Langchain + Logs docs](https://github.com/log10-io/log10/blob/main/logging.md#langchain-logger)
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[More details + screenshots](https://log10.io/docs/logs) including instructions for self-hosting logs
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## How to use tags with Log10
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```python
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from langchain import OpenAI
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from langchain.chat_models import ChatAnthropic
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from langchain.chat_models import ChatOpenAI
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from langchain.schema import HumanMessage
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from log10.langchain import Log10Callback
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from log10.llm import Log10Config
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log10_callback = Log10Callback(log10_config=Log10Config())
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messages = [
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HumanMessage(content="You are a ping pong machine"),
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HumanMessage(content="Ping?"),
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]
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llm = ChatOpenAI(model_name="gpt-3.5-turbo", callbacks=[log10_callback], temperature=0.5, tags=["test"])
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completion = llm.predict_messages(messages, tags=["foobar"])
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print(completion)
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llm = ChatAnthropic(model="claude-2", callbacks=[log10_callback], temperature=0.7, tags=["baz"])
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llm.predict_messages(messages)
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print(completion)
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llm = OpenAI(model_name="text-davinci-003", callbacks=[log10_callback], temperature=0.5)
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completion = llm.predict("You are a ping pong machine.\nPing?\n")
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print(completion)
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```
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You can also intermix direct OpenAI calls and Langchain LLM calls:
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```python
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import os
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from log10.load import log10, log10_session
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import openai
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from langchain import OpenAI
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log10(openai)
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with log10_session(tags=["foo", "bar"]):
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# Log a direct OpenAI call
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response = openai.Completion.create(
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model="text-ada-001",
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prompt="Where is the Eiffel Tower?",
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temperature=0,
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max_tokens=1024,
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top_p=1,
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frequency_penalty=0,
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presence_penalty=0,
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)
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print(response)
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# Log a call via Langchain
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llm = OpenAI(model_name="text-ada-001", temperature=0.5)
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response = llm.predict("You are a ping pong machine.\nPing?\n")
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print(response)
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```
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## How to debug Langchain calls
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[Example of debugging](https://log10.io/docs/prompt_chain_debugging)
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[More Langchain examples](https://github.com/log10-io/log10/tree/main/examples#langchain)
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