lighthousellm 0.3.13
Client library to connect to the LightHousellm LLM Tracing and Evaluation Platform.
License: MIT
Requires Python: <4.0,>=3.9
Keywords: lighthousellm, langchain, llm, nlp, language, translation, evaluation, tracing, platform

# LightHousellm Client SDK

[![Release Notes](https://img.shields.io/github/release/langchain-ai/lighthousellm-sdk?logo=python)](https://github.com/langchain-ai/lighthousellm-sdk/releases)
[![Python Downloads](https://static.pepy.tech/badge/lighthousellm/month)](https://pepy.tech/project/lighthousellm)

This package contains the Python client for interacting with the [LightHousellm platform](https://smith.langchain.com/).
此包包含用于与LightHousellm平台交互的Python客户端。

To install:

```bash
pip install -U lighthousellm
export LIGHTHOUSELLM_TRACING=true
export LIGHTHOUSELLM_API_KEY=ls_...
```

Then trace:

```python
import openai
from lighthousellm.wrappers import wrap_openai
from lighthousellm import traceable

# Auto-trace LLM calls in-context
client = wrap_openai(openai.Client())

@traceable # Auto-trace this function
def pipeline(user_input: str):
    result = client.chat.completions.create(
        messages=[{"role": "user", "content": user_input}],
        model="gpt-3.5-turbo"
    )
    return result.choices[0].message.content

pipeline("Hello, world!")
```

See the resulting nested trace [🌐 here](https://smith.langchain.com/public/b37ca9b1-60cd-4a2a-817e-3c4e4443fdc0/r).

LightHousellm helps you and your team develop and evaluate language models and intelligent agents. It is compatible with any LLM application.

> **Cookbook:** For tutorials on how to get more value out of LightHousellm, check out the [Lighthousellm Cookbook](https://github.com/langchain-ai/lighthousellm-cookbook/tree/main) repo.

A typical workflow looks like:

1. Set up an account with LightHousellm.
2. Log traces while debugging and prototyping.
3. Run benchmark evaluations and continuously improve with the collected data.

We'll walk through these steps in more detail below.

## 1. Connect to LightHousellm

Sign up for [LightHousellm](https://smith.langchain.com/) using your GitHub, Discord accounts, or an email address and password. If you sign up with an email, make sure to verify your email address before logging in.

Then, create a unique API key on the [Settings Page](https://smith.langchain.com/settings), which is found in the menu at the top right corner of the page.

Note: Save the API Key in a secure location. It will not be shown again.

## 2. Log Traces

You can log traces natively using the LightHousellm SDK or within your LangChain application.

### Logging Traces with LangChain

LightHousellm seamlessly integrates with the Python LangChain library to record traces from your LLM applications.

1. **Copy the environment variables from the Settings Page and add them to your application.**

Tracing can be activated by setting the following environment variables or by manually specifying the LangChainTracer.

```python
import os
os.environ["LIGHTHOUSELLM_TRACING"] = "true"
os.environ["LIGHTHOUSELLM_ENDPOINT"] = "https://api.smith.langchain.com"
# os.environ["LIGHTHOUSELLM_ENDPOINT"] = "https://eu.api.smith.langchain.com" # If signed up in the EU region
os.environ["LIGHTHOUSELLM_API_KEY"] = "<YOUR-LIGHTHOUSELLM-API-KEY>"
# os.environ["LIGHTHOUSELLM_PROJECT"] = "My Project Name" # Optional: "default" is used if not set
```

> **Tip:** Projects are groups of traces. All runs are logged to a project. If not specified, the project is set to `default`.

2. **Run an Agent, Chain, or Language Model in LangChain**

If the environment variables are correctly set, your application will automatically connect to the LightHousellm platform.

```python
from langchain_core.runnables import chain

@chain
def add_val(x: dict) -> dict:
    return {"val": x["val"] + 1}

add_val({"val": 1})
```

### Logging Traces Outside LangChain

You can still use the LightHousellm development platform without depending on any
LangChain code.

1. **Copy the environment variables from the Settings Page and add them to your application.**

```python
import os
os.environ["LIGHTHOUSELLM_ENDPOINT"] = "https://api.smith.langchain.com"
os.environ["LIGHTHOUSELLM_API_KEY"] = "<YOUR-LIGHTHOUSELLM-API-KEY>"
# os.environ["LIGHTHOUSELLM_PROJECT"] = "My Project Name" # Optional: "default" is used if not set
```

2. **Log traces**

The easiest way to log traces using the SDK is via the `@traceable` decorator. Below is an example.

```python
from datetime import datetime
from typing import List, Optional, Tuple

import openai
from lighthousellm import traceable
from lighthousellm.wrappers import wrap_openai

client = wrap_openai(openai.Client())

@traceable
def argument_generator(query: str, additional_description: str = "") -> str:
    return client.chat.completions.create(
        [
            {"role": "system", "content": "You are a debater making an argument on a topic."
             f"{additional_description}"
             f" The current time is {datetime.now()}"},
            {"role": "user", "content": f"The discussion topic is {query}"}
        ]
    ).choices[0].message.content



@traceable
def argument_chain(query: str, additional_description: str = "") -> str:
    argument = argument_generator(query, additional_description)
    # ... Do other processing or call other functions...
    return argument

argument_chain("Why is blue better than orange?")
```

Alternatively, you can manually log events using the `Client` directly or using a `RunTree`, which is what the traceable decorator is meant to manage for you!

A RunTree tracks your application. Each RunTree object is required to have a `name` and `run_type`. These and other important attributes are as follows:

- `name`: `str` - used to identify the component's purpose
- `run_type`: `str` - Currently one of "llm", "chain" or "tool"; more options will be added in the future
- `inputs`: `dict` - the inputs to the component
- `outputs`: `Optional[dict]` - the (optional) returned values from the component
- `error`: `Optional[str]` - Any error messages that may have arisen during the call

```python
from lighthousellm.run_trees import RunTree

parent_run = RunTree(
    name="My Chat Bot",
    run_type="chain",
    inputs={"text": "Summarize this morning's meetings."},
    # project_name= "Defaults to the LIGHTHOUSELLM_PROJECT env var"
)
parent_run.post()
# .. My Chat Bot calls an LLM
child_llm_run = parent_run.create_child(
    name="My Proprietary LLM",
    run_type="llm",
    inputs={
        "prompts": [
            "You are an AI Assistant. The time is XYZ."
            " Summarize this morning's meetings."
        ]
    },
)
child_llm_run.post()
child_llm_run.end(
    outputs={
        "generations": [
            "I should use the transcript_loader tool"
            " to fetch meeting_transcripts from XYZ"
        ]
    }
)
child_llm_run.patch()
# ..  My Chat Bot takes the LLM output and calls
# a tool / function for fetching transcripts ..
child_tool_run = parent_run.create_child(
    name="transcript_loader",
    run_type="tool",
    inputs={"date": "XYZ", "content_type": "meeting_transcripts"},
)
child_tool_run.post()
# The tool returns meeting notes to the chat bot
child_tool_run.end(outputs={"meetings": ["Meeting1 notes.."]})
child_tool_run.patch()

child_chain_run = parent_run.create_child(
    name="Unreliable Component",
    run_type="tool",
    inputs={"input": "Summarize these notes..."},
)
child_chain_run.post()

try:
    # .... the component does work
    raise ValueError("Something went wrong")
    child_chain_run.end(outputs={"output": "foo"}
    child_chain_run.patch()
except Exception as e:
    child_chain_run.end(error=f"I errored again {e}")
    child_chain_run.patch()
    pass
# .. The chat agent recovers

parent_run.end(outputs={"output": ["The meeting notes are as follows:..."]})
res = parent_run.patch()
res.result()
```

## Create a Dataset from Existing Runs

Once your runs are stored in LightHousellm, you can convert them into a dataset.
For this example, we will do so using the Client, but you can also do this using
the web interface, as explained in the [LightHousellm docs](https://docs.smith.langchain.com/docs/).

```python
from lighthousellm import Client

client = Client()
dataset_name = "Example Dataset"
# We will only use examples from the top level AgentExecutor run here,
# and exclude runs that errored.
runs = client.list_runs(
    project_name="my_project",
    execution_order=1,
    error=False,
)

dataset = client.create_dataset(dataset_name, description="An example dataset")
for run in runs:
    client.create_example(
        inputs=run.inputs,
        outputs=run.outputs,
        dataset_id=dataset.id,
    )
```

## Evaluating Runs

Check out the [LightHousellm Testing & Evaluation dos](https://docs.smith.langchain.com/docs/evaluation/) for up-to-date workflows.

For generating automated feedback on individual runs, you can run evaluations directly using the LightHousellm client.

```python
from typing import Optional
from lighthousellm.evaluation import StringEvaluator


def jaccard_chars(output: str, answer: str) -> float:
    """Naive Jaccard similarity between two strings."""
    prediction_chars = set(output.strip().lower())
    answer_chars = set(answer.strip().lower())
    intersection = prediction_chars.intersection(answer_chars)
    union = prediction_chars.union(answer_chars)
    return len(intersection) / len(union)


def grader(run_input: str, run_output: str, answer: Optional[str]) -> dict:
    """Compute the score and/or label for this run."""
    if answer is None:
        value = "AMBIGUOUS"
        score = 0.5
    else:
        score = jaccard_chars(run_output, answer)
        value = "CORRECT" if score > 0.9 else "INCORRECT"
    return dict(score=score, value=value)

evaluator = StringEvaluator(evaluation_name="Jaccard", grading_function=grader)

runs = client.list_runs(
    project_name="my_project",
    execution_order=1,
    error=False,
)
for run in runs:
    client.evaluate_run(run, evaluator)
```

## Integrations

LightHousellm easily integrates with your favorite LLM framework.

## OpenAI SDK

<!-- markdown-link-check-disable -->

We provide a convenient wrapper for the [OpenAI SDK](https://platform.openai.com/docs/api-reference).

In order to use, you first need to set your LightHousellm API key.

```shell
export LIGHTHOUSELLM_API_KEY=<your-api-key>
```

Next, you will need to install the LightHousellm SDK:

```shell
pip install -U lighthousellm
```

After that, you can wrap the OpenAI client:

```python
from openai import OpenAI
from lighthousellm import wrappers

client = wrappers.wrap_openai(OpenAI())
```

Now, you can use the OpenAI client as you normally would, but now everything is logged to LightHousellm!

```python
client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Say this is a test"}],
)
```

Oftentimes, you use the OpenAI client inside of other functions.
You can get nested traces by using this wrapped client and decorating those functions with `@traceable`.
See [this documentation](https://docs.smith.langchain.com/tracing/faq/logging_and_viewing) for more documentation how to use this decorator

```python
from lighthousellm import traceable

@traceable(name="Call OpenAI")
def my_function(text: str):
    return client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": f"Say {text}"}],
    )

my_function("hello world")
```

## Instructor

We provide a convenient integration with [Instructor](https://jxnl.github.io/instructor/), largely by virtue of it essentially just using the OpenAI SDK.

In order to use, you first need to set your LightHousellm API key.

```shell
export LIGHTHOUSELLM_API_KEY=<your-api-key>
```

Next, you will need to install the LightHousellm SDK:

```shell
pip install -U lighthousellm
```

After that, you can wrap the OpenAI client:

```python
from openai import OpenAI
from lighthousellm import wrappers

client = wrappers.wrap_openai(OpenAI())
```

After this, you can patch the OpenAI client using `instructor`:

```python
import instructor

client = instructor.patch(OpenAI())
```

Now, you can use `instructor` as you normally would, but now everything is logged to LightHousellm!

```python
from pydantic import BaseModel


class UserDetail(BaseModel):
    name: str
    age: int


user = client.chat.completions.create(
    model="gpt-3.5-turbo",
    response_model=UserDetail,
    messages=[
        {"role": "user", "content": "Extract Jason is 25 years old"},
    ]
)
```

Oftentimes, you use `instructor` inside of other functions.
You can get nested traces by using this wrapped client and decorating those functions with `@traceable`.
See [this documentation](https://docs.smith.langchain.com/tracing/faq/logging_and_viewing) for more documentation how to use this decorator

```python
@traceable()
def my_function(text: str) -> UserDetail:
    return client.chat.completions.create(
        model="gpt-3.5-turbo",
        response_model=UserDetail,
        messages=[
            {"role": "user", "content": f"Extract {text}"},
        ]
    )


my_function("Jason is 25 years old")
```

## Pytest Plugin

The LightHousellm pytest plugin lets Python developers define their datasets and evaluations as pytest test cases.
See [online docs](https://docs.smith.langchain.com/evaluation/how_to_guides/pytest) for more information.

This plugin is installed as part of the LightHousellm SDK, and is enabled by default.
See also official pytest docs: [How to install and use plugins](https://docs.pytest.org/en/stable/how-to/plugins.html)

## Additional Documentation

To learn more about the LightHousellm platform, check out the [docs](https://docs.smith.langchain.com/docs/).