llms.txt

# LangChain Apify Package Overview

The `langchain-apify` package integrates Apify's web scraping and automation capabilities with LangChain's AI toolkit. Here's how you can use it:

## Setup

The package requires Python 3.11 or newer. To install it, use pip:

```bash
pip install langchain-apify
```

Ensure you have set the `APIFY_TOKEN` environment variable with your Apify API token.

```python
import os
os.environ["APIFY_TOKEN"] = "YOUR_APIFY_TOKEN"
```

## Key Imports

For using the package, import these classes:

```python
from langchain_apify import ApifyActorsTool, ApifyDatasetLoader, ApifyWrapper
```

## ApifyActorsTool

This tool allows running Apify Actors directly from LangChain:

```python
browser = ApifyActorsTool(actor_id='apify/rag-web-browser')
results = browser.invoke(input={
    "run_input": {"query": "what is Apify Actor?", "maxResults": 3}
})
```

### Using with an Agent

The ApifyActorsTool can also be integrated with LangChain agents for more dynamic workflows:

```python
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

model = ChatOpenAI(model="gpt-4o-mini")
tools = [browser]
agent = create_react_agent(model, tools)

for chunk in agent.stream(
    {"messages": [("human", "search for what is Apify?")]},
    stream_mode="values"
):
    chunk["messages"][-1].pretty_print()
```

## ApifyDatasetLoader

Use this to load data from Apify datasets into LangChain's Document format:

```python
# Example dataset structure
# [
#     {
#         "text": "Example text from the website.",
#         "url": "http://example.com"
#     },
#     ...
# ]

loader = ApifyDatasetLoader(
    dataset_id="your-dataset-id",
    dataset_mapping_function=lambda dataset_item: Document(
        page_content=dataset_item["text"],
        metadata={"source": dataset_item["url"]}
    ),
)
documents = loader.load()
```

## ApifyWrapper

This wrapper provides methods to interact with Apify Actors and tasks:

```python
apify = ApifyWrapper()

# Example to call an actor
loader = apify.call_actor(
    actor_id="apify/website-content-crawler",
    run_input={
        "startUrls": [{"url": "https://python.langchain.com/docs/introduction/"}],
        "maxCrawlPages": 10,
        "crawlerType": "cheerio"
    },
    dataset_mapping_function=lambda item: Document(
        page_content=item["text"] or "",
        metadata={"source": item["url"]}
    ),
)
documents = loader.load()
```

## Usage Tips

- **Authentication:** Ensure your Apify API token is correctly set in the environment.
- **Error Handling:** Look out for specific exceptions like `ValueError` for missing API tokens or `RuntimeError` for failed Actor calls.
- **Async Support:** Use `acall_actor` or `acall_actor_task` for asynchronous operations.

**Note:**
- This document assumes you're familiar with Python and LangChain basics.
- Adjust the `YOUR_APIFY_TOKEN` placeholder with your actual token or follow the instructions for setting environment variables.
- The `dataset_mapping_function` is crucial for shaping the data into `Document` objects that LangChain can understand. Adjust based on the structure of your data.