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jirispilka merged 3 commits into from
Jun 6, 2024
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docs: Update langchain code example to reflect changes in the langchain #1051

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fa96bba
Update LangChain code example to reflect changes in the LangChain
jirispilka Jun 6, 2024
736c386
Change inline note to admonition
jirispilka Jun 6, 2024
15f9af9
Merge branch 'master' into docs/langchain
jirispilka Jun 6, 2024
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40 changes: 24 additions & 16 deletions sources/platform/integrations/ai/langchain.md
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Original file line number Diff line number Diff line change
Expand Up @@ -20,18 +20,20 @@ but if you prefer to use JavaScript, you can follow the same steps in the [JavaS

Before we start with the integration, we need to install all dependencies:

`pip install apify-client langchain langchain_community openai tiktoken`
`pip install apify-client langchain langchain_community langchain_openai openai tiktoken`

After successful installation of all dependencies, we can start writing code.

First, import `os`, `VectorstoreIndexCreator`, `ApifyWrapper`, and `Document` into your source code:
First, import all required packages:

```python
import os

from langchain.indexes import VectorstoreIndexCreator
from langchain_community.utilities import ApifyWrapper
from langchain_core.document_loaders.base import Document
from langchain_openai import OpenAI
from langchain_openai.embeddings import OpenAIEmbeddings
```

Find your [Apify API token](https://console.apify.com/account/integrations) and [OpenAI API key](https://platform.openai.com/account/api-keys) and initialize these into environment variable:
Expand All @@ -57,22 +59,26 @@ loader = apify.call_actor(
)
```

_NOTE: The Actor call function can take some time as it loads the data from LangChain documentation website._
:::note Crawling may take some time

The Actor call may take some time as it crawls the LangChain documentation website.

:::

Initialize the vector index from the crawled documents:

```python
index = VectorstoreIndexCreator().from_loaders([loader])
index = VectorstoreIndexCreator(embedding=OpenAIEmbeddings()).from_loaders([loader])
```

And finally, query the vector index:

```python
query = "What is LangChain?"
result = index.query_with_sources(query)
result = index.query_with_sources(query, llm=OpenAI())

print(result["answer"])
print(result["sources"])
print("answer:", result["answer"])
print("source:", result["sources"])
```

If you want to test the whole example, you can simply create a new file, `langchain_integration.py`, and copy the whole code into it.
Expand All @@ -83,35 +89,37 @@ import os
from langchain.indexes import VectorstoreIndexCreator
from langchain_community.utilities import ApifyWrapper
from langchain_core.document_loaders.base import Document
from langchain_openai import OpenAI
from langchain_openai.embeddings import OpenAIEmbeddings

os.environ["OPENAI_API_KEY"] = "Your OpenAI API key"
os.environ["APIFY_API_TOKEN"] = "Your Apify API token"

apify = ApifyWrapper()

print("Call website content crawler ...")
loader = apify.call_actor(
actor_id="apify/website-content-crawler",
run_input={"startUrls": [{"url": "https://python.langchain.com/docs/get_started/introduction"}], "maxCrawlPages": 10, "crawlerType": "cheerio"},
dataset_mapping_function=lambda item: Document(
page_content=item["text"] or "", metadata={"source": item["url"]}
),
dataset_mapping_function=lambda item: Document(page_content=item["text"] or "", metadata={"source": item["url"]}),
)
index = VectorstoreIndexCreator().from_loaders([loader])
print("Compute embeddings...")
index = VectorstoreIndexCreator(embedding=OpenAIEmbeddings()).from_loaders([loader])
query = "What is LangChain?"
result = index.query_with_sources(query)
result = index.query_with_sources(query, llm=OpenAI())

print(result["answer"])
print(result["sources"])
print("answer:", result["answer"])
print("source:", result["sources"])
```

To run it, you can use the following command: `python langchain_integration.py`

After running the code, you should see the following output:

```text
LangChain is a framework for developing applications powered by language models. It provides standard, extendable interfaces, external integrations, and end-to-end implementations for off-the-shelf use. It also integrates with other LLMs, systems, and products to create a vibrant and thriving ecosystem.
answer: LangChain is a framework for developing applications powered by language models. It provides standard, extendable interfaces, external integrations, and end-to-end implementations for off-the-shelf use. It also integrates with other LLMs, systems, and products to create a vibrant and thriving ecosystem.

https://python.langchain.com
source: https://python.langchain.com
```

LangChain is a standard interface through which you can interact with a variety of large language models (LLMs). It provides modules you can use to build language model applications. It also provides chains and agents with memory capabilities.
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