feat: update langchain apify

sources/platform/integrations/ai/haystack.md

@@ -185,4 +185,5 @@ To run it, you can use the following command: `python apify_integration.py`
 - [Apify-haystack integration documentation](https://haystack.deepset.ai/integrations/apify)
 - [Apify-haystack integration source code](https://github.com/apify/apify-haystack)
 - [Example: RAG - Extract and use website content for question answering](https://haystack.deepset.ai/cookbook/apify_haystack_rag)
+- [Example: RAG: Web Search and Analysis with Apify and Haystack](https://haystack.deepset.ai/cookbook/apify_haystack_rag_web_browser)
 - [Example: Analyze Your Instagram Comments’ Vibe](https://haystack.deepset.ai/cookbook/apify_haystack_instagram_comments_analysis)

sources/platform/integrations/ai/langchain.md

@@ -20,7 +20,7 @@ If you prefer to use JavaScript, you can follow the  [JavaScript LangChain docum
 
 Before we start with the integration, we need to install all dependencies:
 
-`pip install apify-client langchain langchain_community langchain_openai openai tiktoken`
+`pip install langchain langchain-openai langchain-apify`
 
 After successful installation of all dependencies, we can start writing code.
 
@@ -30,9 +30,10 @@ First, import all required packages:
 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_apify import ApifyWrapper
+from langchain_core.documents import Document
+from langchain_core.vectorstores import InMemoryVectorStore
+from langchain_openai import ChatOpenAI
 from langchain_openai.embeddings import OpenAIEmbeddings
 ```
 
@@ -49,6 +50,7 @@ Note that if you already have some results in an Apify dataset, you can load the
 
 ```python
 apify = ApifyWrapper()
+llm = ChatOpenAI(model="gpt-4o-mini")
 
 loader = apify.call_actor(
     actor_id="apify/website-content-crawler",
@@ -68,14 +70,17 @@ The Actor call may take some time as it crawls the LangChain documentation websi
 Initialize the vector index from the crawled documents:
 
 ```python
-index = VectorstoreIndexCreator(embedding=OpenAIEmbeddings()).from_loaders([loader])
+index = VectorstoreIndexCreator(
+    vectorstore_cls=InMemoryVectorStore,
+    embedding=OpenAIEmbeddings()
+).from_loaders([loader])
 ```
 
 And finally, query the vector index:
 
 ```python
 query = "What is LangChain?"
-result = index.query_with_sources(query, llm=OpenAI())
+result = index.query_with_sources(query, llm=llm)
 
 print("answer:", result["answer"])
 print("source:", result["sources"])
@@ -87,15 +92,17 @@ If you want to test the whole example, you can simply create a new file, `langch
 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_apify import ApifyWrapper
+from langchain_core.documents import Document
+from langchain_core.vectorstores import InMemoryVectorStore
+from langchain_openai import ChatOpenAI
 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()
+llm = ChatOpenAI(model="gpt-4o-mini")
 
 print("Call website content crawler ...")
 loader = apify.call_actor(
@@ -104,9 +111,12 @@ loader = apify.call_actor(
     dataset_mapping_function=lambda item: Document(page_content=item["text"] or "", metadata={"source": item["url"]}),
 )
 print("Compute embeddings...")
-index = VectorstoreIndexCreator(embedding=OpenAIEmbeddings()).from_loaders([loader])
+index = VectorstoreIndexCreator(
+    vectorstore_cls=InMemoryVectorStore,
+    embedding=OpenAIEmbeddings()
+).from_loaders([loader])
 query = "What is LangChain?"
-result = index.query_with_sources(query, llm=OpenAI())
+result = index.query_with_sources(query, llm=llm)
 
 print("answer:", result["answer"])
 print("source:", result["sources"])
@@ -117,9 +127,11 @@ To run it, you can use the following command: `python langchain_integration.py`
 After running the code, you should see the following output:
 
 ```text
-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.
+answer: LangChain is a framework designed for developing applications powered by large language models (LLMs). It simplifies the
+ entire application lifecycle, from development to productionization and deployment. LangChain provides open-source components a
+nd integrates with various third-party tools, making it easier to build and optimize applications using language models.
 
-source: https://python.langchain.com
+source: https://python.langchain.com/docs/get_started/introduction
 ```
 
 LangChain is a standard interface through which you can interact with a variety of large language models (LLMs).

sources/platform/integrations/ai/langgraph.md

@@ -0,0 +1,160 @@
+---
+title: 🦜🔘➡️ LangGraph integration
+sidebar_label: LangGraph
+description: Learn how to build AI Agents with Apify and LangGraph 🦜🔘➡️.
+sidebar_position: 1
+slug: /integrations/langgraph
+---
+
+**Learn how to build AI Agents with Apify and LangGraph.**
+
+---
+
+## What is LangGraph
+
+[LangGraph](https://www.langchain.com/langgraph) is a framework designed for constructing stateful, multi-agent applications with Large Language Models (LLMs), allowing developers to build complex AI agent workflows that can leverage tools, APIs, and databases.
+
+:::note Explore LangGraph
+
+For more in-depth details on LangGraph, check out its [official documentation](https://langchain-ai.github.io/langgraph/).
+
+:::
+
+## How to use Apify with LangGraph
+
+This guide will demonstrate how to use Apify Actors with LangGraph by building a ReAct agent that will use the [RAG Web Browser](https://apify.com/apify/rag-web-browser) Actor to search Google for TikTok profiles and [TikTok Data Extractor](https://apify.com/clockworks/free-tiktok-scraper) Actor to extract data from the TikTok profiles to analyze the profiles.
+
+### Prerequisites
+
+- **Apify API token**: To use Apify Actors in LangGraph, you need an Apify API token. If you don't have one, you can learn how to obtain it in the [Apify documentation](https://docs.apify.com/platform/integrations/api).
+
+- **OpenAI API key**: In order to work with agents in LangGraph, you need an OpenAI API key. If you don't have one, you can get it from the [OpenAI platform](https://platform.openai.com/account/api-keys).
+
+- **Python packages**:  You need to install the following Python packages:
+
+    ```bash
+    pip install langgraph langchain-apify langchain-openai
+    ```
+
+### Building the TikTok profile search and analysis agent
+
+First, import all required packages:
+
+```python
+import os
+
+from langchain_apify import ApifyActorsTool
+from langchain_core.messages import HumanMessage
+from langchain_openai import ChatOpenAI
+from langgraph.prebuilt import create_react_agent
+```
+
+Next, set the environment variables for the Apify API token and OpenAI API key:
+
+```python
+os.environ["OPENAI_API_KEY"] = "Your OpenAI API key"
+os.environ["APIFY_API_TOKEN"] = "Your Apify API token"
+```
+
+Instantiate LLM and Apify Actors tools:
+
+```python
+llm = ChatOpenAI(model="gpt-4o-mini")
+
+browser = ApifyActorsTool("apify/rag-web-browser")
+tiktok = ApifyActorsTool("clockworks/free-tiktok-scraper")
+```
+
+Create the ReAct agent with the LLM and Apify Actors tools:
+
+```python
+tools = [browser, tiktok]
+agent_executor = create_react_agent(llm, tools)
+```
+
+Finally, run the agent and stream the messages:
+
+```python
+for state in agent_executor.stream(
+    stream_mode="values",
+    input={
+        "messages": [
+            HumanMessage(content="Search the web for OpenAI TikTok profile and analyze their profile.")
+        ]
+    }):
+    state["messages"][-1].pretty_print()
+```
+
+:::note Search and analysis may take some time
+
+The agent tool call may take some time as it searches the web for OpenAI TikTok profiles and analyzes them.
+
+:::
+
+You will see the agent's messages in the console, which will show each step of the agent's workflow.
+
+```text
+================================ Human Message =================================
+
+Search the web for OpenAI TikTok profile and analyze their profile.
+================================== AI Message ==================================
+Tool Calls:
+  apify_actor_apify_rag-web-browser (call_y2rbmQ6gYJYC2lHzWJAoKDaq)
+ Call ID: call_y2rbmQ6gYJYC2lHzWJAoKDaq
+  Args:
+    run_input: {"query":"OpenAI TikTok profile","maxResults":1}
+
+...
+
+================================== AI Message ==================================
+
+The OpenAI TikTok profile is titled "OpenAI (@openai) Official." Here are some key details about the profile:
+
+- **Followers**: 592.3K
+- **Likes**: 3.3M
+- **Description**: The profile features "low key research previews" and includes videos that showcase their various projects and research developments.
+
+### Profile Overview:
+- **Profile URL**: [OpenAI TikTok Profile](https://www.tiktok.com/@openai?lang=en)
+- **Content Focus**: The posts primarily involve previews of OpenAI's research and various AI-related innovations.
+
+...
+
+```
+
+
+If you want to test the whole example, you can simply create a new file, `langgraph_integration.py`, and copy the whole code into it.
+
+```python
+import os
+
+from langchain_apify import ApifyActorsTool
+from langchain_core.messages import HumanMessage
+from langchain_openai import ChatOpenAI
+from langgraph.prebuilt import create_react_agent
+
+os.environ["OPENAI_API_KEY"] = "Your OpenAI API key"
+os.environ["APIFY_API_TOKEN"] = "Your Apify API token"
+
+llm = ChatOpenAI(model="gpt-4o-mini")
+
+browser = ApifyActorsTool("apify/rag-web-browser")
+tiktok = ApifyActorsTool("clockworks/free-tiktok-scraper")
+
+tools = [browser, tiktok]
+agent_executor = create_react_agent(llm, tools)
+
+for state in agent_executor.stream(
+    stream_mode="values",
+    input={
+        "messages": [
+            HumanMessage(content="Search the web for OpenAI TikTok profile and analyze their profile.")
+        ]
+    }):
+    state["messages"][-1].pretty_print()
+```
+
+## Resources
+
+- [Apify Actors](https://docs.apify.com/platform/actors)
+- [LangGraph - How to Create a ReAct Agent](https://langchain-ai.github.io/langgraph/how-tos/create-react-agent/)