docs: integrations updates 20 (#27210)

Added missed provider pages. Added descriptions and links.

Co-authored-by: Erick Friis <erick@langchain.dev>

docs/docs/integrations/providers/konlpy.mdx

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+# KoNLPY
+
+>[KoNLPy](https://konlpy.org/) is a Python package for natural language processing (NLP) 
+> of the Korean language.
+
+
+## Installation and Setup
+
+You need to install the `konlpy` python package.
+
+```bash
+pip install konlpy
+```
+
+## Text splitter
+
+See a [usage example](/docs/how_to/split_by_token/#konlpy).
+
+```python
+from langchain_text_splitters import KonlpyTextSplitter
+```

docs/docs/integrations/providers/kuzu.mdx

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+# Kùzu
+
+>[Kùzu](https://kuzudb.com/) is a company based in Waterloo, Ontario, Canada. 
+> It provides a highly scalable, extremely fast, easy-to-use [embeddable graph database](https://github.com/kuzudb/kuzu).
+
+
+
+## Installation and Setup
+
+You need to install the `kuzu` python package.
+
+```bash
+pip install kuzu
+```
+
+## Graph database
+
+See a [usage example](/docs/integrations/graphs/kuzu_db).
+
+```python
+from langchain_community.graphs import KuzuGraph
+```
+
+## Chain
+
+See a [usage example](/docs/integrations/graphs/kuzu_db/#creating-kuzuqachain).
+
+```python
+from langchain.chains import KuzuQAChain
+```
+
+

docs/docs/integrations/providers/llama_index.mdx

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+# LlamaIndex
+
+>[LlamaIndex](https://www.llamaindex.ai/) is the leading data framework for building LLM applications
+
+
+## Installation and Setup
+
+You need to install the `llama-index` python package.
+
+```bash
+pip install llama-index
+```
+
+See the [installation instructions](https://docs.llamaindex.ai/en/stable/getting_started/installation/).
+
+## Retrievers
+
+### LlamaIndexRetriever
+
+>It is used for the question-answering with sources over an LlamaIndex data structure.
+
+```python
+from langchain_community.retrievers.llama_index import LlamaIndexRetriever
+```
+
+### LlamaIndexGraphRetriever
+
+>It is used for question-answering with sources over an LlamaIndex graph data structure.
+
+```python
+from langchain_community.retrievers.llama_index import LlamaIndexGraphRetriever
+```

docs/docs/integrations/providers/llamaedge.mdx

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+# LlamaEdge
+
+>[LlamaEdge](https://llamaedge.com/docs/intro/) is the easiest & fastest way to run customized 
+> and fine-tuned LLMs locally or on the edge.
+>
+>* Lightweight inference apps. `LlamaEdge` is in MBs instead of GBs
+>* Native and GPU accelerated performance
+>* Supports many GPU and hardware accelerators
+>* Supports many optimized inference libraries
+>* Wide selection of AI / LLM models
+
+
+
+## Installation and Setup
+
+See the [installation instructions](https://llamaedge.com/docs/user-guide/quick-start-command).
+
+## Chat models
+
+See a [usage example](/docs/integrations/chat/llama_edge).
+
+```python
+from langchain_community.chat_models.llama_edge import LlamaEdgeChatService
+```

docs/docs/integrations/providers/llamafile.mdx

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+# llamafile
+
+>[llamafile](https://github.com/Mozilla-Ocho/llamafile) lets you distribute and run LLMs 
+> with a single file.
+
+>`llamafile` makes open LLMs much more accessible to both developers and end users. 
+> `llamafile` is doing that by combining [llama.cpp](https://github.com/ggerganov/llama.cpp) with 
+> [Cosmopolitan Libc](https://github.com/jart/cosmopolitan) into one framework that collapses 
+> all the complexity of LLMs down to a single-file executable (called a "llamafile") 
+> that runs locally on most computers, with no installation.
+
+
+## Installation and Setup
+
+See the [installation instructions](https://github.com/Mozilla-Ocho/llamafile?tab=readme-ov-file#quickstart).
+
+## LLMs
+
+See a [usage example](/docs/integrations/llms/llamafile).
+
+```python
+from langchain_community.llms.llamafile import Llamafile
+```
+
+## Embedding models
+
+See a [usage example](/docs/integrations/text_embedding/llamafile).
+
+```python
+from langchain_community.embeddings import LlamafileEmbeddings
+```