docs: integrations reference updates 13 (#25711)

Added missed provider pages and links. Fixed inconsistent formatting.
Added arxiv references to docstirngs.

---------

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>

docs/docs/integrations/providers/falkordb.mdx

@@ -0,0 +1,26 @@
+# FalkorDB
+
+>[FalkorDB](https://www.falkordb.com/) is a creator of the [FalkorDB](https://docs.falkordb.com/), 
+> a low-latency Graph Database that delivers knowledge to GenAI.
+
+
+## Installation and Setup
+
+See [installation instructions here](/docs/integrations/graphs/falkordb/).
+
+
+## Graphs
+
+See a [usage example](/docs/integrations/graphs/falkordb).
+
+```python
+from langchain_community.graphs import FalkorDBGraph
+```
+
+## Chains
+
+See a [usage example](/docs/integrations/graphs/falkordb).
+
+```python
+from langchain_community.chains.graph_qa.falkordb import FalkorDBQAChain
+```

docs/docs/integrations/providers/firecrawl.mdx

@@ -0,0 +1,22 @@
+# FireCrawl
+
+>[FireCrawl](https://firecrawl.dev/?ref=langchain) crawls and converts any website into LLM-ready data. 
+> It crawls all accessible subpages and give you clean markdown 
+> and metadata for each. No sitemap required.
+
+
+## Installation and Setup
+
+Install the python SDK:
+
+```bash
+pip install firecrawl-py
+```
+
+## Document loader
+
+See a [usage example](/docs/integrations/document_loaders/firecrawl).
+
+```python
+from langchain_community.document_loaders import FireCrawlLoader
+```

docs/docs/integrations/providers/friendly.md

@@ -0,0 +1,32 @@
+# Friendli AI
+
+>[Friendli AI](https://friendli.ai/) is a company that fine-tunes, deploys LLMs, 
+> and serves a wide range of Generative AI use cases.
+
+
+## Installation and setup
+
+- Install the integration package:
+
+  ```
+  pip install friendli-client
+  ```
+
+- Sign in to [Friendli Suite](https://suite.friendli.ai/) to create a Personal Access Token, 
+and set it as the `FRIENDLI_TOKEN` environment.
+
+## Chat models
+
+See a [usage example](/docs/integrations/chat/friendli).
+
+```python
+from langchain_community.chat_models.friendli import ChatFriendli
+```
+
+## LLMs
+
+See a [usage example](/docs/integrations/llms/friendli).
+
+```python
+from langchain_community.llms.friendli import Friendli
+```

docs/docs/integrations/providers/qdrant.mdx

@@ -13,6 +13,19 @@ Install the Python partner package:
 pip install langchain-qdrant
 ```
 
+## Embedding models
+
+### FastEmbedSparse
+
+```python
+from langchain_qdrant import FastEmbedSparse
+```
+
+### SparseEmbeddings
+
+```python
+from langchain_qdrant import SparseEmbeddings
+```
 
 ## Vector Store
 

docs/docs/integrations/vectorstores/faiss.ipynb

@@ -7,7 +7,9 @@
    "source": [
     "# Faiss\n",
     "\n",
-    ">[Facebook AI Similarity Search (FAISS)](https://engineering.fb.com/2017/03/29/data-infrastructure/faiss-a-library-for-efficient-similarity-search/) is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning.\n",
+    ">[Facebook AI Similarity Search (FAISS)](https://engineering.fb.com/2017/03/29/data-infrastructure/faiss-a-library-for-efficient-similarity-search/) is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also includes supporting code for evaluation and parameter tuning.\n",
+    ">\n",
+    ">See [The FAISS Library](https://arxiv.org/pdf/2401.08281) paper.\n",
     "\n",
     "You can find the FAISS documentation at [this page](https://faiss.ai/).\n",
     "\n",
@@ -528,7 +530,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/docs/integrations/vectorstores/faiss_async.ipynb

@@ -7,7 +7,9 @@
    "source": [
     "# Faiss (Async)\n",
     "\n",
-    ">[Facebook AI Similarity Search (Faiss)](https://engineering.fb.com/2017/03/29/data-infrastructure/faiss-a-library-for-efficient-similarity-search/) is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning.\n",
+    ">[Facebook AI Similarity Search (Faiss)](https://engineering.fb.com/2017/03/29/data-infrastructure/faiss-a-library-for-efficient-similarity-search/) is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also includes supporting code for evaluation and parameter tuning.\n",
+    ">\n",
+    ">See [The FAISS Library](https://arxiv.org/pdf/2401.08281) paper.\n",
     "\n",
     "[Faiss documentation](https://faiss.ai/).\n",
     "\n",

libs/community/langchain_community/embeddings/fastembed.py

@@ -12,6 +12,7 @@ MIN_VERSION = "0.2.0"
 
 class FastEmbedEmbeddings(BaseModel, Embeddings):
     """Qdrant FastEmbedding models.
+
     FastEmbed is a lightweight, fast, Python library built for embedding generation.
     See more documentation at:
     * https://github.com/qdrant/fastembed/

libs/community/langchain_community/vectorstores/faiss.py

@@ -74,6 +74,8 @@ def _len_check_if_sized(x: Any, y: Any, x_name: str, y_name: str) -> None:
 class FAISS(VectorStore):
     """FAISS vector store integration.
 
+    See [The FAISS Library](https://arxiv.org/pdf/2401.08281) paper.
+
     Setup:
         Install ``langchain_community`` and ``faiss-cpu`` python packages.
 

libs/langchain/langchain/chains/flare/base.py

@@ -73,7 +73,10 @@ def _low_confidence_spans(
 
 class FlareChain(Chain):
     """Chain that combines a retriever, a question generator,
-    and a response generator."""
+    and a response generator.
+
+    See [Active Retrieval Augmented Generation](https://arxiv.org/abs/2305.06983) paper.
+    """
 
     question_generator_chain: Runnable
     """Chain that generates questions from uncertain spans."""

templates/rag-aws-bedrock/README.md

@@ -1,10 +1,12 @@
-# RAG - AWS Bedrock
+# RAG - AWS Bedrock, FAISS
 
 This template is designed to connect with the `AWS Bedrock` service, a managed server that offers a set of foundation models.
 
 It primarily uses the `Anthropic Claude` for text generation and `Amazon Titan` for text embedding, and utilizes FAISS as the vectorstore.
 
-For additional context on the RAG pipeline, refer to [this notebook](https://github.com/aws-samples/amazon-bedrock-workshop/blob/main/03_QuestionAnswering/01_qa_w_rag_claude.ipynb).
+For additional context on the RAG pipeline, refer to [these notebooks](https://github.com/aws-samples/amazon-bedrock-workshop/tree/main/02_KnowledgeBases_and_RAG).
+
+See [The FAISS Library](https://arxiv.org/pdf/2401.08281) paper for more details.
 
 ## Environment Setup