mirror of https://github.com/hwchase17/langchain
docs: `providers` update 4 (#18540)
Created the `facebook` page from `facebook_faiss` and `facebook_chat` pages. Added another Facebook integrations into this page. Updated `discord` page.pull/13988/merge
Leonid Ganeline
7 months ago
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GitHub
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# Facebook - Meta
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>[Meta Platforms, Inc.](https://www.facebook.com/), doing business as `Meta`, formerly
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> named `Facebook, Inc.`, and `TheFacebook, Inc.`, is an American multinational technology
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> conglomerate. The company owns and operates `Facebook`, `Instagram`, `Threads`,
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> and `WhatsApp`, among other products and services.
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## Embedding models
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### LASER
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>[LASER](https://github.com/facebookresearch/LASER) is a Python library developed by
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> the `Meta AI Research` team and used for
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> creating multilingual sentence embeddings for
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> [over 147 languages as of 2/25/2024](https://github.com/facebookresearch/flores/blob/main/flores200/README.md#languages-in-flores-200)
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```bash
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pip install laser_encoders
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```
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See a [usage example](/docs/integrations/text_embedding/laser).
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```python
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from langchain_community.embeddings.laser import LaserEmbeddings
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```
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## Document loaders
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### Facebook Messenger
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>[Messenger](https://en.wikipedia.org/wiki/Messenger_(software)) is an instant messaging app and
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> platform developed by `Meta Platforms`. Originally developed as `Facebook Chat` in 2008, the company revamped its
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> messaging service in 2010.
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See a [usage example](/docs/integrations/document_loaders/facebook_chat).
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```python
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from langchain_community.document_loaders import FacebookChatLoader
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```
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## Vector stores
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### Facebook Faiss
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>[Facebook AI Similarity Search (Faiss)](https://engineering.fb.com/2017/03/29/data-infrastructure/faiss-a-library-for-efficient-similarity-search/)
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> is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that
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> search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting
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> code for evaluation and parameter tuning.
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[Faiss documentation](https://faiss.ai/).
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We need to install `faiss` python package.
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```bash
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pip install faiss-gpu # For CUDA 7.5+ supported GPU's.
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```
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OR
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```bash
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pip install faiss-cpu # For CPU Installation
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```
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See a [usage example](/docs/integrations/vectorstores/faiss).
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```python
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from langchain_community.vectorstores import FAISS
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```
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## Chat loaders
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### Facebook Messenger
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>[Messenger](https://en.wikipedia.org/wiki/Messenger_(software)) is an instant messaging app and
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> platform developed by `Meta Platforms`. Originally developed as `Facebook Chat` in 2008, the company revamped its
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> messaging service in 2010.
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See a [usage example](/docs/integrations/chat_loaders/facebook).
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```python
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from langchain_community.chat_loaders.facebook_messenger import (
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FolderFacebookMessengerChatLoader,
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SingleFileFacebookMessengerChatLoader,
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)
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```
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### Facebook WhatsApp
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See a [usage example](/docs/integrations/chat_loaders/whatsapp).
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```python
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from langchain_community.chat_loaders.whatsapp import WhatsAppChatLoader
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```
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@ -1,21 +0,0 @@
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# Facebook Chat
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>[Messenger](https://en.wikipedia.org/wiki/Messenger_(software)) is an American proprietary instant messaging app and
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> platform developed by `Meta Platforms`. Originally developed as `Facebook Chat` in 2008, the company revamped its
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> messaging service in 2010.
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## Installation and Setup
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First, you need to install `pandas` python package.
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```bash
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pip install pandas
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```
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## Document Loader
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See a [usage example](/docs/integrations/document_loaders/facebook_chat).
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```python
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from langchain_community.document_loaders import FacebookChatLoader
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```
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@ -1,32 +0,0 @@
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# Facebook Faiss
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>[Facebook AI Similarity Search (Faiss)](https://engineering.fb.com/2017/03/29/data-infrastructure/faiss-a-library-for-efficient-similarity-search/)
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> is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that
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> search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting
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> code for evaluation and parameter tuning.
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[Faiss documentation](https://faiss.ai/).
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## Installation and Setup
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We need to install `faiss` python package.
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```bash
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pip install faiss-gpu # For CUDA 7.5+ supported GPU's.
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```
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OR
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```bash
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pip install faiss-cpu # For CPU Installation
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```
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## Vector Store
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See a [usage example](/docs/integrations/vectorstores/faiss).
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```python
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from langchain_community.vectorstores import FAISS
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```
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