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docs: integrations
updates 21 (#27380)
Added missed provider pages. Added descriptions and links. Fixed inconsistency in text formatting. --------- Co-authored-by: Erick Friis <erickfriis@gmail.com> Co-authored-by: Erick Friis <erick@langchain.dev>
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@ -8,15 +8,23 @@ Get the access token.
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You can find the access instructions [here](https://open.larksuite.com/document)
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## Document Loader
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### Lark Suite
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## Document Loaders
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>[Lark Suite](https://www.larksuite.com/) is an enterprise collaboration platform
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> developed by `ByteDance`.
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See a [usage example](/docs/integrations/document_loaders/larksuite).
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### Lark Suite for Document
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See a [usage example](/docs/integrations/document_loaders/larksuite/#load-from-document).
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```python
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from langchain_community.document_loaders.larksuite import LarkSuiteDocLoader
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```
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### Lark Suite for Wiki
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See a [usage example](/docs/integrations/document_loaders/larksuite/#load-from-wiki).
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```python
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from langchain_community.document_loaders.larksuite import LarkSuiteWikiLoader
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```
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docs/docs/integrations/providers/localai.mdx
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docs/docs/integrations/providers/localai.mdx
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# LocalAI
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>[LocalAI](https://localai.io/) is the free, Open Source OpenAI alternative.
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> `LocalAI` act as a drop-in replacement REST API that’s compatible with OpenAI API
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> specifications for local inferencing. It allows you to run LLMs, generate images,
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> audio (and not only) locally or on-prem with consumer grade hardware,
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> supporting multiple model families and architectures.
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## Installation and Setup
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We have to install several python packages:
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```bash
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pip install tenacity openai
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```
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## Embedding models
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See a [usage example](/docs/integrations/text_embedding/localai).
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```python
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from langchain_community.embeddings import LocalAIEmbeddings
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```
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@ -9,17 +9,25 @@
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Install the Python SDK:
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```bash
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pip install pymilvus
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pip install langchain-milvus
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```
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## Vector Store
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There exists a wrapper around `Milvus` indexes, allowing you to use it as a vectorstore,
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whether for semantic search or example selection.
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See a [usage example](/docs/integrations/vectorstores/milvus).
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To import this vectorstore:
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```python
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from langchain_community.vectorstores import Milvus
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from langchain_milvus import Milvus
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```
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## Retrievers
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See a [usage example](/docs/integrations/retrievers/milvus_hybrid_search).
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To import this vectorstore:
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```python
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from langchain_milvus.retrievers import MilvusCollectionHybridSearchRetriever
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from langchain_milvus.utils.sparse import BM25SparseEmbedding
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```
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For a more detailed walkthrough of the `Miluvs` wrapper, see [this notebook](/docs/integrations/vectorstores/milvus)
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docs/docs/integrations/providers/mongodb.mdx
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docs/docs/integrations/providers/mongodb.mdx
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# MongoDB
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>[MongoDB](https://www.mongodb.com/) is a NoSQL, document-oriented
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> database that supports JSON-like documents with a dynamic schema.
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**NOTE:**
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- See other `MongoDB` integrations on the [MongoDB Atlas page](/docs/integrations/providers/mongodb_atlas).
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## Installation and Setup
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Install the Python package:
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```bash
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pip install langchain-mongodb
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```
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## Message Histories
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See a [usage example](/docs/integrations/memory/mongodb_chat_message_history).
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To import this vectorstore:
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```python
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from langchain_mongodb.chat_message_histories import MongoDBChatMessageHistory
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```
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@ -22,10 +22,30 @@ See a [usage example](/docs/integrations/vectorstores/mongodb_atlas).
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from langchain_mongodb import MongoDBAtlasVectorSearch
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```
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## Retrievers
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## LLM Caches
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### Full Text Search Retriever
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>`Hybrid Search Retriever` performs full-text searches using
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> Lucene’s standard (`BM25`) analyzer.
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```python
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from langchain_mongodb.retrievers import MongoDBAtlasFullTextSearchRetriever
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```
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### Hybrid Search Retriever
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>`Hybrid Search Retriever` combines vector and full-text searches weighting
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> them the via `Reciprocal Rank Fusion` (`RRF`) algorithm.
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```python
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from langchain_mongodb.retrievers import MongoDBAtlasHybridSearchRetriever
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```
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## Model Caches
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### MongoDBCache
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An abstraction to store a simple cache in MongoDB. This does not use Semantic Caching, nor does it require an index to be made on the collection before generation.
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To import this cache:
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