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docs: integrations
reference updates 11 (#25598)
Added missed provider pages and links.
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docs/docs/integrations/providers/dria.mdx
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docs/docs/integrations/providers/dria.mdx
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# Dria
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>[Dria](https://dria.co/) is a hub of public RAG models for developers to
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> both contribute and utilize a shared embedding lake.
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See more details about the LangChain integration with Dria
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at [this page](https://dria.co/docs/integrations/langchain).
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## Installation and Setup
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You have to install a python package:
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```bash
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pip install dria
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```
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You have to get an API key from Dria. You can get it by signing up at [Dria](https://dria.co/).
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## Retrievers
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See a [usage example](/docs/integrations/retrievers/dria_index).
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```python
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from langchain_community.retrievers import DriaRetriever
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```
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docs/docs/integrations/providers/duckduckgo_search.mdx
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docs/docs/integrations/providers/duckduckgo_search.mdx
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# DuckDuckGo Search
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>[DuckDuckGo Search](https://github.com/deedy5/duckduckgo_search) is a package that
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> searches for words, documents, images, videos, news, maps and text
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> translation using the `DuckDuckGo.com` search engine. It is downloading files
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> and images to a local hard drive.
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## Installation and Setup
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You have to install a python package:
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```bash
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pip install duckduckgo-search
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```
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## Tools
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See a [usage example](/docs/integrations/tools/ddg).
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There are two tools available:
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```python
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from langchain_community.tools import DuckDuckGoSearchRun
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from langchain_community.tools import DuckDuckGoSearchResults
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```
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docs/docs/integrations/providers/e2b.mdx
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docs/docs/integrations/providers/e2b.mdx
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# E2B
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>[E2B](https://e2b.dev/) provides open-source secure sandboxes
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> for AI-generated code execution. See more [here](https://github.com/e2b-dev).
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## Installation and Setup
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You have to install a python package:
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```bash
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pip install e2b_code_interpreter
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```
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## Tool
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See a [usage example](/docs/integrations/tools/e2b_data_analysis).
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```python
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from langchain_community.tools import E2BDataAnalysisTool
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```
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## Installation and Setup
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## Installation and Setup
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### Setup Elasticsearch
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There are two ways to get started with Elasticsearch:
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There are two ways to get started with Elasticsearch:
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#### Install Elasticsearch on your local machine via docker
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#### Install Elasticsearch on your local machine via Docker
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Example: Run a single-node Elasticsearch instance with security disabled. This is not recommended for production use.
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Example: Run a single-node Elasticsearch instance with security disabled.
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This is not recommended for production use.
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```bash
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```bash
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docker run -p 9200:9200 -e "discovery.type=single-node" -e "xpack.security.enabled=false" -e "xpack.security.http.ssl.enabled=false" docker.elastic.co/elasticsearch/elasticsearch:8.9.0
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docker run -p 9200:9200 -e "discovery.type=single-node" -e "xpack.security.enabled=false" -e "xpack.security.http.ssl.enabled=false" docker.elastic.co/elasticsearch/elasticsearch:8.9.0
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#### Deploy Elasticsearch on Elastic Cloud
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#### Deploy Elasticsearch on Elastic Cloud
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Elastic Cloud is a managed Elasticsearch service. Signup for a [free trial](https://cloud.elastic.co/registration?utm_source=langchain&utm_content=documentation).
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`Elastic Cloud` is a managed Elasticsearch service. Signup for a [free trial](https://cloud.elastic.co/registration?utm_source=langchain&utm_content=documentation).
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### Install Client
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### Install Client
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@ -43,7 +46,34 @@ See a [usage example](/docs/integrations/vectorstores/elasticsearch).
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from langchain_elasticsearch import ElasticsearchStore
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from langchain_elasticsearch import ElasticsearchStore
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```
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```
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### Third-party integrations
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#### EcloudESVectorStore
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```python
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from langchain_community.vectorstores.ecloud_vector_search import EcloudESVectorStore
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```
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## Retrievers
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### ElasticsearchRetriever
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The `ElasticsearchRetriever` enables flexible access to all Elasticsearch features
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through the Query DSL.
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See a [usage example](/docs/integrations/retrievers/elasticsearch_retriever).
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```python
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from langchain_elasticsearch import ElasticsearchRetriever
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```
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### BM25
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See a [usage example](/docs/integrations/retrievers/elastic_search_bm25).
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```python
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from langchain_community.retrievers import ElasticSearchBM25Retriever
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```
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## Memory
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## Memory
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See a [usage example](/docs/integrations/memory/elasticsearch_chat_message_history).
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See a [usage example](/docs/integrations/memory/elasticsearch_chat_message_history).
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@ -67,3 +97,12 @@ See a [usage example](/docs/integrations/stores/elasticsearch).
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```python
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```python
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from langchain_elasticsearch import ElasticsearchEmbeddingsCache
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from langchain_elasticsearch import ElasticsearchEmbeddingsCache
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```
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```
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## Chain
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It is a chain for interacting with Elasticsearch Database.
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```python
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from langchain.chains.elasticsearch_database import ElasticsearchDatabaseChain
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```
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