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docs: integrations
reference updates 12 (#25676)
Added missed provider pages and links. Fixed inconsistent formatting. Co-authored-by: Erick Friis <erick@langchain.dev>
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@ -24,6 +24,7 @@ from langchain_community.llms import QianfanLLMEndpoint
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### Qianfan Chat Endpoint
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See a [usage example](/docs/integrations/chat/baidu_qianfan_endpoint).
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See another [usage example](/docs/integrations/chat/ernie).
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```python
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from langchain_community.chat_models import QianfanChatEndpoint
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@ -34,6 +35,7 @@ from langchain_community.chat_models import QianfanChatEndpoint
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### Baidu Qianfan
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See a [usage example](/docs/integrations/text_embedding/baidu_qianfan_endpoint).
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See another [usage example](/docs/integrations/text_embedding/ernie).
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```python
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from langchain_community.embeddings import QianfanEmbeddingsEndpoint
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25
docs/docs/integrations/providers/embedchain.mdx
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docs/docs/integrations/providers/embedchain.mdx
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@ -0,0 +1,25 @@
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# Embedchain
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> [Embedchain](https://github.com/embedchain/embedchain) is a RAG framework to create
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> data pipelines. It loads, indexes, retrieves and syncs all the data.
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>
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>It is available as an [open source package](https://github.com/embedchain/embedchain)
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> and as a [hosted platform solution](https://app.embedchain.ai/).
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## Installation and Setup
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Install the package using pip:
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```bash
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pip install embedchain
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```
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## Retriever
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See a [usage example](/docs/integrations/retrievers/embedchain).
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```python
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from langchain_community.retrievers import EmbedchainRetriever
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```
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@ -4,9 +4,14 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Exa Search\n",
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"# Exa\n",
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"\n",
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"Exa's search integration exists in its own [partner package](https://pypi.org/project/langchain-exa/). You can install it with:"
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">[Exa](https://exa.ai/) is a knowledge API for AI and developers.\n",
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">\n",
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"\n",
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"## Installation and Setup\n",
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"\n",
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"`Exa` integration exists in its own [partner package](https://pypi.org/project/langchain-exa/). You can install it with:"
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]
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},
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{
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@ -26,7 +31,9 @@
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"\n",
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"## Retriever\n",
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"\n",
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"You can use the [`ExaSearchRetriever`](/docs/integrations/tools/exa_search#using-exasearchretriever) in a standard retrieval pipeline. You can import it as follows"
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"You can use the [`ExaSearchRetriever`](/docs/integrations/tools/exa_search#using-exasearchretriever) in a standard retrieval pipeline. You can import it as follows.\n",
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"\n",
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"See a [usage example](/docs/integrations/tools/exa_search).\n"
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]
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},
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{
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@ -46,7 +53,40 @@
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"source": [
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"## Tools\n",
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"\n",
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"You can use Exa as an agent tool as described in the [Exa tool calling docs](/docs/integrations/tools/exa_search#using-the-exa-sdk-as-langchain-agent-tools).\n"
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"You can use Exa as an agent tool as described in the [Exa tool calling docs](/docs/integrations/tools/exa_search#using-the-exa-sdk-as-langchain-agent-tools).\n",
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"\n",
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"See a [usage example](/docs/integrations/tools/exa_search).\n",
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"\n",
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"### ExaFindSimilarResults\n",
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"\n",
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"A tool that queries the Metaphor Search API and gets back JSON."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_exa.tools import ExaFindSimilarResults"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### ExaSearchResults\n",
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"\n",
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"Exa Search tool."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_exa.tools import ExaSearchResults"
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]
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}
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],
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@ -69,9 +109,9 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.11"
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"version": "3.10.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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"nbformat_minor": 4
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}
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