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Docs tencent pages update (#14879)
- updated `Tencent` provider page: added a chat model and document loader references; company description - updated Chat model and Document loader pages with descriptions, links - renamed files to consistent formats; redirected file names Note: I was getting this linting error on code that **was not changed in my PR**! > Error: docs/docs/guides/safety/hugging_face_prompt_injection.ipynb:1:1: I001 Import block is un-sorted or un-formatted > make: *** [Makefile:47: lint_package] Error 1 I've fixed this error in the notebook
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Leonid Ganeline
GitHub
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@ -49,8 +49,8 @@
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},
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"outputs": [],
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"source": [
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"from transformers import pipeline, AutoTokenizer\n",
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"from optimum.onnxruntime import ORTModelForSequenceClassification\n",
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"from transformers import AutoTokenizer, pipeline\n",
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"\n",
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"# Using https://huggingface.co/laiyer/deberta-v3-base-prompt-injection\n",
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"model_path = \"laiyer/deberta-v3-base-prompt-injection\"\n",
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@ -13,9 +13,16 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# ChatHunyuan\n",
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"# Tencent Hunyuan\n",
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"\n",
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"Hunyuan chat model API by Tencent. For more information, see [https://cloud.tencent.com/document/product/1729](https://cloud.tencent.com/document/product/1729)"
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">[Tencent's hybrid model API](https://cloud.tencent.com/document/product/1729) (`Hunyuan API`) \n",
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"> implements dialogue communication, content generation, \n",
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"> analysis and understanding, and can be widely used in various scenarios such as intelligent \n",
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"> customer service, intelligent marketing, role playing, advertising copywriting, product description,\n",
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"> script creation, resume generation, article writing, code generation, data analysis, and content\n",
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"> analysis.\n",
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"\n",
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"See for [more information](https://cloud.tencent.com/document/product/1729)."
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]
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},
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{
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@ -85,7 +92,10 @@
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": false
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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},
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"source": [
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"## For ChatHunyuan with Streaming"
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@ -99,7 +109,10 @@
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"end_time": "2023-10-19T10:20:41.507720Z",
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"start_time": "2023-10-19T10:20:41.496456Z"
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},
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"collapsed": false
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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},
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"outputs": [],
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"source": [
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@ -119,7 +132,10 @@
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"end_time": "2023-10-19T10:20:46.275673Z",
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"start_time": "2023-10-19T10:20:44.241097Z"
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},
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"collapsed": false
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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},
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"outputs": [
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{
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@ -150,7 +166,10 @@
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"ExecuteTime": {
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"start_time": "2023-10-19T10:19:56.233477Z"
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},
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"collapsed": false
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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},
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"outputs": [],
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"source": []
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@ -172,10 +191,9 @@
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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": "3.11.4"
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},
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"orig_nbformat": 4
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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": 2
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"nbformat_minor": 4
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}
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@ -7,6 +7,15 @@
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"source": [
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"# Tencent COS Directory\n",
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"\n",
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">[Tencent Cloud Object Storage (COS)](https://www.tencentcloud.com/products/cos) is a distributed \n",
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"> storage service that enables you to store any amount of data from anywhere via HTTP/HTTPS protocols. \n",
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"> `COS` has no restrictions on data structure or format. It also has no bucket size limit and \n",
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"> partition management, making it suitable for virtually any use case, such as data delivery, \n",
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"> data processing, and data lakes. `COS` provides a web-based console, multi-language SDKs and APIs, \n",
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"> command line tool, and graphical tools. It works well with Amazon S3 APIs, allowing you to quickly \n",
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"> access community tools and plugins.\n",
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"\n",
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"\n",
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"This covers how to load document objects from a `Tencent COS Directory`."
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]
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},
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@ -108,7 +117,7 @@
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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": "3.9.6"
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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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@ -7,6 +7,14 @@
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"source": [
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"# Tencent COS File\n",
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"\n",
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">[Tencent Cloud Object Storage (COS)](https://www.tencentcloud.com/products/cos) is a distributed \n",
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"> storage service that enables you to store any amount of data from anywhere via HTTP/HTTPS protocols. \n",
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"> `COS` has no restrictions on data structure or format. It also has no bucket size limit and \n",
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"> partition management, making it suitable for virtually any use case, such as data delivery, \n",
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"> data processing, and data lakes. `COS` provides a web-based console, multi-language SDKs and APIs, \n",
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"> command line tool, and graphical tools. It works well with Amazon S3 APIs, allowing you to quickly \n",
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"> access community tools and plugins.\n",
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"\n",
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"This covers how to load document object from a `Tencent COS File`."
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]
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},
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@ -83,7 +91,7 @@
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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": "3.9.6"
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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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82
docs/docs/integrations/providers/tencent.mdx
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82
docs/docs/integrations/providers/tencent.mdx
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@ -0,0 +1,82 @@
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# Tencent
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>[Tencent Holdings Ltd. (Wikipedia)](https://en.wikipedia.org/wiki/Tencent) (Chinese: 腾讯; pinyin: Téngxùn)
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> is a Chinese multinational technology conglomerate and holding company headquartered
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> in Shenzhen. `Tencent` is one of the highest grossing multimedia companies in the
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> world based on revenue. It is also the world's largest company in the video game industry
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> based on its equity investments.
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## Chat model
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>[Tencent's hybrid model API](https://cloud.tencent.com/document/product/1729) (`Hunyuan API`)
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> implements dialogue communication, content generation,
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> analysis and understanding, and can be widely used in various scenarios such as intelligent
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> customer service, intelligent marketing, role playing, advertising, copyrighting, product description,
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> script creation, resume generation, article writing, code generation, data analysis, and content
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> analysis.
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For more information, see [this notebook](/docs/integrations/chat/tencent_hunyuan)
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```python
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from langchain.chat_models import ChatHunyuan
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```
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## Vector Store
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>[Tencent Cloud VectorDB](https://www.tencentcloud.com/products/vdb) is a fully managed,
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> self-developed enterprise-level distributed database service
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>dedicated to storing, retrieving, and analyzing multidimensional vector data. The database supports a variety of index
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>types and similarity calculation methods, and a single index supports 1 billion vectors, millions of QPS, and
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>millisecond query latency. `Tencent Cloud Vector Database` can not only provide an external knowledge base for large
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>models and improve the accuracy of large models' answers, but also be widely used in AI fields such as
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>recommendation systems, NLP services, computer vision, and intelligent customer service.
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Install the Python SDK:
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```bash
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pip install tcvectordb
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```
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For more information, see [this notebook](/docs/integrations/vectorstores/tencentvectordb)
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```python
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from langchain.vectorstores import TencentVectorDB
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```
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## Document Loaders
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### Tencent COS
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>[Tencent Cloud Object Storage (COS)](https://www.tencentcloud.com/products/cos) is a distributed
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> storage service that enables you to store any amount of data from anywhere via HTTP/HTTPS protocols.
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> `COS` has no restrictions on data structure or format. It also has no bucket size limit and
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> partition management, making it suitable for virtually any use case, such as data delivery,
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> data processing, and data lakes. COS provides a web-based console, multi-language SDKs and APIs,
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> command line tool, and graphical tools. It works well with Amazon S3 APIs, allowing you to quickly
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> access community tools and plugins.
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Install the Python SDK:
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```bash
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pip install cos-python-sdk-v5
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```
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#### Tencent COS Directory
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For more information, see [this notebook](/docs/integrations/document_loaders/tencent_cos_directory)
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```python
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from langchain.document_loaders import TencentCOSDirectoryLoader
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from qcloud_cos import CosConfig
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```
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#### Tencent COS File
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For more information, see [this notebook](/docs/integrations/document_loaders/tencent_cos_file)
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```python
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from langchain.document_loaders import TencentCOSFileLoader
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from qcloud_cos import CosConfig
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```
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@ -1,15 +0,0 @@
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# TencentVectorDB
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This page covers how to use the TencentVectorDB ecosystem within LangChain.
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### VectorStore
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There exists a wrapper around TencentVectorDB, allowing you to use it as a vectorstore,
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whether for semantic search or example selection.
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To import this vectorstore:
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```python
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from langchain.vectorstores import TencentVectorDB
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```
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For a more detailed walkthrough of the TencentVectorDB wrapper, see [this notebook](/docs/integrations/vectorstores/tencentvectordb)
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@ -1,5 +1,13 @@
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{
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"redirects": [
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{
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"source": "/docs/integrations/providers/tencentvectordb",
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"destination": "/docs/integrations/providers/tencent"
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},
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{
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"source": "/docs/integrations/chat/hunyuan",
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"destination": "/docs/integrations/chat/tencent_hunyuan"
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},
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{
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"source": "/docs/integrations/providers/aws_dynamodb",
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"destination": "/docs/integrations/platforms/aws#aws-dynamodb"
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