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- 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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# Tencent | ||
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>[Tencent Holdings Ltd. (Wikipedia)](https://en.wikipedia.org/wiki/Tencent) (Chinese: 腾讯; pinyin: Téngxùn) | ||
> is a Chinese multinational technology conglomerate and holding company headquartered | ||
> in Shenzhen. `Tencent` is one of the highest grossing multimedia companies in the | ||
> world based on revenue. It is also the world's largest company in the video game industry | ||
> 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`) | ||
> implements dialogue communication, content generation, | ||
> analysis and understanding, and can be widely used in various scenarios such as intelligent | ||
> customer service, intelligent marketing, role playing, advertising, copyrighting, product description, | ||
> script creation, resume generation, article writing, code generation, data analysis, and content | ||
> analysis. | ||
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For more information, see [this notebook](/docs/integrations/chat/tencent_hunyuan) | ||
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```python | ||
from langchain.chat_models import ChatHunyuan | ||
``` | ||
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## Vector Store | ||
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>[Tencent Cloud VectorDB](https://www.tencentcloud.com/products/vdb) is a fully managed, | ||
> self-developed enterprise-level distributed database service | ||
>dedicated to storing, retrieving, and analyzing multidimensional vector data. The database supports a variety of index | ||
>types and similarity calculation methods, and a single index supports 1 billion vectors, millions of QPS, and | ||
>millisecond query latency. `Tencent Cloud Vector Database` can not only provide an external knowledge base for large | ||
>models and improve the accuracy of large models' answers, but also be widely used in AI fields such as | ||
>recommendation systems, NLP services, computer vision, and intelligent customer service. | ||
Install the Python SDK: | ||
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```bash | ||
pip install tcvectordb | ||
``` | ||
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For more information, see [this notebook](/docs/integrations/vectorstores/tencentvectordb) | ||
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```python | ||
from langchain.vectorstores import TencentVectorDB | ||
``` | ||
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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 | ||
> storage service that enables you to store any amount of data from anywhere via HTTP/HTTPS protocols. | ||
> `COS` has no restrictions on data structure or format. It also has no bucket size limit and | ||
> partition management, making it suitable for virtually any use case, such as data delivery, | ||
> data processing, and data lakes. COS provides a web-based console, multi-language SDKs and APIs, | ||
> command line tool, and graphical tools. It works well with Amazon S3 APIs, allowing you to quickly | ||
> access community tools and plugins. | ||
Install the Python SDK: | ||
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```bash | ||
pip install cos-python-sdk-v5 | ||
``` | ||
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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 | ||
from langchain.document_loaders import TencentCOSDirectoryLoader | ||
from qcloud_cos import CosConfig | ||
``` | ||
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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 | ||
from langchain.document_loaders import TencentCOSFileLoader | ||
from qcloud_cos import CosConfig | ||
``` |
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