mirror of https://github.com/hwchase17/langchain
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 notebookpull/14882/head
Leonid Ganeline
10 months ago
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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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# 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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