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[PR #3552] docs: Add Tencent Cloud CosVectors integration documentation #3597

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opened 2026-06-05 18:23:37 -04:00 by yindo · 0 comments
yindo commented 2026-06-05 18:23:37 -04:00
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📋 Pull Request Information

Original PR: https://github.com/langchain-ai/docs/pull/3552
Author: @hushengquan
Created: 4/12/2026
Status: 🔄 Open

Base: main ← Head: add-cosvectors-integration

📝 Commits (1)

  • a03881b docs: Add CosVectors integration documentation

📊 Changes

4 files changed (+485 additions, -0 deletions)

View changed files

📝 src/oss/python/integrations/providers/all_providers.mdx (+8 -0)
➕ src/oss/python/integrations/providers/cosvectors.mdx (+162 -0)
➕ src/oss/python/integrations/vectorstores/cosvectors.mdx (+313 -0)
📝 src/oss/python/integrations/vectorstores/index.mdx (+2 -0)

📄 Description

Adds comprehensive documentation for Tencent Cloud CosVectors LangChain integration:

  • Provider overview page with setup instructions
  • Vector store integration with similarity search
  • Added CosVectors to provider list and vector store index

Features documented:

  • Vector storage buckets on Tencent Cloud COS
  • Cosine and Euclidean distance metrics
  • Metadata filtering
  • MMR (Maximal Marginal Relevance) search
  • Batch operations with configurable batch size
  • Auto-creation of buckets and indexes
  • Environment variable-based credential management

All code examples verified against langchain-cos-vectors v0.1.0

Overview

This PR adds complete documentation for the CosVectors integration (langchain-cos-vectors package, published to PyPI at
https://pypi.org/project/langchain-cos-vectors/).

CosVectors is a vector storage bucket product from Tencent Cloud Object Storage (COS) that provides vector storage, retrieval, and similarity search capabilities, making it well-suited for RAG workflows and semantic search applications.

Pages added:

  1. Provider overview (providers/cosvectors.mdx) — Installation, Tencent Cloud credential setup, embedding model selection (HuggingFace / OpenAI), and quick-start examples for vector store and retriever usage
  2. Vector store (vectorstores/cosvectors.mdx) — Complete guide for vector search with cosine/Euclidean distance metrics, metadata filtering, MMR search, batch operations, and RAG integration

Pages updated:
3. All providers index (providers/all_providers.mdx) — Added CosVectors provider card (alphabetically placed)
4. Vector stores index (vectorstores/index.mdx) — Added CosVectors to feature comparison table and "All vector stores" card list

Type of change

Type: New documentation page

Related issues/PRs

Checklist

  • I have read the contributing guidelines
  • I have tested my changes locally using docs dev
  • All code examples have been tested and work correctly
  • I have used root relative paths for internal links
  • I have updated navigation in src/docs.json if needed — N/A (pages auto-discovered via frontmatter)

Additional notes

API Verification

All code examples were verified against the actual implementation in langchain-cos-vectors v0.1.0:

  • Checked parameter names (embedding, cos_config, bucket, index, create_if_not_exists)
  • Verified method signatures (add_documents, add_texts, similarity_search, similarity_search_with_score, similarity_search_by_vector, max_marginal_relevance_search)
  • Tested from_texts class method with environment variable fallback

Technical Accuracy

Technical claims verified against Tencent Cloud COS official documentation:

  • Vector storage bucket capabilities
  • Supported distance metrics (cosine, euclidean)
  • Endpoint auto-generation format (vectors.{Region}.coslake.com)
  • Credential configuration (SecretId, SecretKey, Token)

Documentation Structure

Follows existing LangChain docs patterns:

  • Frontmatter format matches other integrations (CockroachDB, Couchbase, etc.)
  • Internal links use /oss/integrations/... pattern
  • Code examples follow established conventions
  • Embedding model selection uses <Tabs> component (HuggingFace / OpenAI)
  • Configuration parameters documented in tables

Files Modified

  • src/oss/python/integrations/providers/all_providers.mdx — Added CosVectors card
  • src/oss/python/integrations/vectorstores/index.mdx — Added CosVectors to feature comparison table and card list
  • src/oss/python/integrations/providers/cosvectors.mdx — NEW
  • src/oss/python/integrations/vectorstores/cosvectors.mdx — NEW

Related Resources

🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.

## 📋 Pull Request Information **Original PR:** https://github.com/langchain-ai/docs/pull/3552 **Author:** [@hushengquan](https://github.com/hushengquan) **Created:** 4/12/2026 **Status:** 🔄 Open **Base:** `main` ← **Head:** `add-cosvectors-integration` --- ### 📝 Commits (1) - [`a03881b`](https://github.com/langchain-ai/docs/commit/a03881b8f07f7fed387dc2831978b90d6e53d9f8) docs: Add CosVectors integration documentation ### 📊 Changes **4 files changed** (+485 additions, -0 deletions) <details> <summary>View changed files</summary> 📝 `src/oss/python/integrations/providers/all_providers.mdx` (+8 -0) ➕ `src/oss/python/integrations/providers/cosvectors.mdx` (+162 -0) ➕ `src/oss/python/integrations/vectorstores/cosvectors.mdx` (+313 -0) 📝 `src/oss/python/integrations/vectorstores/index.mdx` (+2 -0) </details> ### 📄 Description Adds comprehensive documentation for Tencent Cloud CosVectors LangChain integration: - Provider overview page with setup instructions - Vector store integration with similarity search - Added CosVectors to provider list and vector store index Features documented: - Vector storage buckets on Tencent Cloud COS - Cosine and Euclidean distance metrics - Metadata filtering - MMR (Maximal Marginal Relevance) search - Batch operations with configurable batch size - Auto-creation of buckets and indexes - Environment variable-based credential management All code examples verified against langchain-cos-vectors v0.1.0 ## Overview This PR adds complete documentation for the CosVectors integration (`langchain-cos-vectors` package, published to PyPI at https://pypi.org/project/langchain-cos-vectors/). CosVectors is a vector storage bucket product from Tencent Cloud Object Storage (COS) that provides vector storage, retrieval, and similarity search capabilities, making it well-suited for RAG workflows and semantic search applications. **Pages added:** 1. **Provider overview** (`providers/cosvectors.mdx`) — Installation, Tencent Cloud credential setup, embedding model selection (HuggingFace / OpenAI), and quick-start examples for vector store and retriever usage 2. **Vector store** (`vectorstores/cosvectors.mdx`) — Complete guide for vector search with cosine/Euclidean distance metrics, metadata filtering, MMR search, batch operations, and RAG integration **Pages updated:** 3. **All providers index** (`providers/all_providers.mdx`) — Added CosVectors provider card (alphabetically placed) 4. **Vector stores index** (`vectorstores/index.mdx`) — Added CosVectors to feature comparison table and "All vector stores" card list ## Type of change **Type:** New documentation page ## Related issues/PRs - Feature PR: N/A (package already published and maintained) - Package: https://pypi.org/project/langchain-cos-vectors/ (v0.1.0) ## Checklist - [x] I have read the [contributing guidelines](README.md) - [x] I have tested my changes locally using `docs dev` - [x] All code examples have been tested and work correctly - [x] I have used **root relative** paths for internal links - [x] I have updated navigation in `src/docs.json` if needed — N/A (pages auto-discovered via frontmatter) ## Additional notes ### API Verification All code examples were verified against the actual implementation in `langchain-cos-vectors` v0.1.0: - Checked parameter names (`embedding`, `cos_config`, `bucket`, `index`, `create_if_not_exists`) - Verified method signatures (`add_documents`, `add_texts`, `similarity_search`, `similarity_search_with_score`, `similarity_search_by_vector`, `max_marginal_relevance_search`) - Tested `from_texts` class method with environment variable fallback ### Technical Accuracy Technical claims verified against Tencent Cloud COS official documentation: - Vector storage bucket capabilities - Supported distance metrics (cosine, euclidean) - Endpoint auto-generation format (`vectors.{Region}.coslake.com`) - Credential configuration (SecretId, SecretKey, Token) ### Documentation Structure Follows existing LangChain docs patterns: - Frontmatter format matches other integrations (CockroachDB, Couchbase, etc.) - Internal links use `/oss/integrations/...` pattern - Code examples follow established conventions - Embedding model selection uses `<Tabs>` component (HuggingFace / OpenAI) - Configuration parameters documented in tables ### Files Modified - `src/oss/python/integrations/providers/all_providers.mdx` — Added CosVectors card - `src/oss/python/integrations/vectorstores/index.mdx` — Added CosVectors to feature comparison table and card list - `src/oss/python/integrations/providers/cosvectors.mdx` — **NEW** - `src/oss/python/integrations/vectorstores/cosvectors.mdx` — **NEW** ### Related Resources - Package: https://pypi.org/project/langchain-cos-vectors/ - Tencent Cloud COS Documentation: https://cloud.tencent.com/document/product/436 - Tencent Cloud Console: https://console.cloud.tencent.com --- <sub>🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.</sub>
yindo added the pull-request label 2026-06-05 18:23:37 -04:00
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Reference: langchain-ai/docs#3597
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