| Name | langchain-kusto JSON |
| Version |
0.0.1
JSON |
| download |
| home_page | None |
| Summary | LangChain vector store implementation for Azure Data Explorer (Kusto) - PRE-ALPHA VERSION |
| upload_time | 2025-09-04 01:20:28 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | The MIT License (MIT)
Copyright (c) 2025 Daniel Dror
Permission is hereby granted, free of charge, to any person obtaining a copy
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THE SOFTWARE. |
| keywords |
langchain
kusto
azure
eventhouse
fabric
data-explorer
vector-store
llm
|
| VCS |
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# LangChain Kusto Vector Store
**⚠️ PRE-ALPHA VERSION - This package is in very early development and not recommended for production use.**
A LangChain vector store implementation for Azure Data Explorer (Kusto), Microsoft Fabric Eventhouse, and other Kusto-compatible databases.
## Current Status
This is a **very initial version** with **only retrieval capabilities**. Document storage functionality is not yet implemented.
## Features
- ✅ Retrieve vector embeddings from Azure Data Explorer (Kusto) or Microsoft Fabric Eventhouse
- ✅ Compatible with LangChain's vector store interface
- ✅ Similarity search with cosine similarity metric
- ❌ Document storage (not yet implemented)
- ❌ Batch operations (not yet implemented)
## Installation
```bash
pip install langchain-kusto
```
## Quick Start
```python
from langchain_kusto import KustoVectorStore
from langchain_openai import AzureOpenAIEmbeddings
from azure.identity import DefaultAzureCredential
# Initialize embeddings
embeddings = AzureOpenAIEmbeddings(
azure_endpoint="your-openai-endpoint",
azure_deployment="your-embedding-deployment",
openai_api_version="2023-05-15"
)
# Initialize the vector store (retrieval only)
vector_store = KustoVectorStore(
connection="https://your-cluster.kusto.windows.net", # or KustoConnectionStringBuilder
database="your_database",
collection_name="your_table",
embedding=embeddings,
embedding_column="embedding_text", # optional, defaults to "embedding"
id_column="vector_id", # optional, defaults to "id"
content_column="doc_text" # optional, defaults to "text"
)
# Search for similar documents (this requires pre-existing data in Kusto)
results = vector_store.similarity_search("your query text", k=5)
```
## Complete Example
See [demo.py](demo.py) for a complete working example using Azure OpenAI embeddings and a RAG (Retrieval-Augmented Generation) pipeline.
## Requirements
- Python >= 3.8
- Azure Data Explorer cluster or Microsoft Fabric Eventhouse with pre-existing vector data
- LangChain Core >= 0.1.0
- Azure authentication (DefaultAzureCredential)
## Data Prerequisites
Since this version only supports retrieval, you need to have your vector embeddings already stored in a Kusto table with the following structure:
```kql
.create table your_table (
vector_id: string,
doc_text: string,
embedding_text: dynamic // Array of float values representing the vector
// ... other metadata columns
)
```
## Development Status
This package is currently in pre-alpha development. APIs may change significantly between versions. The current version only supports reading existing vector data from Kusto - document ingestion and storage capabilities will be added in future releases.
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
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"description": "# LangChain Kusto Vector Store\r\n\r\n**\u26a0\ufe0f PRE-ALPHA VERSION - This package is in very early development and not recommended for production use.**\r\n\r\nA LangChain vector store implementation for Azure Data Explorer (Kusto), Microsoft Fabric Eventhouse, and other Kusto-compatible databases.\r\n\r\n## Current Status\r\n\r\nThis is a **very initial version** with **only retrieval capabilities**. Document storage functionality is not yet implemented.\r\n\r\n## Features\r\n\r\n- \u2705 Retrieve vector embeddings from Azure Data Explorer (Kusto) or Microsoft Fabric Eventhouse\r\n- \u2705 Compatible with LangChain's vector store interface\r\n- \u2705 Similarity search with cosine similarity metric\r\n- \u274c Document storage (not yet implemented)\r\n- \u274c Batch operations (not yet implemented)\r\n\r\n## Installation\r\n\r\n```bash\r\npip install langchain-kusto\r\n```\r\n\r\n## Quick Start\r\n\r\n```python\r\nfrom langchain_kusto import KustoVectorStore\r\nfrom langchain_openai import AzureOpenAIEmbeddings\r\nfrom azure.identity import DefaultAzureCredential\r\n\r\n# Initialize embeddings\r\nembeddings = AzureOpenAIEmbeddings(\r\n azure_endpoint=\"your-openai-endpoint\",\r\n azure_deployment=\"your-embedding-deployment\",\r\n openai_api_version=\"2023-05-15\"\r\n)\r\n\r\n# Initialize the vector store (retrieval only)\r\nvector_store = KustoVectorStore(\r\n connection=\"https://your-cluster.kusto.windows.net\", # or KustoConnectionStringBuilder\r\n database=\"your_database\",\r\n collection_name=\"your_table\",\r\n embedding=embeddings,\r\n embedding_column=\"embedding_text\", # optional, defaults to \"embedding\"\r\n id_column=\"vector_id\", # optional, defaults to \"id\"\r\n content_column=\"doc_text\" # optional, defaults to \"text\"\r\n)\r\n\r\n# Search for similar documents (this requires pre-existing data in Kusto)\r\nresults = vector_store.similarity_search(\"your query text\", k=5)\r\n```\r\n\r\n## Complete Example\r\n\r\nSee [demo.py](demo.py) for a complete working example using Azure OpenAI embeddings and a RAG (Retrieval-Augmented Generation) pipeline.\r\n\r\n## Requirements\r\n\r\n- Python >= 3.8\r\n- Azure Data Explorer cluster or Microsoft Fabric Eventhouse with pre-existing vector data\r\n- LangChain Core >= 0.1.0\r\n- Azure authentication (DefaultAzureCredential)\r\n\r\n## Data Prerequisites\r\n\r\nSince this version only supports retrieval, you need to have your vector embeddings already stored in a Kusto table with the following structure:\r\n\r\n```kql\r\n.create table your_table (\r\n vector_id: string,\r\n doc_text: string,\r\n embedding_text: dynamic // Array of float values representing the vector\r\n // ... other metadata columns\r\n)\r\n```\r\n\r\n## Development Status\r\n\r\nThis package is currently in pre-alpha development. APIs may change significantly between versions. The current version only supports reading existing vector data from Kusto - document ingestion and storage capabilities will be added in future releases.\r\n\r\n## Contributing\r\n\r\nContributions are welcome! Please feel free to submit a Pull Request.\r\n\r\n## License\r\n\r\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\r\n",
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"license": "The MIT License (MIT)\r\n \r\n Copyright (c) 2025 Daniel Dror\r\n \r\n Permission is hereby granted, free of charge, to any person obtaining a copy\r\n of this software and associated documentation files (the \"Software\"), to deal\r\n in the Software without restriction, including without limitation the rights\r\n to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\r\n copies of the Software, and to permit persons to whom the Software is\r\n furnished to do so, subject to the following conditions:\r\n \r\n The above copyright notice and this permission notice shall be included in\r\n all copies or substantial portions of the Software.\r\n \r\n THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\r\n IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\r\n FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\r\n AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\r\n LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\r\n OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\r\n THE SOFTWARE.",
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