semvec/pypi-packages huggingface dataset:
toika-loom-server 1.0
Web server that drives a Toika dobby loom
Requires Python: >=3.11

# Web server that controls a Toika dobby loom

This server is intended to allow you to control your loom from any phone, tablet or other device that has wifi and a web browser.

For for usage and installation instructions please see the [documentation](https://r-owen.github.io/toika_loom_server/).

Links:

* [Homepage](https://pypi.org/project/toika-loom-server/)
* [Documentation](https://r-owen.github.io/toika_loom_server/)
* [Source](https://github.com/r-owen/toika_loom_server)
* [Issues](https://github.com/r-owen/toika_loom_server/issues)
langchain-agensgraph 0.1.0
AgensGraph integration for LangChain graph and vector stores
Requires Python: <4.0,>=3.9
Keywords: langchain, agensgraph, graph store, vector store, integration

# 🦜️🔗 LangChain AgensGraph

This plugin provides support for GraphStore and VectorStore integration in [langchain](https://www.langchain.com/), for persisting graphs directly in [AgensGraph](https://github.com/skaiworldwide-oss/agensgraph).

See the associated guide below:

- [Agens Graph Store](./examples/agensgraph.ipynb)

## 📦 Installation

```bash
pip install -U langchain-agensgraph
```

## 💻 Usage

### AgensGraph
The AgensGraph class provides functionality to interact with agensgraph as
a graphstore in langchain.

```python
from langchain_agensgraph.graphs.agensgraph import AgensGraph

conf = {
    "dbname": "",
    "user": "",
    "password": "",
    "host": "",
    "port": ,
}

graph = AgensGraph(graph_name="", conf=conf)
graph.query("MATCH (n) RETURN n LIMIT 1;")
```

### AgensgraphVector

The `AgensgraphVector` class provides functionality for managing an AgensGraph vector store.
It enables you to create new vector indexes, add vectors to existing indexes, and perform queries using vector indexes.

```python
from langchain.docstore.document import Document
from langchain_openai import OpenAIEmbeddings

from langchain_agensgraph.vectorstores.agensgraph_vector import AgensgraphVector

# Create a vector store from some documents and embeddings
docs = [
    Document(
        page_content=(
            "LangChain is a framework to build "
            "with LLMs by chaining interoperable components."
        ),
    )
]
embeddings = OpenAIEmbeddings(
    model="text-embedding-3-large",
    api_key="sk-...",  # Replace with your OpenAI API key
)
db = AgensgraphVector.from_documents(
    docs,
    embeddings,
    url="postgresql://username:password@host:port/dbname"
)
# Query the vector store for similar documents
docs_with_score = db.similarity_search_with_score("What is LangChain?", k=1)
```