semvec/pypi-packages huggingface dataset:
nocturne-api 0.4
Terminal API workbench for REST, GraphQL, and WebSockets using Textual.
Requires Python: >=3.10

# Nocturne API Workbench

Interactive terminal UI for exploring REST, GraphQL, and WebSocket APIs using [Textual](https://textual.textualize.io/).

## Features
- Request builder for common HTTP verbs with configurable headers and body.
- GraphQL console with query and variables editors.
- WebSocket client with live activity log and connect/disconnect controls.
- Clean Textual layout with status feedback and response formatting.

## Setup
```bash
python -m venv .venv
.venv\Scripts\activate          # On PowerShell
pip install -r requirements.txt
```

## Run
```bash
python app.py
```
# Or
```bash
nocturne
```
## Tips
- Headers use `Key: Value` on separate lines.
- GraphQL variables must be valid JSON.
- WebSocket messages are sent as plain text; responses stream into the activity panel.
llama-index-retrievers-bedrock 0.5.1
llama-index retrievers bedrock integration
Requires Python: <4.0,>=3.9

# LlamaIndex Retrievers Integration: Bedrock

## Knowledge Bases

> [Knowledge bases for Amazon Bedrock](https://aws.amazon.com/bedrock/knowledge-bases/) is an Amazon Web Services (AWS) offering which lets you quickly build RAG applications by using your private data to customize FM response.

> Implementing `RAG` requires organizations to perform several cumbersome steps to convert data into embeddings (vectors), store the embeddings in a specialized vector database, and build custom integrations into the database to search and retrieve text relevant to the user’s query. This can be time-consuming and inefficient.

> With `Knowledge Bases for Amazon Bedrock`, simply point to the location of your data in `Amazon S3`, and `Knowledge Bases for Amazon Bedrock` takes care of the entire ingestion workflow into your vector database. If you do not have an existing vector database, Amazon Bedrock creates an Amazon OpenSearch Serverless vector store for you.

> Knowledge base can be configured through [AWS Console](https://aws.amazon.com/console/) or by using [AWS SDKs](https://aws.amazon.com/developer/tools/).

## Installation

```
pip install llama-index-retrievers-bedrock
```

## Usage

```
from llama_index.retrievers.bedrock import AmazonKnowledgeBasesRetriever

retriever = AmazonKnowledgeBasesRetriever(
    knowledge_base_id="<knowledge-base-id>",
    retrieval_config={
        "vectorSearchConfiguration": {
            "numberOfResults": 4,
            "overrideSearchType": "HYBRID",
            "filter": {"equals": {"key": "tag", "value": "space"}},
        }
    },
)

query = "How big is Milky Way as compared to the entire universe?"
retrieved_results = retriever.retrieve(query)

# Prints the first retrieved result
print(retrieved_results[0].get_content())
```

## Notebook

Explore the retriever using Notebook present at:
https://docs.llamaindex.ai/en/latest/examples/retrievers/bedrock_retriever/