semvec/pypi-packages huggingface dataset:
langchain-voyageai 0.3.2
An integration package connecting VoyageAI and LangChain
License: MIT
Requires Python: <=3.13,>=3.10

# langchain-voyageai

This package contains the LangChain integrations for VoyageAI through their `voyageai` client package.

## Installation and Setup

- Install the LangChain partner package
```bash
pip install langchain-voyageai
```
- Get an VoyageAI api key and set it as an environment variable (`VOYAGE_API_KEY`) or use the API key as a parameter in the Client.



## Text Embedding Model

See a [usage example](https://python.langchain.com/docs/integrations/text_embedding/voyageai)

```python
from langchain_voyageai import VoyageAIEmbeddings
```

### Voyage-4 Family Models

The latest generation of VoyageAI embedding models with improved quality and flexibility:

```python
# voyage-4: Balanced model for general-purpose and multilingual retrieval
embeddings = VoyageAIEmbeddings(model="voyage-4")

# voyage-4-lite: Optimized for latency and cost, highest batch throughput (1M tokens/batch)
embeddings = VoyageAIEmbeddings(model="voyage-4-lite")

# voyage-4-large: Best retrieval quality for demanding applications
embeddings = VoyageAIEmbeddings(model="voyage-4-large")
```

All voyage-4 family models support flexible output dimensions (256, 512, 1024, 2048):

```python
embeddings = VoyageAIEmbeddings(
    model="voyage-4",
    output_dimension=512  # Choose from: 256, 512, 1024, 2048
)
```

### Custom API Endpoint

You can specify a custom API endpoint using the `base_url` parameter:

```python
embeddings = VoyageAIEmbeddings(
    model="voyage-3.5",
    base_url="https://ai.mongodb.com/v1"
)
```

This is useful for MongoDB Atlas users or custom deployments.
ukraine 0.4.0
Ukraine: A Data Science Toolkit
License: MIT
Requires Python: >=3.7

# Ukraine

Ukraine is a deep learning toolkit that includes research models, approaches and utils.

## Installation

```bash
pip install -U ukraine[langchain_llama]
```

```python
from ukraine.agents.rag import PDFLlamaRAGAgent

agent = PDFLlamaRAGAgent(
    file_path="PATH_TO_PDF",
    system_prompt="""Provide answers based on the document."{context}"""
)
result = agent.chat("What is this document about?")
print(result["answer"])
```
[View this example in the cookbook](./cookbook/rag_cookbook.ipynb)

### 🧠 Introducing Complex AI Agents

A new file has been added:  `ukraine/agents/complex.py`

Includes the `OpenAIJiraNoteAgent` class for automated conversion of audio file that contains meeting recording into Jira tasks using OpenAI + LangChain integrations.

**Capabilities:**
- Transcribes audio to text using `whisper-1`
- Breaks down transcribed instructions into actionable tasks
- Creates structured Jira tickets via LangChain `JiraToolkit`

---

## Installation

```bash
pip install -U ukraine[jira]
```

---

## Example usage

```python
from ukraine.agents.complex import OpenAIJiraNoteAgent

agent = OpenAIJiraNoteAgent()
transcription = agent.transcribe_audio("audio.ogg")
tasks = agent.convert_text_to_tasks(transcription, max_tokens=368)
agent.create_jira_tickets(tasks, project_key="KAN", language="ukrainian")
```

---

## Environment variables required

```bash
OPENAI_API_KEY=<your_openai_key>
JIRA_API_TOKEN=<your_jira_token>
JIRA_USERNAME=<your_email>
JIRA_INSTANCE_URL=https://yourproject.atlassian.net
JIRA_CLOUD=True
```