etoile-generate 2025.5.160702

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etoile_generate

etoile_generate is a Python package developed for the Mistral AI <> a16z London Hackathon held on 06/10/2024. This package facilitates the generation of structured data from natural language queries using machine learning models, integrating with LangChain and HuggingFace technologies to provide seamless experience in transforming text into actionable insights within knowledge graphs.

Installation

To install etoile_generate, use pip:

pip install etoile_generate

Usage

Here is a detailed example of using etoile_generate to extract nodes from natural language texts and transform them into structured JSON format:

Define the Model

from langchain_mistralai import ChatMistralAI

MODEL = "mistral-large-latest"
llm = ChatMistralAI(
    model=MODEL,
    temperature=0,
    max_retries=2
)

Define the Pydantic Models

from langchain_core.pydantic_v1 import BaseModel, Field
from typing import List

class Node(BaseModel):
    node_name: str = Field(..., title="Name of the node")

class Nodes(BaseModel):
    nodes: List[Node] = Field(..., title="List of nodes")

Generate Nodes

from etoile_generate import generate
from langchain_huggingface import HuggingFaceEmbeddings

texts = ["Your text data..."]
query = """
Generate nodes from the given texts.
Nodes are the basic building blocks of a knowledge graph.
The output will be a list of nodes.
Example:
{
    "nodes": [
        {
            "node_name": "Node 1"
        },
        {
            "node_name": "Node 2"
        }
    ]
}
Write the response in JSON format within ``` tags and provide the response only, without any additional explanation.
"""

# Invoke the generation function
result = generate(
    model=llm,
    embeddings=HuggingFaceEmbeddings(),
    pydantic_object=Nodes,
    texts=texts,
    query=query,
    chat_history=[]
)
print(result)
# Example Output: Nodes(nodes=[Node(node_name='Playwright'), ... (list all example nodes) ...])

Image Processing

etoile_generate can also process images by accepting image URLs as part of the input:

after_query = HumanMessage(content=[{
    "type": "image_url",
    "image_url": {"url": "https://example.com/path/to/image.jpg"}
}])

result = generate(
    model=llm,
    embeddings=HuggingFaceEmbeddings(),
    pydantic_object=TestContentSafety,
    texts=[],
    query="Query to analyze the image content for safety",
    chat_history=[],
    after_query=after_query,
    verbose=True
)
print(result)

Features

• Generate structured JSON outputs from natural language inputs.
• Integrated vector storage and retrieval through FAISS.
• Utilizes state-of-the-art embeddings and LLMs from HuggingFace and LangChain.

Contributing

Contributions, issues, and feature requests are welcome! Please feel free to check the issues page.

License

etoile_generate is licensed under the MIT License.

Acknowledgements

This package was created by Evgenii (Eugene) Evstafev for the project Mistral Étoile at the Mistral AI <> a16z London Hackathon. More details about the event can be found here.