MongoDB Handshake

Export Chonkie’s Chunks into a MongoDB collection.

The MongoDBHandshake class provides integration between Chonkie’s chunking system and MongoDB, a popular NoSQL database.

Embed and store your Chonkie chunks in MongoDB directly from the Chonkie SDK.

Installation

Before using the MongoDB handshake, make sure to install the required dependencies:

pip install chonkie[mongodb]

Initialization

Initialize using chonkie

from chonkie import MongoDBHandshake

# Initialize with default settings (auto-generated database/collection)
handshake = MongoDBHandshake(uri="mongodb://localhost:27017")

import pymongo
client = pymongo.MongoClient("mongodb://localhost:27017")
handshake = MongoDBHandshake(
    client=client, 
    db_name="my_db", 
    collection_name="my_collection"
)

handshake = MongoDBHandshake(
    uri="mongodb://localhost:27017",
    db_name="my_db",
    collection_name="my_collection",
    embedding_model="minishlab/potion-retrieval-32M"
)

Parameters

client · Optional[pymongo.MongoClient] · default: "None"

MongoDB client instance. If not provided, a new client will be created based on other parameters.

uri · Optional[str] · default: "None"

MongoDB connection URI.

username · Optional[str] · default: "None"

MongoDB username for authentication.

password · Optional[str] · default: "None"

MongoDB password for authentication.

hostname · Optional[str] · default: "None"

MongoDB host address.

port · Optional[Union[int, str]] · default: "None"

MongoDB port number.

db_name · Union[str, Literal['random']] · default: "random"

Name of the database to use. If “random”, a unique name will be generated.

collection_name · Union[str, Literal['random']] · default: "random"

Name of the collection to use. If “random”, a unique name will be generated.

embedding_model · Union[str, BaseEmbeddings] · default: "minishlab/potion-retrieval-32M"

Embedding model to use. Can be a model name or a BaseEmbeddings instance.

**kwargs · dict[str, Any] · default: "{}"

Additional keyword arguments to pass to the MongoDB client or collection creation.

Writing Chunks to MongoDB

from chonkie import MongoDBHandshake, SemanticChunker    

# Initialize the handshake
handshake = MongoDBHandshake(
    uri="mongodb://localhost:27017",
    db_name="my_documents",
    collection_name="my_collection"
)

# Create some chunks
chunker = SemanticChunker()
chunks = chunker.chunk("Chonkie loves to chonk your texts!")

# Write chunks to MongoDB
handshake.write(chunks)

Searching Chunks in MongoDB

You can retrieve the most similar chunks from your MongoDB collection using the search method:

search using a query

from chonkie import MongoDBHandshake

# Initialize the handshake
handshake = MongoDBHandshake(
    uri="mongodb://localhost:27017",
    db_name="my_documents",
    collection_name="my_collection"
)
results = handshake.search(query="chonk your texts", limit=2)
for result in results:
    print(result["score"], result["text"])

from chonkie import MongoDBHandshake

# Initialize the handshake
handshake = MongoDBHandshake(
    uri="mongodb://localhost:27017",
    db_name="my_documents",
    collection_name="my_collection"
)
embedding = handshake.embedding_model.embed("chonk your texts").tolist()
results = handshake.search(embedding=embedding, limit=2)
for result in results:
    print(result["score"], result["text"])

from chonkie import MongoDBHandshake, SemanticChunker

# Initialize the handshake
handshake = MongoDBHandshake(
    uri="mongodb://localhost:27017",
    db_name="my_documents",
    collection_name="my_collection"
)

# Create some chunks
chunker = SemanticChunker(embedding_model=handshake.embedding_model)
chunks = chunker.chunk("Chonkie loves to chonk your texts!")

# Search the handshake
results = handshake.search(
    embedding=chunks[0].sentences[0].embedding, 
    limit=2,
)
for result in results:
    print(result["score"], result["text"])