Chroma Handshake

from chonkie import ChromaHandshake

# Initialize the handshake
handshake = ChromaHandshake(collection_name="my_documents")
results = handshake.search(query="best chonker", limit=2)
for result in results:
    print(result["score"], result["text"])

from chonkie import ChromaHandshake

# Initialize the handshake
handshake = ChromaHandshake(collection_name="my_documents")

# Generate an embedding using the handshake's configured embedding function
embedding = handshake.embedding_function("best chonker").tolist()

results = handshake.search(embedding=embedding, limit=2)
for result in results:
    print(result["score"], result["text"])

from chonkie import ChromaHandshake, SemanticChunker

# Initialize the handshake
handshake = ChromaHandshake(collection_name="my_documents")

# Create some chunks
chunker = SemanticChunker()
chunks = chunker.chunk("Chonkie is the best chonker ever!")

# The chunker creates chunks but doesn't store their embeddings on the object.
# We can generate an embedding from a chunk's text to use for the search.
if chunks:
    search_embedding = handshake.embedding_function(chunks.text).tolist()

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