Upload File

This endpoint is used to directly upload local files to Carbon. The POST request should be a multipart form request. Note that the set_page_as_boundary query parameter is applicable only to PDFs for now. When this value is set, PDF chunks are at most one page long. Additional information can be retrieved for each chunk, however, namely the coordinates of the bounding box around the chunk (this can be used for things like text highlighting). Following is a description of all possible query parameters:

• chunk_size: the chunk size (in tokens) applied when splitting the document
• chunk_overlap: the chunk overlap (in tokens) applied when splitting the document
• skip_embedding_generation: whether or not to skip the generation of chunks and embeddings
• set_page_as_boundary: described above
• embedding_model: the model used to generate embeddings for the document chunks
• use_ocr: whether or not to use OCR as a preprocessing step prior to generating chunks (only valid for PDFs currently)
• generate_sparse_vectors: whether or not to generate sparse vectors for the file. Required for hybrid search.
• prepend_filename_to_chunks: whether or not to prepend the filename to the chunk text

Carbon supports multiple models for use in generating embeddings for files. For images, we support Vertex AI’s multimodal model; for text, we support OpenAI’s text-embedding-ada-002 and Cohere’s embed-multilingual-v3.0. The model can be specified via the embedding_model parameter (in the POST body for /embeddings, and a query parameter in /uploadfile). If no model is supplied, the text-embedding-ada-002 is used by default. When performing embedding queries, embeddings from files that used the specified model will be considered in the query. For example, if files A and B have embeddings generated with OPENAI, and files C and D have embeddings generated with COHERE_MULTILINGUAL_V3, then by default, queries will only consider files A and B. If COHERE_MULTILINGUAL_V3 is specified as the embedding_model in /embeddings, then only files C and D will be considered. Make sure that the set of all files you want considered for a query have embeddings generated via the same model. For now, do not set VERTEX_MULTIMODAL as an embedding_model. This model is used automatically by Carbon when it detects an image file.