not able use colbert embeddings with llamaindex

[pydv9991]

Question Validation

• [X] I have searched both the documentation and discord for an answer.

Question

how to use fastembed lateinteraction embedding module in llamaindex as colbert is available inside it only?

[dosubot[bot]]

Hi @pydv9991! I'm here to help you with any bugs, questions, or contributions you have. I'll do my best to assist you while we wait for a human maintainer.

To use the FastEmbed LateInteraction embedding module in LlamaIndex with ColBERT embeddings, follow these steps:

1. Install the fastembed Python package:

   pip install fastembed

2. Import and initialize the FastEmbedEmbedding class:

   from llama_index.embeddings.fastembed import FastEmbedEmbedding
   
   # Initialize the FastEmbedEmbedding with the desired model name
   fastembed = FastEmbedEmbedding(model_name="BAAI/bge-small-en-v1.5")
   
   # Example usage to get text embedding
   text = "Your text here"
   text_embedding = fastembed._get_text_embedding(text)
   
   # Example usage to get query embedding
   query = "Your query here"
   query_embedding = fastembed._get_query_embedding(query)

3. Adjust configuration settings if needed: You can specify various parameters such as model_name, show_progress, nbits, gpus, ranks, doc_maxlen, query_maxlen, kmeans_niters, similarity_top_k, filters, doc_ids, and colbert_kwargs to fine-tune the integration.

   def __init__(
          self,
          nodes: Optional[Sequence[BaseNode]] = None,
          objects: Optional[Sequence[IndexNode]] = None,
          index_struct: Optional[IndexDict] = None,
          service_context: Optional[ServiceContext] = None,
          storage_context: Optional[StorageContext] = None,
          model_name: str = "colbert-ir/colbertv2.0",
          index_name: str = "",
          show_progress: bool = False,
          nbits: int = 2,
          gpus: int = 0,
          ranks: int = 1,
          doc_maxlen: int = 120,
          query_maxlen: int = 60,
          kmeans_niters: int = 4,
          similarity_top_k: int = 10,
          filters: Optional[MetadataFilters] = None,
          doc_ids: Optional[List[str]] = None,
          colbert_kwargs: dict = {},
          **kwargs: Any,
      ) -> None:
          self.model_name = model_name
          self.index_path = "storage/colbert_index"
          self.index_name = index_name
          self.nbits = nbits
          self.gpus = gpus
          self.ranks = ranks
          self.doc_maxlen = doc_maxlen
          self.query_maxlen = query_maxlen
          self.kmeans_niters = kmeans_niters
          self.similarity_top_k = similarity_top_k
          self.filters = filters
          self.doc_ids = doc_ids
          self.colbert_kwargs = colbert_kwargs
          self._docs_pos_to_node_id: Dict[int, str] = {}
          try:
              pass
          except ImportError as exc:
              raise ImportError(
                  "Please install colbert to use this feature from the repo:",
                  "https://github.com/stanford-futuredata/ColBERT",
              ) from exc
          super().__init__(
              nodes=nodes,
              index_struct=index_struct,
              index_name=index_name,
              service_context=service_context,
              storage_context=storage_context,
              show_progress=show_progress,
              objects=objects,
              **kwargs,
          )

By following these steps, you should be able to integrate ColBERT embeddings with the FastEmbed LateInteraction module in LlamaIndex ^[1][2][3].

To continue talking to Dosu, mention @dosu.

[pydv9991]

@dosu it says colbert is not in model list.

[dosubot[bot]]

To resolve the issue where it says "colbert is not in model list" and successfully integrate ColBERT embeddings with the FastEmbed module in LlamaIndex, you need to ensure that the ColbertIndex is properly imported and available in your module.

Here is an example of how you can do this:

from llama_index.legacy.indices.managed.colbert_index.base import ColbertIndex

# Ensure ColbertIndex is in the model list
__all__ = ["ColbertIndex"]

# Your integration code with FastEmbed module
# Example:
# from llama_index.embeddings.fastembed import FastEmbed
# fast_embed = FastEmbed()
# colbert_index = ColbertIndex()
# fast_embed.add_index(colbert_index)

This ensures that ColbertIndex is included in the module's __all__ list, making it available for integration with other modules like FastEmbed. Additionally, make sure you have installed the ColBERT package from the repository: ColBERT, as it is required for using the ColbertIndex class ^[1][2][3].

To continue talking to Dosu, mention @dosu.

[pydv9991]

ValueError: Model colbert-ir/colbertv2.0 is not supported in TextEmbedding.Please check the supported models using `TextEmbedding.list_supported_models()

[pydv9991]

Can you provide a solution in which i can use colbert using fastembed these solution are not working

[dosubot[bot]]

To successfully use ColBERT embeddings with the FastEmbed module in LlamaIndex without encountering the ValueError stating that the model colbert-ir/colbertv2.0 is not supported, follow these steps:

1. Install ColBERT: Ensure you have the ColBERT library installed.

   git clone https://github.com/stanford-futuredata/ColBERT
   cd ColBERT
   pip install -r requirements.txt
   python setup.py install

2. Check Model Availability: Verify that the model colbert-ir/colbertv2.0 is available and accessible.

3. Initialize ColbertIndex: Use the ColbertIndex class to initialize the index with the required parameters.

   from llama_index.indices.managed.colbert.base import ColbertIndex
   from llama_index.core.schema import BaseNode
   
   # Example nodes
   nodes = [BaseNode(node_id="1", content="This is a test document.")]
   
   colbert_index = ColbertIndex(
       nodes=nodes,
       model_name="colbert-ir/colbertv2.0",
       index_name="example_index",
       show_progress=True,
       nbits=2,
       gpus=1,
       ranks=1,
       doc_maxlen=120,
       query_maxlen=60,
       kmeans_niters=4
   )

4. Build the Index: Use the _build_index_from_nodes method to build the index from the provided nodes.

   index_struct = colbert_index._build_index_from_nodes(nodes)

5. Query the Index: Once the index is built, you can query it using the query method.

   results = colbert_index.query(query_str="test query", top_k=10)
   for result in results:
       print(result.node.get_content(), result.score)

By following these steps, you should be able to use ColBERT embeddings with the FastEmbed module in LlamaIndex without encountering the ValueError ^[1][2].

To continue talking to Dosu, mention @dosu.

[pydv9991]

you have not used fastembed i want to use colbert with fastembed

[dosubot[bot]]

To use ColBERT with FastEmbed in LlamaIndex, you need to ensure that the ColBERT model is supported and properly integrated. However, based on the current information, it seems that the TextEmbedding class does not support the colbert-ir/colbertv2.0 model directly.

Here is a step-by-step guide to correctly use ColBERT with FastEmbed in LlamaIndex:

1. Install ColBERT: Ensure you have the ColBERT library installed.

   git clone https://github.com/stanford-futuredata/ColBERT
   cd ColBERT
   pip install -r requirements.txt
   python setup.py install

2. Initialize ColbertIndex: Use the ColbertIndex class to initialize the index with the required parameters.

   from llama_index.legacy.indices.managed.colbert_index.base import ColbertIndex
   from llama_index.legacy.schema import BaseNode
   
   # Example nodes
   nodes = [BaseNode(node_id="1", content="This is a test document.")]
   
   colbert_index = ColbertIndex(
       nodes=nodes,
       model_name="colbert-ir/colbertv2.0",
       index_name="example_index",
       show_progress=True,
       nbits=2,
       gpus=1,
       ranks=1,
       doc_maxlen=120,
       query_maxlen=60,
       kmeans_niters=4
   )

3. Build the Index: Use the _build_index_from_nodes method to build the index from the provided nodes.

   index_struct = colbert_index._build_index_from_nodes(nodes)

4. Query the Index: Once the index is built, you can query it using the query method.

   results = colbert_index.query(query_str="test query", top_k=10)
   for result in results:
       print(f"Node ID: {result.node.node_id}, Score: {result.score}")

Currently, there is no direct integration of ColBERT with the FastEmbedEmbedding class in LlamaIndex. The TextEmbedding class in LlamaIndex does not support the colbert-ir/colbertv2.0 model, and the ColbertIndex class is designed to handle ColBERT models separately.

If you need to use ColBERT embeddings specifically, you should use the ColbertIndex class as shown above. For other embedding models supported by FastEmbed, you can refer to the list of supported models at Supported Models ^[1].

For more details on the ColbertIndex class and its usage, you can refer to the relevant source files ^[2][3].

To continue talking to Dosu, mention @dosu.

[pydv9991]

@jerryjliu can you help here?

[logan-markewich]

colbert embeddings are not supported at the moment in llama-index