[Question]: Reranking using custom embedings, chromadb, vectorstoreindex

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Code to Reranking using custom embedings, chromadb, vectorstoreindex - RAG

[dosubot[bot]]

I couldn't find specific examples or documentation on reranking using custom embeddings with ChromaDB in LlamaIndex. However, I can guide you on how to integrate custom embeddings with ChromaDB and perform reranking using a VectorStoreIndex.

1. Integrate Custom Embeddings with ChromaDB:

   • Initialize the Chroma client and create a collection.
   • Use the ChromaVectorStore class to assign Chroma as the vector store in a StorageContext.
   • Initialize your VectorStoreIndex using the StorageContext and your custom embedding model.

2. Rerank Results:

   • Set up a retriever using VectorIndexRetriever to get initial results.
   • Use a reranker like RankLLMRerank or ColbertRerank to reorder the retrieved results based on your custom embeddings.

Here's a basic outline of how you might set this up:

import chromadb
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.chroma import ChromaVectorStore
from llama_index.core import StorageContext
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.core.retrievers import VectorIndexRetriever
from llama_index.postprocessor.rankLLM_rerank import RankLLMRerank

# Load documents
documents = SimpleDirectoryReader("./data").load_data()

# Initialize Chroma client
db = chromadb.PersistentClient(path="./chroma_db")
chroma_collection = db.get_or_create_collection("quickstart")

# Define custom embedding model
embed_model = HuggingFaceEmbedding(model_name="your-custom-model")

# Assign Chroma as the vector store
vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
storage_context = StorageContext.from_defaults(vector_store=vector_store)

# Create VectorStoreIndex
index = VectorStoreIndex.from_documents(documents, storage_context=storage_context, embed_model=embed_model)

# Retrieve and rerank
query_bundle = QueryBundle("Your query here")
retriever = VectorIndexRetriever(index=index, similarity_top_k=10)
retrieved_nodes = retriever.retrieve(query_bundle)

reranker = RankLLMRerank(top_n=3, model="your-rerank-model")
reranked_nodes = reranker.postprocess_nodes(retrieved_nodes, query_bundle)

# Output reranked results
for node in reranked_nodes:
    print(node)

This code demonstrates how to set up a VectorStoreIndex with custom embeddings and perform reranking. Adjust the embedding model and reranking model to fit your specific needs.

To continue talking to Dosu, mention @dosu.

[Kitdit]

Code to rerank using Cohere using bearer token?

[dosubot[bot]]

To implement reranking using Cohere with a bearer token, you can use the CohereRerank class. Here's a sample implementation:

1. Ensure you have the cohere package installed. If not, install it using pip install cohere.

2. Set your Cohere API key as an environment variable COHERE_API_KEY or pass it directly to the CohereRerank class.

3. Initialize the CohereRerank class with the desired model and top_n parameters.

4. Use the _postprocess_nodes method to rerank your nodes based on a query.

Here's a sample code snippet:

import os
from cohere import Client
from llama_index.core.postprocessor.types import BaseNodePostprocessor
from llama_index.core.schema import NodeWithScore, QueryBundle

class CohereRerank(BaseNodePostprocessor):
    def __init__(self, top_n=2, model="rerank-english-v2.0", api_key=None, base_url=None):
        api_key = api_key or os.environ["COHERE_API_KEY"]
        self._client = Client(api_key=api_key, base_url=base_url)
        super().__init__(top_n=top_n, model=model)

    def _postprocess_nodes(self, nodes, query_bundle=None):
        if query_bundle is None:
            raise ValueError("Missing query bundle in extra info.")
        if len(nodes) == 0:
            return []

        texts = [node.node.get_content(metadata_mode=MetadataMode.EMBED) for node in nodes]
        results = self._client.rerank(
            model=self.model,
            top_n=self.top_n,
            query=query_bundle.query_str,
            documents=texts,
        )
        new_nodes = []
        for result in results.results:
            new_node_with_score = NodeWithScore(
                node=nodes[result.index].node, score=result.relevance_score
            )
            new_nodes.append(new_node_with_score)

        return new_nodes

# Example usage
api_key = "your_cohere_api_key"
reranker = CohereRerank(api_key=api_key)
nodes = [...]  # List of NodeWithScore objects
query_bundle = QueryBundle(query_str="your_query")
reranked_nodes = reranker._postprocess_nodes(nodes, query_bundle)

Replace "your_cohere_api_key" and "your_query" with your actual API key and query string ^[1].

To continue talking to Dosu, mention @dosu.

[Kitdit]

Object of type Selector Prompt Template is not JSON serializable using colbert_reranker

[dosubot[bot]]

The error you're encountering likely stems from trying to serialize an object that includes non-serializable components. The SelectorPromptTemplate might contain complex data types that aren't directly JSON serializable.

To address this, you can implement a custom serialization method. Here's a general approach:

1. Custom Serialization: Convert the non-serializable parts of the SelectorPromptTemplate into a JSON-friendly format, such as strings or dictionaries.

2. Use Utilities: If the codebase includes utilities for serialization, like the serialize function, use them to handle complex data types ^[1].

Here's a basic example of how you might serialize a complex object:

import json

def custom_serialize(obj):
    if isinstance(obj, SelectorPromptTemplate):
        # Convert to a dictionary or another serializable format
        return {
            "default_template": str(obj.default_template),
            "conditionals": [(str(cond), str(template)) for cond, template in obj.conditionals]
        }
    # Add more custom serialization logic as needed
    raise TypeError(f"Object of type {obj.__class__.__name__} is not JSON serializable")

# Usage
try:
    json_data = json.dumps(your_object, default=custom_serialize)
except TypeError as e:
    print(e)

Adjust the serialization logic to fit the specific attributes of your SelectorPromptTemplate.

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[Kitdit]

def colbert_reranker(index, query, top_n): colbert_reranker = ColbertRerank( top_n=top_n, model="colbert-ir/colbertv2.0", tokenizer="colbert-ir/colbertv2.0", keep_retrieval_score=True, )

query_engine = index.as_query_engine(
    similarity_top_k=10,
    node_postprocessors=[colbert_reranker],
)

response = query_engine.query(query)

# Print response in a safe manner
for node in response.source_nodes:
    try:
        print(f"Node ID: {node.id_}")
        print(f"Content: {node.node.get_content()}")
        print(f"Reranking Score: {node.score}")
        print(f"Retrieval Score: {node.node.metadata['retrieval_score']}")
        print("=====================================")
    except Exception as e:
        print(f"Error processing node: {e}")

return response