Use LlamaIndex with different embeddings model – Python

The Solutions:

Solution 1: Use the LangchainEmbedding wrapper and set it in a service_context

To use a different embedding model, such as "all-roberta-large-v1" from the sentence-transformers library, you can use the LangchainEmbedding wrapper provided by LlamaIndex. This wrapper allows you to use any embedding model that implements the Embedding interface from the langchain library.

Here’s how you can set up the LangchainEmbedding wrapper and use it in a service_context:

from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from llama_index import LangchainEmbedding, ServiceContext

# Create the embedding model using HuggingFaceEmbeddings
embed_model = LangchainEmbedding(
    HuggingFaceEmbeddings(model_name="sentence-transformers/all-roberta-large-v1")
)

# Create a service_context with the embedding model
service_context = ServiceContext.from_defaults(embed_model=embed_model)

# Set the global service_context (optional)
from llama_index import set_global_service_context
set_global_service_context(service_context)

Once you have set up the service_context with the desired embedding model, you can use it to create an index and query the index as usual.

Solution 3: Use different embedding models along with LlamaIndex

This solution leverages the flexibility of LlamaIndex to use different embedding models for document indexing and language model retrieval. It involves using one StorageContext for indexing, based on Chroma for vector storage and sentence-transformers/all-MiniLM-L6-v2 for embeddings, and another StorageContext for querying the LLM, employing OpenAI’s GPT-3.5 model.

Here’s the breakdown of the solution:

1. Environment Setup: Set up the environment by defining the necessary environment variables for the OpenAI API key and the cache directory for LlamaIndex.

2. Document Loading: Load documents using the SimpleDirectoryReader, which reads documents from a specified directory and converts them into a list of Document objects.

3. Index Setup: Create a VectorStoreIndex, using a ChromaVectorStore as the vector store and HuggingFaceEmbeddings as the embedding model. This index is used for document indexing and retrieval.

4. Query Engine Setup: Set up the RetrieverQueryEngine, which combines the VectorIndexRetriever for retrieving documents based on similarity and a response synthesizer for generating responses from the LLM. The response synthesizer uses OpenAI’s GPT-3.5 model.

5. Query Execution: Execute a query on the query engine, providing a user input. The result is a response from GPT-3.5 based on the retrieved documents.

This solution allows for the use of various embedding models for indexing and retrieval, providing flexibility and customization options for building more tailored search and retrieval systems.

Solution 4: Use LlamaIndex with different embeddings model

This solution demonstrates how to use LlamaIndex with a different embedding model, specifically “sentence-transformers/all-MiniLM-L6-v2,” and OpenAI as the LLM query. It begins by setting up the environment, logging, and loading user-defined variables.

Next, it initializes the embedding model using HuggingFaceEmbeddings. It attempts to load an existing index; if that fails, it creates a new one from documents loaded using a SimpleDirectoryReader. The index is then persisted to a specified directory.

A VectorIndexRetriever is initialized and used to retrieve nodes based on a user query. The results are then passed to an OpenAI LLM service context and response synthesizer. Finally, a RetrieverQueryEngine is created and used to execute the query and generate a response.

Video Explanation:

The following video, titled "How to use Llama Index with a local model instead of OpenAI ...", provides additional insights and in-depth exploration related to the topics discussed in this post.

Install Llama/Alpaca: https://github.com/cocktailpeanut/dalai LlamaIndex w/Custom LLM: ...