How do I use Langchain document loaders with Llamaindex...

@kapa.ai How do I use Langchain document loaders with Llamaindex?

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Please stand by @cablecutter as I search the knowledge base. I normally take about 20 seconds or so. :wave:

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LlamaIndex allows you to use any data loader, including those from Langchain, within the LlamaIndex core repository or in LlamaHub as an "on-demand" data query Tool within a LangChain agent. The Tool will load data using the data loader, index the data, and query the data, returning the response in an ad-hoc manner.

Here is an example of how you can use a LlamaIndex query engine along with a Langchain agent:

Plain Text

from llama_index.langchain_helpers.agents import (
    IndexToolConfig,
    LlamaIndexTool,
)

tool_config = IndexToolConfig(
    query_engine=query_engine,
    name=f"Vector Index",
    description=f"useful for when you want to answer queries about X",
    tool_kwargs={"return_direct": True},
)

tool = LlamaIndexTool.from_tool_config(tool_config)

In this example, a "Tool" is created from a QueryEngine directly. This tool can then be used within a Langchain agent.

For more detailed instructions and examples, you can refer to the LangChain integration guide () and the OnDemandLoaderTool Tutorial () provided in the LlamaIndex documentation.
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ccablecutter

@kapa.ai Can you tell me how to use E5-large embedding model with llamaindex?

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

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To use the E5-large embedding model with LlamaIndex, you would typically set it up in the service context object. However, the specific code for setting up the E5-large model is not provided in the extracts.

Here is a general example of how to set up an embedding model with LlamaIndex:

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from llama_index import ServiceContext
from llama_index.embeddings import OpenAIEmbedding

embed_model = OpenAIEmbedding()  # replace with your specific model setup
service_context = ServiceContext.from_defaults(embed_model=embed_model)

# Optionally set a global service context to avoid passing it into other objects every time
from llama_index import set_global_service_context

set_global_service_context(service_context)

In this example, OpenAIEmbedding() is used as a placeholder. You would replace this with the specific setup for the E5-large model.

Please refer to the LlamaIndex documentation () or the model's specific documentation for more detailed instructions on how to set up the E5-large model.
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How do I use Langchain document loaders with Llamaindex?