I want help regarding the node retreival process for my...

DDhruv Patel

I want help regarding the node retreival process for my RAG chatbot for any particular query the retreiver is fetching nodes that are not related to the question asked

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kkapa.ai

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

kkapa.ai

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In LlamaIndex, the retrieval process is an important step in the RAG (Retrieve and Generate) pipeline. The retriever is responsible for fetching the most relevant document chunks or nodes based on the user's query.

Here is a basic example of how to retrieve relevant documents against a query using LlamaIndex:

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retriever = index.as_retriever(similarity_top_k=2)
retrieved_nodes = retriever.retrieve("What is DoRA?")

In this example, the retrieve method of the retriever object is used to fetch the most relevant document chunks for the query "What is DoRA?".

If you're finding that the retrieved nodes are not relevant to the question asked, it could be due to the similarity_top_k parameter. This parameter determines the number of most similar document chunks to retrieve. You might want to experiment with different values for this parameter to see if it improves the relevance of the retrieved nodes.

Additionally, LlamaIndex also supports re-ranking, which involves reordering nodes based on relevance to the query and choosing the top nodes. Different evaluations are performed based on the number of nodes returned after re-ranking. This could also help in improving the relevance of the retrieved nodes. Here is an example of how to rerank the nodes:

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ranker = LLMRerank(
    choice_batch_size=5, top_n=3, llm=OpenAI(model="gpt-4o-mini")
)
new_nodes = ranker.postprocess_nodes(
    ev.nodes, query_str=ctx.data.get("query")
)

In this example, the LLMRerank object is used to rerank the nodes based on their relevance to the query.

kkapa.ai

For more detailed information, you can refer to the LlamaIndex documentation () and the GitHub repository ().
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DDhruv Patel

this is my main code for the RAG chatbot

DDhruv Patel

@kapa.ai this is my main code for the RAG chatbot

kkapa.ai

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

kkapa.ai

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I'm sorry, but it seems like you didn't provide the code for your RAG chatbot. Could you please provide the code so that I can assist you better?
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Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
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DDhruv Patel

@kapa.ai cant you read file inpputs ?

kkapa.ai

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

kkapa.ai

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Yes, LlamaIndex provides a SimpleDirectoryReader class that can read file inputs. It is the most commonly used data connector that works by simply passing in an input directory or a list of files. It will select the best file reader based on the file extensions.

Here is an example of how to use SimpleDirectoryReader to read a specific file:

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from llama_index.core import SimpleDirectoryReader

reader = SimpleDirectoryReader(
    input_files=["./data/paul_graham/paul_graham_essay1.txt"]
)

docs = reader.load_data()
print(f"Loaded {len(docs)} docs")

In this example, SimpleDirectoryReader is initialized with a list of input files. The load_data method is then called to read the data from the files and load it into the docs variable.

You can also use SimpleDirectoryReader to read all the top-level files from a directory:

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reader = SimpleDirectoryReader(input_dir="./data/paul_graham/")

docs = reader.load_data()
print(f"Loaded {len(docs)} docs")

In this example, SimpleDirectoryReader is initialized with an input directory. The load_data method is then called to read the data from all the top-level files in the directory and load it into the docs variable.

For more information, you can refer to the LlamaIndex documentation ().
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Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
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I want help regarding the node retreival process for my RAG chatbot for any particular que