[Question]: Inaccurate Responses in RAG System Using LlamaIndex and Open-Source Models

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Hello everyone,

I've been implementing a RAG system using Llama-Index and open-source models for both Embedding and the Large Language Model (LLM). However, the responses I'm getting are not as expected.

I've experimented with various embedding models, including BAAI large, BAAI small, and sentence-transformers/all-MiniLM-L6-v2. Despite adjusting different parameters and using hybrid search, the responses remain inaccurate.

Additionally, I've tried different vector databases like Qdrant, Chroma, and Elasticsearch. Some documents suggested that Elasticsearch could be a good option, but it also didn't work as expected.

The only thing I haven't tried yet is using OpenAI models.

Can anyone help me understand why the results are not accurate? Is the issue related to the embedding models or the vector store?

Thank you!

The inaccuracy in your Retrieval-Augmented Generation (RAG) system responses could be due to several factors, including the generator component, embedding models, or vector store configurations. Here are some steps to diagnose and improve the accuracy:

Generator Component: Ensure that the generator (LLM) is effectively utilizing the retrieved context. Sometimes, even if the correct context is retrieved, the generator might not produce accurate responses. Adjusting the generation prompts and evaluation prompts can help improve the accuracy.
Embedding Models: Evaluate the quality of the embeddings used in your RAG pipeline. Use metrics like Hit-Rate and Mean-Reciprocal-Rank (MRR) to determine the most effective embedding type for your dataset. Fine-tuning the embedding models can also lead to significant improvements.
Vector Store Configuration: Optimize the parameters of your vector databases (Qdrant, Chroma, Elasticsearch) for better retrieval performance. For example, you can adjust the similarity_top_k parameter to retrieve more context or enable hybrid search features.
Evaluation Tools: Use tools like UpTrain to perform evaluations on the generated responses. UpTrain provides grades for various preconfigured checks, performs root cause analysis on failure cases, and gives insights on how to resolve them.
Advanced Retrieval Techniques: Consider using advanced retrieval techniques such as Activeloop's Deep Memory, which introduces a tiny neural network layer trained to match user queries with relevant data from a corpus, boosting retrieval accuracy.

Here is an example of how to set up and use UpTrain with LlamaIndex:

%pip install -qU uptrain llama-index

import httpx
import os
import openai
import pandas as pd

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings
from uptrain import Evals, EvalLlamaIndex, Settings as UpTrainSettings

# Create the dataset folder for the query engine
url = "https://uptrain-assets.s3.ap-south-1.amazonaws.com/data/nyc_text.txt"
if not os.path.exists("nyc_wikipedia"):
    os.makedirs("nyc_wikipedia")
dataset_path = os.path.join("./nyc_wikipedia", "nyc_text.txt")

if not os.path.exists(dataset_path):
    r = httpx.get(url)
    with open(dataset_path, "wb") as f:
        f.write(r.content)

# Make the list of queries
data = [
    {"question": "What is the population of New York City?"},
    {"question": "What is the area of New York City?"},
    {"question": "What is the largest borough in New York City?"},
    {"question": "What is the average temperature in New York City?"},
    {"question": "What is the main airport in New York City?"},
    {"question": "What is the famous landmark in New York City?"},
    {"question": "What is the official language of New York City?"},
    {"question": "What is the currency used in New York City?"},
    {"question": "What is the time zone of New York City?"},
    {"question": "What is the famous sports team in New York City?"},
]

# Set OpenAI API key
openai.api_key = "sk-************************"

# Create a query engine using LlamaIndex
Settings.chunk_size = 512
documents = SimpleDirectoryReader("./nyc_wikipedia/").load_data()
vector_index = VectorStoreIndex.from_documents(documents)
query_engine = vector_index.as_query_engine()

# Setup UpTrain settings
settings = UpTrainSettings(openai_api_key=openai.api_key)

# Create the EvalLlamaIndex object
llamaindex_object = EvalLlamaIndex(settings=settings, query_engine=query_engine)

# Run the evaluation
results = llamaindex_object.evaluate(
    project_name="uptrain-llama-index",
    evaluation_name="nyc_wikipedia",
    data=data,
    checks=[Evals.CONTEXT_RELEVANCE, Evals.RESPONSE_CONCISENESS],
)

pd.DataFrame(results)

By carefully evaluating and selecting the appropriate embedding type, using tools like UpTrain for evaluation, and leveraging advanced retrieval techniques, you can significantly improve the retrieval component and the overall performance of your RAG system ^[1]^[2]^[3].

To continue talking to Dosu, mention @dosu.

@dosu, could you provide some guidance on when to use different types of vector databases? For instance, in which scenarios should I use Elasticsearch as a vector database, and when should I opt for Chroma or Qdrant?

What factors should I consider when choosing a vector database?

When choosing between Elasticsearch, Chroma, and Qdrant for a vector database, consider the following factors:

Deployment Type:
- Elasticsearch: Available as self-hosted or cloud.
- Chroma: Self-hosted.
- Qdrant: Available as self-hosted or cloud.
Metadata Filtering:
- All three options (Elasticsearch, Chroma, Qdrant) support metadata filtering.
Hybrid Search:
- Elasticsearch: Supports hybrid search.
- Chroma: Does not support hybrid search.
- Qdrant: Supports hybrid search.
Delete Operations:
- All three options support delete operations.
Store Documents:
- All three options support storing documents.
Async Operations:
- Elasticsearch: Supports async operations.
- Chroma: Does not support async operations.
- Qdrant: Supports async operations.

Summary:

Elasticsearch: Best if you need a versatile solution with hybrid search and async operations, and you are comfortable with either self-hosted or cloud deployment.
Chroma: Suitable for simpler, self-hosted setups where hybrid search and async operations are not required.
Qdrant: Good for both self-hosted and cloud deployments, with support for hybrid search and async operations.

Additionally, LlamaIndex supports integration with these vector stores, allowing you to use them as storage backends for VectorStoreIndex. This can be particularly useful if you are building applications that leverage large language models (LLMs) and need efficient vector storage and retrieval.

Choose based on your specific needs for deployment type, hybrid search, async operations, and integration with frameworks like LlamaIndex ^[1]^[2].

To continue talking to Dosu, mention @dosu.

@dosu, I recently implemented my RAG system using Elasticsearch as the vector database, Ollama for the LLM (Llama3), and the embedding model (znbang/bge:large-en-v1.5-f32).

Unfortunately, the output from the RAG system, especially from Elasticsearch, has been very poor.

I've shared my code below. I'm not sure what might be going wrong. I verified that the ingestion process was successful and confirmed that the documents have been stored in Elasticsearch successfully.

from llama_index.llms.ollama import Ollama
from llama_index.embeddings.ollama import OllamaEmbedding
from llama_index.vector_stores.elasticsearch import ElasticsearchStore

from llama_index.core import (
    Settings,
    StorageContext,
    VectorStoreIndex,
    SimpleDirectoryReader,
)

def define_embedding_function():
    ollama_embedding = OllamaEmbedding(
        model_name="znbang/bge:large-en-v1.5-f32",
        base_url="http://localhost:11434",
        ollama_additional_kwargs={"mirostat": 0},
    )
    Settings.embed_model = ollama_embedding

# Function to set up the language model
def setup_language_model():
    llm = Ollama(
        model="llama3", base_url="http://localhost:11434", request_timeout=120.0
    )
    # Optional: Uncomment to set the language model in Settings
    # Settings.llm = llm
    Settings.llm = None

# Function to load documents
def load_documents(directory_path):
    return SimpleDirectoryReader(directory_path).load_data()

# Function to define the vector store
def define_vector_store(es_url, index_name):
    return ElasticsearchStore(
        es_url=es_url,
        index_name=index_name,
    )

# Function to create an index from documents
def create_index(documents, vector_store):
    storage_context = StorageContext.from_defaults(vector_store=vector_store)
    return VectorStoreIndex.from_documents(documents, storage_context=storage_context)

# Function to query data
def query_data(index, query_text):
    query_engine = index.as_query_engine()
    response = query_engine.query(query_text)
    return response

def main():

    define_embedding_function()
    setup_language_model()

    documents = load_documents("./data")
    vector_store = define_vector_store(
        es_url="http://localhost:9200", index_name="my_index"
    )
    index = create_index(documents, vector_store)

    response = query_data(index, "What are the benefits of prepaid cards?")
    print(response)

    response = query_data(index, "What are the benefits of home loans?")
    print(response)

# Run the main function
if __name__ == "__main__":
    main()

Any insights would be greatly appreciated.

To improve the accuracy of the responses from your Retrieval-Augmented Generation (RAG) system using Elasticsearch, Ollama, and the znbang/bge:large-en-v1.5-f32 embedding model, consider the following steps:

Fine-Tune the Embedding Model: Fine-tuning the embedding model can lead to more meaningful embedding representations over a training distribution of data, which in turn improves retrieval performance. You can generate a synthetic question/answer dataset using LlamaIndex and fine-tune the znbang/bge:large-en-v1.5-f32 embedding model.
Evaluate and Optimize Retrieval: Use tools like UpTrain to evaluate the retrieval performance and identify areas for improvement. Ensure that the generator (LLM) is effectively utilizing the retrieved context.
Advanced Retrieval Techniques: Consider using advanced retrieval techniques such as Activeloop's Deep Memory, which introduces a tiny neural network layer trained to match user queries with relevant data from a corpus, boosting retrieval accuracy.
Optimize Elasticsearch Configuration: Ensure that Elasticsearch is properly configured for vector search. Adjust parameters like similarity_top_k to retrieve more relevant context.

Here is an example of how to fine-tune the embedding model and integrate advanced retrieval techniques:

Fine-Tuning the Embedding Model

# Example code to fine-tune the embedding model
from llama_index.embeddings.ollama import OllamaEmbedding
from llama_index.core import Settings

def fine_tune_embedding_model():
    ollama_embedding = OllamaEmbedding(
        model_name="znbang/bge:large-en-v1.5-f32",
        base_url="http://localhost:11434",
        ollama_additional_kwargs={"mirostat": 0},
    )
    # Fine-tune the embedding model with your dataset
    # Example: ollama_embedding.fine_tune(dataset)
    Settings.embed_model = ollama_embedding

fine_tune_embedding_model()

Integrating Deep Memory

# Example code to integrate Deep Memory
from llama_index.retrievers.deep_memory import DeepMemoryRetriever

def setup_deep_memory_retriever():
    deep_memory_retriever = DeepMemoryRetriever(
        model_name="deep_memory_model",
        base_url="http://localhost:11434",
    )
    # Train the Deep Memory model with your dataset
    # Example: deep_memory_retriever.train(dataset)
    return deep_memory_retriever

deep_memory_retriever = setup_deep_memory_retriever()

Optimizing Elasticsearch Configuration

# Example code to optimize Elasticsearch configuration
from llama_index.vector_stores.elasticsearch import ElasticsearchStore

def define_vector_store(es_url, index_name):
    return ElasticsearchStore(
        es_url=es_url,
        index_name=index_name,
        similarity_top_k=10,  # Adjust this parameter as needed
    )

vector_store = define_vector_store(
    es_url="http://localhost:9200", index_name="my_index"
)

By fine-tuning the embedding model, leveraging advanced retrieval techniques like Deep Memory, and optimizing Elasticsearch configuration, you can significantly improve the accuracy of your RAG system ^[1]^[2]^[3]^[4].

To continue talking to Dosu, mention @dosu.

@dosu, In my case, the RAG system needs to handle vast amounts of data, potentially including anything that customers or users upload. These documents are not related to any specific concepts or topics.

Given this, how should I approach training the embedding model? On what kind of data should I base the training?

Would training be the best option to fine-tune the RAG system in my case?

To approach training the embedding model for a Retrieval-Augmented Generation (RAG) system that handles diverse and vast amounts of user-uploaded data, you should consider optimizing context embeddings. This involves ensuring that the embeddings are tailored to better retrieve information from your specific data corpus, as pre-trained models may not capture the salient properties relevant to your use case.

Key Techniques

Decoupling Chunks Used for Retrieval vs. Chunks Used for Synthesis:
- Embed a document summary: This helps in retrieving relevant documents at a high level before retrieving specific chunks.
- Embed a sentence: This allows for finer-grained retrieval of relevant context, ensuring enough context for LLM synthesis.
Structured Retrieval for Larger Document Sets:
- Metadata Filters + Auto Retrieval: Tag each document with metadata and use these tags to filter documents during retrieval.
- Store Document Hierarchies (summaries -> raw chunks) + Recursive Retrieval: Embed document summaries and map them to chunks per document, fetching at the document level first before chunk level.
Dynamically Retrieve Chunks Depending on Your Task:
- Use modules like the router and data agent to perform task-specific retrieval, combining structured and unstructured data queries.
Optimize Context Embeddings:
- Fine-tuning the embedding model: This can be done over an unstructured text corpus in a label-free way to better capture the properties of your data.

Training Benefits

Training the embedding model can be beneficial in your case as it allows for:

Improved retrieval accuracy by capturing the specific characteristics of your data.
Enhanced performance in handling diverse and vast amounts of user-uploaded data.
Reduction in hallucinations and better synthesis of detailed answers.

Important Concepts within RAG

Understanding the stages within RAG is crucial for effectively training and utilizing your embedding model:

Loading: Getting your data from various sources into your pipeline.
Indexing: Creating vector embeddings and other metadata strategies for querying.
Storing: Saving your indexed data to avoid re-indexing.
Querying: Utilizing LLMs and data structures to perform various query strategies.
Evaluation: Measuring the effectiveness of your pipeline.

Advanced Techniques

For higher accuracy or recall during searches, consider employing advanced retrieval techniques. These methods might involve varying data chunk sizes, rewriting queries multiple times, and more, potentially increasing latency and costs. Activeloop's Deep Memory, a feature available to Activeloop Deep Lake users, addresses these issues by introducing a tiny neural network layer trained to match user queries with relevant data from a corpus. This addition can boost retrieval accuracy by up to 27% while remaining cost-effective and simple to use, without requiring any additional advanced RAG techniques.

Multi-Modal Retrieval-Augmented Generation

Given the diverse nature of user-uploaded data, you might also benefit from leveraging multi-modal models. LlamaIndex supports multi-modal models that can handle text and images, which could be particularly useful if your data includes various types of media.

Example Usage Pattern for Multi-Modal Models

Loading Image Documents:

from llama_index.multi_modal_llms.openai import OpenAIMultiModal
from llama_index.core.multi_modal_llms.generic_utils import load_image_urls
from llama_index.core import SimpleDirectoryReader

# Load image documents from URLs
image_documents = load_image_urls(image_urls)

# Load image documents from local directory
image_documents = SimpleDirectoryReader(local_directory).load_data()

Creating Multi-Modal Vector Stores/Index:

from llama_index.core.indices import MultiModalVectorStoreIndex
from llama_index.vector_stores.qdrant import QdrantVectorStore
from llama_index.core import SimpleDirectoryReader, StorageContext

import qdrant_client

# Create a local Qdrant vector store
client = qdrant_client.QdrantClient(path="qdrant_mm_db")

text_store = QdrantVectorStore(client=client, collection_name="text_collection")
image_store = QdrantVectorStore(client=client, collection_name="image_collection")

storage_context = StorageContext.from_defaults(vector_store=text_store, image_store=image_store)
documents = SimpleDirectoryReader("./data_folder/").load_data()

# Create the Multi-Modal index
index = MultiModalVectorStoreIndex.from_documents(documents, storage_context=storage_context)

Using Multi-Modal Retriever and Query Engine:

from llama_index.multi_modal_llms.openai import OpenAIMultiModal
from llama_index.core import PromptTemplate
from llama_index.core.query_engine import SimpleMultiModalQueryEngine

retriever_engine = index.as_retriever(similarity_top_k=3, image_similarity_top_k=3)
retrieval_results = retriever_engine.retrieve(response)

qa_tmpl_str = (
   "Context information is below.\n"
   "---------------------\n"
   "{context_str}\n"
   "---------------------\n"
   "Given the context information and not prior knowledge, "
   "answer the query.\n"
   "Query: {query_str}\n"
   "Answer: "
)
qa_tmpl = PromptTemplate(qa_tmpl_str)

query_engine = index.as_query_engine(multi_modal_llm=openai_mm_llm, text_qa_template=qa_tmpl)
query_str = "Tell me more about the Porsche"
response = query_engine.query(query_str)

For detailed guidance on fine-tuning the embedding model and understanding the stages within RAG, you can refer to the LlamaIndex documentation ^[1]^[2]^[3]^[4].

To continue talking to Dosu, mention @dosu.

@dosu, I have some questions based on your previous response, particularly about summarizing user-uploaded documents.

You mentioned summarizing the document and storing it in a separate index. My question is, for summarization, should I use RAG and LLM? If I upload a document to RAG, it will extract particular chunks from the document rather than the entire document for summarization. For example, how can I summarize an entire document if it has more than 50 pages?
Another question is, even if I store the document summary in the index, how can I use this to extract the actual context from the vector database? As I understand it, one index will contain the document summary, and another index will contain each chunk of the document. How can I combine these to get an accurate RAG result?

Summarizing Large Documents: To summarize large documents, you can use a combination of RAG and LLM techniques. Here’s a step-by-step approach:

Chunking: Break the document into manageable chunks (e.g., paragraphs or sections).
Summarization: Use an LLM to generate summaries for each chunk.
Aggregation: Combine these chunk summaries into a comprehensive summary for the entire document.

For a document with more than 50 pages, you can automate this process by iterating over the chunks and summarizing them sequentially. Here’s a simplified example:

from llama_index.llms.ollama import Ollama
from llama_index.core import SimpleDirectoryReader, VectorStoreIndex, StorageContext

def summarize_document(document_text, llm):
   # Split document into chunks
   chunks = document_text.split('\n\n')  # Example: split by paragraphs
   summaries = []
   for chunk in chunks:
       response = llm.generate(chunk, max_tokens=100)  # Adjust max_tokens as needed
       summaries.append(response['text'])
   return ' '.join(summaries)

def main():
   llm = Ollama(model="llama3", base_url="http://localhost:11434", request_timeout=120.0)
   documents = SimpleDirectoryReader("./data").load_data()
   for doc in documents:
       summary = summarize_document(doc.text, llm)
       print(summary)  # Store this summary in your summary index

if __name__ == "__main__":
   main()

Combining Summaries and Chunks for Accurate RAG Results: To combine document summaries and chunks for accurate RAG results, you can use a two-step retrieval process:

Step 1: Retrieve Relevant Summaries: Use the summary index to retrieve the most relevant document summaries based on the query.
Step 2: Retrieve Detailed Chunks: Use the retrieved summaries to identify and fetch the detailed chunks from the chunk index.

Here’s how you can implement this:

from llama_index.vector_stores.elasticsearch import ElasticsearchStore
from llama_index.core import VectorStoreIndex, StorageContext

def retrieve_relevant_summaries(query, summary_index):
   query_engine = summary_index.as_query_engine()
   response = query_engine.query(query)
   return response['results']  # Extract relevant summaries

def retrieve_detailed_chunks(summary_ids, chunk_index):
   query_engine = chunk_index.as_query_engine()
   detailed_chunks = []
   for summary_id in summary_ids:
       response = query_engine.query(f"summary_id:{summary_id}")
       detailed_chunks.extend(response['results'])
   return detailed_chunks

def main():
   summary_vector_store = ElasticsearchStore(es_url="http://localhost:9200", index_name="summary_index")
   chunk_vector_store = ElasticsearchStore(es_url="http://localhost:9200", index_name="chunk_index")

   summary_index = VectorStoreIndex.from_defaults(vector_store=summary_vector_store)
   chunk_index = VectorStoreIndex.from_defaults(vector_store=chunk_vector_store)

   query = "What are the benefits of prepaid cards?"
   relevant_summaries = retrieve_relevant_summaries(query, summary_index)
   summary_ids = [summary['id'] for summary in relevant_summaries]
   detailed_chunks = retrieve_detailed_chunks(summary_ids, chunk_index)

   # Use detailed_chunks for LLM synthesis
   print(detailed_chunks)

if __name__ == "__main__":
   main()

By following this approach, you can effectively summarize large documents and combine summaries with detailed chunks to achieve accurate RAG results ^[1]^[2].

To continue talking to Dosu, mention @dosu.

run-llama / llama_index