Questions tagged [retrieval-augmented-generation]
Retrieval Augmented Generation (RAG) is where LLM based generative AI systems use additional retrieval steps to augment the prompt used for inference. The usage of this tag should be followed by the tags for specific LLMs (e.g. GPT-3) or databases (e.g. Pinecone) if applicable.
retrieval-augmented-generation
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Unpredictable, bad performance of vector similarity search in Postgres database with `pgvector`
Problem
I have a Postgres query in the context of a Retrieval Augmented Generation (RAQ) application that is wrapped in a database function which shows poor, unpredictable and varying performance. I ...
- 547
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Unable to create a vectorstore retriever using Chroma
I am trying to implement RAG with the GPT-3.5 api. However, my code execution gets stuck while trying to create the retriever. I didn't get this issue on Google Colab but I started getting this issue ...
- 11
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44
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How to merge multiple (at least two) existing LlamaIndex VectorStoreIndex instances?
I'm working with LlamaIndex and have created two separate VectorStoreIndex instances, each from different documents. Now, I want to merge these two indexes into a single index. Here's my current setup:...
- 43
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160
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RAG using Langchain / Chroma - Unable to save more than 99 Records to Database
I'm using the following code to load the content of markdown files (only one file, in my case), split it into chunks and then embed and store the chunks one by one. My file is split into 801 chunks. ...
- 11
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DSPy: How to get the number of tokens available for the input fields?
This is a cross-post from Issue #1245 of DSPy GitHub Repo. There were no responses in the past week, am I am working on a project with a tight schedule.
When running a DSPy module with a given ...
- 86
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30
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How to Combine Semantic Search with SQL Analytical Queries?
I'm creating an LLM-agent that can provide insights from a complex database. The database includes several columns of different types (datetime, numeric, and text).
For simplicity, let's assume I have ...
- 43
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Which RAG methods/concepts can I use for a benchmark?
I am writing a practical assignment for my uni. There I have to analyse different RAG methods and compare them. Since I am in my 2nd semester of information systems and I lack of experience within the ...
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Converting PDFs to Markdown for Higher Quality Embeddings with Langchain.js
I am working on RAG LLM projects with Langchain.js using Node.js. Most of the data I retrieve are PDFs and a bit of JSON.
For higher quality, I would like to convert my PDFs into Markdown before ...
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26
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I am getting this error while building a RAG model
I am getting this error while building a RAG model while using qwen2 model instead of the default llama2 which chroma uses.
My code:
from langchain_community.embeddings import OllamaEmbeddings
from ...
- 11
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36
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Running entirely local RAG system in Colab over GDrive files?
I am trying to run an entirely local RAG using Colab on my google drive, without sending any tokens to an external language model API. I downloaded the model into a Drive folder (here just called path,...
1
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answer
280
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LlamaParse not able to parse documents inside directory
Whenever I try to use LlamaParse I get an error that states the file_input must be a file path string, file bytes, or buffer object.
parser = LlamaParse(result_type="markdown")
...
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48
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Huggingface library not being able to replace separators in create_documents: "AttributeError: 'dict' object has no attribute 'replace'"
I'm a beginner in the chatbot developer world and currently building a rag code to create a context based chatbot, but I keep getting this error, I believe it happens when the text is being split, ...
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46
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ChromaDB terminates Flask without exception
I'm creating an API with Flask. The other side will send me a file and I will save it to chroma database on my side. Chroma.add will terminates my program without any exception. When I save a smaller ...
- 13
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2
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66
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BedrockEmbeddings - botocore.errorfactory.ModelTimeoutException
I am trying to get vector embeddings on scale for documents.
Importing, from langchain_community.embeddings import BedrockEmbeddings package.
Using embeddings = BedrockEmbeddings( ...
- 6,328
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82
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get metadata from vector store to output using Langchain LCEL RAG chain
I have a langchain LCEL RAG chain with chat history as follows
chain = (
{
"question": itemgetter("messages") | RunnableLambda(extract_user_query_string),
"...
- 301