An sophisticated Google Cloud LlamaIndex RAG implementation Introduction. RAG is changing how it construct Large Language Model (LLM)-powered apps, but unlike tabular machine learning, where XGBoost is the best, there’s no “go-to” option. Developers need fast ways to test retrieval methods. This article shows how to quickly prototype and evaluate RAG solutions utilizing Llamaindex, Streamlit, RAGAS, and Google Cloud’s Gemini models. Beyond basic lessons, it’ll develop reusable components, expand frameworks, and consistently test performance.
LlamaIndex RAG
Building RAG apps with LlamaIndex is powerful. With LLMs, linking, arranging, and querying data is easier. The LlamaIndex RAG workflow breakdown:
- Indexing and storage chunking, embedding, organizing, and structuring queryable documents.
- How to obtain user-queried document parts. Nodes are LlamaIndex index-retrieved document chunks.
- After analyzing a collection of relevant nodes, rerank them to make them more relevant.
- Given a final collection of relevant nodes, curate a user response.
From keyword search to agentic methods, LlamaIndex provides several combinations and integrations to fulfill these stages.
Storing and indexing
The indexing and storing process is complicated. You must construct distinct indexes for diverse data sources, choose algorithms, parse, chunk, and embed, and extract information. Despite its complexity, indexing and storage include pre-processing a bunch of documents so a retrieval system may retrieve important sections and storing them.
The Document AI Layout Parser, available from Google Cloud, can process HTML, PDF, DOCX, and PPTX (in preview) and identify text blocks, paragraphs, tables, lists, titles, headings, and page headers and footers out of the box, making path selection easier. In order to retrieve context-aware information, Layout Parser maintains the document’s organizational structure via a thorough layout analysis.
It must generate LlamaIndex nodes from chunked documents. LlamaIndex nodes include metadata attributes to monitor parent document structure. LlamaIndex may express a lengthy text broken into parts as a doubly-linked list of nodes with PREV and NEXT relationships set to the node IDs.
Pre-processing LlamaIndex nodes before embedding for advanced retrieval methods like auto-merging retrieval is possible. The Hierarchical Node Parser groups nodes from a document into a hierarchy. Each level of the hierarchy reflects a bigger piece of a document, starting with 512-character leaf chunks and linking to 1024-character parent chunks. Only the leaf chunks are embedded in this hierarchy; the remainder are stored in a document store for ID queries. At retrieval time, the vector similarity just on leaf chunks and exploit the hierarchical relationship to get more context from bigger document parts. LlamaIndex Auto-merging Retriever applies this reasoning.
Embed the nodes and pick how and where to store them for later retrieval. Vector databases are clear, but it may need to store content in another fashion to enable hybrid search with semantic retrieval. It demonstrate how to establish a hybrid store in Google Cloud’s Vertex AI Vector Store and Firestore to store document chunks as embedded vectors and key-value stores. It may use this to query documents by vector similarity or id/metadata match.
Multiple indices should be created to compare approach combinations. As an alternative to the hierarchical index, it may design a flat index of fixed-sized pieces.
Retrieval
Retrieval brings a limited number of relevant documents from its vector store/docstore combo to an LLM for context-based response. The LlamaIndex Retriever module abstracts this work well. Subclasses of this module implement the _retrieve function, which accepts a query and returns a list of NodesWithScore, or document chunks with scored relevance to the inquiry. Retrievers in LlamaIndex are popular. Always attempt a baseline retriever that uses vector similarity search to get the top k NodesWithScore.
Automatic retrieval
Baseline_retriever does not include the hierarchical index structure was established before. A document store’s hierarchy of chunks enables an auto-merging retriever to recover nodes based on vector similarity and the source document. It may obtain extra material that may encompass the original node pieces. The baseline_retriever may retrieve five node chunks based on vector similarity.
If its question is complicated, such chunks (512 characters) may not have enough information to answer it. Three of the five chunks may be from the same page and reference distinct paragraphs within a section. The auto-merging retriever may “walk” the hierarchy, getting bigger chunks and providing a larger piece of the document for the LLM to build a response since they recorded their hierarchy, relation to larger chunks, and togetherness. This balances shorter chunk sizes’ retrieval precision with the LLM’s need for relevant data.
LlamaIndex Search
With a collection of NodesWithScores, it must determine their ideal arrangement. Formatting or deleting PII may be necessary. It must then give these pieces to an LLM to get the user’s intended response. The LlamaIndex QueryEngine manages retrieval, node post-processing, and answer synthesis. Passing a retriever, node-post-processing method (if applicable), and response synthesizer as inputs creates a QueryEngine. QueryEngine’s query and aquery (asynchronous query) methods accept a string query and return a Response object with the LLM-generated response and a list of NodeWithScores.
Imagined document embedding
Enveloping the user’s query and calculating vector similarity with the vector storage is how most Llama-index retrievers work. Due to the question’s and answer’s different language structures, this may be unsatisfactory. Hypothetical document embedding (HyDE) uses LLM hallucination to address this. Hallucinate a response to the user’s inquiry without context, then embed it in the vector storage for vector similarity search.
Reranking LLM nodes
A Node Post-Processor in Llamaindex implements _postprocess_nodes, which takes the query and list of NodesWithScores as input and produces a new list. Googles may need to rerank the nodes from the retriever by LLM relevancy to improve their ranking. There are explicit models for re-ranking pieces for a query, or it may use a general LLM.
Reply synthesis
Many techniques exist to direct an LLM to respond to a list of NodeWithScores. Google Cloud may summarize huge nodes before requesting the LLM for a final answer. It may wish to offer the LLM another opportunity to improve or amend an initial answer. The LlamaIndex Response Synthesizer helps us decide how the LLM will respond to a list of nodes.
REACT agent
Google Cloud add a reasoning loop to its query pipeline using ReAct (Yao, et al. 2022). This lets an LLM use chain-of-thought reasoning to answer complicated questions that need several retrieval processes. Its query_engine is exposed to the ReAct agent as a tool for thinking and acting in Llamaindex to design a ReAct loop. Multiple tools may be added here to let the ReAct agent chose or condense results.
Final QueryEngine Creation
After choosing many ways from the stages above, you must write logic to construct your QueryEngine depending on an input configuration. Function examples are here.
Methods for evaluation
After creating a QueryEngine object, it can easily send queries and get RAG pipeline replies and context. Next, it may create the QueryEngine object as part of a backend service like FastAPI and a small front-end to play with it (conversation vs. batch).
When conversing with the RAG pipeline, the query, obtained context, and response may be utilized to analyze the response. It can compute evaluation metrics and objectively compare replies using these three areas. Based on this triad, RAGAS gives heuristic measures for response fidelity, answer relevancy, and context relevancy. With each chat exchange, the calculate and present these.
Expert annotation should also be used to find ground-truth responses. RAG pipeline performance may be better assessed using ground truth. It may determine LLM-graded accuracy by asking an LLM whether the response matches the ground truth or other RAGAS measures like context precision and recall.
Deployment
The FastAPI backend will provide /query_rag and /eval_batch. queries/rag/ is used for one-time interactions with the query engine that can evaluate the response on the fly. Users may choose an eval_set from a Cloud Storage bucket and conduct batch evaluation using query engine parameters with /eval_batch.
In addition to establishing sliders and input forms to match its specifications, Streamlit’s Chat components make it simple to whip up a UI and communicate with the QueryEngine object via a FastAPI backend.
Conclusion
Building a sophisticated RAG application on GCP using modular technologies like LlamaIndex, RAGAS, FastAPI, and streamlit gives you maximum flexibility as you experiment with different approaches and RAG pipeline tweaks. Maybe you’ll discover the “XGBoost” equivalent for your RAG issue in a miraculous mix of settings, prompts, and algorithms.
