From 5d1d291a91b0c3810538783a2dbb2a9acf58a0b3 Mon Sep 17 00:00:00 2001 From: Vipul Maheshwari Date: Fri, 11 Oct 2024 12:39:01 +0530 Subject: [PATCH 01/15] Adding RAPTOR with RAG --- docs/articles/improve-raptor-with-rag.md | 555 ++++++++++++++++++ .../improve-raptor-with-rag/raptor-1.png | Bin 0 -> 4431126 bytes .../improve-raptor-with-rag/raptor-2.png | Bin 0 -> 2144521 bytes .../improve-raptor-with-rag/raptor-3.png | Bin 0 -> 198485 bytes .../improve-raptor-with-rag/raptor-4.png | Bin 0 -> 204469 bytes .../improve-raptor-with-rag/raptor-5.png | Bin 0 -> 18957 bytes .../improve-raptor-with-rag/raptor-6.png | Bin 0 -> 267391 bytes 7 files changed, 555 insertions(+) create mode 100644 docs/articles/improve-raptor-with-rag.md create mode 100644 docs/assets/use_cases/improve-raptor-with-rag/raptor-1.png create mode 100644 docs/assets/use_cases/improve-raptor-with-rag/raptor-2.png create mode 100644 docs/assets/use_cases/improve-raptor-with-rag/raptor-3.png create mode 100644 docs/assets/use_cases/improve-raptor-with-rag/raptor-4.png create mode 100644 docs/assets/use_cases/improve-raptor-with-rag/raptor-5.png create mode 100644 docs/assets/use_cases/improve-raptor-with-rag/raptor-6.png diff --git a/docs/articles/improve-raptor-with-rag.md b/docs/articles/improve-raptor-with-rag.md new file mode 100644 index 000000000..504d41ce1 --- /dev/null +++ b/docs/articles/improve-raptor-with-rag.md @@ -0,0 +1,555 @@ +![image.png](../assets/use_cases/improve-raptor-with-rag/raptor-1.png) + +Traditional [RAG](https://vipul-maheshwari.github.io/2024/02/14/rag-application-with-langchain) setups often split documents into fixed-size chunks, but this can lead to problems in maintaining the semantic coherence of the text. If a key concept spans multiple chunks, and we only retrieve one chunk, the LLM might lack the full understanding of the idea, leading to incomplete or misleading responses. As a result, crucial ideas and relationships between concepts may be overlooked, leading to incomplete or inaccurate responses. + +Additionally, In a flat retrieval structure where all the retrieved chunks are treated equally, this can dilute the importance of critical sections. For example, if one section of the document has key insights but gets buried among less relevant chunks, the model won't know which parts to prioritize unless we introduce more intelligent weighting or hierarchical structures. I mean it becomes really difficult during the retrieval to weigh which chunk is more important and might be better suitable as a context. + +### What is RAPTOR? + +RAPTOR, which stands for Recursive Abstractive Processing for Tree Organized Retrieval, is a new technique which solves the problems mentioned before. Think of RAPTOR as a librarian who organizes information in a tree-like structure. Instead of simply stacking books in a pile, it clusters similar titles together, creating a hierarchy that narrows as you ascend. Each cluster of books represents a group of related documents, and at the top of each cluster, there’s a summary that encapsulates the key points from all the books below it. This process continues all the way to the top of the tree, providing a comprehensive view of the information—it's like having both a magnifying glass and a telescope! + +To visualize this further, think of the leaves of the tree as document chunks. These chunks are grouped into clusters to generate meaningful summaries, which then become the new leaves of the tree. This recursive process repeats until reaching the top. + +### Key terms to look out for + +Before we dive in, let’s quickly review some key terms that will be useful as we explore **RAPTOR** tech. I just want to put it up here to make sure you are comfortable with the nitty tech details as we go along. + +1. **GMM Clustering**: Gaussian Mixture Models (GMM) group data into clusters based on statistical probabilities. So instead of rigidly classifying each instance into one category like K-means, GMM generates K-Gaussian distributions that consider the entire training space. This means that each point can belong to one or more distributions. +2. **Dimensionality Reduction**: This process simplifies the data by reducing the number of variables while retaining essential features. It’s particularly important for understanding high-dimensional datasets like embeddings. +3. **UMAP**: Uniform Manifold Approximation and Projection (UMAP) is a powerful dimensionality reduction algorithm we’ll use to shrink the size of our data point embeddings. This reduction makes it easier for clustering algorithms like GMM to cluster high-dimensional embeddings. +4. **BIC and Elbow Method**: Both techniques help identify the optimal number of clusters in a dataset. The Bayesian Information Criterion (BIC) evaluates models based on their fit to the data while penalizing complexity. The Elbow Method plots explained variance against the number of clusters, helping to pinpoint where adding more clusters offers diminishing returns. For our purposes, we’ll leverage both methods to determine the best number of clusters. + +### How it actually works? + +Now that you’re familiar with the key terms (and if not, no worries—you’ll catch on as we go!), let’s dive into how everything actually works under the hood of RAPTOR. + +- **Starting Documents as Leaves**: The leaves of the tree represent a set of initial documents, which are our text chunks. +- **Embedding and Clustering**: The leaves are embedded and clustered. The authors utilize the UMAP dimensionality reduction algorithm to minimize the embedding size of these chunks. For clustering, Gaussian Mixture Models (GMM) are employed to ensure effective grouping, addressing the challenges posed by high-dimensional vector embeddings. +- **Summarizing Clusters**: Once clustered, these groups of similar chunks are summarised into higher-level abstractions nodes. Each cluster acts like a basket for similar documents, and the individual summaries encapsulate the essence of all nodes within that cluster. This process builds from the bottom up, where nodes are clustered together to create summaries that are then passed up the hierarchy. +- **Recursive Process**: This entire procedure is recursive, resulting in a tree structure that transitions from raw documents (the leaves) to more abstract summaries, with each summary derived from the clusters of various nodes. + +![image.png](../assets/use_cases/improve-raptor-with-rag/raptor-2.png) + +### Building the RAPTOR + +Now that we’ve unpacked how it all works (and you’re still with me hopefully, right?), let’s shift gears and talk about how we actually build the RAPTOR tree. + +**Setup and Imports** + +```python +pip install lancedb scikit-learn openai torch sentence_transformers tiktoken umap-learn PyPDF2 +``` + +```python +import os +import uuid +import tiktoken +import re +import numpy as np +import pandas as pd +import transformers +import torch +import umap.umap_ as umap +import matplotlib.pyplot as plt +from openai import OpenAI +from typing import List, Tuple, Optional, Dict +from sklearn.mixture import GaussianMixture +from sentence_transformers import SentenceTransformer + +openai_api_key = "sk-XXXXXXXXXXXXXXX" +client = OpenAI(api_key=openai_api_key) +``` + +### Creating the Chunks + +Setting up RAPTOR is pretty straightforward and builds on what we’ve already covered. The first step is to break down our textual documents into smaller chunks. Once we have those, we can convert them into dense vector embeddings. + +```python +import os +import PyPDF2 + +# Function to extract text from a PDF file +def extract_pdf_text(file_path): + with open(file_path, 'rb') as file: + reader = PyPDF2.PdfReader(file) + text = "" + for page_num in range(len(reader.pages)): + page = reader.pages[page_num] + text += page.extract_text() + return text + +# Function to split text into chunks with overlap +def split_text(text, chunk_size=1000, chunk_overlap=50): + chunks = [] + start = 0 + while start < len(text): + end = min(start + chunk_size, len(text)) + chunks.append(text[start:end]) + start += chunk_size - chunk_overlap + return chunks + +# Function to process all PDFs in a directory +def process_directory(directory_path, chunk_size=1000, chunk_overlap=50): + all_chunks = [] + # Iterate over all PDF files in the directory + for filename in os.listdir(directory_path): + if filename.endswith(".pdf"): + file_path = os.path.join(directory_path, filename) + print(f"Processing file: {file_path}") + + # Step 1: Extract text from the PDF + pdf_text = extract_pdf_text(file_path) + + # Step 2: Split the extracted text into chunks + chunks = split_text(pdf_text, chunk_size=chunk_size, chunk_overlap=chunk_overlap) + + # Append chunks from this file to the overall list + all_chunks.extend(chunks) + + return all_chunks + +directory_path = os.path.join(os.getcwd(), "data") # Specify your directory path +chunk_size = 1000 +chunk_overlap = 50 + +# Process all PDF files in the directory and get the chunks +chunks = process_directory(directory_path, chunk_size=chunk_size, chunk_overlap=chunk_overlap) + +# Optional: Print the number of chunks and preview some of the chunks +print(f"Total number of chunks: {len(chunks)}") +for i, chunk in enumerate(chunks[:5]): # Show first 5 chunks as a preview + print(f"Chunk {i+1}:\n{chunk}\n") +``` + +Now that we have our chunks, it’s time to dive into the recursive processing to create summarized nodes. For the embedding part, I’ll be using the `all-MiniLM-L6-v2` model from Sentence Transformers, but feel free to choose any embedding model that suits your needs—it’s entirely up to you! + +```python +embedding_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2') +``` + +### Clustering and Dimensionality Reduction + +Now we have our embedded chunks, it’s time to step on for the next set of tasks. When diving into RAPTOR, one of the biggest hurdles we encounter is the high dimensionality of vector embeddings. Traditional clustering methods like Gaussian Mixture Models (GMM) often struggle with this complexity, making it tough to effectively cluster high-dimensional data chunks. To tackle this challenge, we turn to **Uniform Manifold Approximation and Projection (UMAP)**. UMAP excels at simplifying data while preserving the essential structures that matter most. + +A key factor in UMAP's effectiveness is the **`n_neighbors`** parameter. This setting dictates how much of the data's neighbourhood UMAP considers during dimensionality reduction. In simpler terms, it helps you choose between zooming in on details or taking a broader view: + +- **Higher `n_neighbors`:** A higher value encourages UMAP to "look at many neighbors," which helps maintain the **global structure** of the data. This results in larger, more general clusters. +- **Lower `n_neighbors`:** Conversely, lowering `n_neighbors` prompts UMAP to "focus on close relationships," enabling it to preserve the **local structure** and form smaller, more detailed clusters. + +**Think of it this way:** Imagine you’re at a party. If you take a step back and look around (high `n_neighbors`), you can see the whole room—where the groups are forming, who’s mingling, and the general vibe. But if you lean in closer to a specific group (low `n_neighbors`), you can hear their conversation and pick up on the nuances, like inside jokes or shared interests. Both perspectives are valuable; it just depends on what you want to understand. + +In RAPTOR, we leverage this flexibility in `n_neighbors` to create a **hierarchical clustering structure**. We first run UMAP with a higher `n_neighbors` to identify the **global clusters**—the broad categories. Then, we narrow the focus by lowering the value to uncover **local clusters** within those broader groups. This two-step approach ensures we capture both large-scale patterns and intricate details. + +### Well, TL;DR: + +1. **Dimensionality Reduction** helps manage high-dimensional data, and UMAP is our primary tool for that. +2. The **`n_neighbors`** parameter controls the balance between seeing the "big picture" and honing in on local details. +3. The clustering process begins with **global clusters** (using high `n_neighbors`), followed by a focus on **local clusters** with a lower setting of `n_neighbors`. + +```python +def dimensionality_reduction( + embeddings: np.ndarray, + target_dim: int, + clustering_type: str, + metric: str = "cosine", +) -> np.ndarray: + if clustering_type == "local": + n_neighbors = max(2, min(10, len(embeddings) - 1)) + min_dist = 0.01 + elif clustering_type == "global": + n_neighbors = max(2, min(int((len(embeddings) - 1) ** 0.5), len(embeddings) // 10, len(embeddings) - 1)) + min_dist = 0.1 + else: + raise ValueError("clustering_type must be either 'local' or 'global'") + + umap_model = umap.UMAP( + n_neighbors=n_neighbors, + min_dist=min_dist, + n_components=target_dim, + metric=metric, + ) + return umap_model.fit_transform(embeddings) +``` + +I plan to leverage both the Elbow Method and the Bayesian Information Criterion (BIC) to pinpoint the optimal number of clusters for our analysis. + +```python +def compute_inertia(embeddings: np.ndarray, labels: np.ndarray, centroids: np.ndarray) -> float: + return np.sum(np.min(np.sum((embeddings[:, np.newaxis] - centroids) ** 2, axis=2), axis=1)) + +def optimal_cluster_number( + embeddings: np.ndarray, + max_clusters: int = 50, + random_state: int = SEED +) -> int: + max_clusters = min(max_clusters, len(embeddings)) + number_of_clusters = np.arange(1, max_clusters + 1) + inertias = [] + bic_scores = [] + + for n in number_of_clusters: + gmm = GaussianMixture(n_components=n, random_state=random_state) + labels = gmm.fit_predict(embeddings) + centroids = gmm.means_ + inertia = compute_inertia(embeddings, labels, centroids) + inertias.append(inertia) + bic_scores.append(gmm.bic(embeddings)) + + inertia_changes = np.diff(inertias) + elbow_optimal = number_of_clusters[np.argmin(inertia_changes) + 1] + bic_optimal = number_of_clusters[np.argmin(bic_scores)] + + return max(elbow_optimal, bic_optimal) + +def gmm_clustering( + embeddings: np.ndarray, + threshold: float, + random_state: int = SEED +) -> Tuple[List[np.ndarray], int]: + n_clusters = optimal_cluster_number(embeddings, random_state=random_state) + gm = GaussianMixture(n_components=n_clusters, random_state=random_state, n_init=2) + gm.fit(embeddings) + probs = gm.predict_proba(embeddings) + labels = [np.where(prob > threshold)[0] for prob in probs] + return labels, n_clusters +``` + +### Tree Construction + +Now that we’ve wrapped up the clustering part, let’s talk about how we build our hierarchical tree. After several rounds of clustering and summarization (while keeping track of how deep we go), here’s what we have: + +- **Leaf Nodes:** These are our original text chunks, forming the base of the tree. +- **Summary Nodes:** As we go up the tree, each node acts like a quick summary of its child nodes, capturing the main idea of the cluster. +- **Hierarchical Embeddings:** The summary nodes can also become the new nodes at their level. Each of these nodes gets its own vector embedding, representing the summarized meaning. So, we’re essentially adding more nodes while enriching them with summaries. + +The process flows nicely: we embed the chunks, reduce their dimensions using UMAP, cluster them with Gaussian Mixture Models, start with a broad overview, and then zoom in for more detailed clusters before summarizing. + +```python +def clustering_algorithm( + embeddings: np.ndarray, + target_dim: int, + threshold: float, + random_state: int = SEED +) -> Tuple[List[np.ndarray], int]: + if len(embeddings) <= target_dim + 1: + return [np.array([0]) for _ in range(len(embeddings))], 1 + + # Global clustering + reduced_global_embeddings = dimensionality_reduction(embeddings, target_dim, "global") + global_clusters, n_global_clusters = gmm_clustering(reduced_global_embeddings, threshold, random_state=random_state) + + all_local_clusters = [np.array([]) for _ in range(len(embeddings))] + total_clusters = 0 + + # Local clustering within each global cluster + for i in range(n_global_clusters): + global_cluster_mask = np.array([i in gc for gc in global_clusters]) + global_cluster_embeddings = embeddings[global_cluster_mask] + + if len(global_cluster_embeddings) <= target_dim + 1: + # Assign all points in this global cluster to a single local cluster + for idx in np.where(global_cluster_mask)[0]: + all_local_clusters[idx] = np.append(all_local_clusters[idx], total_clusters) + total_clusters += 1 + continue + + try: + reduced_local_embeddings = dimensionality_reduction(global_cluster_embeddings, target_dim, "local") + local_clusters, n_local_clusters = gmm_clustering(reduced_local_embeddings, threshold, random_state=random_state) + + # Assign local cluster IDs + for j in range(n_local_clusters): + local_cluster_mask = np.array([j in lc for lc in local_clusters]) + global_indices = np.where(global_cluster_mask)[0] + local_indices = global_indices[local_cluster_mask] + for idx in local_indices: + all_local_clusters[idx] = np.append(all_local_clusters[idx], j + total_clusters) + + total_clusters += n_local_clusters + except Exception as e: + print(f"Error in local clustering for global cluster {i}: {str(e)}") + # Assign all points in this global cluster to a single local cluster + for idx in np.where(global_cluster_mask)[0]: + all_local_clusters[idx] = np.append(all_local_clusters[idx], total_clusters) + total_clusters += 1 + + return all_local_clusters, total_clusters + + def generate_summary(context: str) -> str: + prompt = f""" + Provide the Summary for the given context. Here are some additional instructions for you: + + Instructions: + 1. Don't make things up, Just use the contexts and generate the relevant summary. + 2. Don't mix the numbers, Just use the numbers in the context. + 3. Don't try to use fancy words, stick to the basics of the language that is being used in the context. + + Context: {context} + """ + response = client.chat.completions.create( + model="gpt-4", + messages=[ + {"role": "system", "content": "You are a helpful assistant that summarizes text."}, + {"role": "user", "content": prompt} + ], + max_tokens=200, + n=1, + stop=None, + temperature=0.7 + ) + summary = response.choices[0].message.content.strip() + return summary + +def embed_clusters( + texts: List[str], + target_dim: int = 10, + threshold: float = 0.1 +) -> pd.DataFrame: + textual_embeddings = np.array(embedding_model.encode(texts)) + clusters, number_of_clusters = clustering_algorithm(textual_embeddings, target_dim, threshold) + print(f"Number of clusters: {number_of_clusters}") + return pd.DataFrame({ + "texts": texts, + "embedding": list(textual_embeddings), + "clusters": clusters + }) + +def embed_cluster_summaries( + texts: List[str], + level: int, + target_dim: int = 10, + threshold: float = 0.1 +) -> Tuple[pd.DataFrame, pd.DataFrame]: + df_clusters = embed_clusters(texts, target_dim, threshold) + main_list = [] + + for _, row in df_clusters.iterrows(): + for cluster in row["clusters"]: + main_list.append({ + "text": row["texts"], + "embedding": row["embedding"], + "clusters": cluster + }) + + main_df = pd.DataFrame(main_list) + unique_clusters = main_df["clusters"].unique() + if len(unique_clusters) == 0: + return df_clusters, pd.DataFrame(columns=["summaries", "level", "clusters"]) + + print(f"--Generated {len(unique_clusters)} clusters--") + + summaries = [] + for cluster in unique_clusters: + text_in_df = main_df[main_df["clusters"] == cluster] + unique_texts = text_in_df["text"].tolist() + text = "------\n------".join(unique_texts) + summary = generate_summary(text) + summaries.append(summary) + + df_summaries = pd.DataFrame({ + "summaries": summaries, + "level": [level] * len(summaries), + "clusters": unique_clusters + }) + + return df_clusters, df_summaries + +def recursive_embedding_with_cluster_summarization( + texts: List[str], + number_of_levels: int = 3, + level: int = 1, + target_dim: int = 10, + threshold: float = 0.1 +) -> Dict[int, Tuple[pd.DataFrame, pd.DataFrame]]: + if level > number_of_levels: + return {} + + results = {} + df_clusters, df_summaries = embed_cluster_summaries(texts, level, target_dim, threshold) + results[level] = (df_clusters, df_summaries) + + if df_summaries.empty or len(df_summaries['clusters'].unique()) == 1: + print(f"No more unique clusters found at level {level}. Stopping recursion.") + return results + + if level < number_of_levels: + next_level_texts = df_summaries['summaries'].tolist() + next_level_results = recursive_embedding_with_cluster_summarization( + next_level_texts, + number_of_levels, + level + 1, + target_dim, + threshold + ) + results.update(next_level_results) + + return results + +``` + +Okay, the code might seem a bit daunting at first glance, but don’t worry! Just give it a couple of looks, and it will start to make sense. Essentially, we’re just following the flow I mentioned earlier. + +```python +def process_text_hierarchy( + texts: List[str], + number_of_levels: int = 3, + target_dim: int = 10, + threshold: float = 0.1 +) -> Dict[str, pd.DataFrame]: + hierarchy_results = recursive_embedding_with_cluster_summarization( + texts, number_of_levels, target_dim=target_dim, threshold=threshold + ) + + processed_results = {} + for level, (df_clusters, df_summaries) in hierarchy_results.items(): + if df_clusters.empty or df_summaries.empty: + print(f"No data for level {level}. Skipping.") + continue + processed_results[f"level_{level}_clusters"] = df_clusters + processed_results[f"level_{level}_summaries"] = df_summaries + + return processed_results + +results = process_text_hierarchy(chunks, number_of_levels=3) +``` + +![image.png](../assets/use_cases/improve-raptor-with-rag/raptor-3.png) + +### Inference + +Now that we have our tree structure with leaf nodes at the bottom and summarized nodes in between, it’s time to query the RAG. There are two main methods for navigating the RAPTOR tree: Tree Traversal and Collapsed Tree Retrieval. + +1. **Tree Traversal Retrieval:** This method systematically explores the tree, starting from the root node. It first selects the top-k most relevant root nodes based on their cosine similarity to the query embedding. Then, for each selected root node, its children are considered in the next layer, where the top-k nodes are again selected based on their cosine similarity to the query vector. This process repeats until we reach the leaf nodes. Finally, the text from all the selected nodes is concatenated to form the retrieved context. +2. **Collapsed Tree Retrieval:** This approach simplifies things by viewing the tree as a single layer. Here, it directly compares the query embedding to the vector embeddings of all the leaf nodes (the original text chunks) and summary nodes. This method works best for factual, keyword-based queries where you need specific details. + +![Reference : https://arxiv.org/pdf/2401.18059](../assets/use_cases/improve-raptor-with-rag/raptor-4.png) + +In the collapsed tree retrieval, we flatten the tree into one layer, retrieving nodes based on cosine similarity until we reach a specified number of ***top k documents***. In our code, we’ll gather the textual chunks from earlier, along with the summarized nodes at each level for all the clusters, to create one big list of texts that includes both the root documents and the summarized nodes. + +To be honest, if you look closely, we’ve been essentially adding more data points (chunks) to our RAG setup all along. Using RAPTOR, we now have both the original chunks and the summarized chunks for each cluster. Now, we’ll simply embed all these new data points and store them in a vector database along with their embeddings and use them for RAG. + +```python +raptor_texts = [] +for level, row in results.items(): + if level.endswith("clusters"): + raptor_texts.extend(row["texts"]) + else: + raptor_texts.extend(row["summaries"]) + +raptor_embeddings = embedding_model.encode(raptor_texts) +len(raptor_embeddings) +``` + +### Setting up Vector Database and RAG + +Now it’s smooth sailing! We’ll just set up a LanceDB vector database to store our embeddings and query our RAG setup. + +```python +raptor_embeddings = embedding_model.encode(raptor_texts) + +raptor_dict = {"texts": [], "embeddings": []} +for texts, embeddings in zip(raptor_texts, raptor_embeddings): + raptor_dict["texts"].append(texts) + raptor_dict["embeddings"].append(embeddings.tolist()) + +``` + +```python +import lancedb +import pyarrow as pa +from lancedb.pydantic import Vector, LanceModel + +uri = "lancedb_database" +db = lancedb.connect(uri) + +class RAG(LanceModel): + texts : str + embeddings : Vector(384) + +table_name = "rag_with_raptor" +raptor_table = db.create_table(table_name, schema = RAG, mode="overwrite") +raptor_table.add(rag_raptor_df) +raptor_table.create_fts_index("texts", replace=True) +``` + +Time to generate the results.. + +```python +def generate_results( + query : str, + context_text : str +) -> str: + + prompt = f""" + Based on the context provided, use it to answer the query. + + query : {query} + + Instructions: + 1. Don't make things up, Just use the contexts and generate the relevant answer. + 2. Don't mix the numbers, Just use the numbers in the context. + 3. Don't try to use fancy words, stick to the basics of the language that is being used in the context. + + {context_text} + """ + response = client.chat.completions.create( + model="gpt-4", + messages=[ + {"role": "system", "content": "You are a helpful assistant that answers query and give the answers."}, + {"role": "user", "content": prompt} + ], + max_tokens=200, + n=1, + stop=None, + temperature=0.7 + ) + answer = response.choices[0].message.content.strip() + return answer +``` + +```python +query = "NTT DATA's net income attributable to shareholders increased from ¥69,227 million in Q3 FY2020 to ¥110,191 million in Q3 FY2021. How does this growth align with their acquisition strategy, particularly considering their stated reasons for acquiring Nexient, LLC and the provisional goodwill recorded in this transaction?" +``` + +Now In our query, there are several key points that must be addressed when crafting the answers. First, we need to note the increase in net income from ¥69,227 million in Q3 FY2020 to ¥110,191 million in Q3 FY2021. Second, we should examine how this growth aligns with NTT DATA's acquisition strategy, particularly their reasons for acquiring Nexient, LLC, and the provisional goodwill recorded in the transaction. With this context in mind, I created a VANILLA RAG to compare its results with those of RAPTOR RAG. + +```python +normal_embeddings = embedding_model.encode(chunks) # default chunks from our data + +normal_dict = {"texts": [], "embeddings": []} +for texts, embeddings in zip(chunks, normal_embeddings): + normal_dict["texts"].append(texts) + normal_dict["embeddings"].append(embeddings.tolist()) + +rag_normal_df = pd.DataFrame(normal_dict) + +table_name = "rag_without_raptor" +normal_table = db.create_table(table_name, schema = RAG, mode="overwrite") +normal_table.add(rag_normal_df) +normal_table.create_fts_index("texts", replace=True) +``` + +With RAPTOR, we now have an increased number of chunks due to the addition of cluster-level summary nodes alongside the default chunks we had earlier. + +![image.png](../assets/use_cases/improve-raptor-with-rag/raptor-5.png) + +### D-Day + +```python +raptor_contexts = raptor_table.search(query).limit(5).select(["texts"]).to_list() +raptor_context_text = "------\n\n".join([context["texts"] for context in raptor_contexts]) +raptor_context_text = "------\n\n" + raptor_context_text + +normal_contexts = normal_table.search(query).limit(5).select(["texts"]).to_list() +normal_context_text = "------\n\n".join([context["texts"] for context in normal_contexts]) +normal_context_text = "------\n\n" + normal_context_text + +raptor_answer = generate_results(query, raptor_context_text) +normal_answer = generate_results(query, normal_context_text) +``` + +![image.png](../assets/use_cases/improve-raptor-with-rag/raptor-6.png) + +When we are comparing RAPTOR RAG with Vanilla RAG, it’s clear that RAPTOR performs better. Not only does RAPTOR retrieve details about the financial growth, but it also effectively connects this growth to the broader acquisition strategy, pulling relevant context from multiple sources. It excels in situations like this, where the query requires insights from various pages, making it more adept at handling complex, layered information retrieval. + +And that’s a wrap for this article! If you want to dig into the intricacies of how everything works, I’d suggest checking out the official RAPTOR [GitHub repository](https://github.com/parthsarthi03/raptor/tree/master) for more info and resources. For an even deeper dive, the official [paper](https://arxiv.org/pdf/2401.18059) is a great read and highly recommended! 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      ASb`U7;pHxtc zK6A@i{3>xidG%VC{ihd5lFJ2DzJT76t{5POP?8zec@(kfzl=0UFq?YBpLkPwrtFVa z50~%J>xP}F+BK%ZlJnq9kCjt&zxuEB5@w?I%Fz9})hJ6SFN=Fp;6$=f9(IFTJnv^t!Qa<7l23S% zrh~6vUYIKX5Xw?e9XN@-z2+e;wGQ}sQ}b9~51)jWb`SW?($gq}w7F~0ISc3DR_wIL zz)G#renTq9?i>MY0aN$^I);|9B&!6BT8>qVSuKz#by(LpSaI-q?gXQ1vHK@x2)C&I zQVQt`Vvt?)J`owCFF`(JAHWHE*?|ObCko7jiI?YO*#0_hea+{jI^O+l;Q|n}umL1x zx1IwH;+4i++C{PpfP`oG+d!){`Y z%|MwsZkmBlxAT5ml+#KLU^{V~^|byQ&hz8cWe_Z3Nl|2goVMJJ$s3Rp;XDoO3e z%i`~#bszt>e1Cq_7CkL!IZQP&^E=SvH_Qk4*g>b7lE`CP^S}KPm#-MTYKp$7QRtst zuiw)ip$Brlu<$V4-&gSGS%4EBQUXVGz7jO}Cr1BuOauFA(lJSqfAwR2|3+{zq+?>l zW$yo5V9F7>kX*3DwlYKh-YtJW%fGg&PH@Ek-;jS+!RY^Q$e+RTf4U)ihOt_D5wCpc R#ZQ61NB5NP=E)nq{x6tFi`W1F literal 0 HcmV?d00001 From b68f113b102973524d7d58acf4c7b77b0c089da1 Mon Sep 17 00:00:00 2001 From: Vipul Maheshwari Date: Thu, 17 Oct 2024 10:56:00 +0530 Subject: [PATCH 02/15] requested changes --- docs/articles/improve-raptor-with-rag.md | 36 +++++++++--------- .../improve-raptor-with-rag/raptor-7.png | Bin 0 -> 110087 bytes 2 files changed, 18 insertions(+), 18 deletions(-) create mode 100644 docs/assets/use_cases/improve-raptor-with-rag/raptor-7.png diff --git a/docs/articles/improve-raptor-with-rag.md b/docs/articles/improve-raptor-with-rag.md index 504d41ce1..62f4afce1 100644 --- a/docs/articles/improve-raptor-with-rag.md +++ b/docs/articles/improve-raptor-with-rag.md @@ -1,18 +1,18 @@ -![image.png](../assets/use_cases/improve-raptor-with-rag/raptor-1.png) +![Implementation of RAPTOR](../assets/use_cases/improve-raptor-with-rag/raptor-1.png) Traditional [RAG](https://vipul-maheshwari.github.io/2024/02/14/rag-application-with-langchain) setups often split documents into fixed-size chunks, but this can lead to problems in maintaining the semantic coherence of the text. If a key concept spans multiple chunks, and we only retrieve one chunk, the LLM might lack the full understanding of the idea, leading to incomplete or misleading responses. As a result, crucial ideas and relationships between concepts may be overlooked, leading to incomplete or inaccurate responses. -Additionally, In a flat retrieval structure where all the retrieved chunks are treated equally, this can dilute the importance of critical sections. For example, if one section of the document has key insights but gets buried among less relevant chunks, the model won't know which parts to prioritize unless we introduce more intelligent weighting or hierarchical structures. I mean it becomes really difficult during the retrieval to weigh which chunk is more important and might be better suitable as a context. +Additionally, In a flat retrieval structure where all the retrieved chunks are treated equally, this can dilute the importance of critical sections. For example, if one section of the document has key insights but gets buried among less relevant chunks, the model won't know which parts to prioritize unless we introduce more intelligent weighting or hierarchical structures. I mean it becomes really difficult during the retrieval to weigh which chunk is more important and might be better suitable as a context. ### What is RAPTOR? -RAPTOR, which stands for Recursive Abstractive Processing for Tree Organized Retrieval, is a new technique which solves the problems mentioned before. Think of RAPTOR as a librarian who organizes information in a tree-like structure. Instead of simply stacking books in a pile, it clusters similar titles together, creating a hierarchy that narrows as you ascend. Each cluster of books represents a group of related documents, and at the top of each cluster, there’s a summary that encapsulates the key points from all the books below it. This process continues all the way to the top of the tree, providing a comprehensive view of the information—it's like having both a magnifying glass and a telescope! +RAPTOR, or Recursive Abstractive Processing for Tree Organized Retrieval, addresses the challenges mentioned earlier by organizing information hierarchically. It clusters related document chunks together, and for each cluster, it generates a summary that captures the key points. This process is repeated recursively, building higher-level summaries until reaching a comprehensive overview of the whole information. The recursive clustering and summarization structure provides both detailed and broad views of the data, similar to navigating a hierarchy where each level distills the content below. -To visualize this further, think of the leaves of the tree as document chunks. These chunks are grouped into clusters to generate meaningful summaries, which then become the new leaves of the tree. This recursive process repeats until reaching the top. +![High Level Overview of RAPTOR](../assets/use_cases/improve-raptor-with-rag/raptor-7.png) ### Key terms to look out for -Before we dive in, let’s quickly review some key terms that will be useful as we explore **RAPTOR** tech. I just want to put it up here to make sure you are comfortable with the nitty tech details as we go along. +Before we dive in, let’s quickly review some key terms that will be useful as we explore **RAPTOR** tech. I just want to put it up here to make sure you are comfortable with the nitty tech details as we go along. 1. **GMM Clustering**: Gaussian Mixture Models (GMM) group data into clusters based on statistical probabilities. So instead of rigidly classifying each instance into one category like K-means, GMM generates K-Gaussian distributions that consider the entire training space. This means that each point can belong to one or more distributions. 2. **Dimensionality Reduction**: This process simplifies the data by reducing the number of variables while retaining essential features. It’s particularly important for understanding high-dimensional datasets like embeddings. @@ -21,18 +21,18 @@ Before we dive in, let’s quickly review some key terms that will be useful as ### How it actually works? -Now that you’re familiar with the key terms (and if not, no worries—you’ll catch on as we go!), let’s dive into how everything actually works under the hood of RAPTOR. +Now that you’re familiar with the key terms (and if not, no worries—you’ll catch on as we go!), let’s dive into how everything actually works under the hood of RAPTOR. - **Starting Documents as Leaves**: The leaves of the tree represent a set of initial documents, which are our text chunks. - **Embedding and Clustering**: The leaves are embedded and clustered. The authors utilize the UMAP dimensionality reduction algorithm to minimize the embedding size of these chunks. For clustering, Gaussian Mixture Models (GMM) are employed to ensure effective grouping, addressing the challenges posed by high-dimensional vector embeddings. -- **Summarizing Clusters**: Once clustered, these groups of similar chunks are summarised into higher-level abstractions nodes. Each cluster acts like a basket for similar documents, and the individual summaries encapsulate the essence of all nodes within that cluster. This process builds from the bottom up, where nodes are clustered together to create summaries that are then passed up the hierarchy. +- **Summarizing Clusters**: Once clustered, these groups of similar chunks are summarized into higher-level abstractions nodes. Each cluster acts like a basket for similar documents, and the individual summaries encapsulate the essence of all nodes within that cluster. This process builds from the bottom up, where nodes are clustered together to create summaries that are then passed up the hierarchy. - **Recursive Process**: This entire procedure is recursive, resulting in a tree structure that transitions from raw documents (the leaves) to more abstract summaries, with each summary derived from the clusters of various nodes. -![image.png](../assets/use_cases/improve-raptor-with-rag/raptor-2.png) +![Raptor Workflow](../assets/use_cases/improve-raptor-with-rag/raptor-2.png) ### Building the RAPTOR -Now that we’ve unpacked how it all works (and you’re still with me hopefully, right?), let’s shift gears and talk about how we actually build the RAPTOR tree. +Now that we’ve unpacked how it all works (and you’re still with me hopefully, right?), let’s shift gears and talk about how we actually build the RAPTOR tree. **Setup and Imports** @@ -131,7 +131,7 @@ embedding_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2') Now we have our embedded chunks, it’s time to step on for the next set of tasks. When diving into RAPTOR, one of the biggest hurdles we encounter is the high dimensionality of vector embeddings. Traditional clustering methods like Gaussian Mixture Models (GMM) often struggle with this complexity, making it tough to effectively cluster high-dimensional data chunks. To tackle this challenge, we turn to **Uniform Manifold Approximation and Projection (UMAP)**. UMAP excels at simplifying data while preserving the essential structures that matter most. -A key factor in UMAP's effectiveness is the **`n_neighbors`** parameter. This setting dictates how much of the data's neighbourhood UMAP considers during dimensionality reduction. In simpler terms, it helps you choose between zooming in on details or taking a broader view: +A key factor in UMAP's effectiveness is the **`n_neighbors`** parameter. This setting dictates how much of the data's neighborhood UMAP considers during dimensionality reduction. In simpler terms, it helps you choose between zooming in on details or taking a broader view: - **Higher `n_neighbors`:** A higher value encourages UMAP to "look at many neighbors," which helps maintain the **global structure** of the data. This results in larger, more general clusters. - **Lower `n_neighbors`:** Conversely, lowering `n_neighbors` prompts UMAP to "focus on close relationships," enabling it to preserve the **local structure** and form smaller, more detailed clusters. @@ -387,7 +387,7 @@ def recursive_embedding_with_cluster_summarization( ``` -Okay, the code might seem a bit daunting at first glance, but don’t worry! Just give it a couple of looks, and it will start to make sense. Essentially, we’re just following the flow I mentioned earlier. +Okay, the code might seem a bit daunting at first glance, but don’t worry! Just give it a couple of looks, and it will start to make sense. Essentially, we’re just following the flow I mentioned earlier. ```python def process_text_hierarchy( @@ -413,7 +413,7 @@ def process_text_hierarchy( results = process_text_hierarchy(chunks, number_of_levels=3) ``` -![image.png](../assets/use_cases/improve-raptor-with-rag/raptor-3.png) +![Building a RAPTOR Tree](../assets/use_cases/improve-raptor-with-rag/raptor-3.png) ### Inference @@ -422,7 +422,7 @@ Now that we have our tree structure with leaf nodes at the bottom and summarized 1. **Tree Traversal Retrieval:** This method systematically explores the tree, starting from the root node. It first selects the top-k most relevant root nodes based on their cosine similarity to the query embedding. Then, for each selected root node, its children are considered in the next layer, where the top-k nodes are again selected based on their cosine similarity to the query vector. This process repeats until we reach the leaf nodes. Finally, the text from all the selected nodes is concatenated to form the retrieved context. 2. **Collapsed Tree Retrieval:** This approach simplifies things by viewing the tree as a single layer. Here, it directly compares the query embedding to the vector embeddings of all the leaf nodes (the original text chunks) and summary nodes. This method works best for factual, keyword-based queries where you need specific details. -![Reference : https://arxiv.org/pdf/2401.18059](../assets/use_cases/improve-raptor-with-rag/raptor-4.png) +![How the Retrieval happens](../assets/use_cases/improve-raptor-with-rag/raptor-4.png) In the collapsed tree retrieval, we flatten the tree into one layer, retrieving nodes based on cosine similarity until we reach a specified number of ***top k documents***. In our code, we’ll gather the textual chunks from earlier, along with the summarized nodes at each level for all the clusters, to create one big list of texts that includes both the root documents and the summarized nodes. @@ -442,7 +442,7 @@ len(raptor_embeddings) ### Setting up Vector Database and RAG -Now it’s smooth sailing! We’ll just set up a LanceDB vector database to store our embeddings and query our RAG setup. +Now it’s smooth sailing! We’ll just set up a LanceDB vector database to store our embeddings and query our RAG setup. ```python raptor_embeddings = embedding_model.encode(raptor_texts) @@ -529,9 +529,9 @@ normal_table.add(rag_normal_df) normal_table.create_fts_index("texts", replace=True) ``` -With RAPTOR, we now have an increased number of chunks due to the addition of cluster-level summary nodes alongside the default chunks we had earlier. +With RAPTOR, we now have an increased number of chunks due to the addition of cluster-level summary nodes alongside the default chunks we had earlier. -![image.png](../assets/use_cases/improve-raptor-with-rag/raptor-5.png) +![Number of chunks in RAPTOR](../assets/use_cases/improve-raptor-with-rag/raptor-5.png) ### D-Day @@ -548,8 +548,8 @@ raptor_answer = generate_results(query, raptor_context_text) normal_answer = generate_results(query, normal_context_text) ``` -![image.png](../assets/use_cases/improve-raptor-with-rag/raptor-6.png) +![Comparison for a Query](../assets/use_cases/improve-raptor-with-rag/raptor-6.png) When we are comparing RAPTOR RAG with Vanilla RAG, it’s clear that RAPTOR performs better. Not only does RAPTOR retrieve details about the financial growth, but it also effectively connects this growth to the broader acquisition strategy, pulling relevant context from multiple sources. It excels in situations like this, where the query requires insights from various pages, making it more adept at handling complex, layered information retrieval. -And that’s a wrap for this article! If you want to dig into the intricacies of how everything works, I’d suggest checking out the official RAPTOR [GitHub repository](https://github.com/parthsarthi03/raptor/tree/master) for more info and resources. For an even deeper dive, the official [paper](https://arxiv.org/pdf/2401.18059) is a great read and highly recommended! Here is the Google [colab](https://colab.research.google.com/drive/1I3WI0U4sgb2nc1QTQm51kThZb2q4MXyr?usp=sharing) for your reference. \ No newline at end of file +And that’s a wrap for this article! If you want to dig into the intricacies of how everything works, I’d suggest checking out the official RAPTOR [GitHub repository](https://github.com/parthsarthi03/raptor/tree/master) for more info and resources. For an even deeper dive, the official [paper](https://arxiv.org/pdf/2401.18059) is a great read and highly recommended! 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changed, 19 insertions(+), 13 deletions(-) diff --git a/docs/articles/improve-raptor-with-rag.md b/docs/articles/improve-raptor-with-rag.md index 504d41ce1..75e4872a9 100644 --- a/docs/articles/improve-raptor-with-rag.md +++ b/docs/articles/improve-raptor-with-rag.md @@ -1,18 +1,19 @@ -![image.png](../assets/use_cases/improve-raptor-with-rag/raptor-1.png) +# Improving RAPTOR with RAG -Traditional [RAG](https://vipul-maheshwari.github.io/2024/02/14/rag-application-with-langchain) setups often split documents into fixed-size chunks, but this can lead to problems in maintaining the semantic coherence of the text. If a key concept spans multiple chunks, and we only retrieve one chunk, the LLM might lack the full understanding of the idea, leading to incomplete or misleading responses. As a result, crucial ideas and relationships between concepts may be overlooked, leading to incomplete or inaccurate responses. +Traditional [RAG](https://vipul-maheshwari.github.io/2024/02/14/rag-application-with-langchain) setups often split documents into fixed-size chunks. This creates problems when key concepts span multiple chunks. The embeddings can lose the semantic coherence of the original text, and LLM queries that retrieve single chunks often miss their relationship to crucial pieces of information buried inside other chunks, leading to incomplete or misleading responses. Without any weighting or hierarchical structure in our chunk embeddings, traditional RAG's flat retrieval returns results based simply on similarity or relevance scores. Key insights are often lost. -Additionally, In a flat retrieval structure where all the retrieved chunks are treated equally, this can dilute the importance of critical sections. For example, if one section of the document has key insights but gets buried among less relevant chunks, the model won't know which parts to prioritize unless we introduce more intelligent weighting or hierarchical structures. I mean it becomes really difficult during the retrieval to weigh which chunk is more important and might be better suitable as a context. +So, is there a way of preserving the relationships and hierarchical structure that exists within source documents in our embeddings, so that our retrieval can surface key insights? +Yes. It's a form of semantic chunking called hierarchical-cluster-embedding-based chunking... RAPTOR (Recursive Abstractive Processing for Tree Organized Retrieval) is an example.. -### What is RAPTOR? +## RAPTOR -RAPTOR, which stands for Recursive Abstractive Processing for Tree Organized Retrieval, is a new technique which solves the problems mentioned before. Think of RAPTOR as a librarian who organizes information in a tree-like structure. Instead of simply stacking books in a pile, it clusters similar titles together, creating a hierarchy that narrows as you ascend. Each cluster of books represents a group of related documents, and at the top of each cluster, there’s a summary that encapsulates the key points from all the books below it. This process continues all the way to the top of the tree, providing a comprehensive view of the information—it's like having both a magnifying glass and a telescope! +RAPTOR organizes information in a tree-like structure, progressively narrowing as it moves upwards, representing original documents at higher and higher levels of abstraction. RAPTOR handles each document by splitting it into chunks, embedding these chunks, grouping these embeddings into clusters, and then summarizing each cluster. Summaries become a new set of chunks, which RAPTOR then embeds, then clusters, then summarizes again... - thus preserving the relationships and hierarchical structures of the original source. At the top of each cluster, there’s a summary that encapsulates the key points from all the data below it. RAPTOR moves upwards like this, recursively, to the very top of the tree, where we have a comprehensive view of the information — it's like having both a magnifying glass and a telescope! -To visualize this further, think of the leaves of the tree as document chunks. These chunks are grouped into clusters to generate meaningful summaries, which then become the new leaves of the tree. This recursive process repeats until reaching the top. +![image.png](../assets/use_cases/improve-raptor-with-rag/raptor-1.png) ### Key terms to look out for -Before we dive in, let’s quickly review some key terms that will be useful as we explore **RAPTOR** tech. I just want to put it up here to make sure you are comfortable with the nitty tech details as we go along. +Before we dive in, let’s quickly review some key terms that will be useful as we explore **RAPTOR** tech. 1. **GMM Clustering**: Gaussian Mixture Models (GMM) group data into clusters based on statistical probabilities. So instead of rigidly classifying each instance into one category like K-means, GMM generates K-Gaussian distributions that consider the entire training space. This means that each point can belong to one or more distributions. 2. **Dimensionality Reduction**: This process simplifies the data by reducing the number of variables while retaining essential features. It’s particularly important for understanding high-dimensional datasets like embeddings. @@ -23,14 +24,14 @@ Before we dive in, let’s quickly review some key terms that will be useful as Now that you’re familiar with the key terms (and if not, no worries—you’ll catch on as we go!), let’s dive into how everything actually works under the hood of RAPTOR. -- **Starting Documents as Leaves**: The leaves of the tree represent a set of initial documents, which are our text chunks. -- **Embedding and Clustering**: The leaves are embedded and clustered. The authors utilize the UMAP dimensionality reduction algorithm to minimize the embedding size of these chunks. For clustering, Gaussian Mixture Models (GMM) are employed to ensure effective grouping, addressing the challenges posed by high-dimensional vector embeddings. -- **Summarizing Clusters**: Once clustered, these groups of similar chunks are summarised into higher-level abstractions nodes. Each cluster acts like a basket for similar documents, and the individual summaries encapsulate the essence of all nodes within that cluster. This process builds from the bottom up, where nodes are clustered together to create summaries that are then passed up the hierarchy. -- **Recursive Process**: This entire procedure is recursive, resulting in a tree structure that transitions from raw documents (the leaves) to more abstract summaries, with each summary derived from the clusters of various nodes. +- **Starting Documents (Leaves)**: RAPTOR first splits raw documents into chunks. +- **Embedding and Clustering**: Next, we embed these chunks ([RAPTOR authors](https://arxiv.org/pdf/2401.18059) use the UMAP dimensionality reduction algorithm to minimize chunk embedding size). Then, based on their vector embeddings, we cluster our text chunks (using Gaussian Mixture Models (GMM) to ensure effective grouping of high-dimensional vector embeddings). +- **Summarizing Clusters**: Next, we summarize the clusters of similar chunks into a node of higher-level abstractions. +- **Recursive Iteration**: We treat these nodes in turn as chunks, clustering them, summarizing these clusters.. iteratively, building a tree-like structure that encapsulates the relationships and hierarchical structures inherent in the raw documents we started from. ![image.png](../assets/use_cases/improve-raptor-with-rag/raptor-2.png) -### Building the RAPTOR +### Building the RAPTOR tree Now that we’ve unpacked how it all works (and you’re still with me hopefully, right?), let’s shift gears and talk about how we actually build the RAPTOR tree. @@ -552,4 +553,9 @@ normal_answer = generate_results(query, normal_context_text) When we are comparing RAPTOR RAG with Vanilla RAG, it’s clear that RAPTOR performs better. Not only does RAPTOR retrieve details about the financial growth, but it also effectively connects this growth to the broader acquisition strategy, pulling relevant context from multiple sources. It excels in situations like this, where the query requires insights from various pages, making it more adept at handling complex, layered information retrieval. -And that’s a wrap for this article! If you want to dig into the intricacies of how everything works, I’d suggest checking out the official RAPTOR [GitHub repository](https://github.com/parthsarthi03/raptor/tree/master) for more info and resources. For an even deeper dive, the official [paper](https://arxiv.org/pdf/2401.18059) is a great read and highly recommended! Here is the Google [colab](https://colab.research.google.com/drive/1I3WI0U4sgb2nc1QTQm51kThZb2q4MXyr?usp=sharing) for your reference. \ No newline at end of file +And that’s a wrap for this article! If you want to dig into the intricacies of how everything works, I’d suggest checking out the official RAPTOR [GitHub repository](https://github.com/parthsarthi03/raptor/tree/master) for more info and resources. For an even deeper dive, the official [paper](https://arxiv.org/pdf/2401.18059) is a great read and highly recommended! Here is the Google [colab](https://colab.research.google.com/drive/1I3WI0U4sgb2nc1QTQm51kThZb2q4MXyr?usp=sharing) for your reference. + +## Contributors + +- [Vipul, author](https://in.linkedin.com/in/vipul-maheshwari-19b98423) +- [Robert Turner, editor](https://www.linkedin.com/in/robertdhayanturner/) \ No newline at end of file From d6e4780ba559d704200a7eb1f882efbeb216ecd7 Mon Sep 17 00:00:00 2001 From: Robert Turner <143536791+robertdhayanturner@users.noreply.github.com> Date: Tue, 22 Oct 2024 17:01:22 -0400 Subject: [PATCH 04/15] update article --- docs/articles/improve-raptor-with-rag.md | 178 +++++++++++------- .../improve-raptor-with-rag/raptor-7.png | Bin 0 -> 109523 bytes 2 files changed, 108 insertions(+), 70 deletions(-) create mode 100644 docs/assets/use_cases/improve-raptor-with-rag/raptor-7.png diff --git a/docs/articles/improve-raptor-with-rag.md b/docs/articles/improve-raptor-with-rag.md index 75e4872a9..1a87404b8 100644 --- a/docs/articles/improve-raptor-with-rag.md +++ b/docs/articles/improve-raptor-with-rag.md @@ -1,41 +1,38 @@ -# Improving RAPTOR with RAG +# Improving RAG with RAPTOR -Traditional [RAG](https://vipul-maheshwari.github.io/2024/02/14/rag-application-with-langchain) setups often split documents into fixed-size chunks. This creates problems when key concepts span multiple chunks. The embeddings can lose the semantic coherence of the original text, and LLM queries that retrieve single chunks often miss their relationship to crucial pieces of information buried inside other chunks, leading to incomplete or misleading responses. Without any weighting or hierarchical structure in our chunk embeddings, traditional RAG's flat retrieval returns results based simply on similarity or relevance scores. Key insights are often lost. +Traditional [RAG](https://vipul-maheshwari.github.io/2024/02/14/rag-application-with-langchain) setups often split documents into fixed-size chunks. But this creates problems. If key concepts span multiple chunks, the embeddings can lose the semantic coherence of the original text, and LLM queries that retrieve single chunks often miss their relationship to crucial pieces of information buried inside other chunks, leading to incomplete or misleading responses. Because its chunk embeddings lack any weighting or hierarchical structure, traditional RAG's flat retrieval returns results based simply on similarity or relevance scores. Key insights are often lost. -So, is there a way of preserving the relationships and hierarchical structure that exists within source documents in our embeddings, so that our retrieval can surface key insights? -Yes. It's a form of semantic chunking called hierarchical-cluster-embedding-based chunking... RAPTOR (Recursive Abstractive Processing for Tree Organized Retrieval) is an example.. +So, is there a way of getting our embeddings to preserve the relationships and hierarchical structure that exists within source documents, so that our retrieval can surface key insights, and do it efficiently? +Yes! It's a form of semantic chunking called hierarchical-cluster-embedding-based chunking. Below, we'll look closely at a recent and innovative implementation of hierarchical clustering - [RAPTOR](https://arxiv.org/pdf/2401.18059) (Recursive Abstractive Processing for Tree Organized Retrieval). -## RAPTOR +We'll walk you step-by-step through setting up RAPTOR RAG and then run an example query on a financial news document to evaluate how RAPTOR performs against vanilla RAG. -RAPTOR organizes information in a tree-like structure, progressively narrowing as it moves upwards, representing original documents at higher and higher levels of abstraction. RAPTOR handles each document by splitting it into chunks, embedding these chunks, grouping these embeddings into clusters, and then summarizing each cluster. Summaries become a new set of chunks, which RAPTOR then embeds, then clusters, then summarizes again... - thus preserving the relationships and hierarchical structures of the original source. At the top of each cluster, there’s a summary that encapsulates the key points from all the data below it. RAPTOR moves upwards like this, recursively, to the very top of the tree, where we have a comprehensive view of the information — it's like having both a magnifying glass and a telescope! +Let's get started! -![image.png](../assets/use_cases/improve-raptor-with-rag/raptor-1.png) +## RAPTOR basic steps -### Key terms to look out for +RAPTOR organizes information in a tree-like structure, progressively narrowing as it moves upwards, representing original documents at higher and higher levels of abstraction, so it can retain the original meaning in full. Let's break this into steps. -Before we dive in, let’s quickly review some key terms that will be useful as we explore **RAPTOR** tech. +![High level overview of RAPTOR](../assets/use_cases/improve-raptor-with-rag/raptor-7.png) -1. **GMM Clustering**: Gaussian Mixture Models (GMM) group data into clusters based on statistical probabilities. So instead of rigidly classifying each instance into one category like K-means, GMM generates K-Gaussian distributions that consider the entire training space. This means that each point can belong to one or more distributions. -2. **Dimensionality Reduction**: This process simplifies the data by reducing the number of variables while retaining essential features. It’s particularly important for understanding high-dimensional datasets like embeddings. -3. **UMAP**: Uniform Manifold Approximation and Projection (UMAP) is a powerful dimensionality reduction algorithm we’ll use to shrink the size of our data point embeddings. This reduction makes it easier for clustering algorithms like GMM to cluster high-dimensional embeddings. -4. **BIC and Elbow Method**: Both techniques help identify the optimal number of clusters in a dataset. The Bayesian Information Criterion (BIC) evaluates models based on their fit to the data while penalizing complexity. The Elbow Method plots explained variance against the number of clusters, helping to pinpoint where adding more clusters offers diminishing returns. For our purposes, we’ll leverage both methods to determine the best number of clusters. +*Above: "Figure 1. Tree construction process" from [RAPTOR](https://arxiv.org/pdf/2401.18059)* -### How it actually works? +**RAPTOR tree construction - basic steps:** -Now that you’re familiar with the key terms (and if not, no worries—you’ll catch on as we go!), let’s dive into how everything actually works under the hood of RAPTOR. +- **starting documents (leaves)**: RAPTOR first splits each raw document into chunks +- **embedding and clustering**: we embed these chunks ([RAPTOR authors](https://arxiv.org/pdf/2401.18059) use the UMAP dimensionality reduction algorithm to minimize chunk embedding size), and, based on their vector embeddings, cluster these chunks (using Gaussian Mixture Models (GMM), which generate distributions that consider the entire training space; each point can belong to more than one distribution) to ensure effective grouping of high-dimensional vector embeddings +- **summarizing clusters**: we summarize the clusters of similar chunks into a node of higher-level abstractions +- **recursive iteration**: we treat these nodes in turn as chunks, clustering them, summarizing these clusters, iteratively, building a tree-like structure that encapsulates the relationships and hierarchical structures inherent in the raw documents we started from -- **Starting Documents (Leaves)**: RAPTOR first splits raw documents into chunks. -- **Embedding and Clustering**: Next, we embed these chunks ([RAPTOR authors](https://arxiv.org/pdf/2401.18059) use the UMAP dimensionality reduction algorithm to minimize chunk embedding size). Then, based on their vector embeddings, we cluster our text chunks (using Gaussian Mixture Models (GMM) to ensure effective grouping of high-dimensional vector embeddings). -- **Summarizing Clusters**: Next, we summarize the clusters of similar chunks into a node of higher-level abstractions. -- **Recursive Iteration**: We treat these nodes in turn as chunks, clustering them, summarizing these clusters.. iteratively, building a tree-like structure that encapsulates the relationships and hierarchical structures inherent in the raw documents we started from. +![Raptor workflow](../assets/use_cases/improve-raptor-with-rag/raptor-2.png) -![image.png](../assets/use_cases/improve-raptor-with-rag/raptor-2.png) +In short, RAPTOR chunks raw documents, embeds the chunks, groups embeddings into clusters, summarizes each cluster, then (recursively) chunks these summaries, embeds, summarizes again, and so on, moving upwards to a comprehensive and efficient data representation, preserving nuances and key points. -### Building the RAPTOR tree +## Building a RAPTOR tree -Now that we’ve unpacked how it all works (and you’re still with me hopefully, right?), let’s shift gears and talk about how we actually build the RAPTOR tree. +Now that we’ve laid out RAPTOR's basic steps, let’s walk through how to *implement* a RAPTOR tree. -**Setup and Imports** +We start with our basic setup and imports. ```python pip install lancedb scikit-learn openai torch sentence_transformers tiktoken umap-learn PyPDF2 @@ -63,10 +60,20 @@ client = OpenAI(api_key=openai_api_key) ### Creating the Chunks -Setting up RAPTOR is pretty straightforward and builds on what we’ve already covered. The first step is to break down our textual documents into smaller chunks. Once we have those, we can convert them into dense vector embeddings. +First, create a directory_path to your source documents. (For simplication purposes, our use case example will use just [one financial document](https://www.nttdata.com/global/en/-/media/nttdataglobal/1_files/investors/financial-results/2021/fy2021_fs_3q.pdf) as a source. You can of course load any source documents (and alter the query below) in your own experiments!) + +```python +directory_path = '/content/data' +if not os.path.exists(directory_path): + os.makedirs(directory_path) + print(f"Directory '{directory_path}' created successfully.") +else: + print(f"Directory '{directory_path}' already exists.") +``` + +Now, let's break our text documents down into smaller chunks. ```python -import os import PyPDF2 # Function to extract text from a PDF file @@ -122,30 +129,17 @@ for i, chunk in enumerate(chunks[:5]): # Show first 5 chunks as a preview print(f"Chunk {i+1}:\n{chunk}\n") ``` -Now that we have our chunks, it’s time to dive into the recursive processing to create summarized nodes. For the embedding part, I’ll be using the `all-MiniLM-L6-v2` model from Sentence Transformers, but feel free to choose any embedding model that suits your needs—it’s entirely up to you! +Now that we have our chunks, let's move on and set up our embedding process, first by initializing our embedding model. We'll use `all-MiniLM-L6-v2` (Sentence Transformers), but you can choose any embedding model that fits your use case. ```python embedding_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2') ``` -### Clustering and Dimensionality Reduction - -Now we have our embedded chunks, it’s time to step on for the next set of tasks. When diving into RAPTOR, one of the biggest hurdles we encounter is the high dimensionality of vector embeddings. Traditional clustering methods like Gaussian Mixture Models (GMM) often struggle with this complexity, making it tough to effectively cluster high-dimensional data chunks. To tackle this challenge, we turn to **Uniform Manifold Approximation and Projection (UMAP)**. UMAP excels at simplifying data while preserving the essential structures that matter most. - -A key factor in UMAP's effectiveness is the **`n_neighbors`** parameter. This setting dictates how much of the data's neighbourhood UMAP considers during dimensionality reduction. In simpler terms, it helps you choose between zooming in on details or taking a broader view: - -- **Higher `n_neighbors`:** A higher value encourages UMAP to "look at many neighbors," which helps maintain the **global structure** of the data. This results in larger, more general clusters. -- **Lower `n_neighbors`:** Conversely, lowering `n_neighbors` prompts UMAP to "focus on close relationships," enabling it to preserve the **local structure** and form smaller, more detailed clusters. +### Hierarchical clustering and dimensionality reduction - setup -**Think of it this way:** Imagine you’re at a party. If you take a step back and look around (high `n_neighbors`), you can see the whole room—where the groups are forming, who’s mingling, and the general vibe. But if you lean in closer to a specific group (low `n_neighbors`), you can hear their conversation and pick up on the nuances, like inside jokes or shared interests. Both perspectives are valuable; it just depends on what you want to understand. +To preserve the essential structures and meaning of our data, we need to cluster it so we can easily access both its broad categories and its intricate details. One efficient way of doing this is with a **hierarchical clustering structure**. This is where UMAP comes in. Traditional clustering methods like Gaussian Mixture Models (GMM) often struggle with clustering high-dimensional data chunks. We can make things easier for our GMM by first using UMAP (Uniform Manifold Approximation and Projection) to simplify our data but preserve its essential structures. -In RAPTOR, we leverage this flexibility in `n_neighbors` to create a **hierarchical clustering structure**. We first run UMAP with a higher `n_neighbors` to identify the **global clusters**—the broad categories. Then, we narrow the focus by lowering the value to uncover **local clusters** within those broader groups. This two-step approach ensures we capture both large-scale patterns and intricate details. - -### Well, TL;DR: - -1. **Dimensionality Reduction** helps manage high-dimensional data, and UMAP is our primary tool for that. -2. The **`n_neighbors`** parameter controls the balance between seeing the "big picture" and honing in on local details. -3. The clustering process begins with **global clusters** (using high `n_neighbors`), followed by a focus on **local clusters** with a lower setting of `n_neighbors`. +UMAP's **`n_neighbors`** parameter dictates how much of the data's neighborhood UMAP considers during dimensionality reduction. Higher `n_neighbors` tells UMAP to look at many neighbors, maintaining the data's global structure and producing larger, more general clusters - **global clusters**. Lower `n_neighbors`, conversely, prompts UMAP to focus on close relationships, preserving local structure and forming smaller, more detailed, **local clusters**. ```python def dimensionality_reduction( @@ -172,7 +166,9 @@ def dimensionality_reduction( return umap_model.fit_transform(embeddings) ``` -I plan to leverage both the Elbow Method and the Bayesian Information Criterion (BIC) to pinpoint the optimal number of clusters for our analysis. +To determine the number of clusters that optimally captures our data's relationships and structure, we'll use the Elbow Method and the Bayesian Information Criterion (BIC). The Elbow Method plots explain variance against the number of clusters, helping us know when adding more clusters results in diminishing returns, while BIC evaluates models based on their fit to the data while penalizing complexity. + +Once we've determined our `optimal_cluster_number`, we'll fit a GMM with it, predict the probability of each embedding belonging to each cluster, and assign labels to our embeddings. ```python def compute_inertia(embeddings: np.ndarray, labels: np.ndarray, centroids: np.ndarray) -> float: @@ -215,17 +211,21 @@ def gmm_clustering( return labels, n_clusters ``` -### Tree Construction +So far, we've chunked our documents and defined functions - for embedding our chunks, reducing their dimensions using UMAP, and clustering them. Now let's set up the recursive hierarchical clustering that is RAPTOR's defining feature. + +## Hierarchical clustering -Now that we’ve wrapped up the clustering part, let’s talk about how we build our hierarchical tree. After several rounds of clustering and summarization (while keeping track of how deep we go), here’s what we have: +To set up our hierarchical clustering, we define several functions: -- **Leaf Nodes:** These are our original text chunks, forming the base of the tree. -- **Summary Nodes:** As we go up the tree, each node acts like a quick summary of its child nodes, capturing the main idea of the cluster. -- **Hierarchical Embeddings:** The summary nodes can also become the new nodes at their level. Each of these nodes gets its own vector embedding, representing the summarized meaning. So, we’re essentially adding more nodes while enriching them with summaries. +- a clustering_algorithm function to manage both global and local clustering using GMMs - first globally, then locally within each global cluster (start with a broad overview, and then zoom in for more detailed clusters before summarizing) +- a generate_summary function to generate text summaries using a language model +- an embed clusters function manages the embedding and initial clustering +- an embed cluster summaries function for summarizing each cluster +- a recursive_embedding_with_cluster_summarization function that leverages these methods to create a multi-level hierarchical clustering and summarization structure -The process flows nicely: we embed the chunks, reduce their dimensions using UMAP, cluster them with Gaussian Mixture Models, start with a broad overview, and then zoom in for more detailed clusters before summarizing. ```python +#define our clustering algorithm def clustering_algorithm( embeddings: np.ndarray, target_dim: int, @@ -385,10 +385,9 @@ def recursive_embedding_with_cluster_summarization( results.update(next_level_results) return results - ``` -Okay, the code might seem a bit daunting at first glance, but don’t worry! Just give it a couple of looks, and it will start to make sense. Essentially, we’re just following the flow I mentioned earlier. +Now we define a process_text_hierarchy function that takes a hierarchical text dataset, processes it to generate embeddings and cluster summaries, and returns the processed results organized by hierarchical level. ```python def process_text_hierarchy( @@ -414,20 +413,52 @@ def process_text_hierarchy( results = process_text_hierarchy(chunks, number_of_levels=3) ``` -![image.png](../assets/use_cases/improve-raptor-with-rag/raptor-3.png) +After several rounds of clustering and summarization, we have our RAPTOR tree. + +### RAPTOR tree + +- **leaf nodes (base level)** - at the tree's base, we have our original, unprocessed text chunks - the raw data inputs that RAPTOR RAG uses for analysis and retrieval +- **intermediate summary nodes** - moving up the tree, we cluster our chunks, and summarize the cluster, creating summary nodes - each summary node captures the meaning of the cluster it represents, facilitating efficient retrieval and accurate contextual understanding +- **hierarchical embeddings (multi-level summarization)** - each level above the base (leaf) has summary nodes capturing the meaning of lower level summary nodes, continuing until we reach the top (or root node) of the tree + +With our RAPTOR tree built, starting from leaf nodes at the bottom and summarized nodes in between, it’s time to query the RAG. + +### Our query example + +For our use case we'll use a straightforward example - a query about financial news, aimed at understanding an increase in NTT DATA's net income attributable to shareholders: + +```markdown +query = "NTT DATA's net income attributable to shareholders increased from ¥69,227 million in Q3 FY2020 to ¥110,191 million in Q3 FY2021. How does this growth align with their acquisition strategy, particularly considering their stated reasons for acquiring Nexient, LLC and the provisional goodwill recorded in this transaction?" +``` + +To keep things simple in our implementation, our data source will be [this pdf](https://www.nttdata.com/global/en/-/media/nttdataglobal/1_files/investors/financial-results/2021/fy2021_fs_3q.pdf). + +Now that we have our query and raw source document, let's choose the right RAPTOR method for querying our RAPTOR tree. + +### RAPTOR query methods + +RAPTOR has two distinct strategies for querying the RAPTOR tree: tree traversal and collapsed tree. -### Inference +1. **tree traversal** method - traverses the tree layer by layer, first selecting the most relevant (top-k in query vector cosine similarity) root nodes, then (in the next layer) navigating each selected root node's children to again find the top-k most relevant nodes, continuing on until it reaches the leaf nodes (original text chunks). Then, it concatenates the text from all the selected nodes, returning it as the query result. -Now that we have our tree structure with leaf nodes at the bottom and summarized nodes in between, it’s time to query the RAG. There are two main methods for navigating the RAPTOR tree: Tree Traversal and Collapsed Tree Retrieval. +2. **collapsed tree** method - flattens the tree into a single layer, directly comparing the query embedding to the vector embeddings of all the leaf nodes (the original text chunks) and summary nodes. For factual, keyword-based queries where you need specific details, collapsed tree returns more relevant and accurate results (with lower latency) than the tree traversal method. -1. **Tree Traversal Retrieval:** This method systematically explores the tree, starting from the root node. It first selects the top-k most relevant root nodes based on their cosine similarity to the query embedding. Then, for each selected root node, its children are considered in the next layer, where the top-k nodes are again selected based on their cosine similarity to the query vector. This process repeats until we reach the leaf nodes. Finally, the text from all the selected nodes is concatenated to form the retrieved context. -2. **Collapsed Tree Retrieval:** This approach simplifies things by viewing the tree as a single layer. Here, it directly compares the query embedding to the vector embeddings of all the leaf nodes (the original text chunks) and summary nodes. This method works best for factual, keyword-based queries where you need specific details. +![How the retrieval happens](../assets/use_cases/improve-raptor-with-rag/raptor-4.png) +from: https://arxiv.org/pdf/2401.18059 page 5 -![Reference : https://arxiv.org/pdf/2401.18059](../assets/use_cases/improve-raptor-with-rag/raptor-4.png) +If our query demanded complex multi-level reasoning, and a contextually rich and precise result, it would make sense to use tree traversal. But for specific queries requiring specific factual information - like our financial news query, we want to be able to directly compare our query embedding with the vector embeddings of all nodes (both leaf and summary), efficiently bypassing RAPTOR's hierarchical structure and going straight to the most relevant data points. -In the collapsed tree retrieval, we flatten the tree into one layer, retrieving nodes based on cosine similarity until we reach a specified number of ***top k documents***. In our code, we’ll gather the textual chunks from earlier, along with the summarized nodes at each level for all the clusters, to create one big list of texts that includes both the root documents and the summarized nodes. +But even though the collapsed tree method's retrieval bypasses the RAPTOR tree's hierarchy, it still capitalizes on the RAPTOR tree's hierarchical encapsulation of meaning to retrieve context. Because the collapsed tree method treats summarized nodes from higher levels simply as additional (same level) chunks, we can pull in higher-level summaries (the global perspective) alongside granular details in just one pass. We want our retrieval to get both an overall perspective and pinpoint very specific details of a particular company's financial quarter. -To be honest, if you look closely, we’ve been essentially adding more data points (chunks) to our RAG setup all along. Using RAPTOR, we now have both the original chunks and the summarized chunks for each cluster. Now, we’ll simply embed all these new data points and store them in a vector database along with their embeddings and use them for RAG. +For our purposes, the collapsed tree method is a better fit than tree traversal. + +So how do we implement it? + +## Implementing collapsed tree retrieval + +Collapsed tree retrieval retrieves top-k nodes (highest cosine similarity to the query vector). To do this, we gather our original root document text chunks and summarized nodes at each level for all the clusters into one big list. Next, we embed all these data points, storing them in a vector database, which we'll query with our RAG. + +First, the embeddings. ```python raptor_texts = [] @@ -441,9 +472,9 @@ raptor_embeddings = embedding_model.encode(raptor_texts) len(raptor_embeddings) ``` -### Setting up Vector Database and RAG +### Setting up vector database and RAG -Now it’s smooth sailing! We’ll just set up a LanceDB vector database to store our embeddings and query our RAG setup. +Now, we set up a LanceDB vector database. ```python raptor_embeddings = embedding_model.encode(raptor_texts) @@ -452,7 +483,6 @@ raptor_dict = {"texts": [], "embeddings": []} for texts, embeddings in zip(raptor_texts, raptor_embeddings): raptor_dict["texts"].append(texts) raptor_dict["embeddings"].append(embeddings.tolist()) - ``` ```python @@ -473,7 +503,7 @@ raptor_table.add(rag_raptor_df) raptor_table.create_fts_index("texts", replace=True) ``` -Time to generate the results.. +Next, we define our results generation function... ```python def generate_results( @@ -508,11 +538,13 @@ def generate_results( return answer ``` +...and, finally, input our query. + ```python query = "NTT DATA's net income attributable to shareholders increased from ¥69,227 million in Q3 FY2020 to ¥110,191 million in Q3 FY2021. How does this growth align with their acquisition strategy, particularly considering their stated reasons for acquiring Nexient, LLC and the provisional goodwill recorded in this transaction?" ``` -Now In our query, there are several key points that must be addressed when crafting the answers. First, we need to note the increase in net income from ¥69,227 million in Q3 FY2020 to ¥110,191 million in Q3 FY2021. Second, we should examine how this growth aligns with NTT DATA's acquisition strategy, particularly their reasons for acquiring Nexient, LLC, and the provisional goodwill recorded in the transaction. With this context in mind, I created a VANILLA RAG to compare its results with those of RAPTOR RAG. +We want our retrieval to address our query's key points. First, we expect our retrieval to take account of the provided factual data regarding NTT DATA's increased shareholder income. Second, we need our retrieval to answer how this increase fits in with NTT Data's acquisition strategy (in particular the Nexient acquisition rationale). Let's keep these in mind when we examine our RAPTOR RAG results. For comparison purposes, we'll also create a vanilla (i.e., normal) RAG: ```python normal_embeddings = embedding_model.encode(chunks) # default chunks from our data @@ -530,11 +562,13 @@ normal_table.add(rag_normal_df) normal_table.create_fts_index("texts", replace=True) ``` -With RAPTOR, we now have an increased number of chunks due to the addition of cluster-level summary nodes alongside the default chunks we had earlier. +Because we now have cluster-level summary nodes in addition to our original text chunks, our RAPTOR RAG has more chunks than our vanilla / normal RAG. + +![Number of chunks in RAPTOR](../assets/use_cases/improve-raptor-with-rag/raptor-5.png) -![image.png](../assets/use_cases/improve-raptor-with-rag/raptor-5.png) +### How did our RAPTOR RAG do? -### D-Day +Let's generate and then compare and evaluate our results. ```python raptor_contexts = raptor_table.search(query).limit(5).select(["texts"]).to_list() @@ -549,11 +583,15 @@ raptor_answer = generate_results(query, raptor_context_text) normal_answer = generate_results(query, normal_context_text) ``` -![image.png](../assets/use_cases/improve-raptor-with-rag/raptor-6.png) +![Comparison for a Query](../assets/use_cases/improve-raptor-with-rag/raptor-6.png) + +RAPTOR RAG performs better than vanilla RAG. RAPTOR retrieves specific details about NTT's financial growth for the specified quarter, and connects this growth to the broader acquisition strategy, pulling relevant context from our example [source document](https://www.nttdata.com/global/en/-/media/nttdataglobal/1_files/investors/financial-results/2021/fy2021_fs_3q.pdf). (Note: collapsed tree retrieval will work well in this kind of use case - a factual, keyword-based query requiring specific details - even if you have many source documents.) Whereas, our vanilla RAG, while correctly identifying the specific details, fails to elucidate either NTT's strategy or a relationship between their quarterly growth, the broader strategy, and the particular (Nexient) acquisition in question. + +## Your turn -When we are comparing RAPTOR RAG with Vanilla RAG, it’s clear that RAPTOR performs better. Not only does RAPTOR retrieve details about the financial growth, but it also effectively connects this growth to the broader acquisition strategy, pulling relevant context from multiple sources. It excels in situations like this, where the query requires insights from various pages, making it more adept at handling complex, layered information retrieval. +Now it's your turn to try out RAPTOR RAG! Here's the Google [colab](https://colab.research.google.com/drive/1I3WI0U4sgb2nc1QTQm51kThZb2q4MXyr?usp=sharing). -And that’s a wrap for this article! If you want to dig into the intricacies of how everything works, I’d suggest checking out the official RAPTOR [GitHub repository](https://github.com/parthsarthi03/raptor/tree/master) for more info and resources. For an even deeper dive, the official [paper](https://arxiv.org/pdf/2401.18059) is a great read and highly recommended! Here is the Google [colab](https://colab.research.google.com/drive/1I3WI0U4sgb2nc1QTQm51kThZb2q4MXyr?usp=sharing) for your reference. +To learn more about the intricacies of RAPTOR, check out the official [GitHub repository](https://github.com/parthsarthi03/raptor/tree/master). For an even deeper dive, we highly recommend the official [paper](https://arxiv.org/pdf/2401.18059)! ## Contributors diff --git a/docs/assets/use_cases/improve-raptor-with-rag/raptor-7.png b/docs/assets/use_cases/improve-raptor-with-rag/raptor-7.png new file mode 100644 index 0000000000000000000000000000000000000000..c2487fab11cbd4ffced94ddad55c059fe6b3655d GIT binary patch literal 109523 zcmbrlby!qi*Ei0<07DP0ATUTtgOv18(jh4zA)V4C9Ri{P(%s!HARQ9Y9nxLW9lmG$ z-uLso*YCaWzkXcTaL(*=_S$=|+H0-Po{uU@(m0r8m`F%SII=R5sz^v+I1&=*Jp>JC zc}&krgM@^wXel9~A}b*QQ*pF2v$QcqLX!CytA+krt&b#4SCtwJk%Y@_D?+~#z~vyU zXW=j=Y7D5{XAH6KaJI5#9ejKi=0?n{84y1CZV~e*BRAGhs2^fo9Q5}4lcDfO!+KPlH+DNg3Un^BOOquFCOvyzb3Wlu~i+aF5$`WRVM`7)3RN?x{wvq#GFkB+V& z#jRW%Zk#LnJ)5`2l`HkV_%oeFN7RRk1RI6sV{HToh2o+3eS_pwc_D?W_9hSa$1^!R z?h<&u4eR2OODXJNl}jn>&_c6aHEw{4g8WK`wthYbjcf-B6~iMjT(&uMmTB?B=%BY^ z`_b`y5|HDkp|%+~DbZyU=J~$4(bs%~XZVUDqE6sqqsJ`kNR@Arex{%L=B@e0H@)dT zdNNf}WZoxk6wBG!B;1#XpVzeuZH>*5ekoRhgB14ZL*Rwnz<_EHZNdI4a{m-8C$t2p 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zTpxXC`;U^MK`VbklAl|ERiFv`YqA4fyZ%uzVg#_nkcUB_?%zuR?zZ!R;(F`O*d_gw zq$qXJ3KLgv9*C9wJ1MmBy|Dc~H+t}C*N-XveUbe6_ Kb49Ax!u|u6_fiu8 literal 0 HcmV?d00001 From 683c74ddef037af1b75c869b29d3ed3e13e80687 Mon Sep 17 00:00:00 2001 From: Robert Turner <143536791+robertdhayanturner@users.noreply.github.com> Date: Wed, 23 Oct 2024 09:27:43 -0400 Subject: [PATCH 05/15] small update to article --- docs/articles/improve-raptor-with-rag.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/articles/improve-raptor-with-rag.md b/docs/articles/improve-raptor-with-rag.md index 1a87404b8..cd6ef3b53 100644 --- a/docs/articles/improve-raptor-with-rag.md +++ b/docs/articles/improve-raptor-with-rag.md @@ -585,13 +585,13 @@ normal_answer = generate_results(query, normal_context_text) ![Comparison for a Query](../assets/use_cases/improve-raptor-with-rag/raptor-6.png) -RAPTOR RAG performs better than vanilla RAG. RAPTOR retrieves specific details about NTT's financial growth for the specified quarter, and connects this growth to the broader acquisition strategy, pulling relevant context from our example [source document](https://www.nttdata.com/global/en/-/media/nttdataglobal/1_files/investors/financial-results/2021/fy2021_fs_3q.pdf). (Note: collapsed tree retrieval will work well in this kind of use case - a factual, keyword-based query requiring specific details - even if you have many source documents.) Whereas, our vanilla RAG, while correctly identifying the specific details, fails to elucidate either NTT's strategy or a relationship between their quarterly growth, the broader strategy, and the particular (Nexient) acquisition in question. +RAPTOR RAG performs better than vanilla RAG at handling retrieval on our hierarchically chunked and embedded source document. RAPTOR retrieves specific details about NTT's financial growth for the specified quarter, and connects this growth to the broader acquisition strategy, pulling relevant context from our example [source document](https://www.nttdata.com/global/en/-/media/nttdataglobal/1_files/investors/financial-results/2021/fy2021_fs_3q.pdf). (Note: collapsed tree retrieval will work well in this kind of use case - a factual, keyword-based query requiring specific details - whether you have multiple source chunks or even many source documents.) Whereas, our vanilla RAG, while correctly identifying the specific details, fails to elucidate either NTT's strategy or a relationship between their quarterly growth, broader strategy, and the particular (Nexient) acquisition in question. ## Your turn Now it's your turn to try out RAPTOR RAG! Here's the Google [colab](https://colab.research.google.com/drive/1I3WI0U4sgb2nc1QTQm51kThZb2q4MXyr?usp=sharing). -To learn more about the intricacies of RAPTOR, check out the official [GitHub repository](https://github.com/parthsarthi03/raptor/tree/master). For an even deeper dive, we highly recommend the official [paper](https://arxiv.org/pdf/2401.18059)! +To learn more about the intricacies of RAPTOR, check out their official [GitHub repository](https://github.com/parthsarthi03/raptor/tree/master). For an even deeper dive, we highly recommend the official [paper](https://arxiv.org/pdf/2401.18059)! ## Contributors From 84eeed72a88efe5429ad9223caaeb4b68c9055b0 Mon Sep 17 00:00:00 2001 From: Robert Turner <143536791+robertdhayanturner@users.noreply.github.com> Date: Wed, 23 Oct 2024 15:32:07 -0400 Subject: [PATCH 06/15] final changes to article --- docs/articles/improve-raptor-with-rag.md | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/docs/articles/improve-raptor-with-rag.md b/docs/articles/improve-raptor-with-rag.md index cd6ef3b53..d314e9f05 100644 --- a/docs/articles/improve-raptor-with-rag.md +++ b/docs/articles/improve-raptor-with-rag.md @@ -1,11 +1,10 @@ # Improving RAG with RAPTOR -Traditional [RAG](https://vipul-maheshwari.github.io/2024/02/14/rag-application-with-langchain) setups often split documents into fixed-size chunks. But this creates problems. If key concepts span multiple chunks, the embeddings can lose the semantic coherence of the original text, and LLM queries that retrieve single chunks often miss their relationship to crucial pieces of information buried inside other chunks, leading to incomplete or misleading responses. Because its chunk embeddings lack any weighting or hierarchical structure, traditional RAG's flat retrieval returns results based simply on similarity or relevance scores. Key insights are often lost. +Traditional [RAG](https://superlinked.com/vectorhub/articles/retrieval-augmented-generation) setups often split documents into fixed-size chunks. But this creates problems. If key concepts span multiple chunks, the embeddings can lose the semantic coherence of the original text, and LLM queries that retrieve single chunks often miss their relationship to crucial pieces of information buried inside other chunks, leading to incomplete or misleading responses. Because its chunk embeddings lack any weighting or hierarchical structure, traditional RAG's flat retrieval returns results based simply on similarity or relevance scores. Key insights are often lost. So, is there a way of getting our embeddings to preserve the relationships and hierarchical structure that exists within source documents, so that our retrieval can surface key insights, and do it efficiently? -Yes! It's a form of semantic chunking called hierarchical-cluster-embedding-based chunking. Below, we'll look closely at a recent and innovative implementation of hierarchical clustering - [RAPTOR](https://arxiv.org/pdf/2401.18059) (Recursive Abstractive Processing for Tree Organized Retrieval). -We'll walk you step-by-step through setting up RAPTOR RAG and then run an example query on a financial news document to evaluate how RAPTOR performs against vanilla RAG. +Yes! It's a form of semantic chunking called hierarchical-cluster-embedding-based chunking. Below, we'll look closely at a recent and innovative implementation of hierarchical clustering called [RAPTOR](https://arxiv.org/pdf/2401.18059) (Recursive Abstractive Processing for Tree Organized Retrieval). We'll walk you step-by-step through setting up RAPTOR RAG and then run an example query on a financial news document to evaluate how RAPTOR performs against vanilla RAG. Let's get started! @@ -58,9 +57,13 @@ openai_api_key = "sk-XXXXXXXXXXXXXXX" client = OpenAI(api_key=openai_api_key) ``` +Note: Don't forget to input your open_ai_key above (we'll be using gpt-4 below). + ### Creating the Chunks -First, create a directory_path to your source documents. (For simplication purposes, our use case example will use just [one financial document](https://www.nttdata.com/global/en/-/media/nttdataglobal/1_files/investors/financial-results/2021/fy2021_fs_3q.pdf) as a source. You can of course load any source documents (and alter the query below) in your own experiments!) +First, create a directory_path to your source documents. To simplify things, our use case example will use just [one financial document](https://www.nttdata.com/global/en/-/media/nttdataglobal/1_files/investors/financial-results/2021/fy2021_fs_3q.pdf) as a source. You can of course load any source documents (and alter the query below) in your own experiments! + +(Note: make sure you use an unencrypted version of this file to avoid errors when PyPDF2 tries to read the file.) ```python directory_path = '/content/data' @@ -456,7 +459,7 @@ So how do we implement it? ## Implementing collapsed tree retrieval -Collapsed tree retrieval retrieves top-k nodes (highest cosine similarity to the query vector). To do this, we gather our original root document text chunks and summarized nodes at each level for all the clusters into one big list. Next, we embed all these data points, storing them in a vector database, which we'll query with our RAG. +Collapsed tree retrieval retrieves top-k nodes (highest cosine similarity to the query vector). To do this, we gather our original root document text chunks and summarized nodes at each level for all the clusters into one big list. Next, we embed all these data points, storing them in a [vector database](https://superlinked.com/vector-db-comparison), which we'll query with our RAG. First, the embeddings. @@ -585,7 +588,7 @@ normal_answer = generate_results(query, normal_context_text) ![Comparison for a Query](../assets/use_cases/improve-raptor-with-rag/raptor-6.png) -RAPTOR RAG performs better than vanilla RAG at handling retrieval on our hierarchically chunked and embedded source document. RAPTOR retrieves specific details about NTT's financial growth for the specified quarter, and connects this growth to the broader acquisition strategy, pulling relevant context from our example [source document](https://www.nttdata.com/global/en/-/media/nttdataglobal/1_files/investors/financial-results/2021/fy2021_fs_3q.pdf). (Note: collapsed tree retrieval will work well in this kind of use case - a factual, keyword-based query requiring specific details - whether you have multiple source chunks or even many source documents.) Whereas, our vanilla RAG, while correctly identifying the specific details, fails to elucidate either NTT's strategy or a relationship between their quarterly growth, broader strategy, and the particular (Nexient) acquisition in question. +RAPTOR RAG performed better than vanilla RAG at handling retrieval on our hierarchically chunked and embedded source document. RAPTOR retrieved specific details about NTT's financial growth for the specified quarter, and connected this growth to the broader acquisition strategy, pulling relevant context from our example [source document](https://www.nttdata.com/global/en/-/media/nttdataglobal/1_files/investors/financial-results/2021/fy2021_fs_3q.pdf). (Note: collapsed tree retrieval will work well in this kind of use case - a factual, keyword-based query requiring specific details - whether you have multiple source chunks or even many source documents.) Whereas, our vanilla RAG, while correctly identifying the specific details, failed to elucidate either NTT's strategy or a relationship between their quarterly growth, broader strategy, and the particular (Nexient) acquisition in question. ## Your turn From 8eff69cbf4d8f4d2428f2b5f40e44d5f43633206 Mon Sep 17 00:00:00 2001 From: Vipul Maheshwari Date: Fri, 25 Oct 2024 11:46:33 +0530 Subject: [PATCH 07/15] changed the title of the piece --- ...with-rag.md => improve-rag-with-raptor.md} | 25 +++++++++--------- .../raptor-1.png | Bin .../raptor-2.png | Bin .../raptor-3.png | Bin .../raptor-4.png | Bin .../raptor-5.png | Bin .../raptor-6.png | Bin .../raptor-7.png | Bin 8 files changed, 12 insertions(+), 13 deletions(-) rename docs/articles/{improve-raptor-with-rag.md => improve-rag-with-raptor.md} (97%) rename docs/assets/use_cases/{improve-raptor-with-rag => improve-rag-with-raptor}/raptor-1.png (100%) rename docs/assets/use_cases/{improve-raptor-with-rag => improve-rag-with-raptor}/raptor-2.png (100%) rename docs/assets/use_cases/{improve-raptor-with-rag => improve-rag-with-raptor}/raptor-3.png (100%) rename docs/assets/use_cases/{improve-raptor-with-rag => improve-rag-with-raptor}/raptor-4.png (100%) rename docs/assets/use_cases/{improve-raptor-with-rag => improve-rag-with-raptor}/raptor-5.png (100%) rename docs/assets/use_cases/{improve-raptor-with-rag => improve-rag-with-raptor}/raptor-6.png (100%) rename docs/assets/use_cases/{improve-raptor-with-rag => improve-rag-with-raptor}/raptor-7.png (100%) diff --git a/docs/articles/improve-raptor-with-rag.md b/docs/articles/improve-rag-with-raptor.md similarity index 97% rename from docs/articles/improve-raptor-with-rag.md rename to docs/articles/improve-rag-with-raptor.md index d314e9f05..42082aa2d 100644 --- a/docs/articles/improve-raptor-with-rag.md +++ b/docs/articles/improve-rag-with-raptor.md @@ -2,7 +2,7 @@ Traditional [RAG](https://superlinked.com/vectorhub/articles/retrieval-augmented-generation) setups often split documents into fixed-size chunks. But this creates problems. If key concepts span multiple chunks, the embeddings can lose the semantic coherence of the original text, and LLM queries that retrieve single chunks often miss their relationship to crucial pieces of information buried inside other chunks, leading to incomplete or misleading responses. Because its chunk embeddings lack any weighting or hierarchical structure, traditional RAG's flat retrieval returns results based simply on similarity or relevance scores. Key insights are often lost. -So, is there a way of getting our embeddings to preserve the relationships and hierarchical structure that exists within source documents, so that our retrieval can surface key insights, and do it efficiently? +So, is there a way of getting our embeddings to preserve the relationships and hierarchical structure that exists within source documents, so that our retrieval can surface key insights, and do it efficiently? Yes! It's a form of semantic chunking called hierarchical-cluster-embedding-based chunking. Below, we'll look closely at a recent and innovative implementation of hierarchical clustering called [RAPTOR](https://arxiv.org/pdf/2401.18059) (Recursive Abstractive Processing for Tree Organized Retrieval). We'll walk you step-by-step through setting up RAPTOR RAG and then run an example query on a financial news document to evaluate how RAPTOR performs against vanilla RAG. @@ -12,7 +12,7 @@ Let's get started! RAPTOR organizes information in a tree-like structure, progressively narrowing as it moves upwards, representing original documents at higher and higher levels of abstraction, so it can retain the original meaning in full. Let's break this into steps. -![High level overview of RAPTOR](../assets/use_cases/improve-raptor-with-rag/raptor-7.png) +![High level overview of RAPTOR](../assets/use_cases/improve-rag-with-raptor/raptor-7.png) *Above: "Figure 1. Tree construction process" from [RAPTOR](https://arxiv.org/pdf/2401.18059)* @@ -23,7 +23,7 @@ RAPTOR organizes information in a tree-like structure, progressively narrowing a - **summarizing clusters**: we summarize the clusters of similar chunks into a node of higher-level abstractions - **recursive iteration**: we treat these nodes in turn as chunks, clustering them, summarizing these clusters, iteratively, building a tree-like structure that encapsulates the relationships and hierarchical structures inherent in the raw documents we started from -![Raptor workflow](../assets/use_cases/improve-raptor-with-rag/raptor-2.png) +![Raptor workflow](../assets/use_cases/improve-rag-with-raptor/raptor-2.png) In short, RAPTOR chunks raw documents, embeds the chunks, groups embeddings into clusters, summarizes each cluster, then (recursively) chunks these summaries, embeds, summarizes again, and so on, moving upwards to a comprehensive and efficient data representation, preserving nuances and key points. @@ -226,7 +226,6 @@ To set up our hierarchical clustering, we define several functions: - an embed cluster summaries function for summarizing each cluster - a recursive_embedding_with_cluster_summarization function that leverages these methods to create a multi-level hierarchical clustering and summarization structure - ```python #define our clustering algorithm def clustering_algorithm( @@ -390,7 +389,7 @@ def recursive_embedding_with_cluster_summarization( return results ``` -Now we define a process_text_hierarchy function that takes a hierarchical text dataset, processes it to generate embeddings and cluster summaries, and returns the processed results organized by hierarchical level. +Now we define a process_text_hierarchy function that takes a hierarchical text dataset, processes it to generate embeddings and cluster summaries, and returns the processed results organized by hierarchical level. ```python def process_text_hierarchy( @@ -418,7 +417,7 @@ results = process_text_hierarchy(chunks, number_of_levels=3) After several rounds of clustering and summarization, we have our RAPTOR tree. -### RAPTOR tree +### RAPTOR tree - **leaf nodes (base level)** - at the tree's base, we have our original, unprocessed text chunks - the raw data inputs that RAPTOR RAG uses for analysis and retrieval - **intermediate summary nodes** - moving up the tree, we cluster our chunks, and summarize the cluster, creating summary nodes - each summary node captures the meaning of the cluster it represents, facilitating efficient retrieval and accurate contextual understanding @@ -442,14 +441,14 @@ Now that we have our query and raw source document, let's choose the right RAPTO RAPTOR has two distinct strategies for querying the RAPTOR tree: tree traversal and collapsed tree. -1. **tree traversal** method - traverses the tree layer by layer, first selecting the most relevant (top-k in query vector cosine similarity) root nodes, then (in the next layer) navigating each selected root node's children to again find the top-k most relevant nodes, continuing on until it reaches the leaf nodes (original text chunks). Then, it concatenates the text from all the selected nodes, returning it as the query result. +1. **Tree traversal** method - traverses the tree layer by layer, first selecting the most relevant (top-k in query vector cosine similarity) root nodes, then (in the next layer) navigating each selected root node's children to again find the top-k most relevant nodes, continuing on until it reaches the leaf nodes (original text chunks). Then, it concatenates the text from all the selected nodes, returning it as the query result. -2. **collapsed tree** method - flattens the tree into a single layer, directly comparing the query embedding to the vector embeddings of all the leaf nodes (the original text chunks) and summary nodes. For factual, keyword-based queries where you need specific details, collapsed tree returns more relevant and accurate results (with lower latency) than the tree traversal method. +2. **Collapsed tree** method - flattens the tree into a single layer, directly comparing the query embedding to the vector embeddings of all the leaf nodes (the original text chunks) and summary nodes. For factual, keyword-based queries where you need specific details, collapsed tree returns more relevant and accurate results (with lower latency) than the tree traversal method. -![How the retrieval happens](../assets/use_cases/improve-raptor-with-rag/raptor-4.png) +![How the retrieval happens](../assets/use_cases/improve-rag-with-raptor/raptor-4.png) from: https://arxiv.org/pdf/2401.18059 page 5 -If our query demanded complex multi-level reasoning, and a contextually rich and precise result, it would make sense to use tree traversal. But for specific queries requiring specific factual information - like our financial news query, we want to be able to directly compare our query embedding with the vector embeddings of all nodes (both leaf and summary), efficiently bypassing RAPTOR's hierarchical structure and going straight to the most relevant data points. +If our query demanded complex multi-level reasoning, and a contextually rich and precise result, it would make sense to use tree traversal. But for specific queries requiring specific factual information - like our financial news query, we want to be able to directly compare our query embedding with the vector embeddings of all nodes (both leaf and summary), efficiently bypassing RAPTOR's hierarchical structure and going straight to the most relevant data points. But even though the collapsed tree method's retrieval bypasses the RAPTOR tree's hierarchy, it still capitalizes on the RAPTOR tree's hierarchical encapsulation of meaning to retrieve context. Because the collapsed tree method treats summarized nodes from higher levels simply as additional (same level) chunks, we can pull in higher-level summaries (the global perspective) alongside granular details in just one pass. We want our retrieval to get both an overall perspective and pinpoint very specific details of a particular company's financial quarter. @@ -567,7 +566,7 @@ normal_table.create_fts_index("texts", replace=True) Because we now have cluster-level summary nodes in addition to our original text chunks, our RAPTOR RAG has more chunks than our vanilla / normal RAG. -![Number of chunks in RAPTOR](../assets/use_cases/improve-raptor-with-rag/raptor-5.png) +![Number of chunks in RAPTOR](../assets/use_cases/improve-rag-with-raptor/raptor-5.png) ### How did our RAPTOR RAG do? @@ -586,7 +585,7 @@ raptor_answer = generate_results(query, raptor_context_text) normal_answer = generate_results(query, normal_context_text) ``` -![Comparison for a Query](../assets/use_cases/improve-raptor-with-rag/raptor-6.png) +![Comparison for a Query](../assets/use_cases/improve-rag-with-raptor/raptor-6.png) RAPTOR RAG performed better than vanilla RAG at handling retrieval on our hierarchically chunked and embedded source document. RAPTOR retrieved specific details about NTT's financial growth for the specified quarter, and connected this growth to the broader acquisition strategy, pulling relevant context from our example [source document](https://www.nttdata.com/global/en/-/media/nttdataglobal/1_files/investors/financial-results/2021/fy2021_fs_3q.pdf). (Note: collapsed tree retrieval will work well in this kind of use case - a factual, keyword-based query requiring specific details - whether you have multiple source chunks or even many source documents.) Whereas, our vanilla RAG, while correctly identifying the specific details, failed to elucidate either NTT's strategy or a relationship between their quarterly growth, broader strategy, and the particular (Nexient) acquisition in question. @@ -599,4 +598,4 @@ To learn more about the intricacies of RAPTOR, check out their official [GitHub ## Contributors - [Vipul, author](https://in.linkedin.com/in/vipul-maheshwari-19b98423) -- [Robert Turner, editor](https://www.linkedin.com/in/robertdhayanturner/) \ No newline at end of file +- [Robert Turner, editor](https://www.linkedin.com/in/robertdhayanturner/) diff --git a/docs/assets/use_cases/improve-raptor-with-rag/raptor-1.png b/docs/assets/use_cases/improve-rag-with-raptor/raptor-1.png similarity index 100% rename from docs/assets/use_cases/improve-raptor-with-rag/raptor-1.png rename to docs/assets/use_cases/improve-rag-with-raptor/raptor-1.png diff --git a/docs/assets/use_cases/improve-raptor-with-rag/raptor-2.png b/docs/assets/use_cases/improve-rag-with-raptor/raptor-2.png similarity index 100% rename from docs/assets/use_cases/improve-raptor-with-rag/raptor-2.png rename to docs/assets/use_cases/improve-rag-with-raptor/raptor-2.png diff --git a/docs/assets/use_cases/improve-raptor-with-rag/raptor-3.png b/docs/assets/use_cases/improve-rag-with-raptor/raptor-3.png similarity index 100% rename from docs/assets/use_cases/improve-raptor-with-rag/raptor-3.png rename to docs/assets/use_cases/improve-rag-with-raptor/raptor-3.png diff --git a/docs/assets/use_cases/improve-raptor-with-rag/raptor-4.png b/docs/assets/use_cases/improve-rag-with-raptor/raptor-4.png similarity index 100% rename from docs/assets/use_cases/improve-raptor-with-rag/raptor-4.png rename to docs/assets/use_cases/improve-rag-with-raptor/raptor-4.png diff --git a/docs/assets/use_cases/improve-raptor-with-rag/raptor-5.png b/docs/assets/use_cases/improve-rag-with-raptor/raptor-5.png similarity index 100% rename from docs/assets/use_cases/improve-raptor-with-rag/raptor-5.png rename to docs/assets/use_cases/improve-rag-with-raptor/raptor-5.png diff --git a/docs/assets/use_cases/improve-raptor-with-rag/raptor-6.png b/docs/assets/use_cases/improve-rag-with-raptor/raptor-6.png similarity index 100% rename from docs/assets/use_cases/improve-raptor-with-rag/raptor-6.png rename to docs/assets/use_cases/improve-rag-with-raptor/raptor-6.png diff --git a/docs/assets/use_cases/improve-raptor-with-rag/raptor-7.png b/docs/assets/use_cases/improve-rag-with-raptor/raptor-7.png similarity index 100% rename from docs/assets/use_cases/improve-raptor-with-rag/raptor-7.png rename to docs/assets/use_cases/improve-rag-with-raptor/raptor-7.png From 03dc26d154bf7987b7abaea83c63a16c66a44953 Mon Sep 17 00:00:00 2001 From: Robert Turner <143536791+robertdhayanturner@users.noreply.github.com> Date: Fri, 25 Oct 2024 09:12:15 -0400 Subject: [PATCH 08/15] final commit --- docs/articles/improve-rag-with-raptor.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/articles/improve-rag-with-raptor.md b/docs/articles/improve-rag-with-raptor.md index 42082aa2d..f2d409fdd 100644 --- a/docs/articles/improve-rag-with-raptor.md +++ b/docs/articles/improve-rag-with-raptor.md @@ -14,7 +14,7 @@ RAPTOR organizes information in a tree-like structure, progressively narrowing a ![High level overview of RAPTOR](../assets/use_cases/improve-rag-with-raptor/raptor-7.png) -*Above: "Figure 1. Tree construction process" from [RAPTOR](https://arxiv.org/pdf/2401.18059)* +*Above: "Figure 1. Tree construction process" from the [RAPTOR paper](https://arxiv.org/pdf/2401.18059)* **RAPTOR tree construction - basic steps:** @@ -446,7 +446,7 @@ RAPTOR has two distinct strategies for querying the RAPTOR tree: tree traversal 2. **Collapsed tree** method - flattens the tree into a single layer, directly comparing the query embedding to the vector embeddings of all the leaf nodes (the original text chunks) and summary nodes. For factual, keyword-based queries where you need specific details, collapsed tree returns more relevant and accurate results (with lower latency) than the tree traversal method. ![How the retrieval happens](../assets/use_cases/improve-rag-with-raptor/raptor-4.png) -from: https://arxiv.org/pdf/2401.18059 page 5 +*Above: "Figure 2: Illustration of the tree traversal and collapsed tree retrieval mechanisms" from the [RAPTOR paper](https://arxiv.org/pdf/2401.1805)* If our query demanded complex multi-level reasoning, and a contextually rich and precise result, it would make sense to use tree traversal. But for specific queries requiring specific factual information - like our financial news query, we want to be able to directly compare our query embedding with the vector embeddings of all nodes (both leaf and summary), efficiently bypassing RAPTOR's hierarchical structure and going straight to the most relevant data points. From ec7df181d41aa64b20e230c7c9e15bbaeb36f422 Mon Sep 17 00:00:00 2001 From: Arunesh Singh <43724007+AruneshSingh@users.noreply.github.com> Date: Mon, 4 Nov 2024 08:05:52 +0000 Subject: [PATCH 09/15] Update Stars, Pulls & Downloads --- docs/tools/vdb_table/data/activeloop.json | 6 +++--- docs/tools/vdb_table/data/aerospike.json | 4 ++-- docs/tools/vdb_table/data/anariai.json | 4 ++-- docs/tools/vdb_table/data/apachecassandra.json | 16 ++++++++-------- docs/tools/vdb_table/data/apachesolr.json | 16 ++++++++-------- docs/tools/vdb_table/data/aperturedb.json | 8 ++++---- docs/tools/vdb_table/data/azureai.json | 8 ++++---- docs/tools/vdb_table/data/chroma.json | 14 +++++++------- docs/tools/vdb_table/data/clickhouse.json | 14 +++++++------- docs/tools/vdb_table/data/couchbase.json | 16 ++++++++-------- docs/tools/vdb_table/data/cratedb.json | 16 ++++++++-------- docs/tools/vdb_table/data/datastaxastra.json | 8 ++++---- docs/tools/vdb_table/data/elasticsearch.json | 16 ++++++++-------- docs/tools/vdb_table/data/epsilla.json | 6 +++--- docs/tools/vdb_table/data/kdbai.json | 4 ++-- docs/tools/vdb_table/data/lancedb.json | 14 +++++++------- docs/tools/vdb_table/data/marqo.json | 10 +++++----- docs/tools/vdb_table/data/meilisearch.json | 10 +++++----- docs/tools/vdb_table/data/milvus.json | 14 +++++++------- docs/tools/vdb_table/data/mongo.json | 10 +++++----- docs/tools/vdb_table/data/myscale.json | 8 ++++---- docs/tools/vdb_table/data/neo4j.json | 14 +++++++------- docs/tools/vdb_table/data/nucliadb.json | 10 +++++----- docs/tools/vdb_table/data/opensearch.json | 16 ++++++++-------- docs/tools/vdb_table/data/oramasearch.json | 6 +++--- docs/tools/vdb_table/data/paradedb.json | 4 ++-- docs/tools/vdb_table/data/pgvecto_rs.json | 8 ++++---- docs/tools/vdb_table/data/pgvector.json | 14 +++++++------- docs/tools/vdb_table/data/pinecone.json | 12 ++++++------ docs/tools/vdb_table/data/qdrant.json | 12 ++++++------ docs/tools/vdb_table/data/redis.json | 12 ++++++------ docs/tools/vdb_table/data/rockset.json | 8 ++++---- docs/tools/vdb_table/data/singlestoredb.json | 10 +++++----- docs/tools/vdb_table/data/turbopuffer.json | 8 ++++---- docs/tools/vdb_table/data/txtai.json | 14 +++++++------- docs/tools/vdb_table/data/typesense.json | 10 +++++----- docs/tools/vdb_table/data/usearch.json | 14 +++++++------- docs/tools/vdb_table/data/vald.json | 10 +++++----- docs/tools/vdb_table/data/vectara.json | 6 +++--- docs/tools/vdb_table/data/vespa.json | 8 ++++---- docs/tools/vdb_table/data/weaviate.json | 16 ++++++++-------- 41 files changed, 217 insertions(+), 217 deletions(-) diff --git a/docs/tools/vdb_table/data/activeloop.json b/docs/tools/vdb_table/data/activeloop.json index eed7fe35f..f2f351ce7 100644 --- a/docs/tools/vdb_table/data/activeloop.json +++ b/docs/tools/vdb_table/data/activeloop.json @@ -157,7 +157,7 @@ "comment": "" }, "github_stars": { - "value": 8117, + "value": 8135, "source_url": "https://github.com/activeloopai/deeplake", "comment": "", 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"value_90_days": 0 }, "npm_downloads": { - "value": 5132, + "value": 5148, "source_url": "https://www.npmjs.com/package/epsillajs", "comment": "", - "value_90_days": 628 + "value_90_days": 633 }, "crates_io_downloads": { "value": 0, diff --git a/docs/tools/vdb_table/data/apachecassandra.json b/docs/tools/vdb_table/data/apachecassandra.json index d0593f294..242c43ec3 100644 --- a/docs/tools/vdb_table/data/apachecassandra.json +++ b/docs/tools/vdb_table/data/apachecassandra.json @@ -157,33 +157,33 @@ "comment": "via Lucene" }, "github_stars": { - "value": 8804, + "value": 8820, "source_url": "https://github.com/apache/cassandra", "comment": "", "value_90_days": 0 }, "docker_pulls": { - "value": 216244777, + "value": 216439023, "source_url": "https://hub.docker.com/_/cassandra", "comment": "", "value_90_days": 0 }, "pypi_downloads": { - "value": 86110753, + "value": 86791449, "source_url": "https://pypi.org/project/cassandra-driver/", "comment": "", - "value_90_days": 9251369 + 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b/docs/tools/vdb_table/data/chroma.json index 27ed9da85..8d08d6d2d 100644 --- a/docs/tools/vdb_table/data/chroma.json +++ b/docs/tools/vdb_table/data/chroma.json @@ -157,7 +157,7 @@ "comment": "" }, "github_stars": { - "value": 15097, + "value": 15212, "source_url": "https://github.com/chroma-core/chroma", "comment": "", "value_90_days": 0 @@ -169,21 +169,21 @@ "value_90_days": 0 }, "pypi_downloads": { - "value": 20502882, + "value": 21033316, "source_url": "https://pypi.org/project/chromadb/", "comment": "", - "value_90_days": 5610849 + "value_90_days": 5748860 }, "npm_downloads": { - "value": 1837352, + "value": 1870554, "source_url": "https://www.npmjs.com/package/chromadb", "comment": "", - "value_90_days": 501691 + "value_90_days": 511955 }, "crates_io_downloads": { - "value": 15009, + "value": 15269, "source_url": "https://crates.io/crates/chromadb", "comment": "", - "value_90_days": 1569 + "value_90_days": 1539 } } \ No newline at end of file diff --git a/docs/tools/vdb_table/data/clickhouse.json b/docs/tools/vdb_table/data/clickhouse.json index 9edf6bdcc..4cc7793fa 100644 --- a/docs/tools/vdb_table/data/clickhouse.json +++ b/docs/tools/vdb_table/data/clickhouse.json @@ -156,7 +156,7 @@ "comment": "HNSW via USearch" }, "github_stars": { - "value": 37360, + "value": 37460, "source_url": "https://github.com/ClickHouse/ClickHouse", "comment": "", "value_90_days": 0 @@ -168,21 +168,21 @@ "value_90_days": 0 }, "pypi_downloads": { - "value": 202650, + "value": 203228, "source_url": "https://pypi.org/project/clickhouse/", "comment": "", - "value_90_days": 7863 + "value_90_days": 7634 }, "npm_downloads": { - "value": 12045811, + "value": 12493410, "source_url": "https://www.npmjs.com/package/@clickhouse/client", "comment": "", - "value_90_days": 5238702 + "value_90_days": 5374750 }, "crates_io_downloads": { - "value": 467732, + "value": 478692, "source_url": "https://crates.io/crates/clickhouse", "comment": "", - "value_90_days": 44889 + 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b/docs/tools/vdb_table/data/opensearch.json @@ -167,33 +167,33 @@ "comment": "via Lucene" }, "github_stars": { - "value": 9702, + "value": 9739, "source_url": "https://github.com/opensearch-project/OpenSearch", "comment": "", "value_90_days": 0 }, "docker_pulls": { - "value": 90711620, + "value": 91292496, "source_url": "https://hub.docker.com/r/opensearchproject/opensearch", "comment": "", "value_90_days": 0 }, "pypi_downloads": { - "value": 363749, + "value": 365181, "source_url": "https://pypi.org/project/opensearch/", "comment": "", - "value_90_days": 20618 + "value_90_days": 20188 }, "npm_downloads": { - "value": 26084497, + "value": 26410262, "source_url": "https://www.npmjs.com/package/@opensearch-project/opensearch", "comment": "", - "value_90_days": 5709368 + "value_90_days": 5763071 }, "crates_io_downloads": { - "value": 669880, + "value": 684513, "source_url": "https://crates.io/crates/opensearch", "comment": "", - "value_90_days": 174938 + "value_90_days": 179271 } } \ No newline at end of file diff --git a/docs/tools/vdb_table/data/oramasearch.json b/docs/tools/vdb_table/data/oramasearch.json index 13dbc36fc..ca461ee50 100644 --- a/docs/tools/vdb_table/data/oramasearch.json +++ b/docs/tools/vdb_table/data/oramasearch.json @@ -149,7 +149,7 @@ "comment": "" }, "github_stars": { - "value": 8641, + "value": 8666, "source_url": "https://github.com/oramasearch/orama", "comment": "", "value_90_days": 0 @@ -167,10 +167,10 @@ "value_90_days": 0 }, "npm_downloads": { - "value": 1197893, + "value": 1224152, "source_url": "https://www.npmjs.com/package/@orama/orama", "comment": "", - "value_90_days": 335092 + "value_90_days": 338691 }, "crates_io_downloads": { "value": 0, diff --git a/docs/tools/vdb_table/data/paradedb.json b/docs/tools/vdb_table/data/paradedb.json index b9021cd71..8518a1559 100644 --- a/docs/tools/vdb_table/data/paradedb.json +++ b/docs/tools/vdb_table/data/paradedb.json @@ -158,13 +158,13 @@ "comment": "" }, "github_stars": { - "value": 6135, + 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"_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + } + } + } + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "### Installing the relevant dependencies" + ], + "metadata": { + "id": "pXh2tc9vKHxu" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "o-2_U0HGaU9S" + }, + "outputs": [], + "source": [ + "%%capture\n", + "!pip install lancedb scikit-learn openai torch sentence_transformers tiktoken umap-learn PyPDF2 tantivy" + ] + }, + { + "cell_type": "code", + "source": [ + "import os\n", + "import uuid\n", + "import tiktoken\n", + "import re\n", + "import numpy as np\n", + "import pandas as pd\n", + "import transformers\n", + "import torch\n", + "import umap.umap_ as umap\n", + "import matplotlib.pyplot as plt\n", + "from openai import OpenAI\n", + "from typing import List, Tuple, Optional, Dict\n", + "from sklearn.mixture import GaussianMixture\n", + "from sentence_transformers import SentenceTransformer\n", + "\n", + "openai_api_key = \"******\"\n", + "embedding_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')\n", + "client = OpenAI(api_key=openai_api_key)\n", + "SEED = 1234" + ], + "metadata": { + "id": "XGmxiiQNbNOG", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 426, + "referenced_widgets": [ + "be703a5360454336827d77474de87803", + "ec15c209ef564b899b2467902bb7e9d5", + "211f483b55b84c2eb656bf770ef0e4d4", + "224c78f30362472ab4a68f4d80aa7491", + "303e08d66f9e43e6afe3e5b75a23bdd9", + "67b93205f7e3426abc804282d377cb0c", + "48c5b440c4244a2aa287b630d4f1fc27", + "070eb5d597104ec59a1c5b32a791a50b", + "584db1dfb9f04c0db7b3b405027ca5a8", + "32dc8c442c8f4c708b023b13e15cd4f2", + "9e670b17e23d43dca51947973397a24d", + "936efe41e7324e10a759dc261a5a8f6f", + "137e355e929b49cf905b22031b0dda0a", + "0ab443d03e3a469dbc47d2ff5e48f905", + 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"23d35eb6c5d84e2498a26d572585dbab", + "f3679e160bd5484292aa5822e6157dd2", + "cf9d6b043bbd49c2a036d5877b03ffd2", + "677616ac487248c79a5488b17640fc6d", + "24a4d38ae53d4b77b7949e7914e3cd78", + "fe4cf5db788a47b6bc1238a0901bdbf7", + "786b94bea5bc4e5bac510fa471690b07", + "cb6b79fbe1894dfebda7ca100bfb8a27", + "fca9216926cd4b9cb4b79361c4e28c8f", + "294ebbf2245d417590768daa5ecbd6b9", + "ca34b303478745148a8cf2ffb9608050", + "33d706fc338f4b5190e4d411e7a19ed3", + "02f763bd8c534b6cab5a71c7e4f96d5c", + "2382010f15044771bae63bfc907e6c76", + "e0ec9206e3b1463fbf13ecb1c2c5af28", + "5e65cc6748c9499db70f566d8c85640c", + "a0bc08ce4fde43e8bf3bb38ab1f6d8ea", + "8b49e94d81f0463b8f0e127533c0169c", + "ed2cfa42d6b84a6b82d4e2d0a15cb68e", + "2e0a2884d45f43beb4f2e1766a3aea97", + "ee402e17a3f14f17b2a444b953bc6c4e", + "d0fc2fabcb284d528a4a7439b21a3ece", + "7ca4b89fe55946bbbf4db559883ab88b", + "ad3bb94f158f4f6db10318ff4f5fa4f1", + "259215f6bdf541e198747feeb23fe131", + "b4e24a22fae541fbba241cad0ef3c893", + "7778e2dde0ff4e7083763c8add1568fe", + "301b6d78b8df4835aacfdbd085aa0233", + "06f3f9ea472d472191ec6df978539d26", + "690ae35b96304f45a00d8c856b107199", + "526f3ff32b134b6d90903ab2c7fb9011", + "15db9ca50cc34cca89c06e9ac5b56c2c", + "0fb2805f19f346cea205368fc1047356", + "4b382d8ca91c430e862bb7e54c800596", + "d73101f6560c4ac0b5b8d7eb1ea677a4", + "54e1cd8f1fb04cb698ea68af80d8c234", + "542d51359bca4906ab18672b21c55c48", + "f4f5d9a78cfc41eca41255a8e4607baa", + "5a0623b782e34075af7520ccabb936c2", + "ecf89a48bed1412e97e62a2e9209b165", + "ce427887152f4d07a986b032cd96856a", + "66c27e12558b4a8d9c0c06d5250d5687", + "7c915ca33f464c6382095af94830e5fe", + "207ec4736935444a999962514544ba4f", + "d3976255a98544fcae91e46189da54ed", + "8421f5bf16054eeda179b9664d595312", + "01eadd6ce62b45a0a067e6a59b996840", + "cdd546ed070a4691aa6f4bc14ad214b5", + "aa58345cc8a74a94bf07449949467792", + "144cb8c372b54a0f9e3f7f2d0dfd4abd", + "e1d0c2d48de34b18999dfe8b12cf568a", + "ee13d97e607d4171bf5667bd71d39291", + "9280f62d04ce45d9bd00c3efb33f3c8e", + "df1309e8f1f843b2b5dd1085a8230816", + "0470c8d796fc46f3a370e74ec8192c0c" + ] + }, + "outputId": "dfea26c3-cb8c-4c56-f36a-13bb2e36eb5a" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "modules.json: 0%| | 0.00/349 [00:00 np.ndarray:\n", + " if clustering_type == \"local\":\n", + " n_neighbors = max(2, min(10, len(embeddings) - 1))\n", + " min_dist = 0.01\n", + " elif clustering_type == \"global\":\n", + " n_neighbors = max(2, min(int((len(embeddings) - 1) ** 0.5), len(embeddings) // 10, len(embeddings) - 1))\n", + " min_dist = 0.1\n", + " else:\n", + " raise ValueError(\"clustering_type must be either 'local' or 'global'\")\n", + "\n", + " umap_model = umap.UMAP(\n", + " n_neighbors=n_neighbors,\n", + " min_dist=min_dist,\n", + " n_components=target_dim,\n", + " metric=metric,\n", + " )\n", + " return umap_model.fit_transform(embeddings)" + ], + "metadata": { + "id": "ZJ_LvhKYgepc" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Optimal Number of Clusters\n", + "\n" + ], + "metadata": { + "id": "QRRbuxB5qpUa" + } + }, + { + "cell_type": "markdown", + "source": [ + "I plan to leverage both the Elbow Method and the Bayesian Information Criterion (BIC) to pinpoint the optimal number of clusters for our analysis." + ], + "metadata": { + "id": "LRy5NHTkq2kl" + } + }, + { + "cell_type": "code", + "source": [ + "def compute_inertia(embeddings: np.ndarray, labels: np.ndarray, centroids: np.ndarray) -> float:\n", + " return np.sum(np.min(np.sum((embeddings[:, np.newaxis] - centroids) ** 2, axis=2), axis=1))\n", + "\n", + "def optimal_cluster_number(\n", + " embeddings: np.ndarray,\n", + " max_clusters: int = 50,\n", + " random_state: int = SEED\n", + ") -> int:\n", + " max_clusters = min(max_clusters, len(embeddings))\n", + " number_of_clusters = np.arange(1, max_clusters + 1)\n", + " inertias = []\n", + " bic_scores = []\n", + "\n", + " for n in number_of_clusters:\n", + " gmm = GaussianMixture(n_components=n, random_state=random_state)\n", + " labels = gmm.fit_predict(embeddings)\n", + " centroids = gmm.means_\n", + " inertia = compute_inertia(embeddings, labels, centroids)\n", + " inertias.append(inertia)\n", + " bic_scores.append(gmm.bic(embeddings))\n", + "\n", + " inertia_changes = np.diff(inertias)\n", + " elbow_optimal = number_of_clusters[np.argmin(inertia_changes) + 1]\n", + " bic_optimal = number_of_clusters[np.argmin(bic_scores)]\n", + "\n", + " return max(elbow_optimal, bic_optimal)\n", + "\n", + "def gmm_clustering(\n", + " embeddings: np.ndarray,\n", + " threshold: float,\n", + " random_state: int = SEED\n", + ") -> Tuple[List[np.ndarray], int]:\n", + " n_clusters = optimal_cluster_number(embeddings, random_state=random_state)\n", + " gm = GaussianMixture(n_components=n_clusters, random_state=random_state, n_init=2)\n", + " gm.fit(embeddings)\n", + " probs = gm.predict_proba(embeddings)\n", + " labels = [np.where(prob > threshold)[0] for prob in probs]\n", + " return labels, n_clusters" + ], + "metadata": { + "id": "zsQA9H0vgs5o" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Tree Construction\n", + "\n", + "Now that we’ve wrapped up the clustering part, let’s talk about how we build our hierarchical tree. After several rounds of clustering and summarization (while keeping track of how deep we go), here’s what we have:\n", + "\n", + "- **Leaf Nodes:** These are our original text chunks, forming the base of the tree.\n", + "- **Summary Nodes:** As we go up the tree, each node acts like a quick summary of its child nodes, capturing the main idea of the cluster.\n", + "- **Hierarchical Embeddings:** The summary nodes can also become the new nodes at their level. Each of these nodes gets its own vector embedding, representing the summarized meaning. So, we’re essentially adding more nodes while enriching them with summaries.\n", + "\n", + "The process flows nicely: we embed the chunks, reduce their dimensions using UMAP, cluster them with Gaussian Mixture Models, start with a broad overview, and then zoom in for more detailed clusters before summarizing." + ], + "metadata": { + "id": "-w-MJXDqrQxl" + } + }, + { + "cell_type": "code", + "source": [ + "def clustering_algorithm(\n", + " embeddings: np.ndarray,\n", + " target_dim: int,\n", + " threshold: float,\n", + " random_state: int = SEED\n", + ") -> Tuple[List[np.ndarray], int]:\n", + " if len(embeddings) <= target_dim + 1:\n", + " return [np.array([0]) for _ in range(len(embeddings))], 1\n", + "\n", + " # Global clustering\n", + " reduced_global_embeddings = dimensionality_reduction(embeddings, target_dim, \"global\")\n", + " global_clusters, n_global_clusters = gmm_clustering(reduced_global_embeddings, threshold, random_state=random_state)\n", + "\n", + " all_local_clusters = [np.array([]) for _ in range(len(embeddings))]\n", + " total_clusters = 0\n", + "\n", + " # Local clustering within each global cluster\n", + " for i in range(n_global_clusters):\n", + " global_cluster_mask = np.array([i in gc for gc in global_clusters])\n", + " global_cluster_embeddings = embeddings[global_cluster_mask]\n", + "\n", + " if len(global_cluster_embeddings) <= target_dim + 1:\n", + " # Assign all points in this global cluster to a single local cluster\n", + " for idx in np.where(global_cluster_mask)[0]:\n", + " all_local_clusters[idx] = np.append(all_local_clusters[idx], total_clusters)\n", + " total_clusters += 1\n", + " continue\n", + "\n", + " try:\n", + " reduced_local_embeddings = dimensionality_reduction(global_cluster_embeddings, target_dim, \"local\")\n", + " local_clusters, n_local_clusters = gmm_clustering(reduced_local_embeddings, threshold, random_state=random_state)\n", + "\n", + " # Assign local cluster IDs\n", + " for j in range(n_local_clusters):\n", + " local_cluster_mask = np.array([j in lc for lc in local_clusters])\n", + " global_indices = np.where(global_cluster_mask)[0]\n", + " local_indices = global_indices[local_cluster_mask]\n", + " for idx in local_indices:\n", + " all_local_clusters[idx] = np.append(all_local_clusters[idx], j + total_clusters)\n", + "\n", + " total_clusters += n_local_clusters\n", + " except Exception as e:\n", + " print(f\"Error in local clustering for global cluster {i}: {str(e)}\")\n", + " # Assign all points in this global cluster to a single local cluster\n", + " for idx in np.where(global_cluster_mask)[0]:\n", + " all_local_clusters[idx] = np.append(all_local_clusters[idx], total_clusters)\n", + " total_clusters += 1\n", + "\n", + " return all_local_clusters, total_clusters\n", + "\n", + "def generate_summary(context: str) -> str:\n", + " prompt = f\"\"\"\n", + " Provide the Summary for the given context. Here are some additional instructions for you:\n", + "\n", + " Instructions:\n", + " 1. Don't make things up, Just use the contexts and generate the relevant summary.\n", + " 2. Don't mix the numbers, Just use the numbers in the context.\n", + " 3. Don't try to use fancy words, stick to the basics of the language that is being used in the context.\n", + "\n", + " Context: {context}\n", + " \"\"\"\n", + " response = client.chat.completions.create(\n", + " model=\"gpt-4\",\n", + " messages=[\n", + " {\"role\": \"system\", \"content\": \"You are a helpful assistant that summarizes text.\"},\n", + " {\"role\": \"user\", \"content\": prompt}\n", + " ],\n", + " max_tokens=200,\n", + " n=1,\n", + " stop=None,\n", + " temperature=0.7\n", + " )\n", + " summary = response.choices[0].message.content.strip()\n", + " return summary\n", + "\n", + "def embed_clusters(\n", + " texts: List[str],\n", + " target_dim: int = 10,\n", + " threshold: float = 0.1\n", + ") -> pd.DataFrame:\n", + " textual_embeddings = np.array(embedding_model.encode(texts))\n", + " clusters, number_of_clusters = clustering_algorithm(textual_embeddings, target_dim, threshold)\n", + " print(f\"Number of clusters: {number_of_clusters}\")\n", + " return pd.DataFrame({\n", + " \"texts\": texts,\n", + " \"embedding\": list(textual_embeddings),\n", + " \"clusters\": clusters\n", + " })\n", + "\n", + "def embed_cluster_summaries(\n", + " texts: List[str],\n", + " level: int,\n", + " target_dim: int = 10,\n", + " threshold: float = 0.1\n", + ") -> Tuple[pd.DataFrame, pd.DataFrame]:\n", + " df_clusters = embed_clusters(texts, target_dim, threshold)\n", + " main_list = []\n", + "\n", + " for _, row in df_clusters.iterrows():\n", + " for cluster in row[\"clusters\"]:\n", + " main_list.append({\n", + " \"text\": row[\"texts\"],\n", + " \"embedding\": row[\"embedding\"],\n", + " \"clusters\": cluster\n", + " })\n", + "\n", + " main_df = pd.DataFrame(main_list)\n", + " unique_clusters = main_df[\"clusters\"].unique()\n", + " if len(unique_clusters) == 0:\n", + " return df_clusters, pd.DataFrame(columns=[\"summaries\", \"level\", \"clusters\"])\n", + "\n", + " print(f\"--Generated {len(unique_clusters)} clusters--\")\n", + "\n", + " summaries = []\n", + " for cluster in unique_clusters:\n", + " text_in_df = main_df[main_df[\"clusters\"] == cluster]\n", + " unique_texts = text_in_df[\"text\"].tolist()\n", + " text = \"------\\n------\".join(unique_texts)\n", + " summary = generate_summary(text)\n", + " summaries.append(summary)\n", + "\n", + " df_summaries = pd.DataFrame({\n", + " \"summaries\": summaries,\n", + " \"level\": [level] * len(summaries),\n", + " \"clusters\": unique_clusters\n", + " })\n", + "\n", + " return df_clusters, df_summaries\n", + "\n", + "\n", + "def recursive_embedding_with_cluster_summarization(\n", + " texts: List[str],\n", + " number_of_levels: int = 3,\n", + " level: int = 1,\n", + " target_dim: int = 10,\n", + " threshold: float = 0.1\n", + ") -> Dict[int, Tuple[pd.DataFrame, pd.DataFrame]]:\n", + " if level > number_of_levels:\n", + " return {}\n", + "\n", + " results = {}\n", + " df_clusters, df_summaries = embed_cluster_summaries(texts, level, target_dim, threshold)\n", + " results[level] = (df_clusters, df_summaries)\n", + "\n", + " if df_summaries.empty or len(df_summaries['clusters'].unique()) == 1:\n", + " print(f\"No more unique clusters found at level {level}. Stopping recursion.\")\n", + " return results\n", + "\n", + " if level < number_of_levels:\n", + " next_level_texts = df_summaries['summaries'].tolist()\n", + " next_level_results = recursive_embedding_with_cluster_summarization(\n", + " next_level_texts,\n", + " number_of_levels,\n", + " level + 1,\n", + " target_dim,\n", + " threshold\n", + " )\n", + " results.update(next_level_results)\n", + "\n", + " return results\n" + ], + "metadata": { + "id": "FcAEBwFIiKNX" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def process_text_hierarchy(\n", + " texts: List[str],\n", + " number_of_levels: int = 3,\n", + " target_dim: int = 10,\n", + " threshold: float = 0.1\n", + ") -> Dict[str, pd.DataFrame]:\n", + " hierarchy_results = recursive_embedding_with_cluster_summarization(\n", + " texts, number_of_levels, target_dim=target_dim, threshold=threshold\n", + " )\n", + "\n", + " processed_results = {}\n", + " for level, (df_clusters, df_summaries) in hierarchy_results.items():\n", + " if df_clusters.empty or df_summaries.empty:\n", + " print(f\"No data for level {level}. Skipping.\")\n", + " continue\n", + " processed_results[f\"level_{level}_clusters\"] = df_clusters\n", + " processed_results[f\"level_{level}_summaries\"] = df_summaries\n", + "\n", + " return processed_results\n", + "\n", + "\n", + "results = process_text_hierarchy(chunks, number_of_levels=3)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "M5Cq7FziHFjc", + "outputId": "2f3362a0-59a7-4ab3-da87-e7ef3dc4d18f" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Number of clusters: 4\n", + "--Generated 4 clusters--\n", + "Number of clusters: 1\n", + "--Generated 1 clusters--\n", + "No more unique clusters found at level 2. Stopping recursion.\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### RAG Formation\n", + "\n", + "Now it’s smooth sailing! We’ll set up a LanceDB vector database to store our embeddings and facilitate querying our RAG setup. To compare the relevant results from both RAPTOR RAG and VANILLA RAG, I’ll configure both RAG systems." + ], + "metadata": { + "id": "Wyu9ec6nrWPz" + } + }, + { + "cell_type": "code", + "source": [ + "raptor_texts = []\n", + "for level, row in results.items():\n", + " if level.endswith(\"clusters\"):\n", + " raptor_texts.extend(row[\"texts\"])\n", + " else:\n", + " raptor_texts.extend(row[\"summaries\"])\n", + "\n", + "raptor_embeddings = embedding_model.encode(raptor_texts) # new raptor embeddings\n", + "normal_embeddings = embedding_model.encode(chunks) # default chunks from our data\n", + "print(raptor_embeddings)\n", + "print(normal_embeddings)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JRi7Z254IKOb", + "outputId": "5856f39c-1a64-4f0d-c6f2-523af7755832" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[[-0.02867434 -0.03404145 0.03183713 ... -0.11637738 -0.03496148\n", + " 0.02426555]\n", + " [-0.02260254 -0.05349045 0.01649414 ... -0.07463644 0.06521226\n", + " 0.02849963]\n", + " [-0.04479891 -0.05722785 0.07431144 ... -0.12457766 -0.07554325\n", + " 0.08023182]\n", + " ...\n", + " [-0.00617281 -0.02009849 -0.00419744 ... -0.09979296 -0.10421827\n", + " -0.01685225]\n", + " [-0.0961774 -0.01634985 -0.02820352 ... -0.08988537 -0.03870759\n", + " -0.0209068 ]\n", + " [-0.07575317 -0.00782455 -0.00337196 ... -0.11301762 -0.02062772\n", + " 0.02530929]]\n", + "[[-2.8674338e-02 -3.4041446e-02 3.1837128e-02 ... -1.1637738e-01\n", + " -3.4961481e-02 2.4265550e-02]\n", + " [-2.2602541e-02 -5.3490449e-02 1.6494140e-02 ... -7.4636437e-02\n", + " 6.5212265e-02 2.8499626e-02]\n", + " [-4.4798914e-02 -5.7227850e-02 7.4311443e-02 ... -1.2457766e-01\n", + " -7.5543255e-02 8.0231816e-02]\n", + " ...\n", + " [-6.6004984e-02 -7.7177368e-02 2.0053916e-02 ... -8.4871434e-02\n", + " 5.2336227e-02 -1.4862130e-06]\n", + " [ 1.5349649e-02 -9.5823780e-02 1.1927984e-02 ... -8.5330136e-02\n", + " 6.6217750e-02 3.6647685e-02]\n", + " [-8.2652569e-02 -2.1616790e-02 -1.9847509e-02 ... -8.3741397e-02\n", + " 4.1552845e-02 2.0205928e-02]]\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "With RAPTOR, we now have an increased number of chunks due to the addition of cluster-level summary nodes alongside the default chunks we had earlier." + ], + "metadata": { + "id": "5G0mDUuRs3Ac" + } + }, + { + "cell_type": "code", + "source": [ + "# Compute lengths of the embeddings\n", + "raptor_length = len(raptor_embeddings)\n", + "normal_length = len(normal_embeddings)\n", + "\n", + "# Bar graph data\n", + "labels = ['Raptor RAG', 'Normal RAG']\n", + "lengths = [raptor_length, normal_length]\n", + "\n", + "# Create the bar graph\n", + "plt.bar(labels, lengths, color=['blue', 'green'])\n", + "\n", + "# Add title and labels\n", + "plt.title('Comparison of Number of Chunks')\n", + "plt.ylabel('Number of Chunks')\n", + "\n", + "# Show the plot\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 452 + }, + "id": "GdxyN80FYarp", + "outputId": "aebcc6f5-d178-42e6-864b-0ef7df4f7542" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "

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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "raptor_dict = {\"texts\": [], \"embeddings\": []}\n", + "for texts, embeddings in zip(raptor_texts, raptor_embeddings):\n", + " raptor_dict[\"texts\"].append(texts)\n", + " raptor_dict[\"embeddings\"].append(embeddings.tolist())\n", + "\n", + "normal_dict = {\"texts\": [], \"embeddings\": []}\n", + "for texts, embeddings in zip(chunks, normal_embeddings):\n", + " normal_dict[\"texts\"].append(texts)\n", + " normal_dict[\"embeddings\"].append(embeddings.tolist())" + ], + "metadata": { + "id": "To8QBJ-MIMqD" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "rag_raptor_df = pd.DataFrame(raptor_dict)\n", + "print(rag_raptor_df.shape)\n", + "\n", + "rag_normal_df = pd.DataFrame(normal_dict)\n", + "print(rag_normal_df.shape)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "dyKzF8L8IiMI", + "outputId": "a1587ef6-3654-442a-9e0b-d1708830ddd9" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(26, 2)\n", + "(17, 2)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import lancedb\n", + "import pyarrow as pa\n", + "from lancedb.pydantic import Vector, LanceModel\n", + "\n", + "uri = \"lancedb_database\"\n", + "db = lancedb.connect(uri)\n", + "\n", + "class RAG(LanceModel):\n", + " texts : str\n", + " embeddings : Vector(384)\n", + "\n", + "table_name = \"rag_with_raptor\"\n", + "raptor_table = db.create_table(table_name, schema = RAG, mode=\"overwrite\")\n", + "raptor_table.add(rag_raptor_df)\n", + "raptor_table.create_fts_index(\"texts\", replace=True)\n", + "\n", + "table_name = \"rag_without_raptor\"\n", + "normal_table = db.create_table(table_name, schema = RAG, mode=\"overwrite\")\n", + "normal_table.add(rag_normal_df)\n", + "normal_table.create_fts_index(\"texts\", replace=True)" + ], + "metadata": { + "id": "682ORFjhIPQL" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def generate_results(\n", + " query : str,\n", + " context_text : str\n", + ") -> str:\n", + "\n", + " prompt = f\"\"\"\n", + " Based on the context provided, use it to answer the query.\n", + "\n", + " query : {query}\n", + "\n", + " Instructions:\n", + " 1. Don't make things up, Just use the contexts and generate the relevant answer.\n", + " 2. Don't mix the numbers, Just use the numbers in the context.\n", + " 3. Don't try to use fancy words, stick to the basics of the language that is being used in the context.\n", + "\n", + " {context_text}\n", + " \"\"\"\n", + " response = client.chat.completions.create(\n", + " model=\"gpt-4\",\n", + " messages=[\n", + " {\"role\": \"system\", \"content\": \"You are a helpful assistant that answers query and give the answers.\"},\n", + " {\"role\": \"user\", \"content\": prompt}\n", + " ],\n", + " max_tokens=200,\n", + " n=1,\n", + " stop=None,\n", + " temperature=0.7\n", + " )\n", + " answer = response.choices[0].message.content.strip()\n", + " return answer" + ], + "metadata": { + "id": "q45B_5TyIRcD" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "query = \"NTT DATA's net income attributable to shareholders increased from ¥69,227 million in Q3 FY2020 to ¥110,191 million in Q3 FY2021. How does this growth align with their acquisition strategy, particularly considering their stated reasons for acquiring Nexient, LLC and the provisional goodwill recorded in this transaction?\"\n", + "raptor_contexts = raptor_table.search(query).limit(5).select([\"texts\"]).to_list()\n", + "raptor_context_text = \"------\\n\\n\".join([context[\"texts\"] for context in raptor_contexts])\n", + "raptor_context_text = \"------\\n\\n\" + raptor_context_text\n", + "\n", + "normal_contexts = normal_table.search(query).limit(5).select([\"texts\"]).to_list()\n", + "normal_context_text = \"------\\n\\n\".join([context[\"texts\"] for context in normal_contexts])\n", + "normal_context_text = \"------\\n\\n\" + normal_context_text\n", + "\n", + "raptor_answer = generate_results(query, raptor_context_text)\n", + "normal_answer = generate_results(query, normal_context_text)" + ], + "metadata": { + "id": "aoVxfQRjIT6J" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "raptor_answer" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 165 + }, + "id": "_0zQwN9LJWtH", + "outputId": "a64d2cf0-b4dd-40b3-f926-803a50653ea5" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "\"The growth in NTT DATA's net income attributable to shareholders from ¥69,227 million in Q3 FY2020 to ¥110,191 million in Q3 FY2021 aligns with their acquisition strategy as it suggests a positive financial performance that can support such growth initiatives. The acquisition of Nexient, LLC, for instance, is expected to enhance NTT DATA's capabilities and better meet clients' needs, which could potentially lead to further growth in income. The cost of this acquisition was 286 million yen, indicating that the company is investing in strategic acquisitions to drive growth. The fact that no significant changes were made in subsidiaries, accounting policies, or accounting estimates, nor were there revisions to the forecasts of dividends and consolidated results, further indicates a stable financial condition that can support such growth-oriented strategies.\"" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 41 + } + ] + }, + { + "cell_type": "code", + "source": [ + "normal_answer" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 109 + }, + "id": "ITtuQpERZgZh", + "outputId": "47cae890-88cc-4150-e880-3434d0b7ccd4" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "\"Based on the context provided, it can be seen that NTT DATA's net income attributable to shareholders has significantly increased from ¥69,227 million in Q3 FY2020 to ¥110,191 million in Q3 FY2021. However, the context does not provide information regarding how this growth aligns with their acquisition strategy. Similarly, there is no specific mention of their reasons for acquiring Nexient, LLC or the provisional goodwill recorded in this transaction. Therefore, it is not possible to provide a detailed answer to the query without making assumptions or adding additional information.\"" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 42 + } + ] + } + ] +} \ No newline at end of file From 2f52cac9518be5bf31b62d3d3780475ebd49fa05 Mon Sep 17 00:00:00 2001 From: robertturner <143536791+robertdhayanturner@users.noreply.github.com> Date: Wed, 6 Nov 2024 14:29:28 -0500 Subject: [PATCH 11/15] Update improve-rag-with-raptor.md add ../assets/use_cases/improve-rag-with-raptor/raptor_with_rag.ipynb link --- docs/articles/improve-rag-with-raptor.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/articles/improve-rag-with-raptor.md b/docs/articles/improve-rag-with-raptor.md index 41baeaca2..1ef87684c 100644 --- a/docs/articles/improve-rag-with-raptor.md +++ b/docs/articles/improve-rag-with-raptor.md @@ -592,7 +592,7 @@ RAPTOR RAG performed **better than vanilla RAG** at handling retrieval on our hi ## Your turn -Now it's your turn to try out RAPTOR RAG! Here's the Google [colab](https://colab.research.google.com/drive/1I3WI0U4sgb2nc1QTQm51kThZb2q4MXyr?usp=sharing). +Now it's your turn to try out RAPTOR RAG! Here's the Google [colab](../assets/use_cases/improve-rag-with-raptor/raptor_with_rag.ipynb). To learn more about the intricacies of RAPTOR, check out their official [GitHub repository](https://github.com/parthsarthi03/raptor/tree/master). For an even deeper dive, we highly recommend the official [paper](https://arxiv.org/pdf/2401.18059)! From f931d0e8b636105543bd3ef697c91d499598531f Mon Sep 17 00:00:00 2001 From: robertturner <143536791+robertdhayanturner@users.noreply.github.com> Date: Wed, 6 Nov 2024 14:42:09 -0500 Subject: [PATCH 12/15] Update improve-rag-with-raptor.md update with correct file path: https://colab.research.google.com/github/superlinked/VectorHub/blob/main/docs/assets/use_cases/improve-rag-with-raptor/raptor_with_rag.ipynb --- docs/articles/improve-rag-with-raptor.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/articles/improve-rag-with-raptor.md b/docs/articles/improve-rag-with-raptor.md index 1ef87684c..f9f48ad7d 100644 --- a/docs/articles/improve-rag-with-raptor.md +++ b/docs/articles/improve-rag-with-raptor.md @@ -592,7 +592,7 @@ RAPTOR RAG performed **better than vanilla RAG** at handling retrieval on our hi ## Your turn -Now it's your turn to try out RAPTOR RAG! Here's the Google [colab](../assets/use_cases/improve-rag-with-raptor/raptor_with_rag.ipynb). +Now it's your turn to try out RAPTOR RAG! Here's the Google [colab](https://colab.research.google.com/github/superlinked/VectorHub/blob/main/docs/assets/use_cases/improve-rag-with-raptor/raptor_with_rag.ipynb). To learn more about the intricacies of RAPTOR, check out their official [GitHub repository](https://github.com/parthsarthi03/raptor/tree/master). For an even deeper dive, we highly recommend the official [paper](https://arxiv.org/pdf/2401.18059)! From 9128a347c3bddb1541561d9db5d14cea4bcb45aa Mon Sep 17 00:00:00 2001 From: robertturner <143536791+robertdhayanturner@users.noreply.github.com> Date: Wed, 6 Nov 2024 15:51:17 -0500 Subject: [PATCH 13/15] add semantic search news article (#507) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * add semantic search news article * update semantic search news * update semantic search news article * update article - with a couple questions * update semantic search news article * update article - add conclusion * update article, basically done * update article, check in w Mor re time question * update article, with question for Mór * update article, question for Mór * update article - passing to Mor for review * final commit --------- Co-authored-by: Arunesh Singh <43724007+AruneshSingh@users.noreply.github.com> --- docs/articles/semantic_search_news.md | 374 ++++++++++++++++++ .../count_article-by-year_publication.png | Bin 0 -> 89217 bytes .../microsoft_acquires_linkedin.png | Bin 0 -> 438213 bytes .../microsoft_linkedin_recency_upweighted.png | Bin 0 -> 423719 bytes .../semantic_search_news/musk_twitter.png | Bin 0 -> 397843 bytes .../musk_twitter_recency.png | Bin 0 -> 414700 bytes .../semantic_search_news/recency_scores.png | Bin 0 -> 124358 bytes .../semantic_search_news/sneak_peek.png | Bin 0 -> 333239 bytes 8 files changed, 374 insertions(+) create mode 100644 docs/articles/semantic_search_news.md create mode 100644 docs/assets/use_cases/semantic_search_news/count_article-by-year_publication.png create mode 100644 docs/assets/use_cases/semantic_search_news/microsoft_acquires_linkedin.png create mode 100644 docs/assets/use_cases/semantic_search_news/microsoft_linkedin_recency_upweighted.png create mode 100644 docs/assets/use_cases/semantic_search_news/musk_twitter.png create mode 100644 docs/assets/use_cases/semantic_search_news/musk_twitter_recency.png create mode 100644 docs/assets/use_cases/semantic_search_news/recency_scores.png create mode 100644 docs/assets/use_cases/semantic_search_news/sneak_peek.png diff --git a/docs/articles/semantic_search_news.md b/docs/articles/semantic_search_news.md new file mode 100644 index 000000000..e5201205e --- /dev/null +++ b/docs/articles/semantic_search_news.md @@ -0,0 +1,374 @@ +# Semantic search in business news - a notebook article + +Semantic search is revolutionizing how we discover and consume news articles, offering a more intuitive and efficient method for finding relevant content and curating personalized news feeds. By embedding the nuances and underlying concepts of text documents in vectors, we can retrieve articles that align closely with the user's interests, preferences, and browsing history. + +Still, implementing effective semantic search for news articles presents **challenges**, including: + +- **Response optimization**: you need to figure out how to weight data attributes in your semantic search algorithms +- **Scalability and performance**: you need efficient indexing and retrieval mechanisms to handle the vast volume of news articles + +Superlinked is designed to handle these challenges, empowering you to **scale efficiently** and - using Superlinked Spaces - **prioritize semantic relevance and/or recency so you can recommend highly relevant news articles to your users *without having to re-embed your dataset*.** + +To illustrate, we'll take you step by step through building a **semantic-search-powered business news recommendation app**, using the following parts of Superlinked's library: + +- **[Recency space](https://github.com/superlinked/superlinked/blob/main/notebook/feature/recency_embedding.ipynb)** - to encode the recency of a data point +- **[TextSimilarity space](https://github.com/superlinked/superlinked/blob/main/notebook/feature/text_embedding.ipynb)** - to encode the semantic meaning of text data +- **[Query time weights](https://github.com/superlinked/superlinked/blob/main/notebook/feature/query_time_weights.ipynb)** - to prioritize different attributes in your queries, without having to re-embed the whole dataset + +Using these spaces to embed our articles' headlines, text, and publication dates, we'll be able to skew our results towards older or more recent news as desired, and also search using specific search terms or a specific news article. + +Ready to begin? Let's get started! + +Let's take a quick look at our dataset, then use Superlinked to embed the articles smartly and handle our queries. + +## Our dataset and embeddings + +Our dataset of [news articles](https://www.kaggle.com/datasets/rmisra/news-category-dataset) is filtered for news in the 'BUSINESS' category. + +We'll embed: + +- headlines +- article text (short descriptions) +- publication (release) dates + +## Setup + +First, we **install Superlinked**. + +```python +%pip install superlinked==12.19.1 +``` + +Now we **import all our dependencies**... + +```python +from datetime import datetime, timedelta, timezone + +import os +import sys +import altair as alt +import pandas as pd + +from superlinked.evaluation.charts.recency_plotter import RecencyPlotter +from superlinked.framework.common.dag.context import CONTEXT_COMMON, CONTEXT_COMMON_NOW +from superlinked.framework.common.dag.period_time import PeriodTime +from superlinked.framework.common.schema.schema import Schema +from superlinked.framework.common.schema.schema_object import String, Timestamp +from superlinked.framework.common.schema.id_schema_object import IdField +from superlinked.framework.common.parser.dataframe_parser import DataFrameParser +from superlinked.framework.common.util.interactive_util import get_altair_renderer +from superlinked.framework.dsl.executor.in_memory.in_memory_executor import ( + InMemoryExecutor, + InMemoryApp, +) +from superlinked.framework.dsl.index.index import Index +from superlinked.framework.dsl.query.param import Param +from superlinked.framework.dsl.query.query import Query +from superlinked.framework.dsl.query.result import Result +from superlinked.framework.dsl.source.in_memory_source import InMemorySource +from superlinked.framework.dsl.space.text_similarity_space import TextSimilaritySpace +from superlinked.framework.dsl.space.recency_space import RecencySpace + +alt.renderers.enable(get_altair_renderer()) +alt.data_transformers.disable_max_rows() +pd.set_option("display.max_colwidth", 190) +``` + +...and **declare our constants**. + +```python +YEAR_IN_DAYS = 365 +TOP_N = 10 +DATASET_URL = "https://storage.googleapis.com/superlinked-notebook-news-dataset/business_news.json" +# as the dataset contains articles from 2022 and before, we can set our application's "NOW" to this date +END_OF_2022_TS = int(datetime(2022, 12, 31, 23, 59).timestamp()) +EXECUTOR_DATA = {CONTEXT_COMMON: {CONTEXT_COMMON_NOW: END_OF_2022_TS}} +``` + +## Prepare & explore dataset + +Let's read our data... + +```python +NROWS = int(os.getenv("NOTEBOOK_TEST_ROW_LIMIT", str(sys.maxsize))) +business_news = pd.read_json(DATASET_URL, convert_dates=True).head(NROWS) +``` + +...then turn the current index into a column ("id"), and convert the date column timestamp into UTC. + +```python +# we are going to need an id column +business_news = business_news.reset_index().rename(columns={"index": "id"}) +# convert the date timestamp into utc timezone +business_news["date"] = [ + int(date.replace(tzinfo=timezone.utc).timestamp()) for date in business_news.date +] +``` + +Let's take a sneak peak. + +```python +num_rows = business_news.shape[0] +print(f"Our dataset contains {num_rows} articles.") +business_news.head() +``` + +Our dataset has 5992 articles. Here are the first 5. + +![sneak peak into data](../assets/use_cases/semantic_search_news/sneak_peek.png) + +### Understand release date distribution + +So that we can set some recency time periods, we'll take a closer look at how our articles distribute over time. + +```python +# some quick transformations and an altair histogram +years_to_plot: pd.DataFrame = pd.DataFrame( + { + "year_of_publication": [ + int(datetime.fromtimestamp(ts).year) for ts in business_news["date"] + ] + } +) +alt.Chart(years_to_plot).mark_bar().encode( + alt.X("year_of_publication:N", bin=True, title="Year of publication"), + y=alt.Y("count()", title="Count of articles"), +).properties(width=400, height=400) +``` + +![count of articles by year of publication](../assets/use_cases/semantic_search_news/count_article-by-year_publication.png) + +Because our oldest article was published in 2012 and we want to be able to query all our dataset articles, we should set our longer time period inclusively to around 11 years. + +The vast majority of our articles are distributed from 2012 through 2017, so it makes sense to differentiate that period by creating another more recent 4-year period (2018-2022) when the article count is much lower. + +We can make sure our retrieval appropriately represents the small differences between articles in our publication-dense period (2012-2017) articles by giving them additional weight. This way, differences in our publication-scarce period (2018-2022), which will be larger than in the dense period, aren't overrepresented. + +Now let's set up Superlinked so we can efficiently optimize our retrieval. + +## Set up Superlinked + +First, we'll define a schema for our news articles. + +```python +# set up schema to accommodate our inputs +class NewsSchema(Schema): + description: String + headline: String + release_timestamp: Timestamp + id: IdField +``` + +```python +news = NewsSchema() +``` + +Next, to embed the characteristics of our text, we use a sentence-transformers model to create a `description_space` for news article descriptions and a `headline_space` for our headlines, and, finally, we encode each article's release date using a `recency_space`. + +```python +# textual characteristics are embedded using a sentence-transformers model +description_space = TextSimilaritySpace( + text=news.description, model="sentence-transformers/all-mpnet-base-v2" +) +headline_space = TextSimilaritySpace( + text=news.headline, model="sentence-transformers/all-mpnet-base-v2" +) +# release date is encoded using our recency embedding algorithm +recency_space = RecencySpace( + timestamp=news.release_timestamp, + period_time_list=[ + PeriodTime(timedelta(days=4 * YEAR_IN_DAYS), weight=1), + PeriodTime(timedelta(days=11 * YEAR_IN_DAYS), weight=2), + ], + negative_filter=0.0, +) +``` + +To query our data, we'll need to create an index of our spaces... + +```python +news_index = Index(spaces=[description_space, headline_space, recency_space]) +``` + +...and set up a **simple query** and a **news query**. + +**Simple query** lets us use a search term to retrieve from both the headline and the description. Simple query also gives us the option to weight certain inputs' importance. + +```python +simple_query = ( + Query( + news_index, + weights={ + description_space: Param("description_weight"), + headline_space: Param("headline_weight"), + recency_space: Param("recency_weight"), + }, + ) + .find(news) + .similar(description_space.text, Param("query_text")) + .similar(headline_space.text, Param("query_text")) + .limit(Param("limit")) +) +``` + +**News query** will search our database using the vector for a specific news article. News query, like simple query, can be weighted. + +```python +news_query = ( + Query( + news_index, + weights={ + description_space: Param("description_weight"), + headline_space: Param("headline_weight"), + recency_space: Param("recency_weight"), + }, + ) + .find(news) + .with_vector(news, Param("news_id")) + .limit(Param("limit")) +) +``` + +Next we parse our dataframe,... + +```python +dataframe_parser = DataFrameParser( + schema=news, + mapping={news.release_timestamp: "date", news.description: "short_description"}, +) +``` + +...create an InMemorySource object to accept the data (which is stored in an InMemoryVectorDatabase), and set up our executor (with our article dataset and index) so that it takes account of context data. The executor creates vectors based on the index's grouping of Spaces. + +```python +source: InMemorySource = InMemorySource(news, parser=dataframe_parser) +executor: InMemoryExecutor = InMemoryExecutor( + sources=[source], indices=[news_index], context_data=EXECUTOR_DATA +) +app: InMemoryApp = executor.run() +``` + +It's time to **input our business news data**. + +```python +source.put([business_news]) + +``` + +### Understanding recency + +With our business news finished inputting, let's plot our recency scores. + +```python +recency_plotter = RecencyPlotter(recency_space, context_data=EXECUTOR_DATA) +recency_plotter.plot_recency_curve() +``` + +![recency scores for our time periods](../assets/use_cases/semantic_search_news/recency_scores.png) + +## Queries + +To see our query results when we run them, we'll set up a helper to present them in a notebook. + +```python +def present_result( + result_to_present: Result, + cols_to_keep: list[str] | None = None, +) -> pd.DataFrame: + if cols_to_keep is None: + cols_to_keep = [ + "description", + "headline", + "release_date", + "id", + "similarity_score", + ] + # parse result to dataframe + df: pd.DataFrame = result_to_present.to_pandas() + # transform timestamp back to release year + df["release_date"] = [ + datetime.fromtimestamp(timestamp, tz=timezone.utc).date() + for timestamp in df["release_timestamp"] + ] + return df[cols_to_keep] +``` + +Now, say we wanted to read articles about Microsoft acquiring LinkedIn - one of the biggest acquisitions of the last decade. We input our query text as follows, weighting headline and description at 1. Recency weight doesn't matter yet so we'll set it to 0. + +```python +result = app.query( + simple_query, + query_text="Microsoft acquires LinkedIn", + description_weight=1, + headline_weight=1, + recency_weight=0, + limit=TOP_N, +) + +present_result(result) +``` + +Let's take a look at our results. + +![microsoft acquires linkedin](../assets/use_cases/semantic_search_news/microsoft_acquires_linkedin.png) + +The first result is about the deal. Other results are related to some aspect of the query. Let's try upweighting recency to see we can surface other, more recent, big acquisitions. + +```python +result = app.query( + simple_query, + query_text="Microsoft acquires LinkedIn", + description_weight=1, + headline_weight=1, + recency_weight=1, + limit=TOP_N, +) + +present_result(result) +``` + +![microsoft linkedin recency upweighted](../assets/use_cases/semantic_search_news/microsoft_linkedin_recency_upweighted.png) + +With recency upweighted, our second result is an article about the much more recent Elon Musk Twitter offer. + +Now let's take this article and perform a search with its `news_id`, resetting recency to 0. + +```python +result = app.query( + news_query, + description_weight=1, + headline_weight=1, + recency_weight=0, + news_id="849", + limit=TOP_N, +) + +present_result(result) +``` + +![musk acquires twitter](../assets/use_cases/semantic_search_news/musk_twitter.png) + +Because our dataset is significantly biased towards older articles, and recency is set to 0, our query retrieves articles highly relevant to the content of our search article - focused on either Elon Musk or Twitter. + +To get more recent articles into the mix, we can start biasing toward recency, navigating the tradeoff between recency and text similarity. + +```python +result = app.query( + news_query, + description_weight=1, + headline_weight=1, + recency_weight=1, + news_id="849", + limit=TOP_N, +) + +present_result(result) +``` + +![musk twitter recency](../assets/use_cases/semantic_search_news/musk_twitter_recency.png) + +## In sum + +Whatever your semantic search use case, Superlinked Spaces enables you up to optimize your vector retrieval with a high degree of control, without incurring the time and resource costs of re-embedding your dataset. By embedding smartly (attribute by attribute) with our Recency and TextSimilarity spaces, you can prioritize or deprioritize different attributes as needed at query time. + +Now it's your turn! Try your own simple_query and news_query in the [notebook](https://github.com/superlinked/superlinked/blob/main/notebook/semantic_search_news.ipynb). 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