diff --git a/docs/docs.json b/docs/docs.json index e23a4fe..b1d9c7a 100644 --- a/docs/docs.json +++ b/docs/docs.json @@ -299,7 +299,14 @@ "group": "Tutorials", "pages": [ "tutorials/index", - "tutorials/search/index", + { + "group": "Search", + "expanded": true, + "pages": [ + "tutorials/search/index", + "tutorials/search/multivector-needle-in-a-haystack" + ] + }, { "group": "RAG & Agents", "expanded": true, diff --git a/docs/search/multivector-search.mdx b/docs/search/multivector-search.mdx index e4e80ef..ac69745 100644 --- a/docs/search/multivector-search.mdx +++ b/docs/search/multivector-search.mdx @@ -1,19 +1,19 @@ --- title: "Multivector Search" sidebarTitle: Multivector search -description: Learn how to perform multivector search in LanceDB to handle multiple vector embeddings per document, ideal for late interaction models like ColBERT and ColPaLi. +description: Learn how to perform multivector search in LanceDB to handle multiple vector embeddings per document, which is ideal for late-interaction models like ColBERT and ColPaLi. icon: "braille" --- -LanceDB's multivector support enables you to store and search multiple vector embeddings for a single item. +LanceDB's multivector support enables you to store and search multiple vector embeddings for a single item. -This capability is particularly valuable when working with late interaction models like ColBERT and ColPaLi that generate multiple embeddings per document. +This capability is particularly valuable when working with late-interaction models like ColBERT and ColPaLi, which generate multiple embeddings per document. -In this tutorial, you'll create a table with multiple vector embeddings per document and learn how to perform multivector search. [For all the code - open in Colab](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/saas_examples/python_notebook/Multivector_on_LanceDB_Cloud.ipynb) +In this tutorial, you'll create a table with multiple vector embeddings per document and learn how to perform multivector search. [For the full code, open the Colab notebook](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/saas_examples/python_notebook/Multivector_on_LanceDB_Cloud.ipynb). ## Multivector Support -Each item in your dataset can have a column containing multiple vectors, which LanceDB can efficiently index and search. When performing a search, you can query using either a single vector embedding or multiple vector embeddings. +Each item in your dataset can have a column containing multiple vectors, which LanceDB can efficiently index and search. When performing a search, you can query with either a single vector embedding or multiple vector embeddings. Currently, only the `cosine` metric is supported for multivector search. The vector value type can be `float16`, `float32`, or `float64`. @@ -21,7 +21,7 @@ Currently, only the `cosine` metric is supported for multivector search. The vec ## Computing Similarity -MaxSim (Maximum Similarity) is a key concept in late interaction models that: +MaxSim (Maximum Similarity) is a key concept in late-interaction models that: - Computes the maximum similarity between each query embedding and all document embeddings - Sums these maximum similarities to get the final relevance score @@ -33,24 +33,19 @@ $$ \text{MaxSim}(Q, D) = \sum_{i=1}^{|Q|} \max_{j=1}^{|D|} \text{sim}(q_i, d_j) $$ -Where $sim$ is the similarity function (e.g. cosine similarity). +Where $sim$ is the similarity function (e.g., cosine similarity). $$ Q = \{q_1, q_2, ..., q_{|Q|}\} $$ -$Q$ represents the query vector, and $D = \{d_1, d_2, ..., d_{|D|}\}$ represents the document vectors. - - -For now, you should use only the `cosine` metric for multivector search. -The vector value type can be `float16`, `float32` or `float64`. - +$Q$ represents the query embeddings, and $D = \{d_1, d_2, ..., d_{|D|}\}$ represents the document embeddings. ## Using Multivector Search ### 1. Setup -Connect to LanceDB and import required libraries for data management. +Connect to LanceDB and import the required libraries. ```python Python icon="python" @@ -68,7 +63,7 @@ db = lancedb.connect( ### 2. Define Schema -Define a schema that specifies a multivector field. A multivector field is a nested list structure where each document contains multiple vectors. In this case, we'll create a schema with: +Define a schema that specifies a multivector field. A multivector field is a nested list structure in which each document contains multiple vectors. In this case, we'll create a schema with: 1. An ID field as an integer (int64) 2. A vector field that is a list of lists of float32 values @@ -91,7 +86,7 @@ schema = pa.schema( ### 3. Generate Multivectors -Generate sample data where each document contains multiple vector embeddings, which could represent different aspects or views of the same document. +Generate sample data where each document contains multiple vector embeddings, which can represent different aspects or views of the same document. In this example, we create **1024 documents** where each document has **2 random vectors** of **dimension 256**, simulating a real-world scenario where you might have multiple embeddings per item. @@ -119,7 +114,7 @@ tbl = db.create_table("multivector_example", data=data, schema=schema) ### 5. Build an Index -Only cosine similarity is supported as the distance metric for multivector search operations. +Only cosine similarity is supported for multivector search operations. For faster search, build the standard `IVF_PQ` index over your vectors: @@ -141,7 +136,7 @@ results_single = tbl.search(query).limit(5).to_pandas() ### 7. Query Multiple Vectors -With multiple vector queries, LanceDB calculates similarity using late interaction - a technique that computes relevance by finding the best matching pairs between query and document vectors. This approach provides more nuanced matching while maintaining fast retrieval speeds. +With multiple query vectors, LanceDB calculates similarity using late interaction, a late-interaction technique that computes relevance by finding the best-matching pairs between query and document vectors. This approach provides more nuanced matching while maintaining fast retrieval speeds. ```python Python icon="python" @@ -151,7 +146,7 @@ results_multi = tbl.search(query_multi).limit(5).to_pandas() -Visit the [Hugging Face embedding integration](/integrations/embedding/huggingface/) page for info on embedding models. +Visit the [Hugging Face embedding integration](/integrations/embedding/huggingface/) page for information on embedding models. ## Simple Example: ColBERT Embeddings @@ -217,18 +212,18 @@ print(out[["doc_id", "text"]]) ``` -Late interaction models implementations evolve rapidly, so it's recommended to check the latest popular models -when trying out multivector search. +Late-interaction model implementations evolve rapidly, so it's a good idea to check the latest popular models +when trying multivector search. ## Advanced Example: XTR Embeddings [ConteXtualized Token Retriever (XTR)](https://arxiv.org/abs/2304.01982) is a late-interaction retrieval model that represents text as token-level vectors instead of a single embedding. This lets search score token-to-token matches (MaxSim), which can improve fine-grained relevance. -The notebook linked below shows how to integrate XTR (ConteXtualized Token Retriever), which prioritizes critical document -tokens during the initial retrieval stage and removes the gathering stage to significantly improve performance. +The notebook linked below shows how to integrate XTR, which prioritizes critical document +tokens during the initial retrieval stage and removes the gathering stage to improve performance significantly. By focusing on the most semantically salient tokens early in the process, XTR reduces computational complexity -with improved recall, ensuring rapid identification of candidate documents. +while improving recall and ensuring rapid identification of candidate documents. pa.RecordBatch:\n start = batch_idx * rows_per_batch\n stop = start + rows_per_batch\n row_ids = list(range(start, stop))\n vectors = pa.array(\n [[random() for _ in range(VECTOR_DIM)] for _ in row_ids],\n type=pa.list_(pa.float32(), VECTOR_DIM),\n )\n return pa.RecordBatch.from_arrays(\n [\n pa.array(row_ids, type=pa.int64()),\n vectors,\n ],\n schema=schema,\n )\n\ndef make_batch_reader(\n num_batches: int, rows_per_batch: int\n) -> pa.RecordBatchReader:\n return pa.RecordBatchReader.from_batches(\n schema,\n (make_batch(batch_idx, rows_per_batch) for batch_idx in range(num_batches)),\n )\n\ndef make_large_scannable(num_batches: int, rows_per_batch: int) -> Scannable:\n total_rows = num_batches * rows_per_batch\n return Scannable(\n schema=schema,\n num_rows=total_rows,\n reader=lambda: make_batch_reader(num_batches, rows_per_batch),\n )\n\ndb = tmp_db\ntable = db.create_table(\n \"bulk_ingest_concurrent\",\n make_large_scannable(num_batches=1000, rows_per_batch=10000),\n mode=\"overwrite\",\n)\n"; + export const TsAddColumnsCalculated = "// Add a discounted price column (10% discount)\nawait schemaAddTable.addColumns([\n {\n name: \"discounted_price\",\n valueSql: \"cast((price * 0.9) as float)\",\n },\n]);\n"; export const TsAddColumnsDefaultValues = "// Add a stock status column with default value\nawait schemaAddTable.addColumns([\n {\n name: \"in_stock\",\n valueSql: \"cast(true as boolean)\",\n },\n]);\n"; diff --git a/docs/static/assets/images/search/multivector/multivector-1.png b/docs/static/assets/images/search/multivector/multivector-1.png new file mode 100644 index 0000000..f34f11c Binary files /dev/null and b/docs/static/assets/images/search/multivector/multivector-1.png differ diff --git a/docs/static/assets/images/search/multivector/multivector-2.png b/docs/static/assets/images/search/multivector/multivector-2.png new file mode 100644 index 0000000..1a99c7b Binary files /dev/null and b/docs/static/assets/images/search/multivector/multivector-2.png differ diff --git a/docs/static/assets/images/search/multivector/multivector-3.png b/docs/static/assets/images/search/multivector/multivector-3.png new file mode 100644 index 0000000..2f2f23b Binary files /dev/null and b/docs/static/assets/images/search/multivector/multivector-3.png differ diff --git a/docs/static/assets/images/search/multivector/multivector-4.png b/docs/static/assets/images/search/multivector/multivector-4.png new file mode 100644 index 0000000..3333610 Binary files /dev/null and b/docs/static/assets/images/search/multivector/multivector-4.png differ diff --git a/docs/static/assets/images/search/multivector/multivector-5.png b/docs/static/assets/images/search/multivector/multivector-5.png new file mode 100644 index 0000000..4aa7715 Binary files /dev/null and b/docs/static/assets/images/search/multivector/multivector-5.png differ diff --git a/docs/static/assets/images/search/multivector/multivector-6.png b/docs/static/assets/images/search/multivector/multivector-6.png new file mode 100644 index 0000000..b33a3cf Binary files /dev/null and b/docs/static/assets/images/search/multivector/multivector-6.png differ diff --git a/docs/tables/create.mdx b/docs/tables/create.mdx index 95c3699..28ad054 100644 --- a/docs/tables/create.mdx +++ b/docs/tables/create.mdx @@ -42,7 +42,7 @@ In LanceDB, tables store records with a defined schema that specifies column nam - PyArrow schemas for explicit schema control - `LanceModel` for Pydantic-based validation -## Create a LanceDB Table +## Create a table with data Initialize a LanceDB connection and create a table @@ -217,9 +217,9 @@ for a `created_at` field. When you run this code it, should raise the `ValidationError`. -### Use Iterators / Write Large Datasets +### From Batch Iterators -For large ingests, prefer batching instead of adding one row at a time. Python and Rust can create a table directly from Arrow batch iterators or readers. In TypeScript, the practical pattern today is to create an empty table and append Arrow batches in chunks. +For bulk ingestion on large datasets, prefer batching instead of adding one row at a time. Python and Rust can create a table directly from Arrow batch iterators or readers. In TypeScript, the practical pattern today is to create an empty table and append Arrow batches in chunks. @@ -235,25 +235,23 @@ For large ingests, prefer batching instead of adding one row at a time. Python a +Use this pattern when: + +- Your source data already arrives in Arrow batches, readers, datasets, or streams. +- Materializing the entire ingest as one giant in-memory list or array would be too expensive. +- You want to control chunk size explicitly during ingestion. + Python can also consume iterators of other supported types like Pandas DataFrames or Python lists. -## Open existing tables +### Write with Concurrency -If you forget the name of your table, you can always get a listing of all table names. +For Python users who want to speed up bulk ingest jobs, it is usually better to write from Arrow-native sources that already produce batches, such as readers, datasets, or scanners, instead of first materializing everything as one large Python list. - - - {OpenExistingTable} - +This is most useful when you are writing large amounts of data from an existing Arrow pipeline or another batch-oriented source. - - {TsOpenExistingTable} - +The current codebase also contains a lower-level ingest mechanism for describing a batch source together with extra metadata such as row counts and retry behavior. However, that path is not accepted by the released Python `create_table(...)` and `add(...)` workflow in `lancedb==0.30.0`, so we are not showing it as a docs example yet. - - {RsOpenExistingTable} - - +In Rust, the same lower-level ingest mechanism is available, but the common batch-reader example above is usually the better starting point unless you specifically need to define your own batch source or provide size and retry hints. In TypeScript, this lower-level mechanism is not exposed publicly, so chunked Arrow batch writes remain the recommended pattern. ## Create empty table You can create an empty table for scenarios where you want to add data to the table later. @@ -289,6 +287,25 @@ that has been extended to support LanceDB specific types like `Vector`. Once the empty table has been created, you can append to it or modify its contents, as explained in the [updating and modifying tables](/tables/update) section. +## Open an existing table + +You can open an existing table by specifying the name of the table to the `open_table` / `openTable` method. +If you forget the name of your table, you can always get a listing of all table names. + + + + {OpenExistingTable} + + + + {TsOpenExistingTable} + + + + {RsOpenExistingTable} + + + ## Drop a table Use the `drop_table()` method on the database to remove a table. diff --git a/docs/tutorials/search/index.mdx b/docs/tutorials/search/index.mdx index 129b271..90e7d41 100644 --- a/docs/tutorials/search/index.mdx +++ b/docs/tutorials/search/index.mdx @@ -1,6 +1,6 @@ --- title: "Search Tutorials" -sidebarTitle: "Search" +sidebarTitle: "Overview" description: "Learn how vector, full-text and hybrid search work in LanceDB." layout: wide --- @@ -14,7 +14,8 @@ The table below shows examples of applications built with LanceDB for search use | **Semantic result merging**
Open In Colab [View on GitHub](https://github.com/lancedb/vectordb-recipes/tree/main/examples/Vector-Arithmetic-with-LanceDB) | Explore the concept of vector arithmetic with LanceDB. This notebook demonstrates how you can manipulate vector embeddings to capture more complex relationships in your data. For instance, you can modify a search query by adding or subtracting vector representations of different concepts, enabling more nuanced and powerful semantic search. | | **Reddit concept summarizer**
Open In Colab [View on GitHub](https://github.com/lancedb/vectordb-recipes/tree/main/examples/Reddit-summarization-and-search) | This project showcases a complete pipeline for acquiring text data from Reddit, transforming it into meaningful vector representations using embeddings, and then storing and managing those vectors in LanceDB. It demonstrates how to build applications on top of this data, such as summarization and powerful semantic search. | | **NER-powered vector search**
Open In Colab [View on GitHub](https://github.com/lancedb/vectordb-recipes/tree/main/tutorials/NER-powered-Semantic-Search) | This example demonstrates how to use Named Entity Recognition (NER) to power vector search. By extracting entities (like people, places, and organizations) from text and creating vector embeddings of them, you can significantly improve the accuracy of your search results. | -| **Multivector search with XTR**
Open In Colab [View on GitHub](https://github.com/lancedb/vectordb-recipes/tree/main/examples/multivector_xtr) | This notebook dives into LanceDB's advanced multivector search capabilities, enhanced by the XTR (ConteXtualized Token Retriever) technique. It shows how to represent complex data with multiple vectors for more nuanced meaning and how XTR speeds up retrieval by prioritizing the most important tokens. | +| **Multi-vector search with XTR**
Open In Colab [View on GitHub](https://github.com/lancedb/vectordb-recipes/tree/main/examples/multivector_xtr) | This notebook dives into LanceDB's advanced multivector search capabilities, enhanced by the XTR (ConteXtualized Token Retriever) technique. It shows how to represent complex data with multiple vectors for more nuanced meaning and how XTR speeds up retrieval by prioritizing the most important tokens. | +| **Needle-in-a-haystack multi-vector search**

[Read the tutorial](/tutorials/search/multivector-needle-in-a-haystack/)
| This tutorial complements the XTR multi-vector example by comparing several retrieval strategies on a token-level "needle in a haystack" benchmark. It shows when full multi-vector search, pooling, and reranking help or hurt when the goal is to find an exact page rather than just a relevant document. | ## Read the docs diff --git a/docs/tutorials/search/multivector-needle-in-a-haystack.mdx b/docs/tutorials/search/multivector-needle-in-a-haystack.mdx new file mode 100644 index 0000000..3e97e60 --- /dev/null +++ b/docs/tutorials/search/multivector-needle-in-a-haystack.mdx @@ -0,0 +1,332 @@ +--- +title: "Finding the Needle in a Haystack: Comparing Multi-vector Search Strategies" +sidebarTitle: "Multi-vector search: Needle in a Haystack" +description: "Tutorial on token-level retrieval with LanceDB multivector search." +--- + +![](/static/assets/images/search/multivector/multivector-1.png) + +In the development of advanced search and retrieval systems, moving from keyword matching to semantic understanding is a critical step. However, a key distinction exists between finding a relevant document and locating a specific piece of information within that document with precision. While there are techniques that perform well for retrieving documents, most of them work by extracting summarized semantic meaning of the document. + +This can be seen as these models trying to understand the "gist" of the documents. Both single-vector search and late-interaction approaches work well for these conditions with various tradeoffs involved. But there's another type of problem where the goal is not just to understand the overall topic of a document in general, but to also specifically account for the requested detail within the document. This "needle in a haystack" problem is a significant challenge, and addressing it is essential for building high-precision retrieval systems. + +This guide provides a technical analysis of multivector search for high-precision information retrieval. We will examine various optimization strategies and analyze their performance. This guide should be seen as complementary to resources like [this blog post by Answer.AI](https://www.answer.ai/posts/colbert-pooling.html) on ColBERT pooling, which explains how pooling strategies can be effective for document-level retrieval. Here, we will demonstrate why those same techniques can be counterproductive when precision at an intra-document, token level is the primary objective. + +To reproduce the work below, see the code [here](https://github.com/lancedb/research/tree/main/multivector-needle-haystack-bench). + +## The Dataset + +This task is different from benchmarks like BEIR, which focus on text-based doc retrieval, finding the most relevant documents from a large collection. Here, we want *intra-document localization*, where the goal is to find a precise piece of information within a single, dense document, in a multimodal setting. + +### The Task: The Document Haystack Dataset + +Our benchmark is built on the **[AmazonScience/document-haystack](https://huggingface.co/datasets/AmazonScience/document-haystack)** dataset, which contains 25 visually complex source documents (e.g., financial reports, academic papers). To create a rigorous test, our evaluation follows a per-document methodology: + +* We process each of the 25 source documents independently. +* **Table Creation:** For a single source document (e.g., "AIG"), we ingest all pages from all of its page-length variants (from 5 to 200 pages long). This creates a temporary LanceDB table containing approximately 1,230 pages. +* **The Task:** We then query this table using a set of "needle" questions, where the goal is to retrieve the **exact page number** containing the answer. A successful retrieval means the correct page number is within the top K results. +* **Target metric:** We measure both retrieval accuracy (Hit@K) and the average search latency for each query against this table. + +**Dataset example** +The documents contain "text needles" like these + +![](/static/assets/images/search/multivector/multivector-2.png) +![](/static/assets/images/search/multivector/multivector-3.png) + +During evaluation, the queries processed are somewhat like this: +``` +What is the secret currency in the document? +What is the secret object #3 in the document? +``` +The intention of this task is to find the page which has the text needle that answers this questions + + +## Models and Architectures + +Our testbed includes a baseline single-vector model and a family of advanced multivector models. + +### Single-Vector (Bi-Encoder) Baseline: `openai/clip-vit-base-patch32` + +A bi-encoder maps an entire piece of content (a query, a document page) to a *single* vector. The search process is simple: pre-compute one vector for every page, and at query time, find the page vector closest to the query vector. + +* **Strength:** Speed and simplicity. +* **Weakness:** This creates an **information bottleneck**. All the nuanced details, keywords, and semantic relationships on a page must be compressed into a single, fixed-size vector. For finding a needle, this is like trying to describe a specific person's face using only one word. + +### Multi-vector (Late-Interaction) Models + +Multi-vector models, pioneered by ColBERT, take a different approach. Instead of one vector per page, they generate a *set of vectors* for each page—one for every token (or image patch). + +* **Mechanism (MaxSim):** The search process is more sophisticated. For each token in the query, the system finds the most similar token on the page. These maximum similarity scores are then summed up to get the final relevance score. This "late-interaction" preserves fine-grained, token-level details. +* **The Models:** We used several vision-language models adapted for this architecture, including `ColPali`, `ColQwen2`, and `ColSmol`. While their underlying transformer backbones differ, they all share the ColBERT philosophy of representing documents as a bag of contextualized token embeddings. + +![](/static/assets/images/search/multivector/multivector-4.png) + +## Different Retrieval Strategies Used + +A full multivector search is powerful but computationally intensive. Here are five strategies for managing it, complete with LanceDB implementation details. + + +### 1. `base`: The Gold Standard (Full Multi-vector Search) + +This is the pure, baseline late-interaction search. It offers the highest potential for accuracy by considering every token. + +**LanceDB also integrates with ConteXtualized Token Retriever (XTR)** , an advanced retrieval model that prioritizes the most semantically important document tokens during search. This integration enhances the quality of search results by focusing on the most relevant token matches. +**LanceDB Implementation:** + +```python Python icon="python" +import lancedb +import pyarrow as pa + +# Schema for multivector data +# Assumes embeddings are 128-dimensional +schema = pa.schema([ + pa.field("page_num", pa.int32()), + pa.field("vector", pa.list_(pa.list_(pa.float32(), 128))) +]) + +db = lancedb.connect("./lancedb") +tbl = db.create_table("document_pages_base", schema=schema) + +# Ingesting multi-token embeddings for a page +# multi_token_embeddings is a NumPy array of shape (num_tokens, 128) +tbl.add([{"page_num": 1, "vector": multi_token_embeddings.tolist()}]) + +# Searching with a multi-token query +# query_multi_vector is also shape (num_query_tokens, 128) +results = tbl.search(query_multi_vector).limit(5).to_list() +``` + +### 2. `flatten`: Mean Pooling + +This strategy "flattens" the set of token vectors into a single vector by averaging them. This transforms the search into a standard, fast approximate nearest neighbor (ANN) search. + +**LanceDB Implementation:** + +```python Python icon="python" +# Schema for single-vector data +schema_flat = pa.schema([ + pa.field("page_num", pa.int32()), + pa.field("vector", pa.list_(pa.float32(), 128)) +]) +tbl_flat = db.create_table("document_pages_flat", schema=schema_flat) + +# Ingesting the mean-pooled vector +mean_vector = multi_token_embeddings.mean(axis=0) +tbl_flat.add([{"page_num": 1, "vector": mean_vector.tolist()}]) + +# Searching with a single averaged query vector +query_mean_vector = query_multi_vector.mean(axis=0) +results = tbl_flat.search(query_mean_vector).limit(5).to_list() +``` + +### 3. `max_pooling` + +This is a variation of `flatten`. `max_pooling` takes the element-wise max across all token vectors instead of the mean. The implementation is identical to `flatten`, just with a different aggregation method (`.max(axis=0)`). + +### 4. `flatten and multivector rerank`: The Hybrid "Optimization" + +This two-stage strategy aims for the best of both worlds. First, use a fast, pooled-vector search to find a set of promising candidates. Then, run the full, accurate multivector search on *only* those candidates. + +**LanceDB Implementation:** +This requires a table with two vector columns. + +```python Python icon="python" +# Schema with both flat and multivector columns +schema_rerank = pa.schema([ + pa.field("page_num", pa.int32()), + pa.field("vector_flat", pa.list_(pa.float32(), 128)), + pa.field("vector_multi", pa.list_(pa.list_(pa.float32(), 128))) +]) +tbl_rerank = db.create_table("document_pages_rerank", schema=schema_rerank) + +# Ingest both vectors +tbl_rerank.add([ + { + "page_num": 1, + "vector_flat": multi_token_embeddings.mean(axis=0).tolist(), + "vector_multi": multi_token_embeddings.tolist() + } +]) + +# --- Two-Stage Search --- +# Stage 1: Fast search on the flat vector +query_flat = query_multi_vector.mean(axis=0) +candidates = tbl_rerank.search(query_flat, vector_column_name="vector_flat") \ + .limit(100) \ + .with_row_id(True) \ + .to_pandas() + +# Stage 2: Precise multivector search on candidates +candidate_ids = tuple(candidates["_rowid"].to_list()) +final_results = tbl_rerank.search(query_multi_vector, vector_column_name="vector_multi") \ + .where(f"_rowid IN {candidate_ids}") \ + .limit(5) \ + .to_list() +``` + +### 5. `hierarchical token pooling`: Compressing the Haystack + +This is an indexing-time strategy that aims to reduce the storage footprint and computational cost of multivector search by reducing the number of vectors per document. Instead of using every token vector, it clusters semantically similar tokens together and replaces them with a single, averaged vector. + +* **Mechanism:** For each document, it computes the similarity between all token vectors, performs hierarchical clustering to group them, and then mean-pools the vectors within each cluster. This results in a smaller, more compact set of token vectors representing the document. +* **Goal:** To reduce memory and disk usage while attempting to preserve the most important semantic information, potentially offering a middle ground between the high accuracy of `base` search and the speed of pooled methods. + +**LanceDB Implementation:** +The schema is identical to the `base` multivector search, but the data is pre-processed before ingestion. + +```python Python icon="python" +from utils import pool_embeddings_hierarchical +import numpy as np + +# Schema is the same as the base multivector schema +schema = pa.schema([ + pa.field("page_num", pa.int32()), + pa.field("vector", pa.list_(pa.list_(pa.float32(), 128))) +]) +tbl_hierarchical = db.create_table("document_pages_hierarchical", schema=schema) + +# Pool the embeddings before ingestion +# multi_token_embeddings is a NumPy array of shape (num_tokens, 128) +pooled_embeddings = pool_embeddings_hierarchical( + multi_token_embeddings, + pool_factor=4 # Reduce vector count by a factor of 4 +) + +# Ingest the smaller set of multi-token embeddings +# pooled_embeddings is now shape (approx. num_tokens / 4, 128) +tbl_hierarchical.add([{"page_num": 1, "vector": pooled_embeddings.tolist()}]) + +# Search is identical to the base multivector search +results = tbl_hierarchical.search(query_multi_vector).limit(5).to_list() +``` + +## The Results + +For a "needle in a haystack" task, retrieval accuracy is the primary metric of success. The benchmark results reveal a significant performance gap between the full multivector search strategy and common optimization techniques. + +### Baseline Performance: Single-Vector Bi-Encoder + +First, we establish a baseline using a standard single-vector bi-encoder model, `openai/clip-vit-base-patch32`. This represents a common approach to semantic search but, as the data shows, is ill-suited for this task's precision requirements. + +| Model | Strategy | Hit@1 | Hit@5 | Hit@20 | Avg. Latency (s) | +| :--- | :--- | :--- | :--- | :--- | :--- | +| `openai/clip-vit-base-patch32` | `base` | 1.6% | 4.7% | 11.8% | **0.008 s** | + +With a Hit@20 rate of just under 12%, the baseline model struggles to reliably locate the correct page. This performance level is insufficient for applications requiring high precision. + +### Multi-vector Model Performance + +We now examine the performance of multivector models using different strategies. The following table compares the `base` (full multivector), `flatten` (mean pooling), and `rerank` (hybrid) strategies across several late-interaction models. + +| Model | Strategy | Hit@1 | Hit@5 | Hit@20 | Avg. Latency (s) | +| :--- | :--- | :--- | :--- | :--- | :--- | +| `vidore/colqwen2-v1.0` | `flatten` | 1.9% | 5.5% | 11.9% | 0.010 s | +| `vidore/colqwen2-v1.0` | `flatten and multivector rerank` | 0.3% | 1.5% | 7.3% | 0.692 s | +| **`vidore/colqwen2-v1.0`** | **`hierarchical token pooling`** | **13.7%** | **60.5%** | **91.6%** | **0.144 s** | +| **`vidore/colqwen2-v1.0`** | **`base`** | **14.0%** | **65.4%** | **95.5%** | 0.668 s | +| `vidore/colpali-v1.3` | `flatten` | 1.7% | 4.5% | 9.3% | 0.008 s | +| `vidore/colpali-v1.3` | `flatten and multivector rerank` | 0.6% | 2.3% | 6.9% | 0.949 s | +| **`vidore/colpali-v1.3`** | **`hierarchical token pooling`** | **10.8%** | **41.7%** | **64.8%** | **0.189 s** | +| **`vidore/colpali-v1.3`** | **`base`** | **11.3%** | **42.3%** | **65.6%** | 0.936 s | +| `vidore/colSmol-256M` | `flatten` | 1.6% | 4.7% | 10.5% | 0.008 s | +| `vidore/colSmol-256M` | `flatten and multivector rerank` | 0.3% | 1.6% | 7.0% | 0.853 s | +| **`vidore/colSmol-256M`** | **`base`** | **14.4%** | **64.0%** | **91.7%** | 0.848 s | + +The data shows a consistent pattern: the `base` strategy outperforms all other techniques. The flattned pooling and reranking strategies perform no better than the single-vector baseline. However, hierarchical token pooling seems like a decent alternative to base considering speed vs accuracy tradeoff. Let's look at the numbers in detail. + +### In-Depth Analysis of Pooling Strategies + +To further understand the failure of optimization techniques, we compared different methods for pooling token vectors into a single vector: `mean` (`flatten`), `max`. + +| Model & Pooling Strategy | Hit@1 | Hit@5 | Hit@20 | Avg. Latency (s) | +| :--- | :--- | :--- | :--- | :--- | +| `vidore/colqwen2-v1.0` (`mean_pooling`) | 1.9% | 5.5% | 11.9% | 0.010 s | +| `vidore/colqwen2-v1.0` (`max_pooling`) | 1.4% | 4.2% | 11.2% | 0.011 s | +| **`vidore/colqwen2-v1.0` (`base`)** | **14.0%** | **65.4%** | **95.5%** | 0.668 s | + +All flattened pooling methods perform poorly, confirming that the aggregation of token vectors into a single representation loses the fine-grained detail required for this task. + +**Finding the Right Trade-Off** + +![](/static/assets/images/search/multivector/multivector-5.png) + +1. **The Failure of Simple Pooling:** The `flatten` (mean pooling) and `max_pooling` strategies fail to improve upon the baseline. This is because their aggressive compression **destroys the essential localization signal**. The resulting single vector represents the *topic* of the page, not the *specific needle* on it. +2. **The Failure of `flatten and multivector rerank`:** This hybrid strategy is the *worst-performing* of all. The reason is a fundamental flaw in its design for this task: the first stage uses a simple pooled vector to retrieve candidates. Since this pooling eliminates the localization signal, the initial candidate set is effectively random. +3. **`hierarchical token pooling`:** By clustering and pooling tokens at indexing time, it reduces the number of vectors per page (in our case, by a factor of 4). This intelligently compresses the data, while preserving enough token-level detail in multivector setting. It achieves a **Hit@20 of 91.6%**, only slightly behind the `base` strategy's 95.5%, but is significantly faster. +4. **`base` multivector Search:** The vanilla, un-optimized `base` multivector search remains the most accurate strategy. Preserving every token vector provides the highest guarantee of finding the needle, but this comes at the highest computational cost. + +### Latency: + +![](/static/assets/images/search/multivector/multivector-6.png) + + +The "optimizations" are not all created equal. While simple pooling is fast, its inaccuracy makes it unusable. Hierarchical pooling, however, offers a compelling balance of speed and accuracy. + +| Strategy (on `vidore/colqwen2-v1.0`) | Avg. Search Latency (s) | Hit@20 Accuracy | +| :--- | :--- | :--- | +| `flatten` (Fast but Ineffective) | **0.010 s** | 11.9% | +| `flatten and multivector rerank` (Slower and Ineffective) | 0.692 s | 7.3% | +| **`hierarchical token pooling` (Accurate & Fast)** | **0.144 s** | **91.6%** | +| `base` (Most Accurate) | 0.668 s | **95.5%** | +_Latency reported is as seen on NVIDIA H100 GPUs_ + +## Practical Considerations + +The accuracy of `base` multivector search is impressive, but its computational intensity has historically limited its use. `hierarchical token pooling` as a viable strategy creates a new, practical sweet spot on the accuracy-latency curve, making high-precision search accessible for a wider range of applications. + +### Search Latency and Computational Complexity + + +As the benchmark data shows, the search latency for `base` multivector search is orders of magnitude higher than for single-vector (or pooled-vector) search. It's important to note that the reported ~670ms latency is an average from per-document evaluations. In this benchmark, each of the 25 documents is processed independently. All pages from a single document's variants (ranging from 5 to 200 pages) are ingested into a temporary table, resulting in a table size of approximately **1,230 rows (pages)** per evaluation. The search is performed on this table, and then the table is discarded. This highlights a significant performance cost even on a relatively small, per-document scale. This stems from a fundamental difference in computational complexity: + +* **Modern ANN Search (for single vectors):** Algorithms like HNSW (Hierarchical Navigable Small World) provide sub-linear search times, often close to `O(log N)`, where `N` is the number of items in the index. This allows them to scale to billions of vectors with millisecond-level latency. +* **Late-Interaction Search (Multi-vector):** The search process is far more intensive. For each query, it must compute similarity scores between query tokens and the tokens of many candidate documents. The complexity is closer to `O(M * Q * D)`, where `M` is the number of candidate documents to score, `Q` is the number of query tokens, and `D` is the average number of tokens per document. `Hierarchical token pooling` directly attacks this problem by reducing `D`, leading to a significant reduction in search latency. + +### When to Use Multi-Vector Search + +Given these constraints, the choice of strategy depends on the specific requirements of the application. + +* **For Maximum Precision (`base`):** In domains where the cost of missing the needle is extremely high, the full `base` search is the most reliable option. +* **For a Balance of Precision and Performance (`hierarchical token pooling`):** This is the ideal choice for many applications. It makes high-precision search practical for larger datasets and more interactive use cases where the sub-second latency of the `base` search may be too high. It significantly lowers the barrier to entry for adopting multivector search. It should still not be seen as a drop-in replacement for ANN, as it still requires more computational resources than single-vector search. +* **For General-Purpose Document Retrieval (`flatten` / single-vector):** For large-scale retrieval where understanding the "gist" is sufficient or where in cases where large-context text-based models suffice, single-vector search remains the most practical and scalable solution. + +## Appendix: Full Benchmark Results + +The full benchmark results are shown below. + +```text +| name | _runtime | _step | _timestamp | _wandb | avg_inference_latency | avg_search_latency | hit_rates | model_name | strategy | +|:--------------------------------------------|-----------:|--------:|--------------:|:-------------------|------------------------:|---------------------:|:------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------|:---------------------| +| vidore/colqwen2-v0.1_base | 13410 | 0 | 1.75873e+09 | {'runtime': 13410} | 0.0418646 | 0.751151 | {'1': 0.1355151515151515, '10': 0.888, '20': 0.9597575757575758, '3': 0.3936969696969697, '5': 0.6349090909090909} | vidore/colqwen2-v0.1 | base | +| vidore/colqwen2-v1.0_rerank | 13008 | 0 | 1.75873e+09 | {'runtime': 13008} | 0.0426252 | 0.692482 | {'1': 0.003393939393939394, '10': 0.03296969696969697, '20': 0.07296969696969698, '3': 0.010666666666666666, '5': 0.015272727272727271} | vidore/colqwen2-v1.0 | rerank | +| vidore/colpali-v1.3_flatten | 2998 | 0 | 1.75872e+09 | {'runtime': 2998} | 0.0296511 | 0.00833224 | {'1': 0.017454545454545455, '10': 0.06448484848484848, '20': 0.09333333333333334, '3': 0.034666666666666665, '5': 0.04509090909090909} | vidore/colpali-v1.3 | flatten | +| vidore/colqwen2-v0.1_rerank | 13512 | 0 | 1.75873e+09 | {'runtime': 13512} | 0.0418855 | 0.662372 | {'1': 0.002909090909090909, '10': 0.026424242424242423, '20': 0.05987878787878788, '3': 0.0075151515151515155, '5': 0.014545454545454544} | vidore/colqwen2-v0.1 | rerank | +| vidore/colqwen2-v1.0_base | 12933 | 0 | 1.75873e+09 | {'runtime': 12933} | 0.0416839 | 0.667898 | {'1': 0.14012121212121212, '10': 0.8846060606060606, '20': 0.9553939393939394, '3': 0.4111515151515152, '5': 0.6538181818181819} | vidore/colqwen2-v1.0 | base | +| vidore/colpali-v1.3_rerank | 12090 | 0 | 1.75873e+09 | {'runtime': 12090} | 0.0310783 | 0.949047 | {'1': 0.006060606060606061, '10': 0.03878787878787879, '20': 0.06933333333333333, '3': 0.014545454545454544, '5': 0.022787878787878788} | vidore/colpali-v1.3 | rerank | +| vidore/colqwen2-v1.0_flatten | 4846 | 0 | 1.75874e+09 | {'runtime': 4846} | 0.0504031 | 0.010443 | {'1': 0.018666666666666668, '10': 0.07854545454545454, '20': 0.11903030303030304, '3': 0.041212121212121214, '5': 0.055030303030303034} | vidore/colqwen2-v1.0 | flatten | +| vidore/colqwen2-v0.1_base | 10838 | 0 | 1.75874e+09 | {'runtime': 10838} | 0.04627 | 0.692836 | {'1': 0.13575757575757577, '10': 0.8870303030303031, '20': 0.96, '3': 0.3941818181818182, '5': 0.6351515151515151} | vidore/colqwen2-v0.1 | base | +| vidore/colqwen2-v0.1_flatten | 4828 | 0 | 1.75874e+09 | {'runtime': 4828} | 0.0486335 | 0.0103028 | {'1': 0.018424242424242423, '10': 0.07224242424242425, '20': 0.10521212121212122, '3': 0.03903030303030303, '5': 0.05212121212121213} | vidore/colqwen2-v0.1 | flatten | +| vidore/colpali-v1.3_base | 10253 | 0 | 1.75874e+09 | {'runtime': 10253} | 0.0312334 | 0.93611 | {'1': 0.11272727272727272, '10': 0.551030303030303, '20': 0.6555151515151515, '3': 0.29987878787878786, '5': 0.4232727272727273} | vidore/colpali-v1.3 | base | +| vidore/colqwen2-v0.1_flatten | 4745 | 0 | 1.75874e+09 | {'runtime': 4745} | 0.0472825 | 0.00990508 | {'1': 0.018424242424242423, '10': 0.07296969696969698, '20': 0.10496969696969696, '3': 0.03951515151515152, '5': 0.05236363636363636} | vidore/colqwen2-v0.1 | flatten | +| vidore/colqwen2.5-v0.2_base | 17218 | 0 | 1.75875e+09 | {'runtime': 17218} | 0.0540356 | 0.694855 | {'1': 0.11903030303030304, '10': 0.7127272727272728, '20': 0.8366060606060606, '3': 0.336, '5': 0.5258181818181819} | vidore/colqwen2.5-v0.2 | base | +| vidore/colqwen2.5-v0.2_rerank | 16859 | 0 | 1.75875e+09 | {'runtime': 16859} | 0.0518383 | 0.71693 | {'1': 0.0026666666666666666, '10': 0.025212121212121213, '20': 0.060848484848484846, '3': 0.006787878787878788, '5': 0.01187878787878788} | vidore/colqwen2.5-v0.2 | rerank | +| vidore/colqwen2-v0.1_rerank | 9484 | 0 | 1.75875e+09 | {'runtime': 9484} | 0.0445599 | 0.691609 | {'1': 0.005333333333333333, '10': 0.030545454545454542, '20': 0.064, '3': 0.010666666666666666, '5': 0.017696969696969697} | vidore/colqwen2-v0.1 | rerank | +| vidore/colSmol-256M_flatten | 6822 | 0 | 1.75875e+09 | {'runtime': 6822} | 0.0404544 | 0.00822329 | {'1': 0.01575757575757576, '10': 0.06836363636363636, '20': 0.10496969696969696, '3': 0.03442424242424243, '5': 0.04678787878787879} | vidore/colSmol-256M | flatten | +| vidore/colSmol-500M_base | 12681 | 0 | 1.75875e+09 | {'runtime': 12681} | 0.0408026 | 0.850902 | {'1': 0.136, '10': 0.8029090909090909, '20': 0.8993939393939394, '3': 0.3806060606060606, '5': 0.5975757575757575} | vidore/colSmol-500M | base | +| vidore/colSmol-500M_rerank | 13066 | 0 | 1.75876e+09 | {'runtime': 13066} | 0.0440974 | 0.927632 | {'1': 0.003636363636363637, '10': 0.028606060606060607, '20': 0.07054545454545455, '3': 0.00896969696969697, '5': 0.015030303030303033} | vidore/colSmol-500M | rerank | +| vidore/colSmol-256M_rerank | 12646 | 0 | 1.75876e+09 | {'runtime': 12646} | 0.0391553 | 0.853279 | {'1': 0.003393939393939394, '10': 0.02909090909090909, '20': 0.07006060606060606, '3': 0.008727272727272728, '5': 0.015515151515151517} | vidore/colSmol-256M | rerank | +| vidore/colqwen2.5-v0.2_flatten | 6348 | 0 | 1.75876e+09 | {'runtime': 6348} | 0.0509971 | 0.00772653 | {'1': 0.017696969696969697, '10': 0.06545454545454546, '20': 0.09284848484848485, '3': 0.03515151515151515, '5': 0.045575757575757575} | vidore/colqwen2.5-v0.2 | flatten | +| vidore/colSmol-256M_base | 11554 | 0 | 1.75876e+09 | {'runtime': 11554} | 0.0366467 | 0.848463 | {'1': 0.1435151515151515, '10': 0.8426666666666667, '20': 0.9173333333333332, '3': 0.40824242424242424, '5': 0.6404848484848484} | vidore/colSmol-256M | base | +| vidore/colSmol-500M_flatten | 6395 | 0 | 1.75876e+09 | {'runtime': 6395} | 0.0384664 | 0.00716238 | {'1': 0.018424242424242423, '10': 0.07345454545454545, '20': 0.11393939393939394, '3': 0.03903030303030303, '5': 0.05090909090909091} | vidore/colSmol-500M | flatten | +| openai/clip-vit-base-patch32_base | 815 | 0 | 1.75876e+09 | {'runtime': 815} | 0.00533487 | 0.00794629 | {'1': 0.016, '10': 0.07636363636363637, '20': 0.11757575757575756, '3': 0.03296969696969697, '5': 0.04703030303030303} | openai/clip-vit-base-patch32 | base | +| vidore/colqwen2-v0.1_max_pooling | 8758 | 0 | 1.7595e+09 | {'runtime': 8758} | 0.0985753 | 0.0106716 | {'1': 0.015515151515151517, '10': 0.07296969696969698, '20': 0.11248484848484848, '3': 0.032484848484848484, '5': 0.04703030303030303} | vidore/colqwen2-v0.1 | max_pooling | +| vidore/colpali-v1.3_max_pooling | 4728 | 0 | 1.75949e+09 | {'runtime': 4728} | 0.0718573 | 0.0103053 | {'1': 0.011393939393939394, '10': 0.05672727272727273, '20': 0.08872727272727272, '3': 0.02666666666666667, '5': 0.03709090909090909} | vidore/colpali-v1.3 | max_pooling | +| vidore/colqwen2-v1.0_max_pooling | 8760 | 0 | 1.7595e+09 | {'runtime': 8760} | 0.0981102 | 0.0106316 | {'1': 0.013575757575757576, '10': 0.06933333333333333, '20': 0.112, '3': 0.02812121212121212, '5': 0.041939393939393936} | vidore/colqwen2-v1.0 | max_pooling | +| vidore/colqwen2-v0.1_max_pooling | 7696 | 0 | 1.7595e+09 | {'runtime': 7696} | 0.105203 | 0.011907 | {'1': 0.016242424242424242, '10': 0.07321212121212121, '20': 0.1132121212121212, '3': 0.03442424242424243, '5': 0.04945454545454545} | vidore/colqwen2-v0.1 | max_pooling | +| vidore/colSmol-256M_max_pooling | 13982 | 0 | 1.75951e+09 | {'runtime': 13982} | 0.0999708 | 0.0121211 | {'1': 0.00993939393939394, '10': 0.05818181818181818, '20': 0.09187878787878788, '3': 0.025212121212121213, '5': 0.037575757575757575} | vidore/colSmol-256M | max_pooling | +| vidore/colSmol-500M_max_pooling | 14191 | 0 | 1.75951e+09 | {'runtime': 14191} | 0.111858 | 0.0121703 | {'1': 0.012363636363636365, '10': 0.07539393939393939, '20': 0.13187878787878787, '3': 0.02787878787878788, '5': 0.0416969696969697} | vidore/colSmol-500M | max_pooling | +| vidore/colqwen2-v0.1_hierarchical_pooling | 4507 | 0 | 1.7599e+09 | {'runtime': 4507} | 0.0320023 | 0.133653 | {'1': 0.1296969696969697, '10': 0.8504242424242424, '20': 0.9343030303030304, '3': 0.37527272727272726, '5': 0.6041212121212122} | vidore/colqwen2-v0.1 | hierarchical_pooling | +| vidore/colpali-v1.3_hierarchical_pooling | 5816 | 0 | 1.7599e+09 | {'runtime': 5816} | 0.0217625 | 0.188727 | {'1': 0.10763636363636364, '10': 0.5372121212121213, '20': 0.6482424242424243, '3': 0.29333333333333333, '5': 0.4167272727272727} | vidore/colpali-v1.3 | hierarchical_pooling | +| vidore/colqwen2.5-v0.2_hierarchical_pooling | 8430 | 0 | 1.75991e+09 | {'runtime': 8430} | 0.043073 | 0.141276 | {'1': 0.11103030303030303, '10': 0.6892121212121212, '20': 0.8203636363636364, '3': 0.3185454545454545, '5': 0.4989090909090909} | vidore/colqwen2.5-v0.2 | hierarchical_pooling | +| vidore/colqwen2-v1.0_hierarchical_pooling | 5685 | 0 | 1.75991e+09 | {'runtime': 5685} | 0.0343824 | 0.144062 | {'1': 0.13745454545454547, '10': 0.822060606060606, '20': 0.9156363636363636, '3': 0.3856969696969697, '5': 0.6050909090909091} | vidore/colqwen2-v1.0 | hierarchical_pooling | +``` \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 792e4c8..f48419a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,13 +5,17 @@ description = "Add your description here" readme = "README.md" requires-python = ">=3.12,<3.14" dependencies = [ - "lancedb>=0.29.2", - "lance-namespace>=0.5.2", - "pandas>=2.3.3", - "polars>=1.35.2", - "pydantic>=2.12.4", + "lancedb>=0.30.0", + "pyarrow>=23.0.1", + "lance-namespace>=0.6.1", + "pandas>=3.0.1", + "polars>=1.39.2", "pytest>=9.0.1", "pytest-asyncio>=1.3.0", +<<<<<<< Updated upstream "Pillow>=11.0.0", "geneva>=0.11.0", +======= + "Pillow>=12.1.1", +>>>>>>> Stashed changes ] diff --git a/tests/py/test_tables.py b/tests/py/test_tables.py index 394a7f2..1f9e601 100644 --- a/tests/py/test_tables.py +++ b/tests/py/test_tables.py @@ -272,6 +272,14 @@ def test_table_creation_from_iterator(tmp_db): # --8<-- [start:create_table_from_iterator] import pyarrow as pa + schema = pa.schema( + [ + pa.field("vector", pa.list_(pa.float32(), 4)), + pa.field("item", pa.utf8()), + pa.field("price", pa.float32()), + ] + ) + def make_batches(): for i in range(5): yield pa.RecordBatch.from_arrays( @@ -286,18 +294,10 @@ def make_batches(): ["vector", "item", "price"], ) - schema = pa.schema( - [ - pa.field("vector", pa.list_(pa.float32(), 4)), - pa.field("item", pa.utf8()), - pa.field("price", pa.float32()), - ] - ) db = tmp_db - db.create_table("batched_tale", make_batches(), schema=schema, mode="overwrite") + db.create_table("batched_table", make_batches(), schema=schema, mode="overwrite") # --8<-- [end:create_table_from_iterator] - def test_open_existing_table(tmp_db): # --8<-- [start:open_existing_table] db = tmp_db diff --git a/uv.lock b/uv.lock index 2204e0c..0b18356 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,6 @@ version = 1 revision = 3 +<<<<<<< Updated upstream requires-python = ">=3.12, 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