Visualize Embeddings as a 3d Plotly scatter plot from Redis Vector Store

[rahulkrprajapati]

How do I visualize the embeddings as a 3d plot using plotly. My vector store is defined as:

vector_store = RedisVectorStore(
index_name=namespace,
redis_url=self.config.get('REDIS_VECTOR_STORE'),
metadata_fields=['namespace']
)

[dosubot[bot]]

Hey @rahulkrprajapati! Great to see you diving into another exciting project with us. 🚀

To visualize the embeddings from a RedisVectorStore as a 3D scatter plot using Plotly, you can follow these steps:

1. Query the RedisVectorStore for Embeddings: Use the query method to retrieve embeddings from the RedisVectorStore. Since the query method requires a VectorStoreQuery object, you'll need to create one with the desired query parameters. Note that the method returns a VectorStoreQueryResult containing the nodes, their IDs, and similarities (which can be interpreted as distances in some contexts).

2. Extract Embeddings and IDs: From the VectorStoreQueryResult, extract the embeddings and their corresponding IDs. The embeddings might need to be converted back from their buffer representation using a utility function if they are not directly usable.

3. Prepare Data for Plotly: Convert the embeddings into a format suitable for Plotly. This typically involves creating lists or arrays for the x, y, and z coordinates of each point in the 3D space.

4. Create a 3D Scatter Plot with Plotly: Use Plotly's plotly.graph_objects.Scatter3d to create a 3D scatter plot. You will need to pass the x, y, and z coordinates, and you can also customize the plot with IDs as hover information or different colors.

Here's an example code snippet that demonstrates these steps:

import plotly.graph_objects as go
from llama_index.core.vector_stores.types import VectorStoreQuery

# Assuming `redis_vector_store` is an instance of RedisVectorStore
# and `query_embedding` is your query vector

# Step 1: Query the RedisVectorStore for embeddings
query = VectorStoreQuery(query_embedding=query_embedding, similarity_top_k=10)
query_result = redis_vector_store.query(query)

# Step 2: Extract embeddings and IDs
# This step assumes embeddings are directly usable; adjust as necessary
ids = [node.id for node in query_result.nodes]
embeddings = [node.get_embedding() for node in query_result.nodes]  # Adjust this line based on how embeddings are stored

# Convert embeddings into x, y, z lists for Plotly
x = [embedding[0] for embedding in embeddings]
y = [embedding[1] for embedding in embeddings]
z = [embedding[2] for embedding in embeddings]

# Step 3: Prepare Data for Plotly
# Already done above by extracting x, y, z

# Step 4: Create a 3D Scatter Plot with Plotly
fig = go.Figure(data=[go.Scatter3d(x=x, y=y, z=z, mode='markers',
                                   marker=dict(size=5),
                                   text=ids)])  # Use IDs as hover text

# Customize the layout
fig.update_layout(title='3D Scatter Plot of Embeddings',
                  scene=dict(xaxis_title='X',
                             yaxis_title='Y',
                             zaxis_title='Z'))
fig.show()

This example assumes that the embeddings are 3-dimensional and directly usable after retrieval. If the embeddings are stored in a different format (e.g., as buffers), you'll need to convert them to a usable format before plotting. Additionally, adjust the VectorStoreQuery as needed for your specific query requirements.

For more details on how the query method is implemented in the RedisVectorStore class, you can refer to the source code.

Sources

• llama-index-integrations/vector_stores/llama-index-vector-stores-redis/llama_index/vector_stores/redis/base.py
• llama-index-legacy/llama_index/legacy/vector_stores/redis.py

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[dosubot]

To reduce the dimensionality of your 768-dimensional embeddings stored in a RedisVectorStore to 3 dimensions for visualization on a 3D scatter plot using Plotly, you can follow these steps:

1. Extract Embeddings from RedisVectorStore: First, you need to retrieve the embeddings from your RedisVectorStore. Unfortunately, the provided code does not include a direct method for fetching all embeddings, but you can use the query method to retrieve embeddings based on specific queries or modify the code to add a method that fetches all embeddings if needed.

2. Dimensionality Reduction: Once you have the embeddings, use a dimensionality reduction technique like PCA (Principal Component Analysis), t-SNE, or UMAP to reduce the embeddings from 768 dimensions to 3 dimensions. Here's an example using PCA with scikit-learn:

from sklearn.decomposition import PCA
import numpy as np

# Assuming `embeddings` is a NumPy array of shape (n_samples, 768) where n_samples is the number of embeddings
pca = PCA(n_components=3)
reduced_embeddings = pca.fit_transform(embeddings)

3. Visualization with Plotly: After reducing the dimensions of your embeddings, you can visualize them on a 3D scatter plot using Plotly. Here's how you can do it:

import plotly.graph_objects as go

# Assuming `reduced_embeddings` is the output from the PCA with shape (n_samples, 3)
fig = go.Figure(data=[go.Scatter3d(
    x=reduced_embeddings[:, 0],
    y=reduced_embeddings[:, 1],
    z=reduced_embeddings[:, 2],
    mode='markers',
    marker=dict(
        size=5,
        opacity=0.8,
    )
)])

fig.update_layout(margin=dict(l=0, r=0, b=0, t=0))
fig.show()

Please note that the code snippets provided above are based on the assumption that you can retrieve or have access to the embeddings from your RedisVectorStore. If the current implementation of RedisVectorStore does not support direct retrieval of all embeddings, you may need to extend its functionality or use a different approach to access your data.

Regarding your question about the query method in the RedisVectorStore class, it is designed to retrieve a list of nodes with their associated scores based on a given query. This method is part of the retrieval process in the VectorIndexRetriever class, which interacts with the vector store to perform queries. However, the query method and its related functionality are primarily designed for retrieving a subset of nodes based on similarity to a query embedding, with optional filters and constraints. To retrieve all embeddings, one would typically need a method specifically designed for that purpose, which might involve iterating over all stored embeddings or using a specific query designed to match all entries. Without explicit mention or implementation of such a feature in the provided context, it's not possible to confirm whether the RedisVectorStore class supports retrieving all embeddings directly through the query method or another method.

The RedisVectorStore class in the LlamaIndex codebase is implemented as a subclass of BasePydanticVectorStore. It is designed to interact with a Redis database to store and manage vector embeddings along with their metadata. This class provides several methods for interacting with the stored embeddings, including __init__, client, add, delete, delete_index, query, and persist. Additionally, there are several private methods like _create_index, _index_exists, _create_vector_field, and _to_redis_filters.

I hope this helps! If you have any more questions, feel free to ask.

Sources

• llama-index-integrations/vector_stores/llama-index-vector-stores-redis/llama_index/vector_stores/redis/base.py
• llama-index-core/llama_index/core/indices/vector_store/retrievers/retriever.py
• llama-index-legacy/llama_index/legacy/vector_stores/redis.py

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[rahulkrprajapati]

@dosu-bot could you write the complete code to plot the retrieved node against the query embedding using UMAP. I want a plotly 2d scatter plot

[dosubot]

Sure, I can provide a complete code snippet that retrieves 768-dimensional embeddings from a RedisVectorStore, reduces their dimensionality using UMAP, and visualizes them on a 2D scatter plot using Plotly. Here's how you can do it:

import numpy as np
import plotly.express as px
import umap.umap_ as umap
from llama_index.vector_stores.redis import RedisVectorStore
from llama_index.core.vector_stores.types import VectorStoreQuery

# Initialize the RedisVectorStore
vector_store = RedisVectorStore(
    index_name="your_index_name",
    redis_url="redis://localhost:6379/",
    overwrite=False
)

# Define your query embedding (example: random 768-dimensional vector)
query_embedding = np.random.rand(768)

# Query the RedisVectorStore for similar embeddings
query_result = vector_store.query(
    VectorStoreQuery(
        query_embedding=query_embedding.tolist(),
        similarity_top_k=10
    )
)

# Extract embeddings and IDs from the query result
embeddings = [node.get_embedding() for node in query_result.nodes]
ids = query_result.ids

# Add the query embedding to the list of embeddings for visualization
embeddings.append(query_embedding)
ids.append('query')

# Convert embeddings to a NumPy array
embeddings_array = np.array(embeddings)

# Reduce dimensionality with UMAP
reduced_embeddings = umap.UMAP(n_components=2, random_state=42).fit_transform(embeddings_array)

# Create a scatter plot with Plotly
fig = px.scatter(
    x=reduced_embeddings[:, 0], y=reduced_embeddings[:, 1],
    text=ids,
    title="UMAP projection of the embeddings",
    labels={'x': 'UMAP-1', 'y': 'UMAP-2'}
)

fig.show()

Please replace "your_index_name" with the name of your index and "redis://localhost:6379/" with your Redis URL. This script will retrieve the top 10 embeddings most similar to the query embedding from the RedisVectorStore, reduce their dimensionality to 2D using UMAP, and visualize them on a 2D scatter plot using Plotly. The query embedding is also included in the visualization.

This code snippet assumes that you have the numpy, plotly, umap-learn, and llama_index packages installed in your Python environment. If not, you can install them using pip:

pip install numpy plotly umap-learn llama_index

Please let me know if you have any questions or need further clarification.

Sources

• Visualising Embeddings
• llama-index-integrations/vector_stores/llama-index-vector-stores-redis/llama_index/vector_stores/redis/base.py
• benchmarks/embeddings/bench_embeddings.py

About Dosu

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[rahulkrprajapati]

@dosu-bot also give me the complete code to plot the retrieved node against the query embedding using UMAP. I want a plotly 3d scatter plot. Also explain if you think UMAP would be better than t-SNE or PCA?

[dosubot]

Sure, here's a Python code snippet that retrieves the top 10 embeddings most similar to a given 768-dimensional query embedding from a RedisVectorStore, reduces their dimensionality to 3D using UMAP, and visualizes them on a 3D scatter plot using Plotly:

from llama_index.vector_stores import RedisVectorStore
from llama_index.vector_stores.types import VectorQuery
import umap
import plotly.graph_objects as go
import numpy as np

# Assuming `query_embedding` is your 768-dimensional query embedding
query_embedding = np.random.rand(768)  # Example query embedding

# Initialize RedisVectorStore
vector_store = RedisVectorStore(
    index_name="pg_essays",
    index_prefix="llama",
    redis_url="redis://localhost:6379",
    overwrite=True,
)

# Retrieve top 10 similar embeddings
top_k_results = vector_store.query(
    VectorQuery(vector=query_embedding.tolist(), top_k=10)
)

# Extract embeddings from results
similar_embeddings = np.array([result.vector for result in top_k_results])

# Dimensionality reduction using UMAP
reducer = umap.UMAP(n_components=3, random_state=42)
reduced_embeddings = reducer.fit_transform(similar_embeddings)

# Visualization using Plotly
fig = go.Figure(data=[go.Scatter3d(
    x=reduced_embeddings[:, 0],
    y=reduced_embeddings[:, 1],
    z=reduced_embeddings[:, 2],
    mode='markers',
    marker=dict(
        size=5,
        opacity=0.8,
    )
)])
fig.update_layout(title="3D Scatter Plot of Similar Embeddings")
fig.show()

Regarding your question about UMAP vs. t-SNE vs. PCA for dimensionality reduction:

• UMAP is often preferred for its balance between preserving global and local structure, making it suitable for a wide range of datasets. It's generally faster than t-SNE and can handle higher dimensional data more efficiently.
• t-SNE is excellent at preserving local structure and revealing clusters in the data. However, it can be computationally expensive, especially as the size of the dataset grows, and it might not preserve the global structure as well as UMAP.
• PCA is a linear technique that is very efficient and can be used as a preprocessing step before applying UMAP or t-SNE. However, it might not capture the nonlinear relationships in the data as effectively as UMAP or t-SNE.

In this case, UMAP is a better choice for dimensionality reduction because it efficiently balances the preservation of both local and global structures in the data, which is crucial for visualizing and interpreting high-dimensional embeddings.

For more details, you can refer to the RedisIndexDemo notebook in the LlamaIndex repository.

Sources

• docs/examples/vector_stores/RedisIndexDemo.ipynb
• llama-index-legacy/llama_index/legacy/vector_stores/redis.py

About Dosu

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.