Visualising RAG

Visualising RAG Data

You will need to setup Jupyter Notebook to run the code below. See Jupyter Notebook setup

The example code below assumes you already have your documents loaded into the bionicGPT database. We will create a scatter graph showing the document chunks of data together with the 4 most 'relevant' chunks based on the query you specified. At the end we should have a diagram like this. This can be a useful technique for data scientists to debug RAG settings.

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Setup and Retrieve Query Embeddings

!pip install -q sqlalchemy psycopg2-binary pandas
!pip install -q matplotlib seaborn scikit-learn

import sqlalchemy
import pandas as pd
import requests
from sqlalchemy import text
import json
import numpy as np
from sklearn.manifold import TSNE
from sklearn.cluster import KMeans
import seaborn as sns
import matplotlib.pyplot as plt

url = "http://embeddings-api:80/embeddings"

data = {"input": "what is tokenising","model" : "bge-small-en-v1.5"}
headers = {"Content-Type": "application/json"}

response = requests.post(url, json=data, headers=headers)

Get the database login details using

kubectl get secret -n bionic-gpt database-urls -o jsonpath='{.data.readonly-url}' | base64 --decode

engine = sqlalchemy.create_engine('postgresql://bionic_readonly:6323714616921@bionic-db-cluster-rw:5432/bionic-gpt')
conn = engine.connect()

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Retrieve Document Data from Database

We will now use the query embeddings from above to retrieve all document chunks from the data ordered by 'similarity'

query_embedding = response.json()['data'][0]['embedding']


# query_embedding = response.text # From above embedding call

sql = text(f"""SELECT  document_id, file_name, text, embeddings  FROM  chunks, documents
where documents.id = document_id
and embeddings is not null
ORDER BY embeddings <-> '{query_embedding}'""")

df = pd.read_sql(sql,conn)
df

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Convert Data Retrieved into 2 Dimensional Data

df['embeddings_vec'] = df['embeddings'].apply(lambda x: [float(y) for y in json.loads(x)])
embeddings_list = df['embeddings_vec'].tolist()

df

# Convert chunk embeddings
x = np.array(df['embeddings_vec'].to_list(), dtype=np.float32)

# Reduce emeddings dimensions to 2 for plotting purposes
tsne = TSNE(random_state=0, n_iter=1000)
tsne_results = tsne.fit_transform(x)


df_tsne = pd.DataFrame(tsne_results, columns=['x','y'])
df_tsne['doc'] = df['file_name']
df_tsne

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Plot Results

Different colours refer to the different documents uploaded. The 4 circles in blue highlight the 4 most 'relevant' chunks based on the query used above.

fig, ax = plt.subplots(figsize=(10,8)) # Set figsize
sns.set_style('darkgrid', {"grid.color": ".6", "grid.linestyle": ":"})
sns.scatterplot(data=df_tsne, x='x', y='y', hue='doc', palette='hls')

# Highlight the most 'relevant' chunks for query provided
sns.scatterplot(data=df_tsne.head(5), x='x', y='y', marker="$\circ$", ec="face", hue='doc', s=30)

sns.move_legend(ax, "upper left", bbox_to_anchor=(1, 1))
plt.title('Scatter plot of document chunks in bionic database');
plt.xlabel('x');
plt.ylabel('y');
plt.axis('equal')

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