In the ever-evolving landscape of natural language processing and information retrieval, Retrieval Augmented Generation (RAG) has emerged as a game-changing paradigm.

At the heart of cutting-edge RAG systems lies a powerful duo: pgvector, PostgreSQL’s vector similarity search extension, and Django, the high-level Python web framework. When combined with sophisticated ReRanking techniques, these technologies form the backbone of state-of-the-art information retrieval systems.

This guide will explore the topic of implementing and optimizing ReRanking within RAG systems, with a particular focus on leveraging pgvector and Django. We’ll explore how these tools can be harnessed to create scalable, efficient, and highly accurate retrieval systems that push the boundaries of what’s possible in natural language processing.

Understanding the RAG ecosystem

Before we explore ReRanking with pgvector and Django, let’s take a moment to understand the RAG ecosystem and why these particular technologies are so crucial.

RAG: The foundation of modern NLP

Retrieval Augmented Generation combines the power of large language models with external knowledge bases. This approach allows for more accurate, up-to-date, and controllable text generation. The typical RAG process involves:

  1. Embedding documents in a vector space
  2. Retrieving relevant documents based on a query
  3. Feeding the retrieved documents and query to a language model for generation

What is RAG?

pgvector: PostgreSQL’s vector powerhouse

pgvector github

pgvector is an extension for PostgreSQL that adds support for vector similarity search. It allows for efficient storage and retrieval of high-dimensional vectors, making it an ideal choice for RAG systems. Key features include:

  • Support for L2 distance, inner product, and cosine similarity
  • Indexing for fast similarity search
  • Integration with PostgreSQL’s rich feature set

Django: The web framework of choice

Django is a high-level Python web framework that encourages rapid development and clean, pragmatic design. Its ORM (Object-Relational Mapping) system makes it easy to work with databases, including PostgreSQL with pgvector. Django’s key advantages for RAG systems include:

  • Seamless database integration
  • Robust security features
  • Scalability and performance optimizations

💡
Django is a great framework to build AI applications. Check this article if you are interested to learn more.

The ReRanking revolution

While the basic RAG setup with pgvector and Django can yield impressive results, the introduction of ReRanking takes performance to new heights. ReRanking is a sophisticated technique used to improve the relevance and quality of retrieved documents before they are used for generation. This additional step in the retrieval process can significantly enhance the overall performance of RAG systems, leading to more accurate and contextually appropriate responses.

ReRankers

Understanding ReRankers

At its core, a ReRanker is a model or algorithm designed to refine and reorder a list of retrieved documents based on their relevance to a given query. Unlike the initial retrieval step, which often relies on efficient but somewhat crude similarity measures, ReRankers can employ more complex and nuanced approaches to assess document relevance.

Key characteristics of ReRankers include:

  1. Contextual understanding: ReRankers often have a deeper understanding of the semantic relationship between the query and the documents. They can capture nuances that might be missed by simpler vector similarity searches.
  2. Multi-faceted scoring: While initial retrieval might rely on a single similarity score, ReRankers can consider multiple factors when assessing relevance. This could include semantic similarity, factual correctness, document freshness, and more.
  3. Query-document interaction: Many advanced ReRankers, especially those based on transformer architectures, can model the interaction between the query and the document directly, rather than treating them as independent entities.
  4. Adaptability: ReRankers can often be fine-tuned or adapted to specific domains or tasks, allowing for more specialized and accurate ranking in various contexts.

Types of ReRankers

There are several types of ReRankers, each with its own strengths and use cases:

  1. Cross-encoder models: These are typically based on transformer architectures and process the query and document together, allowing for rich interaction modeling. They are highly accurate but can be computationally expensive.
  2. Bi-encoder models: These models encode the query and documents separately, allowing for faster inference times at the cost of some accuracy. They’re often used in two-stage ranking systems.
  3. Learning to Rank (LTR) models: These models use machine learning techniques to combine multiple relevance signals and learn an optimal ranking function.
  4. Rule-based ReRankers: While less common in modern systems, these use predefined rules or heuristics to reorder documents. They can be useful in specific domains where expert knowledge can be directly encoded.

The ReRanking process

reranking process

The typical ReRanking process in a RAG system follows these steps:

  1. Initial retrieval: Using pgvector, a set of potentially relevant documents is retrieved based on vector similarity to the query.
  2. Candidate selection: From this initial set, a subset of top candidates is selected for ReRanking. This step is crucial for balancing accuracy and computational efficiency.
  3. Feature extraction: For each query-document pair, relevant features are extracted. These could be dense vectors, sparse features, or a combination of both.
  4. Scoring: The ReRanker assigns a relevance score to each query-document pair. This could involve passing the pair through a neural network, applying a learned ranking function, or using other scoring mechanisms.
  5. Reordering: Based on the new relevance scores, the documents are reordered.
  6. Final selection: The top N reranked documents are selected to be passed to the language model for generation.

Benefits of ReRanking in RAG systems

Incorporating ReRanking into RAG systems offers several key benefits:

  1. Improved relevance: By applying more sophisticated relevance assessment, ReRanking helps ensure that the most pertinent documents are used for generation, leading to more accurate and on-topic responses.
  2. Reduced noise: ReRanking can help filter out irrelevant or low-quality documents that might have been retrieved in the initial search, reducing noise in the input to the language model.
  3. Handling of edge cases: ReRankers can be designed to handle specific edge cases or nuances that might be missed by simpler retrieval methods.
  4. Adaptability to different query types: Different types of queries might require different ranking strategies. ReRankers can be designed to adapt their behavior based on query characteristics.
  5. Integration of multiple signals: ReRankers can incorporate various signals beyond just text similarity, such as document freshness, user preferences, or external knowledge bases.
  6. Improved efficiency: While ReRanking itself adds a computational step, it allows for more efficient use of the language model by providing it with higher-quality input.

Challenges in implementing ReRanking

Despite its benefits, implementing ReRanking in RAG systems also comes with challenges:

  1. Computational overhead: ReRanking, especially with complex models, can add significant computational cost to the retrieval process.
  2. Latency concerns: In real-time applications, the additional time required for ReRanking needs to be carefully managed to maintain acceptable response times.
  3. Training data requirements: Many effective ReRankers require large amounts of labeled training data, which can be expensive and time-consuming to create.
  4. Balancing accuracy and efficiency: There’s often a trade-off between the accuracy of ReRanking and its computational efficiency. Finding the right balance is crucial for practical applications.
  5. Integration complexity: Incorporating ReRanking into existing RAG pipelines can add complexity to the system architecture and workflow.

ReRanking with pgvector and Django

When implementing ReRanking in a system built with pgvector and Django, we can leverage the strengths of both technologies:

  1. pgvector for initial retrieval: Use pgvector’s efficient vector similarity search to quickly retrieve an initial set of candidate documents.
  2. Django for data management: Utilize Django’s ORM for efficient management of document data and metadata.
  3. Python-based ReRankers: Implement ReRanking models using Python libraries like PyTorch or TensorFlow, which integrate well with Django.
  4. Asynchronous processing: Use Django’s asynchronous capabilities or Celery for handling computationally intensive ReRanking tasks without blocking the main application thread.
  5. Caching strategies: Implement caching of ReRanking results using Django’s caching framework to improve performance for repeated queries.

By combining the vector search capabilities of pgvector, the web development strengths of Django, and sophisticated ReRanking techniques, we can create RAG systems that are not only fast and scalable but also highly accurate and contextually aware. This combination pushes the boundaries of what’s possible in information retrieval and natural language processing, opening up new possibilities for building intelligent, responsive, and user-centric applications.

Django book

Setting up the environment

To implement ReRanking with pgvector and Django, we first need to set up our environment. Here’s a step-by-step guide:

  1. Install PostgreSQL and pgvector

First, ensure you have PostgreSQL installed. Then, install pgvector:


sudo apt-get install postgresql-server-dev-all
git clone https://github.com/pgvector/pgvector.git
cd pgvector
make
sudo make install
  1. Create a new Django project and app

django-admin startproject rag_project
cd rag_project
python manage.py startapp retrieval
  1. Install necessary Python packages

pip install django psycopg2-binary numpy scikit-learn sentence-transformers
  1. Configure Django settings

In settings.py, add the following database configuration:


DATABASES = {
    'default': {
        'ENGINE': 'django.db.backends.postgresql',
        'NAME': 'rag_db',
        'USER': 'your_username',
        'PASSWORD': 'your_password',
        'HOST': 'localhost',
        'PORT': '5432',
    }
}
  1. Create the database and enable pgvector

CREATE DATABASE rag_db;
\c rag_db
CREATE EXTENSION vector;

Implementing the core RAG system

Now that our environment is set up, let’s implement the core RAG system using pgvector and Django.

  1. Define the document model

In retrieval/models.py:


from django.db import models
from django.contrib.postgres.fields import ArrayField

class Document(models.Model):
    content = models.TextField()
    embedding = ArrayField(models.FloatField(), size=768)  # Adjust size based on your embedding model

    class Meta:
        indexes = [
            models.Index(fields=['embedding'], name='embedding_idx', opclasses=['vector_cosine_ops'])
        ]
  1. Create and apply migrations

python manage.py makemigrations
python manage.py migrate
  1. Implement document embedding

Create a new file retrieval/embeddings.py:


from sentence_transformers import SentenceTransformer
import numpy as np

model = SentenceTransformer('all-MiniLM-L6-v2')

def embed_text(text):
    return model.encode(text).tolist()
  1. Implement document insertion

In retrieval/views.py:


from django.http import JsonResponse
from .models import Document
from .embeddings import embed_text

def insert_document(request):
    content = request.POST.get('content')
    embedding = embed_text(content)
    doc = Document.objects.create(content=content, embedding=embedding)
    return JsonResponse({'id': doc.id, 'content': doc.content})
  1. Implement similarity search

Add to retrieval/views.py:


from django.db.models.expressions import RawSQL

def similarity_search(request):
    query = request.GET.get('query')
    query_embedding = embed_text(query)
    
    similar_docs = Document.objects.annotate(
        similarity=RawSQL(
            "embedding <=> %s",
            (query_embedding,)
        )
    ).order_by('similarity')[:10]
    
    results = [{'id': doc.id, 'content': doc.content, 'similarity': doc.similarity} for doc in similar_docs]
    return JsonResponse({'results': results})

This basic implementation allows us to insert documents into our pgvector-enabled PostgreSQL database and perform similarity searches. However, to truly leverage the power of ReRanking, we need to go a step further.

Implementing ReRanking with pgvector and Django

Now that we have our basic RAG system in place, let’s implement ReRanking to improve the quality of our retrieved documents.

  1. Create a ReRanking model

We’ll use a simple cross-encoder model for ReRanking. Add the following to retrieval/embeddings.py:


from sentence_transformers import CrossEncoder

reranker = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')

def rerank_documents(query, documents):
    pairs = [[query, doc['content']] for doc in documents]
    scores = reranker.predict(pairs)
    reranked = sorted(zip(documents, scores), key=lambda x: x[1], reverse=True)
    return [doc for doc, score in reranked]
  1. Modify the similarity search view

from .embeddings import embed_text, rerank_documents

def similarity_search(request):
    query = request.GET.get('query')
    query_embedding = embed_text(query)
    
    similar_docs = Document.objects.annotate(
        similarity=RawSQL(
            "embedding <=> %s",
            (query_embedding,)
        )
    ).order_by('similarity')[:100]  # Retrieve more documents for reranking
    
    doc_list = [{'id': doc.id, 'content': doc.content, 'similarity': doc.similarity} for doc in similar_docs]
    
    reranked_docs = rerank_documents(query, doc_list)
    
    return JsonResponse({'results': reranked_docs[:10]})  # Return top 10 after reranking

This implementation now performs an initial similarity search using pgvector, retrieves a larger set of potentially relevant documents, and then applies ReRanking to refine the results.

Optimizing ReRanking performance

While the above implementation is functional, there are several optimizations we can make to improve performance and scalability:

  1. Batch processing

When dealing with a large number of documents, we can use batch processing to improve efficiency:


from django.db import connection

def batch_similarity_search(query, batch_size=1000):
    query_embedding = embed_text(query)
    
    with connection.cursor() as cursor:
        cursor.execute("""
            SELECT id, content, embedding <=> %s AS similarity
            FROM retrieval_document
            ORDER BY similarity
            LIMIT 100
        """, [query_embedding])
        
        results = []
        while True:
            batch = cursor.fetchmany(batch_size)
            if not batch:
                break
            results.extend(batch)
    
    return [{'id': r[0], 'content': r[1], 'similarity': r[2]} for r in results]
  1. Caching

Implement caching to store ReRanking results for frequent queries:


from django.core.cache import cache

def cached_rerank(query, documents, cache_timeout=3600):
    cache_key = f"rerank_{hash(query)}_{hash(tuple(d['id'] for d in documents))}"
    cached_result = cache.get(cache_key)
    if cached_result is not None:
        return cached_result
    
    reranked = rerank_documents(query, documents)
    cache.set(cache_key, reranked, cache_timeout)
    return reranked
  1. Asynchronous processing

For long-running ReRanking tasks, consider using asynchronous processing:


from asgiref.sync import async_to_sync
from channels.layers import get_channel_layer

@async_to_sync
async def async_rerank(query, documents):
    reranked = rerank_documents(query, documents)
    channel_layer = get_channel_layer()
    await channel_layer.group_send(
        "search_results",
        {
            "type": "search.results",
            "results": reranked,
        },
    )
  1. Distributed ReRanking

For very large datasets, consider implementing distributed ReRanking:


from celery import group
from .tasks import rerank_subset

def distributed_rerank(query, documents, num_workers=4):
    chunk_size = len(documents) // num_workers
    chunks = [documents[i:i + chunk_size] for i in range(0, len(documents), chunk_size)]
    
    job = group(rerank_subset.s(query, chunk) for chunk in chunks)
    result = job.apply_async()
    
    reranked_chunks = result.get()
    return sorted(sum(reranked_chunks, []), key=lambda x: x['score'], reverse=True)

Advanced ReRanking techniques

Now that we have a solid foundation for ReRanking with pgvector and Django, let’s explore some advanced techniques to further enhance our system.

  1. Hybrid ReRanking

Combine multiple ReRanking models for improved performance:


from sentence_transformers import CrossEncoder

reranker1 = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
reranker2 = CrossEncoder('cross-encoder/mmarco-mMiniLMv2-L12-H384-v1')

def hybrid_rerank(query, documents):
    pairs = [[query, doc['content']] for doc in documents]
    scores1 = reranker1.predict(pairs)
    scores2 = reranker2.predict(pairs)
    
    combined_scores = [0.7 * s1 + 0.3 * s2 for s1, s2 in zip(scores1, scores2)]
    reranked = sorted(zip(documents, combined_scores), key=lambda x: x[1], reverse=True)
    return [doc for doc, score in reranked]
  1. Context-aware ReRanking

Incorporate user context or session information into the ReRanking process:


def context_aware_rerank(query, documents, user_context):
    user_interests = user_context.get('interests', [])
    
    def score_with_context(doc):
        base_score = reranker.predict([[query, doc['content']]])[0]
        context_score = sum(interest in doc['content'].lower() for interest in user_interests)
        return base_score + 0.1 * context_score
    
    return sorted(documents, key=score_with_context, reverse=True)
  1. Diversity-aware ReRanking

Implement a diversity-aware ReRanking strategy to ensure a varied set of results:


from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np

def diversity_aware_rerank(query, documents, lambda_param=0.5):
    tfidf = TfidfVectorizer().fit_transform([doc['content'] for doc in documents])
    diversity_matrix = 1 - cosine_similarity(tfidf)
    
    reranked = []
    remaining = list(range(len(documents)))
    
    while remaining:
        scores = []
        for i in remaining:
            relevance = reranker.predict([[query, documents[i]['content']]])[0]
            if reranked:
                diversity = np.mean([diversity_matrix[i][j] for j in reranked])
            else:
                diversity = 1
            score = lambda_param * relevance + (1 - lambda_param) * diversity
            scores.append(score)
        
        best_idx = remaining[np.argmax(scores)]
        reranked.append(best_idx)
        remaining.remove(best_idx)
    
    return [documents[i] for i in reranked]
  1. Adaptive ReRanking

Implement an adaptive ReRanking strategy that adjusts based on query characteristics:


def adaptive_rerank(query, documents):
    query_length = len(query.split())
    
    if query_length <= 3:
        return rerank_documents(query, documents)  # Use basic reranking for short queries
    elif query_length <= 6:
        return hybrid_rerank(query, documents)  # Use hybrid reranking for medium queries
    else:
        return diversity_aware_rerank(query, documents)  # Use diversity-aware reranking for long queries

Integrating ReRanking with Django REST framework

To make our ReRanking system more accessible and easier to integrate with front-end applications, let’s create a REST API using Django REST Framework.

  1. Install Django REST Framework

pip install djangorestframework
  1. Add REST Framework to INSTALLED_APPS in settings.py

 


INSTALLED_APPS = [
    # ...
    'rest_framework',
    # ...
]
  1. Create serializers

In retrieval/serializers.py:


from rest_framework import serializers
from .models import Document

class DocumentSerializer(serializers.ModelSerializer):
    class Meta:
        model = Document
        fields = ['id', 'content']

class SearchResultSerializer(serializers.Serializer):
    id = serializers.IntegerField()
    content = serializers.CharField()
    similarity = serializers.FloatField()
  1. Create API views

In retrieval/views.py:


from django.db.models.expressions import RawSQL
from rest_framework.response import Response
from rest_framework.views import APIView

from .embeddings import embed_text, rerank_documents
from .serializers import DocumentSerializer, SearchResultSerializer


class DocumentView(APIView):
    def post(self, request):
        serializer = DocumentSerializer(data=request.data)
        if serializer.is_valid():
            content = serializer.validated_data['content']
            embedding = embed_text(content)
            doc = Document.objects.create(content=content, embedding=embedding)
            return Response(DocumentSerializer(doc).data, status=201)
        return Response(serializer.errors, status=400)


class SearchView(APIView):
    def get(self, request):
        query = request.query_params.get('query')
        if not query:
            return Response({'error': 'Query parameter is required'}, status=400)
        query_embedding = embed_text(query)

        similar_docs = Document.objects.annotate(
            similarity=RawSQL(
                "embedding <=> %s",
                (query_embedding,)
            )
        ).order_by('similarity')[:100]
    
        doc_list = [{'id': doc.id, 'content': doc.content, 'similarity': doc.similarity} for doc in similar_docs]
    
        reranked_docs = rerank_documents(query, doc_list)
    
        serializer = SearchResultSerializer(reranked_docs[:10], many=True)
        return Response(serializer.data)

5. Configure URLs

In rag_project/urls.py:


from django.urls import path, include
from retrieval.views import DocumentView, SearchView

urlpatterns = [
    path('api/documents/', DocumentView.as_view(), name='document'),
    path('api/search/', SearchView.as_view(), name='search'),
]

Now we have a RESTful API for our ReRanking system, making it easy to integrate with various front-end applications or other services.

Scaling ReRanking with pgvector and Django

As your RAG system grows, you’ll need to consider scaling strategies to maintain performance. Here are some advanced techniques for scaling ReRanking with pgvector and Django:

  1. Database partitioning

For large datasets, consider partitioning your pgvector-enabled table:


CREATE TABLE documents_partition (
    id BIGINT NOT NULL,
    content TEXT NOT NULL,
    embedding VECTOR(768) NOT NULL
) PARTITION BY RANGE (id);

CREATE TABLE documents_p1 PARTITION OF documents_partition
    FOR VALUES FROM (1) TO (1000000);

CREATE TABLE documents_p2 PARTITION OF documents_partition
    FOR VALUES FROM (1000000) TO (2000000);

-- Create more partitions as needed

Update your Django model to use the partitioned table:


class Document(models.Model):
    content = models.TextField()
    embedding = ArrayField(models.FloatField(), size=768)

    class Meta:
        managed = False
        db_table = 'documents_partition'
  1. Implementing a distributed pgvector setup

For even larger datasets, consider implementing a distributed pgvector setup using PostgreSQL’s built-in replication features:


DATABASES = {
    'default': {
        'ENGINE': 'django.db.backends.postgresql',
        'NAME': 'rag_db',
        'USER': 'your_username',
        'PASSWORD': 'your_password',
        'HOST': 'primary.example.com',
        'PORT': '5432',
    },
    'replica1': {
        'ENGINE': 'django.db.backends.postgresql',
        'NAME': 'rag_db',
        'USER': 'your_username',
        'PASSWORD': 'your_password',
        'HOST': 'replica1.example.com',
        'PORT': '5432',
    },
    'replica2': {
        'ENGINE': 'django.db.backends.postgresql',
        'NAME': 'rag_db',
        'USER': 'your_username',
        'PASSWORD': 'your_password',
        'HOST': 'replica2.example.com',
        'PORT': '5432',
    },
}

Implement a custom database router to distribute read operations:

class ReplicaRouter:
    def db_for_read(self, model, **hints):
        import random
        return random.choice(['replica1', 'replica2'])

    def db_for_write(self, model, **hints):
        return 'default'

    def allow_relation(self, obj1, obj2, **hints):
        return True

    def allow_migrate(self, db, app_label, model_name=None, **hints):
        return db == 'default'

Add the router to your Django settings:


DATABASE_ROUTERS = ['path.to.ReplicaRouter']
  1. Implementing caching layers

Implement multiple caching layers to reduce database load:


CACHES = {
    'default': {
        'BACKEND': 'django.core.cache.backends.memcached.MemcachedCache',
        'LOCATION': '127.0.0.1:11211',
    },
    'redis': {
        'BACKEND': 'django_redis.cache.RedisCache',
        'LOCATION': 'redis://127.0.0.1:6379/1',
        'OPTIONS': {
            'CLIENT_CLASS': 'django_redis.client.DefaultClient',
        }
    }
}

Implement a hierarchical caching strategy:


from django.core.cache import caches

def get_cached_search_results(query):
    # Try to get from Memcached first
    result = caches['default'].get(query)
    if result is not None:
        return result
    
    # If not in Memcached, try Redis
    result = caches['redis'].get(query)
    if result is not None:
        # Store in Memcached for faster future access
        caches['default'].set(query, result, timeout=300)
        return result
    
    # If not in Redis, perform the search
    result = perform_search_and_rerank(query)
    
    # Store in both Redis and Memcached
    caches['redis'].set(query, result, timeout=3600)
    caches['default'].set(query, result, timeout=300)
    
    return result
  1. Asynchronous processing with Celery

Implement asynchronous processing for ReRanking tasks using Celery:


from celery import shared_task
from .embeddings import rerank_documents

@shared_task
def async_rerank(query, documents):
    return rerank_documents(query, documents)

# In your view
from django.http import JsonResponse
from .tasks import async_rerank

def search_view(request):
    query = request.GET.get('query')
    initial_results = perform_initial_search(query)
    
    task = async_rerank.delay(query, initial_results)
    
    return JsonResponse({
        'task_id': task.id,
        'initial_results': initial_results[:10]
    })

def get_reranked_results(request):
    task_id = request.GET.get('task_id')
    task = AsyncResult(task_id)
    
    if task.ready():
        return JsonResponse({'results': task.get()})
    else:
        return JsonResponse({'status': 'pending'})
  1. Implementing a feedback loop

Implement a feedback loop to continuously improve your ReRanking model:


from django.db import models

class UserFeedback(models.Model):
    query = models.TextField()
    document = models.ForeignKey(Document, on_delete=models.CASCADE)
    relevance_score = models.FloatField()
    timestamp = models.DateTimeField(auto_now_add=True)

def collect_feedback(request):
    query = request.POST.get('query')
    doc_id = request.POST.get('document_id')
    relevance_score = float(request.POST.get('relevance_score'))
    
    UserFeedback.objects.create(
        query=query,
        document_id=doc_id,
        relevance_score=relevance_score
    )
    
    return JsonResponse({'status': 'success'})

# Periodically retrain your ReRanking model using collected feedback
@shared_task
def retrain_reranker():
    feedback_data = UserFeedback.objects.all().values('query', 'document__content', 'relevance_score')
    # Use feedback_data to fine-tune your ReRanking model
    # This could involve updating the weights of your cross-encoder model
    pass

Advanced ReRanking techniques with pgvector

Let’s explore some advanced ReRanking techniques that leverage the unique capabilities of pgvector:

  1. Hybrid vector search

Combine multiple embedding models for more robust search results:


from django.db.models import F

def hybrid_vector_search(query, model1, model2, weight1=0.7, weight2=0.3):
    embedding1 = model1.encode(query)
    embedding2 = model2.encode(query)
    
    results = Document.objects.annotate(
        score1=RawSQL("embedding <=> %s", (embedding1,)),
        score2=RawSQL("embedding2 <=> %s", (embedding2,)),
        combined_score=F('score1') * weight1 + F('score2') * weight2
    ).order_by('combined_score')[:100]
    
    return results
  1. Contextual ReRanking with pgvector

Implement contextual ReRanking by considering the user’s recent search history:


def contextual_rerank(query, user_history):
    query_embedding = embed_text(query)
    history_embedding = np.mean([embed_text(q) for q in user_history], axis=0)
    
    results = Document.objects.annotate(
        query_similarity=RawSQL("embedding <=> %s", (query_embedding,)),
        history_similarity=RawSQL("embedding <=> %s", (history_embedding,)),
        combined_score=F('query_similarity') * 0.8 + F('history_similarity') * 0.2
    ).order_by('combined_score')[:100]
    
    return results
  1. Semantic clustering with pgvector

Implement semantic clustering to group similar documents and diversify search results:


from sklearn.cluster import KMeans

def semantic_cluster_rerank(query, n_clusters=5):
    query_embedding = embed_text(query)
    
    initial_results = Document.objects.annotate(
        similarity=RawSQL("embedding <=> %s", (query_embedding,))
    ).order_by('similarity')[:1000]
    
    embeddings = np.array([doc.embedding for doc in initial_results])
    
    kmeans = KMeans(n_clusters=n_clusters)
    clusters = kmeans.fit_predict(embeddings)
    
    reranked_results = []
    for cluster in range(n_clusters):
        cluster_docs = [doc for doc, c in zip(initial_results, clusters) if c == cluster]
        reranked_results.extend(cluster_docs[:20 // n_clusters])
    
    return reranked_results
  1. Time-aware ReRanking

Implement time-aware ReRanking to balance relevance with recency:


from django.db.models import ExpressionWrapper, F, fields
from django.utils import timezone

def time_aware_rerank(query, time_weight=0.2):
    query_embedding = embed_text(query)
    now = timezone.now()
    
    results = Document.objects.annotate(
        similarity=RawSQL("embedding <=> %s", (query_embedding,)),
        age=ExpressionWrapper(now - F('created_at'), output_field=fields.DurationField()),
        time_score=ExpressionWrapper(F('age').total_seconds() / (24 * 60 * 60), output_field=fields.FloatField()),
        combined_score=F('similarity') * (1 - time_weight) + F('time_score') * time_weight
    ).order_by('combined_score')[:100]
    
    return results
  1. Personalized ReRanking with user embeddings

Implement personalized ReRanking by maintaining user embeddings:


class UserProfile(models.Model):
    user = models.OneToOneField(User, on_delete=models.CASCADE)
    embedding = ArrayField(models.FloatField(), size=768)

def update_user_embedding(user, query):
    query_embedding = embed_text(query)
    user_profile, created = UserProfile.objects.get_or_create(user=user)
    
    if created:
        user_profile.embedding = query_embedding
    else:
        user_profile.embedding = [
            0.9 * ue + 0.1 * qe 
            for ue, qe in zip(user_profile.embedding, query_embedding)
        ]
    
    user_profile.save()

def personalized_rerank(query, user):
    query_embedding = embed_text(query)
    user_embedding = UserProfile.objects.get(user=user).embedding
    
    results = Document.objects.annotate(
        query_similarity=RawSQL("embedding <=> %s", (query_embedding,)),
        user_similarity=RawSQL("embedding <=> %s", (user_embedding,)),
        combined_score=F('query_similarity') * 0.7 + F('user_similarity') * 0.3
    ).order_by('combined_score')[:100]
    
    return results

Optimizing pgvector performance

To ensure optimal performance when working with pgvector at scale, consider the following optimizations:

  1. Indexing strategies

Experiment with different indexing strategies to find the best balance between query speed and index build time:


-- Create an HNSW index
CREATE INDEX ON documents USING hnsw (embedding vector_cosine_ops);

-- Create an IVFFlat index
CREATE INDEX ON documents USING ivfflat (embedding vector_cosine_ops) WITH (lists = 100);
  1. Approximate Nearest Neighbor Search

Use approximate nearest neighbor search for faster query times at the cost of some accuracy:


from django.db.models import Func

class ApproximateDistance(Func):
    function = 'vector_cosine_ops'
    template = "%(function)s(%(expressions)s) <?> %%s"

def approximate_search(query):
    query_embedding = embed_text(query)
    
    results = Document.objects.annotate(
        distance=ApproximateDistance('embedding', query_embedding)
    ).order_by('distance')[:100]
    
    return results
  1. Batch processing

Implement batch processing for inserting and updating large numbers of documents:


from django.db import connection

def batch_insert_documents(documents, batch_size=1000):
    with connection.cursor() as cursor:
        values = []
        for doc in documents:
            embedding = embed_text(doc['content'])
            values.append((doc['content'], embedding))
            
            if len(values) >= batch_size:
                cursor.executemany(
                    "INSERT INTO retrieval_document (content, embedding) VALUES (%s, %s)",
                    values
                )
                values = []
        
        if values:
            cursor.executemany(
                "INSERT INTO retrieval_document (content, embedding) VALUES (%s, %s)",
                values
            )
  1. Asynchronous updates

Implement asynchronous updates to keep your vector index fresh without impacting query performance:


from celery import shared_task

@shared_task
def update_document_embedding(doc_id):
    doc = Document.objects.get(id=doc_id)
    new_embedding = embed_text(doc.content)
    doc.embedding = new_embedding
    doc.save()

def update_document(request):
    doc_id = request.POST.get('doc_id')
    new_content = request.POST.get('content')
    
    doc = Document.objects.get(id=doc_id)
    doc.content = new_content
    doc.save()
    
    update_document_embedding.delay(doc_id)
    
    return JsonResponse({'status': 'success'})

Conclusion

ReRanking with pgvector and Django offers a powerful and flexible approach to building advanced RAG systems. By leveraging the vector similarity capabilities of pgvector and the robust web development framework provided by Django, we can create scalable, efficient, and highly accurate information retrieval systems.

Throughout this guide, we’ve explored various techniques for implementing and optimizing ReRanking, from basic setups to advanced strategies like hybrid ReRanking, contextual awareness, and personalization. We’ve also explored scaling considerations, performance optimizations, and integration with other technologies like Django REST Framework and Celery.

Last Update: 13/10/2024