How to implement contextual retrieval for AI applications
Contextual retrieval improves the accuracy and relevancy of AI results. Learn how to implement contextual retrieval, overcome implementation challenges, and evaluate success.
Mar 6, 2025 • 7 Minute Read
If you want to deliver accurate and context-aware results in AI applications like chatbots or search engines, you’re going to need more than traditional retrieval methods.
Contextual retrieval solves this by embedding metadata into data chunks, enhancing relevance during similarity searches. This tutorial explains how developers can use tools like LangChain and vector databases to implement contextual retrieval and tackle challenges like ambiguous queries and metadata optimization.
Explore what leaders need to know about contextual retrieval.
Introducing contextual retrieval
Contextual retrieval is a way to enhance retrieval-augmented generation (RAG) workflows by embedding metadata—such as section titles, timestamps, and summaries—into chunks during preprocessing.
Unlike traditional systems, where chunks are treated as standalone entities, contextual retrieval enriches these with context like section titles, timestamps, and summaries. This added context creates embeddings that more accurately capture the data’s meaning and relevance, improving semantic similarity results.
For example, consider a chatbot tasked with answering, “What is the refund policy?” A traditional retrieval system might pull isolated sentences that mention “refund” but lack actionable insights. Contextual retrieval preprocesses the data to include headings, summaries, and related context (e.g., “Refund Policy for Online Orders”), ensuring that the chatbot retrieves precise answers.
Key points:
- Enhanced semantic search: Contextual retrieval embeds metadata into chunks for better semantic search
- Aligned embeddings: Enriched data creates embeddings that are better aligned with query intent, enhancing precision in RAG workflows.
- Versatile applications: It’s ideal for developers building chatbots, semantic search tools, and enterprise systems.
Why use contextual retrieval?
Contextual retrieval provides significant advantages over traditional retrieval systems, especially for applications that require nuanced and accurate information retrieval. By embedding additional context during preprocessing, contextual retrieval solves common challenges such as irrelevant matches, poor handling of ambiguous queries, and the inability to scale semantic understanding across large datasets.
For developers, contextual retrieval offers a robust foundation for building intelligent systems. It improves the quality of retrieved results, reducing the noise and inaccuracies that often plague keyword-based methods.
This leads to smoother integration into downstream tasks like Retrieval-Augmented Generation (RAG), where the quality of retrieved chunks directly impacts the relevance and coherence of the generated responses. Developers also benefit from open-source tools like Hugging Face and FAISS, which simplify the implementation process and reduce the barriers to entry for adopting advanced techniques.
The value of contextual retrieval is particularly evident in domains like healthcare, finance, and legal services, where precision is non-negotiable. A legal document retrieval system, for instance, could surface case-specific precedents by considering the context of the legal query, saving hours of manual research. Similarly, in healthcare, contextual retrieval ensures that medical professionals can access relevant information tailored to a patient’s specific condition or history.
Key points:
- Enhanced precision: Contextual retrieval provides precise and relevant results by embedding context during preprocessing.
- Retrieval quality: It benefits developers with better retrieval quality and smoother integration into RAG.
- High precision: It’s critical for domains requiring high precision, like healthcare, finance, and legal.
Challenges of implementing contextual retrieval
While contextual retrieval offers major benefits, implementing it can be a challenge. Addressing these hurdles requires careful planning, the right tools, and a clear understanding of the technical and organizational requirements.
1. Scalability
One of the most significant challenges is scalability. Dense embeddings generated during preprocessing consume far more storage and computational resources than traditional keyword-based methods. For organizations managing large datasets with millions of chunks, this can lead to high storage costs and increased query latency. Moreover, ensuring that the system scales efficiently as the dataset grows is critical for maintaining performance.
Solution: Vector databases like FAISS and Pinecone are designed to handle large-scale semantic embeddings. By optimizing how embeddings are stored and queried (e.g., clustering or indexing), these tools ensure retrieval remains efficient even at scale.
2. Complexity of preprocessing
The preprocessing phase, while crucial for contextual retrieval, adds a layer of complexity to the pipeline. Deciding how much context to include in each chunk is a delicate balance. Overloading chunks with excessive metadata or unrelated context can make embeddings noisy and reduce retrieval precision. Conversely, insufficient context might fail to capture the nuances necessary for accurate matching.
Solution: Start with simple context enrichment strategies, such as adding section titles or document metadata, and iteratively refine the preprocessing pipeline based on retrieval performance metrics.
3. Computational costs
Encoding chunks with transformer models, especially for large datasets, can be computationally expensive. Additionally, fine-tuning embeddings for domain-specific use cases may require significant resources.
Solution: Pre-trained models from Hugging Face provide robust embeddings that can often be used without fine-tuning. For domain-specific applications, selective fine-tuning on smaller datasets can reduce costs while maintaining performance.
4. Organizational buy-in
Convincing stakeholders to invest in contextual retrieval may be challenging, particularly if the current retrieval system is already functional. Without clear evidence of ROI, decision-makers may hesitate to adopt a new approach.
Solution: Conduct small-scale pilot projects to demonstrate the value of contextual retrieval. Use clear metrics, such as precision, recall, and user satisfaction scores, to showcase improvements over the existing system.
Key points:
- Scalability challenges arise from the computational demands of dense embeddings.
- Preprocessing requires balancing context enrichment to avoid noisy or incomplete embeddings.
- High computational costs can be mitigated with pre-trained models and selective fine-tuning.
- Pilot projects and clear metrics help secure organizational buy-in.
How to implement contextual retrieval
This code demonstrates a contextual retrieval pipeline with preprocessing, vector database indexing, retrieval, and response generation using a RAG setup. It uses BM25 for comparison and FAISS for dense vector search.
Step 1: Install required libraries
Start by installing the necessary libraries.
pip install transformers sentence-transformers faiss-cpu rank-bm25
Step 2: Load data and preprocess chunks
Preprocessing involves enriching chunks with additional metadata, such as section headers and document titles.
from sentence_transformers import SentenceTransformer
from rank_bm25 import BM25Okapi
import numpy as np
# Sample document with sections and context
document = {
"title": "Refund Policy",
"sections": [
{
"header": "Refund Policy for Online Purchases",
"text": "Refunds are processed within 10 business days after receiving the return."
},
{
"header": "Refund Policy for In-Store Purchases",
"text": "Refunds for in-store purchases must be processed at the original point of sale."
}
]
}
# Preprocess chunks with contextual enrichment
chunks = []
for section in document['sections']:
context_chunk = f"{section['text']} (Section: {section['header']}, Document: {document['title']})"
chunks.append(context_chunk)
# Display preprocessed chunks
print("Preprocessed Chunks:")
print(chunks)
Step 3: Implement BM25
Use BM25 for basic token-based retrieval to compare the results with contextual retrieval’s.
# Tokenize chunks for BM25
tokenized_chunks = [chunk.split() for chunk in chunks]
bm25 = BM25Okapi(tokenized_chunks)
# Query for BM25
query = "refund for online purchases"
tokenized_query = query.split()
bm25_results = bm25.get_top_n(tokenized_query, chunks, n=2)
# Display BM25 results
print("\nBM25 Retrieved Results:")
for result in bm25_results:
print(result)
Step 4: Encode chunks for contextual retrieval
Convert chunks and the query into vectors using a pre-trained model for contextual retrieval.
# Load embedding model for contextual retrieval
model = SentenceTransformer('all-MiniLM-L6-v2')
# Encode enriched chunks into dense embeddings
chunk_embeddings = model.encode(chunks)
# Query embedding
query_embedding = model.encode([query])
Step 5: Store and retrieve using FAISS
Use FAISS for efficient similarity search across dense embeddings.
import faiss
# Create FAISS index
dimension = chunk_embeddings.shape[1]
index = faiss.IndexFlatL2(dimension)
index.add(np.array(chunk_embeddings))
# Perform similarity search
k = 2 # Number of top results to retrieve
distances, indices = index.search(np.array(query_embedding), k)
# Retrieve results
retrieved_chunks = [chunks[i] for i in indices[0]]
print("\nFAISS Retrieved Results:")
for result in retrieved_chunks:
print(result)
Step 6: Combine with RAG for response generation
Use the chunks retrieved in Step 5 to generate responses with a LLM.
from transformers import pipeline
# Combine retrieved chunks
retrieved_context = " ".join(retrieved_chunks)
# Define query prompt
prompt = f"Use the following context to answer:\n{retrieved_context}\n\nQuestion: {query}\nAnswer:"
# Generate response using an LLM
generator = pipeline("text-generation", model="gpt-3.5-turbo")
response = generator(prompt, max_length=100, num_return_sequences=1)
print("\nRAG-Generated Response:")
print(response[0]['generated_text'])
Step 7: Evaluate results
The outputs will compare:
- BM25 results: Token-based, lacking semantic understanding
- FAISS contextual retrieval results: Richer semantic matches with context preprocessing
- RAG-generated response: Contextually accurate answers powered by LLMs
Example outputs
Compare these example results.
BM25 retrieves results based on token frequency but lacks semantic understanding. BM25 results:
Refunds are processed within 10 business days after receiving the return.
Refunds for in-store purchases must be processed at the original point of sale.
FAISS-based dense retrieval uses enriched embeddings to match query intent with chunk context. FAISS retrieved results:
Refunds are processed within 10 business days after receiving the return. (Section: Refund Policy for Online Purchases, Document: Refund Policy)
RAG generation synthesizes retrieved results into a coherent, user-friendly response using an LLM. RAG-generated response:
Answer: Refunds for online purchases are processed within 10 business days after receiving the return.
Metrics to evaluate contextual retrieval
Evaluating contextual retrieval involves tracking technical and user-centered metrics to ensure the system’s effectiveness. Precision measures the relevance of retrieved chunks, while recall assesses completeness. Combining these metrics using the F1 Score provides a balanced evaluation of the system’s accuracy. For engagement, developers should track CTR (click-through rates) and TTA (time-to-answer) to assess user interaction and system efficiency.
For example, if a system retrieves refund-related chunks for a query like “What’s the refund policy?” with 90% precision but only 70% recall, it may need better metadata optimization. Comparing contextual retrieval to baselines like BM25 quantifies the improvements, while A/B testing reveals the real-world impact on user satisfaction and engagement.
Key points:
- Use precision, recall, and F1 Score to evaluate retrieval relevance.
- Track CTR and TTA for insights into user engagement and system efficiency.
- Compare performance with baseline methods (e.g. BM25) and use A/B testing.
- Refine systems using feedback on retrieved chunk quality and relevance.
Conclusion: Contextual retrieval is a powerful tool for more accurate AI workflows
By incorporating contextual retrieval into AI workflows, developers can significantly improve the accuracy and utility of AI applications.
Tools like LangChain and Hugging Face make implementation accessible, while metadata optimization ensures systems are both precise and scalable.
Although challenges like latency and storage exist, this guide provides the foundation for creating advanced retrieval systems tailored to real-world needs.
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