11 — LlamaIndex Indexes and Retrieval¶
Understand how LlamaIndex organizes enterprise data for efficient retrieval, how different index strategies work, how retrievers operate, and how to design production-grade retrieval architectures for RAG applications.
📖 Overview¶
Indexing is one of the most important stages in a LlamaIndex-powered RAG system.
The ingestion pipeline prepares the data:
Indexing transforms those nodes into structures that can efficiently support retrieval:
A simplified LlamaIndex architecture is:
Enterprise Data
↓
Documents
↓
Nodes
↓
Index
↓
Retriever
↓
Relevant Nodes
↓
Query / Response Layer
↓
LLM
The choice of index and retrieval strategy directly influences:
🎯 Learning Objectives¶
After completing this chapter, you will be able to:
- Understand what an index represents in LlamaIndex
- Understand the relationship between nodes and indexes
- Understand VectorStoreIndex
- Understand SummaryIndex
- Understand Tree-based indexing concepts
- Understand keyword-oriented indexing concepts
- Understand property/graph-oriented indexing at a high level
- Understand retrievers
- Understand similarity-based retrieval
- Understand Top-K retrieval
- Understand metadata filtering
- Understand hybrid retrieval concepts
- Understand retrieval configuration
- Design retrieval pipelines for enterprise RAG
- Understand retrieval failure patterns
- Tune retrieval quality
- Design scalable indexing architectures
- Separate indexing from query execution
- Select an appropriate index and retrieval strategy
1. What Is an Index?¶
An index is a data structure that organizes information so that relevant content can be found efficiently.
In a RAG system:
Instead of searching every piece of content manually, the index provides a mechanism for finding relevant information.
2. Indexing vs Retrieval¶
These are different stages.
Indexing¶
Retrieval¶
Therefore:
3. Indexing Architecture¶
flowchart TB
A[Documents] --> B[Nodes]
B --> C[Metadata]
B --> D[Embedding]
D --> E[Index]
C --> E
F[User Query] --> G[Retriever]
G --> E
E --> H[Relevant Nodes]
H --> I[LLM]4. Why Indexing Matters¶
Imagine an enterprise repository containing:
A query such as:
cannot efficiently scan every node in the same way a small in-memory application might.
An appropriate index allows the system to narrow the search space.
5. Index Types¶
LlamaIndex supports multiple indexing approaches for different application requirements.
Common conceptual categories include:
The most important distinction is:
6. VectorStoreIndex¶
For modern RAG systems, VectorStoreIndex is one of the most important LlamaIndex index types.
Conceptually:
At query time:
7. Vector Index Architecture¶
flowchart LR
A[Nodes] --> B[Embedding Model]
B --> C[(Vector Store)]
D[User Query] --> E[Query Embedding]
E --> C
C --> F[Top-K Similar Nodes]
F --> G[Response Layer]8. Creating a VectorStoreIndex¶
A simple example:
from llama_index.core import (
SimpleDirectoryReader,
VectorStoreIndex
)
documents = SimpleDirectoryReader(
"data"
).load_data()
index = VectorStoreIndex.from_documents(
documents
)
Conceptually:
In production, the underlying vector store and embedding model should be explicitly selected according to application requirements.
9. Similarity Search¶
Suppose the query is:
The system converts the query into an embedding:
It then compares that representation with indexed node vectors.
Conceptually:
10. Similarity¶
A vector database may use a similarity or distance function such as:
The exact choice depends on the embedding model and vector-store configuration.
Conceptually:
11. Top-K Retrieval¶
A retriever commonly returns the top K candidates.
Example:
Conceptually:
12. Top-K Trade-off¶
Small K:
but:
Large K:
but:
Therefore:
13. Retrieval Precision vs Recall¶
Retrieval quality can be understood through:
Precision
=
How much retrieved content is relevant?
Recall
=
How much relevant content was retrieved?
Example:
Precision is affected by:
while recall is affected by:
versus:
14. Retrieval Trade-off¶
High Precision
↓
Less Noise
↓
Potentially Lower Recall
High Recall
↓
More Candidate Context
↓
Potentially More Noise
Production RAG systems often use multiple retrieval stages to balance the two.
15. Similarity Threshold¶
A retrieval system may also use a similarity threshold.
Conceptually:
For example:
A threshold could eliminate Node D.
The exact threshold must be calibrated against the embedding model and evaluation dataset.
16. Metadata Filtering¶
Vector similarity alone may not be sufficient.
Consider:
Retrieval becomes:
17. Metadata-Aware Retrieval¶
flowchart TD
A[User Query] --> B[Query]
C[Tenant Filter] --> B
D[Department Filter] --> B
E[Document Type Filter] --> B
B --> F[Retriever]
F --> G[(Vector Index)]
G --> H[Authorized Relevant Nodes]
H --> I[LLM]18. Why Filtering Matters¶
Without filtering:
With filtering:
This improves both:
19. Filtering Is Not Authorization¶
An important enterprise principle:
The application should establish:
Never allow the model to decide whether a user is authorized to access data.
20. Query-Time Filtering¶
Example concept:
The retriever can then restrict the search space.
The exact metadata-filter API depends on the selected LlamaIndex version and vector-store integration.
21. Multiple Index Strategies¶
Not every dataset is best represented as a pure vector index.
Consider:
Different retrieval problems may require different structures.
22. SummaryIndex¶
A summary-oriented index is useful when the application needs to reason over a collection of documents rather than simply retrieve the nearest chunks.
Conceptually:
This can be useful for:
23. Summary Index Architecture¶
flowchart TB
A[Documents] --> B[Nodes]
B --> C[Summary Structure]
D[User Query] --> C
C --> E[Relevant / Summarized Context]
E --> F[LLM]
F --> G[Response]24. Summary Retrieval Trade-off¶
A summary-oriented approach can provide:
but may require:
depending on the implementation and synthesis strategy.
It is therefore different from:
25. Tree-Based Indexing¶
A tree-based approach organizes information hierarchically.
Conceptually:
The hierarchy can help when information needs to be summarized progressively.
26. Tree Retrieval Architecture¶
flowchart TB
A[Root Summary]
A --> B[Branch A]
A --> C[Branch B]
B --> D[Node 1]
B --> E[Node 2]
C --> F[Node 3]
C --> G[Node 4]The exact implementation details should be selected based on the current LlamaIndex APIs and use case.
27. Keyword-Oriented Indexing¶
Keyword-oriented approaches are useful when exact terms matter.
Example:
This may be more useful than semantic similarity when exact terminology is important.
28. Keyword vs Semantic Retrieval¶
Semantic¶
may retrieve:
even if the exact words differ.
Keyword¶
can directly match:
Both approaches have strengths.
29. Keyword Retrieval Architecture¶
flowchart LR
A[Query] --> B[Keyword Extraction]
B --> C[Keyword Index]
C --> D[Matching Nodes]
D --> E[Response Layer]30. Hybrid Retrieval¶
Production RAG systems often combine:
Example:
User Query
│
├───────────────┐
▼ ▼
Keyword Search Vector Search
│ │
▼ ▼
Keyword Results Semantic Results
└───────┬───────┘
▼
Merge / Rank
▼
Final Context
31. Hybrid Retrieval Architecture¶
flowchart TD
A[User Query] --> B[Query Processing]
B --> C[Keyword Retriever]
B --> D[Vector Retriever]
C --> E[Keyword Results]
D --> F[Semantic Results]
E --> G[Result Fusion]
F --> G
G --> H[Ranked Results]
H --> I[LLM]Hybrid retrieval can improve recall when exact terminology and semantic meaning both matter.
32. Retrieval Fusion¶
Suppose:
A fusion strategy can produce:
based on a ranking mechanism.
Advanced ranking and re-ranking techniques will be covered in later retrieval chapters.
33. Property Graph / Graph-Oriented Indexing¶
Some enterprise knowledge is naturally relational.
Example:
A graph-oriented representation can preserve these relationships.
34. Graph Retrieval Architecture¶
flowchart TD
A[Enterprise Data] --> B[Entities]
B --> C[Relationships]
C --> D[Knowledge Graph]
E[User Query] --> F[Graph Retrieval]
F --> D
D --> G[Relevant Entities / Relationships]
G --> H[LLM]This is useful when the question depends on relationships rather than only text similarity.
35. Vector vs Graph Retrieval¶
Vector Retrieval¶
Best suited for:
Graph Retrieval¶
Useful for:
Many enterprise systems may use both.
36. Multi-Index Architecture¶
A sophisticated enterprise knowledge system may maintain:
Architecture:
flowchart TB
A[Enterprise Knowledge] --> B[Ingestion]
B --> C[Vector Index]
B --> D[Keyword Index]
B --> E[Graph Index]
F[User Query] --> G[Query Router]
G --> C
G --> D
G --> E
C --> H[Result Fusion]
D --> H
E --> H
H --> I[Context]
I --> J[LLM]37. Query Routing¶
Different questions may require different retrieval strategies.
Example:
→ Vector retrieval
→ Keyword + vector
→ Graph retrieval
38. Query Router¶
flowchart TD
A[User Query] --> B[Query Router]
B -->|Semantic Question| C[Vector Retriever]
B -->|Exact Term| D[Keyword Retriever]
B -->|Relationship Query| E[Graph Retriever]
C --> F[Results]
D --> F
E --> F
F --> G[Context Builder]
G --> H[LLM]39. Retriever¶
An index stores or organizes information.
A retriever determines:
Conceptually:
The retriever can encapsulate:
40. Basic Retriever¶
Example:
retriever = index.as_retriever(
similarity_top_k=5
)
nodes = retriever.retrieve(
"What is the security policy?"
)
for node in nodes:
print(node.text)
The exact retriever interface may vary with the index and LlamaIndex version.
41. Retriever Configuration¶
Important parameters may include:
Top-K
Similarity Threshold
Metadata Filters
Query Transformations
Vector Store Parameters
Post-Processing
The correct configuration should be determined through evaluation rather than guesswork.
42. Retrieval Pipeline¶
User Query
↓
Query Processing
↓
Retriever
↓
Candidate Retrieval
↓
Filtering
↓
Ranking
↓
Context Selection
↓
LLM
43. Candidate Retrieval¶
A common architecture is:
Example:
This can improve recall while controlling context size.
44. Retrieval Post-Processing¶
Retrieved nodes can be processed before being sent to the LLM.
Conceptually:
45. Deduplication¶
Multiple chunks may contain similar information.
Example:
Sending all of them can waste context.
A post-processing stage can remove redundant results.
46. Retrieval Compression¶
Suppose:
but only:
are relevant to the query.
A compression strategy can reduce the amount of context passed to the model.
Conceptually:
47. Retrieval and Context Engineering¶
Retrieval should not be treated as:
A production pipeline should optimize:
context.
48. Retrieval Quality Pipeline¶
flowchart LR
A[Query] --> B[Candidate Retrieval]
B --> C[Filtering]
C --> D[Deduplication]
D --> E[Ranking]
E --> F[Context Selection]
F --> G[LLM]49. Retrieval Evaluation¶
A retrieval system should be evaluated independently of final answer quality.
Useful metrics include:
For example:
asks whether relevant information appeared in the top five results.
50. Retrieval Evaluation Architecture¶
flowchart TB
A[Test Query] --> B[Retriever]
B --> C[Top-K Results]
C --> D[Ground Truth]
D --> E[Evaluation]
C --> E
E --> F[Precision]
E --> G[Recall]
E --> H[MRR]
E --> I[NDCG]51. Retrieval Dataset¶
A useful evaluation dataset contains:
Example:
Question:
"What is the password expiry period?"
Expected Source:
security-policy.pdf
Relevant Node:
SEC-001-CHUNK-07
52. Retrieval Tuning¶
Retrieval should be tuned using a representative evaluation dataset.
Parameters to evaluate include:
Chunk Size
Chunk Overlap
Top-K
Similarity Threshold
Metadata Filters
Embedding Model
Retrieval Strategy
53. Tuning Loop¶
flowchart LR
A[Evaluation Dataset] --> B[Retrieval Configuration]
B --> C[Run Retrieval]
C --> D[Measure Metrics]
D --> E{Good Enough?}
E -->|No| F[Tune Parameters]
F --> B
E -->|Yes| G[Production Candidate]54. Index Selection Matrix¶
| Requirement | Recommended Starting Point |
|---|---|
| Semantic document search | VectorStoreIndex |
| Similarity-based RAG | VectorStoreIndex |
| Broad document synthesis | Summary-oriented approach |
| Hierarchical summarization | Tree-oriented approach |
| Exact terminology | Keyword-oriented retrieval |
| Entity relationships | Graph-oriented approach |
| Complex enterprise knowledge | Multiple indexes |
| Mixed semantic + exact search | Hybrid retrieval |
These are starting points, not universal rules.
55. VectorStoreIndex in Production¶
For enterprise RAG, a common architecture is:
The vector store should be selected according to:
56. External Vector Stores¶
Production systems commonly integrate with external vector databases or vector-capable databases.
Conceptually:
This allows indexing infrastructure to remain separate from the application process.
57. Persistence¶
Development:
Production:
This enables:
58. Horizontal Scaling¶
A production query layer may have multiple instances:
Load Balancer
│
┌───────────┼───────────┐
▼ ▼ ▼
Query API 1 Query API 2 Query API 3
│ │ │
└───────────┼───────────┘
▼
Vector Store
The retrieval state should not depend on a single application instance.
59. Index Build vs Query Runtime¶
Index building is usually a separate lifecycle from querying.
Index Build¶
Query Runtime¶
This separation improves operational scalability.
60. Index Lifecycle¶
61. Index Versioning¶
Production retrieval systems should consider versioning.
Example:
A new index can be built independently:
rather than modifying the live index blindly.
62. Blue-Green Index Deployment¶
flowchart TB
A[Current Index v1] --> B[Production Queries]
C[Build Index v2] --> D[Validation]
D --> E{Valid?}
E -->|Yes| F[Switch Traffic]
F --> G[Production Queries]
E -->|No| H[Keep v1]This reduces the risk of deploying a broken index.
63. Retrieval Failure Patterns¶
Failure 1 — Wrong Index¶
Failure 2 — Poor Embeddings¶
Failure 3 — Wrong Top-K¶
or:
Failure 4 — Missing Metadata Filters¶
Failure 5 — Duplicate Nodes¶
64. Retrieval Failure Diagram¶
flowchart TD
A[User Query] --> B[Retriever]
B --> C{Good Results?}
C -->|No| D[Possible Causes]
D --> E[Bad Embeddings]
D --> F[Bad Chunking]
D --> G[Wrong Top-K]
D --> H[Missing Filters]
D --> I[Stale Index]
D --> J[Wrong Index Strategy]65. Stale Index¶
A retrieval system can fail even when the retriever is technically working.
Example:
The retriever may return the correct node according to the index.
The index itself is stale.
Therefore:
must both be monitored.
66. Retrieval Observability¶
Useful metrics include:
Retrieval Latency
Top-K
Similarity Scores
Retrieved Node IDs
Filter Results
Empty Retrieval Rate
Retrieval Hit Rate
Context Size
A trace might look like:
Query
↓
Retriever
↓
Candidate Count = 50
↓
Filtered Count = 18
↓
Top-K = 5
↓
Context Tokens = 3200
↓
LLM
67. Retrieval Trace¶
sequenceDiagram
participant U as User
participant Q as Query API
participant R as Retriever
participant V as Vector Store
participant L as LLM
U->>Q: Query
Q->>R: Retrieve
R->>V: Similarity Search
V-->>R: Candidates
R-->>Q: Ranked Nodes
Q->>L: Context + Query
L-->>Q: Answer
Q-->>U: Response68. Retrieval Cost¶
Retrieval cost can include:
For high-volume applications:
can become a significant operational expense.
69. Retrieval Optimization¶
Potential optimizations include:
Metadata Filtering
+
Smaller Candidate Sets
+
Caching
+
Efficient Vector Store
+
Appropriate Top-K
+
Query Batching
+
Result Reuse
70. Retrieval Caching¶
Repeated queries may benefit from caching.
However, cache invalidation must account for:
71. Secure Retrieval Cache¶
A dangerous cache design is:
because different users may have different permissions.
A safer conceptual key is:
72. Retrieval Architecture for Enterprise RAG¶
flowchart TB
A[User] --> B[Authentication]
B --> C[Authorization]
C --> D[Query API]
D --> E[Query Processing]
E --> F[Metadata / Tenant Filter]
F --> G[Retriever]
G --> H[(Vector Store)]
H --> I[Candidate Nodes]
I --> J[Post Processing]
J --> K[Context Builder]
K --> L[LLM]
L --> M[Response]
G --> N[Retrieval Metrics]73. Enterprise Retrieval Principles¶
A production retrieval layer should:
1. Retrieve only authorized data
2. Preserve source metadata
3. Support tenant isolation
4. Track index versions
5. Monitor retrieval quality
6. Monitor latency
7. Monitor empty results
8. Tune Top-K
9. Evaluate embeddings
10. Support index updates
74. Retrieval Strategy Selection¶
Use:
when the problem is primarily:
Use:
when:
Use:
when:
Use:
when:
are both important.
75. Query Complexity¶
Simple query:
may require:
Complex query:
may require:
76. Multi-Stage Retrieval¶
Production systems often use:
Stage 1
Broad Candidate Retrieval
↓
Stage 2
Filtering
↓
Stage 3
Ranking
↓
Stage 4
Context Selection
↓
Stage 5
Generation
This separates:
from:
77. Multi-Stage Retrieval Architecture¶
flowchart LR
A[Query] --> B[Candidate Retrieval]
B --> C[Metadata Filtering]
C --> D[Ranking]
D --> E[Context Selection]
E --> F[LLM]
F --> G[Response]Advanced re-ranking strategies will be covered separately in the production retrieval section.
78. Retrieval and RAG Quality¶
A useful mental model is:
RAG Quality
↓
┌─────┴─────┐
▼ ▼
Retrieval Generation
Quality Quality
│
├── Chunking
├── Embeddings
├── Index
├── Filters
├── Ranking
└── Top-K
Improving the model alone cannot compensate for poor retrieval.
79. Indexing Strategy Decision Tree¶
flowchart TD
A[Knowledge Requirement] --> B{Semantic Search?}
B -->|Yes| C[Vector Index]
B -->|No| D{Exact Terms Important?}
D -->|Yes| E[Keyword Index]
D -->|No| F{Relationships Important?}
F -->|Yes| G[Graph-Oriented Index]
F -->|No| H[Evaluate Summary / Other Strategy]
C --> I{Exact Terms Also Important?}
I -->|Yes| J[Hybrid Retrieval]
I -->|No| K[Vector Retrieval]80. Index Selection Is an Architecture Decision¶
Do not select an index only because:
Instead evaluate:
81. Practical LlamaIndex Example¶
A simple retrieval pipeline:
from llama_index.core import (
SimpleDirectoryReader,
VectorStoreIndex
)
documents = SimpleDirectoryReader(
"data"
).load_data()
index = VectorStoreIndex.from_documents(
documents
)
retriever = index.as_retriever(
similarity_top_k=5
)
nodes = retriever.retrieve(
"How does the company handle security incidents?"
)
for node in nodes:
print(
node.score,
node.node.text
)
Conceptually:
82. Query Engine vs Retriever¶
These concepts should not be confused.
Retriever¶
Returns relevant nodes:
Query Engine¶
Usually coordinates retrieval and response generation:
Therefore:
83. Retrieval-Only Architecture¶
Retrieval can be useful without generation.
Example:
This is useful for:
84. Retrieval + Generation¶
For RAG:
The retrieval layer should remain independently testable.
85. Retrieval Abstraction¶
A useful enterprise architecture can define:
Then implementations can include:
This keeps application code less coupled to a single retrieval implementation.
86. Ports and Adapters Architecture¶
flowchart LR
A[Enterprise Application] --> B[KnowledgeRetriever Interface]
B --> C[LlamaIndex Adapter]
C --> D[VectorStoreIndex]
B --> E[Hybrid Adapter]
E --> F[Keyword + Vector]
B --> G[Graph Adapter]
G --> H[Graph Store]This approach is useful when long-term framework flexibility matters.
87. Production Retrieval Checklist¶
Index¶
- [ ] Index type selected
- [ ] Embedding model selected
- [ ] Vector store selected
- [ ] Persistence configured
- [ ] Index versioning defined
Retrieval¶
- [ ] Retriever configured
- [ ] Top-K tuned
- [ ] Similarity threshold evaluated
- [ ] Metadata filters implemented
- [ ] Deduplication implemented
- [ ] Ranking strategy evaluated
Security¶
- [ ] Authentication
- [ ] Authorization
- [ ] Tenant filtering
- [ ] ACL enforcement
- [ ] Secure cache design
Quality¶
- [ ] Retrieval evaluation dataset
- [ ] Precision@K
- [ ] Recall@K
- [ ] Hit Rate
- [ ] Context relevance
- [ ] Empty-result monitoring
Operations¶
- [ ] Latency monitoring
- [ ] Cost monitoring
- [ ] Index freshness
- [ ] Retrieval tracing
- [ ] Failure handling
- [ ] Index rollback strategy
88. Key Takeaways¶
- Indexing prepares enterprise data for efficient retrieval.
- Indexing and retrieval are separate stages.
VectorStoreIndexis a central pattern for semantic RAG.- Vector retrieval depends heavily on embedding quality.
- Top-K controls the amount of retrieved context.
- Too little context can reduce recall.
- Too much context can increase noise, latency, and cost.
- Metadata filtering can significantly improve retrieval precision.
- Metadata filtering is not a replacement for authorization.
- Summary-oriented indexing can support broad document synthesis.
- Tree-oriented approaches can support hierarchical information processing.
- Keyword-oriented retrieval is useful when exact terms matter.
- Graph-oriented indexing is useful when relationships are important.
- Hybrid retrieval combines complementary retrieval strategies.
- Query routing can select different retrieval strategies for different questions.
- Post-processing can filter, deduplicate, rank, and compress retrieved nodes.
- Retrieval should be evaluated independently from generation.
- Production retrieval requires index lifecycle management.
- Index versioning enables safer production deployments.
- Blue-green index deployment can reduce production risk.
- Retrieval observability should include latency, scores, filters, and result quality.
- Retrieval caches must account for tenant and authorization boundaries.
- Multi-stage retrieval separates recall from precision.
- Index selection should be driven by data shape and query requirements.
- Framework-specific retrieval should be isolated behind application-level interfaces when portability matters.
📝 Quick Revision Notes¶
Indexing¶
Vector Retrieval¶
Hybrid Retrieval¶
Enterprise Retrieval¶
User
↓
Authentication
↓
Authorization
↓
Tenant Filter
↓
Retriever
↓
Index
↓
Post-Processing
↓
Context
↓
LLM
Retrieval Quality¶
Index Lifecycle¶
❓ Interview Questions¶
Beginner¶
- What is an index in LlamaIndex?
- What is the difference between indexing and retrieval?
- What is VectorStoreIndex?
- What is a retriever?
- What is Top-K retrieval?
- What is similarity search?
- Why is metadata filtering important?
- What is the difference between a retriever and a query engine?
Intermediate¶
- Explain how VectorStoreIndex works.
- How do embeddings participate in retrieval?
- How would you choose Top-K?
- What is similarity thresholding?
- What is the difference between precision and recall in retrieval?
- When would you use keyword retrieval?
- When would you use vector retrieval?
- What is hybrid retrieval?
- Why is retrieval post-processing useful?
- How would you evaluate retrieval quality?
- How would you handle stale indexes?
- How would you implement index versioning?
Advanced¶
- Design a production LlamaIndex retrieval architecture.
- How would you design multi-tenant retrieval?
- How would you prevent unauthorized data from entering the retrieval context?
- How would you combine vector, keyword, and graph retrieval?
- How would you design a query router?
- How would you optimize retrieval latency at enterprise scale?
- How would you design index blue-green deployment?
- How would you safely roll back an index?
- How would you tune Top-K using an evaluation dataset?
- How would you evaluate whether a new embedding model improves retrieval?
- How would you design a retrieval cache safely?
- How would you separate LlamaIndex retrieval from business logic?
- How would you design retrieval for millions of documents?
- How would you diagnose an increase in empty retrieval results?
- How would you diagnose retrieval returning semantically related but incorrect documents?
- How would you design a multi-stage retrieval pipeline?
- When should an enterprise use vector retrieval versus graph retrieval?
- How would you balance retrieval recall against LLM context cost?
🛠️ Practical Exercise¶
Build a production-oriented retrieval service using LlamaIndex.
Step 1 — Build the Index¶
Load:
Create:
Step 2 — Implement Retrieval¶
Support:
Step 3 — Add Metadata¶
Every node should contain:
Step 4 — Evaluate¶
Create at least:
Measure:
Step 5 — Compare Retrieval Strategies¶
Compare:
Measure:
🏢 Enterprise Architecture Challenge¶
Design retrieval for:
10 Million Documents
500 Tenants
Multiple Data Sources
Strict Authorization
Continuous Updates
High Query Volume
Requirements:
Semantic Retrieval
+
Keyword Search
+
Metadata Filtering
+
Tenant Isolation
+
Index Versioning
+
Retrieval Evaluation
+
Observability
🧠 Architecture Challenge¶
Design:
User
│
▼
Authentication
│
▼
Authorization
│
▼
Query API
│
▼
Query Router
/ | \
/ | \
▼ ▼ ▼
Vector Keyword Graph
Index Index Index
\ | /
\ | /
▼ ▼ ▼
Result Fusion
│
▼
Ranking
│
▼
Context Selection
│
▼
LLM
│
▼
Response
The architecture should support:
🚀 Production Design Exercise¶
Implement index versioning:
If evaluation fails:
If evaluation succeeds:
📚 References & Further Reading¶
Recommended areas for further study:
- LlamaIndex VectorStoreIndex
- LlamaIndex Retrievers
- LlamaIndex Query Engines
- LlamaIndex Metadata Filtering
- LlamaIndex Vector Store Integrations
- LlamaIndex Summary Indexing
- LlamaIndex Tree-Oriented Indexing
- LlamaIndex Keyword Retrieval
- LlamaIndex Property Graph / Graph Retrieval
- LlamaIndex Retrieval Evaluation
- Vector Search
- Hybrid Search
- Enterprise Search Architecture
- RAG Retrieval Evaluation
- Production Vector Database Architecture
LlamaIndex evolves rapidly. Before implementing production systems, verify the current APIs, index classes, retriever interfaces, metadata-filtering syntax, vector-store integrations, and graph capabilities against the official documentation for the version used by your project.
🧭 Chapter Navigation¶
⬅️ Previous: 10. LlamaIndex Data and Document Ingestion
📚 Part VIII Index: AI Engineering Frameworks & Tooling
➡️ Next: 12. LlamaIndex RAG Pielines
Enterprise AI Engineering Handbook
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