Semantic search system using vector embeddings to find relevant documents
Core Idea: Vector stores enable efficient semantic search by converting text documents into numerical vectors (embeddings) and retrieving the most similar documents to a query based on vector similarity metrics.
Key Elements
Core Components
-
Document Processing Pipeline:
- Document loading from various sources (web, files, databases)
- Text splitting into manageable chunks
- Embedding generation using neural network models
- Vector storage with indexing for fast retrieval
-
Embedding Models:
- Neural networks that convert text to high-dimensional vectors
- Models capture semantic meaning rather than just keywords
- Common options include OpenAI embeddings, BERT, Sentence Transformers
- Embedding dimensions typically range from 384 to 1536
-
Similarity Metrics:
- Cosine similarity (most common): measures the cosine of the angle between vectors
- Euclidean distance: measures straight-line distance between vectors
- Dot product: simple vector multiplication for similarity
-
Indexing Methods:
- Approximate Nearest Neighbors (ANN) algorithms like HNSW, IVF
- Tree-based indexing structures
- Clustering techniques to organize similar documents
Implementation Process
- Document Ingestion:
from langchain_community.document_loaders import WebBaseLoader # Load documents loader = WebBaseLoader(["https://docs.example.com/page1", "https://docs.example.com/page2"]) documents = loader.load()
2. **Text Splitting**:
```python
from langchain.text_splitter import RecursiveCharacterTextSplitter
# Split documents into chunks
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
chunks = text_splitter.split_documents(documents)
```
3. **Embedding Generation**:
```python
from langchain_openai import OpenAIEmbeddings
# Create embeddings
embeddings = OpenAIEmbeddings()
```
4. **Vector Store Creation**:
```python
from langchain_community.vectorstores import Chroma
# Create and persist vector store
vectorstore = Chroma.from_documents(
documents=chunks,
embedding=embeddings,
persist_directory="./docs_vectorstore"
)
```
5. **Query Execution**:
```python
# Perform similarity search
query = "How do I implement feature X?"
results = vectorstore.similarity_search(query, k=3)
```
### Vector Store Options
- **In-memory**: FAISS, for speed and simplicity
- **Persistent**: Chroma, Pinecone, Weaviate, Qdrant
- **Hybrid search**: Combining keyword and semantic search (Elasticsearch with vector extensions)
- **Managed services**: Pinecone, Qdrant Cloud, Weaviate Cloud
### Advanced Techniques
- **Metadata Filtering**: Narrowing search based on document metadata
- **Hybrid Search**: Combining keyword and vector search for better results
- **Dynamic Document Updates**: Incrementally updating vector stores
- **Query Expansion**: Enriching queries to improve retrieval quality
- **Cross-encoder Reranking**: Using a second model to rerank initial results
## Connections
- **Related Concepts**: Retrieval-Augmented Generation, Embedding Models, Semantic Search
- **Applications**: LangGraph Query Tool, MCP Resources
- **Implementation Frameworks**: LangChain, LlamaIndex
- **Integration Methods**: MCP Server Implementation, LLM Tool Use
## References
1. LangChain documentation on vector stores
2. "Neural Information Retrieval: A Literature Review" (academic paper)
3. Pinecone, Chroma, and Weaviate documentation
4. Implementation guides for RAG systems
#VectorStore #Embeddings #SemanticSearch #DocumentRetrieval #RAG #LLM #AITools
---
**Connections:**
-
---
**Sources:**
- From: LangChain - Understanding MCP From Scratch