# Embeddings, Vector Stores and Retrievers — LangChain / LlamaIndex

Source: https://www.geekswithgeeks.com/en/langchain-llamaindex/r-vector

> Index chunks and search them by similarity.

## Store vectors, search by closeness

An **embeddings** model turns text into vectors. A **vector store** saves the vectors with their Documents and supports `similarity_search`. A **retriever** is a runnable that takes a query and returns Documents; `store.as_retriever(search_kwargs={"k": 3})` wraps a store. LangChain has a common interface over many stores (in-memory, FAISS, Chroma, pgvector, Pinecone, Qdrant and others) so you can start in memory and move to a database later. Use **metadata filters** for hard constraints (tenant, language, permissions). The demo below uses a deterministic fake embedding, which has **no meaning**: it only shows the API, and a text is most similar to itself.

## An in-memory vector store, run

I ran this offline in a Python virtual environment with langchain-core 1.6.6, langchain-text-splitters 1.1.2 and llama-index-core 0.14.25. No API key or network call is needed because a fake model or a toy embedding stands in for the real one. Searching with the exact text of the hotel document ranks it first (the `travel` source). Because the fake embedding carries no meaning, the second hit is not semantically related; with a real embedding model the ranking would reflect meaning. The retriever returns the single best match for the laptop text.

```python
from langchain_core.documents import Document
from langchain_core.vectorstores import InMemoryVectorStore
from langchain_core.embeddings import DeterministicFakeEmbedding

docs = [
    Document(page_content="Employees get 24 days of paid leave.", metadata={"src": "hr"}),
    Document(page_content="Hotels are capped at 6000 rupees per night.", metadata={"src": "travel"}),
    Document(page_content="Report lost laptops within 24 hours.", metadata={"src": "security"}),
]
store = InMemoryVectorStore(DeterministicFakeEmbedding(size=32))
store.add_documents(docs)

# The fake embedding is deterministic but has no meaning, so the same text always matches itself best.
hits = store.similarity_search("Hotels are capped at 6000 rupees per night.", k=2)
print([h.metadata["src"] for h in hits])
retriever = store.as_retriever(search_kwargs={"k": 1})
print(retriever.invoke("Report lost laptops within 24 hours.")[0].page_content)

```

Output:

```
['travel', 'hr']
Report lost laptops within 24 hours.
```

**Quiz:** What is a retriever?

- [ ] A prompt template
- [ ] A type of GPU
- [x] A runnable that takes a query and returns relevant Documents
- [ ] A model weight file

*Answer:* A runnable that takes a query and returns relevant Documents. Retrievers hide the search backend behind a simple interface.