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Building a (Very Simple) Vector Store from Scratch#

In this tutorial, we show you how to build a simple in-memory vector store that can store documents along with metadata. It will also expose a query interface that can support a variety of queries:

  • semantic search (with embedding similarity)

  • metadata filtering

NOTE: Obviously this is not supposed to be a replacement for any actual vector store (e.g. Pinecone, Weaviate, Chroma, Qdrant, Milvus, or others within our wide range of vector store integrations). This is more to teach some key retrieval concepts, like top-k embedding search + metadata filtering.

We won’t be covering advanced query/retrieval concepts such as approximate nearest neighbors, sparse/hybrid search, or any of the system concepts that would be required for building an actual database.

Setup#

We load in some documents, and parse them into Node objects - chunks that are ready to be inserted into a vector store.

Load in Documents#

!mkdir data
!wget --user-agent "Mozilla" "https://arxiv.org/pdf/2307.09288.pdf" -O "data/llama2.pdf"

from pathlib import Path
from llama_hub.file.pymu_pdf.base import PyMuPDFReader

loader = PyMuPDFReader()
documents = loader.load(file_path="./data/llama2.pdf")

Parse into Nodes#

from llama_index.node_parser import SentenceSplitter

node_parser = SentenceSplitter(chunk_size=256)
nodes = node_parser.get_nodes_from_documents(documents)

Generate Embeddings for each Node#

from llama_index.embeddings import OpenAIEmbedding

embed_model = OpenAIEmbedding()
for node in nodes:
    node_embedding = embed_model.get_text_embedding(
        node.get_content(metadata_mode="all")
    )
    node.embedding = node_embedding

Build a Simple In-Memory Vector Store#

Now we’ll build our in-memory vector store. We’ll store Nodes within a simple Python dictionary. We’ll start off implementing embedding search, and add metadata filters.

1. Defining the Interface#

We’ll first define the interface for building a vector store. It contains the following items:

  • get

  • add

  • delete

  • query

  • persist (which we will not implement)

from llama_index.vector_stores.types import (
    VectorStore,
    VectorStoreQuery,
    VectorStoreQueryResult,
)
from typing import List, Any, Optional, Dict
from llama_index.schema import TextNode, BaseNode
import os


class BaseVectorStore(VectorStore):
    """Simple custom Vector Store.

    Stores documents in a simple in-memory dict.

    """

    stores_text: bool = True

    def get(self, text_id: str) -> List[float]:
        """Get embedding."""
        pass

    def add(
        self,
        nodes: List[BaseNode],
    ) -> List[str]:
        """Add nodes to index."""
        pass

    def delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None:
        """
        Delete nodes using with ref_doc_id.

        Args:
            ref_doc_id (str): The doc_id of the document to delete.

        """
        pass

    def query(
        self,
        query: VectorStoreQuery,
        **kwargs: Any,
    ) -> VectorStoreQueryResult:
        """Get nodes for response."""
        pass

    def persist(self, persist_path, fs=None) -> None:
        """Persist the SimpleVectorStore to a directory.

        NOTE: we are not implementing this for now.

        """
        pass

At a high-level, we subclass our base VectorStore abstraction. There’s no inherent reason to do this if you’re just building a vector store from scratch. We do it because it makes it easy to plug into our downstream abstractions later.

Let’s look at some of the classes defined here.

  • BaseNode is simply the parent class of our core Node modules. Each Node represents a text chunk + associated metadata.

  • We also use some lower-level constructs, for instance our VectorStoreQuery and VectorStoreQueryResult. These are just lightweight dataclass containers to represent queries and results. We look at the dataclass fields below.

from dataclasses import fields

{f.name: f.type for f in fields(VectorStoreQuery)}

{'query_embedding': typing.Optional[typing.List[float]],
 'similarity_top_k': int,
 'doc_ids': typing.Optional[typing.List[str]],
 'node_ids': typing.Optional[typing.List[str]],
 'query_str': typing.Optional[str],
 'output_fields': typing.Optional[typing.List[str]],
 'embedding_field': typing.Optional[str],
 'mode': <enum 'VectorStoreQueryMode'>,
 'alpha': typing.Optional[float],
 'filters': typing.Optional[llama_index.vector_stores.types.MetadataFilters],
 'mmr_threshold': typing.Optional[float],
 'sparse_top_k': typing.Optional[int]}

{f.name: f.type for f in fields(VectorStoreQueryResult)}

{'nodes': typing.Optional[typing.Sequence[llama_index.schema.BaseNode]],
 'similarities': typing.Optional[typing.List[float]],
 'ids': typing.Optional[typing.List[str]]}

2. Defining add, get, and delete#

We add some basic capabilities to add, get, and delete from a vector store.

The implementation is very simple (everything is just stored in a python dictionary).

class VectorStore2(BaseVectorStore):
    """VectorStore2 (add/get/delete implemented)."""

    stores_text: bool = True

    def __init__(self) -> None:
        """Init params."""
        self.node_dict: Dict[str, BaseNode] = {}

    def get(self, text_id: str) -> List[float]:
        """Get embedding."""
        return self.node_dict[text_id]

    def add(
        self,
        nodes: List[BaseNode],
    ) -> List[str]:
        """Add nodes to index."""
        for node in nodes:
            self.node_dict[node.node_id] = node

    def delete(self, node_id: str, **delete_kwargs: Any) -> None:
        """
        Delete nodes using with node_id.

        Args:
            node_id: str

        """
        del self.node_dict[node_id]

We run some basic tests just to show it works well.

test_node = TextNode(id_="id1", text="hello world")
test_node2 = TextNode(id_="id2", text="foo bar")
test_nodes = [test_node, test_node2]

vector_store = VectorStore2()

vector_store.add(test_nodes)

node = vector_store.get("id1")
print(str(node))

Node ID: id1
Text: hello world

3.b. Supporting Metadata Filtering#

The next extension is adding metadata filter support. This means that we will first filter the candidate set with documents that pass the metadata filters, and then perform semantic querying.

For simplicity we use metadata filters for exact matching with an AND condition.

from llama_index.vector_stores import MetadataFilters
from llama_index.schema import BaseNode
from typing import cast


def filter_nodes(nodes: List[BaseNode], filters: MetadataFilters):
    filtered_nodes = []
    for node in nodes:
        matches = True
        for f in filters.filters:
            if f.key not in node.metadata:
                matches = False
                continue
            if f.value != node.metadata[f.key]:
                matches = False
                continue
        if matches:
            filtered_nodes.append(node)
    return filtered_nodes

We add filter_nodes as a first-pass over the nodes before running semantic search.

def dense_search(query: VectorStoreQuery, nodes: List[BaseNode]):
    """Dense search."""
    query_embedding = cast(List[float], query.query_embedding)
    doc_embeddings = [n.embedding for n in nodes]
    doc_ids = [n.node_id for n in nodes]
    return get_top_k_embeddings(
        query_embedding,
        doc_embeddings,
        doc_ids,
        similarity_top_k=query.similarity_top_k,
    )


class VectorStore3B(VectorStore2):
    """Implements Metadata Filtering."""

    def query(
        self,
        query: VectorStoreQuery,
        **kwargs: Any,
    ) -> VectorStoreQueryResult:
        """Get nodes for response."""
        # 1. First filter by metadata
        nodes = self.node_dict.values()
        if query.filters is not None:
            nodes = filter_nodes(nodes, query.filters)
        if len(nodes) == 0:
            result_nodes = []
            similarities = []
            node_ids = []
        else:
            # 2. Then perform semantic search
            similarities, node_ids = dense_search(query, nodes)
            result_nodes = [self.node_dict[node_id] for node_id in node_ids]
        return VectorStoreQueryResult(
            nodes=result_nodes, similarities=similarities, ids=node_ids
        )

4. Load Data into our Vector Store#

Let’s load our text chunks into the vector store, and run it on different types of queries: dense search, w/ metadata filters, and more.

vector_store = VectorStore3B()
# load data into the vector stores
vector_store.add(nodes)

Define an example question and embed it.

query_str = "Can you tell me about the key concepts for safety finetuning"
query_embedding = embed_model.get_query_embedding(query_str)

Query the vector store with dense search + Metadata Filters#

# filters = MetadataFilters(
#     filters=[
#         ExactMatchFilter(key="page", value=3)
#     ]
# )
filters = MetadataFilters.from_dict({"source": "24"})

query_obj = VectorStoreQuery(
    query_embedding=query_embedding, similarity_top_k=2, filters=filters
)

query_result = vector_store.query(query_obj)
for similarity, node in zip(query_result.similarities, query_result.nodes):
    print(
        "\n----------------\n"
        f"[Node ID {node.node_id}] Similarity: {similarity}\n\n"
        f"{node.get_content(metadata_mode='all')}"
        "\n----------------\n\n"
    )

----------------
[Node ID efe54bc0-4f9f-49ad-9dd5-900395a092fa] Similarity: 0.8190195580569283

total_pages: 77
file_path: ./data/llama2.pdf
source: 24

4.2.2
Safety Supervised Fine-Tuning
In accordance with the established guidelines from Section 4.2.1, we gather prompts and demonstrations
of safe model responses from trained annotators, and use the data for supervised fine-tuning in the same
manner as described in Section 3.1. An example can be found in Table 5.
The annotators are instructed to initially come up with prompts that they think could potentially induce
the model to exhibit unsafe behavior, i.e., perform red teaming, as defined by the guidelines. Subsequently,
annotators are tasked with crafting a safe and helpful response that the model should produce.
4.2.3
Safety RLHF
We observe early in the development of Llama 2-Chat that it is able to generalize from the safe demonstrations
in supervised fine-tuning. The model quickly learns to write detailed safe responses, address safety concerns,
explain why the topic might be sensitive, and provide additional helpful information.
----------------



----------------
[Node ID 619c884b-cdbc-44b2-aec0-2692b44740ee] Similarity: 0.8010811332867503

total_pages: 77
file_path: ./data/llama2.pdf
source: 24

In particular, when
the model outputs safe responses, they are often more detailed than what the average annotator writes.
Therefore, after gathering only a few thousand supervised demonstrations, we switched entirely to RLHF to
teach the model how to write more nuanced responses. Comprehensive tuning with RLHF has the added
benefit that it may make the model more robust to jailbreak attempts (Bai et al., 2022a).
We conduct RLHF by first collecting human preference data for safety similar to Section 3.2.2: annotators
write a prompt that they believe can elicit unsafe behavior, and then compare multiple model responses to
the prompts, selecting the response that is safest according to a set of guidelines. We then use the human
preference data to train a safety reward model (see Section 3.2.2), and also reuse the adversarial prompts to
sample from the model during the RLHF stage.
Better Long-Tail Safety Robustness without Hurting Helpfulness
Safety is inherently a long-tail problem,
where the challenge comes from a small number of very specific cases.
----------------

Build a RAG System with the Vector Store#

Now that we’ve built the RAG system, it’s time to plug it into our downstream system!

from llama_index import VectorStoreIndex

index = VectorStoreIndex.from_vector_store(vector_store)

query_engine = index.as_query_engine()

query_str = "Can you tell me about the key concepts for safety finetuning"

response = query_engine.query(query_str)

print(str(response))

The key concepts for safety fine-tuning include supervised safety fine-tuning, safety RLHF (Reinforcement Learning from Human Feedback), and safety context distillation. Supervised safety fine-tuning involves gathering adversarial prompts and safe demonstrations to align the model with safety guidelines before RLHF. Safety RLHF integrates safety into the RLHF pipeline by training a safety-specific reward model and gathering more challenging adversarial prompts for fine-tuning and optimization. Finally, safety context distillation is used to refine the RLHF pipeline. These techniques aim to mitigate safety risks and ensure that the model aligns with safety guidelines.

Conclusion#

That’s it! We’ve built a simple in-memory vector store that supports very simple inserts, gets, deletes, and supports dense search and metadata filtering. This can then be plugged into the rest of LlamaIndex abstractions.

It doesn’t support sparse search yet and is obviously not meant to be used in any sort of actual app. But this should expose some of what’s going on under the hood!