Building RAG from Scratch (Open-source only!)

In this tutorial, we show you how to build a data ingestion pipeline into a vector database, and then build a retrieval pipeline from that vector database, from scratch.

Notably, we use a fully open-source stack:

• Sentence Transformers as the embedding model
• Postgres as the vector store (we support many other vector stores too!)
• Llama 2 as the LLM (through llama.cpp)

Setup

We setup our open-source components.

1. Sentence Transformers
2. Llama 2
3. We initialize postgres and wrap it with our wrappers/abstractions.

Sentence Transformers

%pip install llama-index-readers-file pymupdf
%pip install llama-index-vector-stores-postgres
%pip install llama-index-embeddings-huggingface
%pip install llama-index-llms-llama-cpp

# sentence transformers
from llama_index.embeddings.huggingface import HuggingFaceEmbedding

embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en")

Llama CPP

In this notebook, we use the llama-2-chat-13b-ggml model, along with the proper prompt formatting.

Check out our Llama CPP guide for full setup instructions/details.

!pip install llama-cpp-python

Requirement already satisfied: llama-cpp-python in /Users/jerryliu/Programming/gpt_index/.venv/lib/python3.10/site-packages (0.2.7)
Requirement already satisfied: numpy>=1.20.0 in /Users/jerryliu/Programming/gpt_index/.venv/lib/python3.10/site-packages (from llama-cpp-python) (1.23.5)
Requirement already satisfied: typing-extensions>=4.5.0 in /Users/jerryliu/Programming/gpt_index/.venv/lib/python3.10/site-packages (from llama-cpp-python) (4.7.1)
Requirement already satisfied: diskcache>=5.6.1 in /Users/jerryliu/Programming/gpt_index/.venv/lib/python3.10/site-packages (from llama-cpp-python) (5.6.3)

[notice] A new release of pip available: 22.3.1 -> 23.2.1
[notice] To update, run: pip install --upgrade pip

from llama_index.llms.llama_cpp import LlamaCPP

# model_url = "https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/resolve/main/llama-2-13b-chat.ggmlv3.q4_0.bin"
model_url = "https://huggingface.co/TheBloke/Llama-2-13B-chat-GGUF/resolve/main/llama-2-13b-chat.Q4_0.gguf"

llm = LlamaCPP(
    # You can pass in the URL to a GGML model to download it automatically
    model_url=model_url,
    # optionally, you can set the path to a pre-downloaded model instead of model_url
    model_path=None,
    temperature=0.1,
    max_new_tokens=256,
    # llama2 has a context window of 4096 tokens, but we set it lower to allow for some wiggle room
    context_window=3900,
    # kwargs to pass to __call__()
    generate_kwargs={},
    # kwargs to pass to __init__()
    # set to at least 1 to use GPU
    model_kwargs={"n_gpu_layers": 1},
    verbose=True,
)

Initialize Postgres

Using an existing postgres running at localhost, create the database we’ll be using.

NOTE: Of course there are plenty of other open-source/self-hosted databases you can use! e.g. Chroma, Qdrant, Weaviate, and many more. Take a look at our vector store guide.

NOTE: You will need to setup postgres on your local system. Here’s an example of how to set it up on OSX: https://www.sqlshack.com/setting-up-a-postgresql-database-on-mac/.

NOTE: You will also need to install pgvector (https://github.com/pgvector/pgvector).

You can add a role like the following:

CREATE ROLE <user> WITH LOGIN PASSWORD '<password>';
ALTER ROLE <user> SUPERUSER;

!pip install psycopg2-binary pgvector asyncpg "sqlalchemy[asyncio]" greenlet

import psycopg2

db_name = "vector_db"
host = "localhost"
password = "password"
port = "5432"
user = "jerry"
# conn = psycopg2.connect(connection_string)
conn = psycopg2.connect(
    dbname="postgres",
    host=host,
    password=password,
    port=port,
    user=user,
)
conn.autocommit = True

with conn.cursor() as c:
    c.execute(f"DROP DATABASE IF EXISTS {db_name}")
    c.execute(f"CREATE DATABASE {db_name}")

from sqlalchemy import make_url
from llama_index.vector_stores.postgres import PGVectorStore

vector_store = PGVectorStore.from_params(
    database=db_name,
    host=host,
    password=password,
    port=port,
    user=user,
    table_name="llama2_paper",
    embed_dim=384,  # openai embedding dimension
)

Build an Ingestion Pipeline from Scratch

We show how to build an ingestion pipeline as mentioned in the introduction.

We fast-track the steps here (can skip metadata extraction). More details can be found in our dedicated ingestion guide.

1. Load Data

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

from pathlib import Path
from llama_index.readers.file import PyMuPDFReader

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

2. Use a Text Splitter to Split Documents

from llama_index.core.node_parser import SentenceSplitter

text_parser = SentenceSplitter(
    chunk_size=1024,
    # separator=" ",
)

text_chunks = []
# maintain relationship with source doc index, to help inject doc metadata in (3)
doc_idxs = []
for doc_idx, doc in enumerate(documents):
    cur_text_chunks = text_parser.split_text(doc.text)
    text_chunks.extend(cur_text_chunks)
    doc_idxs.extend([doc_idx] * len(cur_text_chunks))

3. Manually Construct Nodes from Text Chunks

from llama_index.core.schema import TextNode

nodes = []
for idx, text_chunk in enumerate(text_chunks):
    node = TextNode(
        text=text_chunk,
    )
    src_doc = documents[doc_idxs[idx]]
    node.metadata = src_doc.metadata
    nodes.append(node)

4. Generate Embeddings for each Node

Here we generate embeddings for each Node using a sentence_transformers model.

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

5. Load Nodes into a Vector Store

We now insert these nodes into our PostgresVectorStore.

vector_store.add(nodes)

Build Retrieval Pipeline from Scratch

We show how to build a retrieval pipeline. Similar to ingestion, we fast-track the steps. Take a look at our retrieval guide for more details!

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

1. Generate a Query Embedding

query_embedding = embed_model.get_query_embedding(query_str)

2. Query the Vector Database

# construct vector store query
from llama_index.core.vector_stores import VectorStoreQuery

query_mode = "default"
# query_mode = "sparse"
# query_mode = "hybrid"

vector_store_query = VectorStoreQuery(
    query_embedding=query_embedding, similarity_top_k=2, mode=query_mode
)

# returns a VectorStoreQueryResult
query_result = vector_store.query(vector_store_query)
print(query_result.nodes[0].get_content())

3. Parse Result into a Set of Nodes

from llama_index.core.schema import NodeWithScore
from typing import Optional

nodes_with_scores = []
for index, node in enumerate(query_result.nodes):
    score: Optional[float] = None
    if query_result.similarities is not None:
        score = query_result.similarities[index]
    nodes_with_scores.append(NodeWithScore(node=node, score=score))

4. Put into a Retriever

from llama_index.core import QueryBundle
from llama_index.core.retrievers import BaseRetriever
from typing import Any, List


class VectorDBRetriever(BaseRetriever):
    """Retriever over a postgres vector store."""

    def __init__(
        self,
        vector_store: PGVectorStore,
        embed_model: Any,
        query_mode: str = "default",
        similarity_top_k: int = 2,
    ) -> None:
        """Init params."""
        self._vector_store = vector_store
        self._embed_model = embed_model
        self._query_mode = query_mode
        self._similarity_top_k = similarity_top_k
        super().__init__()

    def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
        """Retrieve."""
        query_embedding = embed_model.get_query_embedding(
            query_bundle.query_str
        )
        vector_store_query = VectorStoreQuery(
            query_embedding=query_embedding,
            similarity_top_k=self._similarity_top_k,
            mode=self._query_mode,
        )
        query_result = vector_store.query(vector_store_query)

        nodes_with_scores = []
        for index, node in enumerate(query_result.nodes):
            score: Optional[float] = None
            if query_result.similarities is not None:
                score = query_result.similarities[index]
            nodes_with_scores.append(NodeWithScore(node=node, score=score))

        return nodes_with_scores

retriever = VectorDBRetriever(
    vector_store, embed_model, query_mode="default", similarity_top_k=2
)

Plug this into our RetrieverQueryEngine to synthesize a response

from llama_index.core.query_engine import RetrieverQueryEngine

query_engine = RetrieverQueryEngine.from_args(retriever, llm=llm)

query_str = "How does Llama 2 perform compared to other open-source models?"

response = query_engine.query(query_str)

print(str(response))

 Based on the results shown in Table 3, Llama 2 outperforms all open-source models on most of the benchmarks, with an average improvement of around 5 points over the next best model (GPT-3.5).

print(response.source_nodes[0].get_content())