Retrieval-Augmented Generation

Retrieval-Augmented Generation (RAG) is a technique in natural language processing that combines retrieval systems with generative models to improve the accuracy and relevance of generated content. It is particularly useful for tasks that require up-to-date or domain-specific knowledge not included in the generative model’s training data.

This example demonstrates how to use the Galadriel API to perform embeddings and LLM inference for question-answering tasks. We will be using Lanchain to load and format a dataset, and ChromaDB, which is an open-source vector database, for storing and querying the embeddings, gotten from the Galadriel API.

First set up the required project requirements

Create an account here
Create an API key on the dashboard

Setup the environment

create a file called .env, and add your API key there, together with the LLM to be used:

GALADRIEL_API_KEY="YOUR GALADRIEL API KEY HERE"
MODEL="llama3.1-70b"
API_URL="https://api.galadriel.com/v1"

Create a python environment

python3 -m venv venv
source venv/bin/activate

Install the required dependencies

pip install openai==1.42.0 chromadb==0.5.18 python-dotenv==1.0.1 langchain==0.3.7 langchain_chroma==0.1.4
langchain_community==0.3.7 bs4==0.0.2

Load the variables

First, in our Python file, we must load the environment variables

import os
from pathlib import Path

from dotenv import load_dotenv

# Load .env variables and ensure they are set correctly
env_path = Path(".env")
load_dotenv(dotenv_path=env_path)

MODEL = os.getenv("MODEL")
assert MODEL is not None, "MODEL missing from .env"
API_KEY = os.getenv("GALADRIEL_API_KEY")
assert API_KEY is not None, "GALADRIEL_API_KEY missing from .env"
API_URL = os.getenv("API_URL")
assert API_URL is not None, "API_URL missing from .env"

EMBEDDING_MODEL = "gte-large-en-v1.5"

Create a vector datastore

Next, we can download the dataset and create a vector datastore, which we will be using later for storing our embeddings and querying the relevant context.

import bs4
import chromadb
from langchain_community.document_loaders import WebBaseLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter


def _load_dataset():
    loader = WebBaseLoader(
        web_paths=("https://lilianweng.github.io/posts/2023-06-23-agent/",),
        bs_kwargs=dict(
            parse_only=bs4.SoupStrainer(
                class_=("post-content", "post-title", "post-header")
            )
        ),
    )
    docs = loader.load()
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=1000, chunk_overlap=200)
    return text_splitter.split_documents(docs)


def _create_vector_db():
    chroma_client = chromadb.Client()
    collection = chroma_client.create_collection(
        name="my_collection")
    return collection


dataset = _load_dataset()
collection = _create_vector_db()

Create embeddings

Now we can create embeddings for our dataset and store them in our vector datastore. We can also create embeddings for our search query.

from openai import OpenAI

QUERY = "What are the approaches to Task Decomposition?"

client = OpenAI(
    base_url=API_URL,
    api_key=API_KEY,
)
print("Creating embeddings...")
embeddings = client.embeddings.create(
    model=EMBEDDING_MODEL,
    input=[s.page_content for s in dataset],
    encoding_format="float"
)

print("Saving embeddings to the vector datastore...\n")
collection.add(
    documents=[s.page_content for s in dataset],
    embeddings=[e.embedding for e in embeddings.data],
    ids=[f"id{i}" for i, _ in enumerate(dataset)],
)

print(f"Querying vector datastore with user query: {QUERY}\n")
# Create embeddings for the query
query_embeddings = client.embeddings.create(
    model=EMBEDDING_MODEL,
    input=["What are the approaches to Task Decomposition?"],
    encoding_format="float"
)
# Query the vector datastore with the query embeddings
results = collection.query(
    query_embeddings=query_embeddings.data[0].embedding,
    n_results=2
)

Call the LLM

To call the LLM, we need to format the prompt, so it includes the user query, and the relevant context retrieved from the vector datastore.

# LLM prompt, where context will be added
PROMPT = """You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.
Question: {question}
Context: {context}
Answer:"""

# Format the query results
context = "\n".join([d for d in results["documents"][0]])
# Add the formatted query results to the prompt
formatted_prompt = PROMPT \
    .replace("{question}", QUERY) \
    .replace("{context}", context)

print("Calling LLM with formatted prompt:")
print(formatted_prompt)
print("\n")
response = client.chat.completions.create(
    model=MODEL,
    temperature=0,
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": formatted_prompt},
    ],
)
print("==== Got LLM response: ====")
print(response.choices[0].message.content)

You should see something like this, when running the code:

==== Got LLM response: ====
There are three approaches to task decomposition:
(1) using a large language model (LLM) with simple prompting,
(2) using task-specific instructions, and
(3) with human inputs.
Additionally, techniques like Chain of Thought (CoT) and
Tree of Thoughts can be employed to break down complex
tasks into smaller, manageable steps.
These methods transform big tasks into multiple simpler tasks,
providing insight into the model's thinking process.

The output of the code also shows the full prompt that was used, which contains the relevant context queried from the datastore!

Conclusion

The above example shows how to build a RAG (Retrieval-Augmented Generation) system using Galadriel, Langchain and ChromaDB. This can be used to build powerful LLM powered systems that have access to real-time data!

Full code example

import os
from pathlib import Path
from typing import List

import bs4
import chromadb
from chromadb.api.models.Collection import Collection
from dotenv import load_dotenv
from langchain_community.document_loaders import WebBaseLoader
from langchain_core.documents import Document
from langchain_text_splitters import RecursiveCharacterTextSplitter
from openai import OpenAI

# Load .env variables and ensure they are set correctly
env_path = Path(".env")
load_dotenv(dotenv_path=env_path)

MODEL = os.getenv("MODEL")
assert MODEL is not None, "MODEL missing from .env"
API_KEY = os.getenv("GALADRIEL_API_KEY")
assert API_KEY is not None, "GALADRIEL_API_KEY missing from .env"
API_URL = os.getenv("API_URL")
assert API_URL is not None, "API_URL missing from .env"

EMBEDDING_MODEL = "gte-large-en-v1.5"

# LLM prompt, where context will be added
PROMPT = """You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.
Question: {question}
Context: {context}
Answer:"""
QUERY = "What is task decomposition?"


def main():
    client = OpenAI(
        base_url=API_URL,
        api_key=API_KEY,
    )

    print("Loading dataset...")
    splits = _load_dataset()
    print(f"Got dataset, with {len(splits)} documents.\n")

    collection = _create_vector_db()

    print("Creating embeddings...")
    embeddings = client.embeddings.create(
        model=EMBEDDING_MODEL,
        input=[s.page_content for s in splits],
        encoding_format="float"
    )

    print("Saving embeddings to the vector datastore...\n")
    collection.add(
        documents=[s.page_content for s in splits],
        embeddings=[e.embedding for e in embeddings.data],
        ids=[f"id{i}" for i, _ in enumerate(splits)],
    )

    print(f"Querying vector datastore with user query: {QUERY}\n")
    # Create embeddings for the query
    query_embeddings = client.embeddings.create(
        model=EMBEDDING_MODEL,
        input=["What are the approaches to Task Decomposition?"],
        encoding_format="float"
    )
    # Query the vector datastore with the query embeddings
    results = collection.query(
        query_embeddings=query_embeddings.data[0].embedding,
        n_results=2
    )
    # Format the query results
    context = "\n".join([d for d in results["documents"][0]])
    # Add the formatted query results to the prompt
    formatted_prompt = PROMPT \
        .replace("{question}", QUERY) \
        .replace("{context}", context)

    print("Calling LLM with formatted prompt:")
    print(formatted_prompt)
    print("\n")
    response = client.chat.completions.create(
        model=MODEL,
        temperature=0,
        messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": formatted_prompt},
        ],
    )
    print("==== Got LLM response: ====")
    print(response.choices[0].message.content)


def _load_dataset() -> List[Document]:
    loader = WebBaseLoader(
        web_paths=("https://lilianweng.github.io/posts/2023-06-23-agent/",),
        bs_kwargs=dict(
            parse_only=bs4.SoupStrainer(
                class_=("post-content", "post-title", "post-header")
            )
        ),
    )
    docs = loader.load()
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=1000, chunk_overlap=200)
    return text_splitter.split_documents(docs)


def _create_vector_db() -> Collection:
    chroma_client = chromadb.Client()
    collection = chroma_client.create_collection(
        name="my_collection")
    return collection


def format_docs(docs):
    return "\n\n".join(doc.page_content for doc in docs)


if __name__ == '__main__':
    main()

About

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Retrieval-Augmented Generation

First set up the required project requirements

Load the variables

Create a vector datastore

Create embeddings

Call the LLM

Conclusion

Full code example

About

For agents developers

For LLM apps developers

API docs

Examples

For nodes

Other

​First set up the required project requirements

​Load the variables

​Create a vector datastore

​Create embeddings

​Call the LLM

​Conclusion

​Full code example

First set up the required project requirements

Load the variables

Create a vector datastore

Create embeddings

Call the LLM

Conclusion

Full code example