RAG with ChromaDB

Retrieval-Augmented Generation (RAG) connects semantic vector search with generative language models. External documents are converted into dense floating-point vector representations, indexed in a vector store, and retrieved to inject factual context into the model’s prompt.

Quickstart

import os
import chromadb
from chromadb import Documents, EmbeddingFunction, Embeddings
from openai import OpenAI

client = OpenAI(
    base_url="https://api.neosantara.xyz/v1",
    api_key=os.environ["NEOSANTARA_API_KEY"]
)

class NeosantaraEmbeddingFunction(EmbeddingFunction):
    def __call__(self, input: Documents) -> Embeddings:
        response = client.embeddings.create(
            model="nusa-embedding-0001",
            input=input
        )
        return [d.embedding for d in response.data]

# Initialize local in-memory ChromaDB vector store
chroma_client = chromadb.Client()
collection = chroma_client.create_collection(
    name="vehicle_manual",
    embedding_function=NeosantaraEmbeddingFunction()
)

# Populate reference documents
collection.add(
    documents=[
        "Cabin temperature is adjusted via the center knob. Turn clockwise to raise heat.",
        "Automatic transmission: P for park, R for reverse, N for neutral, D for drive.",
        "The 12-inch touchscreen controls GPS navigation, media playback, and diagnostics."
    ],
    ids=["doc1", "doc2", "doc3"]
)

# Retrieve top matching passage and synthesize answer
query = "How do I change the cabin temperature?"
results = collection.query(query_texts=[query], n_results=1)
relevant_context = results["documents"][0][0]

response = client.chat.completions.create(
    model="deepseek-v4.1-flash",
    messages=[
        {"role": "system", "content": f"Answer based strictly on this reference context:\n\n{relevant_context}"},
        {"role": "user", "content": query}
    ]
)

print(response.choices[0].message.content)

Embeddings Capability

Specifications for /v1/embeddings parameters and vector formatting.

Model Catalog

Browse available embedding models including nusa-embedding-0001 and nv-embed-v1.

Step-by-Step Implementation

Install Dependencies

Install required Python libraries for OpenAI SDK and ChromaDB:

pip install -U openai chromadb numpy pandas

export NEOSANTARA_API_KEY="nsk_your_api_key_here"

NEOSANTARA_API_KEY=nsk_your_api_key_here

$env:NEOSANTARA_API_KEY="nsk_your_api_key_here"

Define Custom Embedding Function

ChromaDB allows custom embedding wrappers to direct vectorization requests to Neosantara’s /v1/embeddings endpoint:

import chromadb
from chromadb import Documents, EmbeddingFunction, Embeddings
from openai import OpenAI
import os

client = OpenAI(
    base_url="https://api.neosantara.xyz/v1",
    api_key=os.environ["NEOSANTARA_API_KEY"]
)

EMBEDDING_MODEL = "nusa-embedding-0001"
CHAT_MODEL = "deepseek-v4.1-flash"

class NeosantaraEmbeddingFunction(EmbeddingFunction):
    def __call__(self, input: Documents) -> Embeddings:
        response = client.embeddings.create(
            model=EMBEDDING_MODEL,
            input=input
        )
        return [d.embedding for d in response.data]

Index Knowledge Base in ChromaDB

Populate documents into a Chroma collection. The embedding function automatically projects each passage into a 768-dimensional vector space:

def setup_knowledge_base(docs, collection_name="company_sop"):
    chroma_client = chromadb.Client()
    try:
        chroma_client.delete_collection(name=collection_name)
    except Exception:
        pass

    db = chroma_client.create_collection(
        name=collection_name,
        embedding_function=NeosantaraEmbeddingFunction()
    )

    for i, text in enumerate(docs):
        db.add(
            documents=[text],
            ids=[f"id_{i}"]
        )
    return db

Vector Retrieval & Grounded Generation

Retrieve the closest matching passages using cosine similarity and prompt the chat completion model with grounded context:

def generate_grounded_answer(query: str, db, n_results: int = 2):
    # 1. Retrieval
    results = db.query(query_texts=[query], n_results=n_results)
    matched_docs = results["documents"][0] if results and results["documents"] else []
    
    if not matched_docs:
        return "No relevant information found in the knowledge base."

    context_text = "\n---\n".join(matched_docs)

    # 2. Augmented Generation
    prompt = (
        "You are a grounded assistant. Answer the user query using only the "
        "provided reference passages. If the information is not contained within "
        "the context, state clearly that it is not available.\n\n"
        f"REFERENCE CONTEXT:\n{context_text}\n\n"
        f"USER QUERY: {query}\n\n"
        "ANSWER:"
    )

    response = client.chat.completions.create(
        model=CHAT_MODEL,
        messages=[{"role": "user", "content": prompt}],
        temperature=0.2
    )
    return response.choices[0].message.content

Complete Production Script

rag_chroma_complete.py

import os
import chromadb
from chromadb import Documents, EmbeddingFunction, Embeddings
from openai import OpenAI

client = OpenAI(
    base_url="https://api.neosantara.xyz/v1",
    api_key=os.environ["NEOSANTARA_API_KEY"]
)

EMBEDDING_MODEL = "nusa-embedding-0001"
CHAT_MODEL = "deepseek-v4.1-flash"

class NeosantaraEmbeddingFunction(EmbeddingFunction):
    def __call__(self, input: Documents) -> Embeddings:
        response = client.embeddings.create(
            model=EMBEDDING_MODEL,
            input=input
        )
        return [d.embedding for d in response.data]

documents = [
    (
        "Climate Control: Cabin temperature is adjusted via the center console knob. "
        "Turn clockwise to increase heat and counter-clockwise to cool. "
        "Defrost mode directs heated airflow directly onto the windshield to clear fog."
    ),
    (
        "Touchscreen Navigation: The 12-inch display provides live GPS routing, "
        "Bluetooth connectivity, and vehicle profiles. "
        "Press the gear icon on the top right to open diagnostic settings."
    ),
    (
        "Powertrain & Braking: The electric single-speed drive engages instantly. "
        "Depress the brake pedal fully before pressing the Start/Stop button."
    )
]

chroma_client = chromadb.Client()
collection = chroma_client.create_collection(
    name="car_manual",
    embedding_function=NeosantaraEmbeddingFunction()
)

for idx, doc in enumerate(documents):
    collection.add(documents=[doc], ids=[f"doc_{idx}"])

def ask_rag(question: str):
    print(f"\nQuery: {question}")
    results = collection.query(query_texts=[question], n_results=1)
    passage = results["documents"][0][0]

    response = client.chat.completions.create(
        model=CHAT_MODEL,
        messages=[
            {
                "role": "system",
                "content": "Provide concise, grounded answers strictly derived from the reference context."
            },
            {
                "role": "user",
                "content": f"Reference Context:\n{passage}\n\nQuestion: {question}"
            }
        ],
        temperature=0.1
    )
    print(f"AI Answer: {response.choices[0].message.content}")

if __name__ == "__main__":
    ask_rag("How do I clear fog from the windshield?")
    ask_rag("What is the touchscreen display size?")
    ask_rag("Does the vehicle have a coffee maker?")

Set temperature=0 or 0.1 during the generation stage to minimize hallucinations and enforce strict adherence to retrieved text.

The nusa-embedding-0001 model yields 768-dimensional vector embeddings and is included in Neosantara’s tier quota without separate vector charges.

Guides

Operations & Security

Recursive Language Models (RLM)

Advanced AI Techniques

Quickstart

Embeddings Capability

Model Catalog

Step-by-Step Implementation

Complete Production Script

​Quickstart

Embeddings Capability

Model Catalog

​Step-by-Step Implementation

​Complete Production Script

​Related Resources

Quickstart

Step-by-Step Implementation

Complete Production Script

Related Resources