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# Build a RAG pipeline
This documentation page is also available as an interactive notebook. You can launch the notebook in
Kaggle or Colab, or download it for use with an IDE or local Jupyter installation, by clicking one of the
above links.
export const quartoRawHtml = [`
| Use case |
Documents |
Questions |
| Customer support |
Help articles |
“How do I reset my password?” |
| Internal wiki |
Company docs |
“What’s our vacation policy?” |
| Research |
Papers |
“What did the study find about X?” |
`, `
| question |
answer |
| When am I billed? |
You are billed on the first of each month. |
| What are the API rate limits? |
The API rate limits are 1000 requests per minute for standard plans
and 10000 requests per minute for enterprise plans. |
| How do I reset my password? |
To reset your password, go to the login page and click "Forgot
Password". Enter your email address, and you will receive a reset link
within 5 minutes. The link expires after 24 hours. |
`, `
| Component |
Purpose |
| Embedding index |
Fast similarity search |
@pxt.query |
Retrieve context from the database |
@pxt.udf |
Build the augmented prompt |
| Computed columns |
Chain the pipeline together |
`];
Create a retrieval-augmented generation system that answers questions
using your documents as context.
## Problem
You want an LLM to answer questions using your specific documents—not
just its training data. You need to retrieve relevant context and
include it in the prompt.
## Solution
**What’s in this recipe:**
* Embed and index documents for retrieval
* Create a query function that retrieves context
* Generate answers grounded in your documents
You build a pipeline that: (1) embeds documents, (2) finds relevant
chunks for a query, and (3) generates an answer using those chunks as
context.
### Setup
```python theme={null}
%pip install -qU pixeltable openai
```
```python theme={null}
import getpass
import os
if 'OPENAI_API_KEY' not in os.environ:
os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key: ')
```
```python theme={null}
import pixeltable as pxt
from pixeltable.functions.openai import chat_completions, embeddings
```
```python theme={null}
# Create a fresh directory
pxt.drop_dir('rag_demo', force=True)
pxt.create_dir('rag_demo')
```
Connected to Pixeltable database at: postgresql+psycopg://postgres:@/pixeltable?host=/Users/pjlb/.pixeltable/pgdata
Created directory 'rag\_demo'.
\
### Step 1: create document store with embeddings
```python theme={null}
# Create table for document chunks
chunks = pxt.create_table(
'rag_demo/chunks', {'doc_id': pxt.String, 'chunk_text': pxt.String}
)
```
Created table 'chunks'.
```python theme={null}
# Add embedding index for semantic search
chunks.add_embedding_index(
column='chunk_text',
string_embed=embeddings.using(model='text-embedding-3-small'),
)
```
### Step 2: load documents
```python theme={null}
# Sample knowledge base (in production, load from files/database)
documents = [
{
'doc_id': 'password-reset',
'chunk_text': 'To reset your password, go to the login page and click "Forgot Password". Enter your email address and you will receive a reset link within 5 minutes. The link expires after 24 hours.',
},
{
'doc_id': 'password-reset',
'chunk_text': 'Password requirements: minimum 8 characters, at least one uppercase letter, one number, and one special character. Passwords expire every 90 days for security.',
},
{
'doc_id': 'account-settings',
'chunk_text': 'To update your profile, navigate to Settings > Account. You can change your display name, email address, and notification preferences. Changes take effect immediately.',
},
{
'doc_id': 'billing',
'chunk_text': 'Billing occurs on the first of each month. You can view invoices under Settings > Billing. To change your payment method, click "Update Payment" and enter your new card details.',
},
{
'doc_id': 'api-access',
'chunk_text': 'API keys can be generated in Settings > Developer. Each key has configurable permissions. Rate limits are 1000 requests per minute for standard plans, 10000 for enterprise.',
},
]
chunks.insert(documents)
```
Inserting rows into \`chunks\`: 5 rows \[00:00, 345.31 rows/s]
Inserted 5 rows with 0 errors.
5 rows inserted, 15 values computed.
### Step 3: create the RAG query function
```python theme={null}
# Define a query function that retrieves context
@pxt.query
def retrieve_context(query: str, top_k: int = 3):
"""Retrieve the most relevant chunks for a query."""
sim = chunks.chunk_text.similarity(string=query)
return (
chunks.where(sim > 0.5)
.order_by(sim, asc=False)
.limit(top_k)
.select(doc_id=chunks.doc_id, text=chunks.chunk_text)
)
```
```python theme={null}
# View retrieved context for a query
query = 'What are the key features?'
context_chunks = retrieve_context(query)
context_chunks
```
retrieve\_context('What are the key features?')
### Step 4: generate answers with context
```python theme={null}
# Create a table for questions/answers
qa = pxt.create_table('rag_demo/qa', {'question': pxt.String})
```
Created table 'qa'.
```python theme={null}
# Add retrieval step
qa.add_computed_column(context=retrieve_context(qa.question, top_k=3))
```
Added 0 column values with 0 errors.
No rows affected.
```python theme={null}
# Build the RAG prompt
@pxt.udf
def build_rag_prompt(question: str, context: list[dict]) -> str:
context_text = '\n\n'.join(
[f'[{c["doc_id"]}]: {c["text"]}' for c in context]
)
return f"""Answer the question based only on the provided context. If the context doesn't contain the answer, say "I don't have information about that."
Context:
{context_text}
Question: {question}
Answer:"""
qa.add_computed_column(prompt=build_rag_prompt(qa.question, qa.context))
```
Added 0 column values with 0 errors.
No rows affected.
```python theme={null}
# Generate answer
qa.add_computed_column(
response=chat_completions(
messages=[{'role': 'user', 'content': qa.prompt}],
model='gpt-4o-mini',
)
)
qa.add_computed_column(answer=qa.response.choices[0].message.content)
```
Added 0 column values with 0 errors.
Added 0 column values with 0 errors.
No rows affected.
### Ask questions
```python theme={null}
# Insert questions
questions = [
{'question': 'How do I reset my password?'},
{'question': 'What are the API rate limits?'},
{'question': 'When am I billed?'},
]
qa.insert(questions)
```
Inserting rows into \`qa\`: 3 rows \[00:00, 872.12 rows/s]
Inserted 3 rows with 0 errors.
3 rows inserted, 18 values computed.
```python theme={null}
# View answers
qa.select(qa.question, qa.answer).collect()
```
## Explanation
**RAG pipeline flow:**
Question → Embed → Retrieve similar chunks → Build prompt with context → Generate answer
**Key components:**
**Scaling tips:**
* Use `doc-chunk-for-rag` recipe to split long documents
* Adjust `top_k` to balance context size vs. relevance
* Consider metadata filtering for large knowledge bases
## See also
* [Chunk documents for
RAG](/howto/cookbooks/text/doc-chunk-for-rag) -
Split documents into chunks
* [Create text
embeddings](/howto/cookbooks/search/embed-text-openai) -
Embedding fundamentals
* [Semantic text
search](/howto/cookbooks/search/search-semantic-text) -
Search patterns
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