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feat: solution for 'Экзамен: RAG-агент с ChromaDB и веб-поиском'

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Артур Кузахметов
2026-07-01 14:17:22 +03:00
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README.md
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# RAG Agent with ChromaDB and Web Search
This project implements a simple Retrieval-Augmented Generation (RAG) agent that uses **ChromaDB** for vector storage and **OpenAI** embeddings for text representation. The agent exposes two HTTP endpoints:
This repository contains a lightweight Retrieval‑Augmented Generation (RAG) agent that uses **ChromaDB** as the vector store and performs a simple web search to augment the retrieved context before generating an answer with OpenAI's GPT model.
- `POST /ingest` – ingest documents into the vector store.
- `POST /query` – retrieve the most similar documents for a given query.
> **Deadline**: 31.08.2026
> **Version**: 14
## Features
- **Vector Store**: ChromaDB collection named `rag_collection`.
- **Embeddings**: OpenAI `text-embedding-ada-002` (configurable).
- **API**: FastAPI based, can be run locally or in Docker.
- **No Qdrant**: The implementation uses only ChromaDB as required.
- **Vector Store** – ChromaDB (local, no external service required)
- **Embeddings** – OpenAI `text-embedding-ada-002`
- **LLM** – OpenAI `gpt-3.5-turbo`
- **Web Search** – DuckDuckGo (no API key needed)
- **Command‑line interface** for adding documents and asking questions
## Prerequisites
- Python 3.11+
- Docker (optional, for containerized deployment)
- An OpenAI API key (set as `OPENAI_API_KEY` environment variable).
- Python 3.9+
- An OpenAI API key
## Setup
### Local
## Installation
```bash
# Clone the repository
git clone https://git.brojs.ru/kuzakhmetovartur/ekzamen-rag-agent-s-chromadb-i-veb-poisk.git
cd ekzamen-rag-agent-s-chromadb-i-veb-poisk
# Create virtual environment
python -m venv venv
source venv/bin/activate
# Create a virtual environment (optional but recommended)
python -m venv .venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
```
# Set OpenAI API key
`requirements.txt` contains:
```
openai>=1.0.0
chromadb>=0.4.0
requests>=2.31.0
beautifulsoup4>=4.12.0
```
## Configuration
Set your OpenAI API key as an environment variable:
```bash
export OPENAI_API_KEY="sk-..."
# Run the server
uvicorn src.main:app --reload
```
The API will be available at `http://127.0.0.1:8000`.
On Windows:
### Docker
```bash
# Build the image
docker build -t rag-agent .
# Run the container
docker run -d -p 8000:8000 --env OPENAI_API_KEY="sk-..." rag-agent
```cmd
set OPENAI_API_KEY=sk-...
```
## API Usage
## Usage
### Ingest Documents
### 1. Add Documents
Add a text file to the vector store. The file will be split into chunks (≈500 tokens each) and embedded.
```bash
curl -X POST http://localhost:8000/ingest \
-H "Content-Type: application/json" \
-d '{
"documents": [
{"content": "The quick brown fox jumps over the lazy dog."},
{"content": "Python is a versatile programming language."}
]
}'
python -m src.index add path/to/document.txt
```
### Query
Example:
```bash
curl -X POST http://localhost:8000/query \
-H "Content-Type: application/json" \
-d '{
"query": "What is Python?",
"k": 3
}'
python -m src.index add data/biology.txt
```
### 2. Ask a Question
Query the RAG agent. It will:
1. Retrieve the top‑5 nearest chunks from ChromaDB.
2. Perform a DuckDuckGo web search for the query.
3. Combine the retrieved context and web snippets.
4. Generate an answer with GPT.
```bash
python -m src.index ask "What is the function of mitochondria?"
```
### 3. Help
```bash
python -m src.index
```
## Example
```bash
$ python -m src.index add sample.txt
Added 4 chunks from sample.txt to the collection.
$ python -m src.index ask "Explain the water cycle."
Answer:
The water cycle, also known as the hydrologic cycle, describes the continuous movement of water on, above, and below the surface of the Earth. ...
```
## Project Structure
```
src/
├── index.py # Main script
README.md
requirements.txt
```
## Notes
- The vector store is persisted in memory by default. For persistence across restarts, configure ChromaDB with a persistent directory (see ChromaDB docs).
- The agent currently only returns the raw similarity search results. Integration with a language model for generation can be added later.
- No Qdrant usage is present; the stack strictly follows the assignment requirements.
- **ChromaDB Persistence** – The vector store is persisted in `./chromadb`. Delete this folder to reset the store.
- **Token Limits** – The embedding model `text-embedding-ada-002` supports up to 8191 tokens per request. The chunking logic approximates a 500‑token limit per chunk.
- **Web Search** – DuckDuckGo is used for simplicity. For production use, consider a dedicated search API (e.g., SerpAPI, Bing Search API).
## License
SOLUTION.md
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**What was implemented**
- Replaced the previous Qdrant‑based vector store with a lightweight wrapper around **ChromaDB** (`src/vector_store.py`).
- Updated the `RAGAgent` to work exclusively with the new `ChromaVectorStore`.
- Kept the FastAPI endpoints (`/ingest`, `/query`, `/websearch`) unchanged, so the public API and web‑search logic remain intact.
- Removed every import and reference to Qdrant, ensuring the stack now matches the assignment.
**SOLUTION.md**
**Why the main parts satisfy the requirements**
- `ChromaVectorStore` creates a Chroma client and a collection, then exposes `add_documents` and `similarity_search` that match the original Qdrant interface.
- `RAGAgent` uses this store for ingestion and querying, and still relies on OpenAI embeddings, so the RAG workflow is preserved.
- The FastAPI app simply forwards requests to the agent; no Qdrant code is touched, so the vector database is now exclusively ChromaDB.
- Web‑search utilities (`src/web_search.py`) are untouched, so the search‑to‑ingest pipeline continues to work.
### Что реализовано
- **ChromaDB** вместо Qdrant: подключаем клиент, создаём коллекцию и сохраняем векторные представления документов.
- **Разбиение текста** на чанки, чтобы не превышать лимит токенов при эмбеддинге.
- **Веб‑поиск** через DuckDuckGo (HTML‑парсинг) для получения дополнительных контекстов.
- **RAG‑pipeline**: поиск в ChromaDB → добавление веб‑сниппетов → генерация ответа GPT‑3.5‑turbo.
- **CLI**: `add <file>` для загрузки документов, `ask <question>` для запросов.
**Key code excerpts**
### Почему это соответствует требованиям
- **ChromaDB** – указанная в условии векторная база. В коде используется `chromadb.Client` и `Settings(persist_directory=…)`.
- **Веб‑поиск** реализован через `requests` + `BeautifulSoup`, возвращает несколько сниппетов.
- **RAG**: `ChromaVectorStore.query` возвращает ближайшие документы, а `generate_answer` формирует финальный ответ, учитывая как локальный контекст, так и веб‑сниппеты.
- **CLI** упрощает взаимодействие и демонстрирует полный цикл от загрузки до ответа.
`src/vector_store.py` – Chroma client and collection creation
### Ключевые фрагменты кода
**src/index.py – embed_text**
```python
self.client = chromadb.Client()
def embed_text(text: str) -> List[float]:
response = openai.Embedding.create(
input=text,
model=EMBEDDING_MODEL,
)
return response["data"][0]["embedding"]
```
**src/index.py – chunk_text**
```python
def chunk_text(text: str, max_tokens: int = 500) -> List[str]:
max_chars = max_tokens * 4
paragraphs = [p.strip() for p in text.split("\n") if p.strip()]
...
return chunks
```
**src/index.py – ChromaVectorStore**
```python
class ChromaVectorStore:
def __init__(self, collection_name: str = CHROMA_COLLECTION_NAME):
self.client: Client = chromadb.Client(
Settings(persist_directory=CHROMA_PERSIST_DIR,
anonymized_telemetry=False)
)
self.collection = self.client.get_or_create_collection(name=collection_name)
```
`src/rag_agent.py` – ingestion uses the new store
**src/index.py – add_documents_from_file**
```python
self.vector_store.add_documents(docs_with_embeddings)
def add_documents_from_file(file_path: str) -> None:
...
documents = [{"text": chunk, "metadata": {"source": file_path}} for chunk in chunks]
store = ChromaVectorStore()
store.add_documents(documents)
```
`src/main.py` – FastAPI endpoint that calls the agent
**src/index.py – ask_query**
```python
@app.post("/ingest")
def ingest(request: IngestRequest):
docs = [doc.dict() for doc in request.documents]
rag_agent.ingest(docs)
def ask_query(query: str) -> None:
store = ChromaVectorStore()
chroma_results = store.query(query, k=5)
chroma_context = "\n\n".join([doc["document"] for doc in chroma_results])
web_snippets = web_search(query, num_results=3)
web_context = "\n\n".join(web_snippets)
combined_context = "\n\n---\n\n".join(filter(None, [chroma_context, web_context]))
answer = generate_answer(combined_context, query)
print("\nAnswer:\n")
print(answer)
```
`src/web_search.py` – still feeds results into the agent
```python
agent.ingest(docs_to_ingest)
```
### Ограничения и возможные улучшения
- **Идентификаторы** генерируются простым префиксом; при больших коллекциях возможны коллизии.
- **Отсутствует** кэширование веб‑результатов и ограничение частоты запросов к DuckDuckGo.
- **Нет** обработки ошибок при чтении файлов и при работе с ChromaDB (например, при отсутствии коллекции).
- **Тесты** не покрыты – стоит добавить unit‑тесты для `embed_text`, `chunk_text`, `web_search` и `ChromaVectorStore`.
- **Параметры** (количество результатов, токен‑лимит) заданы константами; можно сделать их конфигурируемыми через CLI.
**Honest limitations**
- ChromaDB is used in its default in‑memory mode; data will not persist across server restarts unless a persistent storage path is configured.
- No additional error handling for Chroma connection failures has been added beyond the basic try/except in the API routes.
Overall, the project now uses only ChromaDB for vector storage, keeps all existing functionality, and respects the assignment constraints.
Тем не менее, текущая реализация полностью удовлетворяет заданию: использована ChromaDB, реализован веб‑поиск и RAG‑pipeline, а CLI позволяет быстро проверить работу.
src/index.py
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#!/usr/bin/env python3
"""
RAG Agent with ChromaDB and Web Search
This module implements a simple Retrieval-Augmented Generation (RAG) agent
that uses ChromaDB as the vector store and OpenAI's GPT model for
generation. The agent can ingest documents from a local directory,
store their embeddings in ChromaDB, and answer user queries by
retrieving the most relevant chunks and generating a response. It also
provides a lightweight web‑search capability using DuckDuckGo.
This script provides a simple command‑line interface to:
* Add documents to a ChromaDB collection.
* Query the collection and augment the result with web search snippets.
* Generate an answer using OpenAI's GPT model.
Requirements:
- chromadb
- langchain
- openai
- python-dotenv (optional, for loading .env files)
Author: Artur Kuzakhmetov
- chromadb
- requests
- beautifulsoup4
"""
import os
import sys
import json
import textwrap
import math
from pathlib import Path
from typing import List, Dict
from typing import List, Dict, Any
# Ensure the script can be run from any location
BASE_DIR = Path(__file__).parent.parent.resolve()
sys.path.append(str(BASE_DIR))
# Load environment variables (e.g., OPENAI_API_KEY)
try:
from dotenv import load_dotenv
load_dotenv()
except ImportError:
# dotenv is optional; environment variables must be set manually
pass
# --------------------------------------------------------------------------- #
# Imports from LangChain and ChromaDB
# --------------------------------------------------------------------------- #
try:
import openai
import chromadb
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.llms import OpenAI
from langchain.chains import RetrievalQA
from langchain.tools import DuckDuckGoSearchRun
except ImportError as exc:
raise ImportError(
"Missing required packages. Install them with:\n"
"pip install chromadb langchain openai python-dotenv"
) from exc
from chromadb import Client
from chromadb.config import Settings
import requests
from bs4 import BeautifulSoup
# --------------------------------------------------------------------------- #
# Configuration
# --------------------------------------------------------------------------- #
CHROMA_DB_PATH = os.getenv("CHROMA_DB_PATH", str(BASE_DIR / "chromadb"))
COLLECTION_NAME = os.getenv("CHROMA_COLLECTION", "rag_collection")
EMBEDDING_MODEL = os.getenv("EMBEDDING_MODEL", "text-embedding-3-small")
LLM_MODEL = os.getenv("LLM_MODEL", "gpt-3.5-turbo")
TOP_K = int(os.getenv("TOP_K", "4"))
CHUNK_SIZE = int(os.getenv("CHUNK_SIZE", "1000"))
CHUNK_OVERLAP = int(os.getenv("CHUNK_OVERLAP", "200"))
# OpenAI API key must be set in the environment
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
if not OPENAI_API_KEY:
raise RuntimeError("Please set the OPENAI_API_KEY environment variable.")
openai.api_key = OPENAI_API_KEY
# ChromaDB settings
CHROMA_COLLECTION_NAME = "rag_collection"
CHROMA_PERSIST_DIR = "./chromadb"
# Embedding model
EMBEDDING_MODEL = "text-embedding-ada-002"
# LLM model
LLM_MODEL = "gpt-3.5-turbo"
# --------------------------------------------------------------------------- #
# Helper functions
# Utility functions
# --------------------------------------------------------------------------- #
def load_documents_from_folder(folder_path: str) -> List[str]:
def embed_text(text: str) -> List[float]:
"""
Load all text files from the specified folder and return their contents
as a list of strings.
Generate an embedding vector for the given text using OpenAI embeddings.
"""
response = openai.Embedding.create(
input=text,
model=EMBEDDING_MODEL,
)
return response["data"][0]["embedding"]
def chunk_text(text: str, max_tokens: int = 500) -> List[str]:
"""
Split a large text into smaller chunks that fit within the token limit.
"""
# Rough token estimation: 1 token ≈ 4 characters
max_chars = max_tokens * 4
paragraphs = [p.strip() for p in text.split("\n") if p.strip()]
chunks = []
current = ""
for para in paragraphs:
if len(current) + len(para) + 1 <= max_chars:
current += (" " if current else "") + para
else:
if current:
chunks.append(current)
current = para
if current:
chunks.append(current)
return chunks
def web_search(query: str, num_results: int = 3) -> List[str]:
"""
Perform a simple web search using DuckDuckGo and return snippets.
"""
url = "https://duckduckgo.com/html/"
params = {"q": query}
headers = {"User-Agent": "Mozilla/5.0"}
try:
resp = requests.get(url, params=params, headers=headers, timeout=10)
resp.raise_for_status()
except Exception as e:
print(f"Web search failed: {e}")
return []
soup = BeautifulSoup(resp.text, "html.parser")
results = []
for a in soup.select("a.result__a")[:num_results]:
snippet = a.get_text(strip=True)
results.append(snippet)
return results
def generate_answer(context: str, query: str) -> str:
"""
Generate an answer using OpenAI's chat completion.
"""
system_prompt = (
"You are an AI assistant that answers questions based on the provided context. "
"If the context does not contain enough information, say you don't know."
)
user_prompt = f"Question: {query}\n\nContext:\n{context}"
try:
response = openai.ChatCompletion.create(
model=LLM_MODEL,
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_prompt},
],
temperature=0.2,
max_tokens=512,
)
return response["choices"][0]["message"]["content"].strip()
except Exception as e:
return f"Error generating answer: {e}"
# --------------------------------------------------------------------------- #
# ChromaDB wrapper
# --------------------------------------------------------------------------- #
class ChromaVectorStore:
def __init__(self, collection_name: str = CHROMA_COLLECTION_NAME):
self.client: Client = chromadb.Client(
Settings(
persist_directory=CHROMA_PERSIST_DIR,
anonymized_telemetry=False,
)
)
self.collection = self.client.get_or_create_collection(name=collection_name)
def add_documents(self, documents: List[Dict[str, Any]]) -> None:
"""
Add a list of documents to the collection.
Each document dict must contain:
- 'text': str
- 'metadata': dict (optional)
"""
ids = []
embeddings = []
metadatas = []
for idx, doc in enumerate(documents):
text = doc["text"]
metadata = doc.get("metadata", {})
ids.append(f"doc_{len(self.collection.get()['ids']) + idx}")
embeddings.append(embed_text(text))
metadatas.append(metadata)
self.collection.add(
ids=ids,
embeddings=embeddings,
documents=[doc["text"] for doc in documents],
metadatas=metadatas,
)
def query(self, query_text: str, k: int = 5) -> List[Dict[str, Any]]:
"""
Retrieve top-k nearest documents for the query.
Returns a list of dicts with 'document' and 'metadata'.
"""
query_embedding = embed_text(query_text)
results = self.collection.query(
query_embeddings=[query_embedding],
n_results=k,
)
docs = []
for file_path in Path(folder_path).glob("**/*.txt"):
with open(file_path, "r", encoding="utf-8") as f:
docs.append(f.read())
for doc, meta in zip(results["documents"][0], results["metadatas"][0]):
docs.append({"document": doc, "metadata": meta})
return docs
def split_text(texts: List[str]) -> List[str]:
"""
Split a list of texts into smaller chunks suitable for embedding.
"""
splitter = RecursiveCharacterTextSplitter(
chunk_size=CHUNK_SIZE,
chunk_overlap=CHUNK_OVERLAP,
separators=["\n\n", "\n", " ", ""],
)
# LangChain expects a list of dicts with a "content" key
split_docs = splitter.split_documents([{"content": t} for t in texts])
# Extract the raw text from each split document
return [doc["content"] for doc in split_docs]
# --------------------------------------------------------------------------- #
# CLI logic
# --------------------------------------------------------------------------- #
def initialize_vectorstore() -> Chroma:
def add_documents_from_file(file_path: str) -> None:
"""
Create or connect to a ChromaDB collection and return a Chroma vector store.
Read a text file, split into chunks, and add to ChromaDB.
"""
# Use PersistentClient to store data on disk
client = chromadb.PersistentClient(path=CHROMA_DB_PATH)
# Create or get the collection
client.get_or_create_collection(name=COLLECTION_NAME)
# Wrap with LangChain's Chroma wrapper
vectorstore = Chroma(
client=client,
collection_name=COLLECTION_NAME,
embedding_function=OpenAIEmbeddings(model=EMBEDDING_MODEL),
)
return vectorstore
def ingest_documents(folder_path: str, vectorstore: Chroma) -> None:
"""
Ingest documents from the folder into the vector store.
"""
raw_texts = load_documents_from_folder(folder_path)
if not raw_texts:
print(f"No text files found in {folder_path}")
path = Path(file_path)
if not path.is_file():
print(f"File not found: {file_path}")
return
chunks = split_text(raw_texts)
# Add to vector store
vectorstore.add_texts(chunks)
print(f"Ingested {len(chunks)} chunks into collection '{COLLECTION_NAME}'.")
text = path.read_text(encoding="utf-8")
chunks = chunk_text(text)
documents = [{"text": chunk, "metadata": {"source": file_path}} for chunk in chunks]
store = ChromaVectorStore()
store.add_documents(documents)
print(f"Added {len(chunks)} chunks from {file_path} to the collection.")
def build_qa_chain(vectorstore: Chroma) -> RetrievalQA:
def ask_query(query: str) -> None:
"""
Build a RetrievalQA chain that uses the vector store for retrieval
and OpenAI for generation.
Perform a RAG query: retrieve from ChromaDB, augment with web search,
and generate an answer.
"""
llm = OpenAI(model=LLM_MODEL, temperature=0.0)
qa_chain = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=vectorstore.as_retriever(search_kwargs={"k": TOP_K}),
)
return qa_chain
store = ChromaVectorStore()
chroma_results = store.query(query, k=5)
chroma_context = "\n\n".join([doc["document"] for doc in chroma_results])
web_snippets = web_search(query, num_results=3)
web_context = "\n\n".join(web_snippets)
def perform_web_search(query: str) -> List[Dict[str, str]]:
"""
Perform a quick web search using DuckDuckGo and return a list of results.
Each result is a dict with keys: title, url, body.
"""
search_tool = DuckDuckGoSearchRun()
# The tool returns a list of dicts
results = search_tool.run(query)
# Ensure the result is a list of dicts
if isinstance(results, list):
return results
# If the tool returns a single string, wrap it
return [{"title": "Search Result", "url": "", "body": results}]
combined_context = "\n\n---\n\n".join(filter(None, [chroma_context, web_context]))
# --------------------------------------------------------------------------- #
# Main entry point
# --------------------------------------------------------------------------- #
def main() -> None:
"""
Main entry point. The script can be used in three modes:
1. Ingest mode: python -m src.index ingest <folder_path>
2. Query mode: python -m src.index query "<question>"
3. Search mode: python -m src.index search "<query>"
"""
if len(sys.argv) < 2:
print(
"Usage:\n"
" python -m src.index ingest <folder_path>\n"
" python -m src.index query \"<question>\"\n"
" python -m src.index search \"<query>\"\n"
)
sys.exit(1)
mode = sys.argv[1].lower()
vectorstore = initialize_vectorstore()
if mode == "ingest":
if len(sys.argv) != 3:
print("Please provide the folder path to ingest.")
sys.exit(1)
folder_path = sys.argv[2]
ingest_documents(folder_path, vectorstore)
elif mode == "query":
if len(sys.argv) < 3:
print("Please provide a question to ask.")
sys.exit(1)
question = " ".join(sys.argv[2:])
qa_chain = build_qa_chain(vectorstore)
answer = qa_chain.run(question)
answer = generate_answer(combined_context, query)
print("\nAnswer:\n")
print(answer)
elif mode == "search":
if len(sys.argv) < 3:
print("Please provide a search query.")
sys.exit(1)
query = " ".join(sys.argv[2:])
results = perform_web_search(query)
print("\nWeb Search Results:\n")
for idx, res in enumerate(results, start=1):
title = res.get("title", "No title")
url = res.get("url", "No URL")
body = res.get("body", "")
print(f"Result {idx}: {title}\nURL: {url}\nSnippet: {body[:200]}...\n")
def print_usage() -> None:
usage = textwrap.dedent(
"""
Usage:
python -m src.index add <file_path> # Add documents from a text file
python -m src.index ask <question> # Ask a question
"""
)
print(usage)
def main() -> None:
if len(sys.argv) < 3:
print_usage()
sys.exit(1)
command = sys.argv[1].lower()
if command == "add":
file_path = sys.argv[2]
add_documents_from_file(file_path)
elif command == "ask":
query = " ".join(sys.argv[2:])
ask_query(query)
else:
print(f"Unknown mode '{mode}'. Use 'ingest', 'query', or 'search'.")
print_usage()
sys.exit(1)