> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/JhonHander/obstetrics-rag-benchmark/llms.txt
> Use this file to discover all available pages before exploring further.
# Simple Semantic RAG
> Basic RAG pipeline using semantic search with ChromaDB
## Overview
The Simple Semantic RAG module implements a basic RAG (Retrieval-Augmented Generation) pipeline. It uses a semantic retriever to find relevant documents in a ChromaDB vector store and then uses a language model to generate an answer based on the retrieved context.
**Module:** `src.rag.simple`
**Source:** `src/rag/simple.py`
## Configuration
### Default Models
```python theme={null}
embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
llm = ChatOpenAI(model_name="gpt-4o", temperature=0)
```
### Vector Store
```python theme={null}
vectorstore = Chroma(
persist_directory=str(chroma_db_dir),
embedding_function=embeddings,
collection_name="guia_embarazo_parto"
)
```
### Retriever Configuration
```python theme={null}
retriever = vectorstore.as_retriever(search_kwargs={"k": 5})
```
Retrieves the top 5 most similar documents using semantic search.
## Prompt Template
The module uses a medical-focused prompt template:
```python theme={null}
qa_template = """
You are a medical expert specializing in pregnancy and childbirth.
Your task is to analyze the provided medical context and answer the user's question accurately and concisely.
STRICT INSTRUCTIONS:
1. **Base your answer exclusively on the information within the MEDICAL CONTEXT section.** Do not use any external knowledge.
2. *The context is ordered by relevance.* Give the highest priority to the first few documents (e.g., Documents 1-2) as they are the most relevant. Use subsequent documents to supplement your answer if needed.
3. *Provide a direct and integrated answer.* Your response should be a single, well-written paragraph. Start with a direct answer to the question, then seamlessly incorporate specific details, data, and recommendations from the context to support it.
4. *If the context does not contain enough information to answer the question, state that clearly.* Do not try to invent an answer.
5. *remember always answer in spanish*
MEDICAL CONTEXT (ordered by relevance):
{context}
QUESTION: {question}
DETAILED MEDICAL ANSWER:
"""
```
## Functions
### format\_docs
```python theme={null}
def format_docs(docs: List[Document]) -> str
```
Formats the retrieved documents to be included in the final prompt.
A list of retrieved LangChain Document objects
A formatted string containing the content of the documents with source and page metadata
**Example Output Format:**
```
--- Document 1 ---
Source: guide.pdf, Page: 42
Content: [document content]
--- Document 2 ---
Source: guide.pdf, Page: 43
Content: [document content]
```
### process\_semantic\_query
```python theme={null}
def process_semantic_query(query: str, custom_llm: ChatOpenAI = None) -> Dict[str, Any]
```
Processes a query using the simple semantic RAG pipeline.
The user's question
Custom LLM to use. If None, uses default llm (gpt-4o)
A dictionary containing:
* `answer` (str): The generated answer
* `contexts` (List\[str]): List of retrieved document contents
* `retrieved_documents` (List\[Document]): Full Document objects
* `metrics` (dict): Token usage and cost metrics
* `input_tokens` (int): Number of input tokens
* `output_tokens` (int): Number of output tokens
* `total_tokens` (int): Total tokens used
* `usage_source` (str): Source of usage data
* `cost` (float): Total cost in USD
* `cost_source` (str): Source of cost calculation
### query\_for\_evaluation
```python theme={null}
def query_for_evaluation(
question: str,
llm_model: str = None,
custom_llm: Optional[BaseChatModel] = None
) -> dict
```
A wrapper function for RAG evaluation frameworks like Ragas. This function processes a question and returns a dictionary structured for easy integration with evaluation tools.
The question to process
Model name to use. If None, uses default "gpt-4o"
Pre-configured language model. Takes precedence over llm\_model
A dictionary containing:
* `question` (str): The original question
* `answer` (str): The generated answer
* `contexts` (List\[str]): Retrieved document contents
* `source_documents` (List\[Document]): Full retrieved documents
* `metadata` (dict): Comprehensive metadata including:
* `num_contexts` (int): Number of retrieved contexts
* `retrieval_method` (str): "semantic\_only"
* `llm_model` (str): Model name used
* `provider` (str): Provider (e.g., "openai")
* `model_id` (str): Full model identifier
* `embedding_model` (str): "text-embedding-3-small"
* `execution_time` (float): Total execution time in seconds
* `input_tokens` (int): Input tokens used
* `output_tokens` (int): Output tokens generated
* `total_cost` (float): Total cost in USD
* `tokens_used` (int): Total tokens (input + output)
* `usage_source` (str): Source of usage metrics
* `cost_source` (str): Source of cost calculation
## Usage Example
```python theme={null}
from src.rag.simple import query_for_evaluation
# Basic usage with default model
result = query_for_evaluation(
question="¿Cuáles son los síntomas del embarazo temprano?"
)
print(result["answer"])
print(f"Cost: ${result['metadata']['total_cost']:.6f}")
print(f"Contexts retrieved: {result['metadata']['num_contexts']}")
# Using a custom model
result = query_for_evaluation(
question="¿Qué es la preeclampsia?",
llm_model="gpt-4o-mini"
)
# Using a pre-configured LLM
from langchain_openai import ChatOpenAI
custom_llm = ChatOpenAI(model_name="gpt-4o", temperature=0.3)
result = query_for_evaluation(
question="¿Cuándo debo ir al hospital durante el parto?",
custom_llm=custom_llm
)
```
## Pipeline Flow
1. **Retrieve**: Uses semantic search to find the top 5 most relevant documents from ChromaDB
2. **Format**: Formats documents with source and page metadata
3. **Generate**: Uses the LLM to generate an answer based on the retrieved context
4. **Track**: Captures token usage and cost metrics
## Key Features
* Simple and straightforward semantic search
* Automatic cost and token tracking
* Support for custom LLMs
* Medical domain-specific prompting
* Structured output for evaluation frameworks