rag-implementation

upstream

Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.

ID: rag-implementation

Volver

RAG Implementation

Master Retrieval-Augmented Generation (RAG) to build LLM applications that provide accurate, grounded responses using external knowledge sources.

When to Use This Skill

Building Q&A systems over proprietary documents
Creating chatbots with current, factual information
Implementing semantic search with natural language queries
Reducing hallucinations with grounded responses
Enabling LLMs to access domain-specific knowledge
Building documentation assistants
Creating research tools with source citation

Core Components

1. Vector Databases

Purpose: Store and retrieve document embeddings efficiently

Options:

Pinecone: Managed, scalable, fast queries
Weaviate: Open-source, hybrid search
Milvus: High performance, on-premise
Chroma: Lightweight, easy to use
Qdrant: Fast, filtered search
FAISS: Meta's library, local deployment

2. Embeddings

Purpose: Convert text to numerical vectors for similarity search

Models:

text-embedding-ada-002 (OpenAI): General purpose, 1536 dims
all-MiniLM-L6-v2 (Sentence Transformers): Fast, lightweight
e5-large-v2: High quality, multilingual
Instructor: Task-specific instructions
bge-large-en-v1.5: SOTA performance

3. Retrieval Strategies

Approaches:

Dense Retrieval: Semantic similarity via embeddings
Sparse Retrieval: Keyword matching (BM25, TF-IDF)
Hybrid Search: Combine dense + sparse
Multi-Query: Generate multiple query variations
HyDE: Generate hypothetical documents

4. Reranking

Purpose: Improve retrieval quality by reordering results

Methods:

Cross-Encoders: BERT-based reranking
Cohere Rerank: API-based reranking
Maximal Marginal Relevance (MMR): Diversity + relevance
LLM-based: Use LLM to score relevance

Quick Start

``python


from langchain.document_loaders import DirectoryLoader
from langchain.text_splitters import RecursiveCharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
from langchain.llms import OpenAI


1. Load documents
loader = DirectoryLoader('./docs', glob="**/*.txt")
documents = loader.load()


2. Split into chunks
text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000,
    chunk_overlap=200,
    length_function=len
)
chunks = text_splitter.split_documents(documents)


3. Create embeddings and vector store
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(chunks, embeddings)


4. Create retrieval chain
qa_chain = RetrievalQA.from_chain_type(
    llm=OpenAI(),
    chain_type="stuff",
    retriever=vectorstore.as_retriever(search_kwargs={"k": 4}),
    return_source_documents=True
)


5. Query
result = qa_chain({"query": "What are the main features?"})
print(result['result'])
print(result['source_documents'])




Advanced RAG Patterns


Pattern 1: Hybrid Search

`python


from langchain.retrievers import BM25Retriever, EnsembleRetriever


Sparse retriever (BM25)
bm25_retriever = BM25Retriever.from_documents(chunks)
bm25_retriever.k = 5


Dense retriever (embeddings)
embedding_retriever = vectorstore.as_retriever(search_kwargs={"k": 5})


Combine with weights
ensemble_retriever = EnsembleRetriever(
    retrievers=[bm25_retriever, embedding_retriever],
    weights=[0.3, 0.7]
)




Pattern 2: Multi-Query Retrieval

`python


from langchain.retrievers.multi_query import MultiQueryRetriever


Generate multiple query perspectives
retriever = MultiQueryRetriever.from_llm(
    retriever=vectorstore.as_retriever(),
    llm=OpenAI()
)


Single query ÔåÆ multiple variations ÔåÆ combined results
results = retriever.get_relevant_documents("What is the main topic?")




Pattern 3: Contextual Compression

`python


from langchain.retrievers import ContextualCompressionRetriever
from langchain.retrievers.document_compressors import LLMChainExtractor


compressor = LLMChainExtractor.from_llm(llm)


compression_retriever = ContextualCompressionRetriever(
    base_compressor=compressor,
    base_retriever=vectorstore.as_retriever()
)


Returns only relevant parts of documents
compressed_docs = compression_retriever.get_relevant_documents("query")




Pattern 4: Parent Document Retriever

`python


from langchain.retrievers import ParentDocumentRetriever
from langchain.storage import InMemoryStore


Store for parent documents
store = InMemoryStore()


Small chunks for retrieval, large chunks for context
child_splitter = RecursiveCharacterTextSplitter(chunk_size=400)
parent_splitter = RecursiveCharacterTextSplitter(chunk_size=2000)


retriever = ParentDocumentRetriever(
    vectorstore=vectorstore,
    docstore=store,
    child_splitter=child_splitter,
    parent_splitter=parent_splitter
)




Document Chunking Strategies


Recursive Character Text Splitter

`python


from langchain.text_splitters import RecursiveCharacterTextSplitter


splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000,
    chunk_overlap=200,
    length_function=len,
    separators=["\n\n", "\n", " ", ""]  # Try these in order
)




Token-Based Splitting

`python


from langchain.text_splitters import TokenTextSplitter


splitter = TokenTextSplitter(
    chunk_size=512,
    chunk_overlap=50
)




Semantic Chunking

`python


from langchain.text_splitters import SemanticChunker


splitter = SemanticChunker(
    embeddings=OpenAIEmbeddings(),
    breakpoint_threshold_type="percentile"
)




Markdown Header Splitter

`python


from langchain.text_splitters import MarkdownHeaderTextSplitter


headers_to_split_on = [
    ("#", "Header 1"),
    ("##", "Header 2"),
    ("###", "Header 3"),
]


splitter = MarkdownHeaderTextSplitter(headers_to_split_on=headers_to_split_on)




Vector Store Configurations


Pinecone

`python


import pinecone
from langchain.vectorstores import Pinecone


pinecone.init(api_key="your-api-key", environment="us-west1-gcp")


index = pinecone.Index("your-index-name")


vectorstore = Pinecone(index, embeddings.embed_query, "text")




Weaviate

`python


import weaviate
from langchain.vectorstores import Weaviate


client = weaviate.Client("http://localhost:8080")


vectorstore = Weaviate(client, "Document", "content", embeddings)




Chroma (Local)

`python


from langchain.vectorstores import Chroma


vectorstore = Chroma(
    collection_name="my_collection",
    embedding_function=embeddings,
    persist_directory="./chroma_db"
)




Retrieval Optimization


1. Metadata Filtering

`python


Add metadata during indexing
chunks_with_metadata = []
for i, chunk in enumerate(chunks):
    chunk.metadata = {
        "source": chunk.metadata.get("source"),
        "page": i,
        "category": determine_category(chunk.page_content)
    }
    chunks_with_metadata.append(chunk)


Filter during retrieval
results = vectorstore.similarity_search(
    "query",
    filter={"category": "technical"},
    k=5
)




2. Maximal Marginal Relevance

`python


Balance relevance with diversity
results = vectorstore.max_marginal_relevance_search(
    "query",
    k=5,
    fetch_k=20,  # Fetch 20, return top 5 diverse
    lambda_mult=0.5  # 0=max diversity, 1=max relevance
)




3. Reranking with Cross-Encoder

`python


from sentence_transformers import CrossEncoder


reranker = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')


Get initial results
candidates = vectorstore.similarity_search("query", k=20)


Rerank
pairs = [[query, doc.page_content] for doc in candidates]
scores = reranker.predict(pairs)


Sort by score and take top k
reranked = sorted(zip(candidates, scores), key=lambda x: x[1], reverse=True)[:5]




Prompt Engineering for RAG


Contextual Prompt

`python


prompt_template = """Use the following context to answer the question. If you cannot answer based on the context, say "I don't have enough information."


Context:
{context}


Question: {question}


Answer:"""




With Citations

`python


prompt_template = """Answer the question based on the context below. Include citations using [1], [2], etc.


Context:
{context}


Question: {question}


Answer (with citations):"""




With Confidence

`python


prompt_template = """Answer the question using the context. Provide a confidence score (0-100%) for your answer.


Context:
{context}


Question: {question}


Answer:
Confidence:"""




Evaluation Metrics

`python


def evaluate_rag_system(qa_chain, test_cases):
    metrics = {
        'accuracy': [],
        'retrieval_quality': [],
        'groundedness': []
    }


    for test in test_cases:
        result = qa_chain({"query": test['question']})


        # Check if answer matches expected
        accuracy = calculate_accuracy(result['result'], test['expected'])
        metrics['accuracy'].append(accuracy)


        # Check if relevant docs were retrieved
        retrieval_quality = evaluate_retrieved_docs(
            result['source_documents'],
            test['relevant_docs']
        )
        metrics['retrieval_quality'].append(retrieval_quality)


        # Check if answer is grounded in context
        groundedness = check_groundedness(
            result['result'],
            result['source_documents']
        )
        metrics['groundedness'].append(groundedness)


    return {k: sum(v)/len(v) for k, v in metrics.items()}

Resources

references/vector-databases.md: Detailed comparison of vector DBs
references/embeddings.md: Embedding model selection guide
references/retrieval-strategies.md: Advanced retrieval techniques
references/reranking.md: Reranking methods and when to use them
references/context-window.md: Managing context limits
assets/vector-store-config.yaml: Configuration templates
assets/retriever-pipeline.py: Complete RAG pipeline
assets/embedding-models.md: Model comparison and benchmarks

Best Practices

1. Chunk Size: Balance between context and specificity (500-1000 tokens)

2. Overlap: Use 10-20% overlap to preserve context at boundaries

3. Metadata: Include source, page, timestamp for filtering and debugging

4. Hybrid Search: Combine semantic and keyword search for best results

5. Reranking: Improve top results with cross-encoder

6. Citations: Always return source documents for transparency

7. Evaluation: Continuously test retrieval quality and answer accuracy

8. Monitoring: Track retrieval metrics in production

Common Issues

Poor Retrieval: Check embedding quality, chunk size, query formulation
Irrelevant Results: Add metadata filtering, use hybrid search, rerank
Missing Information: Ensure documents are properly indexed
Slow Queries: Optimize vector store, use caching, reduce k
Hallucinations: Improve grounding prompt, add verification step

Informacion

Estado	Activo
Origen	upstream
Usos	3
Ultimo uso	23/01 16:57
Actualizado	09/01/2026 02:14

Archivo origen

Z:\Repositorios\core\upstream\plugins\llm-application-dev\skills\rag-implementation\SKILL.md