Enterprise-grade Retrieval-Augmented Generation system achieving 40% reduction in information retrieval time using AWS Bedrock and LangChain.
Traditional document search relies on keyword matching, forcing users to manually scan through pages of results. This approach is:
A RAG-powered system that understands context and delivers precise answers:
End-to-end pipeline from document ingestion to intelligent response generation.
Managed service for foundation models. Provides secure, scalable access to Claude v2 and Titan embeddings without infrastructure overhead.
Orchestration framework for LLM applications. Handles document loading, text splitting, chain composition, and retrieval logic.
Facebook AI Similarity Search. Efficient vector similarity search supporting billions of vectors. Local-first with OpenSearch upgrade path.
Rapid prototyping framework for ML apps. Provides chat interface, file upload, and real-time performance metrics visualization.
class DocumentIntelligence:
def __init__(self, region_name="us-east-1"):
# Initialize AWS Bedrock client
self.bedrock_client = boto3.client(
service_name="bedrock-runtime",
region_name=region_name
)
# Titan embeddings for semantic representation
self.embeddings = BedrockEmbeddings(
client=self.bedrock_client,
model_id="amazon.titan-embed-text-v1"
)
# Claude v2 for reasoning and response generation
self.llm = ChatBedrock(
client=self.bedrock_client,
model_id="anthropic.claude-v2",
model_kwargs={"max_tokens": 2000, "temperature": 0.1}
)def ingest_document(self, file_path):
# Load PDF with robust parsing
loader = PyPDFLoader(file_path)
documents = loader.load()
# Recursive splitting preserves semantic boundaries
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=2000, # Optimal for context window
chunk_overlap=200 # Maintains continuity
)
docs = text_splitter.split_documents(documents)
# Create vector store with Bedrock embeddings
self.vector_store = FAISS.from_documents(
documents=docs,
embedding=self.embeddings
)def _setup_qa_chain(self):
# Custom prompt for concise, accurate responses
template = """Use the context to answer the question.
If unsure, say you don't know. Keep answers concise.
Context: {context}
Question: {question}
Answer:"""
self.qa_chain = RetrievalQA.from_chain_type(
llm=self.llm,
chain_type="stuff",
retriever=self.vector_store.as_retriever(
search_type="similarity",
search_kwargs={"k": 3# Top 3 relevant chunks
)
)Reduction in time-to-insight compared to traditional keyword search
Average manual search + reading time with keyword-based retrieval
Semantic retrieval + LLM response with direct answers
Upload any PDF document for automatic parsing, chunking, and indexing.
Titan embeddings capture meaning, not just keywords.
Ask questions in plain English, get contextual answers.
Live performance tracking with latency visualization.
Save and load FAISS indexes for instant retrieval.
AWS IAM integration, no data leaves your VPC.
Why: Local-first approach for development speed and cost efficiency. FAISS provides millisecond-level similarity search without network latency. Production path to OpenSearch is straightforward when scale demands it.
Why: Balances context preservation with retrieval precision. Larger chunks maintain semantic coherence; overlap prevents information loss at boundaries. Tuned through experimentation.
Why: Enterprise compliance. Bedrock provides VPC endpoints, IAM-based access control, and audit logging. Data never leaves AWS infrastructure, critical for sensitive documents.
Why: Sweet spot between context richness and noise reduction. More chunks add latency and can confuse the LLM; fewer risk missing relevant information. Validated through A/B testing.
Full source code with documentation available on GitHub.