LlamaIndex
and Graph RAG
2024-11-26 Memgraph Webinar
What are we talking about?
- What is LlamaIndex
- What is RAG
- How graph RAG works
- Going beyond RAG to agents
- Building agentic workflows
What is LlamaIndex?
Python: docs.llamaindex.ai
TypeScript: ts.llamaindex.ai
LlamaParse
cloud.llamaindex.ai
Free for 1000 pages/day!
LlamaCloud
2. Get on the waitlist!
1. Sign up
LlamaHub
- Data loaders
- Embedding models
- Vector stores
- LLMs
- Agent tools
- Pre-built strategies
- More!
Why LlamaIndex?
- Build faster
- Skip the boilerplate
- Avoid early pitfalls
- Get best practices for free
- Go from prototype to production
Q: What does LlamaIndex actually do?
A: Agentic RAG
Why RAG
is necessary
Sematic search
Ways to do RAG #1:
Text to SQL
Ways to do RAG #2:
Graph RAG
Ways to do RAG #3:
Basic RAG pipeline
Constructing a graph
Vector retrieval
Forms of graph RAG retrieval #1:
Text to Cypher
Forms of graph RAG retrieval #2:
Synonym retrieval
Forms of graph RAG retrieval #3:
Limitations of RAG
Naive RAG failure points #1:
Summarization
Naive RAG failure points #2:
Comparison
Naive RAG failure points #3:
Multi-part questions
RAG is necessary
but not sufficient
Two ways
to improve RAG:
- Improve your data
- Improve your querying
What is an agent anyway?
- Semi-autonomous software
- Accepts a goal
- Uses tools to achieve that goal
- Exact steps to resolution not specified
RAG pipeline
⚠️ Single-shot
⚠️ No query understanding/planning
⚠️ No tool use
⚠️ No reflection, error correction
⚠️ No memory (stateless)
Agentic RAG
✅ Multi-turn
✅ Query / task planning layer
✅ Tool interface for external environment
✅ Reflection
✅ Memory for personalization
From simple to advanced agents
Routing
Conversation memory
Query planning
Tool use
Tools unleash the power of LLMs
Combine agentic strategies
and then go further
- Routing
- Memory
- Planning
- Tool use
Agentic strategies
- Multi-turn
- Reasoning
- Reflection
Full agent
3 agent
reasoning loops
- Sequential
- DAG-based
- Tree-based
Sequential reasoning
DAG-based reasoning
Self reflection
Tree-based reasoning
Exploration vs exploitation
Workflows
Why workflows?
Workflows primer
from llama_index.llms.openai import OpenAI
class OpenAIGenerator(Workflow):
@step
async def generate(self, ev: StartEvent) -> StopEvent:
query = ev.get("query")
llm = OpenAI()
response = await llm.acomplete(query)
return StopEvent(result=str(response))
w = OpenAIGenerator(timeout=10, verbose=False)
result = await w.run(query="What's LlamaIndex?")
print(result)Looping
class LoopExampleFlow(Workflow):
@step
async def answer_query(self, ev: StartEvent | QueryEvent ) -> FailedEvent | StopEvent:
query = ev.query
# try to answer the query
random_number = random.randint(0, 1)
if (random_number == 0):
return FailedEvent(error="Failed to answer the query.")
else:
return StopEvent(result="The answer to your query")
@step
async def improve_query(self, ev: FailedEvent) -> QueryEvent | StopEvent:
# improve the query or decide it can't be fixed
random_number = random.randint(0, 1)
if (random_number == 0):
return QueryEvent(query="Here's a better query.")
else:
return StopEvent(result="Your query can't be fixed.")
l = LoopExampleFlow(timeout=10, verbose=True)
result = await l.run(query="What's LlamaIndex?")
print(result)Visualization
draw_all_possible_flows()Keeping state
class RAGWorkflow(Workflow):
@step
async def ingest(self, ctx: Context, ev: StartEvent) -> Optional[StopEvent]:
dataset_name = ev.dataset
documents = SimpleDirectoryReader("data").load_data()
ctx.set("INDEX", VectorStoreIndex.from_documents(documents=documents))
return StopEvent(result=f"Indexed {len(documents)} documents.")
...Customizability
class MyWorkflow(RAGWorkflow):
@step
def rerank(
self, ctx: Context, ev: Union[RetrieverEvent, StartEvent]
) -> Optional[QueryResult]:
# my custom reranking logic here
w = MyWorkflow(timeout=60, verbose=True)
result = await w.run(query="Who is Paul Graham?")Workflows enable arbitrarily complex applications
Recap
- What is LlamaIndex
- What is RAG
- How graph RAG works
- Going beyond RAG to agents
- Building agentic workflows
What's next?
Thanks!
Follow me on BlueSky:
@seldo.com
Please don't add me on LinkedIn.
LlamaIndex and Graph RAG
By seldo
