# 02. Naive RAG
## Naive RAG
**step**
1. Perform Naive RAG
### Preferences
```python
# Configuration file for managing API keys as environment variables
from dotenv import load_dotenv
# Load API key information
load_dotenv()
```
```
True
```
```python
# Set up LangSmith tracking. https://smith.langchain.com
# !pip install -qU langchain-teddynote
from langchain_teddynote import logging
# Enter a project name.
logging.langsmith("CH17-LangGraph-Structures")
```
```
Start tracking LangSmith.
[Project name]
CH17-LangGraph-Structures
```
### Basic PDF-based Retrieval Chain creation
Here, we create a Retrieval Chain based on PDF documents. Retrieval Chain with the simplest structure.
However, LangGraph creates Retirever and Chain separately. Only then can you do detailed processing for each node.
```python
from rag.pdf import PDFRetrievalChain
# Load a PDF document.
pdf = PDFRetrievalChain(["data/SPRI_AI_Brief_2023년12월호_F.pdf"]).create_chain()
# Create a retriever and a chain.
pdf_retriever = pdf.retriever
pdf_chain = pdf.chain
```
First, use pdf\_retriever to get your search results.
```python
search_result = pdf_retriever.invoke("Please tell us the companies and amounts invested in Anthropic..")
search_result
```
```
[Document (metadata={'source':'data/SPRI_AI_Brief_2023 Year 12 _F.pdf','file_path':'data/SPRI_AI_Brief_2023 Year 12 _F.pdf','page': 13, 'total_ Policy/law 2. Enterprise/Industry 3. Technology / Research 4. Workforce/training\ngoggles create $20 billion investment in Aspics Enhances AI cooperation\nKEY Contents\nn Google has agreed to invest up to $20 billion in Aspics and has invested $500 million first, and Aspics has also signed a contract to use cloud services with \nnn 3rd Cloud Operator Google, Microsoft and Amazon are representative companies of the next generation AI model. Up to $200 billion investment agreement and cloud service delivery to Ansropic'), Document (metadata={'source':' data/SPRI_AI_Brief_2023 December issue_F.pdf','file_path':'data/SPRI_AI_Brief_2023 December issue_F.pdf','page': 13,'total_pages': 23,'Author':'dj', 00'",'ModDate': "D:20231208132838+09'00'",'PDFVersion': '1.4'}, page_content='£Google, up to $200 billion investment agreement in Ansropic... ] 00'",'ModDate': "D:20231208132838+09'00'",'PDFVersion': '1.4'}, page_content='£Google, up to $200 billion investment agreement in Ansropic... ]
```
Pass the previously searched result to the context of the chain.
```python
Copy# Generate answers based on search results.
answer = pdf_chain.invoke(
{
"question": "Please tell us the companies and amounts invested in Anthropic.",
"context": search_result,
"chat_history": [],
}
)
print(answer)
```
```
Google has agreed to invest up to $200 billion in Ansropic, of which $500 million has been invested first. In addition, Google has already invested $550 million in February 2023. Amazon has released an investment plan of up to $400 billion in Antwerp.
**Source**
-data/SPRI_AI_Brief_2023 December issue_F.pdf (page 13)
```
### State definition
`State` : Defines the state of sharing between nodes and nodes in Graph.
Generally `TypedDict` Use format.
```python
from typing import Annotated, TypedDict
from langgraph.graph.message import add_messages
# GraphState State Definition
class GraphState(TypedDict):
question: Annotated[str, "Question"] # question
context: Annotated[str, "Context"] # Search results for the document
answer: Annotated[str, "Answer"] # answerw
messages: Annotated[list, add_messages] # 메시지(누적되는 list)
```
### Node definition
* `Nodes` : Nodes that handle each step. Usually implemented as a Python function. Input and output are state values.
**Reference**
* `State` Updated after performing a defined logic with input `State` Returns.
```python
from langchain_teddynote.messages import messages_to_history
from rag.utils import format_docs
# Document Search Node
def retrieve_document(state: GraphState) -> GraphState:
# Get the question from the state.
latest_question = state["question"]
# Search the documentation to find relevant articles.
retrieved_docs = pdf_retriever.invoke(latest_question)
# Formats the retrieved document (for input into the prompt)
retrieved_docs = format_docs(retrieved_docs)
# Stores the searched document in the context key.
return GraphState(context=retrieved_docs)
# Generate Answer Node
def llm_answer(state: GraphState) -> GraphState:
# Get the question from the state.
latest_question = state["question"]
# Get the searched documents in status.
context = state["context"]
# Call the chain to generate an answer.
response = pdf_chain.invoke(
{
"question": latest_question,
"context": context,
"chat_history": messages_to_history(state["messages"]),
}
)
# Stores generated answers, (user's questions, answers) messages in the state.
return GraphState(
answer=response, messages=[("user", latest_question), ("assistant", response)]
)
```
### Edges
* `Edges` : Currently `State` Run next based on `Node` Python function to determine.
General edges, conditional edges, and more.
```python
from langgraph.graph import END, StateGraph
from langgraph.checkpoint.memory import MemorySaver
# create a graph
workflow = StateGraph(GraphState)
# node definition
workflow.add_node("retrieve", retrieve_document)
workflow.add_node("llm_answer", llm_answer)
# edge definition
workflow.add_edge("retrieve", "llm_answer") # 검색 -> 답변
workflow.add_edge("llm_answer", END) # 답변 -> 종료
# Setting the graph entry point
workflow.set_entry_point("retrieve")
# Set checkpoint
memory = MemorySaver()
# compile
app = workflow.compile(checkpointer=memory)
```
Visualize compa-like graphs.
```
from langchain_teddynote.graphs import visualize_graph
visualize_graph(app)
```
### Graph execution
* `config` Parameters convey the necessary setting information when running the graph.
* `recursion_limit` : Set the maximum number of recurses when running the graph.
* `inputs` : Pass the required input information when running the graph.
**Reference**
* Message output streaming [Everything in LangGraph streaming mode ](https://wikidocs.net/265770)Please refer to.
Under `stream_graph` A function is a function that only streams certain nodes.
You can easily check the streaming output of a specific node.
```python
from langchain_core.runnables import RunnableConfig
from langchain_teddynote.messages import stream_graph, random_uuid
# config settings (max recursion count, thread_id)
config = RunnableConfig(recursion_limit=20, configurable={"thread_id": random_uuid()})
# Enter your question
inputs = GraphState(question="Please tell me the companies and amounts invested in Anthropic.")
# Running the graph
stream_graph(app, inputs, config, ["llm_answer"])
```
```
==================================================
🔄 Node: llm_answer🔄
- - - - - - - - - - - - - - - - - - - - - - - - - - - -
Google has agreed to invest up to $20 billion in Ansropic, of which $500 million has been invested first. Amazon has released an investment plan of up to $400 billion in Antwerp.
**Source**
-data/SPRI_AI_Brief_2023 December issue_F.pdf (page 14)
```
```python
outputs = app.get_state(config).values
print(f'Question: {outputs["question"]}')
print("===" * 20)
print(f'Answer:\n{outputs["answer"]}')
```
```
Question: Please tell us the amount of your investment and the companies that have invested in Ansropic.
============================================================
Answer:
Google has agreed to invest up to $20 billion in Ansropic, of which $500 million has been invested first. Amazon has released an investment plan of up to $400 billion in Antwerp.
**Source**
-data/SPRI_AI_Brief_2023 December issue_F.pdf (page 14)
```