From 7623e81a9f1d1e10f24562fce1c858d180283760 Mon Sep 17 00:00:00 2001
From: Maro <25640912+commitBlob@users.noreply.github.com>
Date: Tue, 30 Sep 2025 12:40:18 +0100
Subject: [PATCH] feat: add LangGraph Agentic RAG implementation (#2)

---
 RAG/.gitignore                           | 119 +++++++++++++++++++++++
 RAG/README.md                            | 115 ++++++++++++++++++++++
 RAG/graph/__init__.py                    |   0
 RAG/graph/chains/__init__.py             |   0
 RAG/graph/chains/answer_grader.py        |  25 +++++
 RAG/graph/chains/generation.py           |   8 ++
 RAG/graph/chains/hallucination_grader.py |  24 +++++
 RAG/graph/chains/retrieval_grader.py     |  25 +++++
 RAG/graph/chains/router.py               |  29 ++++++
 RAG/graph/consts.py                      |   4 +
 RAG/graph/graph.py                       |  99 +++++++++++++++++++
 RAG/graph/nodes/__init__.py              |   6 ++
 RAG/graph/nodes/generate.py              |  21 ++++
 RAG/graph/nodes/grade_documents.py       |  42 ++++++++
 RAG/graph/nodes/retrieve.py              |  20 ++++
 RAG/graph/nodes/web_search.py            |  43 ++++++++
 RAG/graph/state.py                       |  18 ++++
 RAG/graph/tests/__init__.py              |   0
 RAG/graph/tests/test_chains.py           |  76 +++++++++++++++
 RAG/graph_output.png                     | Bin 0 -> 27419 bytes
 RAG/ingestion.py                         |  35 +++++++
 RAG/main.py                              |   9 ++
 22 files changed, 718 insertions(+)
 create mode 100644 RAG/.gitignore
 create mode 100644 RAG/README.md
 create mode 100644 RAG/graph/__init__.py
 create mode 100644 RAG/graph/chains/__init__.py
 create mode 100644 RAG/graph/chains/answer_grader.py
 create mode 100644 RAG/graph/chains/generation.py
 create mode 100644 RAG/graph/chains/hallucination_grader.py
 create mode 100644 RAG/graph/chains/retrieval_grader.py
 create mode 100644 RAG/graph/chains/router.py
 create mode 100644 RAG/graph/consts.py
 create mode 100644 RAG/graph/graph.py
 create mode 100644 RAG/graph/nodes/__init__.py
 create mode 100644 RAG/graph/nodes/generate.py
 create mode 100644 RAG/graph/nodes/grade_documents.py
 create mode 100644 RAG/graph/nodes/retrieve.py
 create mode 100644 RAG/graph/nodes/web_search.py
 create mode 100644 RAG/graph/state.py
 create mode 100644 RAG/graph/tests/__init__.py
 create mode 100644 RAG/graph/tests/test_chains.py
 create mode 100644 RAG/graph_output.png
 create mode 100644 RAG/ingestion.py
 create mode 100644 RAG/main.py

diff --git a/RAG/.gitignore b/RAG/.gitignore
new file mode 100644
index 0000000..38abdb8
--- /dev/null
+++ b/RAG/.gitignore
@@ -0,0 +1,119 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+.chroma/*
+.chroma
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+# VS Code
+.vscode/
+*.code-workspace
+
+# PyCharm
+.idea/
+*.iml
+
+# Jupyter
+.ipynb_checkpoints/
+
+# macOS
+.DS_Store
+
+# Windows
+Thumbs.db
\ No newline at end of file
diff --git a/RAG/README.md b/RAG/README.md
new file mode 100644
index 0000000..866bb61
--- /dev/null
+++ b/RAG/README.md
@@ -0,0 +1,115 @@
+# Playground/RAG
+
+Part of the [playground](https://github.com/commitBlob/playground) repository collection.
+
+## LangGraph Agentic RAG
+
+An intelligent question-answering system built with LangGraph that combines vector store retrieval and web search capabilities with sophisticated answer validation. The system uses an agent-based approach to dynamically decide the best information source and verify the quality of responses.
+
+### Project Location
+
+This project is located in the `RAG` directory of the playground repository:
+```
+playground/
+└── RAG/                  # This project
+    ├── ingestion.py
+    ├── main.py
+    └── graph/
+        └── ...
+```
+
+## Features
+
+- **Smart Routing**: Automatically routes questions to either vector store or web search based on the question's content
+- **Document Relevance Grading**: Evaluates retrieved documents for relevance to the question
+- **Hallucination Detection**: Verifies that generated answers are grounded in the source documents
+- **Answer Quality Assessment**: Ensures generated responses actually answer the user's question
+- **Fallback Mechanisms**: Dynamically switches to web search when vector store results are insufficient
+- **Flexible Architecture**: Built with LangGraph for clear state management and workflow control
+
+## Architecture
+
+The system uses a state-based graph architecture with several key components:
+
+1. **Router**: Determines whether to use vector store or web search based on the question
+2. **Retriever**: Fetches relevant documents from Pinecone vector store
+3. **Document Grader**: Evaluates document relevance
+4. **Generator**: Creates answers based on retrieved documents
+5. **Answer Grader**: Validates answers for hallucinations and relevance
+6. **Web Search**: Provides additional information when needed using Tavily Search
+
+## Setup
+
+1. Clone the repository
+2. Install dependencies:
+   ```bash
+   pip install langchain langchainhub langchain-community langchain-tavily langchain-pinecone langgraph python-dotenv pytest
+   ```
+
+3. Create a `.env` file with the following keys:
+   ```
+   OPENAI_API_KEY=your_openai_key
+   PINECONE_API_KEY=your_pinecone_key
+   TAVILY_API_KEY=your_tavily_key
+   LANGSMITH_API_KEY=your_langsmith_key (optional)
+   LANGSMITH_TRACING=true (optional)
+   LANGSMITH_PROJECT_NAME=your_project_name (optional)
+   
+   ```
+
+## Usage
+
+1. Run the ingestion script to populate the vector store:
+   ```bash
+   python ingestion.py
+   ```
+
+2. Run the main application:
+   ```bash
+   python main.py
+   ```
+
+Example query:
+```python
+from graph.graph import app
+
+result = app.invoke(input={"question": "What is agent memory?"})
+print(result)
+```
+
+## Project Structure
+
+```
+├── ingestion.py          # Document ingestion and vector store setup
+├── main.py              # Main application entry point
+├── graph/
+│   ├── chains/         # LangChain components
+│   │   ├── answer_grader.py
+│   │   ├── generation.py
+│   │   ├── hallucination_grader.py
+│   │   ├── retrieval_grader.py
+│   │   └── router.py
+│   ├── nodes/          # Graph nodes implementation
+│   │   ├── generate.py
+│   │   ├── grade_documents.py
+│   │   ├── retrieve.py
+│   │   └── web_search.py
+│   ├── tests/          # Test cases
+│   ├── graph.py        # Main graph definition
+│   └── state.py        # State management
+```
+
+## Testing
+
+Run the test suite:
+```bash
+pytest . -s -v
+```
+
+## Flow Visualization
+
+The system generates a visual representation of the workflow graph in `graph_output.png`:
+
+![LangGraph Workflow](graph_output.png)
+
+This visualization shows the complete flow of the question-answering system, including routing decisions, document retrieval, grading steps, and generation paths.
\ No newline at end of file
diff --git a/RAG/graph/__init__.py b/RAG/graph/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/RAG/graph/chains/__init__.py b/RAG/graph/chains/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/RAG/graph/chains/answer_grader.py b/RAG/graph/chains/answer_grader.py
new file mode 100644
index 0000000..96fd2a9
--- /dev/null
+++ b/RAG/graph/chains/answer_grader.py
@@ -0,0 +1,25 @@
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.runnables import RunnableSequence
+from langchain_openai import ChatOpenAI
+from pydantic import BaseModel, Field
+
+
+class GradeAnswer(BaseModel):
+    binary_score: bool = Field(
+        description="Answer addresses the question, 'yes' or 'no'"
+    )
+
+llm = ChatOpenAI(temperature=0)
+
+structured_llm_grader = llm.with_structured_output(GradeAnswer)
+
+system = """You are a grader assessing whether an answer addresses / resolves a question \n 
+     Give a binary score 'yes' or 'no'. Yes' means that the answer resolves the question."""
+answer_prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", system),
+        ("human", "User question: \n\n {question} \n\n LLM generation: {generation}"),
+    ]
+)
+
+answer_grader: RunnableSequence = answer_prompt | structured_llm_grader
\ No newline at end of file
diff --git a/RAG/graph/chains/generation.py b/RAG/graph/chains/generation.py
new file mode 100644
index 0000000..d4720f1
--- /dev/null
+++ b/RAG/graph/chains/generation.py
@@ -0,0 +1,8 @@
+from langchain import hub
+from langchain_core.output_parsers import StrOutputParser
+from langchain_openai import ChatOpenAI
+
+llm = ChatOpenAI(temperature=0)
+prompt = hub.pull("rlm/rag-prompt")
+
+generation_chain = prompt | llm | StrOutputParser()
\ No newline at end of file
diff --git a/RAG/graph/chains/hallucination_grader.py b/RAG/graph/chains/hallucination_grader.py
new file mode 100644
index 0000000..d78f112
--- /dev/null
+++ b/RAG/graph/chains/hallucination_grader.py
@@ -0,0 +1,24 @@
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.runnables import RunnableSequence
+from langchain_openai import ChatOpenAI
+from pydantic import BaseModel, Field
+
+llm = ChatOpenAI(temperature=0)
+
+class GradeHallucinations(BaseModel):
+    """Binary score for hallucination prensent in generation answer."""
+
+    binary_score: bool = Field(description="Answer is grounded in the facts, 'yes' or 'no'")
+
+structured_llm_grader = llm.with_structured_output(GradeHallucinations)
+
+system = """You are a grader assessing whether an LLM generation is grounded in / supported by a set of retrieved facts. \n 
+     Give a binary score 'yes' or 'no'. 'Yes' means that the answer is grounded in / supported by the set of facts."""
+hallucination_prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", system),
+        ("human", "Set of facts: \n\n {documents} \n\n LLM generation: {generation}"),
+    ]
+)
+
+hallucination_grader: RunnableSequence = hallucination_prompt | structured_llm_grader
\ No newline at end of file
diff --git a/RAG/graph/chains/retrieval_grader.py b/RAG/graph/chains/retrieval_grader.py
new file mode 100644
index 0000000..7bfd851
--- /dev/null
+++ b/RAG/graph/chains/retrieval_grader.py
@@ -0,0 +1,25 @@
+from langchain_core.prompts import ChatPromptTemplate
+from pydantic import BaseModel, Field
+from langchain_openai import ChatOpenAI
+
+llm = ChatOpenAI(temperature=0)
+
+class GradeDocuments(BaseModel):
+    """Binary score for rlevance check on retrieved documents"""
+
+    binary_score: str = Field(description="Document are relevant to the question, 'yes' or 'no'")
+
+structured_llm_grader = llm.with_structured_output(GradeDocuments)
+
+system = """You are a grader assessing relevance of a retrieved document to a user question. \n 
+    If the document contains keyword(s) or semantic meaning related to the question, grade it as relevant. \n
+    Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question."""
+
+grade_prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", system),
+        ("human", "Retrieved document: \n\n {document} \n\n User question: {question}"),
+    ]
+)
+
+retrieval_grader = grade_prompt | structured_llm_grader
\ No newline at end of file
diff --git a/RAG/graph/chains/router.py b/RAG/graph/chains/router.py
new file mode 100644
index 0000000..3730d00
--- /dev/null
+++ b/RAG/graph/chains/router.py
@@ -0,0 +1,29 @@
+from typing import Literal
+
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_openai import ChatOpenAI
+from pydantic import BaseModel, Field
+
+
+class RouteQuery(BaseModel):
+    """Route a user query to the most relevant datasource."""
+
+    datasource: Literal["vectorstore", "websearch"] = Field(
+        ...,
+        description="Given a user question choose to route it to web search or a vectorstore.",
+    )
+
+llm = ChatOpenAI(temperature=0)
+structured_llm_router = llm.with_structured_output(RouteQuery)
+
+system = """You are an expert at routing a user question to a vectorstore or web search.
+The vectorstore contains documents related to agents, prompt engineering, and adversarial attacks.
+Use the vectorstore for questions on these topics. For all else, use web-search."""
+route_prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", system),
+        ("human", "{question}"),
+    ]
+)
+
+question_router = route_prompt | structured_llm_router
\ No newline at end of file
diff --git a/RAG/graph/consts.py b/RAG/graph/consts.py
new file mode 100644
index 0000000..47eb623
--- /dev/null
+++ b/RAG/graph/consts.py
@@ -0,0 +1,4 @@
+RETRIEVE = "retrieve"
+GRADE_DOCUMENTS = "grade_documents"
+GENERATE = "generate"
+WEBSEARCH = "websearch"
\ No newline at end of file
diff --git a/RAG/graph/graph.py b/RAG/graph/graph.py
new file mode 100644
index 0000000..f38d7e3
--- /dev/null
+++ b/RAG/graph/graph.py
@@ -0,0 +1,99 @@
+from dotenv import load_dotenv
+from langgraph.graph import END, StateGraph
+
+from graph.chains.answer_grader import answer_grader
+from graph.chains.hallucination_grader import hallucination_grader
+from graph.chains.router import RouteQuery, question_router
+from graph.consts import GENERATE, GRADE_DOCUMENTS, RETRIEVE, WEBSEARCH
+from graph.nodes import generate, grade_documents, retrieve, web_search
+from graph.state import GraphState
+
+load_dotenv()
+
+def decide_to_generate(state):
+    print("assessing graded documents...")
+
+    if state["web_search"]:
+        print(
+            "---DECISION: NOT ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, INCLUDE WEB SEARCH---"
+        )
+        return WEBSEARCH
+    else:
+        print("---DECISION: GENERATE---")
+        return GENERATE
+    
+def grade_generation_grounded_in_documents_and_question(state: GraphState) -> str:
+    print("check for hallucinations in generation...")
+    question = state["question"]
+    documents = state["documents"]
+    generation = state["generation"]
+
+    score = hallucination_grader.invoke(
+        {"documents": documents, "generation": generation}
+    )
+
+    if hallucination_grade := score.binary_score:
+        print("decision - generation is grounded in documents")
+        score = answer_grader.invoke(
+            {"generation": generation, "question": question}
+        )
+        if answer_grade := score.binary_score:
+            print("decision - generation answers the question")
+            return "useful"
+        else:
+            print("decision - generation does not answer the question")
+            return "not useful"
+    else:
+        print("decision - generation is NOT grounded in documents")
+        return "not supported"
+    
+def route_question(state: GraphState) -> str:
+    print("routing question...")
+    question = state["question"]
+    source: RouteQuery = question_router.invoke({"question": question})
+    if source.datasource == WEBSEARCH:
+        print("decision - route question to web search")
+        return WEBSEARCH
+    elif source.datasource == "vectorstore":
+        print("decision - route question to rag")
+        return RETRIEVE
+    
+
+
+workflow = StateGraph(GraphState)
+workflow.add_node(RETRIEVE, retrieve)
+workflow.add_node(GRADE_DOCUMENTS, grade_documents)
+workflow.add_node(GENERATE, generate)
+workflow.add_node(WEBSEARCH, web_search)
+
+workflow.set_conditional_entry_point(
+    route_question,
+    {
+        RETRIEVE: RETRIEVE, 
+        WEBSEARCH: WEBSEARCH
+    }
+)
+workflow.add_edge(RETRIEVE, GRADE_DOCUMENTS)
+workflow.add_conditional_edges(
+    GRADE_DOCUMENTS,
+    decide_to_generate,
+    {
+        WEBSEARCH: WEBSEARCH,
+        GENERATE: GENERATE,
+    }
+)
+workflow.add_conditional_edges(
+    GENERATE,
+    grade_generation_grounded_in_documents_and_question,
+    {
+        "useful": END,
+        "not useful": WEBSEARCH,
+        "not supported": GENERATE,
+    }
+)
+workflow.add_edge(WEBSEARCH, GENERATE)
+workflow.add_edge(GENERATE, END)
+
+app = workflow.compile()
+
+app.get_graph().draw_mermaid_png(output_file_path="graph_output.png")
\ No newline at end of file
diff --git a/RAG/graph/nodes/__init__.py b/RAG/graph/nodes/__init__.py
new file mode 100644
index 0000000..cc1cbfa
--- /dev/null
+++ b/RAG/graph/nodes/__init__.py
@@ -0,0 +1,6 @@
+from graph.nodes.generate import generate
+from graph.nodes.grade_documents import grade_documents
+from graph.nodes.retrieve import retrieve
+from graph.nodes.web_search import web_search
+
+__all__ = ["generate", "grade_documents", "retrieve", "web_search"]
\ No newline at end of file
diff --git a/RAG/graph/nodes/generate.py b/RAG/graph/nodes/generate.py
new file mode 100644
index 0000000..a346dc7
--- /dev/null
+++ b/RAG/graph/nodes/generate.py
@@ -0,0 +1,21 @@
+from typing import Any, Dict
+
+from graph.chains.generation import generation_chain
+from graph.state import GraphState
+
+def generate(state: GraphState) -> Dict[str, Any]:
+    """
+    Generate a response based on the question and documents in the state.
+
+    Args:
+        state (GraphState): The current state of the graph.
+
+    Returns:
+        Dict[str, Any]: A dictionary containing the generated response and the original question.
+    """
+    print("Generating response...")
+    question = state["question"]
+    documents = state["documents"]
+    
+    generation = generation_chain.invoke({"context": documents, "question": question})
+    return {"documents": documents, "generation": generation, "question": question}
\ No newline at end of file
diff --git a/RAG/graph/nodes/grade_documents.py b/RAG/graph/nodes/grade_documents.py
new file mode 100644
index 0000000..4abbb1e
--- /dev/null
+++ b/RAG/graph/nodes/grade_documents.py
@@ -0,0 +1,42 @@
+from typing import Any, Dict
+
+from graph import state
+from graph.chains.retrieval_grader import retrieval_grader
+from graph.state import GraphState
+
+
+def grade_documents(state: GraphState) -> Dict[str, Any]:
+    """
+    Determines whether the retrieved documents are relevant to the question
+    If any document is not relevant, we will set a flag to run web search
+
+    Args:
+        state (dict): The current graph state
+
+    Returns:
+        state (dict): Filtered out irrelevant documents and updated web_search state
+    """
+     
+    print("Grading documents...")
+    question = state["question"]
+    documents = state["documents"]
+
+    filtered_docs = []
+    web_search = False
+
+    for document in documents:
+        score = retrieval_grader.invoke(
+            {"document": document.page_content, "question": question}
+        )
+
+        grade = score.binary_score
+
+        if grade.lower() == "yes":
+            print("Document is relevant")
+            filtered_docs.append(document)
+        else:
+            print("Document is not relevant")
+            web_search = True
+            continue
+        
+    return {"documents": filtered_docs, "web_search": web_search, "question": question}
\ No newline at end of file
diff --git a/RAG/graph/nodes/retrieve.py b/RAG/graph/nodes/retrieve.py
new file mode 100644
index 0000000..8031adf
--- /dev/null
+++ b/RAG/graph/nodes/retrieve.py
@@ -0,0 +1,20 @@
+from typing import Any, Dict
+
+from graph.state import GraphState
+from ingestion import retriever
+
+def retrieve(state: GraphState) -> Dict[str, Any]:
+    """
+    Retrieve documents based on the question in the state.
+
+    Args:
+        state (GraphState): The current state of the graph.
+
+    Returns:
+        Dict[str, Any]: A dictionary containing the retrieved documents and the original question.
+    """
+    print("Retrieving documents...")
+    question = state["question"]
+    documents = retriever.invoke(question)
+
+    return {"documents": documents, "question": question}
\ No newline at end of file
diff --git a/RAG/graph/nodes/web_search.py b/RAG/graph/nodes/web_search.py
new file mode 100644
index 0000000..536c71e
--- /dev/null
+++ b/RAG/graph/nodes/web_search.py
@@ -0,0 +1,43 @@
+from typing import Any, Dict
+
+from dotenv import load_dotenv
+from langchain.schema import Document
+from langchain_tavily import TavilySearch
+
+from graph.state import GraphState
+
+load_dotenv()
+web_search_tool = TavilySearch(max_results=3)
+
+def web_search(state: GraphState) -> Dict[str, Any]:
+    """
+    Perform a web search based on the question in the state.
+
+    Args:
+        state (GraphState): The current state of the graph.
+
+    Returns:
+        Dict[str, Any]: A dictionary containing the search results and the original question.
+    """
+    print("Performing web search...")
+    question = state["question"]
+    if "documents" in state:
+        documents = state["documents"]
+    else:
+        documents = None
+
+    tavily_results = web_search_tool.invoke({"query": question})["results"]
+    joined_tavily_result = "\n".join(
+        [tavily_result["content"] for tavily_result in tavily_results]
+    )
+    web_results = Document(page_content=joined_tavily_result)
+
+    if documents is not None:
+        documents.append(web_results)
+    else:
+        documents = [web_results]
+
+    return {"documents": documents, "question": question}
+
+if __name__ == "__main__":
+    web_search(state={"question": "agent memory", "documents": None})
\ No newline at end of file
diff --git a/RAG/graph/state.py b/RAG/graph/state.py
new file mode 100644
index 0000000..2c3dc43
--- /dev/null
+++ b/RAG/graph/state.py
@@ -0,0 +1,18 @@
+from typing import List, TypedDict
+
+class GraphState(TypedDict):
+    """
+    Represents the state of our graph.
+
+    Attributes:
+        question: question
+        generation: LLM generation
+        web_search: whether to add search
+        documents: list of documents
+    """
+
+    question: str
+    generation: str
+    web_search: bool
+    documents: List[str]
+   
\ No newline at end of file
diff --git a/RAG/graph/tests/__init__.py b/RAG/graph/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/RAG/graph/tests/test_chains.py b/RAG/graph/tests/test_chains.py
new file mode 100644
index 0000000..099e815
--- /dev/null
+++ b/RAG/graph/tests/test_chains.py
@@ -0,0 +1,76 @@
+from pprint import pprint
+
+from dotenv import load_dotenv
+
+load_dotenv()
+
+from graph.chains.generation import generation_chain
+from graph.chains.hallucination_grader import (GradeHallucinations,
+                                               hallucination_grader)
+from graph.chains.retrieval_grader import GradeDocuments, retrieval_grader
+from graph.chains.router import RouteQuery, question_router
+from ingestion import retriever
+
+
+def test_retrieval_grader_answer_yes() -> None:
+    question = "agent memory"
+    documents = retriever.invoke(question)
+    doc_txt = documents[0].page_content
+
+    res: GradeDocuments = retrieval_grader.invoke(
+        {"document": doc_txt, "question": question}
+    )
+
+    assert res.binary_score == "yes"
+
+def test_retrieval_grader_answer_no() -> None:
+    question = "agent memory"
+    documents = retriever.invoke(question)
+    doc_txt = documents[1].page_content
+
+    res: GradeDocuments = retrieval_grader.invoke(
+        {"document": doc_txt, "question": "how to make pizza"}
+    )
+
+    assert res.binary_score == "no"
+
+def test_generation_chain() -> None:
+    question = "agent memory"
+    docs = retriever.invoke(question)
+    generation = generation_chain.invoke({"context": docs, "question": question})
+    pprint(generation)
+
+def test_hallucination_grader_answer_yes() -> None:
+    question = "agent memory"
+    docs = retriever.invoke(question)
+
+    generation = generation_chain.invoke({"context": docs, "question": question})
+    res: GradeHallucinations = hallucination_grader.invoke(
+        {"documents": docs, "generation": generation}
+    )
+    assert res.binary_score
+
+def test_hallucination_grader_answer_no() -> None:
+    question = "agent memory"
+    docs = retriever.invoke(question)
+
+    res: GradeHallucinations = hallucination_grader.invoke(
+        {
+            "documents": docs,
+            "generation": "In order to make pizza we need to first start with the dough",
+        }
+    )
+    assert not res.binary_score
+
+def test_router_to_vectorstore() -> None:
+    question = "agent memory"
+
+    res: RouteQuery = question_router.invoke({"question": question})
+    assert res.datasource == "vectorstore"
+
+
+def test_router_to_websearch() -> None:
+    question = "how to make pizza"
+
+    res: RouteQuery = question_router.invoke({"question": question})
+    assert res.datasource == "websearch"
diff --git a/RAG/graph_output.png b/RAG/graph_output.png
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diff --git a/RAG/ingestion.py b/RAG/ingestion.py
new file mode 100644
index 0000000..e71a74c
--- /dev/null
+++ b/RAG/ingestion.py
@@ -0,0 +1,35 @@
+import os
+from dotenv import load_dotenv
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.document_loaders import WebBaseLoader
+from langchain_pinecone.vectorstores import PineconeVectorStore
+from langchain_openai import OpenAIEmbeddings
+
+load_dotenv()
+
+urls = [
+    "https://lilianweng.github.io/posts/2023-06-23-agent/",
+    "https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/",
+    "https://lilianweng.github.io/posts/2023-10-25-adv-attack-llm/",
+]
+
+docs = [WebBaseLoader(url).load() for url in urls]
+docs_list = [item for sublist in docs for item in sublist]
+
+text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
+    chunk_size=250, chunk_overlap=0
+)
+
+doc_splits = text_splitter.split_documents(docs_list)
+
+vectorstore = PineconeVectorStore.from_documents(
+    documents=doc_splits,
+    embedding=OpenAIEmbeddings(),
+    index_name="langgraph-agentic-rag",
+)
+
+retriever = PineconeVectorStore(
+    index_name="langgraph-agentic-rag",
+    embedding=OpenAIEmbeddings(),
+    text_key="text"
+).as_retriever()
\ No newline at end of file
diff --git a/RAG/main.py b/RAG/main.py
new file mode 100644
index 0000000..6c90c11
--- /dev/null
+++ b/RAG/main.py
@@ -0,0 +1,9 @@
+from dotenv import load_dotenv
+
+load_dotenv()
+
+from graph.graph import app
+
+if __name__ == "__main__":
+    print("Hello, LangGraph!")
+    print(app.invoke(input={"question": "What is agent memory?"}))
\ No newline at end of file