diff --git a/.env.template b/.env.template
index bc136d5..7db54a1 100644
--- a/.env.template
+++ b/.env.template
@@ -31,3 +31,11 @@ AZURE_BLOB_CONTAINER_NAME="audio"
 # Azure AI Speech
 AZURE_AI_SPEECH_API_ENDPOINT="https://<speech-api-name>.cognitiveservices.azure.com/"
 AZURE_AI_SPEECH_API_SUBSCRIPTION_KEY="<speech-api-subscription-key>"
+
+# Bing search resource
+BING_SUBSCRIPTION_KEY="<bing-subscription-key>"
+BING_SEARCH_URL="https://api.bing.microsoft.com/v7.0/search"
+
+# LangSmith
+LANGCHAIN_TRACING_V2="true"
+LANGCHAIN_API_KEY="<langchain-api-key>"
diff --git a/.gitignore b/.gitignore
index bdca069..5454ece 100644
--- a/.gitignore
+++ b/.gitignore
@@ -166,3 +166,4 @@ generated/
 *.pt
 *.jpg
 *.jpeg
+.chroma
diff --git a/README.md b/README.md
index 79299ac..6ced8f1 100644
--- a/README.md
+++ b/README.md
@@ -41,6 +41,7 @@ Here are the preferred tools for development.
 | [9_streamlit_azure_document_intelligence](./apps/9_streamlit_azure_document_intelligence/README.md) | Call Azure AI Document Intelligence API with Streamlit  | ![9_streamlit_azure_document_intelligence](./docs/images/9_streamlit_azure_document_intelligence.main.png) |
 | [10_streamlit_batch_transcription](./apps/10_streamlit_batch_transcription/README.md)               | Call Batch Transcription API with Streamlit             | ![10_streamlit_batch_transcription](./docs/images/10_streamlit_batch_transcription.main.png)               |
 | [11_promptflow](./apps/11_promptflow/README.md)                                                     | Get started with Prompt flow                            | No Image                                                                                                   |
+| [12_langgraph_agent](./apps/12_langgraph_agent/README.md)                                           | Create agents with LangGraph                            | No Image                                                                                                   |
 | [99_streamlit_examples](./apps/99_streamlit_examples/README.md)                                     | Code samples for Streamlit                              | ![99_streamlit_examples](./docs/images/99_streamlit_examples.explaindata.png)                              |
 
 ## How to run
diff --git a/apps/12_langgraph_agent/README.md b/apps/12_langgraph_agent/README.md
new file mode 100644
index 0000000..26c9f22
--- /dev/null
+++ b/apps/12_langgraph_agent/README.md
@@ -0,0 +1,62 @@
+# Create agents with LangGraph
+
+This app demonstrates how to implement agents with LangGraph.
+
+## Prerequisites
+
+- Python 3.10 or later
+- Azure OpenAI Service
+
+## Overview
+
+**What is [LangGraph](https://langchain-ai.github.io/langgraph/)?**
+
+LangGraph is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows.
+
+This chapter provides a practical example of how to use LangGraph to create an agent that can interact with users and external tools.
+
+## Usage
+
+1. Get Azure OpenAI Service API key
+1. Copy [.env.template](../../.env.template) to `.env` in the same directory
+1. Set credentials in `.env`
+1. Run main.py
+
+```shell
+# Create a virtual environment
+$ python -m venv .venv
+
+# Activate the virtual environment
+$ source .venv/bin/activate
+
+# Install dependencies
+$ pip install -r requirements.txt
+```
+
+### Examples
+
+#### [reflection_agent](./reflection_agent/main.py)
+
+#### [react_agent](./react_agent/main.py)
+
+#### [advanced_rag_flows](./advanced_rag_flows/main.py)
+
+```shell
+# create vector store
+python apps/12_langgraph_agent/advanced_rag_flows/ingestion.py
+
+# run main.py
+python apps/12_langgraph_agent/advanced_rag_flows/main.py
+```
+
+![Advanced RAG Flows](../../docs/images/12_langgraph_agent_graph.png)
+
+## References
+
+- [LangGraph](https://langchain-ai.github.io/langgraph/)
+- [Udemy > LangGraph- Develop LLM powered agents with LangGraph](https://www.udemy.com/course/langgraph)
+- [emarco177/langgaph-course](https://github.com/emarco177/langgaph-course)
+- [Prompt flow > Tracing](https://microsoft.github.io/promptflow/how-to-guides/tracing/index.html)
+- [Reflection Agents](https://blog.langchain.dev/reflection-agents/)
+- [LangChain > Reflexion](https://langchain-ai.github.io/langgraph/tutorials/reflexion/reflexion/)
+- [LangChain > Bing Search](https://python.langchain.com/docs/integrations/tools/bing_search/)
diff --git a/apps/12_langgraph_agent/advanced_rag_flows/graph/__init__.py b/apps/12_langgraph_agent/advanced_rag_flows/graph/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/apps/12_langgraph_agent/advanced_rag_flows/graph/chains/__init__.py b/apps/12_langgraph_agent/advanced_rag_flows/graph/chains/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/apps/12_langgraph_agent/advanced_rag_flows/graph/chains/answer_grader.py b/apps/12_langgraph_agent/advanced_rag_flows/graph/chains/answer_grader.py
new file mode 100644
index 0000000..103f4d4
--- /dev/null
+++ b/apps/12_langgraph_agent/advanced_rag_flows/graph/chains/answer_grader.py
@@ -0,0 +1,33 @@
+from os import getenv
+
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.runnables import RunnableSequence
+from langchain_openai import AzureChatOpenAI
+from pydantic import BaseModel, Field
+
+
+class GradeAnswer(BaseModel):
+
+    binary_score: bool = Field(description="Answer addresses the question, 'yes' or 'no'")
+
+
+llm = AzureChatOpenAI(
+    temperature=0,
+    api_key=getenv("AZURE_OPENAI_API_KEY"),
+    api_version=getenv("AZURE_OPENAI_API_VERSION"),
+    azure_endpoint=getenv("AZURE_OPENAI_ENDPOINT"),
+    model=getenv("AZURE_OPENAI_GPT_MODEL"),
+)
+
+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
diff --git a/apps/12_langgraph_agent/advanced_rag_flows/graph/chains/generation.py b/apps/12_langgraph_agent/advanced_rag_flows/graph/chains/generation.py
new file mode 100644
index 0000000..686be82
--- /dev/null
+++ b/apps/12_langgraph_agent/advanced_rag_flows/graph/chains/generation.py
@@ -0,0 +1,16 @@
+from os import getenv
+
+from langchain import hub
+from langchain_core.output_parsers import StrOutputParser
+from langchain_openai import AzureChatOpenAI
+
+llm = AzureChatOpenAI(
+    temperature=0,
+    api_key=getenv("AZURE_OPENAI_API_KEY"),
+    api_version=getenv("AZURE_OPENAI_API_VERSION"),
+    azure_endpoint=getenv("AZURE_OPENAI_ENDPOINT"),
+    model=getenv("AZURE_OPENAI_GPT_MODEL"),
+)
+prompt = hub.pull("rlm/rag-prompt")
+
+generation_chain = prompt | llm | StrOutputParser()
diff --git a/apps/12_langgraph_agent/advanced_rag_flows/graph/chains/hallucination_grader.py b/apps/12_langgraph_agent/advanced_rag_flows/graph/chains/hallucination_grader.py
new file mode 100644
index 0000000..1f1631d
--- /dev/null
+++ b/apps/12_langgraph_agent/advanced_rag_flows/graph/chains/hallucination_grader.py
@@ -0,0 +1,34 @@
+from os import getenv
+
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.runnables import RunnableSequence
+from langchain_openai import AzureChatOpenAI
+from pydantic import BaseModel, Field
+
+llm = AzureChatOpenAI(
+    temperature=0,
+    api_key=getenv("AZURE_OPENAI_API_KEY"),
+    api_version=getenv("AZURE_OPENAI_API_VERSION"),
+    azure_endpoint=getenv("AZURE_OPENAI_ENDPOINT"),
+    model=getenv("AZURE_OPENAI_GPT_MODEL"),
+)
+
+
+class GradeHallucinations(BaseModel):
+    """Binary score for hallucination present 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."""  # noqa
+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
diff --git a/apps/12_langgraph_agent/advanced_rag_flows/graph/chains/retrieval_grader.py b/apps/12_langgraph_agent/advanced_rag_flows/graph/chains/retrieval_grader.py
new file mode 100644
index 0000000..3dd046a
--- /dev/null
+++ b/apps/12_langgraph_agent/advanced_rag_flows/graph/chains/retrieval_grader.py
@@ -0,0 +1,34 @@
+from os import getenv
+
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_openai import AzureChatOpenAI
+from pydantic import BaseModel, Field
+
+llm = AzureChatOpenAI(
+    temperature=0,
+    api_key=getenv("AZURE_OPENAI_API_KEY"),
+    api_version=getenv("AZURE_OPENAI_API_VERSION"),
+    azure_endpoint=getenv("AZURE_OPENAI_ENDPOINT"),
+    model=getenv("AZURE_OPENAI_GPT_MODEL"),
+)
+
+
+class GradeDocuments(BaseModel):
+    """Binary score for relevance check on retrieved documents."""
+
+    binary_score: str = Field(description="Documents 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
diff --git a/apps/12_langgraph_agent/advanced_rag_flows/graph/chains/router.py b/apps/12_langgraph_agent/advanced_rag_flows/graph/chains/router.py
new file mode 100644
index 0000000..4b1eb2e
--- /dev/null
+++ b/apps/12_langgraph_agent/advanced_rag_flows/graph/chains/router.py
@@ -0,0 +1,37 @@
+from os import getenv
+from typing import Literal
+
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_openai import AzureChatOpenAI
+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 = AzureChatOpenAI(
+    temperature=0,
+    api_key=getenv("AZURE_OPENAI_API_KEY"),
+    api_version=getenv("AZURE_OPENAI_API_VERSION"),
+    azure_endpoint=getenv("AZURE_OPENAI_ENDPOINT"),
+    model=getenv("AZURE_OPENAI_GPT_MODEL"),
+)
+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
diff --git a/apps/12_langgraph_agent/advanced_rag_flows/graph/chains/tests/__init__.py b/apps/12_langgraph_agent/advanced_rag_flows/graph/chains/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/apps/12_langgraph_agent/advanced_rag_flows/graph/chains/tests/test_chains.py b/apps/12_langgraph_agent/advanced_rag_flows/graph/chains/tests/test_chains.py
new file mode 100644
index 0000000..73b6750
--- /dev/null
+++ b/apps/12_langgraph_agent/advanced_rag_flows/graph/chains/tests/test_chains.py
@@ -0,0 +1,74 @@
+from pprint import pprint
+
+from dotenv import load_dotenv
+from ingestion import retriever
+
+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
+
+load_dotenv()
+
+
+def test_generation_chain() -> None:
+    question = "agent memory"
+    docs = retriever.invoke(question)
+    generation = generation_chain.invoke({"context": docs, "question": question})
+    pprint(generation)
+
+
+def test_retrival_grader_answer_yes() -> None:
+    question = "agent memory"
+    docs = retriever.invoke(question)
+    doc_txt = docs[1].page_content
+
+    res: GradeDocuments = retrieval_grader.invoke({"question": question, "document": doc_txt})
+
+    assert res.binary_score == "yes"
+
+
+def test_retrival_grader_answer_no() -> None:
+    question = "agent memory"
+    docs = retriever.invoke(question)
+    doc_txt = docs[1].page_content
+
+    res: GradeDocuments = retrieval_grader.invoke({"question": "how to make pizaa", "document": doc_txt})
+
+    assert res.binary_score == "no"
+
+
+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/apps/12_langgraph_agent/advanced_rag_flows/graph/consts.py b/apps/12_langgraph_agent/advanced_rag_flows/graph/consts.py
new file mode 100644
index 0000000..7f3c0d4
--- /dev/null
+++ b/apps/12_langgraph_agent/advanced_rag_flows/graph/consts.py
@@ -0,0 +1,4 @@
+RETRIEVE = "retrieve"
+GRADE_DOCUMENTS = "grade_documents"
+GENERATE = "generate"
+WEBSEARCH = "websearch"
diff --git a/apps/12_langgraph_agent/advanced_rag_flows/graph/graph.py b/apps/12_langgraph_agent/advanced_rag_flows/graph/graph.py
new file mode 100644
index 0000000..3cf5f75
--- /dev/null
+++ b/apps/12_langgraph_agent/advanced_rag_flows/graph/graph.py
@@ -0,0 +1,103 @@
+from dotenv import load_dotenv
+from langgraph.checkpoint.memory import MemorySaver
+from langgraph.checkpoint.sqlite import SqliteSaver
+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()
+memory = SqliteSaver.from_conn_string(":memory:")
+memory = MemorySaver()
+
+
+def decide_to_generate(state):
+    print("---ASSESS 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 HALLUCINATIONS---")
+    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---")
+        print("---GRADE GENERATION vs QUESTION---")
+        print(f"hallucination_grade: {hallucination_grade}")
+        score = answer_grader.invoke({"question": question, "generation": generation})
+        if answer_grade := score.binary_score:
+            print("---DECISION: GENERATION ADDRESSES QUESTION---")
+            print(f"answer_grade: {answer_grade}")
+            return "useful"
+        else:
+            print("---DECISION: GENERATION DOES NOT ADDRESS QUESTION---")
+            return "not useful"
+    else:
+        print("---DECISION: GENERATION IS NOT GROUNDED IN DOCUMENTS, RE-TRY---")
+        return "not supported"
+
+
+def route_question(state: GraphState) -> str:
+    print("---ROUTE QUESTION---")
+    question = state["question"]
+    source: RouteQuery = question_router.invoke({"question": question})
+    if source.datasource == WEBSEARCH:
+        print("---ROUTE QUESTION TO WEB SEARCH---")
+        return WEBSEARCH
+    elif source.datasource == "vectorstore":
+        print("---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,
+    {
+        WEBSEARCH: WEBSEARCH,
+        RETRIEVE: RETRIEVE,
+    },
+)
+workflow.add_edge(RETRIEVE, GRADE_DOCUMENTS)
+workflow.add_conditional_edges(
+    GRADE_DOCUMENTS,
+    decide_to_generate,
+    {
+        WEBSEARCH: WEBSEARCH,
+        GENERATE: GENERATE,
+    },
+)
+workflow.add_edge(WEBSEARCH, GENERATE)
+workflow.add_conditional_edges(
+    GENERATE,
+    grade_generation_grounded_in_documents_and_question,
+    {
+        "not supported": GENERATE,
+        "useful": END,
+        "not useful": WEBSEARCH,
+    },
+)
+
+
+app = workflow.compile(checkpointer=memory)
+
+app.get_graph().draw_mermaid_png(output_file_path="docs/images/12_langgraph_agent_graph.png")
diff --git a/apps/12_langgraph_agent/advanced_rag_flows/graph/nodes/__init__.py b/apps/12_langgraph_agent/advanced_rag_flows/graph/nodes/__init__.py
new file mode 100644
index 0000000..4dca550
--- /dev/null
+++ b/apps/12_langgraph_agent/advanced_rag_flows/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"]
diff --git a/apps/12_langgraph_agent/advanced_rag_flows/graph/nodes/generate.py b/apps/12_langgraph_agent/advanced_rag_flows/graph/nodes/generate.py
new file mode 100644
index 0000000..952acaf
--- /dev/null
+++ b/apps/12_langgraph_agent/advanced_rag_flows/graph/nodes/generate.py
@@ -0,0 +1,13 @@
+from typing import Any
+
+from graph.chains.generation import generation_chain
+from graph.state import GraphState
+
+
+def generate(state: GraphState) -> dict[str, Any]:
+    print("---GENERATE---")
+    question = state["question"]
+    documents = state["documents"]
+
+    generation = generation_chain.invoke({"context": documents, "question": question})
+    return {"documents": documents, "question": question, "generation": generation}
diff --git a/apps/12_langgraph_agent/advanced_rag_flows/graph/nodes/grade_documents.py b/apps/12_langgraph_agent/advanced_rag_flows/graph/nodes/grade_documents.py
new file mode 100644
index 0000000..c9a4a99
--- /dev/null
+++ b/apps/12_langgraph_agent/advanced_rag_flows/graph/nodes/grade_documents.py
@@ -0,0 +1,35 @@
+from typing import Any
+
+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("---CHECK DOCUMENT RELEVANCE TO QUESTION---")
+    question = state["question"]
+    documents = state["documents"]
+
+    filtered_docs = []
+    web_search = False
+    for d in documents:
+        score = retrieval_grader.invoke({"question": question, "document": d.page_content})
+        grade = score.binary_score
+        if grade.lower() == "yes":
+            print("---GRADE: DOCUMENT RELEVANT---")
+            filtered_docs.append(d)
+        else:
+            print("---GRADE: DOCUMENT NOT RELEVANT---")
+            web_search = True
+            continue
+    return {"documents": filtered_docs, "question": question, "web_search": web_search}
diff --git a/apps/12_langgraph_agent/advanced_rag_flows/graph/nodes/retrieve.py b/apps/12_langgraph_agent/advanced_rag_flows/graph/nodes/retrieve.py
new file mode 100644
index 0000000..bdc0748
--- /dev/null
+++ b/apps/12_langgraph_agent/advanced_rag_flows/graph/nodes/retrieve.py
@@ -0,0 +1,13 @@
+from typing import Any
+
+from ingestion import retriever
+
+from graph.state import GraphState
+
+
+def retrieve(state: GraphState) -> dict[str, Any]:
+    print("---RETRIEVE---")
+    question = state["question"]
+
+    documents = retriever.invoke(question)
+    return {"documents": documents, "question": question}
diff --git a/apps/12_langgraph_agent/advanced_rag_flows/graph/nodes/web_search.py b/apps/12_langgraph_agent/advanced_rag_flows/graph/nodes/web_search.py
new file mode 100644
index 0000000..ac9de82
--- /dev/null
+++ b/apps/12_langgraph_agent/advanced_rag_flows/graph/nodes/web_search.py
@@ -0,0 +1,33 @@
+from os import getenv
+from typing import Any
+
+from langchain.schema import Document
+from langchain_community.tools.bing_search import BingSearchResults
+from langchain_community.utilities import BingSearchAPIWrapper
+
+from graph.state import GraphState
+
+web_search_tool = BingSearchResults(
+    api_wrapper=BingSearchAPIWrapper(
+        bing_search_url=getenv("BING_SEARCH_URL"),
+        bing_subscription_key=getenv("BING_SUBSCRIPTION_KEY"),
+        k=1,
+    ),
+    num_results=1,
+)
+
+
+def web_search(state: GraphState) -> dict[str, Any]:
+    print("---WEB SEARCH---")
+    question = state["question"]
+    documents = state["documents"]
+
+    docs: str = web_search_tool.invoke({"query": question})
+    docs = eval(docs)
+    web_results = "\n".join([d["snippet"] for d in docs])
+    web_results = Document(page_content=web_results)
+    if documents is not None:
+        documents.append(web_results)
+    else:
+        documents = [web_results]
+    return {"documents": documents, "question": question}
diff --git a/apps/12_langgraph_agent/advanced_rag_flows/graph/state.py b/apps/12_langgraph_agent/advanced_rag_flows/graph/state.py
new file mode 100644
index 0000000..970b781
--- /dev/null
+++ b/apps/12_langgraph_agent/advanced_rag_flows/graph/state.py
@@ -0,0 +1,18 @@
+from typing import 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]
diff --git a/apps/12_langgraph_agent/advanced_rag_flows/ingestion.py b/apps/12_langgraph_agent/advanced_rag_flows/ingestion.py
new file mode 100644
index 0000000..f50377d
--- /dev/null
+++ b/apps/12_langgraph_agent/advanced_rag_flows/ingestion.py
@@ -0,0 +1,57 @@
+from os import getenv
+
+from dotenv import load_dotenv
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_chroma import Chroma
+from langchain_community.document_loaders import WebBaseLoader
+from langchain_openai import AzureOpenAIEmbeddings
+
+load_dotenv()
+
+embedding = AzureOpenAIEmbeddings(
+    api_key=getenv("AZURE_OPENAI_API_KEY"),
+    api_version=getenv("AZURE_OPENAI_API_VERSION"),
+    azure_endpoint=getenv("AZURE_OPENAI_ENDPOINT"),
+    model=getenv("AZURE_OPENAI_EMBEDDING_MODEL"),
+)
+
+COLLECTION_NAME = "rag-chroma"
+PERSIST_DIRECTORY = "./.chroma"
+
+
+retriever = Chroma(
+    collection_name=COLLECTION_NAME,
+    persist_directory=PERSIST_DIRECTORY,
+    embedding_function=embedding,
+).as_retriever()
+
+if __name__ == "__main__":
+    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)
+
+    # Create vector store
+    # import logging
+
+    # logging.basicConfig(level=logging.DEBUG)
+    vectorstore = Chroma.from_documents(
+        documents=doc_splits,
+        collection_name=COLLECTION_NAME,
+        embedding=embedding,
+        persist_directory=PERSIST_DIRECTORY,
+    )
+
+    # retrieve documents
+    docs = retriever.invoke("What is LLM Powered autonomous agent?")
+    print(f"got {len(docs)} documents")
diff --git a/apps/12_langgraph_agent/advanced_rag_flows/main.py b/apps/12_langgraph_agent/advanced_rag_flows/main.py
new file mode 100644
index 0000000..2613a23
--- /dev/null
+++ b/apps/12_langgraph_agent/advanced_rag_flows/main.py
@@ -0,0 +1,14 @@
+from pprint import pprint
+
+from dotenv import load_dotenv
+from graph.graph import app
+
+load_dotenv()
+
+question1 = "What are the types of agent memory?"
+inputs = {"question": question1}
+
+for output in app.stream(inputs, config={"configurable": {"thread_id": "2"}}):
+    for key, value in output.items():
+        pprint(f"Finished running: {key}:")
+pprint(value["generation"])
diff --git a/apps/12_langgraph_agent/react_agent/main.py b/apps/12_langgraph_agent/react_agent/main.py
new file mode 100644
index 0000000..aec1db7
--- /dev/null
+++ b/apps/12_langgraph_agent/react_agent/main.py
@@ -0,0 +1,45 @@
+from dotenv import load_dotenv
+from langchain_core.agents import AgentFinish
+from langgraph.graph import END, StateGraph
+from nodes import execute_tools, run_agent_reasoning_engine
+from promptflow.tracing import start_trace
+from state import AgentState
+
+load_dotenv()
+start_trace()
+
+AGENT_REASON = "agent_reason"
+ACT = "act"
+
+
+def should_continue(state: AgentState) -> str:
+    if isinstance(state["agent_outcome"], AgentFinish):
+        return END
+    else:
+        return ACT
+
+
+flow = StateGraph(AgentState)
+
+flow.add_node(AGENT_REASON, run_agent_reasoning_engine)
+flow.set_entry_point(AGENT_REASON)
+flow.add_node(ACT, execute_tools)
+
+
+flow.add_conditional_edges(
+    AGENT_REASON,
+    should_continue,
+)
+
+flow.add_edge(ACT, AGENT_REASON)
+
+app = flow.compile()
+# app.get_graph().draw_mermaid_png(output_file_path="graph.png")
+
+if __name__ == "__main__":
+    res = app.invoke(
+        input={
+            "input": "When is Microsoft established? List it and then Triple it.",
+        }
+    )
+    print(res["agent_outcome"].return_values["output"])
diff --git a/apps/12_langgraph_agent/react_agent/nodes.py b/apps/12_langgraph_agent/react_agent/nodes.py
new file mode 100644
index 0000000..f4fca62
--- /dev/null
+++ b/apps/12_langgraph_agent/react_agent/nodes.py
@@ -0,0 +1,20 @@
+from dotenv import load_dotenv
+from langgraph.prebuilt.tool_executor import ToolExecutor
+from react import react_agent_runnable, tools
+from state import AgentState
+
+load_dotenv()
+
+
+def run_agent_reasoning_engine(state: AgentState):
+    agent_outcome = react_agent_runnable.invoke(state)
+    return {"agent_outcome": agent_outcome}
+
+
+tool_executor = ToolExecutor(tools)
+
+
+def execute_tools(state: AgentState):
+    agent_action = state["agent_outcome"]
+    output = tool_executor.invoke(agent_action)
+    return {"intermediate_steps": [(agent_action, str(output))]}
diff --git a/apps/12_langgraph_agent/react_agent/react.py b/apps/12_langgraph_agent/react_agent/react.py
new file mode 100644
index 0000000..5d10590
--- /dev/null
+++ b/apps/12_langgraph_agent/react_agent/react.py
@@ -0,0 +1,52 @@
+from os import getenv
+
+from dotenv import load_dotenv
+from langchain import hub
+from langchain.agents import create_react_agent
+from langchain_community.tools.bing_search import BingSearchResults
+from langchain_community.utilities import BingSearchAPIWrapper
+from langchain_core.prompts import PromptTemplate
+from langchain_core.tools import tool
+from langchain_openai import AzureChatOpenAI
+
+load_dotenv()
+
+
+react_prompt: PromptTemplate = hub.pull("hwchase17/react")
+
+
+@tool
+def triple(num: float) -> float:
+    """
+    :param num: a number to triple
+    :return: the number tripled ->  multiplied by 3
+    """
+    return 3 * float(num)
+
+
+web_search_tool = BingSearchResults(
+    api_wrapper=BingSearchAPIWrapper(
+        bing_search_url=getenv("BING_SEARCH_URL"),
+        bing_subscription_key=getenv("BING_SUBSCRIPTION_KEY"),
+        k=3,
+    ),
+    num_results=3,
+)
+tools = [
+    web_search_tool,
+    triple,
+]
+
+llm = AzureChatOpenAI(
+    temperature=0,
+    api_key=getenv("AZURE_OPENAI_API_KEY"),
+    api_version=getenv("AZURE_OPENAI_API_VERSION"),
+    azure_endpoint=getenv("AZURE_OPENAI_ENDPOINT"),
+    model=getenv("AZURE_OPENAI_GPT_MODEL"),
+)
+
+react_agent_runnable = create_react_agent(llm, tools, react_prompt)
+
+if __name__ == "__main__":
+    result = web_search_tool.invoke({"query": "python"})
+    print(result)
diff --git a/apps/12_langgraph_agent/react_agent/state.py b/apps/12_langgraph_agent/react_agent/state.py
new file mode 100644
index 0000000..5570ab5
--- /dev/null
+++ b/apps/12_langgraph_agent/react_agent/state.py
@@ -0,0 +1,10 @@
+import operator
+from typing import Annotated, TypedDict
+
+from langchain_core.agents import AgentAction, AgentFinish
+
+
+class AgentState(TypedDict):
+    input: str
+    agent_outcome: AgentAction | AgentFinish | None
+    intermediate_steps: Annotated[list[tuple[AgentAction, str]], operator.add]
diff --git a/apps/12_langgraph_agent/reflection_agent/chains.py b/apps/12_langgraph_agent/reflection_agent/chains.py
new file mode 100644
index 0000000..54840d0
--- /dev/null
+++ b/apps/12_langgraph_agent/reflection_agent/chains.py
@@ -0,0 +1,39 @@
+from os import getenv
+
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_openai import AzureChatOpenAI
+
+reflection_prompt = ChatPromptTemplate.from_messages(
+    [
+        (
+            "system",
+            "You are a viral twitter influencer grading a tweet. Generate critique and recommendations for the user's tweet."  # noqa
+            "Always provide detailed recommendations, including requests for length, virality, style, etc.",  # noqa
+        ),
+        MessagesPlaceholder(variable_name="messages"),
+    ]
+)
+
+generation_prompt = ChatPromptTemplate.from_messages(
+    [
+        (
+            "system",
+            "You are a twitter techie influencer assistant tasked with writing excellent twitter posts."
+            " Generate the best twitter post possible for the user's request."
+            " If the user provides critique, respond with a revised version of your previous attempts.",  # noqa
+        ),
+        MessagesPlaceholder(variable_name="messages"),
+    ]
+)
+
+
+llm = AzureChatOpenAI(
+    temperature=0,
+    api_key=getenv("AZURE_OPENAI_API_KEY"),
+    api_version=getenv("AZURE_OPENAI_API_VERSION"),
+    azure_endpoint=getenv("AZURE_OPENAI_ENDPOINT"),
+    model=getenv("AZURE_OPENAI_GPT_MODEL"),
+)
+
+generate_chain = generation_prompt | llm
+reflect_chain = reflection_prompt | llm
diff --git a/apps/12_langgraph_agent/reflection_agent/main.py b/apps/12_langgraph_agent/reflection_agent/main.py
new file mode 100644
index 0000000..6e33ba0
--- /dev/null
+++ b/apps/12_langgraph_agent/reflection_agent/main.py
@@ -0,0 +1,50 @@
+from collections.abc import Sequence
+
+from chains import generate_chain, reflect_chain
+from dotenv import load_dotenv
+from langchain_core.messages import BaseMessage, HumanMessage
+from langgraph.graph import END, MessageGraph
+from promptflow.tracing import start_trace
+
+load_dotenv()
+start_trace()
+
+
+REFLECT = "reflect"
+GENERATE = "generate"
+
+
+def generation_node(state: Sequence[BaseMessage]):
+    return generate_chain.invoke({"messages": state})
+
+
+def reflection_node(messages: Sequence[BaseMessage]) -> list[BaseMessage]:
+    res = reflect_chain.invoke({"messages": messages})
+    return [HumanMessage(content=res.content)]
+
+
+builder = MessageGraph()
+builder.add_node(GENERATE, generation_node)
+builder.add_node(REFLECT, reflection_node)
+builder.set_entry_point(GENERATE)
+
+
+def should_continue(state: list[BaseMessage]):
+    if len(state) > 6:
+        return END
+    return REFLECT
+
+
+builder.add_conditional_edges(GENERATE, should_continue)
+builder.add_edge(REFLECT, GENERATE)
+
+graph = builder.compile()
+print(graph.get_graph().draw_mermaid())
+# graph.get_graph().print_ascii()
+
+if __name__ == "__main__":
+    inputs = HumanMessage(
+        content="""Make this tweet better:"
+        Microsoft's Azure OpenAI API is a game-changer for developers."""
+    )
+    response = graph.invoke(inputs)
diff --git a/docs/images/12_langgraph_agent_graph.png b/docs/images/12_langgraph_agent_graph.png
new file mode 100644
index 0000000..a0f3c41
Binary files /dev/null and b/docs/images/12_langgraph_agent_graph.png differ
diff --git a/poetry.lock b/poetry.lock
index 7dd94ee..3e1ef4e 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
 
 [[package]]
 name = "absl-py"
@@ -147,6 +147,24 @@ files = [
 [package.dependencies]
 frozenlist = ">=1.1.0"
 
+[[package]]
+name = "aiosqlite"
+version = "0.20.0"
+description = "asyncio bridge to the standard sqlite3 module"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "aiosqlite-0.20.0-py3-none-any.whl", hash = "sha256:36a1deaca0cac40ebe32aac9977a6e2bbc7f5189f23f4a54d5908986729e5bd6"},
+    {file = "aiosqlite-0.20.0.tar.gz", hash = "sha256:6d35c8c256637f4672f843c31021464090805bf925385ac39473fb16eaaca3d7"},
+]
+
+[package.dependencies]
+typing_extensions = ">=4.0"
+
+[package.extras]
+dev = ["attribution (==1.7.0)", "black (==24.2.0)", "coverage[toml] (==7.4.1)", "flake8 (==7.0.0)", "flake8-bugbear (==24.2.6)", "flit (==3.9.0)", "mypy (==1.8.0)", "ufmt (==2.3.0)", "usort (==1.0.8.post1)"]
+docs = ["sphinx (==7.2.6)", "sphinx-mdinclude (==0.5.3)"]
+
 [[package]]
 name = "altair"
 version = "5.3.0"
@@ -233,6 +251,23 @@ files = [
 [package.extras]
 test = ["coverage", "mypy", "pexpect", "ruff", "wheel"]
 
+[[package]]
+name = "asgiref"
+version = "3.8.1"
+description = "ASGI specs, helper code, and adapters"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "asgiref-3.8.1-py3-none-any.whl", hash = "sha256:3e1e3ecc849832fe52ccf2cb6686b7a55f82bb1d6aee72a58826471390335e47"},
+    {file = "asgiref-3.8.1.tar.gz", hash = "sha256:c343bd80a0bec947a9860adb4c432ffa7db769836c64238fc34bdc3fec84d590"},
+]
+
+[package.dependencies]
+typing-extensions = {version = ">=4", markers = "python_version < \"3.11\""}
+
+[package.extras]
+tests = ["mypy (>=0.800)", "pytest", "pytest-asyncio"]
+
 [[package]]
 name = "async-timeout"
 version = "4.0.3"
@@ -398,6 +433,78 @@ typing-extensions = ">=4.6.0"
 [package.extras]
 aio = ["azure-core[aio] (>=1.28.0)"]
 
+[[package]]
+name = "backoff"
+version = "2.2.1"
+description = "Function decoration for backoff and retry"
+optional = false
+python-versions = ">=3.7,<4.0"
+files = [
+    {file = "backoff-2.2.1-py3-none-any.whl", hash = "sha256:63579f9a0628e06278f7e47b7d7d5b6ce20dc65c5e96a6f3ca99a6adca0396e8"},
+    {file = "backoff-2.2.1.tar.gz", hash = "sha256:03f829f5bb1923180821643f8753b0502c3b682293992485b0eef2807afa5cba"},
+]
+
+[[package]]
+name = "bcrypt"
+version = "4.2.0"
+description = "Modern password hashing for your software and your servers"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "bcrypt-4.2.0-cp37-abi3-macosx_10_12_universal2.whl", hash = "sha256:096a15d26ed6ce37a14c1ac1e48119660f21b24cba457f160a4b830f3fe6b5cb"},
+    {file = "bcrypt-4.2.0-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c02d944ca89d9b1922ceb8a46460dd17df1ba37ab66feac4870f6862a1533c00"},
+    {file = "bcrypt-4.2.0-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1d84cf6d877918620b687b8fd1bf7781d11e8a0998f576c7aa939776b512b98d"},
+    {file = "bcrypt-4.2.0-cp37-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:1bb429fedbe0249465cdd85a58e8376f31bb315e484f16e68ca4c786dcc04291"},
+    {file = "bcrypt-4.2.0-cp37-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:655ea221910bcac76ea08aaa76df427ef8625f92e55a8ee44fbf7753dbabb328"},
+    {file = "bcrypt-4.2.0-cp37-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:1ee38e858bf5d0287c39b7a1fc59eec64bbf880c7d504d3a06a96c16e14058e7"},
+    {file = "bcrypt-4.2.0-cp37-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:0da52759f7f30e83f1e30a888d9163a81353ef224d82dc58eb5bb52efcabc399"},
+    {file = "bcrypt-4.2.0-cp37-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:3698393a1b1f1fd5714524193849d0c6d524d33523acca37cd28f02899285060"},
+    {file = "bcrypt-4.2.0-cp37-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:762a2c5fb35f89606a9fde5e51392dad0cd1ab7ae64149a8b935fe8d79dd5ed7"},
+    {file = "bcrypt-4.2.0-cp37-abi3-win32.whl", hash = "sha256:5a1e8aa9b28ae28020a3ac4b053117fb51c57a010b9f969603ed885f23841458"},
+    {file = "bcrypt-4.2.0-cp37-abi3-win_amd64.whl", hash = "sha256:8f6ede91359e5df88d1f5c1ef47428a4420136f3ce97763e31b86dd8280fbdf5"},
+    {file = "bcrypt-4.2.0-cp39-abi3-macosx_10_12_universal2.whl", hash = "sha256:c52aac18ea1f4a4f65963ea4f9530c306b56ccd0c6f8c8da0c06976e34a6e841"},
+    {file = "bcrypt-4.2.0-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3bbbfb2734f0e4f37c5136130405332640a1e46e6b23e000eeff2ba8d005da68"},
+    {file = "bcrypt-4.2.0-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3413bd60460f76097ee2e0a493ccebe4a7601918219c02f503984f0a7ee0aebe"},
+    {file = "bcrypt-4.2.0-cp39-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:8d7bb9c42801035e61c109c345a28ed7e84426ae4865511eb82e913df18f58c2"},
+    {file = "bcrypt-4.2.0-cp39-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:3d3a6d28cb2305b43feac298774b997e372e56c7c7afd90a12b3dc49b189151c"},
+    {file = "bcrypt-4.2.0-cp39-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:9c1c4ad86351339c5f320ca372dfba6cb6beb25e8efc659bedd918d921956bae"},
+    {file = "bcrypt-4.2.0-cp39-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:27fe0f57bb5573104b5a6de5e4153c60814c711b29364c10a75a54bb6d7ff48d"},
+    {file = "bcrypt-4.2.0-cp39-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:8ac68872c82f1add6a20bd489870c71b00ebacd2e9134a8aa3f98a0052ab4b0e"},
+    {file = "bcrypt-4.2.0-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:cb2a8ec2bc07d3553ccebf0746bbf3d19426d1c6d1adbd4fa48925f66af7b9e8"},
+    {file = "bcrypt-4.2.0-cp39-abi3-win32.whl", hash = "sha256:77800b7147c9dc905db1cba26abe31e504d8247ac73580b4aa179f98e6608f34"},
+    {file = "bcrypt-4.2.0-cp39-abi3-win_amd64.whl", hash = "sha256:61ed14326ee023917ecd093ee6ef422a72f3aec6f07e21ea5f10622b735538a9"},
+    {file = "bcrypt-4.2.0-pp310-pypy310_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:39e1d30c7233cfc54f5c3f2c825156fe044efdd3e0b9d309512cc514a263ec2a"},
+    {file = "bcrypt-4.2.0-pp310-pypy310_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:f4f4acf526fcd1c34e7ce851147deedd4e26e6402369304220250598b26448db"},
+    {file = "bcrypt-4.2.0-pp39-pypy39_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:1ff39b78a52cf03fdf902635e4c81e544714861ba3f0efc56558979dd4f09170"},
+    {file = "bcrypt-4.2.0-pp39-pypy39_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:373db9abe198e8e2c70d12b479464e0d5092cc122b20ec504097b5f2297ed184"},
+    {file = "bcrypt-4.2.0.tar.gz", hash = "sha256:cf69eaf5185fd58f268f805b505ce31f9b9fc2d64b376642164e9244540c1221"},
+]
+
+[package.extras]
+tests = ["pytest (>=3.2.1,!=3.3.0)"]
+typecheck = ["mypy"]
+
+[[package]]
+name = "beautifulsoup4"
+version = "4.12.3"
+description = "Screen-scraping library"
+optional = false
+python-versions = ">=3.6.0"
+files = [
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+]
+
+[package.dependencies]
+soupsieve = ">1.2"
+
+[package.extras]
+cchardet = ["cchardet"]
+chardet = ["chardet"]
+charset-normalizer = ["charset-normalizer"]
+html5lib = ["html5lib"]
+lxml = ["lxml"]
+
 [[package]]
 name = "black"
 version = "24.8.0"
@@ -455,6 +562,31 @@ files = [
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+[[package]]
+name = "build"
+version = "1.2.2"
+description = "A simple, correct Python build frontend"
+optional = false
+python-versions = ">=3.8"
+files = [
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+]
+
+[package.dependencies]
+colorama = {version = "*", markers = "os_name == \"nt\""}
+importlib-metadata = {version = ">=4.6", markers = "python_full_version < \"3.10.2\""}
+packaging = ">=19.1"
+pyproject_hooks = "*"
+tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
+
+[package.extras]
+docs = ["furo (>=2023.08.17)", "sphinx (>=7.0,<8.0)", "sphinx-argparse-cli (>=1.5)", "sphinx-autodoc-typehints (>=1.10)", "sphinx-issues (>=3.0.0)"]
+test = ["build[uv,virtualenv]", "filelock (>=3)", "pytest (>=6.2.4)", "pytest-cov (>=2.12)", "pytest-mock (>=2)", "pytest-rerunfailures (>=9.1)", "pytest-xdist (>=1.34)", "setuptools (>=42.0.0)", "setuptools (>=56.0.0)", "setuptools (>=56.0.0)", "setuptools (>=67.8.0)", "wheel (>=0.36.0)"]
+typing = ["build[uv]", "importlib-metadata (>=5.1)", "mypy (>=1.9.0,<1.10.0)", "tomli", "typing-extensions (>=3.7.4.3)"]
+uv = ["uv (>=0.1.18)"]
+virtualenv = ["virtualenv (>=20.0.35)"]
+
 [[package]]
 name = "cachetools"
 version = "5.4.0"
@@ -651,6 +783,88 @@ files = [
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+description = "Chromas fork of hnswlib"
+optional = false
+python-versions = "*"
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+
+[package.dependencies]
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+
+[[package]]
+name = "chromadb"
+version = "0.5.7"
+description = "Chroma."
+optional = false
+python-versions = ">=3.8"
+files = [
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+]
+
+[package.dependencies]
+bcrypt = ">=4.0.1"
+build = ">=1.0.3"
+chroma-hnswlib = "0.7.6"
+fastapi = ">=0.95.2"
+grpcio = ">=1.58.0"
+httpx = ">=0.27.0"
+importlib-resources = "*"
+kubernetes = ">=28.1.0"
+mmh3 = ">=4.0.1"
+numpy = ">=1.22.5"
+onnxruntime = ">=1.14.1"
+opentelemetry-api = ">=1.2.0"
+opentelemetry-exporter-otlp-proto-grpc = ">=1.2.0"
+opentelemetry-instrumentation-fastapi = ">=0.41b0"
+opentelemetry-sdk = ">=1.2.0"
+orjson = ">=3.9.12"
+overrides = ">=7.3.1"
+posthog = ">=2.4.0"
+pydantic = ">=1.9"
+pypika = ">=0.48.9"
+PyYAML = ">=6.0.0"
+rich = ">=10.11.0"
+tenacity = ">=8.2.3"
+tokenizers = ">=0.13.2"
+tqdm = ">=4.65.0"
+typer = ">=0.9.0"
+typing-extensions = ">=4.5.0"
+uvicorn = {version = ">=0.18.3", extras = ["standard"]}
+
 [[package]]
 name = "click"
 version = "8.1.7"
@@ -676,6 +890,23 @@ files = [
     {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
 ]
 
+[[package]]
+name = "coloredlogs"
+version = "15.0.1"
+description = "Colored terminal output for Python's logging module"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+files = [
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+    {file = "coloredlogs-15.0.1.tar.gz", hash = "sha256:7c991aa71a4577af2f82600d8f8f3a89f936baeaf9b50a9c197da014e5bf16b0"},
+]
+
+[package.dependencies]
+humanfriendly = ">=9.1"
+
+[package.extras]
+cron = ["capturer (>=2.4)"]
+
 [[package]]
 name = "contourpy"
 version = "1.2.1"
@@ -955,6 +1186,16 @@ files = [
     {file = "docstring_parser-0.16.tar.gz", hash = "sha256:538beabd0af1e2db0146b6bd3caa526c35a34d61af9fd2887f3a8a27a739aa6e"},
 ]
 
+[[package]]
+name = "durationpy"
+version = "0.7"
+description = "Module for converting between datetime.timedelta and Go's Duration strings."
+optional = false
+python-versions = "*"
+files = [
+    {file = "durationpy-0.7.tar.gz", hash = "sha256:8447c43df4f1a0b434e70c15a38d77f5c9bd17284bfc1ff1d430f233d5083732"},
+]
+
 [[package]]
 name = "exceptiongroup"
 version = "1.2.0"
@@ -1321,6 +1562,29 @@ gitdb = ">=4.0.1,<5"
 doc = ["sphinx (==4.3.2)", "sphinx-autodoc-typehints", "sphinx-rtd-theme", "sphinxcontrib-applehelp (>=1.0.2,<=1.0.4)", "sphinxcontrib-devhelp (==1.0.2)", "sphinxcontrib-htmlhelp (>=2.0.0,<=2.0.1)", "sphinxcontrib-qthelp (==1.0.3)", "sphinxcontrib-serializinghtml (==1.1.5)"]
 test = ["coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar", "typing-extensions"]
 
+[[package]]
+name = "google-auth"
+version = "2.35.0"
+description = "Google Authentication Library"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "google_auth-2.35.0-py2.py3-none-any.whl", hash = "sha256:25df55f327ef021de8be50bad0dfd4a916ad0de96da86cd05661c9297723ad3f"},
+    {file = "google_auth-2.35.0.tar.gz", hash = "sha256:f4c64ed4e01e8e8b646ef34c018f8bf3338df0c8e37d8b3bba40e7f574a3278a"},
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+
+[package.dependencies]
+cachetools = ">=2.0.0,<6.0"
+pyasn1-modules = ">=0.2.1"
+rsa = ">=3.1.4,<5"
+
+[package.extras]
+aiohttp = ["aiohttp (>=3.6.2,<4.0.0.dev0)", "requests (>=2.20.0,<3.0.0.dev0)"]
+enterprise-cert = ["cryptography", "pyopenssl"]
+pyopenssl = ["cryptography (>=38.0.3)", "pyopenssl (>=20.0.0)"]
+reauth = ["pyu2f (>=0.1.5)"]
+requests = ["requests (>=2.20.0,<3.0.0.dev0)"]
+
 [[package]]
 name = "googleapis-common-protos"
 version = "1.65.0"
@@ -1409,6 +1673,64 @@ files = [
 docs = ["Sphinx", "furo"]
 test = ["objgraph", "psutil"]
 
+[[package]]
+name = "grpcio"
+version = "1.66.1"
+description = "HTTP/2-based RPC framework"
+optional = false
+python-versions = ">=3.8"
+files = [
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 [[package]]
 name = "h11"
 version = "0.14.0"
@@ -1441,6 +1763,54 @@ http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
 trio = ["trio (>=0.22.0,<0.26.0)"]
 
+[[package]]
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+
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+test = ["Cython (>=0.29.24,<0.30.0)"]
+
 [[package]]
 name = "httpx"
 version = "0.27.0"
@@ -1465,6 +1835,54 @@ cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<14)"]
 http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
 
+[[package]]
+name = "huggingface-hub"
+version = "0.25.0"
+description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
+optional = false
+python-versions = ">=3.8.0"
+files = [
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+]
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+fsspec = ">=2023.5.0"
+packaging = ">=20.9"
+pyyaml = ">=5.1"
+requests = "*"
+tqdm = ">=4.42.1"
+typing-extensions = ">=3.7.4.3"
+
+[package.extras]
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+cli = ["InquirerPy (==0.3.4)"]
+dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.5.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
+hf-transfer = ["hf-transfer (>=0.1.4)"]
+inference = ["aiohttp", "minijinja (>=1.0)"]
+quality = ["mypy (==1.5.1)", "ruff (>=0.5.0)"]
+tensorflow = ["graphviz", "pydot", "tensorflow"]
+tensorflow-testing = ["keras (<3.0)", "tensorflow"]
+testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
+torch = ["safetensors[torch]", "torch"]
+typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
+
+[[package]]
+name = "humanfriendly"
+version = "10.0"
+description = "Human friendly output for text interfaces using Python"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+files = [
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+    {file = "humanfriendly-10.0.tar.gz", hash = "sha256:6b0b831ce8f15f7300721aa49829fc4e83921a9a301cc7f606be6686a2288ddc"},
+]
+
+[package.dependencies]
+pyreadline3 = {version = "*", markers = "sys_platform == \"win32\" and python_version >= \"3.8\""}
+
 [[package]]
 name = "identify"
 version = "2.5.35"
@@ -1973,6 +2391,33 @@ files = [
     {file = "kiwisolver-1.4.5.tar.gz", hash = "sha256:e57e563a57fb22a142da34f38acc2fc1a5c864bc29ca1517a88abc963e60d6ec"},
 ]
 
+[[package]]
+name = "kubernetes"
+version = "31.0.0"
+description = "Kubernetes python client"
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "kubernetes-31.0.0-py2.py3-none-any.whl", hash = "sha256:bf141e2d380c8520eada8b351f4e319ffee9636328c137aa432bc486ca1200e1"},
+    {file = "kubernetes-31.0.0.tar.gz", hash = "sha256:28945de906c8c259c1ebe62703b56a03b714049372196f854105afe4e6d014c0"},
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+
+[package.dependencies]
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+durationpy = ">=0.7"
+google-auth = ">=1.0.1"
+oauthlib = ">=3.2.2"
+python-dateutil = ">=2.5.3"
+pyyaml = ">=5.4.1"
+requests = "*"
+requests-oauthlib = "*"
+six = ">=1.9.0"
+urllib3 = ">=1.24.2"
+websocket-client = ">=0.32.0,<0.40.0 || >0.40.0,<0.41.dev0 || >=0.43.dev0"
+
+[package.extras]
+adal = ["adal (>=1.0.2)"]
+
 [[package]]
 name = "langchain"
 version = "0.2.16"
@@ -2000,6 +2445,26 @@ requests = ">=2,<3"
 SQLAlchemy = ">=1.4,<3"
 tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<9.0.0"
 
+[[package]]
+name = "langchain-chroma"
+version = "0.1.4"
+description = "An integration package connecting Chroma and LangChain"
+optional = false
+python-versions = "<4,>=3.8.1"
+files = [
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+    {file = "langchain_chroma-0.1.4.tar.gz", hash = "sha256:5963a79bf72af0f72019084bbc1e610d03ba33ec2df9a4b47b27c0132aa533fb"},
+]
+
+[package.dependencies]
+chromadb = ">=0.4.0,<0.5.4 || >0.5.4,<0.5.5 || >0.5.5,<0.6.0"
+fastapi = ">=0.95.2,<1"
+langchain-core = {version = ">=0.1.40,<0.4", markers = "python_version >= \"3.9\""}
+numpy = [
+    {version = ">=1,<2", markers = "python_version < \"3.12\""},
+    {version = ">=1.26.0,<2.0.0", markers = "python_version >= \"3.12\""},
+]
+
 [[package]]
 name = "langchain-community"
 version = "0.2.12"
@@ -2028,18 +2493,18 @@ tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<9.0.0"
 
 [[package]]
 name = "langchain-core"
-version = "0.2.38"
+version = "0.2.41"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
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 packaging = ">=23.2,<25"
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@@ -2079,18 +2544,64 @@ files = [
 [package.dependencies]
 langchain-core = ">=0.2.10,<0.3.0"
 
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+version = "0.2.23"
+description = "Building stateful, multi-actor applications with LLMs"
+optional = false
+python-versions = "<4.0,>=3.9.0"
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+
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+
+[[package]]
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+version = "1.0.11"
+description = "Library with base interfaces for LangGraph checkpoint savers."
+optional = false
+python-versions = "<4.0.0,>=3.9.0"
+files = [
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+
+[package.dependencies]
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+
+[[package]]
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+version = "1.0.4"
+description = "Library with a SQLite implementation of LangGraph checkpoint saver."
+optional = false
+python-versions = "<4.0.0,>=3.9.0"
+files = [
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+
 [[package]]
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+version = "0.1.125"
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@@ -2353,14 +2864,138 @@ files = [
 
 [package.dependencies]
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     {version = ">=1.23.3", markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
+    {version = ">=1.21.2", markers = "python_version >= \"3.10\" and python_version < \"3.11\""},
     {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
 ]
 
 [package.extras]
 dev = ["absl-py", "pyink", "pylint (>=2.6.0)", "pytest", "pytest-xdist"]
 
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+    {file = "msgpack-1.1.0-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:3180065ec2abbe13a4ad37688b61b99d7f9e012a535b930e0e683ad6bc30155b"},
+    {file = "msgpack-1.1.0-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:c5a91481a3cc573ac8c0d9aace09345d989dc4a0202b7fcb312c88c26d4e71a8"},
+    {file = "msgpack-1.1.0-cp39-cp39-win32.whl", hash = "sha256:f80bc7d47f76089633763f952e67f8214cb7b3ee6bfa489b3cb6a84cfac114cd"},
+    {file = "msgpack-1.1.0-cp39-cp39-win_amd64.whl", hash = "sha256:4d1b7ff2d6146e16e8bd665ac726a89c74163ef8cd39fa8c1087d4e52d3a2325"},
+    {file = "msgpack-1.1.0.tar.gz", hash = "sha256:dd432ccc2c72b914e4cb77afce64aab761c1137cc698be3984eee260bcb2896e"},
+]
+
 [[package]]
 name = "msrest"
 version = "0.7.1"
@@ -2791,6 +3499,48 @@ rsa = ["cryptography (>=3.0.0)"]
 signals = ["blinker (>=1.4.0)"]
 signedtoken = ["cryptography (>=3.0.0)", "pyjwt (>=2.0.0,<3)"]
 
+[[package]]
+name = "onnxruntime"
+version = "1.19.2"
+description = "ONNX Runtime is a runtime accelerator for Machine Learning models"
+optional = false
+python-versions = "*"
+files = [
+    {file = "onnxruntime-1.19.2-cp310-cp310-macosx_11_0_universal2.whl", hash = "sha256:84fa57369c06cadd3c2a538ae2a26d76d583e7c34bdecd5769d71ca5c0fc750e"},
+    {file = "onnxruntime-1.19.2-cp310-cp310-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:bdc471a66df0c1cdef774accef69e9f2ca168c851ab5e4f2f3341512c7ef4666"},
+    {file = "onnxruntime-1.19.2-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e3a4ce906105d99ebbe817f536d50a91ed8a4d1592553f49b3c23c4be2560ae6"},
+    {file = "onnxruntime-1.19.2-cp310-cp310-win32.whl", hash = "sha256:4b3d723cc154c8ddeb9f6d0a8c0d6243774c6b5930847cc83170bfe4678fafb3"},
+    {file = "onnxruntime-1.19.2-cp310-cp310-win_amd64.whl", hash = "sha256:17ed7382d2c58d4b7354fb2b301ff30b9bf308a1c7eac9546449cd122d21cae5"},
+    {file = "onnxruntime-1.19.2-cp311-cp311-macosx_11_0_universal2.whl", hash = "sha256:d863e8acdc7232d705d49e41087e10b274c42f09e259016a46f32c34e06dc4fd"},
+    {file = "onnxruntime-1.19.2-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c1dfe4f660a71b31caa81fc298a25f9612815215a47b286236e61d540350d7b6"},
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+    {file = "onnxruntime-1.19.2-cp312-cp312-win_amd64.whl", hash = "sha256:190103273ea4507638ffc31d66a980594b237874b65379e273125150eb044857"},
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+    {file = "onnxruntime-1.19.2-cp38-cp38-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b2046fc9560f97947bbc1acbe4c6d48585ef0f12742744307d3364b131ac5778"},
+    {file = "onnxruntime-1.19.2-cp38-cp38-win32.whl", hash = "sha256:31c12840b1cde4ac1f7d27d540c44e13e34f2345cf3642762d2a3333621abb6a"},
+    {file = "onnxruntime-1.19.2-cp38-cp38-win_amd64.whl", hash = "sha256:016229660adea180e9a32ce218b95f8f84860a200f0f13b50070d7d90e92956c"},
+    {file = "onnxruntime-1.19.2-cp39-cp39-macosx_11_0_universal2.whl", hash = "sha256:006c8d326835c017a9e9f74c9c77ebb570a71174a1e89fe078b29a557d9c3848"},
+    {file = "onnxruntime-1.19.2-cp39-cp39-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:df2a94179a42d530b936f154615b54748239c2908ee44f0d722cb4df10670f68"},
+    {file = "onnxruntime-1.19.2-cp39-cp39-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:fae4b4de45894b9ce7ae418c5484cbf0341db6813effec01bb2216091c52f7fb"},
+    {file = "onnxruntime-1.19.2-cp39-cp39-win32.whl", hash = "sha256:dc5430f473e8706fff837ae01323be9dcfddd3ea471c900a91fa7c9b807ec5d3"},
+    {file = "onnxruntime-1.19.2-cp39-cp39-win_amd64.whl", hash = "sha256:38475e29a95c5f6c62c2c603d69fc7d4c6ccbf4df602bd567b86ae1138881c49"},
+]
+
+[package.dependencies]
+coloredlogs = "*"
+flatbuffers = "*"
+numpy = ">=1.21.6"
+packaging = "*"
+protobuf = "*"
+sympy = "*"
+
 [[package]]
 name = "openai"
 version = "1.43.1"
@@ -2833,9 +3583,9 @@ files = [
 
 [package.dependencies]
 numpy = [
+    {version = ">=1.23.5", markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
     {version = ">=1.21.4", markers = "python_version >= \"3.10\" and platform_system == \"Darwin\" and python_version < \"3.11\""},
     {version = ">=1.21.2", markers = "platform_system != \"Darwin\" and python_version >= \"3.10\" and python_version < \"3.11\""},
-    {version = ">=1.23.5", markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
     {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
 ]
 
@@ -2857,9 +3607,9 @@ files = [
 
 [package.dependencies]
 numpy = [
+    {version = ">=1.23.5", markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
     {version = ">=1.21.4", markers = "python_version >= \"3.10\" and platform_system == \"Darwin\" and python_version < \"3.11\""},
     {version = ">=1.21.2", markers = "platform_system != \"Darwin\" and python_version >= \"3.10\" and python_version < \"3.11\""},
-    {version = ">=1.23.5", markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
     {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
 ]
 
@@ -2881,9 +3631,9 @@ files = [
 
 [package.dependencies]
 numpy = [
+    {version = ">=1.23.5", markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
     {version = ">=1.21.4", markers = "python_version >= \"3.10\" and platform_system == \"Darwin\" and python_version < \"3.11\""},
     {version = ">=1.21.2", markers = "platform_system != \"Darwin\" and python_version >= \"3.10\" and python_version < \"3.11\""},
-    {version = ">=1.23.5", markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
     {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
 ]
 
@@ -2916,6 +3666,26 @@ files = [
 [package.dependencies]
 opentelemetry-proto = "1.27.0"
 
+[[package]]
+name = "opentelemetry-exporter-otlp-proto-grpc"
+version = "1.27.0"
+description = "OpenTelemetry Collector Protobuf over gRPC Exporter"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "opentelemetry_exporter_otlp_proto_grpc-1.27.0-py3-none-any.whl", hash = "sha256:56b5bbd5d61aab05e300d9d62a6b3c134827bbd28d0b12f2649c2da368006c9e"},
+    {file = "opentelemetry_exporter_otlp_proto_grpc-1.27.0.tar.gz", hash = "sha256:af6f72f76bcf425dfb5ad11c1a6d6eca2863b91e63575f89bb7b4b55099d968f"},
+]
+
+[package.dependencies]
+deprecated = ">=1.2.6"
+googleapis-common-protos = ">=1.52,<2.0"
+grpcio = ">=1.0.0,<2.0.0"
+opentelemetry-api = ">=1.15,<2.0"
+opentelemetry-exporter-otlp-proto-common = "1.27.0"
+opentelemetry-proto = "1.27.0"
+opentelemetry-sdk = ">=1.27.0,<1.28.0"
+
 [[package]]
 name = "opentelemetry-exporter-otlp-proto-http"
 version = "1.27.0"
@@ -2936,6 +3706,64 @@ opentelemetry-proto = "1.27.0"
 opentelemetry-sdk = ">=1.27.0,<1.28.0"
 requests = ">=2.7,<3.0"
 
+[[package]]
+name = "opentelemetry-instrumentation"
+version = "0.48b0"
+description = "Instrumentation Tools & Auto Instrumentation for OpenTelemetry Python"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "opentelemetry_instrumentation-0.48b0-py3-none-any.whl", hash = "sha256:a69750dc4ba6a5c3eb67986a337185a25b739966d80479befe37b546fc870b44"},
+    {file = "opentelemetry_instrumentation-0.48b0.tar.gz", hash = "sha256:94929685d906380743a71c3970f76b5f07476eea1834abd5dd9d17abfe23cc35"},
+]
+
+[package.dependencies]
+opentelemetry-api = ">=1.4,<2.0"
+setuptools = ">=16.0"
+wrapt = ">=1.0.0,<2.0.0"
+
+[[package]]
+name = "opentelemetry-instrumentation-asgi"
+version = "0.48b0"
+description = "ASGI instrumentation for OpenTelemetry"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "opentelemetry_instrumentation_asgi-0.48b0-py3-none-any.whl", hash = "sha256:ddb1b5fc800ae66e85a4e2eca4d9ecd66367a8c7b556169d9e7b57e10676e44d"},
+    {file = "opentelemetry_instrumentation_asgi-0.48b0.tar.gz", hash = "sha256:04c32174b23c7fa72ddfe192dad874954968a6a924608079af9952964ecdf785"},
+]
+
+[package.dependencies]
+asgiref = ">=3.0,<4.0"
+opentelemetry-api = ">=1.12,<2.0"
+opentelemetry-instrumentation = "0.48b0"
+opentelemetry-semantic-conventions = "0.48b0"
+opentelemetry-util-http = "0.48b0"
+
+[package.extras]
+instruments = ["asgiref (>=3.0,<4.0)"]
+
+[[package]]
+name = "opentelemetry-instrumentation-fastapi"
+version = "0.48b0"
+description = "OpenTelemetry FastAPI Instrumentation"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "opentelemetry_instrumentation_fastapi-0.48b0-py3-none-any.whl", hash = "sha256:afeb820a59e139d3e5d96619600f11ce0187658b8ae9e3480857dd790bc024f2"},
+    {file = "opentelemetry_instrumentation_fastapi-0.48b0.tar.gz", hash = "sha256:21a72563ea412c0b535815aeed75fc580240f1f02ebc72381cfab672648637a2"},
+]
+
+[package.dependencies]
+opentelemetry-api = ">=1.12,<2.0"
+opentelemetry-instrumentation = "0.48b0"
+opentelemetry-instrumentation-asgi = "0.48b0"
+opentelemetry-semantic-conventions = "0.48b0"
+opentelemetry-util-http = "0.48b0"
+
+[package.extras]
+instruments = ["fastapi (>=0.58,<1.0)"]
+
 [[package]]
 name = "opentelemetry-proto"
 version = "1.27.0"
@@ -2981,6 +3809,17 @@ files = [
 deprecated = ">=1.2.6"
 opentelemetry-api = "1.27.0"
 
+[[package]]
+name = "opentelemetry-util-http"
+version = "0.48b0"
+description = "Web util for OpenTelemetry"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "opentelemetry_util_http-0.48b0-py3-none-any.whl", hash = "sha256:76f598af93aab50328d2a69c786beaedc8b6a7770f7a818cc307eb353debfffb"},
+    {file = "opentelemetry_util_http-0.48b0.tar.gz", hash = "sha256:60312015153580cc20f322e5cdc3d3ecad80a71743235bdb77716e742814623c"},
+]
+
 [[package]]
 name = "opt-einsum"
 version = "3.3.0"
@@ -3036,8 +3875,6 @@ files = [
     {file = "orjson-3.10.6-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:960db0e31c4e52fa0fc3ecbaea5b2d3b58f379e32a95ae6b0ebeaa25b93dfd34"},
     {file = "orjson-3.10.6-cp312-none-win32.whl", hash = "sha256:a6ea7afb5b30b2317e0bee03c8d34c8181bc5a36f2afd4d0952f378972c4efd5"},
     {file = "orjson-3.10.6-cp312-none-win_amd64.whl", hash = "sha256:874ce88264b7e655dde4aeaacdc8fd772a7962faadfb41abe63e2a4861abc3dc"},
-    {file = "orjson-3.10.6-cp313-none-win32.whl", hash = "sha256:efdf2c5cde290ae6b83095f03119bdc00303d7a03b42b16c54517baa3c4ca3d0"},
-    {file = "orjson-3.10.6-cp313-none-win_amd64.whl", hash = "sha256:8e190fe7888e2e4392f52cafb9626113ba135ef53aacc65cd13109eb9746c43e"},
     {file = "orjson-3.10.6-cp38-cp38-macosx_10_15_x86_64.macosx_11_0_arm64.macosx_10_15_universal2.whl", hash = "sha256:66680eae4c4e7fc193d91cfc1353ad6d01b4801ae9b5314f17e11ba55e934183"},
     {file = "orjson-3.10.6-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:caff75b425db5ef8e8f23af93c80f072f97b4fb3afd4af44482905c9f588da28"},
     {file = "orjson-3.10.6-cp38-cp38-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:3722fddb821b6036fd2a3c814f6bd9b57a89dc6337b9924ecd614ebce3271394"},
@@ -3061,6 +3898,17 @@ files = [
     {file = "orjson-3.10.6.tar.gz", hash = "sha256:e54b63d0a7c6c54a5f5f726bc93a2078111ef060fec4ecbf34c5db800ca3b3a7"},
 ]
 
+[[package]]
+name = "overrides"
+version = "7.7.0"
+description = "A decorator to automatically detect mismatch when overriding a method."
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "overrides-7.7.0-py3-none-any.whl", hash = "sha256:c7ed9d062f78b8e4c1a7b70bd8796b35ead4d9f510227ef9c5dc7626c60d7e49"},
+    {file = "overrides-7.7.0.tar.gz", hash = "sha256:55158fa3d93b98cc75299b1e67078ad9003ca27945c76162c1c0766d6f91820a"},
+]
+
 [[package]]
 name = "packaging"
 version = "24.0"
@@ -3112,8 +3960,8 @@ files = [
 
 [package.dependencies]
 numpy = [
-    {version = ">=1.22.4", markers = "python_version < \"3.11\""},
     {version = ">=1.23.2", markers = "python_version == \"3.11\""},
+    {version = ">=1.22.4", markers = "python_version < \"3.11\""},
     {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
 ]
 python-dateutil = ">=2.8.2"
@@ -3328,6 +4176,29 @@ docs = ["sphinx (>=1.7.1)"]
 redis = ["redis"]
 tests = ["pytest (>=5.4.1)", "pytest-cov (>=2.8.1)", "pytest-mypy (>=0.8.0)", "pytest-timeout (>=2.1.0)", "redis", "sphinx (>=6.0.0)", "types-redis"]
 
+[[package]]
+name = "posthog"
+version = "3.6.6"
+description = "Integrate PostHog into any python application."
+optional = false
+python-versions = "*"
+files = [
+    {file = "posthog-3.6.6-py2.py3-none-any.whl", hash = "sha256:38834fd7f0732582a20d4eb4674c8d5c088e464d14d1b3f8c176e389aecaa4ef"},
+    {file = "posthog-3.6.6.tar.gz", hash = "sha256:1e04783293117109189ad7048f3eedbe21caff0e39bee5e2d47a93dd790fefac"},
+]
+
+[package.dependencies]
+backoff = ">=1.10.0"
+monotonic = ">=1.5"
+python-dateutil = ">2.1"
+requests = ">=2.7,<3.0"
+six = ">=1.5"
+
+[package.extras]
+dev = ["black", "flake8", "flake8-print", "isort", "pre-commit"]
+sentry = ["django", "sentry-sdk"]
+test = ["coverage", "django", "flake8", "freezegun (==0.3.15)", "mock (>=2.0.0)", "pylint", "pytest", "pytest-timeout"]
+
 [[package]]
 name = "pre-commit"
 version = "3.8.0"
@@ -3582,6 +4453,31 @@ numpy = ">=1.16.6"
 [package.extras]
 test = ["cffi", "hypothesis", "pandas", "pytest", "pytz"]
 
+[[package]]
+name = "pyasn1"
+version = "0.6.1"
+description = "Pure-Python implementation of ASN.1 types and DER/BER/CER codecs (X.208)"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "pyasn1-0.6.1-py3-none-any.whl", hash = "sha256:0d632f46f2ba09143da3a8afe9e33fb6f92fa2320ab7e886e2d0f7672af84629"},
+    {file = "pyasn1-0.6.1.tar.gz", hash = "sha256:6f580d2bdd84365380830acf45550f2511469f673cb4a5ae3857a3170128b034"},
+]
+
+[[package]]
+name = "pyasn1-modules"
+version = "0.4.1"
+description = "A collection of ASN.1-based protocols modules"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "pyasn1_modules-0.4.1-py3-none-any.whl", hash = "sha256:49bfa96b45a292b711e986f222502c1c9a5e1f4e568fc30e2574a6c7d07838fd"},
+    {file = "pyasn1_modules-0.4.1.tar.gz", hash = "sha256:c28e2dbf9c06ad61c71a075c7e0f9fd0f1b0bb2d2ad4377f240d33ac2ab60a7c"},
+]
+
+[package.dependencies]
+pyasn1 = ">=0.4.6,<0.7.0"
+
 [[package]]
 name = "pycparser"
 version = "2.22"
@@ -3800,6 +4696,41 @@ files = [
 [package.extras]
 diagrams = ["jinja2", "railroad-diagrams"]
 
+[[package]]
+name = "pypika"
+version = "0.48.9"
+description = "A SQL query builder API for Python"
+optional = false
+python-versions = "*"
+files = [
+    {file = "PyPika-0.48.9.tar.gz", hash = "sha256:838836a61747e7c8380cd1b7ff638694b7a7335345d0f559b04b2cd832ad5378"},
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+optional = false
+python-versions = ">=3.7"
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+[package.extras]
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+
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 version = "8.3.3"
@@ -4245,6 +5176,20 @@ files = [
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+[[package]]
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+version = "4.9"
+description = "Pure-Python RSA implementation"
+optional = false
+python-versions = ">=3.6,<4"
+files = [
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+
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 version = "0.18.6"
@@ -4442,6 +5387,17 @@ files = [
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+description = "Tool to Detect Surrounding Shell"
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@@ -4495,6 +5451,17 @@ CFFI = ">=1.0"
 [package.extras]
 numpy = ["NumPy"]
 
+[[package]]
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 version = "2.0.32"
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 blobfile = ["blobfile (>=2)"]
 
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+docs = ["setuptools-rust", "sphinx", "sphinx-rtd-theme"]
+testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests", "ruff"]
+
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 version = "0.10.2"
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 tutorials = ["matplotlib", "pandas", "tabulate"]
 
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+
 [[package]]
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 version = "4.12.2"
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 zstd = ["zstandard (>=0.18.0)"]
 
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+
 [[package]]
 name = "virtualenv"
 version = "20.25.1"
@@ -5119,6 +6295,101 @@ files = [
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 watchmedo = ["PyYAML (>=3.10)"]
 
+[[package]]
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 [[package]]
 name = "websocket-client"
 version = "1.8.0"
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 optional = ["python-socks", "wsaccel"]
 test = ["websockets"]
 
+[[package]]
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+]
+
 [[package]]
 name = "werkzeug"
 version = "3.0.4"
@@ -5356,4 +6722,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "99d73046ca1ff33543717917ceedad6863515f177f18bb8372e6210fc5cec652"
+content-hash = "02fa2b8c6348dfa3957deb55ec2f3911f2adcd2f109ef60cb77ad386b9a58440"
diff --git a/pyproject.toml b/pyproject.toml
index d7ff27c..ff4941f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -28,6 +28,10 @@ requests = "^2.32.3"
 opencv-python-headless = "^4.10.0.84"
 promptflow = "^1.15.0"
 promptflow-evals = "^0.3.2"
+langgraph = "^0.2.23"
+langchain-chroma = "^0.1.4"
+beautifulsoup4 = "^4.12.3"
+langgraph-checkpoint-sqlite = "^1.0.4"
 
 [tool.poetry.group.dev.dependencies]
 pre-commit = "^3.8.0"
diff --git a/requirements.txt b/requirements.txt
index efbfca4..5579e01 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,18 +2,22 @@ openai==1.43.1
 python-dotenv==1.0.1
 streamlit==1.38.0
 azure-cosmos==4.7.0
-plotly==5.24.0
+plotly==5.24.1
 pandas==2.2.2
 langchain==0.2.16
 langchain-openai==0.1.23
 langchain-community==0.2.12
 azure-search-documents==11.5.1
 azure-identity==1.17.1
-azure-ai-documentintelligence==1.0.0b3
+azure-ai-documentintelligence==1.0.0b4
 azure-storage-blob==12.22.0
 requests==2.32.3
 promptflow==1.15.0
 promptflow-evals==0.3.2
+langgraph==0.2.23
+langchain-chroma==0.1.4
+beautifulsoup4==4.12.3
+langgraph-checkpoint-sqlite==1.0.4
 
 # To run 99_streamlit_examples/pages/10_Object_Detection.py
 # ultralytics==8.2.89