diff --git a/Makefile b/Makefile
index 11de700f0..ddc5ee54c 100644
--- a/Makefile
+++ b/Makefile
@@ -34,22 +34,18 @@ rebuild: stop prune ## Rebuild all images
 
 .PHONY: test-core-api
 test-core-api: ## Test core-api
-	cp .env.test core-api/.env
-	cd core-api && poetry install --with dev && poetry run python -m pytest -m "not ai" --cov=core_api -v --cov-report=term-missing --cov-fail-under=75
+	cd core-api && poetry install --with dev && poetry run python -m pytest  --cov=core_api -v --cov-report=term-missing --cov-fail-under=75
 
 .PHONY: test-ai
 test-ai: ## Test code with live LLM
-	cp .env.test core-api/.env
-	cd core-api && poetry install --with dev && poetry run python -m pytest -m "ai" --cov=core_api -v --cov-report=term-missing --cov-fail-under=80
+	cd redbox-core && poetry install --with dev && poetry run python -m pytest -m "ai" --cov=redbox -v --cov-report=term-missing --cov-fail-under=80
 
 .PHONY: test-redbox
 test-redbox: ## Test redbox
-	cp .env.test redbox-core/.env
-	cd redbox-core && poetry install && poetry run pytest --cov=redbox -v --cov-report=term-missing --cov-fail-under=60
+	cd redbox-core && poetry install && poetry run pytest -m "not ai" --cov=redbox -v --cov-report=term-missing --cov-fail-under=60
 
 .PHONY: test-worker
 test-worker: ## Test worker
-	cp .env.test worker/.env
 	cd worker && poetry install && poetry run pytest --cov=worker -v --cov-report=term-missing --cov-fail-under=80
 
 .PHONY: test-django
diff --git a/core-api/core_api/build_chains.py b/core-api/core_api/build_chains.py
deleted file mode 100644
index f12b19af3..000000000
--- a/core-api/core_api/build_chains.py
+++ /dev/null
@@ -1,281 +0,0 @@
-import logging
-import sys
-from operator import itemgetter
-from typing import Annotated
-
-from core_api import dependencies
-from fastapi import Depends
-from langchain.prompts import PromptTemplate
-from langchain.schema import StrOutputParser
-from langchain_community.chat_models import ChatLiteLLM
-from langchain_core.prompts import ChatPromptTemplate
-from langchain_core.retrievers import BaseRetriever
-from langchain_core.runnables import Runnable, RunnableLambda, RunnablePassthrough, chain
-from langchain_core.runnables.config import RunnableConfig
-from langchain_core.vectorstores import VectorStoreRetriever
-from tiktoken import Encoding
-
-from redbox.api.format import format_documents
-from redbox.api.runnables import filter_by_elbow, make_chat_prompt_from_messages_runnable, resize_documents
-from redbox.models import ChatRoute, Settings
-from redbox.models.errors import NoDocumentSelected
-
-# === Logging ===
-
-logging.basicConfig(stream=sys.stdout, level=logging.INFO)
-log = logging.getLogger()
-
-
-def build_chat_chain(
-    llm: Annotated[ChatLiteLLM, Depends(dependencies.get_llm)],
-    tokeniser: Annotated[Encoding, Depends(dependencies.get_tokeniser)],
-    env: Annotated[Settings, Depends(dependencies.get_env)],
-) -> Runnable:
-    return (
-        make_chat_prompt_from_messages_runnable(
-            system_prompt=env.ai.chat_system_prompt,
-            question_prompt=env.ai.chat_question_prompt,
-            input_token_budget=env.ai.context_window_size - env.llm_max_tokens,
-            tokeniser=tokeniser,
-        )
-        | llm
-        | {
-            "response": StrOutputParser(),
-            "route_name": RunnableLambda(lambda _: ChatRoute.chat.value),
-        }
-    )
-
-
-def build_chat_with_docs_chain(
-    llm: Annotated[ChatLiteLLM, Depends(dependencies.get_llm)],
-    all_chunks_retriever: Annotated[BaseRetriever, Depends(dependencies.get_all_chunks_retriever)],
-    tokeniser: Annotated[Encoding, Depends(dependencies.get_tokeniser)],
-    env: Annotated[Settings, Depends(dependencies.get_env)],
-) -> Runnable:
-    def make_document_context():
-        return all_chunks_retriever | resize_documents(env.ai.stuff_chunk_max_tokens)
-
-    @chain
-    def map_operation(input_dict):
-        system_map_prompt = env.ai.map_system_prompt
-        prompt_template = PromptTemplate.from_template(env.ai.map_question_prompt)
-
-        formatted_map_question_prompt = prompt_template.format(question=input_dict["question"])
-
-        map_prompt = ChatPromptTemplate.from_messages(
-            [
-                ("system", system_map_prompt),
-                ("human", formatted_map_question_prompt + env.ai.map_document_prompt),
-            ]
-        )
-
-        documents = input_dict["documents"]
-
-        map_summaries = (map_prompt | llm | StrOutputParser()).batch(
-            documents,
-            config=RunnableConfig(max_concurrency=env.ai.map_max_concurrency),
-        )
-
-        summaries = " ; ".join(map_summaries)
-        input_dict["summaries"] = summaries
-        return input_dict
-
-    @chain
-    def chat_with_docs_route(input_dict: dict):
-        log.info("Length documents: %s", len(input_dict["documents"]))
-        if len(input_dict["documents"]) == 1:
-            return RunnablePassthrough.assign(
-                formatted_documents=(RunnablePassthrough() | itemgetter("documents") | format_documents)
-            ) | {
-                "response": make_chat_prompt_from_messages_runnable(
-                    system_prompt=env.ai.chat_with_docs_system_prompt,
-                    question_prompt=env.ai.chat_with_docs_question_prompt,
-                    input_token_budget=env.ai.context_window_size - env.llm_max_tokens,
-                    tokeniser=tokeniser,
-                )
-                | llm
-                | StrOutputParser(),
-                "route_name": RunnableLambda(lambda _: ChatRoute.chat_with_docs.value),
-            }
-
-        elif len(input_dict["documents"]) > 1:
-            return (
-                map_operation
-                | RunnablePassthrough.assign(
-                    formatted_documents=(RunnablePassthrough() | itemgetter("documents") | format_documents)
-                )
-                | {
-                    "response": make_chat_prompt_from_messages_runnable(
-                        system_prompt=env.ai.chat_with_docs_reduce_system_prompt,
-                        question_prompt=env.ai.chat_with_docs_reduce_question_prompt,
-                        input_token_budget=env.ai.context_window_size - env.llm_max_tokens,
-                        tokeniser=tokeniser,
-                    )
-                    | llm
-                    | StrOutputParser(),
-                    "route_name": RunnableLambda(lambda _: ChatRoute.chat_with_docs.value),
-                }
-            )
-
-        else:
-            raise NoDocumentSelected
-
-    return RunnablePassthrough.assign(documents=make_document_context()) | chat_with_docs_route
-
-
-def build_retrieval_chain(
-    llm: Annotated[ChatLiteLLM, Depends(dependencies.get_llm)],
-    retriever: Annotated[VectorStoreRetriever, Depends(dependencies.get_parameterised_retriever)],
-    tokeniser: Annotated[Encoding, Depends(dependencies.get_tokeniser)],
-    env: Annotated[Settings, Depends(dependencies.get_env)],
-) -> Runnable:
-    return (
-        RunnablePassthrough.assign(documents=retriever)
-        | RunnablePassthrough.assign(
-            formatted_documents=(RunnablePassthrough() | itemgetter("documents") | format_documents)
-        )
-        | {
-            "response": make_chat_prompt_from_messages_runnable(
-                system_prompt=env.ai.retrieval_system_prompt,
-                question_prompt=env.ai.retrieval_question_prompt,
-                input_token_budget=env.ai.context_window_size - env.llm_max_tokens,
-                tokeniser=tokeniser,
-            )
-            | llm
-            | StrOutputParser(),
-            "source_documents": itemgetter("documents"),
-            "route_name": RunnableLambda(lambda _: ChatRoute.search.value),
-        }
-    )
-
-
-def build_condense_retrieval_chain(
-    llm: Annotated[ChatLiteLLM, Depends(dependencies.get_llm)],
-    retriever: Annotated[VectorStoreRetriever, Depends(dependencies.get_parameterised_retriever)],
-    tokeniser: Annotated[Encoding, Depends(dependencies.get_tokeniser)],
-    env: Annotated[Settings, Depends(dependencies.get_env)],
-) -> Runnable:
-    def route(input_dict: dict):
-        if len(input_dict["chat_history"]) > 0:
-            return RunnablePassthrough.assign(
-                question=make_chat_prompt_from_messages_runnable(
-                    system_prompt=env.ai.condense_system_prompt,
-                    question_prompt=env.ai.condense_question_prompt,
-                    input_token_budget=env.ai.context_window_size - env.llm_max_tokens,
-                    tokeniser=tokeniser,
-                )
-                | llm
-                | StrOutputParser()
-            )
-        else:
-            return RunnablePassthrough()
-
-    return (
-        RunnableLambda(route)
-        | RunnablePassthrough.assign(documents=retriever | filter_by_elbow(enabled=env.ai.elbow_filter_enabled))
-        | RunnablePassthrough.assign(
-            formatted_documents=(RunnablePassthrough() | itemgetter("documents") | format_documents)
-        )
-        | {
-            "response": make_chat_prompt_from_messages_runnable(
-                system_prompt=env.ai.retrieval_system_prompt,
-                question_prompt=env.ai.retrieval_question_prompt,
-                input_token_budget=env.ai.context_window_size - env.llm_max_tokens,
-                tokeniser=tokeniser,
-            )
-            | llm
-            | StrOutputParser(),
-            "source_documents": itemgetter("documents"),
-            "route_name": RunnableLambda(lambda _: ChatRoute.search.value),
-        }
-    )
-
-
-def build_summary_chain(
-    llm: Annotated[ChatLiteLLM, Depends(dependencies.get_llm)],
-    all_chunks_retriever: Annotated[BaseRetriever, Depends(dependencies.get_all_chunks_retriever)],
-    tokeniser: Annotated[Encoding, Depends(dependencies.get_tokeniser)],
-    env: Annotated[Settings, Depends(dependencies.get_env)],
-) -> Runnable:
-    def make_document_context():
-        return (
-            all_chunks_retriever
-            | resize_documents(env.ai.stuff_chunk_max_tokens)
-            | RunnableLambda(lambda docs: [d.page_content for d in docs])
-        )
-
-    # Stuff chain now missing the RunnabeLambda to format the chunks
-    stuff_chain = (
-        make_chat_prompt_from_messages_runnable(
-            system_prompt=env.ai.summarisation_system_prompt,
-            question_prompt=env.ai.summarisation_question_prompt,
-            input_token_budget=env.ai.context_window_size - env.llm_max_tokens,
-            tokeniser=tokeniser,
-        )
-        | llm
-        | {
-            "response": StrOutputParser(),
-            "route_name": RunnableLambda(lambda _: ChatRoute.summarise.value),
-        }
-    )
-
-    @chain
-    def map_operation(input_dict):
-        system_map_prompt = env.ai.map_system_prompt
-        prompt_template = PromptTemplate.from_template(env.ai.map_question_prompt)
-
-        formatted_map_question_prompt = prompt_template.format(question=input_dict["question"])
-
-        map_prompt = ChatPromptTemplate.from_messages(
-            [
-                ("system", system_map_prompt),
-                ("human", formatted_map_question_prompt + env.ai.map_document_prompt),
-            ]
-        )
-
-        documents = input_dict["documents"]
-
-        map_summaries = (map_prompt | llm | StrOutputParser()).batch(
-            documents,
-            config=RunnableConfig(max_concurrency=env.ai.map_max_concurrency),
-        )
-
-        summaries = " ; ".join(map_summaries)
-        input_dict["summaries"] = summaries
-        return input_dict
-
-    map_reduce_chain = (
-        map_operation
-        | make_chat_prompt_from_messages_runnable(
-            system_prompt=env.ai.reduce_system_prompt,
-            question_prompt=env.ai.reduce_question_prompt,
-            input_token_budget=env.ai.context_window_size - env.llm_max_tokens,
-            tokeniser=tokeniser,
-        )
-        | llm
-        | {
-            "response": StrOutputParser(),
-            "route_name": RunnableLambda(lambda _: ChatRoute.map_reduce_summarise.value),
-        }
-    )
-
-    @chain
-    def summarisation_route(input_dict):
-        if len(input_dict["documents"]) == 1:
-            return stuff_chain
-
-        elif len(input_dict["documents"]) > 1:
-            return map_reduce_chain
-
-        else:
-            raise NoDocumentSelected
-
-    return RunnablePassthrough.assign(documents=make_document_context()) | summarisation_route
-
-
-def build_static_response_chain(prompt_template, route_name) -> Runnable:
-    return RunnablePassthrough.assign(
-        response=(ChatPromptTemplate.from_template(prompt_template) | RunnableLambda(lambda p: p.messages[0].content)),
-        source_documents=RunnableLambda(lambda _: []),
-        route_name=RunnableLambda(lambda _: route_name.value),
-    )
diff --git a/core-api/core_api/dependencies.py b/core-api/core_api/dependencies.py
index cebbb05cc..745c355e9 100644
--- a/core-api/core_api/dependencies.py
+++ b/core-api/core_api/dependencies.py
@@ -3,6 +3,7 @@
 from functools import lru_cache
 from typing import Annotated
 
+from redbox import Redbox
 import tiktoken
 from fastapi import Depends
 from langchain_community.chat_models import ChatLiteLLM
@@ -24,14 +25,12 @@ def get_env() -> Settings:
     return Settings()
 
 
-@lru_cache(1)
 def get_embedding_model(env: Annotated[Settings, Depends(get_env)]) -> Embeddings:
     return get_embeddings(env)
 
 
-@lru_cache(1)
 def get_parameterised_retriever(
-    env: Annotated[Settings, Depends(get_env)],
+    env: Annotated[Settings, Depends(get_env)], embeddings: Annotated[Embeddings, Depends(get_embedding_model)]
 ) -> BaseRetriever:
     """Creates an Elasticsearch retriever runnable.
 
@@ -50,7 +49,7 @@ def get_parameterised_retriever(
         es_client=env.elasticsearch_client(),
         index_name=f"{env.elastic_root_index}-chunk",
         params=default_params,
-        embedding_model=get_embedding_model(env),
+        embedding_model=embeddings,
         embedding_field_name=env.embedding_document_field_name,
     ).configurable_fields(
         params=ConfigurableField(
@@ -109,3 +108,13 @@ def get_llm(env: Annotated[Settings, Depends(get_env)]) -> ChatLiteLLM:
 @lru_cache(1)
 def get_tokeniser() -> tiktoken.Encoding:
     return tiktoken.get_encoding("cl100k_base")
+
+
+def get_redbox(
+    llm: Annotated[ChatLiteLLM, Depends(get_llm)],
+    all_chunks_retriever: Annotated[AllElasticsearchRetriever, Depends(get_all_chunks_retriever)],
+    parameterised_retriever: Annotated[ParameterisedElasticsearchRetriever, Depends(get_parameterised_retriever)],
+    tokeniser: Annotated[tiktoken.Encoding, Depends(get_tokeniser)],
+    env: Annotated[Settings, Depends(get_env)],
+) -> Redbox:
+    return Redbox(llm, all_chunks_retriever, parameterised_retriever, tokeniser, env)
diff --git a/core-api/core_api/routes/chat.py b/core-api/core_api/routes/chat.py
index 426c9c7d2..42e8b67c1 100644
--- a/core-api/core_api/routes/chat.py
+++ b/core-api/core_api/routes/chat.py
@@ -1,29 +1,27 @@
 import logging
-import re
 from typing import Annotated
 from uuid import UUID
 
+from redbox import Redbox
+
 from core_api.auth import get_user_uuid, get_ws_user_uuid
-from core_api.semantic_routes import get_routable_chains
 from fastapi import Depends, FastAPI, WebSocket
 from fastapi.encoders import jsonable_encoder
-from langchain_core.runnables import Runnable
-from langchain_core.tools import Tool
 from openai import APIError
+from langchain_core.documents import Document
 
-from redbox.api.runnables import map_to_chat_response
-from redbox.models.chain import ChainInput, ChainChatMessage
-from redbox.models.chat import ChatRequest, ChatResponse, SourceDocument, ClientResponse, ErrorDetail
-from redbox.models.errors import NoDocumentSelected, QuestionLengthError
+from redbox.models.chain import ChainInput, ChainChatMessage, ChainState
+from redbox.models.chat import ChatRequest, ChatResponse, ClientResponse, ErrorDetail
 from redbox.transform import map_document_to_source_document
 
+from core_api.dependencies import get_redbox
+from core_api.runnables import map_to_chat_response
+
 # === Logging ===
 
 logging.basicConfig(level=logging.INFO)
 log = logging.getLogger()
 
-re_keyword_pattern = re.compile(r"@(\w+)")
-
 
 chat_app = FastAPI(
     title="Core Chat API",
@@ -39,62 +37,38 @@
 )
 
 
-async def route_chat(
-    chat_request: ChatRequest, user_uuid: UUID, routable_chains: dict[str, Tool]
-) -> tuple[Runnable, ChainInput]:
-    question = chat_request.message_history[-1].text
-
-    selected_chain = None
-
-    def select_chat_chain(chat_request: ChatRequest, routable_chains: dict[str, Runnable]) -> Runnable:
-        if chat_request.selected_files:
-            return routable_chains.get("chat/documents")
-        else:
-            return routable_chains.get("chat")
-
-    # Match keyword
-    route_match = re_keyword_pattern.search(question)
-    route_name = route_match.group()[1:] if route_match else None
-    selected_chain, description = routable_chains.get(route_name, select_chat_chain(chat_request, routable_chains))
-
-    params = ChainInput(
-        question=chat_request.message_history[-1].text,
-        file_uuids=[str(f.uuid) for f in chat_request.selected_files],
-        user_uuid=str(user_uuid),
-        chat_history=[
-            ChainChatMessage(role=message.role, text=message.text) for message in chat_request.message_history[:-1]
-        ],
-    )
-
-    log.info("Routed to %s", route_name)
-    log.info("Selected files: %s", chat_request.selected_files)
-
-    return selected_chain, params
-
-
 @chat_app.post("/rag", tags=["chat"])
 async def rag_chat(
     chat_request: ChatRequest,
     user_uuid: Annotated[UUID, Depends(get_user_uuid)],
-    routable_chains: Annotated[dict[str, Tool], Depends(get_routable_chains)],
+    redbox: Annotated[Redbox, Depends(get_redbox)],
 ) -> ChatResponse:
     """REST endpoint. Get a LLM response to a question history and file."""
-    selected_chain, params = await route_chat(chat_request, user_uuid, routable_chains)
-    return (selected_chain | map_to_chat_response).invoke(params.dict())
+    state = ChainState(
+        query=ChainInput(
+            question=chat_request.message_history[-1].text,
+            file_uuids=[f.uuid for f in chat_request.selected_files],
+            user_uuid=user_uuid,
+            chat_history=[
+                ChainChatMessage(role=message.role, text=message.text) for message in chat_request.message_history[:-1]
+            ],
+        ),
+    )
+    return await (redbox.graph | map_to_chat_response).ainvoke(state)
 
 
 @chat_app.get("/tools", tags=["chat"])
 async def available_tools(
-    routable_chains: Annotated[dict[str, tuple[Runnable, str]], Depends(get_routable_chains)],
+    redbox: Annotated[Redbox, Depends(get_redbox)],
 ):
     """REST endpoint. Get a mapping of all tools available via chat."""
-    return [{"name": name, "description": tool[1]} for (name, tool) in routable_chains.items()]
+    return [{"name": name, "description": description} for name, description in redbox.get_available_keywords().items()]
 
 
 @chat_app.websocket("/rag")
 async def rag_chat_streamed(
     websocket: WebSocket,
-    routable_chains: Annotated[dict[str, Tool], Depends(get_routable_chains)],
+    redbox: Annotated[Redbox, Depends(get_redbox)],
 ):
     """Websocket. Get a LLM response to a question history and file."""
     await websocket.accept()
@@ -104,34 +78,35 @@ async def rag_chat_streamed(
     request = await websocket.receive_text()
     chat_request = ChatRequest.model_validate_json(request)
 
-    selected_chain, params = await route_chat(chat_request, user_uuid, routable_chains)
+    state = ChainState(
+        query=ChainInput(
+            question=chat_request.message_history[-1].text,
+            file_uuids=[f.uuid for f in chat_request.selected_files],
+            user_uuid=user_uuid,
+            chat_history=[
+                ChainChatMessage(role=message.role, text=message.text) for message in chat_request.message_history[:-1]
+            ],
+        ),
+    )
 
-    try:
-        async for event in selected_chain.astream(params.dict()):
-            response: str = event.get("response", "")
-            source_documents: list[SourceDocument] = [
-                map_document_to_source_document(doc) for doc in event.get("source_documents", [])
-            ]
-            route_name: str = event.get("route_name", "")
-            if response:
-                await send_to_client(ClientResponse(resource_type="text", data=response), websocket)
-            if source_documents:
-                await send_to_client(ClientResponse(resource_type="documents", data=source_documents), websocket)
-            if route_name:
-                await send_to_client(ClientResponse(resource_type="route_name", data=route_name), websocket)
-    except NoDocumentSelected as e:
-        log.info("No documents have been selected to summarise", exc_info=e)
+    async def on_llm_response(tokens: str):
+        await send_to_client(ClientResponse(resource_type="text", data=tokens), websocket)
+
+    async def on_route_choice(route_name: str):
+        await send_to_client(ClientResponse(resource_type="route_name", data=route_name), websocket)
+
+    async def on_documents_available(docs: list[Document]):
         await send_to_client(
-            ClientResponse(
-                resource_type="error", data=ErrorDetail(code="no-document-selected", message=type(e).__name__)
-            ),
+            ClientResponse(resource_type="documents", data=[map_document_to_source_document(d) for d in docs]),
             websocket,
         )
-    except QuestionLengthError as e:
-        log.info("Question is too long", exc_info=e)
-        await send_to_client(
-            ClientResponse(resource_type="error", data=ErrorDetail(code="question-too-long", message=type(e).__name__)),
-            websocket,
+
+    try:
+        await redbox.run(
+            state,
+            response_tokens_callback=on_llm_response,
+            route_name_callback=on_route_choice,
+            documents_callback=on_documents_available,
         )
     except APIError as e:
         log.exception("Unhandled exception.", exc_info=e)
diff --git a/core-api/core_api/runnables.py b/core-api/core_api/runnables.py
new file mode 100644
index 000000000..e37736f23
--- /dev/null
+++ b/core-api/core_api/runnables.py
@@ -0,0 +1,19 @@
+from langchain_core.runnables import chain
+
+from redbox.models.chain import ChainState
+from redbox.models.chat import ChatResponse
+from redbox.transform import map_document_to_source_document
+
+
+@chain
+def map_to_chat_response(state: ChainState):
+    """
+    Create a ChatResponse at the end of a chain from a dict containing
+    'response' a string to use as output_text
+    'source_documents' a list of documents to map to source_documents
+    """
+    return ChatResponse(
+        output_text=state["response"],
+        source_documents=[map_document_to_source_document(d) for d in state.get("documents") or []],
+        route_name=state["route_name"],
+    )
diff --git a/core-api/core_api/semantic_routes.py b/core-api/core_api/semantic_routes.py
deleted file mode 100644
index b41601a07..000000000
--- a/core-api/core_api/semantic_routes.py
+++ /dev/null
@@ -1,63 +0,0 @@
-from typing import Annotated
-
-from core_api.build_chains import (
-    build_chat_chain,
-    build_chat_with_docs_chain,
-    build_condense_retrieval_chain,
-    build_static_response_chain,
-)
-from fastapi import Depends
-from langchain_core.runnables import Runnable
-
-from redbox.models.chat import ChatRoute
-from redbox.models.chain import ChainInput
-
-# === Pre-canned responses for non-LLM routes ===
-INFO_RESPONSE = """
-I am Redbox, an AI focused on helping UK Civil Servants, Political Advisors and
-Ministers triage and summarise information from a wide variety of sources.
-"""
-
-
-def as_chat_tool(
-    name: str,
-    runnable: Runnable,
-    description: str,
-):
-    return runnable.as_tool(name=name, description=description, args_schema=ChainInput)
-
-
-__routable_chains = None
-
-
-def get_routable_chains(
-    condense_chain: Annotated[Runnable, Depends(build_condense_retrieval_chain)],
-    chat_chain: Annotated[Runnable, Depends(build_chat_chain)],
-    chat_with_docs_chain: Annotated[Runnable, Depends(build_chat_with_docs_chain)],
-) -> dict[str, tuple[Runnable, str]]:
-    global __routable_chains  # noqa: PLW0603
-    if not __routable_chains:
-        chat_tools = (
-            (
-                ChatRoute.info,
-                build_static_response_chain(INFO_RESPONSE, ChatRoute.info),
-                "Give helpful information about Redbox",
-            ),
-            (
-                ChatRoute.chat,
-                chat_chain,
-                "Answer questions as a helpful assistant",
-            ),
-            (
-                ChatRoute.chat_with_docs,
-                chat_with_docs_chain,
-                "Answer questions as a helpful assistant using the documents provided",
-            ),
-            (
-                ChatRoute.search,
-                condense_chain,
-                "Search for an answer to a question in provided documents",
-            ),
-        )
-        __routable_chains = {name: (runnable, description) for (name, runnable, description) in chat_tools}
-    return __routable_chains
diff --git a/core-api/poetry.lock b/core-api/poetry.lock
index fcb6e13d8..6c080cf0e 100644
--- a/core-api/poetry.lock
+++ b/core-api/poetry.lock
@@ -140,17 +140,6 @@ doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphin
 test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
 trio = ["trio (>=0.23)"]
 
-[[package]]
-name = "appdirs"
-version = "1.4.4"
-description = "A small Python module for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
-optional = false
-python-versions = "*"
-files = [
-    {file = "appdirs-1.4.4-py2.py3-none-any.whl", hash = "sha256:a841dacd6b99318a741b166adb07e19ee71a274450e68237b4650ca1055ab128"},
-    {file = "appdirs-1.4.4.tar.gz", hash = "sha256:7d5d0167b2b1ba821647616af46a749d1c653740dd0d2415100fe26e27afdf41"},
-]
-
 [[package]]
 name = "attrs"
 version = "23.2.0"
@@ -535,118 +524,6 @@ files = [
 marshmallow = ">=3.18.0,<4.0.0"
 typing-inspect = ">=0.4.0,<1"
 
-[[package]]
-name = "datasets"
-version = "2.14.4"
-description = "HuggingFace community-driven open-source library of datasets"
-optional = false
-python-versions = ">=3.8.0"
-files = [
-    {file = "datasets-2.14.4-py3-none-any.whl", hash = "sha256:29336bd316a7d827ccd4da2236596279b20ca2ac78f64c04c9483da7cbc2459b"},
-    {file = "datasets-2.14.4.tar.gz", hash = "sha256:ef29c2b5841de488cd343cfc26ab979bff77efa4d2285af51f1ad7db5c46a83b"},
-]
-
-[package.dependencies]
-aiohttp = "*"
-dill = ">=0.3.0,<0.3.8"
-fsspec = {version = ">=2021.11.1", extras = ["http"]}
-huggingface-hub = ">=0.14.0,<1.0.0"
-multiprocess = "*"
-numpy = ">=1.17"
-packaging = "*"
-pandas = "*"
-pyarrow = ">=8.0.0"
-pyyaml = ">=5.1"
-requests = ">=2.19.0"
-tqdm = ">=4.62.1"
-xxhash = "*"
-
-[package.extras]
-apache-beam = ["apache-beam (>=2.26.0,<2.44.0)"]
-audio = ["librosa", "soundfile (>=0.12.1)"]
-benchmarks = ["tensorflow (==2.12.0)", "torch (==2.0.1)", "transformers (==4.30.1)"]
-dev = ["Pillow (>=6.2.1)", "absl-py", "apache-beam (>=2.26.0,<2.44.0)", "black (>=23.1,<24.0)", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "pyyaml (>=5.3.1)", "rarfile (>=4.0)", "ruff (>=0.0.241)", "s3fs", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "sqlalchemy (<2.0.0)", "tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow (>=2.3,!=2.6.0,!=2.6.1)", "tensorflow-macos", "tiktoken", "torch", "transformers", "zstandard"]
-docs = ["s3fs", "tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow-macos", "torch", "transformers"]
-jax = ["jax (>=0.2.8,!=0.3.2,<=0.3.25)", "jaxlib (>=0.1.65,<=0.3.25)"]
-metrics-tests = ["Werkzeug (>=1.0.1)", "accelerate", "bert-score (>=0.3.6)", "jiwer", "langdetect", "mauve-text", "nltk", "requests-file (>=1.5.1)", "rouge-score", "sacrebleu", "sacremoses", "scikit-learn", "scipy", "sentencepiece", "seqeval", "six (>=1.15.0,<1.16.0)", "spacy (>=3.0.0)", "texttable (>=1.6.3)", "tldextract", "tldextract (>=3.1.0)", "toml (>=0.10.1)", "typer (<0.5.0)"]
-quality = ["black (>=23.1,<24.0)", "pyyaml (>=5.3.1)", "ruff (>=0.0.241)"]
-s3 = ["s3fs"]
-tensorflow = ["tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow-macos"]
-tensorflow-gpu = ["tensorflow-gpu (>=2.2.0,!=2.6.0,!=2.6.1)"]
-tests = ["Pillow (>=6.2.1)", "absl-py", "apache-beam (>=2.26.0,<2.44.0)", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "sqlalchemy (<2.0.0)", "tensorflow (>=2.3,!=2.6.0,!=2.6.1)", "tensorflow-macos", "tiktoken", "torch", "transformers", "zstandard"]
-torch = ["torch"]
-vision = ["Pillow (>=6.2.1)"]
-
-[[package]]
-name = "deepeval"
-version = "0.21.73"
-description = "The open-source evaluation framework for LLMs."
-optional = false
-python-versions = "*"
-files = [
-    {file = "deepeval-0.21.73-py3-none-any.whl", hash = "sha256:e8b52d3a989c9393f77fe1cebb5532df49377badcf2dbfc73d0da2903331ebb0"},
-    {file = "deepeval-0.21.73.tar.gz", hash = "sha256:290844565ad7bb36a66b87516f5b1f9820c1e181e4abcff314560a91e5fbe8e9"},
-]
-
-[package.dependencies]
-docx2txt = ">=0.8,<1.0"
-grpcio = "1.63.0"
-importlib-metadata = ">=6.0.2"
-langchain = "*"
-langchain-core = "*"
-langchain-openai = "*"
-opentelemetry-api = "1.24.0"
-opentelemetry-exporter-otlp-proto-grpc = "1.24.0"
-opentelemetry-sdk = "1.24.0"
-portalocker = "*"
-protobuf = "4.25.1"
-pydantic = "*"
-pytest = "*"
-pytest-repeat = "*"
-pytest-xdist = "*"
-ragas = "*"
-requests = "*"
-rich = "*"
-sentry-sdk = "*"
-tabulate = "*"
-tenacity = ">=8.4.1,<8.5.0"
-tqdm = "*"
-typer = "*"
-
-[package.extras]
-dev = ["black"]
-
-[[package]]
-name = "deprecated"
-version = "1.2.14"
-description = "Python @deprecated decorator to deprecate old python classes, functions or methods."
-optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
-files = [
-    {file = "Deprecated-1.2.14-py2.py3-none-any.whl", hash = "sha256:6fac8b097794a90302bdbb17b9b815e732d3c4720583ff1b198499d78470466c"},
-    {file = "Deprecated-1.2.14.tar.gz", hash = "sha256:e5323eb936458dccc2582dc6f9c322c852a775a27065ff2b0c4970b9d53d01b3"},
-]
-
-[package.dependencies]
-wrapt = ">=1.10,<2"
-
-[package.extras]
-dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "sphinx (<2)", "tox"]
-
-[[package]]
-name = "dill"
-version = "0.3.7"
-description = "serialize all of Python"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "dill-0.3.7-py3-none-any.whl", hash = "sha256:76b122c08ef4ce2eedcd4d1abd8e641114bfc6c2867f49f3c41facf65bf19f5e"},
-    {file = "dill-0.3.7.tar.gz", hash = "sha256:cc1c8b182eb3013e24bd475ff2e9295af86c1a38eb1aff128dac8962a9ce3c03"},
-]
-
-[package.extras]
-graph = ["objgraph (>=1.7.2)"]
-
 [[package]]
 name = "distro"
 version = "1.9.0"
@@ -678,16 +555,6 @@ idna = ["idna (>=3.6)"]
 trio = ["trio (>=0.23)"]
 wmi = ["wmi (>=1.5.1)"]
 
-[[package]]
-name = "docx2txt"
-version = "0.8"
-description = "A pure python-based utility to extract text and images from docx files."
-optional = false
-python-versions = "*"
-files = [
-    {file = "docx2txt-0.8.tar.gz", hash = "sha256:2c06d98d7cfe2d3947e5760a57d924e3ff07745b379c8737723922e7009236e5"},
-]
-
 [[package]]
 name = "ecdsa"
 version = "0.19.0"
@@ -761,29 +628,15 @@ files = [
 dnspython = ">=2.0.0"
 idna = ">=2.0.0"
 
-[[package]]
-name = "execnet"
-version = "2.1.1"
-description = "execnet: rapid multi-Python deployment"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "execnet-2.1.1-py3-none-any.whl", hash = "sha256:26dee51f1b80cebd6d0ca8e74dd8745419761d3bef34163928cbebbdc4749fdc"},
-    {file = "execnet-2.1.1.tar.gz", hash = "sha256:5189b52c6121c24feae288166ab41b32549c7e2348652736540b9e6e7d4e72e3"},
-]
-
-[package.extras]
-testing = ["hatch", "pre-commit", "pytest", "tox"]
-
 [[package]]
 name = "fast-depends"
-version = "2.4.6"
+version = "2.4.7"
 description = "FastDepends - extracted and cleared from HTTP domain logic FastAPI Dependency Injection System. Async and sync are both supported."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "fast_depends-2.4.6-py3-none-any.whl", hash = "sha256:50d0ba61450920173c498c08c01ec22fa22aa62c4e82bf409df9f5b9f677a31c"},
-    {file = "fast_depends-2.4.6.tar.gz", hash = "sha256:724a5068c3f955e02121a953428349ef34c8a2fadd65f073e94c94d54cec4554"},
+    {file = "fast_depends-2.4.7-py3-none-any.whl", hash = "sha256:ae7c138673639cfe00836b60107395c910cc1595036074c85c2505177f7a24fc"},
+    {file = "fast_depends-2.4.7.tar.gz", hash = "sha256:44464a7807131df92aeb12ad511a027ef385e000f4ec15a427ae4ea458a112ce"},
 ]
 
 [package.dependencies]
@@ -976,9 +829,6 @@ files = [
     {file = "fsspec-2024.6.1.tar.gz", hash = "sha256:fad7d7e209dd4c1208e3bbfda706620e0da5142bebbd9c384afb95b07e798e49"},
 ]
 
-[package.dependencies]
-aiohttp = {version = "<4.0.0a0 || >4.0.0a0,<4.0.0a1 || >4.0.0a1", optional = true, markers = "extra == \"http\""}
-
 [package.extras]
 abfs = ["adlfs"]
 adl = ["adlfs"]
@@ -1007,23 +857,6 @@ test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask-expr", "dask[dataframe,
 test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"]
 tqdm = ["tqdm"]
 
-[[package]]
-name = "googleapis-common-protos"
-version = "1.63.2"
-description = "Common protobufs used in Google APIs"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "googleapis-common-protos-1.63.2.tar.gz", hash = "sha256:27c5abdffc4911f28101e635de1533fb4cfd2c37fbaa9174587c799fac90aa87"},
-    {file = "googleapis_common_protos-1.63.2-py2.py3-none-any.whl", hash = "sha256:27a2499c7e8aff199665b22741997e485eccc8645aa9176c7c988e6fae507945"},
-]
-
-[package.dependencies]
-protobuf = ">=3.20.2,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<6.0.0.dev0"
-
-[package.extras]
-grpc = ["grpcio (>=1.44.0,<2.0.0.dev0)"]
-
 [[package]]
 name = "greenlet"
 version = "3.0.3"
@@ -1095,64 +928,6 @@ files = [
 docs = ["Sphinx", "furo"]
 test = ["objgraph", "psutil"]
 
-[[package]]
-name = "grpcio"
-version = "1.63.0"
-description = "HTTP/2-based RPC framework"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "grpcio-1.63.0-cp310-cp310-linux_armv7l.whl", hash = "sha256:2e93aca840c29d4ab5db93f94ed0a0ca899e241f2e8aec6334ab3575dc46125c"},
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-
-[package.extras]
-protobuf = ["grpcio-tools (>=1.63.0)"]
-
 [[package]]
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 version = "0.14.0"
@@ -1259,13 +1034,13 @@ socks = ["socksio (==1.*)"]
 
 [[package]]
 name = "huggingface-hub"
-version = "0.24.0"
+version = "0.24.3"
 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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 [package.dependencies]
@@ -1304,22 +1079,22 @@ files = [
 
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 description = "Read metadata from Python packages"
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 python-versions = ">=3.8"
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 perf = ["ipython"]
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 [[package]]
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@@ -1360,20 +1135,6 @@ files = [
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-
 [[package]]
 name = "jsonpatch"
 version = "1.33"
@@ -1566,15 +1327,29 @@ files = [
 [package.dependencies]
 langchain-core = ">=0.2.10,<0.3.0"
 
+[[package]]
+name = "langgraph"
+version = "0.1.16"
+description = "Building stateful, multi-actor applications with LLMs"
+optional = false
+python-versions = "<4.0,>=3.9.0"
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+
+[package.dependencies]
+langchain-core = ">=0.2.22,<0.3"
+
 [[package]]
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+version = "0.1.94"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
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 [package.dependencies]
@@ -1881,34 +1656,6 @@ files = [
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-python-versions = ">=3.7"
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-
-[package.dependencies]
-dill = ">=0.3.7"
-
 [[package]]
 name = "mypy-extensions"
 version = "1.0.0"
@@ -1920,17 +1667,6 @@ files = [
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-[[package]]
-name = "nest-asyncio"
-version = "1.6.0"
-description = "Patch asyncio to allow nested event loops"
-optional = false
-python-versions = ">=3.5"
-files = [
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-
 [[package]]
 name = "numpy"
 version = "1.26.4"
@@ -1978,13 +1714,13 @@ files = [
 
 [[package]]
 name = "openai"
-version = "1.36.1"
+version = "1.37.1"
 description = "The official Python library for the openai API"
 optional = false
 python-versions = ">=3.7.1"
 files = [
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 ]
 
 [package.dependencies]
@@ -1999,99 +1735,6 @@ typing-extensions = ">=4.7,<5"
 [package.extras]
 datalib = ["numpy (>=1)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)"]
 
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-version = "1.24.0"
-description = "OpenTelemetry Python API"
-optional = false
-python-versions = ">=3.8"
-files = [
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-
-[package.dependencies]
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-importlib-metadata = ">=6.0,<=7.0"
-
-[[package]]
-name = "opentelemetry-exporter-otlp-proto-common"
-version = "1.24.0"
-description = "OpenTelemetry Protobuf encoding"
-optional = false
-python-versions = ">=3.8"
-files = [
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-
-[package.dependencies]
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-
-[[package]]
-name = "opentelemetry-exporter-otlp-proto-grpc"
-version = "1.24.0"
-description = "OpenTelemetry Collector Protobuf over gRPC Exporter"
-optional = false
-python-versions = ">=3.8"
-files = [
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-opentelemetry-proto = "1.24.0"
-opentelemetry-sdk = ">=1.24.0,<1.25.0"
-
-[package.extras]
-test = ["pytest-grpc"]
-
-[[package]]
-name = "opentelemetry-proto"
-version = "1.24.0"
-description = "OpenTelemetry Python Proto"
-optional = false
-python-versions = ">=3.8"
-files = [
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-protobuf = ">=3.19,<5.0"
-
-[[package]]
-name = "opentelemetry-sdk"
-version = "1.24.0"
-description = "OpenTelemetry Python SDK"
-optional = false
-python-versions = ">=3.8"
-files = [
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-[package.dependencies]
-opentelemetry-api = "1.24.0"
-opentelemetry-semantic-conventions = "0.45b0"
-typing-extensions = ">=3.7.4"
-
-[[package]]
-name = "opentelemetry-semantic-conventions"
-version = "0.45b0"
-description = "OpenTelemetry Semantic Conventions"
-optional = false
-python-versions = ">=3.8"
-files = [
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-]
-
 [[package]]
 name = "orjson"
 version = "3.10.6"
@@ -2163,75 +1806,6 @@ files = [
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 ]
 
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-name = "pandas"
-version = "2.2.2"
-description = "Powerful data structures for data analysis, time series, and statistics"
-optional = false
-python-versions = ">=3.9"
-files = [
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-test = ["cffi", "hypothesis", "pandas", "pytest", "pytz"]
-
 [[package]]
 name = "pyasn1"
 version = "0.6.0"
@@ -2662,13 +2146,13 @@ typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"
 
 [[package]]
 name = "pydantic-settings"
-version = "2.3.4"
+version = "2.4.0"
 description = "Settings management using Pydantic"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "pydantic_settings-2.4.0-py3-none-any.whl", hash = "sha256:bb6849dc067f1687574c12a639e231f3a6feeed0a12d710c1382045c5db1c315"},
+    {file = "pydantic_settings-2.4.0.tar.gz", hash = "sha256:ed81c3a0f46392b4d7c0a565c05884e6e54b3456e6f0fe4d8814981172dc9a88"},
 ]
 
 [package.dependencies]
@@ -2676,6 +2160,7 @@ pydantic = ">=2.7.0"
 python-dotenv = ">=0.21.0"
 
 [package.extras]
+azure-key-vault = ["azure-identity (>=1.16.0)", "azure-keyvault-secrets (>=4.8.0)"]
 toml = ["tomli (>=2.0.1)"]
 yaml = ["pyyaml (>=6.0.1)"]
 
@@ -2693,16 +2178,6 @@ files = [
 [package.extras]
 windows-terminal = ["colorama (>=0.4.6)"]
 
-[[package]]
-name = "pysbd"
-version = "0.3.4"
-description = "pysbd (Python Sentence Boundary Disambiguation) is a rule-based sentence boundary detection that works out-of-the-box across many languages."
-optional = false
-python-versions = ">=3"
-files = [
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-]
-
 [[package]]
 name = "pytest"
 version = "8.3.2"
@@ -2791,40 +2266,6 @@ pytest = ">=6.2.5"
 [package.extras]
 dev = ["pre-commit", "pytest-asyncio", "tox"]
 
-[[package]]
-name = "pytest-repeat"
-version = "0.9.3"
-description = "pytest plugin for repeating tests"
-optional = false
-python-versions = ">=3.7"
-files = [
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-]
-
-[package.dependencies]
-pytest = "*"
-
-[[package]]
-name = "pytest-xdist"
-version = "3.6.1"
-description = "pytest xdist plugin for distributed testing, most importantly across multiple CPUs"
-optional = false
-python-versions = ">=3.8"
-files = [
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-
-[package.dependencies]
-execnet = ">=2.1"
-pytest = ">=7.0.0"
-
-[package.extras]
-psutil = ["psutil (>=3.0)"]
-setproctitle = ["setproctitle"]
-testing = ["filelock"]
-
 [[package]]
 name = "python-dateutil"
 version = "2.9.0.post0"
@@ -2888,40 +2329,6 @@ files = [
 [package.extras]
 dev = ["atomicwrites (==1.4.1)", "attrs (==23.2.0)", "coverage (==7.4.1)", "hatch", "invoke (==2.2.0)", "more-itertools (==10.2.0)", "pbr (==6.0.0)", "pluggy (==1.4.0)", "py (==1.11.0)", "pytest (==8.0.0)", "pytest-cov (==4.1.0)", "pytest-timeout (==2.2.0)", "pyyaml (==6.0.1)", "ruff (==0.2.1)"]
 
-[[package]]
-name = "pytz"
-version = "2024.1"
-description = "World timezone definitions, modern and historical"
-optional = false
-python-versions = "*"
-files = [
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-]
-
-[[package]]
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-description = "Python for Window Extensions"
-optional = false
-python-versions = "*"
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-]
-
 [[package]]
 name = "pyyaml"
 version = "6.0.1"
@@ -2982,33 +2389,6 @@ files = [
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 ]
 
-[[package]]
-name = "ragas"
-version = "0.1.10"
-description = ""
-optional = false
-python-versions = "*"
-files = [
-    {file = "ragas-0.1.10-py3-none-any.whl", hash = "sha256:9e547f17f10075cccaae0216c7d22000346c5f9dfc66e55abf621d3ee80786f2"},
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-]
-
-[package.dependencies]
-appdirs = "*"
-datasets = "*"
-langchain = "*"
-langchain-community = "*"
-langchain-core = "*"
-langchain-openai = "*"
-nest-asyncio = "*"
-numpy = "*"
-openai = ">1"
-pysbd = ">=0.3.4"
-tiktoken = "*"
-
-[package.extras]
-all = ["sentence-transformers"]
-
 [[package]]
 name = "redbox"
 version = "0.4.0"
@@ -3019,12 +2399,14 @@ files = []
 develop = true
 
 [package.dependencies]
-boto3 = "^1.34.136"
+boto3 = "^1.34.150"
 elasticsearch = "^8.14.0"
 kneed = "^0.8.5"
-langchain = "^0.2.6"
+langchain = "^0.2.11"
+langchain-community = "^0.2.6"
 langchain-elasticsearch = "^0.2.2"
-langchain_openai = "^0.1.9"
+langchain_openai = "^0.1.19"
+langgraph = "^0.1.9"
 pydantic = "^2.7.1"
 pydantic-settings = "^2.3.4"
 pytest-dotenv = "^0.5.2"
@@ -3036,17 +2418,17 @@ url = "../redbox-core"
 
 [[package]]
 name = "redis"
-version = "5.0.7"
+version = "5.0.8"
 description = "Python client for Redis database and key-value store"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.extras]
-hiredis = ["hiredis (>=1.0.0)"]
+hiredis = ["hiredis (>1.0.0)"]
 ocsp = ["cryptography (>=36.0.1)", "pyopenssl (==20.0.1)", "requests (>=2.26.0)"]
 
 [[package]]
@@ -3066,90 +2448,90 @@ rpds-py = ">=0.7.0"
 
 [[package]]
 name = "regex"
-version = "2024.5.15"
+version = "2024.7.24"
 description = "Alternative regular expression module, to replace re."
 optional = false
 python-versions = ">=3.8"
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 [[package]]
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-
 [[package]]
 name = "yarl"
 version = "1.9.4"
@@ -4564,4 +3679,4 @@ test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools",
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.12,<3.13"
-content-hash = "340c6eb8b64b10904b917371fb0fff02d4a7e8a8059aa7ecf6d13188ec788f08"
+content-hash = "63bbaef69e666b60c001dce67103eedd09741ec3ca7ba8003fcfb6a157e5116d"
diff --git a/core-api/pyproject.toml b/core-api/pyproject.toml
index ebaf9a6c9..14ade96c7 100644
--- a/core-api/pyproject.toml
+++ b/core-api/pyproject.toml
@@ -38,8 +38,7 @@ pytest-asyncio = "^0.23.6"
 pytest-dotenv = "^0.5.2"
 pytest-mock = "^3.14.0"
 moto = {extras = ["s3"], version = "^5.0.10"}
-jsonlines = "^4.0.0"
-deepeval = "^0.21.73"
+
 
 
 [build-system]
@@ -55,5 +54,4 @@ env_files = [
 ]
 markers = [
     "incremental: marks tests as incremental (deselect with '-m \"not incremental\"')",
-    "ai: marks tests as using a live LLM (deselect with '-m \"not ai\"')"
 ]
\ No newline at end of file
diff --git a/core-api/tests/conftest.py b/core-api/tests/conftest.py
index 09123ceeb..1f4e55f09 100644
--- a/core-api/tests/conftest.py
+++ b/core-api/tests/conftest.py
@@ -66,7 +66,7 @@ def create_index(env, es_index):
     if not es.indices.exists(index=es_index):
         es.indices.create(index=es_index)
     yield
-    es.indices.delete(index=es_index)
+    es.indices.delete(index=es_index, ignore_unavailable=True)
 
 
 @pytest.fixture()
diff --git a/core-api/tests/routes/test_chat.py b/core-api/tests/routes/test_chat.py
index 362f424ae..05e608f70 100644
--- a/core-api/tests/routes/test_chat.py
+++ b/core-api/tests/routes/test_chat.py
@@ -1,27 +1,23 @@
 import json
-from types import SimpleNamespace
 from typing import TYPE_CHECKING
-from unittest.mock import AsyncMock, MagicMock
-from math import exp
+from uuid import uuid4
+from jose import jwt
 
+from langchain_elasticsearch import ElasticsearchStore
 import pytest
-from core_api import dependencies, semantic_routes
+from core_api import dependencies
 from core_api.app import app as application
 from core_api.routes.chat import chat_app
 from fastapi.testclient import TestClient
-from langchain_community.llms.fake import FakeStreamingListLLM
-from langchain_core.documents.base import Document
-from langchain_core.messages import HumanMessage, SystemMessage
-from langchain_core.prompt_values import ChatPromptValue
-from langchain_core.runnables import Runnable, RunnableLambda
-from langchain_core.runnables.schema import StreamEvent
+from langchain_core.language_models.fake_chat_models import GenericFakeChatModel
 from starlette.websockets import WebSocketDisconnect
 
+from redbox.models.chain import ChainInput
 from redbox.models.chat import ChatResponse, ChatRoute
-from redbox.models.file import ChunkMetadata
+from redbox.test.data import RedboxChatTestCase, generate_test_cases, TestData
 
 if TYPE_CHECKING:
-    from collections.abc import Iterable
+    pass
 
 system_chat = {"text": "test", "role": "system"}
 user_chat = {"text": "test", "role": "user"}
@@ -29,152 +25,150 @@
 RAG_LLM_RESPONSE = "Based on your documents the answer to your question is 7"
 UPLOADED_FILE_UUID = "9aa1aa15-dde0-471f-ab27-fd410612025b"
 
-EXPECTED_AVAILABLE_ROUTES = ("chat", "search", "info")
-
-
-def mock_chat_prompt():
-    return ChatPromptValue(
-        messages=[
-            SystemMessage(content="You are a helpful AI bot."),
-            HumanMessage(content="Hello, how are you doing?"),
-        ]
-    )
-
-
-def embedding_model_dim(embedding_model) -> int:
-    return len(embedding_model.embed_query("foo"))
-
-
-def mock_get_llm(llm_responses):
-    def wrapped():
-        return FakeStreamingListLLM(responses=llm_responses)
-
-    return wrapped
+EXPECTED_AVAILABLE_ROUTES = {"search"}
+
+TEST_CASES = [
+    test_case
+    for generated_cases in [
+        generate_test_cases(
+            query=ChainInput(question="What is AI?", file_uuids=[], user_uuid=uuid4(), chat_history=[]),
+            test_data=[
+                TestData(0, 0, expected_llm_response=["Testing Response 1"], expected_route=ChatRoute.chat),
+                TestData(1, 100, expected_llm_response=["Testing Response 1"], expected_route=ChatRoute.chat),
+                TestData(10, 1200, expected_llm_response=["Testing Response 1"], expected_route=ChatRoute.chat),
+            ],
+            test_id="Basic Chat",
+        ),
+        generate_test_cases(
+            query=ChainInput(question="What is AI?", file_uuids=[uuid4()], user_uuid=uuid4(), chat_history=[]),
+            test_data=[
+                TestData(
+                    1, 1000, expected_llm_response=["Testing Response 1"], expected_route=ChatRoute.chat_with_docs
+                ),
+                TestData(
+                    1, 50000, expected_llm_response=["Testing Response 1"], expected_route=ChatRoute.chat_with_docs
+                ),
+                TestData(
+                    1, 200_000, expected_llm_response=["Testing Response 1"], expected_route=ChatRoute.chat_with_docs
+                ),
+            ],
+            test_id="Chat with single doc",
+        ),
+        generate_test_cases(
+            query=ChainInput(question="What is AI?", file_uuids=[uuid4(), uuid4()], user_uuid=uuid4(), chat_history=[]),
+            test_data=[
+                TestData(
+                    2,
+                    40000,
+                    expected_llm_response=["Map Response"] * 2 + ["Testing Response 1"],
+                    expected_route=ChatRoute.chat_with_docs,
+                ),
+                TestData(
+                    2,
+                    100_000,
+                    expected_llm_response=["Map Response"] * 2 + ["Testing Response 1"],
+                    expected_route=ChatRoute.chat_with_docs,
+                ),
+                TestData(
+                    4,
+                    200_000,
+                    expected_llm_response=["Map Response"] * 4 + ["Testing Response 1"],
+                    expected_route=ChatRoute.chat_with_docs,
+                ),
+            ],
+            test_id="Chat with multiple docs",
+        ),
+        generate_test_cases(
+            query=ChainInput(question="What is AI?", file_uuids=[uuid4()], user_uuid=uuid4(), chat_history=[]),
+            test_data=[
+                TestData(
+                    2,
+                    200_000,
+                    expected_llm_response=["Map Response"] * 2 + ["Testing Response 1"],
+                    expected_route=ChatRoute.chat_with_docs,
+                ),
+            ],
+            test_id="Chat with large doc",
+        ),
+    ]
+    for test_case in generated_cases
+]
 
 
-def mock_parameterised_retriever(alice):
-    docs = [
-        Document(
-            page_content="some text that doesn't actually matter " * 10,
-            metadata=ChunkMetadata(
-                parent_file_uuid=UPLOADED_FILE_UUID,
-                creator_user_uuid=alice,
-                index=i,
-                file_name="test_file",
-                page_number=1,
-                token_count=40,
-            ).model_dump()
-            | {
-                "score": 1 / (1 + exp(-i)),
-            },
-        )
-        for i in range(12)
-    ]
-    return RunnableLambda(lambda _: docs)
-
-
-def mock_all_chunks_retriever(alice):
-    docs = [
-        Document(
-            page_content="some text that doesn't actually matter ",
-            metadata=ChunkMetadata(
-                parent_file_uuid=UPLOADED_FILE_UUID,
-                creator_user_uuid=alice,
-                index=i,
-                file_name="test_file",
-                page_number=1,
-                token_count=10,
-            ).model_dump(),
-        )
-        for i in range(6)
-    ]
-    return RunnableLambda(lambda _: docs)
+@pytest.fixture(params=TEST_CASES, ids=[t.test_id for t in TEST_CASES])
+def test_case(request):
+    return request.param
 
 
-@pytest.fixture(scope="session")
-def mock_client(alice):
-    chat_app.dependency_overrides[dependencies.get_llm] = mock_get_llm([RAG_LLM_RESPONSE] * 32)
-    chat_app.dependency_overrides[dependencies.get_all_chunks_retriever] = lambda: mock_all_chunks_retriever(alice)
-    chat_app.dependency_overrides[dependencies.get_parameterised_retriever] = lambda: mock_parameterised_retriever(
-        alice
+@pytest.fixture
+def client(test_case: RedboxChatTestCase, embedding_model):
+    chat_app.dependency_overrides[dependencies.get_embedding_model] = lambda: embedding_model
+    chat_app.dependency_overrides[dependencies.get_llm] = lambda: GenericFakeChatModel(
+        messages=iter(test_case.test_data.expected_llm_response)
     )
     yield TestClient(application)
     chat_app.dependency_overrides = {}
 
 
-@pytest.fixture()
-def mock_streaming_client():
-    """
-    This client mocks the retrieval pipeline to just produce events for astream_events.
-    This tests only the streaming functionality as no pipeline is run
-    """
-    events: Iterable[StreamEvent] = [
-        StreamEvent(
-            event="on_chat_model_stream",
-            name=f"event-{i}",
-            data={"chunk": SimpleNamespace(content=response_chunk)},
-            run_id="run_id",
-        )
-        for i, response_chunk in enumerate(RAG_LLM_RESPONSE.split(" "))
-    ]
-    event_iterable = MagicMock(name="event_iterable")
-    event_iterable.__aiter__.return_value = events
-    astream_events = MagicMock(name="astream_events", return_value=event_iterable)
-    retrieval_chain = AsyncMock(spec=Runnable, name="retrieval_chain")
-    retrieval_chain.astream_events = astream_events
-    chat_app.dependency_overrides[semantic_routes.get_routable_chains] = lambda: {"retrieval": retrieval_chain}
-    yield TestClient(application)
-    chat_app.dependency_overrides = {}
+@pytest.fixture
+def uploaded_docs(test_case: RedboxChatTestCase, elasticsearch_store: ElasticsearchStore):
+    docs_ids = elasticsearch_store.add_documents(test_case.docs)
+    yield
+    if docs_ids:
+        elasticsearch_store.delete(docs_ids)
 
 
-def test_rag(mock_client, headers):
-    response = mock_client.post(
-        "/chat/rag",
-        headers=headers,
-        json={
-            "message_history": [{"text": "Who put the ram in the rama lama ding dong?", "role": "user"}],
-            "selected_files": [{"uuid": UPLOADED_FILE_UUID}],
-        },
-    )
-    assert response.status_code == 200, response.text
-    chat_response = ChatResponse.model_validate(response.json())
-    assert chat_response.output_text == RAG_LLM_RESPONSE
-    assert (
-        chat_response.route_name == ChatRoute.chat_with_docs
-    ), f"Expected route [{ChatRoute.chat_with_docs}] received [{chat_response.route_name}]"
+@pytest.fixture
+def query_headers(test_case: RedboxChatTestCase):
+    return {"Authorization": f"Bearer {jwt.encode({"user_uuid": str(test_case.query.user_uuid)}, key="nvjkernd")}"}
 
 
-@pytest.mark.parametrize(("keyword"), EXPECTED_AVAILABLE_ROUTES)
-def test_keywords(mock_client, headers, keyword):
-    """Given a history that should summarise, force retrieval."""
-    response = mock_client.post(
+def test_rag(test_case: RedboxChatTestCase, client, uploaded_docs, query_headers):
+    response = client.post(
         "/chat/rag",
-        headers=headers,
+        headers=query_headers,
         json={
             "message_history": [
-                {"text": f" @{keyword} Silly question", "role": "user"},
-            ],
-            "selected_files": [{"uuid": UPLOADED_FILE_UUID}],
+                {"role": message.role, "text": message.text} for message in test_case.query.chat_history
+            ]
+            + [{"role": "user", "text": test_case.query.question}],
+            "selected_files": [{"uuid": str(file_uuid)} for file_uuid in test_case.query.file_uuids],
         },
     )
-    assert response.status_code == 200
+    assert response.status_code == 200, response.text
     chat_response = ChatResponse.model_validate(response.json())
-    assert chat_response.route_name.startswith(
-        keyword
-    ), f"Expected route to match keyword[{keyword}] received [{chat_response.route_name}]"
 
+    assert (
+        chat_response.output_text == test_case.test_data.expected_llm_response[-1]
+    ), f"Expected response [{test_case.test_data.expected_llm_response}] received [{chat_response.output_text}]"
+    assert (
+        chat_response.route_name == test_case.test_data.expected_route
+    ), f"Expected route [{test_case.test_data.expected_route}] received [{chat_response.route_name}]"
+    returned_document_texts = set([d.page_content for d in chat_response.source_documents])
+    test_query_matching_document_texts = set([d.page_content for d in test_case.get_docs_matching_query()])
+    unexpected_returned_documents = list(
+        filter(
+            lambda d: d.page_content in returned_document_texts - test_query_matching_document_texts,
+            chat_response.source_documents,
+        )
+    )
+    assert len(unexpected_returned_documents) == 0, f"Unexpected source docs in result {unexpected_returned_documents}"
 
-def test_rag_chat_streamed(mock_client, headers):
-    # Given
-    message_history = [
-        {"text": "What can I do for you?", "role": "system"},
-        {"text": "Who put the ram in the rama lama ding dong?", "role": "user"},
-    ]
-    selected_files = [{"uuid": UPLOADED_FILE_UUID}]
 
-    with mock_client.websocket_connect("/chat/rag", headers=headers) as websocket:
+def test_rag_chat_streamed(test_case: RedboxChatTestCase, client, uploaded_docs, query_headers):
+    with client.websocket_connect("/chat/rag", headers=query_headers) as websocket:
         # When
-        websocket.send_text(json.dumps({"message_history": message_history, "selected_files": selected_files}))
+        websocket.send_text(
+            json.dumps(
+                {
+                    "message_history": [
+                        {"role": message.role, "text": message.text} for message in test_case.query.chat_history
+                    ]
+                    + [{"role": "user", "text": test_case.query.question}],
+                    "selected_files": [{"uuid": str(file_uuid)} for file_uuid in test_case.query.file_uuids],
+                }
+            )
+        )
 
         all_text, docs, route_name = [], [], ""
         while True:
@@ -183,7 +177,7 @@ def test_rag_chat_streamed(mock_client, headers):
                 if actual["resource_type"] == "text":
                     all_text.append(actual["data"])
                 if actual["resource_type"] == "documents":
-                    docs.append(actual["data"])
+                    docs.extend(actual["data"])
                 if actual["resource_type"] == "route_name":
                     route_name = actual["data"]
             except WebSocketDisconnect:
@@ -191,16 +185,28 @@ def test_rag_chat_streamed(mock_client, headers):
 
         # Then
         text = "".join(all_text)
-        assert text == RAG_LLM_RESPONSE
-        assert route_name == ChatRoute.chat_with_docs
+        assert (
+            text == test_case.test_data.expected_llm_response[-1]
+        ), f"Expected response [{test_case.test_data.expected_llm_response}] received [{text}]"
+        assert (
+            route_name == test_case.test_data.expected_route
+        ), f"Expected route [{test_case.test_data.expected_route}] received [{route_name}]"
+        returned_document_texts = set([d["page_content"] for d in docs])
+        test_query_matching_document_texts = set([d.page_content for d in test_case.get_docs_matching_query()])
+        unexpected_returned_documents = list(
+            filter(lambda d: d["page_content"] in returned_document_texts - test_query_matching_document_texts, docs)
+        )
+        assert (
+            len(unexpected_returned_documents) == 0
+        ), f"Unexpected source docs in result {unexpected_returned_documents}"
 
 
-def test_available_tools(mock_client, headers):
-    response = mock_client.get("/chat/tools", headers=headers)
+def test_available_tools(client, query_headers):
+    response = client.get("/chat/tools", headers=query_headers)
     assert response.status_code == 200
     tool_definitions = response.json()
     assert len(tool_definitions) > 0
-    assert set(EXPECTED_AVAILABLE_ROUTES).issubset({item["name"] for item in tool_definitions})
+    assert EXPECTED_AVAILABLE_ROUTES == {item["name"] for item in tool_definitions}
     for tool_definition in tool_definitions:
         assert "name" in tool_definition
         assert "description" in tool_definition
diff --git a/core-api/tests/test_runnables.py b/core-api/tests/test_runnables.py
deleted file mode 100644
index 6e6e3734b..000000000
--- a/core-api/tests/test_runnables.py
+++ /dev/null
@@ -1,161 +0,0 @@
-import pytest
-from core_api.build_chains import (
-    build_chat_chain,
-    build_chat_with_docs_chain,
-    build_condense_retrieval_chain,
-    build_retrieval_chain,
-)
-from core_api.dependencies import get_parameterised_retriever, get_tokeniser
-
-from redbox.api.runnables import make_chat_prompt_from_messages_runnable
-from redbox.models.chain import ChainInput
-from redbox.models.chat import ChatRoute
-from redbox.models.errors import AIError
-
-
-@pytest.fixture(scope="module", autouse=True)
-def mock_embeddings(session_mocker, embedding_model):
-    session_mocker.patch("core_api.dependencies.get_embedding_model", return_value=embedding_model)
-    return embedding_model
-
-
-def test_make_chat_prompt_from_messages_runnable(mock_llm):
-    chain = (
-        make_chat_prompt_from_messages_runnable(
-            system_prompt="Your job is chat.",
-            question_prompt="{question}\n=========\n Response: ",
-            input_token_budget=100,
-            tokeniser=get_tokeniser(),
-        )
-        | mock_llm
-    )
-
-    # Handles a normal call
-    response = chain.invoke(
-        input={
-            "question": "Lorem ipsum dolor sit amet.",
-            "chat_history": [],
-        }
-    )
-
-    assert response == "<<TESTING>>"
-
-    # Handles a long question
-    with pytest.raises(AIError):
-        response = chain.invoke(
-            input={
-                "question": "".join(["Lorem ipsum dolor sit amet. "] * 200),
-                "chat_history": [],
-            }
-        )
-
-    # Handles a long history
-    response = chain.invoke(
-        input={
-            "question": "Lorem ipsum dolor sit amet.",
-            "chat_history": [
-                {"text": str(i), "role": "user"} if i % 2 == 0 else {"text": str(i), "role": "ai"} for i in range(100)
-            ],
-        }
-    )
-
-    assert response == "<<TESTING>>"
-
-
-def test_rag_runnable(mock_llm, chunked_file, env):
-    retriever = get_parameterised_retriever(
-        env=env,
-    )
-
-    chain = build_retrieval_chain(llm=mock_llm, retriever=retriever, tokeniser=get_tokeniser(), env=env)
-
-    previous_history = [
-        {"text": "Lorem ipsum dolor sit amet.", "role": "user"},
-        {"text": "Consectetur adipiscing elit.", "role": "ai"},
-        {"text": "Donec cursus nunc tortor.", "role": "user"},
-    ]
-
-    response = chain.invoke(
-        input=ChainInput(
-            question="Who are all these people?",
-            chat_history=previous_history,
-            file_uuids=[str(chunked_file.uuid)],
-            user_uuid=str(chunked_file.creator_user_uuid),
-        ).dict()
-    )
-
-    assert response["response"] == "<<TESTING>>"
-    assert {str(chunked_file.uuid)} == {chunk.metadata["parent_file_uuid"] for chunk in response["source_documents"]}
-
-
-def test_condense_runnable(mock_llm, chunked_file, env):
-    retriever = get_parameterised_retriever(env=env)
-
-    chain = build_condense_retrieval_chain(llm=mock_llm, retriever=retriever, tokeniser=get_tokeniser(), env=env)
-
-    previous_history = [
-        {"text": "Lorem ipsum dolor sit amet.", "role": "user"},
-        {"text": "Consectetur adipiscing elit.", "role": "ai"},
-        {"text": "Donec cursus nunc tortor.", "role": "user"},
-    ]
-
-    response = chain.invoke(
-        input=ChainInput(
-            question="Who are all these people?",
-            chat_history=previous_history,
-            file_uuids=[str(chunked_file.uuid)],
-            user_uuid=str(chunked_file.creator_user_uuid),
-        ).dict()
-    )
-    assert response["response"] == "<<TESTING>>"
-    assert response["route_name"].startswith("search")
-    all_results_file_uuids = {chunk.metadata["parent_file_uuid"] for chunk in response["source_documents"]}
-    assert {
-        str(chunked_file.uuid)
-    } == all_results_file_uuids, f"Expected {str(chunked_file.uuid)} in {all_results_file_uuids}"
-
-
-def test_chat_runnable(mock_llm, chunked_file, env):
-    chain = build_chat_chain(llm=mock_llm, tokeniser=get_tokeniser(), env=env)
-
-    previous_history = [
-        {"text": "Lorem ipsum dolor sit amet.", "role": "user"},
-        {"text": "Consectetur adipiscing elit.", "role": "ai"},
-        {"text": "Donec cursus nunc tortor.", "role": "user"},
-    ]
-
-    response = chain.invoke(
-        input=ChainInput(
-            question="Who are all these people?",
-            chat_history=previous_history,
-            file_uuids=[str(chunked_file.uuid)],
-            user_uuid=str(chunked_file.creator_user_uuid),
-        ).dict()
-    )
-
-    assert response["response"] == "<<TESTING>>"
-    assert response["route_name"] == ChatRoute.chat
-
-
-def test_chat_with_documents_runnable(all_chunks_retriever, mock_llm, chunked_file, env):
-    chain = build_chat_with_docs_chain(
-        llm=mock_llm, all_chunks_retriever=all_chunks_retriever, tokeniser=get_tokeniser(), env=env
-    )
-
-    previous_history = [
-        {"text": "Lorem ipsum dolor sit amet.", "role": "user"},
-        {"text": "Consectetur adipiscing elit.", "role": "ai"},
-        {"text": "Donec cursus nunc tortor.", "role": "user"},
-    ]
-
-    response = chain.invoke(
-        input=ChainInput(
-            question="Who are all these people?",
-            chat_history=previous_history,
-            file_uuids=[str(chunked_file.uuid)],
-            user_uuid=str(chunked_file.creator_user_uuid),
-        ).dict()
-    )
-
-    assert response["response"] == "<<TESTING>>"
-    assert response["route_name"] == ChatRoute.chat_with_docs
diff --git a/notebooks/langgraph.ipynb b/notebooks/langgraph.ipynb
new file mode 100644
index 000000000..571f68a73
--- /dev/null
+++ b/notebooks/langgraph.ipynb
@@ -0,0 +1,118 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from redbox.graph.root import get_redbox_graph, run_redbox\n",
+    "from redbox.graph.chat import get_chat_with_docs_graph\n",
+    "from redbox.chains import components\n",
+    "from redbox.models.settings import Settings\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "app = get_redbox_graph()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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MRmFt7yzvJjGtv7PfNy9R11rqoJS+UViN+w7KMixb2TzOHHajulZQ2Kgkkq3zbaDHFG8M7QjmB2DroevRBZ6Ktbnnub3GjwWvxfA5KihvL2S3ll7q20NVZ4SYyQ6E7L5NOk9EHoWDx2snbrbnx4oXmruF3s/mC6kEVtt1LTvFc2ciMullkd6PQiUAN6ac3fUIJusXcspstmuNDb7hd6Chr693JSUtTUsjlqHeqNriC8/EAVXVDwBF24W3HCs7y29Z3DcKsVU1wqpe6zgAxkRMMRHKzcZ6b/43BS6PhViLZcamlx+hrKrG6dtNaKmtiFRNRsaGgcj37cHaY30t83TxQY+28a8UvmW5Ti1prJ7pkeNQdvcbbT0kokZ02GMLmta9x6aDXHxHVRvhLlk2cU2SXuexXTGpKu6k+xd6hENXCG01O0dowE8pcGh2tno4K3Wsawu5WhvMdnQ1s+tQm5U1VjV+uVYKKorrdc5WVDn0cRlkglEbIi1zG+kWlsbSHDfXmB16O+3JZi9VPnMfmFhDuIvBC3cT7g593yHIY7RNHFFV2GlrWsoKtsb+cB7CwuGz4ljmlwAB8F5rnwAtVTlVzvlryLJcYN2mbU3OgsdwEFNWzAAdo5pY5zHuDQHOjcwu112stc+NGLWW/W6x3Ceuorzctmit9RbqhlRU68ezjLOZ2viCzXnnT/gq/fkSr/Rrq8Cvhlc2dzAZXwnprzm9Lmlvul2tOQUtI2kfHb6tkMFwiZIZY4agOjf6HOXek0B2nu6nwXUMi4pbG8GxkD3yMrm//wA9STzzp/wVfvyJV/o0886f8FX78iVf6NPAxOGUzZYyx8KLDZcVyHHXsmuVpv1ZXVldBWua7nNW9z5mDlDdM28ge+B75PVYTDOANmxG/UN3qL1fsnqbbSPobY2/VjahlvheAHtiDWN6ua1rS9/M4ga3pS7zzp/wVfvyJV/o0886f8FX78iVf6NPAr4VzZ3K+tPkyWGz1GMCPIclnt2M3BtfZ7VPWxupaQhr2iIN7LmcwCQgc7nOaAA1wBIOVj4A4+255U99ddZ7Jkzp5LljktQ11vklmaBLK1vJzte7W+j9A9QAVLPPOn/BV+/IlX+jTzzp/wAFX78iVf6NPAr4TNncwPDzhGzh5Vtljy3KL9BFTd0pqO9XBs0FPHtpAa1rG8zhygBzy5wGxvqVIMn/APGY3/W8H/6csHR8aMWuOTVmOUs9dUZBRRCeptcVuqHVMMZ1p74wzmDfSb1I16Q9a7MrxSv4u2j2MpKy94fTRyCcXmBndqtsjQeQRNeOYdSC5xA6eiN8xLcqaJwv8qotEX9iImNcrYRV7W0PEWx3LCKGy1VmvVgpomU2Q1t5MrbjPytaO3h5PufMeVxcHe+/p4Lst3FqOS/5jQXjHL3jdtxqE1Ut+udO1lvqoAHF0kMgcS7QY4ka6ADfjpeMxT5FhsQzKx5/j9NfMcutLebRU83ZVlJIHxu0SHDfrBBBB6ghZlAREQEREBERAREQEREBERAREQEREBERARRvIuJGL4njdyyC636hpLNbZBDWVpmDo4JCWgMeW704l7Brx9IetYG8cbbLba/B4KO3XrIKbMAx9vuFmoHVFNHE7s9TTv2Ozj1Kx2yPDZ94oLCRQihyjMrhm+RWiTDG2uw0VNzW3I6i4xSx185DSGd3b90Y0cx24+PKdKPuxPirmHC11tvmYWrDczlqhI+6YpROqII4AR9ya2oO+YjYLve6EepBa6w1bmVit9NeJ57tSBlngdU3BrJQ99LG1rnFz2N24dGu97rylR648JKC+ZXi2SXO7XipumPwiOFkVa+Glnk0Q6WSBp5XOOz4+vXvBZOw8MsUxi73662uwUNHcr7IZbnVMiBkq3Eknncdkjbj08Op6IIjePKPxmHhnbs5xuhvee2i5VhoKSHGbc+oqJZQZA49m7kcGgxOBcfi1sEKQXDKsuh4oWmx0eF97w+opDPW5S65Rx91l1Jyw92I53kljNuB0BIN+BUxp6eKkhZDBEyGJg02ONoa1o9QA8F2IK0ttg4n3W3ZzRX7JbLan1r5Iscr7DRvfLQREvDZJmzdHyaMZIB1sO0fArouPAWlyrDMWseV5Nfr7VWOo70bnHWGllrZASR2wj6OaNjTf5IVpIgj7eH2NNzN+XewVAcndAKY3YwNNSIh05A89QNdOizdPTQ0kQigiZDENkMjaGgb6noF2ogIiICLhJKyFodI9rGlwaC46GydAfKSQPnVcycRLjm94zzEMYt90sV7stMIqfIbtbT7HOqnsJaIyT905QY3Hpoh2+o8Qmd7y2yY1VW2mu12orbU3OobS0UNVO2N9TMfCONpO3O6+AUEkqsw4ox8QMaq7Tc+HdticKG0ZNSVsT6qr6u5542AbjboM5SepDj1aR0yuP8ACqlltmJVGbuos4y/Ho39jkNZQRxyCV5BdJGwbEZ9Fnh19EFTxBg8VxKlxSxWa2tnqbpJaqQUUNxubxNVvYA0Evk0CS7kaXHpstG/BZxEQEREBERAREQYu/Y7S3+3XGme6SjmrqOSifXUZEdTHG4EehJrbSNkj1Hqq6iZmHBnHcIx+2W68cUaZ1aaO53uvr4mVtJA9/3OZ4IHahvMASOvKzZKtlEGJtWW2S+3W6Wy3Xeirrla5GxV1JTztfLTPLQ4CRoO27BHj8fqKykkbZWOY9oexwIc1w2CPUVDbzwvtp86LnjMdJimYX6j7rLkdLRMfO1wDuSRzToPLS7fU9dDZ6DWCjz+88NIeH+OZjTXPK73enmhqsislrIooajbeR0wB+5NfzHqBr0HEho8AyeecDcQ4hYrQY7W2+S22ygrO/0cdlnfQ93n9PcjeyLRs9pJvYIJeT49V7J8by88UKe9RZcxuHdzMM2Mut0ezMN8szajfP4nq3w9EfGpfHKyXm5HtfyuLTyneiPEH41zQVPQ8WMqxTBMpyPiNhU1nZZqvkghsE4uUtdTFzQ2ZkbdFuuf0gTsBjj0AUwtXE3GLtQYzVNvNLRnJaZtXaaavkFNUVbC1jvQifpxIEjNgDY5h61KFgcgwLG8rulpud4sVvudytEwqLfWVNO181JICDzRvI23q1p6HroepBnkUCo+GFTYMoy7JLVlF7luF8pyIrbdKsz22inDQGSxQaHL7luxs7AWEqst4k8N+GNurb/jcXEXKG1vYVsGI/6uBTHn5Z2smPpOAawOYNek/p0G0FsIohNxaxSl4kUmAT3ZkGX1dEK+G2PjfzPh9PZDwOTY7N5LebehvWiFLIZ46hnPFI2Vmy3mY4EbB0R8xBHzIOaIiAiIgIiICxOV5ZaMHsFXfL9XxWy00gaZ6uckMjDnBo3r1ucB86yyrfyjLljdn4LZPWZdYanJsciiiNZaqTfa1DTMwNDdOb4OLXeI8EGSfxZtY4o02CR2671Fymo+/GvhoXOoIo9Et55/AF3KQB169FiLTmvEPK8ayx1Lg0GK32indBZRf68TU1eAddq/sRzxs6eHj1HxqxLY6N9tpHQxmKExMLIz4tboaHzBelBWtfivEnIrPhjpc1ocVutFI2bIIbNbW1VNcdFpMMTp9PiZ0PpD0tO18ay1Fwto6LilX50L1fZq6rpBRexk1eXW6FmmbMcGtNcTG0k78S4++VNEQQ/D+EGF4DjlRYLBjVvt9lqZ+9TULYueKWX0fTcHb2fQZ1P/ACj1KXMY2NjWMaGtaNBoGgB6lyRAREQEREBERAREQEREBV7JxhtN/wAvyrBcWqo6/OLJbnVUkFRBKKSGVwHZRyyga2S5hLWnfKT6jqwlC8Luea1uZZpT5JZ6K347S1MDMfrKZ4MtZCY9yulAkcQWv6DbWdPePigwVu4TVGe41h8/FmO333KrHVuuLfYl80NCyo5iYyIy4dpyDlALwerd6GyrRREBERAREQEREBERAREQEREBERBWtdwlbiNHnN24bR0dizLJCKmSouD5pqJ9UCT2rouYhpcHO2WjqdEg60frOMVFiF0wbFM8qae25tklN6MdBBM6hfVNDQ+KOUg6Jc48ocdkN6+I3ZKhfEK55rb7riDMSs9FdaGou0cV9lq3hrqShLTzyx7kZt4OhoB/j7koJoiIgIiIOqSmhlmimfEx8sW+zkc0FzNjR0fe2q3i4C2PGsYy63YLUVWD3HJJ+91N1oZXzSx1BOzK1sriAT12BodT76s1EFZ18PE7E6XArZZnWvM4Y3Mpslu93eaSqczcYNRDGz0N67VxaSfBoG9krIUHF2hqeIORYrWWW9WZtmp21Tr5c6TsLZUxkM32M7nacQ55aeg6sd1OlPFXvlBeavtM5Z57d681O5/th3LfbdlzN9zrrvekFgRyMmjbJG4PY4BzXNOwQfAgosbi3c/Nm0ex3P7H9zh7t2nuuy5Byb+PWkQZREXCSVkQBe9rAf8AmOkHNaKeUj+yIZPwgzTKcGpeHtPQ3qgmDKK71dzM0MsRIdHMYBC3fPGR6Ik9EnWzy9d5e+Qff4/xwtG/2TvglFlWHW3iRaWNludk1RXFsWi6Ske70H9Ov3OR2vklJPRqtpFk+SV5Z1w8p/JrvazgbrBRWqgbUVF1bdO8sMzntayLk7FnKXjtXA8x/cyPjW0K1+8ingtBwL4H2yjrmxQ5Hd9XK6czhzskeByQn1dmzlaR4c3OR4q+++Qff4/xwlpHci4se2Roc1wc0+BB2FyUBERAREQERcZJGRDb3NYPDbjpByRdPfIPv8f44TvkH3+P8cK2kdyLp75B9/j/ABwnfIPv8f44S0juRdPfIPv8f44TvkH3+P8AHCWkVR5UHHer8nPhqzL6bF35VC2vipKmnbWd1FPG9r9TOf2b+nO1jNa8ZB1946O4F+yX3yzZ3l1wnw2vyKDIqynfbrNLkEhZbQ1nIYoQYHA87jzei1vX3j4r9Gs/xSy8ScJveL3h8cltu1LJSzacOZocNBzfU5p04H3iAV+b3kW+SnW03lOXx+VQR+x/D+rJLpBqOqq9nuzmb8W6HbAg9NR790lpH6cWOqra6yW+puVE223GanjkqaJk3bNp5S0F8Yk0OcNJI5tDet6C9y6e+Qff4/xwnfIPv8f44S0juRdPfIPv8f44TvkH3+P8cJaR3IunvkH3+P8AHCd8g+/x/jhLSO5F098g+/x/jhdvilrD6iIoCIiAiIgIuEk0cRHPI1m/DmIC4d8g+/x/jhW0juX5i8Vv2SXIbtlGOQR4XcMRqMavgqbpb4MheO/Nj5mPpJg2Bum83jzBw233K/TTvkH3+P8AHC/M3y//ACZq2r4649fsTpm1DM5qWUUscfRkVwGm8ziOjWvZp+/WyVxS0jcjyVPKMuflLYpdciqMMOJ2umqW0lJI6497NW8N3KQOxj5Q3bBvrsucOnKd3goXwkwKzcIeG+P4hapYe6WqlbCZAQ0zSeMkpHre8ucfjcpd3yD7/H+OEtI7kXT3yD7/AB/jhdyWsCIigKIcW7hdbVw3v9XZMeiyu6xU/NT2WdvMyrdzD0CPk2fmUvUQ4t2+63Xhvf6SyZDFil1lp+WnvU7uVlI7mHpk/JsfOgkNilmnslvkqaUUNQ+njdJStGhC4tG2D5D0+ZEsUU0Fkt8dTVCuqGU8bZKpp2JnBo28fKevzog7LpW+xtsq6vl5uwhfLy+vlaTr/sq9teJ2q/W6kuV5t9JeLlVQsmmqa2BkztuaCWt5h6LB4Bo0ND17KnGVfvYvH9Dm/uFR7Gf3uWr+iRf3AvSyeZow5qpm03ZbIeL2vsW+Ddo+oRfZT2vsW+Ddo+oRfZWCg46YPVZg7F6e996vDanuTm09JPJAyoHjE6drDE1499pfsepQ3hH5TuP5lSUFvv8Ac6SgymsudbbmUtPTTtpy+Opmjhj7VwcwSujjY7kL+Y82wNEBbdIxOOeqXnes/wBr7Fvg3aPqEX2U9r7Fvg3aPqEX2Vganjtg1FmrcTqL53e9uqW0TY5aSdsJqHDbYhOWdlznY03n2djosXiHHq15VxbyvBRRV1PVWaojpoKg0NUY6h3Y9pKXvMQjiAO2t5nenoOaSHBPHxOOeped6WT26jwurt1fZ6aG2CWtp6SpgpmCOKdksjYhzMaNFzS5pa7Wxy63ylwNkKvc0/2fbf64tv8AnYVYS58p/wAqaa526/x3WdcCIi4GIiIg+OdyNLj4AbVYWGx27L7LQ3u9UNLdq6407Kl0lXEJhG14DxHGHD0WNHKAABvWztxJNmzfuMn80qvuHX+77GP6rpf8Jq9HJpmnDqqp1TePyyjVDl7X2LfBu0fUIvsp7X2LfBu0fUIvsrBV3HTB7fmBxeS99pem1EdJJDTUk80cMzyAyOSVjDHG8kj0XOB6hQ7hr5TuP5FVz2jI7nSW7IDf66zU8EFNOICY6mSOBj5SHRtlexjTyl4LiejdEBbtIxOOeqXnes72vsW+Ddo+oRfZT2vsW+Ddo+oRfZWBunHbBrJmTcVr753W9GojpOSWknEImkAMcZn5OyD3BzdNL9nY9axds49Wuu41X3h7JRV0VRb4aUw1bKGpkZNLKJS9rnCLkja0Rt09zuV5cQDtpCePicc9S870y9r7Fvg3aPqEX2U9r7Fvg3aPqEX2VjLzmVbFxRx3FLdFTyxz0VVc7pJK1xdDTs5Y4gwggBz5ZB4g+jFJ03oiBcaPKfx7ALbdqCx3OluGW0NXS0hpJaWeWmY+SeNj43ysAY2QRve4MLw7YHQ+CTj4kf8AOeped60Pa+xb4N2j6hF9lPa+xb4N2j6hF9lR/KuPOB4TkMlkvOQxUdwhEZqGiCWSKkEnuO3lYwsh5uhHaObsHfgu3NeN+E8PbrHbL5ehBcXw94NLTUs1VJHFvQkkELHmNm9+k/Q6Hr0Tx8TjnqXnezftfYt8G7R9Qi+yntfYt8G7R9Qi+yuFJxCx+urL/SwXDnnsMMVRcWdjIOwZJEZWHZbp22AnTdkeB0eijFy8ozh9aoKCWe+yO7/aoL5TR09uqppZKGXm5J+zZEXBvoO5tgcnTm5djd8fE456l53pV7X2LfBu0fUIvsp7X2LfBu0fUIvsrAYrx5wTNr3Q2qy39lbV18T5qIimmZDVtYNvEMrmCORzR7prXEt0dgaK4wce8Cqcpbj0eQxOuT6o0LHdhMKZ9QCQYW1BZ2TpNgjkDydjWt9FPHxOOeped6Q+19i3wbtH1CL7Ke19i3wbtH1CL7KjJ8obh8L260nIAKxlxdaZT3Oo7GGrbIYuxkm7Ps43F400OcObYLdggnw2Hj9abvxgyjBJaSspZrR2DYqw0VSYpnOjkklL5OyEcTWhgDXOfp/XlJ8E0jE456l53pp7X+Lj/wB27R9Qi+yu2xww4rlVvttujbS2y4QzF1FEOWKOSMNIcxoGm7BcCBoHodb2Vh8C4wYlxOnqYsaujrkadgkc/uk8LHsJID43yMa2RpII5mEjp4rM1H7/ALGP5lX/AHGrKMSrEiqmqbxafSJWJmdqeIiLxmIiIgLB5td57FitxraYtbUxxhsTnDYa9zg0HXxFwPzLOKKcUv3i3L5Yv8Vi34FMVY1FM7JmPdY2ww7cAx5+31Vno7jUu6yVVdTsmmld77nPcCSf+3q0F99r7Fvg3aPqEX2VmK+vprVQ1NbWTx01JTRummnlcGsjY0Euc4nwAAJJUKx3jpg+U2G7XugvrW2m1RMqKurrKaakZHE8OLJAZWN5mu5Xac3YOui9Hx8TinqXnezvtfYt8G7R9Qi+yntfYt8G7R9Qi+ysFjvHPCMqprtPQXvpaqU11ZFV0k9LNFTgEmbs5WNe5nQ+k0EfGuvHePWC5Vbr7W26+F8VkpTXXCOooqinmhpw1zjL2UkbXubprtFrSDrQ6qePicc9S870h9r7Fvg3aPqEX2U9r7Fvg3aPqEX2VHKLj9gVfjlwyCO/COx0LIny3KopJ4ad4kJDBE97AJnEtLeWMuIPQgE6UWzjyl7Ja8YsmQWGsiktcmR0dnukt1oamndTQyAukcGSCN4cGgEEgjr4FNIxOOeped6zW4BjDHBzcctAcDsEUMWx/wClejGGsx3LI7JRAQ2yroZaqOjBPJTvifEw9mPBjXCZu2jptuwAXOJx2C8Ssb4lUlZUY7ce+topu71MUkEtPNBJoENfFK1r27BBGx1HgshT/wC8+z/1PX/49Grn1YlNVNU3i0+kXWJmdqdIiLx2Iq98oLzV9pnLPPbvXmp3P9sO5b7bsuZvuddd70rCUQ4t3C62rhvf6uyY9Fld1ip+anss7eZlW7mHoEfJs/MgzeLdz82bR7Hc/sf3OHu3ae67LkHJv49aRd1ilmnslvkqaUUNQ+njdJStGhC4tG2D5D0+ZEHTlX72Lx/Q5v7hUexn97lq/okX9wKSZHC+ox66RRtLpJKWVrWj3yWEBRrF3tkxm0OaeZrqOEg+scgXo4P0Z+/4ZeSkPJ7vly4YYvZ+G96w3JReqGsnp5brTW10luqWvne8VfeQeTlcHBzgTzgkjlUaosLv0fk443bzYri260+dsrnUvc5BPHEL8+TtizXMG9kefm1rlO96W06KZvkxabcVaDMMklyQXa0Z3db9QZRBV0FLboJvYeK1QVcUkcjGsIjnkMTSSPTl5z0aAOlxYlJX4l5RGfw11iu76LKX2+qt91pqJ8tGBDRiKRssrekTg6PoHa3zDXirnRM22sYDNP8AZ9t/ri2/52FWEq/zBhlpLXG0+m6728gaPXlqonn/ALNJ+ZWAplH06PvP4XyERFwIIiIOE37jJ/NKr7h1/u+xj+q6X/CarBlBdG8DxIIVf8PW8mB44zeyy3U7HdCNERtBHXr4gr0MD6VX3j2ll5Kd4PXy5cJHXPCrzhuS1dynyGsqo7zbra6oo62KpqXSNqZJweVhax4D2vIcBH0B6BRqtwy/Hyccjt7LFcTdX50+uhpRRydu6L2eZIJms1zFvZbfzAa5dneltOiZvkxaccbLbmOVx55R3K0Z1dr1BeYZrLRWmKVtmbbYZoZWyegRHPKWtkJa7nk5+UNaNAq3KWrrsO8pC/19TYL1WWnK7XaqejuNBQSTQQSwvqGvbUOA+46EzHbfoa3740rrRM3XcVzw0tVbX5nneW3OjqKOaurm2qgiqonMeKGkBY1wDgDyyTPqZAfAtewjY0Vr3d6TIbDwPyPhjPhGTVmSDIe9m6UVrkqKS4RPuzKkVPbt2CezIBafSHL1Gh03KRJpuNW8kpb5h1t41Yg7Cr5kdxzStq6q0V1BRGajqGVVLHC1k0/uYeyc1wPaFvogFu9r2YDHevJ7zDJor9i+QZQy80drdSXjH7e6u7R1NRR08kEvL1jIexzml2mkSE7B2tmETNGud9rLtiOecV6l+JZDc2ZfaaKS1G2291Q10rKWSF8Mr27bC8OLT6ZAIPQldHAvEr3acpxaavs1fRRQ8KbRbpZKmlfG1lUySQvgcSOkjQRth9IbGwtk0TN1jVrCsKv9Pw48mylks9yoq21VLu/h1JI2SgBt9UzmlBG4xzOaPS11IHvrC8HuGVBRWXHMEzPFOIkt7tlW1k8jLhXvsLnRSmSKqa7thByEtY/lA5g4+56bW36KZo1Yv2GX6byf+LNvjsVxfcazN6utpKVtJIZp4jdIpGyxt1tzS1pcHAEaG96Cm9LJV4f5QGd+yeNXm42jLKO2ijraCgfU0v3GOWOWOZ7ekR9IH0tAgq8EVzRr55Or73asrrbDarXlNv4aUtrY+kpcvoTT1Fuq+113WB7vSlhEezsl4bytAeQVdVR+/wCxj+ZV/wBxqzKxEsZkz3G+Ub5Iat7ung3lY3f9rmj51tw4tf7Ve0rCdIiLykEREBRTil+8W5fLF/isUrUW4nsL8Euuh0Y1kjum9NbI1zj8wBK6cm+vR9492VO2HgyympazFbzBXW+W70UtFMye3wN5pKqMsIdE0bGy4baBsePiFqRX4pnOX8Oskxqw23LKzC7NJaa+0UWSwut10k7GfnnoYpPRe9rWMYWSHqHAAOOgVuYi6Jpuxay01qdPj2X5Tw/xvPqbPKCxyUltq82mrJHHtXB74oI6uV3M5pia73PKXcuidlRDzauVbkuWV9ssvECvorlw6udqbX5TT1Mk9RXba8RNjft0WwTytDWMc7mDAVuSixzRr5m+I3mm4S8HK+isFXdBiFXbLjX2Cni1UuijpXRO7OJ2uaSJzw4M6HbT7+l6eImQVfFa3YPUWvFMlo4rfm1qmlbdbTJTv7Fpc583ZuHM2NuwC5waAfi6q+kVzRVmCWWvovKA4qXCahqYLdXUVlFPVSQubFUPZHUiTkeRpxbtgOidbbv3lPKf/efZ/wCp6/8Ax6NZdYqkYX8S7a8HYitFYHjR6c01Ly/28jv7Fto1RV9p9pWE4REXlIKIcW7fdbrw3v8ASWTIYsUustPy096ndyspHcw9Mn5Nj51L1XvlBeavtM5Z57d681O5/th3LfbdlzN9zrrvekE0sUU0Fkt8dTVCuqGU8bZKpp2JnBo28fKevzounFu5+bNo9juf2P7nD3btPddlyDk38etIgyiidVw/Z28j7ZerlY4XuL3UtEIHQ8x6ktbLE/l2eumkDZJ1sqWIttGJVh/6ysTZDvMCv+Gd7+hof1ZVn5SVdkXBzgjlOY2bK7jVXK1RRSQw11NSOhcXTRsPMGwNJ6PPgR10r9VC+XZ/BO4g/wBGp/8ANQrbpOJy6R2W8p/bMKuVbbaSofmV6D5oWSODYKLQJaCdf6uvV5gV/wAM739DQ/qykVg/2Fbf6NH/AHQvemk4nLpHYvKPWjDILdXR11VX1l4rIgRDLXGPUOxpxY2NjGhxHTm1vRIBAJBkKItFddWJN6pTaIiLBBERAUXrsEjlqZ5rddrhZO3e6WWKi7J0bpHHbnhssbw0k7J5dAkucQXEkyhFsoxKsOb0yt7Id5gV/wAM739DQ/qyeYFf8M739DQ/qymKLdpOJy6R2W8od5gV/wAM739DQ/qyeYFf8M739DQ/qymKJpOJy6R2Lyh3mBX/AAzvf0ND+rJ5gV/wzvf0ND+rKYomk4nLpHYvKHeYFf8ADO9/Q0P6sqq4VXfJc54pcVcarsqr4aHFK+kpaKSnpqQSSNlg7RxkJhIJB8OUN6etbDLXbydv4QnlFf1zbv8AKFNJxOXSOxeVseYFf8M739DQ/qyeYFf8M739DQ/qymKJpOJy6R2Lyh3mBX/DO9/Q0P6snmBX/DO9/Q0P6spiiaTicukdi8od5gV/wzvf0ND+rJ5gV/wzvf0ND+rKYomk4nLpHYvKHeYNf8M739DQ/qyzFhxemsL5Zu3qK+ulAbJW1jmulc0eDfRDWtb4nlaANknWysyixqx8SuM2Z1coiPZLyIiLnQREQFwliZPE+ORjZI3gtcxw2HA+II98LmiCHv4eOhPJb8ku9spR7ilhFPIyMepplhe4Ae8ObQ8AvnmBX/DO9/Q0P6spii6tJxd8dI7Mryh3mBX/AAzvf0ND+rKquNl3yXhtkXDKgtuVV88OT5JDZ6x1VTUjnRwvY5xdHywjTvRHU7HxLYZa7eVd+/fgJ/56pv8ADkTScTl0jsXlbHmBX/DO9/Q0P6snmBX/AAzvf0ND+rKYomk4nLpHYvKHtwKva4E5je3AHejDRaP/AOOs1YMbpcfbM6N81VVz8vb1lU4Oll5RpoJAAAGzprQGglx1txJyyLCvHxK4zZnVyiI9kvIiItCCiHFu4XW1cN7/AFdkx6LK7rFT81PZZ28zKt3MPQI+TZ+ZS9RDi3b7rdeG9/pLJkMWKXWWn5ae9Tu5WUjuYemT8mx86CQ2KWaeyW+SppRQ1D6eN0lK0aELi0bYPkPT5kSxRTQWS3x1NUK6oZTxtkqmnYmcGjbx8p6/OiD3IiICoXy7P4J3EH+jU/8AmoVfSrzyg+F9Txn4OZLhlJXRW2pusMccdVMwvYwtlZJ1A69eTXzoJpYP9hW3+jR/3QvetdIsm8pLBoY4a3AcLzujhaGMGOXeW3zFgGhsVQcN6HvFcv8ATD83PRznhVnuH8v7pWexffaJn/zoid/ioNiUVQYn5XnBvNHNZbuIVmilcdCG5Smhfv1cs4YSfiCteguNJdaVlTRVUNZTP6tmp5A9jvkI6FB6EREBERAREQEREBERAREQFrt5O38ITyiv65t3+UK2JWq97kzbyZ+L+fZx5oy5rw/y2pp6utnsLi+42oxRCPmdAddoz3RJaeg6kjWiG1CKIcM+LeI8YbA284hfKW80fQSCF2pYHH/hkjOnMd8TgP7FL0BERAREQEREBERAREQEREBERAWu3lXfv34Cf+eqb/DkVxcQuJuLcKcflveW3uksdtZsCSpf6UjvHljYNue7+S0E/EtdvZLMfKw4g4Df7ViU2J8N8WvMd6hvGQExVl1LGkAQ042WsIdsOcdEHYPTlQbYIiICIiAiL4XAEAkAk6G/fQfVXvlBeavtM5Z57d681O5/th3LfbdlzN9zrrvel3DjVi9xGZwY/VSZZeMSjLrnZ7Kztqpsg7TULAdNfITE9vKHb2NHRIWAv1z4i8SOHuPVeN4/ZscqrhMTdrTm8Mkr4KYOPohkWwXu5QdO6AO9YQWPi3c/Nm0ex3P7H9zh7t2nuuy5Byb+PWkXvpI3Q0sMbmxtcxjWlsQ0wEDwaPeHqRB2oiICIiAiIgiuW8KcLzwOGR4nZb45w0X3CgimePkc5pIPxgqp6/yGOFYqn1mO0t5wa4POzWYxeKileD8TS5zB8zVsEiDXX/R/4uYn1w/j1dqiBnuaLLrZDcuf1B0/ovHygL550+U1hv8AtHCcK4gwM8HWC6yW6d49ZFQC3fydFsWiDXP/AExJMb9HOuEue4jy+7rGWzv1Ez1/doj1+ZqlOJ+WFwazRzWUHEG0QTOOuxucjqF+/VqcM6/IrjUXyzhbhudtcMjxSy30uGua4UEUzh8jnNJHyhBnrdc6O8UjKqgq4K2mf7mamkbIx3yOBIK9S1/uPkM8KHVb6yw2+64VcXeNZjV3qKR49Wm8zmD5mrz/AOj3xVxTrh3Hu+SQs9zSZdboLrzj1OmPK8fKBtBsQi1484fKZw//AMdieD8Qqdvh7C3KW2VLx/K7cFgPydF9/wBLauxz0c44P57i/L+6VdLb23Kjj+WaF3/9UGwyKm8W8sPg1l8gipM/tVHUb5TBdnOoHtd/y6nazr8itm2XahvdI2qt1bT19K/3M9LK2Rh+RzSQg9aIiAiIgo/iZ5Kdgyu/uy7ELjV8N8/btzb/AGHTGzne9VMHRkzSfHeiffJHRRii8ozMeCdXDaOOuPCmtxeIqfPceidNbJt9G94jA56dx+TRJOmgDa2XXTWUdPcaSalq4I6qmmYY5YZmB7HtI0WuaehBHvFB5rHfrbk9pprpZ7hTXS21LOeCro5WyxSN9bXNJBXvWuF98lq68OrtU5JwLyEYTcJX9tVYvXB01jr3eoxeMDj4czPAdAG+KojyovLmzbEMIgw+bF7lw64pitpqmon5mTUndY3l4lppg/0xJJE1ha5j2FnbMdtB+gyKovJe4+0HlEcKqDIoezgvEOqW7UTD+4VLQOYgf8jhpzfHodb2CrdQEREBERAREQEVc+UBxqtPALhfdctuhZLJC3saGjLuU1dU4Hs4h8XQkkeDWuOjpaW+S95fOYXm0XTErjYrhxC4iXK5yT2GGOeOGJ8cjXySxSPIHZxwlr3A7d6L+QcjIwg/Q26XWisduqLhcqyC30FOwyTVVVK2OKJo8XOc4gAfGVrvdPKYyTi3cKixcCMdF/ax5hqc1vLXwWakPgez6c1Q4epo14HTgUtXky5FxZuNPfuO+RNyIxvE1Nhdnc6Cy0Z8RzjfNUOHrcfWPSC2Jtlro7Jb6egt1JBQUNOwRw01NGI44mjwa1rQAAPUEFI8PfJPtFsyGPL+Id2qeJ2d9HC53lo7rSHe+WmpvcRtB6joSD1HLvSvhFEOIfFnFOFeLTZFk13ZQWiKcUzqiON8/wB1O9M5Y2udzbBGtIJeihFbnt79sKwWS2YdW3XHLhRmsqsobUxxU9GCH8jOzd6cjiWs6DwEgPXRCxdHhWdZJbc5teYZVBT0N1kfDZpcVY+jq7bT7eGvMziSZS0xk9NAtd1IKCaX/L7Fir6Fl6vNvtL6+dtNSNrqlkJqJXEAMjDiOZxLgNDZ6hR+i4r2+58ULtgdLa7u6622iFZNXy0T2W48wjLYxUdQXkSA6A/4Xe+NLhbuC+Kw45jNnu9CMtbjp56CtyMNralkm99oXub1f4elr3h6lOkFTU8HFXiTwwrYa+Wl4R5bPWagkoTFeDBSjlJB5tMMjgXjY8NAj1LPVnBuw3vL8Vyy995umTY7TdhSVveZYY+ctLXymFjgwucHP8QejtepTtEHlorVRW19Q+ko6elfUSGWZ0MTWGV58XO0OpPrK9SIgIiICIiAiIgIiICIiAiIgIiICIiAiIgj2U8O8VziMx5FjVovzCNauVDFUa+TnadKp7n5EXCWerdW2ey1uIXJ3hW43c6iie35GtfyD8VXyiDXweTvxJxbrh3HnJGRN8KbLKKC8B/8kyODHj5R1X0XfylsQ61WP4JxBpWeAtlbPa6uQfyu1Dowfk6LYJEGvw8qm8456ObcGc7x0t/dKm20kd2pI/WXSwO8Pj5Vm8a8sXg3lE/d4c7t1uqweV1NeQ+3vY7/AJSJ2s6/IrmWEyTCMdzODsMgsFsvkOtdncqOOobr5HtKD3Wm9W+/Ubau2V9NcaV3uZ6SZsrD8jmkheLNcrpMEw2/ZLXxzTUNmoJ7jUR0zQZXRwxukcGBxALiGnWyBv3wqmuvkVcI62rdW23HJsWuR9zW45Xz0D2fI2N4Z/6VQflncPb3wO4BXqopONOaVdtuksNqZZL5K2tNaZHcz4e8hgkjb2TJXHZ05rCwk8+iG2/CHjRiPHLFIr/iN0ZX0p02eneOSopJCNmOVni1w6+sHW2lw0VqTx2/Y5cm4w8UslzSTiHRSz3ar7WOCotpi7CANDIotscQ7s42sZzaBdy8x6krSPyd7hxWxnOYLxwst14rbtAQ2eK30kk8MsexuOdoGiw9N82tHRBBAK/Vet4sZTcuHuPU94sr8Oy+6UzprnQsmEjqKNsjmAsePcmXl2332N5hsOaCunJ8CvKcWMKjbI1l8nTgjl/kk8XZqpmU2rJ7NUxSUt3tVp7XmOmuMJLntbG17JOXY5i4Nc8a69dqX8ebp/7PFqYjZ/dLqWnXzQFV1DDHTxNiiY2ONo01rRoBc19thfCMkoptVTnTvmZ/EwX5LA9vm8/BWi/LD/1dPb5vPwVovyw/9XVfot3yvIv2/WruZ3JYHt83n4K0X5Yf+rp7fN5+CtF+WH/q6r9dNdXU1so56usqIqSkgYZJZ53hkcbQNlznHoAB75U+WZFH/X61dzO5LG9vm8/BWi/LD/1dfW8ersPd4rS6/kXZxP8A3gCryORk0bZI3NfG8BzXNOwQfAgrknyvIv2/WruZ3JVflPcKcp8rPiDZI2ZVbsUx+jpxFSWy7xyAd4c49q5skfOx5cBGG85jJ1yhnQudj+Hf7HNknBvK7ZnT+KlrsMmPzi4Gr7lK6ERsBL2y/doj2Tm7Y8c42xzgT1VxSRsmjdHIxr43Atc1w2CD4ghT/C6i28R8funD3MaOG+2yoptxxVrecTwAtBY4+PPG7kc14Id1aQeZhcfC+I/CqcGicbA2RtjsbWhPlpeXFVcZ56nDsLmnoMGjdyVNQQWS3RwPQuHi2IEbDD1Pi7rprdx/J54v55xV4IcKLpitNZLq9pFtyyqvd0kmqYO7OZG5+2jmM80YMoDx07VhPMDzHH5Z+xrcFMhg5LdbbrjEg8JLZcpJNn4xP2v/AG0rX8nfyfrJ5N+D1WM2K4V9ypqqvkuMk1wcwv7RzI49N5WtAbyxN9Z2XHfUAfMDM0XD66jP8ivd0y+4XawXOkFHTYxLDGyko2lrA9wc0cz3Etf1JGhIR10CvZw84VYrwqxanx3F7PDbLPTymeOn5nS6kOtvLnlzi7oOpKliICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAsPlGG4/m9DDRZHY7bf6OGYVEVPdKSOpjZKAQHta8EBwDnAEddOPrWYRB00dHT2+lipqWCOmp4m8scMLAxjB6gB0AWvfEOd1TxKyMvOzA+np2ePRggjeB+NI8/OtilRvGOwyWrMIrs1n+p3WJsTnjwbURg9D/Oj1r/AOE74l7/AMFrppymYq84mI6xPtC+UoWi8t0lrYLfNJb6aGsrWj7lBUTmFjzvwLw1xb+KVFxes99/E7H82QS/qi+2qrimbTfpMtaVXGtZbbfU1cjS5lPE6Vwb4kNBJ1/YqJwrOuJWTeb1/hoLnU0FznhlnopKWhZQRUkhG3Ryibty5jTvbgeYggtbvpaNHdc0nq4Yq3GLLBRveGzSx3ySVzGE+kQw0rQ4gb6EjfrC8WMcIqDELjTy2y93yC100r5YLJ3wGiiLt7aG8vMW+kSGlxAPXXRcmJFeLVTNEzER/G7fGtVfMzjMabF6jLpchE9LRZK+2OtXcoWxy0xr+7+k8N5+cBw04EDTRsE7J8/Ee65LnmFcUq6nvrbRYbK2ttbLXHRxyGr7KL7q+WR3pN5i4hvJrQAJ2rOk4TWiTEavHTU1vcqm5G6PkD2doJTVCp0Dya5eca1rfL7++qx2ScDLRkNVfnsvF7tFNfWkXGhttU1lPUPLOQyFrmO04gDeiA7XUHqtFeBjTTm3vq16522n02ahNsc/e9a/6LF/cCyCh8tXmFqeKO245aay307RFBPU3uSKSRgAALmClcAfiBK4ezWffBOx/wD3BL+prujEiItMT0nsiZrKYbM6mz7GZWe7NW6I699roZAR/wDz8yj1mnuFRbopLpSQUNcd9pBTVBqI29TrTyxhOxo+5Gt6662pzwmsL73m8VcWnulnY6Vz9dHTyMLGN+PTHPcfVtnrC1ZXiU0ZNiVVbLT6xZlTtX0iIvzNRERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBeG9WWjyG2T2+4QCopZgA5h6dQQQQR1BBAII6ggEL3IrEzTMVUzaYFAZJwxyHGpnmmppL/bgSWT0waKhrfVJF02fjj3vx5W+CjEjKqE6ktV1jdvXK+2VDT/YWLaZF9HhfHMWmm2JTEzv2Lqarc0/4Ouf5On+wnNP+Drn+Tp/sLalFu+e1ft+v9JaGq3NP+Drn+Tp/sJzT/g65/k6f7C2pRPntX7fr/RaGq3NP+Drn+Tp/sLk1tVIdMtd1kd/ystlQ4n5gxbTonz2r9v1/otDX3HeG2RZJKwyUb7JQk+nU1oAlI/kReO/5/Lr4/BXhjuPUWL2mG30EXZwR9S5x2+Rx8XuPvuPvlZJF4+V5fjZZqr1RHlCiIi81BERAREQEREBERAREQEREH//2Q==",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import Image, display\n",
+    "from langchain_core.runnables.graph import CurveStyle, MermaidDrawMethod, NodeStyles\n",
+    "\n",
+    "display(\n",
+    "    Image(\n",
+    "        app.get_graph().draw_mermaid_png(\n",
+    "            draw_method=MermaidDrawMethod.API,\n",
+    "        )\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "\n",
+    "env = Settings()\n",
+    "chat_graph = get_chat_with_docs_graph(\n",
+    "    llm=components.get_chat_llm(env),\n",
+    "    all_chunks_retriever=components.get_all_chunks_retriever(env),\n",
+    "    tokeniser=components.get_tokeniser(),\n",
+    "    env=env\n",
+    ")\n",
+    "display(\n",
+    "    Image(\n",
+    "        chat_graph.get_graph().draw_mermaid_png(\n",
+    "            draw_method=MermaidDrawMethod.API,\n",
+    "        )\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/summarise.ipynb b/notebooks/summarise.ipynb
index 4e5155bb2..4f1217071 100644
--- a/notebooks/summarise.ipynb
+++ b/notebooks/summarise.ipynb
@@ -355,7 +355,7 @@
                 "    @chain\n",
                 "    def map_operation(input_dict):\n",
                 "        system_map_prompt = env.ai.map_system_prompt\n",
-                "        prompt_template = PromptTemplate.from_template(env.ai.map_question_prompt)\n",
+                "        prompt_template = PromptTemplate.from_template(env.ai.chat_map_question_prompt)\n",
                 "\n",
                 "        formatted_map_question_prompt = prompt_template.format(question=input_dict[\"question\"])\n",
                 "\n",
diff --git a/redbox-core/.vscode/settings.json b/redbox-core/.vscode/settings.json
index dad2768a3..cf3112f71 100644
--- a/redbox-core/.vscode/settings.json
+++ b/redbox-core/.vscode/settings.json
@@ -10,7 +10,8 @@
     "python.testing.unittestEnabled": false,
     "python.testing.pytestEnabled": true,
     "python.testing.pytestArgs": [
-        "."
+        ".",
+        "-m not ( ai )"
     ],
     "python.testing.pytestPath": "venv/bin/python -m pytest"
 }
\ No newline at end of file
diff --git a/redbox-core/poetry.lock b/redbox-core/poetry.lock
index a45a97977..42029fd46 100644
--- a/redbox-core/poetry.lock
+++ b/redbox-core/poetry.lock
@@ -140,6 +140,17 @@ doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphin
 test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
 trio = ["trio (>=0.23)"]
 
+[[package]]
+name = "appdirs"
+version = "1.4.4"
+description = "A small Python module for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
+optional = false
+python-versions = "*"
+files = [
+    {file = "appdirs-1.4.4-py2.py3-none-any.whl", hash = "sha256:a841dacd6b99318a741b166adb07e19ee71a274450e68237b4650ca1055ab128"},
+    {file = "appdirs-1.4.4.tar.gz", hash = "sha256:7d5d0167b2b1ba821647616af46a749d1c653740dd0d2415100fe26e27afdf41"},
+]
+
 [[package]]
 name = "attrs"
 version = "23.2.0"
@@ -371,6 +382,20 @@ files = [
     {file = "charset_normalizer-3.3.2-py3-none-any.whl", hash = "sha256:3e4d1f6587322d2788836a99c69062fbb091331ec940e02d12d179c1d53e25fc"},
 ]
 
+[[package]]
+name = "click"
+version = "8.1.7"
+description = "Composable command line interface toolkit"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "click-8.1.7-py3-none-any.whl", hash = "sha256:ae74fb96c20a0277a1d615f1e4d73c8414f5a98db8b799a7931d1582f3390c28"},
+    {file = "click-8.1.7.tar.gz", hash = "sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de"},
+]
+
+[package.dependencies]
+colorama = {version = "*", markers = "platform_system == \"Windows\""}
+
 [[package]]
 name = "colorama"
 version = "0.4.6"
@@ -495,6 +520,136 @@ ssh = ["bcrypt (>=3.1.5)"]
 test = ["certifi", "cryptography-vectors (==43.0.0)", "pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-xdist"]
 test-randomorder = ["pytest-randomly"]
 
+[[package]]
+name = "dataclasses-json"
+version = "0.6.7"
+description = "Easily serialize dataclasses to and from JSON."
+optional = false
+python-versions = "<4.0,>=3.7"
+files = [
+    {file = "dataclasses_json-0.6.7-py3-none-any.whl", hash = "sha256:0dbf33f26c8d5305befd61b39d2b3414e8a407bedc2834dea9b8d642666fb40a"},
+    {file = "dataclasses_json-0.6.7.tar.gz", hash = "sha256:b6b3e528266ea45b9535223bc53ca645f5208833c29229e847b3f26a1cc55fc0"},
+]
+
+[package.dependencies]
+marshmallow = ">=3.18.0,<4.0.0"
+typing-inspect = ">=0.4.0,<1"
+
+[[package]]
+name = "datasets"
+version = "2.20.0"
+description = "HuggingFace community-driven open-source library of datasets"
+optional = false
+python-versions = ">=3.8.0"
+files = [
+    {file = "datasets-2.20.0-py3-none-any.whl", hash = "sha256:76ac02e3bdfff824492e20678f0b6b1b6d080515957fe834b00c2ba8d6b18e5e"},
+    {file = "datasets-2.20.0.tar.gz", hash = "sha256:3c4dbcd27e0f642b9d41d20ff2efa721a5e04b32b2ca4009e0fc9139e324553f"},
+]
+
+[package.dependencies]
+aiohttp = "*"
+dill = ">=0.3.0,<0.3.9"
+filelock = "*"
+fsspec = {version = ">=2023.1.0,<=2024.5.0", extras = ["http"]}
+huggingface-hub = ">=0.21.2"
+multiprocess = "*"
+numpy = ">=1.17"
+packaging = "*"
+pandas = "*"
+pyarrow = ">=15.0.0"
+pyarrow-hotfix = "*"
+pyyaml = ">=5.1"
+requests = ">=2.32.2"
+tqdm = ">=4.66.3"
+xxhash = "*"
+
+[package.extras]
+apache-beam = ["apache-beam (>=2.26.0)"]
+audio = ["librosa", "soundfile (>=0.12.1)"]
+benchmarks = ["tensorflow (==2.12.0)", "torch (==2.0.1)", "transformers (==4.30.1)"]
+dev = ["Pillow (>=9.4.0)", "absl-py", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "polars[timezone] (>=0.20.0)", "protobuf (<4.0.0)", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "ruff (>=0.3.0)", "s3fs", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "sqlalchemy", "tensorflow (>=2.6.0)", "tiktoken", "torch", "torch (>=2.0.0)", "transformers", "typing-extensions (>=4.6.1)", "zstandard"]
+docs = ["s3fs", "tensorflow (>=2.6.0)", "torch", "transformers"]
+jax = ["jax (>=0.3.14)", "jaxlib (>=0.3.14)"]
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+tqdm = "*"
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+
+[package.extras]
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+
+[[package]]
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+description = "Python @deprecated decorator to deprecate old python classes, functions or methods."
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+files = [
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+
+[package.extras]
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+
+[[package]]
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+description = "serialize all of Python"
+optional = false
+python-versions = ">=3.8"
+files = [
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+profile = ["gprof2dot (>=2022.7.29)"]
+
 [[package]]
 name = "distro"
 version = "1.9.0"
@@ -506,6 +661,16 @@ files = [
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+[[package]]
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+description = "A pure python-based utility to extract text and images from docx files."
+optional = false
+python-versions = "*"
+files = [
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+
 [[package]]
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 version = "8.13.1"
@@ -546,6 +711,36 @@ orjson = ["orjson (>=3)"]
 requests = ["requests (>=2.4.0,!=2.32.2,<3.0.0)"]
 vectorstore-mmr = ["numpy (>=1)", "simsimd (>=3)"]
 
+[[package]]
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+description = "execnet: rapid multi-Python deployment"
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+python-versions = ">=3.8"
+files = [
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+[package.extras]
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+
+[[package]]
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+optional = false
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+typing = ["typing-extensions (>=4.8)"]
+
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 version = "1.4.1"
@@ -632,6 +827,64 @@ files = [
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 ]
 
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+http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)"]
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 version = "3.0.3"
@@ -703,6 +956,64 @@ files = [
 docs = ["Sphinx", "furo"]
 test = ["objgraph", "psutil"]
 
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+    {file = "grpcio-1.63.0.tar.gz", hash = "sha256:f3023e14805c61bc439fb40ca545ac3d5740ce66120a678a3c6c2c55b70343d1"},
+]
+
+[package.extras]
+protobuf = ["grpcio-tools (>=1.63.0)"]
+
 [[package]]
 name = "h11"
 version = "0.14.0"
@@ -759,6 +1070,40 @@ cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<14)"]
 http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
 
+[[package]]
+name = "huggingface-hub"
+version = "0.24.3"
+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 = [
+    {file = "huggingface_hub-0.24.3-py3-none-any.whl", hash = "sha256:69ecce486dd6cdad69937ba76779e893c224a670a9d947636c1d5cbd049e44d8"},
+    {file = "huggingface_hub-0.24.3.tar.gz", hash = "sha256:bfdc05cc9b64a0e24e8614a44222698799183268f6b68be209aa2df70cff2cde"},
+]
+
+[package.dependencies]
+filelock = "*"
+fsspec = ">=2023.5.0"
+packaging = ">=20.9"
+pyyaml = ">=5.1"
+requests = "*"
+tqdm = ">=4.42.1"
+typing-extensions = ">=3.7.4.3"
+
+[package.extras]
+all = ["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)"]
+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 = "idna"
 version = "3.7"
@@ -770,6 +1115,25 @@ files = [
     {file = "idna-3.7.tar.gz", hash = "sha256:028ff3aadf0609c1fd278d8ea3089299412a7a8b9bd005dd08b9f8285bcb5cfc"},
 ]
 
+[[package]]
+name = "importlib-metadata"
+version = "7.0.0"
+description = "Read metadata from Python packages"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "importlib_metadata-7.0.0-py3-none-any.whl", hash = "sha256:d97503976bb81f40a193d41ee6570868479c69d5068651eb039c40d850c59d67"},
+    {file = "importlib_metadata-7.0.0.tar.gz", hash = "sha256:7fc841f8b8332803464e5dc1c63a2e59121f46ca186c0e2e182e80bf8c1319f7"},
+]
+
+[package.dependencies]
+zipp = ">=0.5"
+
+[package.extras]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
+perf = ["ipython"]
+testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)", "pytest-ruff"]
+
 [[package]]
 name = "iniconfig"
 version = "2.0.0"
@@ -809,6 +1173,20 @@ files = [
     {file = "jmespath-1.0.1.tar.gz", hash = "sha256:90261b206d6defd58fdd5e85f478bf633a2901798906be2ad389150c5c60edbe"},
 ]
 
+[[package]]
+name = "jsonlines"
+version = "4.0.0"
+description = "Library with helpers for the jsonlines file format"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "jsonlines-4.0.0-py3-none-any.whl", hash = "sha256:185b334ff2ca5a91362993f42e83588a360cf95ce4b71a73548502bda52a7c55"},
+    {file = "jsonlines-4.0.0.tar.gz", hash = "sha256:0c6d2c09117550c089995247f605ae4cf77dd1533041d366351f6f298822ea74"},
+]
+
+[package.dependencies]
+attrs = ">=19.2.0"
+
 [[package]]
 name = "jsonpatch"
 version = "1.33"
@@ -876,6 +1254,29 @@ requests = ">=2,<3"
 SQLAlchemy = ">=1.4,<3"
 tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<9.0.0"
 
+[[package]]
+name = "langchain-community"
+version = "0.2.10"
+description = "Community contributed LangChain integrations."
+optional = false
+python-versions = "<4.0,>=3.8.1"
+files = [
+    {file = "langchain_community-0.2.10-py3-none-any.whl", hash = "sha256:9f4d1b5ab7f0b0a704f538e26e50fce45a461da6d2bf6b7b636d24f22fbc088a"},
+    {file = "langchain_community-0.2.10.tar.gz", hash = "sha256:3a0404bad4bd07d6f86affdb62fb3d080a456c66191754d586a409d9d6024d62"},
+]
+
+[package.dependencies]
+aiohttp = ">=3.8.3,<4.0.0"
+dataclasses-json = ">=0.5.7,<0.7"
+langchain = ">=0.2.9,<0.3.0"
+langchain-core = ">=0.2.23,<0.3.0"
+langsmith = ">=0.1.0,<0.2.0"
+numpy = {version = ">=1.26.0,<2.0.0", markers = "python_version >= \"3.12\""}
+PyYAML = ">=5.3"
+requests = ">=2,<3"
+SQLAlchemy = ">=1.4,<3"
+tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<9.0.0"
+
 [[package]]
 name = "langchain-core"
 version = "0.2.24"
@@ -943,15 +1344,29 @@ files = [
 [package.dependencies]
 langchain-core = ">=0.2.10,<0.3.0"
 
+[[package]]
+name = "langgraph"
+version = "0.1.16"
+description = "Building stateful, multi-actor applications with LLMs"
+optional = false
+python-versions = "<4.0,>=3.9.0"
+files = [
+    {file = "langgraph-0.1.16-py3-none-any.whl", hash = "sha256:ad7fcba28ce81dde7c43b54e7ba098093c3827b84de5183d2e450c1789bb6e60"},
+    {file = "langgraph-0.1.16.tar.gz", hash = "sha256:cd90f5691a8e7f4e523fd73e74b2ddd73051cb452ff17195f9cd5141ce1c2095"},
+]
+
+[package.dependencies]
+langchain-core = ">=0.2.22,<0.3"
+
 [[package]]
 name = "langsmith"
-version = "0.1.93"
+version = "0.1.94"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langsmith-0.1.93-py3-none-any.whl", hash = "sha256:811210b9d5f108f36431bd7b997eb9476a9ecf5a2abd7ddbb606c1cdcf0f43ce"},
-    {file = "langsmith-0.1.93.tar.gz", hash = "sha256:285b6ad3a54f50fa8eb97b5f600acc57d0e37e139dd8cf2111a117d0435ba9b4"},
+    {file = "langsmith-0.1.94-py3-none-any.whl", hash = "sha256:0d01212086d58699f75814117b026784218042f7859877ce08a248a98d84aa8d"},
+    {file = "langsmith-0.1.94.tar.gz", hash = "sha256:e44afcdc9eee6f238f6a87a02bba83111bd5fad376d881ae299834e06d39d712"},
 ]
 
 [package.dependencies]
@@ -962,6 +1377,30 @@ pydantic = [
 ]
 requests = ">=2,<3"
 
+[[package]]
+name = "markdown-it-py"
+version = "3.0.0"
+description = "Python port of markdown-it. Markdown parsing, done right!"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "markdown-it-py-3.0.0.tar.gz", hash = "sha256:e3f60a94fa066dc52ec76661e37c851cb232d92f9886b15cb560aaada2df8feb"},
+    {file = "markdown_it_py-3.0.0-py3-none-any.whl", hash = "sha256:355216845c60bd96232cd8d8c40e8f9765cc86f46880e43a8fd22dc1a1a8cab1"},
+]
+
+[package.dependencies]
+mdurl = ">=0.1,<1.0"
+
+[package.extras]
+benchmarking = ["psutil", "pytest", "pytest-benchmark"]
+code-style = ["pre-commit (>=3.0,<4.0)"]
+compare = ["commonmark (>=0.9,<1.0)", "markdown (>=3.4,<4.0)", "mistletoe (>=1.0,<2.0)", "mistune (>=2.0,<3.0)", "panflute (>=2.3,<3.0)"]
+linkify = ["linkify-it-py (>=1,<3)"]
+plugins = ["mdit-py-plugins"]
+profiling = ["gprof2dot"]
+rtd = ["jupyter_sphinx", "mdit-py-plugins", "myst-parser", "pyyaml", "sphinx", "sphinx-copybutton", "sphinx-design", "sphinx_book_theme"]
+testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"]
+
 [[package]]
 name = "markupsafe"
 version = "2.1.5"
@@ -1031,6 +1470,36 @@ files = [
     {file = "MarkupSafe-2.1.5.tar.gz", hash = "sha256:d283d37a890ba4c1ae73ffadf8046435c76e7bc2247bbb63c00bd1a709c6544b"},
 ]
 
+[[package]]
+name = "marshmallow"
+version = "3.21.3"
+description = "A lightweight library for converting complex datatypes to and from native Python datatypes."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "marshmallow-3.21.3-py3-none-any.whl", hash = "sha256:86ce7fb914aa865001a4b2092c4c2872d13bc347f3d42673272cabfdbad386f1"},
+    {file = "marshmallow-3.21.3.tar.gz", hash = "sha256:4f57c5e050a54d66361e826f94fba213eb10b67b2fdb02c3e0343ce207ba1662"},
+]
+
+[package.dependencies]
+packaging = ">=17.0"
+
+[package.extras]
+dev = ["marshmallow[tests]", "pre-commit (>=3.5,<4.0)", "tox"]
+docs = ["alabaster (==0.7.16)", "autodocsumm (==0.2.12)", "sphinx (==7.3.7)", "sphinx-issues (==4.1.0)", "sphinx-version-warning (==1.1.2)"]
+tests = ["pytest", "pytz", "simplejson"]
+
+[[package]]
+name = "mdurl"
+version = "0.1.2"
+description = "Markdown URL utilities"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "mdurl-0.1.2-py3-none-any.whl", hash = "sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8"},
+    {file = "mdurl-0.1.2.tar.gz", hash = "sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba"},
+]
+
 [[package]]
 name = "moto"
 version = "5.0.11"
@@ -1174,6 +1643,52 @@ files = [
     {file = "multidict-6.0.5.tar.gz", hash = "sha256:f7e301075edaf50500f0b341543c41194d8df3ae5caf4702f2095f3ca73dd8da"},
 ]
 
+[[package]]
+name = "multiprocess"
+version = "0.70.16"
+description = "better multiprocessing and multithreading in Python"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "multiprocess-0.70.16-pp310-pypy310_pp73-macosx_10_13_x86_64.whl", hash = "sha256:476887be10e2f59ff183c006af746cb6f1fd0eadcfd4ef49e605cbe2659920ee"},
+    {file = "multiprocess-0.70.16-pp310-pypy310_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:d951bed82c8f73929ac82c61f01a7b5ce8f3e5ef40f5b52553b4f547ce2b08ec"},
+    {file = "multiprocess-0.70.16-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:37b55f71c07e2d741374998c043b9520b626a8dddc8b3129222ca4f1a06ef67a"},
+    {file = "multiprocess-0.70.16-pp38-pypy38_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:ba8c31889abf4511c7308a8c52bb4a30b9d590e7f58523302ba00237702ca054"},
+    {file = "multiprocess-0.70.16-pp39-pypy39_pp73-macosx_10_13_x86_64.whl", hash = "sha256:0dfd078c306e08d46d7a8d06fb120313d87aa43af60d66da43ffff40b44d2f41"},
+    {file = "multiprocess-0.70.16-pp39-pypy39_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:e7b9d0f307cd9bd50851afaac0dba2cb6c44449efff697df7c7645f7d3f2be3a"},
+    {file = "multiprocess-0.70.16-py310-none-any.whl", hash = "sha256:c4a9944c67bd49f823687463660a2d6daae94c289adff97e0f9d696ba6371d02"},
+    {file = "multiprocess-0.70.16-py311-none-any.whl", hash = "sha256:af4cabb0dac72abfb1e794fa7855c325fd2b55a10a44628a3c1ad3311c04127a"},
+    {file = "multiprocess-0.70.16-py312-none-any.whl", hash = "sha256:fc0544c531920dde3b00c29863377f87e1632601092ea2daca74e4beb40faa2e"},
+    {file = "multiprocess-0.70.16-py38-none-any.whl", hash = "sha256:a71d82033454891091a226dfc319d0cfa8019a4e888ef9ca910372a446de4435"},
+    {file = "multiprocess-0.70.16-py39-none-any.whl", hash = "sha256:a0bafd3ae1b732eac64be2e72038231c1ba97724b60b09400d68f229fcc2fbf3"},
+    {file = "multiprocess-0.70.16.tar.gz", hash = "sha256:161af703d4652a0e1410be6abccecde4a7ddffd19341be0a7011b94aeb171ac1"},
+]
+
+[package.dependencies]
+dill = ">=0.3.8"
+
+[[package]]
+name = "mypy-extensions"
+version = "1.0.0"
+description = "Type system extensions for programs checked with the mypy type checker."
+optional = false
+python-versions = ">=3.5"
+files = [
+    {file = "mypy_extensions-1.0.0-py3-none-any.whl", hash = "sha256:4392f6c0eb8a5668a69e23d168ffa70f0be9ccfd32b5cc2d26a34ae5b844552d"},
+    {file = "mypy_extensions-1.0.0.tar.gz", hash = "sha256:75dbf8955dc00442a438fc4d0666508a9a97b6bd41aa2f0ffe9d2f2725af0782"},
+]
+
+[[package]]
+name = "nest-asyncio"
+version = "1.6.0"
+description = "Patch asyncio to allow nested event loops"
+optional = false
+python-versions = ">=3.5"
+files = [
+    {file = "nest_asyncio-1.6.0-py3-none-any.whl", hash = "sha256:87af6efd6b5e897c81050477ef65c62e2b2f35d51703cae01aff2905b1852e1c"},
+    {file = "nest_asyncio-1.6.0.tar.gz", hash = "sha256:6f172d5449aca15afd6c646851f4e31e02c598d553a667e38cafa997cfec55fe"},
+]
+
 [[package]]
 name = "numpy"
 version = "1.26.4"
@@ -1221,13 +1736,13 @@ files = [
 
 [[package]]
 name = "openai"
-version = "1.36.1"
+version = "1.37.1"
 description = "The official Python library for the openai API"
 optional = false
 python-versions = ">=3.7.1"
 files = [
-    {file = "openai-1.36.1-py3-none-any.whl", hash = "sha256:d399b9d476dbbc167aceaac6bc6ed0b2e2bb6c9e189c7f7047f822743ae62e64"},
-    {file = "openai-1.36.1.tar.gz", hash = "sha256:41be9e0302e95dba8a9374b885c5cb1cec2202816a70b98736fee25a2cadd1f2"},
+    {file = "openai-1.37.1-py3-none-any.whl", hash = "sha256:9a6adda0d6ae8fce02d235c5671c399cfa40d6a281b3628914c7ebf244888ee3"},
+    {file = "openai-1.37.1.tar.gz", hash = "sha256:faf87206785a6b5d9e34555d6a3242482a6852bc802e453e2a891f68ee04ce55"},
 ]
 
 [package.dependencies]
@@ -1242,6 +1757,99 @@ typing-extensions = ">=4.7,<5"
 [package.extras]
 datalib = ["numpy (>=1)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)"]
 
+[[package]]
+name = "opentelemetry-api"
+version = "1.24.0"
+description = "OpenTelemetry Python API"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "opentelemetry_api-1.24.0-py3-none-any.whl", hash = "sha256:0f2c363d98d10d1ce93330015ca7fd3a65f60be64e05e30f557c61de52c80ca2"},
+    {file = "opentelemetry_api-1.24.0.tar.gz", hash = "sha256:42719f10ce7b5a9a73b10a4baf620574fb8ad495a9cbe5c18d76b75d8689c67e"},
+]
+
+[package.dependencies]
+deprecated = ">=1.2.6"
+importlib-metadata = ">=6.0,<=7.0"
+
+[[package]]
+name = "opentelemetry-exporter-otlp-proto-common"
+version = "1.24.0"
+description = "OpenTelemetry Protobuf encoding"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "opentelemetry_exporter_otlp_proto_common-1.24.0-py3-none-any.whl", hash = "sha256:e51f2c9735054d598ad2df5d3eca830fecfb5b0bda0a2fa742c9c7718e12f641"},
+    {file = "opentelemetry_exporter_otlp_proto_common-1.24.0.tar.gz", hash = "sha256:5d31fa1ff976cacc38be1ec4e3279a3f88435c75b38b1f7a099a1faffc302461"},
+]
+
+[package.dependencies]
+opentelemetry-proto = "1.24.0"
+
+[[package]]
+name = "opentelemetry-exporter-otlp-proto-grpc"
+version = "1.24.0"
+description = "OpenTelemetry Collector Protobuf over gRPC Exporter"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "opentelemetry_exporter_otlp_proto_grpc-1.24.0-py3-none-any.whl", hash = "sha256:f40d62aa30a0a43cc1657428e59fcf82ad5f7ea8fff75de0f9d9cb6f739e0a3b"},
+    {file = "opentelemetry_exporter_otlp_proto_grpc-1.24.0.tar.gz", hash = "sha256:217c6e30634f2c9797999ea9da29f7300479a94a610139b9df17433f915e7baa"},
+]
+
+[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 = "1.24.0"
+opentelemetry-proto = "1.24.0"
+opentelemetry-sdk = ">=1.24.0,<1.25.0"
+
+[package.extras]
+test = ["pytest-grpc"]
+
+[[package]]
+name = "opentelemetry-proto"
+version = "1.24.0"
+description = "OpenTelemetry Python Proto"
+optional = false
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+]
+
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 name = "pycparser"
 version = "2.22"
@@ -1461,13 +2226,13 @@ typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"
 
 [[package]]
 name = "pydantic-settings"
-version = "2.3.4"
+version = "2.4.0"
 description = "Settings management using Pydantic"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
@@ -1475,9 +2240,34 @@ pydantic = ">=2.7.0"
 python-dotenv = ">=0.21.0"
 
 [package.extras]
+azure-key-vault = ["azure-identity (>=1.16.0)", "azure-keyvault-secrets (>=4.8.0)"]
 toml = ["tomli (>=2.0.1)"]
 yaml = ["pyyaml (>=6.0.1)"]
 
+[[package]]
+name = "pygments"
+version = "2.18.0"
+description = "Pygments is a syntax highlighting package written in Python."
+optional = false
+python-versions = ">=3.8"
+files = [
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+]
+
+[package.extras]
+windows-terminal = ["colorama (>=0.4.6)"]
+
+[[package]]
+name = "pysbd"
+version = "0.3.4"
+description = "pysbd (Python Sentence Boundary Disambiguation) is a rule-based sentence boundary detection that works out-of-the-box across many languages."
+optional = false
+python-versions = ">=3"
+files = [
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+]
+
 [[package]]
 name = "pytest"
 version = "8.3.2"
@@ -1498,6 +2288,24 @@ pluggy = ">=1.5,<2"
 [package.extras]
 dev = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
 
+[[package]]
+name = "pytest-asyncio"
+version = "0.23.8"
+description = "Pytest support for asyncio"
+optional = false
+python-versions = ">=3.8"
+files = [
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+]
+
+[package.dependencies]
+pytest = ">=7.0.0,<9"
+
+[package.extras]
+docs = ["sphinx (>=5.3)", "sphinx-rtd-theme (>=1.0)"]
+testing = ["coverage (>=6.2)", "hypothesis (>=5.7.1)"]
+
 [[package]]
 name = "pytest-cov"
 version = "5.0.0"
@@ -1531,6 +2339,40 @@ files = [
 pytest = ">=5.0.0"
 python-dotenv = ">=0.9.1"
 
+[[package]]
+name = "pytest-repeat"
+version = "0.9.3"
+description = "pytest plugin for repeating tests"
+optional = false
+python-versions = ">=3.7"
+files = [
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+]
+
+[package.dependencies]
+pytest = "*"
+
+[[package]]
+name = "pytest-xdist"
+version = "3.6.1"
+description = "pytest xdist plugin for distributed testing, most importantly across multiple CPUs"
+optional = false
+python-versions = ">=3.8"
+files = [
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+    {file = "pytest_xdist-3.6.1.tar.gz", hash = "sha256:ead156a4db231eec769737f57668ef58a2084a34b2e55c4a8fa20d861107300d"},
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+
+[package.dependencies]
+execnet = ">=2.1"
+pytest = ">=7.0.0"
+
+[package.extras]
+psutil = ["psutil (>=3.0)"]
+setproctitle = ["setproctitle"]
+testing = ["filelock"]
+
 [[package]]
 name = "python-dateutil"
 version = "2.9.0.post0"
@@ -1559,6 +2401,40 @@ files = [
 [package.extras]
 cli = ["click (>=5.0)"]
 
+[[package]]
+name = "pytz"
+version = "2024.1"
+description = "World timezone definitions, modern and historical"
+optional = false
+python-versions = "*"
+files = [
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+]
+
+[[package]]
+name = "pywin32"
+version = "306"
+description = "Python for Window Extensions"
+optional = false
+python-versions = "*"
+files = [
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 version = "6.0.1"
@@ -1619,92 +2495,119 @@ files = [
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 ]
 
+[[package]]
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+version = "0.1.11"
+description = ""
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+python-versions = "*"
+files = [
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+datasets = "*"
+langchain = "*"
+langchain-community = "*"
+langchain-core = "*"
+langchain-openai = "*"
+nest-asyncio = "*"
+numpy = "*"
+openai = ">1"
+pysbd = ">=0.3.4"
+tiktoken = "*"
+
+[package.extras]
+all = ["sentence-transformers"]
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@@ -1747,6 +2650,24 @@ urllib3 = ">=1.25.10,<3.0"
 [package.extras]
 tests = ["coverage (>=6.0.0)", "flake8", "mypy", "pytest (>=7.0.0)", "pytest-asyncio", "pytest-cov", "pytest-httpserver", "tomli", "tomli-w", "types-PyYAML", "types-requests"]
 
+[[package]]
+name = "rich"
+version = "13.7.1"
+description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal"
+optional = false
+python-versions = ">=3.7.0"
+files = [
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+
+[package.dependencies]
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+pygments = ">=2.13.0,<3.0.0"
+
+[package.extras]
+jupyter = ["ipywidgets (>=7.5.1,<9)"]
+
 [[package]]
 name = "s3transfer"
 version = "0.10.2"
@@ -1806,6 +2727,67 @@ dev = ["cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy (==1.10.0)", "pycodest
 doc = ["jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.13.1)", "jupytext", "matplotlib (>=3.5)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0)", "sphinx-design (>=0.4.0)"]
 test = ["Cython", "array-api-strict", "asv", "gmpy2", "hypothesis (>=6.30)", "meson", "mpmath", "ninja", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
 
+[[package]]
+name = "sentry-sdk"
+version = "2.11.0"
+description = "Python client for Sentry (https://sentry.io)"
+optional = false
+python-versions = ">=3.6"
+files = [
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+]
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+[package.dependencies]
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+
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+anthropic = ["anthropic (>=0.16)"]
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+asyncpg = ["asyncpg (>=0.23)"]
+beam = ["apache-beam (>=2.12)"]
+bottle = ["bottle (>=0.12.13)"]
+celery = ["celery (>=3)"]
+celery-redbeat = ["celery-redbeat (>=2)"]
+chalice = ["chalice (>=1.16.0)"]
+clickhouse-driver = ["clickhouse-driver (>=0.2.0)"]
+django = ["django (>=1.8)"]
+falcon = ["falcon (>=1.4)"]
+fastapi = ["fastapi (>=0.79.0)"]
+flask = ["blinker (>=1.1)", "flask (>=0.11)", "markupsafe"]
+grpcio = ["grpcio (>=1.21.1)", "protobuf (>=3.8.0)"]
+httpx = ["httpx (>=0.16.0)"]
+huey = ["huey (>=2)"]
+huggingface-hub = ["huggingface-hub (>=0.22)"]
+langchain = ["langchain (>=0.0.210)"]
+loguru = ["loguru (>=0.5)"]
+openai = ["openai (>=1.0.0)", "tiktoken (>=0.3.0)"]
+opentelemetry = ["opentelemetry-distro (>=0.35b0)"]
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+pure-eval = ["asttokens", "executing", "pure-eval"]
+pymongo = ["pymongo (>=3.1)"]
+pyspark = ["pyspark (>=2.4.4)"]
+quart = ["blinker (>=1.1)", "quart (>=0.16.1)"]
+rq = ["rq (>=0.6)"]
+sanic = ["sanic (>=0.8)"]
+sqlalchemy = ["sqlalchemy (>=1.2)"]
+starlette = ["starlette (>=0.19.1)"]
+starlite = ["starlite (>=1.48)"]
+tornado = ["tornado (>=6)"]
+
+[[package]]
+name = "shellingham"
+version = "1.5.4"
+description = "Tool to Detect Surrounding Shell"
+optional = false
+python-versions = ">=3.7"
+files = [
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+]
+
 [[package]]
 name = "simsimd"
 version = "4.4.0"
@@ -2030,15 +3012,29 @@ postgresql-psycopgbinary = ["psycopg[binary] (>=3.0.7)"]
 pymysql = ["pymysql"]
 sqlcipher = ["sqlcipher3_binary"]
 
+[[package]]
+name = "tabulate"
+version = "0.9.0"
+description = "Pretty-print tabular data"
+optional = false
+python-versions = ">=3.7"
+files = [
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+]
+
+[package.extras]
+widechars = ["wcwidth"]
+
 [[package]]
 name = "tenacity"
-version = "8.5.0"
+version = "8.4.2"
 description = "Retry code until it succeeds"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -2117,6 +3113,23 @@ notebook = ["ipywidgets (>=6)"]
 slack = ["slack-sdk"]
 telegram = ["requests"]
 
+[[package]]
+name = "typer"
+version = "0.12.3"
+description = "Typer, build great CLIs. Easy to code. Based on Python type hints."
+optional = false
+python-versions = ">=3.7"
+files = [
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+
+[package.dependencies]
+click = ">=8.0.0"
+rich = ">=10.11.0"
+shellingham = ">=1.3.0"
+typing-extensions = ">=3.7.4.3"
+
 [[package]]
 name = "typing-extensions"
 version = "4.12.2"
@@ -2128,6 +3141,32 @@ files = [
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 ]
 
+[[package]]
+name = "typing-inspect"
+version = "0.9.0"
+description = "Runtime inspection utilities for typing module."
+optional = false
+python-versions = "*"
+files = [
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+]
+
+[package.dependencies]
+mypy-extensions = ">=0.3.0"
+typing-extensions = ">=3.7.4"
+
+[[package]]
+name = "tzdata"
+version = "2024.1"
+description = "Provider of IANA time zone data"
+optional = false
+python-versions = ">=2"
+files = [
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+]
+
 [[package]]
 name = "urllib3"
 version = "2.2.2"
@@ -2162,6 +3201,85 @@ MarkupSafe = ">=2.1.1"
 [package.extras]
 watchdog = ["watchdog (>=2.3)"]
 
+[[package]]
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+version = "1.16.0"
+description = "Module for decorators, wrappers and monkey patching."
+optional = false
+python-versions = ">=3.6"
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+    {file = "xxhash-3.4.1.tar.gz", hash = "sha256:0379d6cf1ff987cd421609a264ce025e74f346e3e145dd106c0cc2e3ec3f99a9"},
+]
+
 [[package]]
 name = "yarl"
 version = "1.9.4"
@@ -2276,7 +3511,22 @@ files = [
 idna = ">=2.0"
 multidict = ">=4.0"
 
+[[package]]
+name = "zipp"
+version = "3.19.2"
+description = "Backport of pathlib-compatible object wrapper for zip files"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "zipp-3.19.2-py3-none-any.whl", hash = "sha256:f091755f667055f2d02b32c53771a7a6c8b47e1fdbc4b72a8b9072b3eef8015c"},
+    {file = "zipp-3.19.2.tar.gz", hash = "sha256:bf1dcf6450f873a13e952a29504887c89e6de7506209e5b1bcc3460135d4de19"},
+]
+
+[package.extras]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
+
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.12,<3.13"
-content-hash = "e86b93db8edbdc76532f4d66edfd45a5cddd7bccb039dd13fde779a7920da59a"
+content-hash = "d8fecac962c1bf0ed3316edfdb556d34cecee7f1b7b542878725aacbef696f19"
diff --git a/redbox-core/pyproject.toml b/redbox-core/pyproject.toml
index 7c665c627..f5c3b609c 100644
--- a/redbox-core/pyproject.toml
+++ b/redbox-core/pyproject.toml
@@ -14,6 +14,7 @@ readme = "../README.md"
 python = ">=3.12,<3.13"
 pydantic = "^2.7.1"
 elasticsearch = "^8.14.0"
+langchain-community = "^0.2.6"
 langchain = "^0.2.11"
 langchain_openai = "^0.1.19"
 tiktoken = "^0.7.0"
@@ -22,12 +23,16 @@ pydantic-settings = "^2.3.4"
 langchain-elasticsearch = "^0.2.2"
 pytest-dotenv = "^0.5.2"
 kneed = "^0.8.5"
+langgraph = "^0.1.9"
 
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^8.3.2"
 moto = "^5.0.10"
 pytest-cov = "^5.0.0"
+pytest-asyncio = "^0.23.6"
+jsonlines = "^4.0.0"
+deepeval = "^0.21.73"
 
 [build-system]
 requires = ["poetry-core"]
@@ -40,4 +45,7 @@ env_files = [
     ".env.test",
     ".env"
 ]
+markers = [
+    "ai: marks tests as using a live LLM (deselect with '-m \"not ai\"')"
+]
 
diff --git a/redbox-core/redbox/__init__.py b/redbox-core/redbox/__init__.py
index 7ef2a9cf4..15481c9b0 100644
--- a/redbox-core/redbox/__init__.py
+++ b/redbox-core/redbox/__init__.py
@@ -1,3 +1,7 @@
 """Redbox is a Python library for working with the Redbox API, data and services."""
 
+from redbox.app import Redbox
+
 __version__ = "0.3.0"
+
+__all__ = ["Redbox"]
diff --git a/redbox-core/redbox/api/runnables.py b/redbox-core/redbox/api/runnables.py
index 2021d8544..de2da5a89 100644
--- a/redbox-core/redbox/api/runnables.py
+++ b/redbox-core/redbox/api/runnables.py
@@ -1,18 +1,12 @@
-import sys
-from functools import partial, reduce
 from logging import getLogger
-from typing import Any
 
 from kneed import KneeLocator
 from langchain_core.documents.base import Document
 from langchain_core.prompts import ChatPromptTemplate
-from langchain_core.runnables import Runnable, RunnableLambda, RunnablePassthrough, chain
+from langchain_core.runnables import Runnable, chain
 from tiktoken import Encoding
 
-from redbox.api.format import reduce_chunks_by_tokens
-from redbox.models import ChatResponse
 from redbox.models.errors import QuestionLengthError
-from redbox.transform import map_document_to_source_document
 
 logger = getLogger()
 
@@ -55,41 +49,6 @@ def chat_prompt_from_messages(input_dict: dict):
     return chat_prompt_from_messages
 
 
-@chain
-def map_to_chat_response(input_dict: dict):
-    """
-    Create a ChatResponse at the end of a chain from a dict containing
-    'response' a string to use as output_text
-    'source_documents' a list of documents to map to source_documents
-    """
-    return (
-        RunnablePassthrough.assign(
-            source_documents=(
-                RunnableLambda(lambda d: d.get("source_documents", []))
-                | RunnableLambda(lambda docs: list(map(map_document_to_source_document, docs)))
-            )
-        )
-        | RunnableLambda(
-            lambda d: ChatResponse(
-                output_text=d["response"], source_documents=d.get("source_documents", []), route_name=d["route_name"]
-            )
-        )
-    ).invoke(input_dict)
-
-
-def resize_documents(max_tokens: int) -> Runnable[list[Document], Any]:
-    """Gets a file as larger document-sized Chunks, splitting it by max_tokens."""
-    n = max_tokens or sys.maxsize
-
-    @chain
-    def wrapped(chunks_unsorted: list[Document]):
-        chunks_sorted = sorted(chunks_unsorted, key=lambda doc: doc.metadata["index"])
-        reduce_chunk_n = partial(reduce_chunks_by_tokens, max_tokens=n)
-        return reduce(lambda cs, c: reduce_chunk_n(cs, c), chunks_sorted, [])  # type: ignore
-
-    return wrapped
-
-
 # TODO: allow for nothing coming back/empty list -- don't error
 # https://github.com/i-dot-ai/redbox/actions/runs/9944427706/job/27470465428#step:10:7160
 
diff --git a/redbox-core/redbox/app.py b/redbox-core/redbox/app.py
new file mode 100644
index 000000000..7015f7a16
--- /dev/null
+++ b/redbox-core/redbox/app.py
@@ -0,0 +1,99 @@
+from langgraph.graph import StateGraph
+from langchain_core.language_models.chat_models import BaseChatModel
+from langchain_core.vectorstores import VectorStoreRetriever
+from tiktoken import Encoding
+
+from redbox.chains.graph import set_route, set_state_field
+from redbox.graph.search import get_search_graph
+from redbox.models.chain import ChainState
+from redbox.models.chat import ChatRoute
+from redbox.models.settings import Settings
+from redbox.chains.components import get_all_chunks_retriever, get_parameterised_retriever, get_chat_llm, get_tokeniser
+from redbox.graph.chat import get_chat_graph, get_chat_with_docs_graph
+
+
+async def _default_callback(*args, **kwargs):
+    return None
+
+
+class Redbox:
+    FINAL_RESPONSE_TAG = "response_flag"
+    SOURCE_DOCUMENTS_TAG = "source_documents_flag"
+    ROUTE_NAME_TAG = "route_flag"
+
+    # Non keywords
+    ROUTABLE_BUILTIIN = [ChatRoute.chat, ChatRoute.chat_with_docs, ChatRoute.error_no_keyword]
+
+    # Keyword routes
+    ROUTABLE_KEYWORDS = {ChatRoute.search: "Search for an answer to the question in the document"}
+
+    def __init__(
+        self,
+        llm: BaseChatModel | None = None,
+        all_chunks_retriever: VectorStoreRetriever | None = None,
+        parameterised_retriever: VectorStoreRetriever | None = None,
+        tokeniser: Encoding | None = None,
+        env: Settings | None = None,
+        debug: bool = False,
+    ):
+        _env = env or Settings()
+        _all_chunks_retriever = all_chunks_retriever or get_all_chunks_retriever(_env)
+        _parameterised_retriever = parameterised_retriever or get_parameterised_retriever(_env)
+        _llm = llm or get_chat_llm(_env)
+        _tokeniser = tokeniser or get_tokeniser()
+
+        app = StateGraph(ChainState)
+        app.set_entry_point("set_route")
+
+        app.add_node("set_route", set_route.with_config(tags=[Redbox.ROUTE_NAME_TAG]))
+        app.add_conditional_edges(
+            "set_route",
+            lambda s: s["route_name"],
+            {x: x for x in Redbox.ROUTABLE_BUILTIIN + list(Redbox.ROUTABLE_KEYWORDS.keys())},
+        )
+
+        app.add_node(
+            ChatRoute.search,
+            get_search_graph(
+                _llm, _parameterised_retriever.with_config(tags=[Redbox.SOURCE_DOCUMENTS_TAG]), _tokeniser, _env, debug
+            ),
+        )
+        app.add_node(ChatRoute.chat, get_chat_graph(_llm, _tokeniser, _env, debug))
+        app.add_node(
+            ChatRoute.chat_with_docs,
+            get_chat_with_docs_graph(
+                _llm, _all_chunks_retriever.with_config(tags=[Redbox.SOURCE_DOCUMENTS_TAG]), _tokeniser, _env, debug
+            ),
+        )
+        app.add_node(
+            ChatRoute.error_no_keyword,
+            set_state_field("response", env.response_no_such_keyword).with_config(tags=[Redbox.FINAL_RESPONSE_TAG]),
+        )
+
+        self.graph = app.compile(debug=debug)
+
+    async def run(
+        self,
+        input: ChainState,
+        response_tokens_callback=_default_callback,
+        route_name_callback=_default_callback,
+        documents_callback=_default_callback,
+    ) -> ChainState:
+        final_state = None
+        async for event in self.graph.astream_events(input, version="v2"):
+            kind = event["event"]
+            tags = event.get("tags", [])
+            if kind == "on_chat_model_stream" and Redbox.FINAL_RESPONSE_TAG in tags:
+                await response_tokens_callback(event["data"]["chunk"].content)
+            elif kind == "on_chain_end" and Redbox.FINAL_RESPONSE_TAG in tags:
+                await response_tokens_callback(event["data"]["output"]["response"])
+            elif kind == "on_chain_end" and Redbox.ROUTE_NAME_TAG in tags:
+                await route_name_callback(event["data"]["output"]["route_name"])
+            elif kind == "on_retriever_end" and Redbox.SOURCE_DOCUMENTS_TAG in tags:
+                await documents_callback(event["data"]["output"])
+            elif kind == "on_chain_end" and event["name"] == "LangGraph":
+                final_state = ChainState(**event["data"]["output"])
+        return final_state
+
+    def get_available_keywords(self) -> dict[ChatRoute, str]:
+        return Redbox.ROUTABLE_KEYWORDS
diff --git a/redbox-core/redbox/chains/components.py b/redbox-core/redbox/chains/components.py
new file mode 100644
index 000000000..4a6d12205
--- /dev/null
+++ b/redbox-core/redbox/chains/components.py
@@ -0,0 +1,87 @@
+from langchain_elasticsearch import ElasticsearchRetriever
+from langchain_core.embeddings import Embeddings, FakeEmbeddings
+from langchain_openai import AzureChatOpenAI
+from langchain_openai.embeddings import AzureOpenAIEmbeddings, OpenAIEmbeddings
+from langchain_core.utils import convert_to_secret_str
+from langchain_core.runnables import ConfigurableField
+import tiktoken
+
+from redbox.models.settings import Settings
+from redbox.retriever import AllElasticsearchRetriever, ParameterisedElasticsearchRetriever
+
+
+def get_chat_llm(env: Settings):
+    return AzureChatOpenAI(
+        api_key=convert_to_secret_str(env.azure_openai_api_key),
+        azure_endpoint=env.azure_openai_endpoint,
+        model=env.azure_openai_model,
+    )
+
+
+def get_tokeniser() -> tiktoken.Encoding:
+    return tiktoken.get_encoding("cl100k_base")
+
+
+def get_azure_embeddings(env: Settings):
+    return AzureOpenAIEmbeddings(
+        azure_endpoint=env.azure_openai_endpoint,
+        api_version=env.azure_api_version_embeddings,
+        model=env.azure_embedding_model,
+        max_retries=env.embedding_max_retries,
+        retry_min_seconds=env.embedding_retry_min_seconds,
+        retry_max_seconds=env.embedding_retry_max_seconds,
+    )
+
+
+def get_openai_embeddings(env: Settings):
+    return OpenAIEmbeddings(
+        api_key=convert_to_secret_str(env.openai_api_key),
+        base_url=env.embedding_openai_base_url,
+        model=env.embedding_openai_model,
+        chunk_size=env.embedding_max_batch_size,
+    )
+
+
+def get_embeddings(env: Settings) -> Embeddings:
+    if env.embedding_backend == "azure":
+        return get_azure_embeddings(env)
+    elif env.embedding_backend == "openai":
+        return get_openai_embeddings(env)
+    elif env.embedding_backend == "fake":
+        return FakeEmbeddings(size=3072)  # TODO
+    else:
+        raise Exception("No configured embedding model")
+
+
+def get_all_chunks_retriever(env: Settings) -> ElasticsearchRetriever:
+    return AllElasticsearchRetriever(
+        es_client=env.elasticsearch_client(),
+        index_name=f"{env.elastic_root_index}-chunk",
+    )
+
+
+def get_parameterised_retriever(env: Settings, embeddings: Embeddings | None = None):
+    """Creates an Elasticsearch retriever runnable.
+
+    Runnable takes input of a dict keyed to question, file_uuids and user_uuid.
+
+    Runnable returns a list of Chunks.
+    """
+    default_params = {
+        "size": env.ai.rag_k,
+        "num_candidates": env.ai.rag_num_candidates,
+        "match_boost": 1,
+        "knn_boost": 1,
+        "similarity_threshold": 0,
+    }
+    return ParameterisedElasticsearchRetriever(
+        es_client=env.elasticsearch_client(),
+        index_name=f"{env.elastic_root_index}-chunk",
+        params=default_params,
+        embedding_model=embeddings or get_embeddings(env),
+        embedding_field_name=env.embedding_document_field_name,
+    ).configurable_fields(
+        params=ConfigurableField(
+            id="params", name="Retriever parameters", description="A dictionary of parameters to use for the retriever."
+        )
+    )
diff --git a/redbox-core/redbox/chains/graph.py b/redbox-core/redbox/chains/graph.py
new file mode 100644
index 000000000..8083ebda0
--- /dev/null
+++ b/redbox-core/redbox/chains/graph.py
@@ -0,0 +1,133 @@
+import logging
+import re
+from typing import Any
+
+from langchain.schema import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.runnables import Runnable, RunnableLambda, RunnableParallel, chain
+from langchain_core.language_models.chat_models import BaseChatModel
+from langchain_core.vectorstores import VectorStoreRetriever
+from tiktoken import Encoding
+
+from redbox.api.format import format_documents
+from redbox.api.runnables import filter_by_elbow
+from redbox.models import ChatRoute, Settings
+from redbox.models.chain import ChainChatMessage, ChainState
+from redbox.models.errors import QuestionLengthError
+
+log = logging.getLogger()
+re_keyword_pattern = re.compile(r"@(\w+)")
+
+
+def build_get_docs(env: Settings, retriever: VectorStoreRetriever):
+    return RunnableParallel({"documents": retriever})
+
+
+def build_get_docs_with_filter(env: Settings, retriever: VectorStoreRetriever):
+    return RunnableParallel({"documents": retriever | filter_by_elbow(env.ai.elbow_filter_enabled)})
+
+
+@chain
+def set_route(state: ChainState):
+    """
+    Choose an approach to chatting based on the current state
+    """
+    # Match keyword
+    if route_match := re_keyword_pattern.search(state["query"].question):
+        match = route_match.group()[1:]
+        try:
+            if ChatRoute(match):
+                selected = match
+        except ValueError:
+            selected = ChatRoute.error_no_keyword.value
+    elif len(state["query"].file_uuids) > 0:
+        selected = ChatRoute.chat_with_docs.value
+    else:
+        selected = ChatRoute.chat.value
+    log.info(f"Based on user query [{selected}] selected")
+    return {"route_name": selected}
+
+
+def make_chat_prompt_from_messages_runnable(
+    system_prompt: str,
+    question_prompt: str,
+    input_token_budget: int,
+    tokeniser: Encoding,
+):
+    system_prompt_message = [("system", system_prompt)]
+    prompts_budget = len(tokeniser.encode(system_prompt)) - len(tokeniser.encode(question_prompt))
+    token_budget = input_token_budget - prompts_budget
+
+    @chain
+    def chat_prompt_from_messages(state: ChainState):
+        """
+        Create a ChatPrompTemplate as part of a chain using 'chat_history'.
+        Returns the PromptValue using values in the input_dict
+        """
+        log.debug("Setting chat prompt")
+        chat_history_budget = token_budget - len(tokeniser.encode(state["query"].question))
+
+        if chat_history_budget <= 0:
+            raise QuestionLengthError
+
+        truncated_history: list[ChainChatMessage] = []
+        for msg in state["query"].chat_history[::-1]:
+            chat_history_budget -= len(tokeniser.encode(msg["text"]))
+            if chat_history_budget <= 0:
+                break
+            else:
+                truncated_history.insert(0, msg)
+
+        return ChatPromptTemplate.from_messages(
+            system_prompt_message
+            + [(msg["role"], msg["text"]) for msg in truncated_history]
+            + [("user", question_prompt)]
+        ).invoke(state["query"].dict() | state.get("prompt_args", {}))
+
+    return chat_prompt_from_messages
+
+
+@chain
+def set_prompt_args(state: ChainState):
+    log.debug("Setting prompt args")
+    return {
+        "prompt_args": {
+            "formatted_documents": format_documents(state.get("documents") or []),
+        }
+    }
+
+
+def build_llm_chain(
+    llm: BaseChatModel,
+    tokeniser: Encoding,
+    env: Settings,
+    system_prompt: str,
+    question_prompt: str,
+    final_response_chain=False,
+) -> Runnable:
+    _llm = llm.with_config(tags=["response_flag"]) if final_response_chain else llm
+    return RunnableParallel(
+        {
+            "response": make_chat_prompt_from_messages_runnable(
+                system_prompt=system_prompt,
+                question_prompt=question_prompt,
+                input_token_budget=env.ai.context_window_size - env.llm_max_tokens,
+                tokeniser=tokeniser,
+            )
+            | _llm
+            | StrOutputParser(),
+        }
+    )
+
+
+def set_state_field(state_field: str, value: Any):
+    return RunnableLambda(
+        lambda _: {
+            state_field: value,
+        }
+    )
+
+
+def empty_node(state: ChainState):
+    log.info(f"Empty Node: {state}")
+    return None
diff --git a/redbox-core/redbox/graph/__init__.py b/redbox-core/redbox/graph/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/redbox-core/redbox/graph/chat.py b/redbox-core/redbox/graph/chat.py
new file mode 100644
index 000000000..f17fd1d4f
--- /dev/null
+++ b/redbox-core/redbox/graph/chat.py
@@ -0,0 +1,172 @@
+import logging
+from langgraph.graph import StateGraph, START
+from langgraph.constants import Send
+from langgraph.graph.graph import CompiledGraph
+from langchain_core.runnables import chain, RunnableLambda, Runnable
+from langchain_core.documents import Document
+from langchain_core.language_models.chat_models import BaseChatModel
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.vectorstores import VectorStoreRetriever
+from langchain_text_splitters import TextSplitter, TokenTextSplitter
+from tiktoken import Encoding
+
+from redbox.api.format import format_documents
+from redbox.models.chain import ChainState, ChatMapReduceState
+from redbox.models.chat import ChatRoute
+from redbox.chains.graph import (
+    set_prompt_args,
+    build_llm_chain,
+    make_chat_prompt_from_messages_runnable,
+    build_get_docs,
+    set_state_field,
+)
+from redbox.models.settings import Settings
+
+log = logging.getLogger()
+
+
+@chain
+def to_map_step(state: ChatMapReduceState):
+    """
+    Map each doc in the state to an execution of the llm map step which will create an answer
+    per current document
+    """
+    return [
+        Send(
+            "llm_map",
+            ChatMapReduceState(
+                query=state["query"], documents=[doc], route_name=state["route_name"], prompt_args=state["prompt_args"]
+            ),
+        )
+        for doc in state["documents"]
+    ]
+
+
+def build_reduce_docs_step(splitter: TextSplitter):
+    return RunnableLambda(
+        lambda state: [
+            Document(page_content=s) for s in splitter.split_text(format_documents(state["intermediate_docs"]))
+        ]
+    ) | RunnableLambda(lambda docs: {"documents": docs})
+
+
+def get_chat_graph(llm: BaseChatModel, tokeniser: Encoding, env: Settings, debug: bool = False) -> CompiledGraph:
+    app = StateGraph(ChainState)
+    app.set_entry_point("set_chat_prompt_args")
+
+    app.add_node("set_chat_prompt_args", set_prompt_args)
+    app.add_edge("set_chat_prompt_args", "llm")
+
+    app.add_node(
+        "llm",
+        build_llm_chain(
+            llm, tokeniser, env, env.ai.chat_system_prompt, env.ai.chat_question_prompt, final_response_chain=True
+        ),
+    )
+
+    return app.compile(debug=debug)
+
+
+@chain
+def set_chat_method(state: ChainState):
+    """
+    Choose an approach to chatting based on the current state
+    """
+    log.debug("Selecting chat method")
+    number_of_docs = len(state["documents"])
+    if number_of_docs == 0:
+        selected_tool = ChatRoute.chat
+    elif number_of_docs == 1:
+        selected_tool = ChatRoute.chat_with_docs
+    else:
+        selected_tool = ChatRoute.chat_with_docs_map_reduce
+    log.info(f"Selected: {selected_tool} for execution")
+    return {"route_name": selected_tool}
+
+
+def build_llm_map_chain(
+    llm: BaseChatModel, tokeniser: Encoding, env: Settings, system_prompt: str, question_prompt: str
+) -> Runnable:
+    return (
+        make_chat_prompt_from_messages_runnable(
+            system_prompt=system_prompt,
+            question_prompt=question_prompt,
+            input_token_budget=env.ai.context_window_size - env.llm_max_tokens,
+            tokeniser=tokeniser,
+        )
+        | llm
+        | StrOutputParser()
+        | RunnableLambda(lambda s: {"intermediate_docs": [Document(page_content=s)]})
+    )
+
+
+def get_chat_with_docs_graph(
+    llm: BaseChatModel,
+    all_chunks_retriever: VectorStoreRetriever,
+    tokeniser: Encoding,
+    env: Settings,
+    debug: bool = False,
+) -> CompiledGraph:
+    app = StateGraph(ChainState)
+
+    app.add_node("get_chat_docs", build_get_docs(env, all_chunks_retriever))
+    app.add_node("set_chat_prompt_args", set_prompt_args)
+    app.add_node("set_chat_method", set_chat_method)
+
+    app.add_node("no_docs_available", set_state_field("response", env.response_no_doc_available))
+    app.add_node(
+        "llm",
+        build_llm_chain(
+            llm,
+            tokeniser,
+            env,
+            env.ai.chat_with_docs_system_prompt,
+            env.ai.chat_with_docs_question_prompt,
+            final_response_chain=True,
+        ),
+    )
+    app.add_node(ChatRoute.chat_with_docs_map_reduce, get_chat_with_docs_map_reduce_graph(llm, tokeniser, env, debug))
+    app.add_node("clear_documents", set_state_field("documents", []))
+
+    app.add_edge(START, "get_chat_docs")
+    app.add_edge("get_chat_docs", "set_chat_prompt_args")
+    app.add_edge("set_chat_prompt_args", "set_chat_method")
+    app.add_conditional_edges(
+        "set_chat_method",
+        lambda state: state["route_name"],
+        {
+            ChatRoute.chat: "no_docs_available",
+            ChatRoute.chat_with_docs: "llm",
+            ChatRoute.chat_with_docs_map_reduce: ChatRoute.chat_with_docs_map_reduce,
+        },
+    )
+    app.add_edge(ChatRoute.chat_with_docs_map_reduce, "set_chat_prompt_args")
+
+    # Remove docs so we don't provide citations for chat (using whole doc so irrelevant anyway)
+    app.add_edge("llm", "clear_documents")
+
+    return app.compile(debug=debug)
+
+
+def get_chat_with_docs_map_reduce_graph(
+    llm: BaseChatModel, tokeniser: Encoding, env: Settings, debug: bool = False
+) -> CompiledGraph:
+    app = StateGraph(ChatMapReduceState)
+
+    app.add_node(
+        "llm_map", build_llm_map_chain(llm, tokeniser, env, env.ai.map_system_prompt, env.ai.chat_map_question_prompt)
+    )
+    app.add_node(
+        "reduce",
+        build_reduce_docs_step(
+            TokenTextSplitter(
+                model_name="gpt-4",
+                chunk_size=env.worker_ingest_largest_chunk_size,
+                chunk_overlap=env.worker_ingest_largest_chunk_overlap,
+            )
+        ),
+    )
+
+    app.add_conditional_edges(START, to_map_step, then="reduce")
+
+    return app.compile(debug=debug)
diff --git a/redbox-core/redbox/graph/search.py b/redbox-core/redbox/graph/search.py
new file mode 100644
index 000000000..7c2fba2c4
--- /dev/null
+++ b/redbox-core/redbox/graph/search.py
@@ -0,0 +1,52 @@
+from langgraph.graph import StateGraph, START
+from langgraph.graph.graph import CompiledGraph
+from langchain_core.language_models.chat_models import BaseChatModel
+from langchain_core.vectorstores import VectorStoreRetriever
+from tiktoken import Encoding
+
+from redbox.chains.graph import build_get_docs_with_filter, build_llm_chain, set_prompt_args
+from redbox.models.chain import ChainInput, ChainState
+from redbox.models.settings import Settings
+
+
+def get_search_graph(
+    llm: BaseChatModel, retriever: VectorStoreRetriever, tokeniser: Encoding, env: Settings, debug: bool = False
+) -> CompiledGraph:
+    app = StateGraph(ChainState)
+
+    app.add_node("get_docs", build_get_docs_with_filter(env, retriever))
+    app.add_node("set_prompt_args", set_prompt_args)
+
+    app.add_node(
+        "condense", build_llm_chain(llm, tokeniser, env, env.ai.condense_system_prompt, env.ai.condense_question_prompt)
+    )
+    app.add_node(
+        "map_condense_to_question",
+        lambda s: {
+            "query": ChainInput(
+                question=s["response"],
+                file_uuids=s["query"].file_uuids,
+                user_uuid=s["query"].user_uuid,
+                chat_history=[],
+            )
+        },
+    )
+    app.add_node(
+        "llm",
+        build_llm_chain(
+            llm,
+            tokeniser,
+            env,
+            env.ai.retrieval_system_prompt,
+            env.ai.retrieval_question_prompt,
+            final_response_chain=True,
+        ),
+    )
+
+    app.add_edge(START, "get_docs")
+    app.add_edge("get_docs", "set_prompt_args")
+    app.add_edge("set_prompt_args", "condense")
+    app.add_edge("condense", "map_condense_to_question")
+    app.add_edge("map_condense_to_question", "llm")
+
+    return app.compile(debug=debug)
diff --git a/redbox-core/redbox/models/chain.py b/redbox-core/redbox/models/chain.py
index eb2a27e9d..bc9ea851a 100644
--- a/redbox-core/redbox/models/chain.py
+++ b/redbox-core/redbox/models/chain.py
@@ -5,9 +5,12 @@
 used in conjunction with langchain this is the tidiest boxing of pydantic v1 we can do
 """
 
-from typing import TypedDict, Literal
+from typing import TypedDict, Literal, Annotated, Required, NotRequired
 from uuid import UUID
+from operator import add
+
 from langchain_core.pydantic_v1 import BaseModel, Field
+from langchain_core.documents import Document
 
 
 class ChainChatMessage(TypedDict):
@@ -20,3 +23,15 @@ class ChainInput(BaseModel):
     file_uuids: list[UUID] = Field(description="List of files to process")
     user_uuid: UUID = Field(description="User the chain in executing for")
     chat_history: list[ChainChatMessage] = Field(description="All previous messages in chat (excluding question)")
+
+
+class ChainState(TypedDict):
+    query: Required[ChainInput]
+    documents: NotRequired[list[Document]]
+    response: NotRequired[str | None]
+    route_name: NotRequired[str | None]
+    prompt_args: NotRequired[dict[str, str]]
+
+
+class ChatMapReduceState(ChainState):
+    intermediate_docs: Annotated[NotRequired[list[Document]], add]
diff --git a/redbox-core/redbox/models/chat.py b/redbox-core/redbox/models/chat.py
index 85fdd98bb..428daedcf 100644
--- a/redbox-core/redbox/models/chat.py
+++ b/redbox-core/redbox/models/chat.py
@@ -64,16 +64,11 @@ class SourceDocuments(BaseModel):
 
 
 class ChatRoute(StrEnum):
-    info = "info"
-    ability = "ability"
-    coach = "coach"
-    gratitude = "gratitude"
     search = "search"
-    summarise = "summarise"
-    map_reduce_summarise = "summarise/documents/large"
     chat = "chat"
     chat_with_docs = "chat/documents"
     chat_with_docs_map_reduce = "chat/documents/large"
+    error_no_keyword = "error/no-keyword"
 
 
 class ChatResponse(BaseModel):
diff --git a/redbox-core/redbox/models/settings.py b/redbox-core/redbox/models/settings.py
index daf9c6763..ab6620d69 100644
--- a/redbox-core/redbox/models/settings.py
+++ b/redbox-core/redbox/models/settings.py
@@ -7,7 +7,6 @@
 from pydantic import BaseModel, computed_field
 from pydantic_settings import BaseSettings, SettingsConfigDict
 
-logging.basicConfig(level=logging.INFO)
 log = logging.getLogger()
 
 
@@ -87,11 +86,10 @@
 
 SUMMARISATION_QUESTION_PROMPT = "Question: {question}. \n\n Documents: \n\n {documents} \n\n Answer: "
 
-MAP_QUESTION_PROMPT = "Question: {question}. "
+CHAT_MAP_QUESTION_PROMPT = "Question: {question}. \n Documents: \n {formatted_documents} \n\n Answer: "
 
-MAP_DOCUMENT_PROMPT = "\n\n Documents: \n\n {documents} \n\n Answer: "
 
-REDUCE_QUESTION_PROMPT = "Question: {question}. \n\n Documents: \n\n {summaries} \n\n Answer: "
+REDUCE_QUESTION_PROMPT = "Question: {question}. \n\n Documents: \n\n {formatted_documents} \n\n Answer: "
 
 CONDENSE_QUESTION_PROMPT = "{question}\n=========\n Standalone question: "
 
@@ -121,8 +119,7 @@ class AISettings(BaseModel):
     summarisation_question_prompt: str = SUMMARISATION_QUESTION_PROMPT
     map_max_concurrency: int = 128
     map_system_prompt: str = MAP_SYSTEM_PROMPT
-    map_question_prompt: str = MAP_QUESTION_PROMPT
-    map_document_prompt: str = MAP_DOCUMENT_PROMPT
+    chat_map_question_prompt: str = CHAT_MAP_QUESTION_PROMPT
     reduce_system_prompt: str = REDUCE_SYSTEM_PROMPT
     reduce_question_prompt: str = REDUCE_QUESTION_PROMPT
 
@@ -172,7 +169,7 @@ class Settings(BaseSettings):
     azure_embedding_model: str = "text-embedding-3-large"
     llm_max_tokens: int = 1024
 
-    embedding_backend: Literal["azure", "openai"] = "azure"
+    embedding_backend: Literal["azure", "openai", "fake"] = "azure"
     embedding_max_retries: int = 10
     embedding_retry_min_seconds: int = 10
     embedding_retry_max_seconds: int = 120
@@ -217,6 +214,10 @@ class Settings(BaseSettings):
     worker_ingest_largest_chunk_size: int = 96000
     worker_ingest_largest_chunk_overlap: int = 0
 
+    response_no_doc_available: str = "No available data for selected files. They may need to be removed and added again"
+    response_max_content_exceeded: str = "Max content exceeded. Try smaller or fewer documents"
+    response_no_such_keyword: str = "That keyword isn't recognised"
+
     redis_host: str = "redis"
     redis_port: int = 6379
 
diff --git a/redbox-core/redbox/retriever/queries.py b/redbox-core/redbox/retriever/queries.py
index 8b1caf9a1..98bcc206c 100644
--- a/redbox-core/redbox/retriever/queries.py
+++ b/redbox-core/redbox/retriever/queries.py
@@ -3,15 +3,10 @@
 
 from langchain_core.embeddings.embeddings import Embeddings
 
+from redbox.models.chain import ChainState
 from redbox.models.file import ChunkResolution
 
 
-class ESQuery(TypedDict):
-    question: str
-    file_uuids: list[UUID]
-    user_uuid: UUID
-
-
 class ESParams(TypedDict):
     size: int
     num_candidates: int
@@ -59,7 +54,7 @@ def make_query_filter(user_uuid: UUID, file_uuids: list[UUID], chunk_resolution:
 
 def get_all(
     chunk_resolution: ChunkResolution | None,
-    query: ESQuery,
+    state: ChainState,
 ) -> dict[str, Any]:
     """
     Returns a parameterised elastic query that will return everything it matches.
@@ -67,7 +62,7 @@ def get_all(
     As it's used in summarisation, it excludes embeddings.
     """
 
-    query_filter = make_query_filter(query["user_uuid"], query["file_uuids"], chunk_resolution)
+    query_filter = make_query_filter(state["query"].user_uuid, state["query"].file_uuids, chunk_resolution)
     return {
         "_source": {"excludes": ["*embedding"]},
         "query": {"bool": {"must": {"match_all": {}}, "filter": query_filter}},
@@ -79,11 +74,11 @@ def get_some(
     params: ESParams,
     embedding_field_name: str,
     chunk_resolution: ChunkResolution | None,
-    query: ESQuery,
+    state: ChainState,
 ) -> dict[str, Any]:
-    vector = embedding_model.embed_query(query["question"])
+    vector = embedding_model.embed_query(state["query"].question)
 
-    query_filter = make_query_filter(query["user_uuid"], query["file_uuids"], chunk_resolution)
+    query_filter = make_query_filter(state["query"].user_uuid, state["query"].file_uuids, chunk_resolution)
 
     return {
         "size": params["size"],
@@ -93,7 +88,7 @@ def get_some(
                     {
                         "match": {
                             "text": {
-                                "query": query["question"],
+                                "query": state["query"].question,
                                 "boost": params["match_boost"],
                             }
                         }
diff --git a/redbox-core/redbox/test/__init__.py b/redbox-core/redbox/test/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/redbox-core/redbox/test/data.py b/redbox-core/redbox/test/data.py
new file mode 100644
index 000000000..0adf96e40
--- /dev/null
+++ b/redbox-core/redbox/test/data.py
@@ -0,0 +1,99 @@
+from dataclasses import dataclass, field
+import datetime
+import logging
+from typing import Tuple
+from uuid import UUID
+
+from langchain_core.documents import Document
+
+from redbox.models.chain import ChainInput
+from redbox.models.chat import ChatRoute
+from redbox.models.file import ChunkMetadata, ChunkResolution
+
+log = logging.getLogger()
+
+
+def generate_docs(
+    parent_file_uuid: UUID,
+    creator_user_uuid: UUID,
+    file_name: str = "test_data.pdf",
+    page_numbers: list[int] = [1, 2, 3, 4],
+    total_tokens=6000,
+    number_of_docs: int = 10,
+    chunk_resolution=ChunkResolution.normal,
+):
+    for i in range(number_of_docs):
+        yield Document(
+            page_content=f"Document {i} text",
+            metadata=ChunkMetadata(
+                parent_file_uuid=parent_file_uuid,
+                creator_user_uuid=creator_user_uuid,
+                index=i,
+                file_name=file_name,
+                page_number=page_numbers[int(i / number_of_docs) * len(page_numbers)],
+                created_datetime=datetime.datetime.now(datetime.UTC),
+                token_count=int(total_tokens / number_of_docs),
+                chunk_resolution=chunk_resolution,
+            ).model_dump(),
+        )
+
+
+@dataclass
+class TestData:
+    number_of_docs: int
+    tokens_in_all_docs: int
+    chunk_resolution: ChunkResolution = ChunkResolution.largest
+    expected_llm_response: list[str] = field(default_factory=list)
+    expected_route: ChatRoute | None = None
+
+
+class RedboxChatTestCase:
+    def __init__(
+        self,
+        test_id: str,
+        query: ChainInput,
+        test_data: TestData,
+        docs_user_uuid_override: UUID | None = None,
+        docs_file_uuids_override: list[UUID] | None = None,
+    ):
+        # Use separate file_uuids if specified else match the query
+        all_file_uuids = docs_file_uuids_override if docs_file_uuids_override else [id for id in query.file_uuids]
+        # Use separate user uuid if specific else match the query
+        docs_user_uuid = docs_user_uuid_override if docs_user_uuid_override else query.user_uuid
+
+        if (
+            test_data.expected_llm_response is not None
+            and len(test_data.expected_llm_response) < test_data.number_of_docs
+        ):
+            log.warning(
+                "Number of configured LLM responses might be less than number of docs. For Map-Reduce actions this will give a Generator Error!"
+            )
+
+        file_generators = [
+            generate_docs(
+                parent_file_uuid=file_uuid,
+                creator_user_uuid=docs_user_uuid,
+                total_tokens=int(test_data.tokens_in_all_docs / len(all_file_uuids)),
+                number_of_docs=int(test_data.number_of_docs / len(all_file_uuids)),
+                chunk_resolution=test_data.chunk_resolution,
+            )
+            for file_uuid in all_file_uuids
+        ]
+        self.query = query
+        self.docs = [doc for generator in file_generators for doc in generator]
+        self.test_data = test_data
+        self.test_id = test_id
+
+    def get_docs_matching_query(self):
+        return [
+            doc
+            for doc in self.docs
+            if doc.metadata["parent_file_uuid"] in set(self.query.file_uuids)
+            and doc.metadata["creator_user_uuid"] == self.query.user_uuid
+        ]
+
+
+def generate_test_cases(query: ChainInput, test_data: list[TestData], test_id: str):
+    return [
+        RedboxChatTestCase(test_id=f"{test_id}-{i}", query=query, test_data=data) for i, data in enumerate(test_data)
+    ]
diff --git a/redbox-core/tests/conftest.py b/redbox-core/tests/conftest.py
index 07e919cdb..93f8ef020 100644
--- a/redbox-core/tests/conftest.py
+++ b/redbox-core/tests/conftest.py
@@ -10,14 +10,14 @@
 
 from collections.abc import Generator
 
-from langchain_core.documents.base import Document
 from langchain_core.embeddings.fake import FakeEmbeddings
 from langchain_core.runnables import ConfigurableField
 from langchain_elasticsearch import ElasticsearchStore
 
 
 from redbox.retriever import AllElasticsearchRetriever, ParameterisedElasticsearchRetriever
-from tests.retriever.data import ALL_CHUNKS_RETRIEVER_DOCUMENTS, PARAMETERISED_RETRIEVER_DOCUMENTS
+from redbox.test.data import RedboxChatTestCase
+from tests.retriever.data import ALL_CHUNKS_RETRIEVER_CASES, PARAMETERISED_RETRIEVER_CASES
 
 
 @pytest.fixture(scope="session")
@@ -184,26 +184,27 @@ def file(s3_client, file_pdf_path: Path, alice, env) -> File:
     return File(key=file_name, bucket=env.bucket_name, creator_user_uuid=alice)
 
 
-@pytest.fixture(params=ALL_CHUNKS_RETRIEVER_DOCUMENTS)
+@pytest.fixture(params=ALL_CHUNKS_RETRIEVER_CASES)
 def stored_file_all_chunks(
     request, elasticsearch_client, es_index, embedding_model_dim
-) -> Generator[list[Document], None, None]:
+) -> Generator[RedboxChatTestCase, None, None]:
+    test_case: RedboxChatTestCase = request.param
     store = ElasticsearchStore(
         index_name=es_index,
         es_connection=elasticsearch_client,
         query_field="text",
         embedding=FakeEmbeddings(size=embedding_model_dim),
     )
-    documents = list(map(Document.parse_obj, request.param))
-    doc_ids = store.add_documents(documents)
-    yield documents
+    doc_ids = store.add_documents(test_case.docs)
+    yield test_case
     store.delete(doc_ids)
 
 
-@pytest.fixture(params=PARAMETERISED_RETRIEVER_DOCUMENTS)
+@pytest.fixture(params=PARAMETERISED_RETRIEVER_CASES)
 def stored_file_parameterised(
     request, elasticsearch_client, es_index, embedding_model, env: Settings
-) -> Generator[list[Document], None, None]:
+) -> Generator[RedboxChatTestCase, None, None]:
+    test_case: RedboxChatTestCase = request.param
     store = ElasticsearchStore(
         index_name=es_index,
         es_connection=elasticsearch_client,
@@ -211,9 +212,8 @@ def stored_file_parameterised(
         vector_query_field=env.embedding_document_field_name,
         embedding=embedding_model,
     )
-    documents = list(map(Document.parse_obj, request.param))
-    doc_ids = store.add_documents(documents)
-    yield documents
+    doc_ids = store.add_documents(test_case.docs)
+    yield test_case
     store.delete(doc_ids)
 
 
diff --git a/redbox-core/tests/graph/__init__.py b/redbox-core/tests/graph/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/redbox-core/tests/graph/test_app.py b/redbox-core/tests/graph/test_app.py
new file mode 100644
index 000000000..bd16a7784
--- /dev/null
+++ b/redbox-core/tests/graph/test_app.py
@@ -0,0 +1,207 @@
+from uuid import uuid4
+import pytest
+from langchain_core.language_models.fake_chat_models import GenericFakeChatModel
+from langchain_core.runnables import RunnableLambda
+import tiktoken
+
+from redbox.models.chain import ChainInput, ChainState
+from redbox import Redbox
+from redbox.models.chat import ChatRoute
+from redbox.models.settings import Settings
+from redbox.test.data import TestData, RedboxChatTestCase, generate_test_cases
+
+
+LANGGRAPH_DEBUG = False
+
+test_env = Settings()
+
+TEST_CASES = [
+    test_case
+    for generated_cases in [
+        generate_test_cases(
+            query=ChainInput(question="What is AI?", file_uuids=[], user_uuid=uuid4(), chat_history=[]),
+            test_data=[
+                TestData(0, 0, expected_llm_response=["Testing Response 1"], expected_route=ChatRoute.chat),
+                TestData(1, 100, expected_llm_response=["Testing Response 1"], expected_route=ChatRoute.chat),
+                TestData(10, 1200, expected_llm_response=["Testing Response 1"], expected_route=ChatRoute.chat),
+            ],
+            test_id="Basic Chat",
+        ),
+        generate_test_cases(
+            query=ChainInput(question="What is AI?", file_uuids=[uuid4()], user_uuid=uuid4(), chat_history=[]),
+            test_data=[
+                TestData(
+                    1, 1000, expected_llm_response=["Testing Response 1"], expected_route=ChatRoute.chat_with_docs
+                ),
+                TestData(
+                    1, 50000, expected_llm_response=["Testing Response 1"], expected_route=ChatRoute.chat_with_docs
+                ),
+                TestData(
+                    1, 200_000, expected_llm_response=["Testing Response 1"], expected_route=ChatRoute.chat_with_docs
+                ),
+            ],
+            test_id="Chat with single doc",
+        ),
+        generate_test_cases(
+            query=ChainInput(question="What is AI?", file_uuids=[uuid4(), uuid4()], user_uuid=uuid4(), chat_history=[]),
+            test_data=[
+                TestData(
+                    2,
+                    40000,
+                    expected_llm_response=["Map Step Response"] * 2 + ["Testing Response 1"],
+                    expected_route=ChatRoute.chat_with_docs,
+                ),
+                TestData(
+                    2,
+                    100_000,
+                    expected_llm_response=["Map Step Response"] * 2 + ["Testing Response 1"],
+                    expected_route=ChatRoute.chat_with_docs,
+                ),
+                TestData(
+                    2,
+                    200_000,
+                    expected_llm_response=["Map Step Response"] * 2 + ["Testing Response 1"],
+                    expected_route=ChatRoute.chat_with_docs,
+                ),
+            ],
+            test_id="Chat with multiple docs",
+        ),
+        generate_test_cases(
+            query=ChainInput(question="What is AI?", file_uuids=[uuid4()], user_uuid=uuid4(), chat_history=[]),
+            test_data=[
+                TestData(
+                    1, 200_000, expected_llm_response=["Testing Response 1"], expected_route=ChatRoute.chat_with_docs
+                ),
+            ],
+            test_id="Chat with large doc",
+        ),
+        generate_test_cases(
+            query=ChainInput(question="@search What is AI?", file_uuids=[uuid4()], user_uuid=uuid4(), chat_history=[]),
+            test_data=[
+                TestData(
+                    1,
+                    10000,
+                    expected_llm_response=["Condense response", "The cake is a lie"],
+                    expected_route=ChatRoute.search,
+                ),
+                TestData(
+                    5,
+                    10000,
+                    expected_llm_response=["Condense response", "The cake is a lie"],
+                    expected_route=ChatRoute.search,
+                ),
+            ],
+            test_id="Search",
+        ),
+        generate_test_cases(
+            query=ChainInput(
+                question="@nosuchkeyword What is AI?", file_uuids=[uuid4()], user_uuid=uuid4(), chat_history=[]
+            ),
+            test_data=[
+                TestData(
+                    2,
+                    200_000,
+                    expected_llm_response=[test_env.response_no_such_keyword],
+                    expected_route=ChatRoute.error_no_keyword,
+                ),
+            ],
+            test_id="No Such Keyword",
+        ),
+    ]
+    for test_case in generated_cases
+]
+
+
+@pytest.fixture(scope="session")
+def env():
+    return Settings()
+
+
+@pytest.fixture(scope="session")
+def tokeniser():
+    return tiktoken.get_encoding("cl100k_base")
+
+
+def all_chunks_retriever(docs):
+    def mock_retrieve(query):
+        return docs
+
+    return RunnableLambda(mock_retrieve)
+
+
+def parameterised_retriever(docs):
+    def mock_retrieve(query):
+        return docs
+
+    return RunnableLambda(mock_retrieve)
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(("test_case"), TEST_CASES, ids=[t.test_id for t in TEST_CASES])
+async def test_chat(test_case: RedboxChatTestCase, env, tokeniser):
+    app = Redbox(
+        llm=GenericFakeChatModel(messages=iter(test_case.test_data.expected_llm_response)),
+        all_chunks_retriever=all_chunks_retriever(test_case.docs),
+        parameterised_retriever=parameterised_retriever(test_case.docs),
+        tokeniser=tokeniser,
+        env=env,
+        debug=LANGGRAPH_DEBUG,
+    )
+    response = await app.run(
+        input=ChainState(query=test_case.query),
+    )
+    final_state = ChainState(response)
+    assert (
+        final_state["response"] == test_case.test_data.expected_llm_response[-1]
+    ), f"Expected LLM response: '{test_case.test_data.expected_llm_response[-1]}'. Received '{final_state["response"]}'"
+    assert (
+        final_state["route_name"] == test_case.test_data.expected_route
+    ), f"Expected Route: '{ test_case.test_data.expected_route}'. Received '{final_state["route_name"]}'"
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(("test_case"), TEST_CASES, ids=[t.test_id for t in TEST_CASES])
+async def test_streaming(test_case: RedboxChatTestCase, env, tokeniser):
+    app = Redbox(
+        llm=GenericFakeChatModel(messages=iter(test_case.test_data.expected_llm_response)),
+        all_chunks_retriever=all_chunks_retriever(test_case.docs),
+        parameterised_retriever=parameterised_retriever(test_case.docs),
+        tokeniser=tokeniser,
+        env=env,
+        debug=LANGGRAPH_DEBUG,
+    )
+
+    token_events = []
+
+    async def streaming_response_handler(tokens: str):
+        token_events.append(tokens)
+
+    response = await app.run(
+        input=ChainState(query=test_case.query), response_tokens_callback=streaming_response_handler
+    )
+    final_state = ChainState(response)
+
+    # Bit of a bodge to retain the ability to check that the LLM streaming is working in most cases
+    if not final_state["route_name"].startswith("error"):
+        assert len(token_events) > 1, f"Expected tokens as a stream. Received: {token_events}"
+    llm_response = "".join(token_events)
+    assert (
+        final_state["response"] == llm_response
+    ), f"Expected LLM response: '{llm_response}'. Received '{final_state["response"]}'"
+    assert (
+        final_state["route_name"] == test_case.test_data.expected_route
+    ), f"Expected Route: '{ test_case.test_data.expected_route}'. Received '{final_state["route_name"]}'"
+
+
+def get_available_keywords():
+    app = Redbox(
+        llm=GenericFakeChatModel(messages=iter([])),
+        all_chunks_retriever=all_chunks_retriever([]),
+        parameterised_retriever=parameterised_retriever([]),
+        tokeniser=tokeniser,
+        env=env,
+        debug=LANGGRAPH_DEBUG,
+    )
+    keywords = {ChatRoute.search}
+
+    assert keywords == set(app.get_available_keywords().keys())
diff --git a/redbox-core/tests/retriever/data.py b/redbox-core/tests/retriever/data.py
index 6e331d1d1..010b08852 100644
--- a/redbox-core/tests/retriever/data.py
+++ b/redbox-core/tests/retriever/data.py
@@ -1,139 +1,30 @@
-from langchain_core.documents.base import Document
+from uuid import uuid4
 
-ALL_CHUNKS_RETRIEVER_DOCUMENTS = [
-    [
-        Document(
-            page_content="ABC",
-            metadata={
-                "parent_file_uuid": "abcd",
-                "creator_user_uuid": "xabcd",
-                "index": 1,
-                "page_number": 1,
-                "languages": ["en"],
-                "link_texts": [],
-                "link_urls": [],
-                "links": [],
-                "created_datetime": "2024-06-01T12:00:00Z",
-                "token_count": 12,
-                "chunk_resolution": "largest",
-            },
-        ),
-        Document(
-            page_content="DEF",
-            metadata={
-                "parent_file_uuid": "abcd",
-                "creator_user_uuid": "xabcd",
-                "index": 2,
-                "page_number": 1,
-                "languages": ["en"],
-                "link_texts": [],
-                "link_urls": [],
-                "links": [],
-                "created_datetime": "2024-06-01T12:00:00Z",
-                "token_count": 12,
-                "chunk_resolution": "largest",
-            },
-        ),
-        Document(
-            page_content="GHI",
-            metadata={
-                "parent_file_uuid": "abcd",
-                "creator_user_uuid": "xabcd",
-                "index": 3,
-                "page_number": 1,
-                "languages": ["en"],
-                "link_texts": [],
-                "link_urls": [],
-                "links": [],
-                "created_datetime": "2024-06-01T12:00:00Z",
-                "token_count": 12,
-                "chunk_resolution": "largest",
-            },
-        ),
-        Document(
-            page_content="JKL",
-            metadata={
-                "parent_file_uuid": "abcd",
-                "creator_user_uuid": "xabcd",
-                "index": 4,
-                "page_number": 1,
-                "languages": ["en"],
-                "link_texts": [],
-                "link_urls": [],
-                "links": [],
-                "created_datetime": "2024-06-01T12:00:00Z",
-                "token_count": 12,
-                "chunk_resolution": "largest",
-            },
-        ),
+
+from redbox.models.chain import ChainInput
+from redbox.models.file import ChunkResolution
+from redbox.test.data import TestData, generate_test_cases
+
+ALL_CHUNKS_RETRIEVER_CASES = [
+    test_case
+    for generator in [
+        generate_test_cases(
+            query=ChainInput(question="Irrelevant Question", file_uuids=[uuid4()], user_uuid=uuid4(), chat_history=[]),
+            test_data=[TestData(8, 8000)],
+            test_id="Successful Path",
+        )
     ]
+    for test_case in generator
 ]
 
-PARAMETERISED_RETRIEVER_DOCUMENTS = [
-    [
-        Document(
-            page_content="ABC",
-            metadata={
-                "parent_file_uuid": "abcd",
-                "creator_user_uuid": "xabcd",
-                "index": 1,
-                "page_number": 1,
-                "languages": ["en"],
-                "link_texts": [],
-                "link_urls": [],
-                "links": [],
-                "created_datetime": "2024-06-01T12:00:00Z",
-                "token_count": 12,
-                "chunk_resolution": "normal",
-            },
-        ),
-        Document(
-            page_content="DEF",
-            metadata={
-                "parent_file_uuid": "abcd",
-                "creator_user_uuid": "xabcd",
-                "index": 2,
-                "page_number": 1,
-                "languages": ["en"],
-                "link_texts": [],
-                "link_urls": [],
-                "links": [],
-                "created_datetime": "2024-06-01T12:00:00Z",
-                "token_count": 12,
-                "chunk_resolution": "normal",
-            },
-        ),
-        Document(
-            page_content="GHI",
-            metadata={
-                "parent_file_uuid": "abcd",
-                "creator_user_uuid": "xabcd",
-                "index": 3,
-                "page_number": 1,
-                "languages": ["en"],
-                "link_texts": [],
-                "link_urls": [],
-                "links": [],
-                "created_datetime": "2024-06-01T12:00:00Z",
-                "token_count": 12,
-                "chunk_resolution": "normal",
-            },
-        ),
-        Document(
-            page_content="JKL",
-            metadata={
-                "parent_file_uuid": "abcd",
-                "creator_user_uuid": "xabcd",
-                "index": 4,
-                "page_number": 1,
-                "languages": ["en"],
-                "link_texts": [],
-                "link_urls": [],
-                "links": [],
-                "created_datetime": "2024-06-01T12:00:00Z",
-                "token_count": 12,
-                "chunk_resolution": "normal",
-            },
-        ),
+PARAMETERISED_RETRIEVER_CASES = [
+    test_case
+    for generator in [
+        generate_test_cases(
+            query=ChainInput(question="Irrelevant Question", file_uuids=[uuid4()], user_uuid=uuid4(), chat_history=[]),
+            test_data=[TestData(8, 8000, ChunkResolution.normal)],
+            test_id="Successful Path",
+        )
     ]
+    for test_case in generator
 ]
diff --git a/redbox-core/tests/retriever/test_all_chunks.py b/redbox-core/tests/retriever/test_all_chunks.py
index febf4c275..12ecc8191 100644
--- a/redbox-core/tests/retriever/test_all_chunks.py
+++ b/redbox-core/tests/retriever/test_all_chunks.py
@@ -1,18 +1,13 @@
-from langchain_core.documents.base import Document
-
+from redbox.models.chain import ChainState
 from redbox.retriever import AllElasticsearchRetriever
+from redbox.test.data import RedboxChatTestCase
+
 
+def test_all_chunks_retriever(
+    all_chunks_retriever: AllElasticsearchRetriever, stored_file_all_chunks: RedboxChatTestCase
+):
+    result = all_chunks_retriever.invoke(ChainState(query=stored_file_all_chunks.query))
 
-def test_all_chunks_retriever(all_chunks_retriever: AllElasticsearchRetriever, stored_file_all_chunks: list[Document]):
-    result = all_chunks_retriever.invoke(
-        {
-            "question": "is this real?",
-            "file_uuids": [stored_file_all_chunks[0].metadata["parent_file_uuid"]],
-            "user_uuid": stored_file_all_chunks[0].metadata["creator_user_uuid"],
-        }
-    )
-    assert len(result) == len(stored_file_all_chunks)
-    assert {c.page_content for c in result} == {c.page_content for c in stored_file_all_chunks}
-    assert {c.metadata["parent_file_uuid"] for c in result} == {
-        str(c.metadata["parent_file_uuid"]) for c in stored_file_all_chunks
-    }
+    assert len(result) == len(stored_file_all_chunks.get_docs_matching_query())
+    assert {c.page_content for c in result} | {c.page_content for c in stored_file_all_chunks.docs}
+    assert {c.metadata["parent_file_uuid"] for c in result} == set(map(str, stored_file_all_chunks.query.file_uuids))
diff --git a/redbox-core/tests/retriever/test_parameterised.py b/redbox-core/tests/retriever/test_parameterised.py
index c5d7e4c11..ad6b5924f 100644
--- a/redbox-core/tests/retriever/test_parameterised.py
+++ b/redbox-core/tests/retriever/test_parameterised.py
@@ -1,7 +1,8 @@
 import pytest
-from langchain_core.documents.base import Document
 
+from redbox.models.chain import ChainState
 from redbox.retriever import ParameterisedElasticsearchRetriever
+from redbox.test.data import RedboxChatTestCase
 
 test_chain_parameters = (
     {
@@ -9,14 +10,14 @@
         "num_candidates": 100,
         "match_boost": 1,
         "knn_boost": 2,
-        "similarity_threshold": 0.7,
+        "similarity_threshold": 0,
     },
     {
         "size": 2,
         "num_candidates": 100,
         "match_boost": 1,
         "knn_boost": 2,
-        "similarity_threshold": 0.7,
+        "similarity_threshold": 0,
     },
 )
 
@@ -25,13 +26,9 @@
 def test_parameterised_retriever(
     chain_params,
     parameterised_retriever: ParameterisedElasticsearchRetriever,
-    stored_file_parameterised: list[Document],
+    stored_file_parameterised: RedboxChatTestCase,
 ):
     result = parameterised_retriever.with_config(configurable={"params": chain_params}).invoke(
-        {
-            "question": "is this real?",
-            "file_uuids": [stored_file_parameterised[0].metadata["parent_file_uuid"]],
-            "user_uuid": stored_file_parameterised[0].metadata["creator_user_uuid"],
-        }
+        ChainState(query=stored_file_parameterised.query)
     )
     assert len(result) == chain_params["size"], result
diff --git a/core-api/tests/test_ai.py b/redbox-core/tests/test_ai.py
similarity index 95%
rename from core-api/tests/test_ai.py
rename to redbox-core/tests/test_ai.py
index f759e8ada..7cd93c97a 100644
--- a/core-api/tests/test_ai.py
+++ b/redbox-core/tests/test_ai.py
@@ -22,9 +22,9 @@
 from elasticsearch.helpers import bulk, scan
 from pydantic import BaseModel, Field
 
-from core_api.build_chains import build_retrieval_chain
-from core_api.dependencies import get_llm, get_parameterised_retriever, get_tokeniser
-from redbox.models.chain import ChainInput
+from redbox.chains.components import get_chat_llm, get_parameterised_retriever, get_tokeniser
+from redbox.graph.search import get_search_graph
+from redbox.models.chain import ChainInput, ChainState
 
 if TYPE_CHECKING:
     from collections.abc import Callable, Generator
@@ -93,7 +93,7 @@ def ai_experiment_data() -> ExperimentData:
 
 @pytest.fixture(scope="session")
 def llm(env: Settings) -> ChatLiteLLM:
-    return get_llm(env)
+    return get_chat_llm(env)
 
 
 @pytest.fixture(scope="session")
@@ -124,7 +124,7 @@ async def a_generate(self, prompt: str) -> str:
         def get_model_name(self):
             return "Custom LiteLLM Model"
 
-    return ChatLiteLLMDeepEval(model=get_llm(env))
+    return ChatLiteLLMDeepEval(model=get_chat_llm(env))
 
 
 @pytest.fixture(scope="session")
@@ -188,7 +188,7 @@ def make_test_case(
 
     retriever = get_parameterised_retriever(env=env)
 
-    rag_chain = build_retrieval_chain(
+    rag_chain = get_search_graph(
         llm=llm,
         retriever=retriever,
         tokeniser=get_tokeniser(),
@@ -201,13 +201,16 @@ def _make_test_case(
         for case in experiment.test_cases:
             if case.input == prompt:
                 return case
-        chain_input = ChainInput(
-            question=prompt,
-            file_uuids=[],
-            user_uuid=str(user_uuid),
-            chat_history=[],
+        answer: ChainState = rag_chain.invoke(
+            input=ChainState(
+                query=ChainInput(
+                    question=prompt,
+                    file_uuids=[],
+                    user_uuid=str(user_uuid),
+                    chat_history=[],
+                )
+            )
         )
-        answer = rag_chain.invoke(input=chain_input.model_dump(mode="json"))
 
         return LLMTestCase(
             input=prompt,
diff --git a/worker/poetry.lock b/worker/poetry.lock
index a24813458..58ea16baa 100644
--- a/worker/poetry.lock
+++ b/worker/poetry.lock
@@ -653,13 +653,13 @@ crt = ["awscrt (==0.20.11)"]
 
 [[package]]
 name = "botocore-stubs"
-version = "1.34.145"
+version = "1.34.150"
 description = "Type annotations and code completion for botocore"
 optional = false
 python-versions = "<4.0,>=3.8"
 files = [
-    {file = "botocore_stubs-1.34.145-py3-none-any.whl", hash = "sha256:4c579734492713c142773c7405cf62b466dfaf5f05371b7510d3cd308b0ba6a5"},
-    {file = "botocore_stubs-1.34.145.tar.gz", hash = "sha256:69bcfc0c81eee294e681dc8049b7ca2a47a8f0510b88c05710f07dba6d0785c7"},
+    {file = "botocore_stubs-1.34.150-py3-none-any.whl", hash = "sha256:5f3d9eca8a7173e450c903ee28b934a7152ab5c2da2bfe71f72f4a9e89ef4923"},
+    {file = "botocore_stubs-1.34.150.tar.gz", hash = "sha256:e2308ec1f983f7c93fc0e42c1b60f91c75746bcb564a9cb8240c742fd7d6483a"},
 ]
 
 [package.dependencies]
@@ -1246,13 +1246,13 @@ files = [
 
 [[package]]
 name = "fast-depends"
-version = "2.4.6"
+version = "2.4.7"
 description = "FastDepends - extracted and cleared from HTTP domain logic FastAPI Dependency Injection System. Async and sync are both supported."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "fast_depends-2.4.6-py3-none-any.whl", hash = "sha256:50d0ba61450920173c498c08c01ec22fa22aa62c4e82bf409df9f5b9f677a31c"},
-    {file = "fast_depends-2.4.6.tar.gz", hash = "sha256:724a5068c3f955e02121a953428349ef34c8a2fadd65f073e94c94d54cec4554"},
+    {file = "fast_depends-2.4.7-py3-none-any.whl", hash = "sha256:ae7c138673639cfe00836b60107395c910cc1595036074c85c2505177f7a24fc"},
+    {file = "fast_depends-2.4.7.tar.gz", hash = "sha256:44464a7807131df92aeb12ad511a027ef385e000f4ec15a427ae4ea458a112ce"},
 ]
 
 [package.dependencies]
@@ -1803,13 +1803,13 @@ socks = ["socksio (==1.*)"]
 
 [[package]]
 name = "huggingface-hub"
-version = "0.24.0"
+version = "0.24.3"
 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 = [
-    {file = "huggingface_hub-0.24.0-py3-none-any.whl", hash = "sha256:7ad92edefb93d8145c061f6df8d99df2ff85f8379ba5fac8a95aca0642afa5d7"},
-    {file = "huggingface_hub-0.24.0.tar.gz", hash = "sha256:6c7092736b577d89d57b3cdfea026f1b0dc2234ae783fa0d59caf1bf7d52dfa7"},
+    {file = "huggingface_hub-0.24.3-py3-none-any.whl", hash = "sha256:69ecce486dd6cdad69937ba76779e893c224a670a9d947636c1d5cbd049e44d8"},
+    {file = "huggingface_hub-0.24.3.tar.gz", hash = "sha256:bfdc05cc9b64a0e24e8614a44222698799183268f6b68be209aa2df70cff2cde"},
 ]
 
 [package.dependencies]
@@ -2202,13 +2202,13 @@ six = "*"
 
 [[package]]
 name = "langsmith"
-version = "0.1.93"
+version = "0.1.94"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langsmith-0.1.93-py3-none-any.whl", hash = "sha256:811210b9d5f108f36431bd7b997eb9476a9ecf5a2abd7ddbb606c1cdcf0f43ce"},
-    {file = "langsmith-0.1.93.tar.gz", hash = "sha256:285b6ad3a54f50fa8eb97b5f600acc57d0e37e139dd8cf2111a117d0435ba9b4"},
+    {file = "langsmith-0.1.94-py3-none-any.whl", hash = "sha256:0d01212086d58699f75814117b026784218042f7859877ce08a248a98d84aa8d"},
+    {file = "langsmith-0.1.94.tar.gz", hash = "sha256:e44afcdc9eee6f238f6a87a02bba83111bd5fad376d881ae299834e06d39d712"},
 ]
 
 [package.dependencies]
@@ -2772,24 +2772,24 @@ files = [
 
 [[package]]
 name = "mypy-boto3-dynamodb"
-version = "1.34.131"
-description = "Type annotations for boto3.DynamoDB 1.34.131 service generated with mypy-boto3-builder 7.24.0"
+version = "1.34.148"
+description = "Type annotations for boto3.DynamoDB 1.34.148 service generated with mypy-boto3-builder 7.25.0"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "mypy_boto3_dynamodb-1.34.131-py3-none-any.whl", hash = "sha256:62e4fd85c621561f145828de14752a51380182d492cb039043d7f46bef872c34"},
-    {file = "mypy_boto3_dynamodb-1.34.131.tar.gz", hash = "sha256:d23c857568ae7c8c8fc1fbd96709a1dd00c140f917d74e09423fd44677097abf"},
+    {file = "mypy_boto3_dynamodb-1.34.148-py3-none-any.whl", hash = "sha256:f1a7aabff5c6e926b9b272df87251c9d6dfceb4c1fb159fb5a2df52062cd7e87"},
+    {file = "mypy_boto3_dynamodb-1.34.148.tar.gz", hash = "sha256:c85489b92cbbbe4f6997070372022df914d4cb8eb707fdc73aa18ce6ba25c578"},
 ]
 
 [[package]]
 name = "mypy-boto3-ec2"
-version = "1.34.145"
-description = "Type annotations for boto3.EC2 1.34.145 service generated with mypy-boto3-builder 7.25.0"
+version = "1.34.149"
+description = "Type annotations for boto3.EC2 1.34.149 service generated with mypy-boto3-builder 7.25.0"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "mypy_boto3_ec2-1.34.145-py3-none-any.whl", hash = "sha256:d6a200a341e65b584cc9af9861e8a331dc85d19a5316d9d0637d192824ec9375"},
-    {file = "mypy_boto3_ec2-1.34.145.tar.gz", hash = "sha256:f676e9382ca2ad1e419804a2759779018d7f0006ac86bd602479855073fafc79"},
+    {file = "mypy_boto3_ec2-1.34.149-py3-none-any.whl", hash = "sha256:9f5b13d7ffcb7fb1d57688a728bcefbeb12ab74f2047ee5a4577b8064f7a3fe3"},
+    {file = "mypy_boto3_ec2-1.34.149.tar.gz", hash = "sha256:034f8d160535f5cf77fe35c95588454790cd2709f656274604bc36f76406211f"},
 ]
 
 [[package]]
@@ -3072,7 +3072,6 @@ description = "Nvidia JIT LTO Library"
 optional = false
 python-versions = ">=3"
 files = [
-    {file = "nvidia_nvjitlink_cu12-12.5.82-py3-none-manylinux2014_aarch64.whl", hash = "sha256:98103729cc5226e13ca319a10bbf9433bbbd44ef64fe72f45f067cacc14b8d27"},
     {file = "nvidia_nvjitlink_cu12-12.5.82-py3-none-manylinux2014_x86_64.whl", hash = "sha256:f9b37bc5c8cf7509665cb6ada5aaa0ce65618f2332b7d3e78e9790511f111212"},
     {file = "nvidia_nvjitlink_cu12-12.5.82-py3-none-win_amd64.whl", hash = "sha256:e782564d705ff0bf61ac3e1bf730166da66dd2fe9012f111ede5fc49b64ae697"},
 ]
@@ -3203,13 +3202,13 @@ sympy = "*"
 
 [[package]]
 name = "openai"
-version = "1.36.1"
+version = "1.37.1"
 description = "The official Python library for the openai API"
 optional = false
 python-versions = ">=3.7.1"
 files = [
-    {file = "openai-1.36.1-py3-none-any.whl", hash = "sha256:d399b9d476dbbc167aceaac6bc6ed0b2e2bb6c9e189c7f7047f822743ae62e64"},
-    {file = "openai-1.36.1.tar.gz", hash = "sha256:41be9e0302e95dba8a9374b885c5cb1cec2202816a70b98736fee25a2cadd1f2"},
+    {file = "openai-1.37.1-py3-none-any.whl", hash = "sha256:9a6adda0d6ae8fce02d235c5671c399cfa40d6a281b3628914c7ebf244888ee3"},
+    {file = "openai-1.37.1.tar.gz", hash = "sha256:faf87206785a6b5d9e34555d6a3242482a6852bc802e453e2a891f68ee04ce55"},
 ]
 
 [package.dependencies]
@@ -3991,13 +3990,13 @@ typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"
 
 [[package]]
 name = "pydantic-settings"
-version = "2.3.4"
+version = "2.4.0"
 description = "Settings management using Pydantic"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "pydantic_settings-2.3.4-py3-none-any.whl", hash = "sha256:11ad8bacb68a045f00e4f862c7a718c8a9ec766aa8fd4c32e39a0594b207b53a"},
-    {file = "pydantic_settings-2.3.4.tar.gz", hash = "sha256:c5802e3d62b78e82522319bbc9b8f8ffb28ad1c988a99311d04f2a6051fca0a7"},
+    {file = "pydantic_settings-2.4.0-py3-none-any.whl", hash = "sha256:bb6849dc067f1687574c12a639e231f3a6feeed0a12d710c1382045c5db1c315"},
+    {file = "pydantic_settings-2.4.0.tar.gz", hash = "sha256:ed81c3a0f46392b4d7c0a565c05884e6e54b3456e6f0fe4d8814981172dc9a88"},
 ]
 
 [package.dependencies]
@@ -4005,6 +4004,7 @@ pydantic = ">=2.7.0"
 python-dotenv = ">=0.21.0"
 
 [package.extras]
+azure-key-vault = ["azure-identity (>=1.16.0)", "azure-keyvault-secrets (>=4.8.0)"]
 toml = ["tomli (>=2.0.1)"]
 yaml = ["pyyaml (>=6.0.1)"]
 
@@ -4394,104 +4394,104 @@ files = [
 
 [[package]]
 name = "rapidfuzz"
-version = "3.9.4"
+version = "3.9.5"
 description = "rapid fuzzy string matching"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "rapidfuzz-3.9.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c9b9793c19bdf38656c8eaefbcf4549d798572dadd70581379e666035c9df781"},
-    {file = "rapidfuzz-3.9.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:015b5080b999404fe06ec2cb4f40b0be62f0710c926ab41e82dfbc28e80675b4"},
-    {file = "rapidfuzz-3.9.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:acc5ceca9c1e1663f3e6c23fb89a311f69b7615a40ddd7645e3435bf3082688a"},
-    {file = "rapidfuzz-3.9.4-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1424e238bc3f20e1759db1e0afb48a988a9ece183724bef91ea2a291c0b92a95"},
-    {file = "rapidfuzz-3.9.4-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:ed01378f605aa1f449bee82cd9c83772883120d6483e90aa6c5a4ce95dc5c3aa"},
-    {file = "rapidfuzz-3.9.4-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:eb26d412271e5a76cdee1c2d6bf9881310665d3fe43b882d0ed24edfcb891a84"},
-    {file = "rapidfuzz-3.9.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8f37e9e1f17be193c41a31c864ad4cd3ebd2b40780db11cd5c04abf2bcf4201b"},
-    {file = "rapidfuzz-3.9.4-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:d070ec5cf96b927c4dc5133c598c7ff6db3b833b363b2919b13417f1002560bc"},
-    {file = "rapidfuzz-3.9.4-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:10e61bb7bc807968cef09a0e32ce253711a2d450a4dce7841d21d45330ffdb24"},
-    {file = "rapidfuzz-3.9.4-cp310-cp310-musllinux_1_2_ppc64le.whl", hash = "sha256:31a2fc60bb2c7face4140010a7aeeafed18b4f9cdfa495cc644a68a8c60d1ff7"},
-    {file = "rapidfuzz-3.9.4-cp310-cp310-musllinux_1_2_s390x.whl", hash = "sha256:fbebf1791a71a2e89f5c12b78abddc018354d5859e305ec3372fdae14f80a826"},
-    {file = "rapidfuzz-3.9.4-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:aee9fc9e3bb488d040afc590c0a7904597bf4ccd50d1491c3f4a5e7e67e6cd2c"},
-    {file = "rapidfuzz-3.9.4-cp310-cp310-win32.whl", hash = "sha256:005a02688a51c7d2451a2d41c79d737aa326ff54167211b78a383fc2aace2c2c"},
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 ]
 
 [package.extras]
@@ -4507,19 +4507,16 @@ files = []
 develop = false
 
 [package.dependencies]
-boto3 = "^1.34.136"
+boto3 = "^1.34.150"
 elasticsearch = "^8.14.0"
 kneed = "^0.8.5"
-langchain = "^0.2.6"
+langchain = "^0.2.11"
 langchain-elasticsearch = "^0.2.2"
-langchain_openai = "^0.1.9"
+langchain_openai = "^0.1.19"
 pydantic = "^2.7.1"
 pydantic-settings = "^2.3.4"
 pytest-dotenv = "^0.5.2"
 tiktoken = "^0.7.0"
-torch = "2.2.2"
-unstructured = {version = "<0.14.9", extras = ["all-docs"]}
-unstructured-inference = "^0.7.36"
 
 [package.source]
 type = "git"
@@ -4530,105 +4527,105 @@ subdirectory = "redbox-core"
 
 [[package]]
 name = "redis"
-version = "5.0.7"
+version = "5.0.8"
 description = "Python client for Redis database and key-value store"
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 python-versions = ">=3.7"
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 ]
 
 [package.extras]
-hiredis = ["hiredis (>=1.0.0)"]
+hiredis = ["hiredis (>1.0.0)"]
 ocsp = ["cryptography (>=36.0.1)", "pyopenssl (==20.0.1)", "requests (>=2.26.0)"]
 
 [[package]]
 name = "regex"
-version = "2024.5.15"
+version = "2024.7.24"
 description = "Alternative regular expression module, to replace re."
 optional = false
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 [[package]]
@@ -5244,13 +5241,13 @@ blobfile = ["blobfile (>=2)"]
 
 [[package]]
 name = "timm"
-version = "1.0.7"
+version = "1.0.8"
 description = "PyTorch Image Models"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
@@ -5498,19 +5495,19 @@ telegram = ["requests"]
 
 [[package]]
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 description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
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 python-versions = ">=3.8.0"
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 [package.dependencies]
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 huggingface-hub = ">=0.23.2,<1.0"
-numpy = ">=1.17,<2.0"
+numpy = ">=1.17"
 packaging = ">=20.0"
 pyyaml = ">=5.1"
 regex = "!=2019.12.17"
@@ -5522,14 +5519,14 @@ tqdm = ">=4.27"
 [package.extras]
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-dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.4.4)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.19,<0.20)", "urllib3 (<2.0.0)"]
+dev = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "decord (==0.6.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "librosa", "nltk", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.4.4)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "scipy (<1.13.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "timm (<=0.9.16)", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
+dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "librosa", "nltk", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.4.4)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.19,<0.20)", "urllib3 (<2.0.0)"]
 dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.4.4)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm (<=0.9.16)", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
 flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)", "scipy (<1.13.0)"]
 flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
@@ -5552,15 +5549,15 @@ sigopt = ["sigopt"]
 sklearn = ["scikit-learn"]
 speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
 testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk", "parameterized", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.4.4)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
-tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
-tf-cpu = ["keras (>2.9,<2.16)", "keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>2.9,<2.16)", "tensorflow-probability (<0.24)", "tensorflow-text (<2.16)", "tf2onnx"]
+tf = ["keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
+tf-cpu = ["keras (>2.9,<2.16)", "keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow-cpu (>2.9,<2.16)", "tensorflow-probability (<0.24)", "tensorflow-text (<2.16)", "tf2onnx"]
 tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
 timm = ["timm (<=0.9.16)"]
 tokenizers = ["tokenizers (>=0.19,<0.20)"]
 torch = ["accelerate (>=0.21.0)", "torch"]
 torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
 torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
-torchhub = ["filelock", "huggingface-hub (>=0.23.2,<1.0)", "importlib-metadata", "numpy (>=1.17,<2.0)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.19,<0.20)", "torch", "tqdm (>=4.27)"]
+torchhub = ["filelock", "huggingface-hub (>=0.23.2,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.19,<0.20)", "torch", "tqdm (>=4.27)"]
 video = ["av (==9.2.0)", "decord (==0.6.0)"]
 vision = ["Pillow (>=10.0.1,<=15.0)"]
 
@@ -5759,13 +5756,13 @@ xlsx = ["networkx", "openpyxl", "pandas", "xlrd"]
 
 [[package]]
 name = "unstructured-client"
-version = "0.24.1"
+version = "0.25.0"
 description = "Python Client SDK for Unstructured API"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "unstructured-client-0.24.1.tar.gz", hash = "sha256:1bd82a532497783dd77b30ed4e56837d6abfae8cc6d61442acac0bcacbd568c8"},
-    {file = "unstructured_client-0.24.1-py3-none-any.whl", hash = "sha256:044dab0c3079f908f6adf7088ad44f0e17476b47e2b04e0de608134a482bd0e3"},
+    {file = "unstructured-client-0.25.0.tar.gz", hash = "sha256:01d716b1423f6d2829a7121e1ff1f27ebcc77657615f6d13e6d0fb98e52a988c"},
+    {file = "unstructured_client-0.25.0-py3-none-any.whl", hash = "sha256:04263164d5cc041dff2979e36d2fe216e0a1e95c465f9c9b29ae605091e924a0"},
 ]
 
 [package.dependencies]