diff --git a/.env.django b/.env.django index 46a1157d8..0957eb86d 100644 --- a/.env.django +++ b/.env.django @@ -2,6 +2,7 @@ ANTHROPIC_API_KEY= OPENAI_API_KEY= +OPENAI_MODEL= LLM_MAX_TOKENS=1024 # === Development === diff --git a/.env.docker_build b/.env.docker_build new file mode 100644 index 000000000..f5e31737c --- /dev/null +++ b/.env.docker_build @@ -0,0 +1 @@ +EMBEDDING_MODEL=all-mpnet-base-v2 \ No newline at end of file diff --git a/.env.uktrade b/.env.uktrade new file mode 100644 index 000000000..b18e2ae8e --- /dev/null +++ b/.env.uktrade @@ -0,0 +1,80 @@ +ANALYTICS_LINK=REPLACE_WITH_GITLAB_SECRET +ANALYTICS_TAG=REPLACE_WITH_GITLAB_SECRET + +AUTHBROKER_CLIENT_ID=REPLACE_WITH_GITLAB_SECRET +AUTHBROKER_CLIENT_SECRET=REPLACE_WITH_GITLAB_SECRET +AUTHBROKER_URL=https://sso.trade.gov.uk + +AWS_ACCESS_KEY=GET_FROM_AWS_ACCESS_PORTAL +AWS_SECRET_KEY=GET_FROM_AWS_ACCESS_PORTAL + +AWS_REGION=eu-west-2 + +BUCKET_NAME=dbt-default + +CHAT_BACKEND=openai + +COMPRESSION_ENABLED=false + +CONTACT_EMAIL=redbox@businessandtrade.gov.uk + +CORE_API_PORT=5002 +EMAIL_BACKEND_TYPE=CONSOLE +FROM_EMAIL=REPLACE_ME + +DEV_MODE=false + +DJANGO_SETTINGS_MODULE=redbox_app.settings +DEBUG=false +DJANGO_SECRET_KEY=REPLACE_WITH_GITLAB_SECRET +DJANGO_LOG_LEVEL=DEBUG + +ELASTIC__HOST=elasticsearch.internal +ELASTIC__VERSION=8.11.0 +ELASTIC__USER=elastic +ELASTIC__PASSWORD=REPLACE_WITH_GITLAB_SECRET +ELASTIC__PORT=9200 +ELASTIC__SCHEME=http + +EMBEDDING_MODEL=all-mpnet-base-v2 +EMBEDDING_BACKEND=sentencetransformers +EMBEDDING_OPENAI_MODEL=all-mpnet-base-v2 + +EMBED_QUEUE_NAME=redbox-embedder-queue + +ENVIRONMENT=DEV + +FILE_EXPIRY_IN_DAYS=30 + +INGEST_QUEUE_NAME=redbox-ingester-queue + +LLM_MAX_TOKENS=4096 + +LOGIN_METHOD=sso + +OBJECT_STORE=s3 + +OPENAI_API_KEY=REPLACE_WITH_GITLAB_SECRET +OPENAI_MODEL=gpt-4o-mini + +POSTGRES_DB=redbox +POSTGRES_PASSWORD=REPLACE_WITH_GITLAB_SECRET +POSTGRES_USER=postgres + +REDIS_PORT=6379 + +USE_STREAMING=false + +MAX_SECURITY_CLASSIFICATION=OFFICIAL + +SESSION_COOKIE_SECURE=false + +SUPERUSER_EMAIL=isobel.daley@digital.trade.gov.uk + +REPO_OWNER=uktrade + +WORKER_INGEST_MAX_CHUNK_SIZE=600 +WORKER_INGEST_MIN_CHUNK_SIZE=400 + +ANALYTICS_TAG=REPLACE_WITH_GITLAB_SECRET +ANALYTICS_LINK=REPLACE_WITH_GITLAB_SECRET \ No newline at end of file diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md index 47f72b836..74f1ea66e 100644 --- a/.github/PULL_REQUEST_TEMPLATE.md +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -18,3 +18,4 @@ - [ ] I have added any new ENV vars in all deployed environments - [ ] I have tested any code added or changed - [ ] I have run integration tests +- [ ] I have added/updated any DBT specific changes to .env.uktrade diff --git a/.github/workflows/pull-upstream-release.yml b/.github/workflows/pull-upstream-release.yml new file mode 100644 index 000000000..3f96341f9 --- /dev/null +++ b/.github/workflows/pull-upstream-release.yml @@ -0,0 +1,37 @@ +name: Detect upstream release + +on: + schedule: + - cron: '0 8 * * *' # run every day at 8am + workflow_dispatch: + +jobs: + check-upstream-release: + runs-on: ubuntu-latest + steps: + - name: Checkout repository + uses: actions/checkout@v2 + + - name: Fetch upstream release_version + run: | + git remote add upstream https://github.com/i-dot-ai/redbox.git + git fetch upstream --tags + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + + - name: Check for new release tag + id: check_tag + run: | + latest_tag=$(git describe --tags `git rev-list --tags --max-count=1`) + echo "Latest tag: $latest_tag" + echo "latest_tag=$latest_tag" >> $GITHUB_ENV + + - name: Create new branch if new release found + if: github.event_name == 'schedule' + run: | + branch_name=upstream/release-${{ env.latest_tag }} + echo branch_name: $branch_name + git checkout -b $branch_name + git push origin $branch_name + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} \ No newline at end of file diff --git a/.github/workflows/python-app.yml b/.github/workflows/python-app.yml index d47bf3aeb..788170ff0 100644 --- a/.github/workflows/python-app.yml +++ b/.github/workflows/python-app.yml @@ -83,7 +83,7 @@ jobs: cp .env.test worker/.env cd worker poetry install - poetry run python -m nltk.downloader punkt + poetry run python -m nltk.downloader punkt_tab poetry run pytest --cov=worker -v --cov-report=term-missing --cov-fail-under=80 test-redbox: diff --git a/.gitignore b/.gitignore index 3faf21b3d..6cef7ecb7 100644 --- a/.gitignore +++ b/.gitignore @@ -219,3 +219,7 @@ django_app/tests/staticfiles infrastructure/aws/cleanup_lambda/*.zip infrastructure/aws/cleanup_lambda/layer/* + +# chat history files and personal data +*chat_history_* +notebooks/evaluation/results \ No newline at end of file diff --git a/Makefile b/Makefile index 11de700f0..191810185 100644 --- a/Makefile +++ b/Makefile @@ -33,7 +33,8 @@ rebuild: stop prune ## Rebuild all images docker compose build --no-cache .PHONY: test-core-api -test-core-api: ## Test core-api +test-core-api: stop ## Test core-api + docker compose up -d --wait elasticsearch minio cp .env.test core-api/.env cd core-api && poetry install --with dev && poetry run python -m pytest -m "not ai" --cov=core_api -v --cov-report=term-missing --cov-fail-under=75 @@ -124,15 +125,17 @@ docs-build: ## Build documentation poetry run mkdocs build # Docker +AWS_ACCOUNT_ID=REPLACE_ME + AWS_REGION=eu-west-2 -APP_NAME=redbox +APP_NAME=dbt ECR_URL=$(AWS_ACCOUNT_ID).dkr.ecr.$(AWS_REGION).amazonaws.com ECR_REPO_URL=$(ECR_URL)/$(ECR_REPO_NAME) -IMAGE=$(ECR_REPO_URL):$(IMAGE_TAG) +IMAGE=latest ECR_REPO_NAME=$(APP_NAME) -PREV_IMAGE_TAG=$$(git rev-parse HEAD~1) -IMAGE_TAG=$$(git rev-parse HEAD) +PREV_IMAGE_TAG=latest +IMAGE_TAG=latest tf_build_args=-var "image_tag=$(IMAGE_TAG)" DOCKER_SERVICES=$$(docker compose config --services | grep -v mlflow) @@ -169,7 +172,7 @@ docker_build: ## Build the docker container PREV_IMAGE="$(ECR_REPO_URL)-$$service:$(PREV_IMAGE_TAG)"; \ echo "Pulling previous image: $$PREV_IMAGE"; \ docker pull $$PREV_IMAGE; \ - docker compose build $$service; \ + DOCKER_DEFAULT_PLATFORM=linux/amd64 docker compose build $$service; \ else \ echo "Skipping $$service uses default image"; \ fi; \ diff --git a/README.md b/README.md index 6766d13f9..8a2d6faf4 100644 --- a/README.md +++ b/README.md @@ -13,7 +13,6 @@ Redbox is a retrieval augmented generation (RAG) app that uses GenAI to chat wit - **Better retrieval**. Redbox increases organisational memory by indexing documents - **Faster, accurate summarisation**. Redbox can summarise reports read months ago, supplement them with current work, and produce a first draft that lets civil servants focus on what they do best - https://github.com/i-dot-ai/redbox-copilot/assets/8233643/e7984242-1403-4c93-9e68-03b3f065b38d # Setup @@ -90,7 +89,7 @@ redbox/ # Contributing -We welcome contributions to this project. Please see the [CONTRIBUTING.md](./CONTRIBUTING.md) file for more information. +We welcome contributions to this project. Please see the [CONTRIBUTING.md](./docs/contributing.md) file for more information. # License @@ -184,9 +183,9 @@ On initial app setup you will need to run `poetry run python manage.py collectst checkout the `main` branch of the following repos: -* https://github.com/i-dot-ai/redbox -* https://github.com/i-dot-ai/i-ai-core-infrastructure/ -* https://github.com/i-dot-ai/redbox-copilot-infra-config +- https://github.com/i-dot-ai/redbox +- https://github.com/i-dot-ai/i-ai-core-infrastructure/ +- https://github.com/i-dot-ai/redbox-copilot-infra-config If, and only if, you want to deploy something other than HEAD then replace `var.image_tag` in `infrastructure/aws/ecs.tf` with the hash of the build you want deployed. @@ -197,3 +196,13 @@ cd redbox make tf_init make tf_apply env= ``` + +Local set up for DBT + +``` +cp .env.uktrade .env +docker compose build +docker compose up -d +docker compose exec django-app bash +venv/bin/django-admin createsuperuser +``` diff --git a/core-api/Dockerfile b/core-api/Dockerfile index f4cffc60e..38590e51d 100644 --- a/core-api/Dockerfile +++ b/core-api/Dockerfile @@ -1,4 +1,4 @@ -FROM python:3.12-bookworm as builder +FROM python:3.12-bookworm AS builder RUN apt-get update && apt-get install --yes build-essential curl > /dev/null @@ -6,12 +6,15 @@ RUN apt-get update && apt-get install --yes build-essential curl > /dev/null ADD redbox-core/ /app/redbox-core ADD core-api/ /app/core-api ADD README.md /app/README.md +ADD download_embedder.py /app/ WORKDIR /app/core-api RUN pip install poetry RUN poetry install +RUN type=cache poetry run python ../download_embedder.py --embedding_model "all-mpnet-base-v2" + # Run FastAPI EXPOSE 5002 CMD ["poetry", "run", "uvicorn", "core_api.app:app", "--host", "0.0.0.0", "--port", "5002"] diff --git a/core-api/core_api/build_chains.py b/core-api/core_api/build_chains.py index f12b19af3..0f16fd5f3 100644 --- a/core-api/core_api/build_chains.py +++ b/core-api/core_api/build_chains.py @@ -3,22 +3,31 @@ 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 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.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 +from tiktoken import Encoding + +from core_api import dependencies # === Logging === @@ -46,9 +55,32 @@ def build_chat_chain( ) +def build_coach_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.coach_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.coach.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)], + 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: @@ -60,7 +92,9 @@ 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"]) + formatted_map_question_prompt = prompt_template.format( + question=input_dict["question"] + ) map_prompt = ChatPromptTemplate.from_messages( [ @@ -85,7 +119,9 @@ 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) + formatted_documents=( + RunnablePassthrough() | itemgetter("documents") | format_documents + ) ) | { "response": make_chat_prompt_from_messages_runnable( system_prompt=env.ai.chat_with_docs_system_prompt, @@ -102,37 +138,51 @@ def chat_with_docs_route(input_dict: dict): return ( map_operation | RunnablePassthrough.assign( - formatted_documents=(RunnablePassthrough() | itemgetter("documents") | format_documents) + 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, + 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), + "route_name": RunnableLambda( + lambda _: ChatRoute.chat_with_docs.value + ), } ) else: raise NoDocumentSelected - return RunnablePassthrough.assign(documents=make_document_context()) | chat_with_docs_route + 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)], + 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) + formatted_documents=( + RunnablePassthrough() | itemgetter("documents") | format_documents + ) ) | { "response": make_chat_prompt_from_messages_runnable( @@ -151,7 +201,9 @@ def build_retrieval_chain( def build_condense_retrieval_chain( llm: Annotated[ChatLiteLLM, Depends(dependencies.get_llm)], - retriever: Annotated[VectorStoreRetriever, Depends(dependencies.get_parameterised_retriever)], + retriever: Annotated[ + VectorStoreRetriever, Depends(dependencies.get_parameterised_retriever) + ], tokeniser: Annotated[Encoding, Depends(dependencies.get_tokeniser)], env: Annotated[Settings, Depends(dependencies.get_env)], ) -> Runnable: @@ -172,9 +224,13 @@ def route(input_dict: dict): 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) + 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( @@ -193,7 +249,9 @@ def route(input_dict: dict): def build_summary_chain( llm: Annotated[ChatLiteLLM, Depends(dependencies.get_llm)], - all_chunks_retriever: Annotated[BaseRetriever, Depends(dependencies.get_all_chunks_retriever)], + 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: @@ -224,7 +282,9 @@ 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"]) + formatted_map_question_prompt = prompt_template.format( + question=input_dict["question"] + ) map_prompt = ChatPromptTemplate.from_messages( [ @@ -255,7 +315,9 @@ def map_operation(input_dict): | llm | { "response": StrOutputParser(), - "route_name": RunnableLambda(lambda _: ChatRoute.map_reduce_summarise.value), + "route_name": RunnableLambda( + lambda _: ChatRoute.map_reduce_summarise.value + ), } ) @@ -270,12 +332,18 @@ def summarisation_route(input_dict): else: raise NoDocumentSelected - return RunnablePassthrough.assign(documents=make_document_context()) | summarisation_route + 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)), + 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..86d5b0d53 100644 --- a/core-api/core_api/dependencies.py +++ b/core-api/core_api/dependencies.py @@ -9,11 +9,14 @@ from langchain_core.embeddings import Embeddings from langchain_core.retrievers import BaseRetriever from langchain_core.runnables import ConfigurableField - from redbox.api.callbacks import LoggerCallbackHandler -from redbox.retriever import AllElasticsearchRetriever, ParameterisedElasticsearchRetriever -from redbox.models import Settings from redbox.embeddings import get_embeddings +from redbox.models import Settings +from redbox.retriever import ( + AllElasticsearchRetriever, + ParameterisedElasticsearchRetriever, +) +from redbox.storage import ElasticsearchStorageHandler logging.basicConfig(level=logging.INFO) log = logging.getLogger() @@ -54,7 +57,9 @@ def get_parameterised_retriever( 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." + id="params", + name="Retriever parameters", + description="A dictionary of parameters to use for the retriever.", ) ) @@ -78,6 +83,8 @@ def get_llm(env: Annotated[Settings, Depends(get_env)]) -> ChatLiteLLM: streaming=True, openai_key=env.openai_api_key, callbacks=[logger_callback], + model=env.openai_model, + max_tokens=env.llm_max_tokens, ) elif env.chat_backend == "azure": log.info("Creating Azure LLM Client") diff --git a/core-api/core_api/routes/chat.py b/core-api/core_api/routes/chat.py index 426c9c7d2..dd3eaff97 100644 --- a/core-api/core_api/routes/chat.py +++ b/core-api/core_api/routes/chat.py @@ -48,7 +48,7 @@ async def route_chat( def select_chat_chain(chat_request: ChatRequest, routable_chains: dict[str, Runnable]) -> Runnable: if chat_request.selected_files: - return routable_chains.get("chat/documents") + return routable_chains.get("summarise") else: return routable_chains.get("chat") diff --git a/core-api/core_api/semantic_routes.py b/core-api/core_api/semantic_routes.py index b41601a07..98504379e 100644 --- a/core-api/core_api/semantic_routes.py +++ b/core-api/core_api/semantic_routes.py @@ -3,14 +3,14 @@ from core_api.build_chains import ( build_chat_chain, build_chat_with_docs_chain, + build_coach_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 +from redbox.models.chat import ChatRoute # === Pre-canned responses for non-LLM routes === INFO_RESPONSE = """ @@ -34,6 +34,7 @@ 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)], + coach_chain: Annotated[Runnable, Depends(build_coach_chain)], ) -> dict[str, tuple[Runnable, str]]: global __routable_chains # noqa: PLW0603 if not __routable_chains: @@ -49,7 +50,7 @@ def get_routable_chains( "Answer questions as a helpful assistant", ), ( - ChatRoute.chat_with_docs, + ChatRoute.summarise, chat_with_docs_chain, "Answer questions as a helpful assistant using the documents provided", ), @@ -58,6 +59,14 @@ def get_routable_chains( condense_chain, "Search for an answer to a question in provided documents", ), + ( + ChatRoute.coach, + coach_chain, + "Check the chat history for any imporvement recommendations", + ), ) - __routable_chains = {name: (runnable, description) for (name, runnable, description) in chat_tools} + __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 3d10e4ac1..a88dd1d3c 100644 --- a/core-api/poetry.lock +++ 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"pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] +enabler = ["pytest-enabler (>=2.2)"] +test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"] +type = ["pytest-mypy"] [metadata] lock-version = "2.0" python-versions = ">=3.12,<3.13" -content-hash = "4279df152392a8b6ff6e29b3e7f88b42fa4e44b3b43b0d21415563d7e8d3f9f9" +content-hash = "fb2f73f90957c91f25a4f616fe2005b3718e4b7c04eaaf10d3b88702c9810740" diff --git a/core-api/pyproject.toml b/core-api/pyproject.toml index 6e4acd08a..8248b7584 100644 --- a/core-api/pyproject.toml +++ b/core-api/pyproject.toml @@ -18,8 +18,8 @@ pydantic = "^2.7.1" langchain = "^0.2.6" tiktoken = "^0.7.0" boto3 = "^1.34.139" -faststream = {extras = ["redis"], version = "<0.5.0"} -fastapi = "^0.111.0" +faststream = {extras = ["redis", "cli"], version = "^0.5.28"} +fastapi = "^0.115.2" uvicorn = "^0.30.1" python-multipart = "^0.0.9" python-jose = "^3.3.0" @@ -50,8 +50,7 @@ build-backend = "poetry.core.masonry.api" addopts = "--import-mode=importlib" env_override_existing_values = 1 env_files = [ - "../.env.test", - "../.env" + "../.env.test" ] markers = [ "incremental: marks tests as incremental (deselect with '-m \"not incremental\"')", diff --git a/core-api/tests/routes/test_chat.py b/core-api/tests/routes/test_chat.py index 362f424ae..331a161a6 100644 --- a/core-api/tests/routes/test_chat.py +++ b/core-api/tests/routes/test_chat.py @@ -1,11 +1,10 @@ import json +from math import exp from types import SimpleNamespace from typing import TYPE_CHECKING from unittest.mock import AsyncMock, MagicMock -from math import exp import pytest -from core_api import dependencies, semantic_routes from core_api.app import app as application from core_api.routes.chat import chat_app from fastapi.testclient import TestClient @@ -15,10 +14,11 @@ from langchain_core.prompt_values import ChatPromptValue from langchain_core.runnables import Runnable, RunnableLambda from langchain_core.runnables.schema import StreamEvent -from starlette.websockets import WebSocketDisconnect - from redbox.models.chat import ChatResponse, ChatRoute from redbox.models.file import ChunkMetadata +from starlette.websockets import WebSocketDisconnect + +from core_api import dependencies, semantic_routes if TYPE_CHECKING: from collections.abc import Iterable @@ -29,7 +29,7 @@ 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") +EXPECTED_AVAILABLE_ROUTES = ("chat", "search", "info", "coach") def mock_chat_prompt(): @@ -93,10 +93,14 @@ def mock_all_chunks_retriever(alice): @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 + 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) ) yield TestClient(application) chat_app.dependency_overrides = {} @@ -122,7 +126,9 @@ def mock_streaming_client(): 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} + chat_app.dependency_overrides[semantic_routes.get_routable_chains] = lambda: { + "retrieval": retrieval_chain + } yield TestClient(application) chat_app.dependency_overrides = {} @@ -132,7 +138,9 @@ def test_rag(mock_client, headers): "/chat/rag", headers=headers, json={ - "message_history": [{"text": "Who put the ram in the rama lama ding dong?", "role": "user"}], + "message_history": [ + {"text": "Who put the ram in the rama lama ding dong?", "role": "user"} + ], "selected_files": [{"uuid": UPLOADED_FILE_UUID}], }, ) @@ -144,6 +152,32 @@ def test_rag(mock_client, headers): ), f"Expected route [{ChatRoute.chat_with_docs}] received [{chat_response.route_name}]" +def test_coach(mock_client, headers): + + message_history = [ + {"text": "What can I do for you?", "role": "system"}, + {"text": "Who put the ram in the rama lama ding dong?", "role": "user"}, + {"text": "The lama put the ram in rama", "role": "ai"}, + {"text": "@coach", "role": "user"}, + ] + selected_files = [{"uuid": UPLOADED_FILE_UUID}] + + response = mock_client.post( + "/chat/rag", + headers=headers, + json={ + "message_history": message_history, + "selected_files": selected_files, + }, + ) + 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.coach + ), f"Expected route [{ChatRoute.coach}] received [{chat_response.route_name}]" + + @pytest.mark.parametrize(("keyword"), EXPECTED_AVAILABLE_ROUTES) def test_keywords(mock_client, headers, keyword): """Given a history that should summarise, force retrieval.""" @@ -174,7 +208,11 @@ def test_rag_chat_streamed(mock_client, headers): with mock_client.websocket_connect("/chat/rag", headers=headers) as websocket: # When - websocket.send_text(json.dumps({"message_history": message_history, "selected_files": selected_files})) + websocket.send_text( + json.dumps( + {"message_history": message_history, "selected_files": selected_files} + ) + ) all_text, docs, route_name = [], [], "" while True: @@ -200,7 +238,9 @@ def test_available_tools(mock_client, 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 set(EXPECTED_AVAILABLE_ROUTES).issubset( + {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 index 6e6e3734b..ac2845fd1 100644 --- a/core-api/tests/test_runnables.py +++ b/core-api/tests/test_runnables.py @@ -2,11 +2,11 @@ from core_api.build_chains import ( build_chat_chain, build_chat_with_docs_chain, + build_coach_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 @@ -15,7 +15,9 @@ @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) + session_mocker.patch( + "core_api.dependencies.get_embedding_model", return_value=embedding_model + ) return embedding_model @@ -54,7 +56,12 @@ def test_make_chat_prompt_from_messages_runnable(mock_llm): 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) + ( + {"text": str(i), "role": "user"} + if i % 2 == 0 + else {"text": str(i), "role": "ai"} + ) + for i in range(100) ], } ) @@ -67,7 +74,9 @@ def test_rag_runnable(mock_llm, chunked_file, env): env=env, ) - chain = build_retrieval_chain(llm=mock_llm, retriever=retriever, tokeniser=get_tokeniser(), 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"}, @@ -85,13 +94,39 @@ def test_rag_runnable(mock_llm, chunked_file, env): ) assert response["response"] == "<>" - assert {str(chunked_file.uuid)} == {chunk.metadata["parent_file_uuid"] for chunk in response["source_documents"]} + assert {str(chunked_file.uuid)} == { + chunk.metadata["parent_file_uuid"] for chunk in response["source_documents"] + } + + +def test_coach_runnable(mock_llm, chunked_file, env): + chain = build_coach_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="@coach", + chat_history=previous_history, + file_uuids=[str(chunked_file.uuid)], + user_uuid=str(chunked_file.creator_user_uuid), + ).dict() + ) + + assert response["response"] == "<>" + assert response["route_name"] == ChatRoute.coach 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) + 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"}, @@ -109,10 +144,14 @@ def test_condense_runnable(mock_llm, chunked_file, env): ) assert response["response"] == "<>" assert response["route_name"].startswith("search") - all_results_file_uuids = {chunk.metadata["parent_file_uuid"] for chunk in response["source_documents"]} + 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}" + } == all_results_file_uuids, ( + f"Expected {str(chunked_file.uuid)} in {all_results_file_uuids}" + ) def test_chat_runnable(mock_llm, chunked_file, env): @@ -137,9 +176,14 @@ def test_chat_runnable(mock_llm, chunked_file, env): assert response["route_name"] == ChatRoute.chat -def test_chat_with_documents_runnable(all_chunks_retriever, mock_llm, chunked_file, env): +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 + llm=mock_llm, + all_chunks_retriever=all_chunks_retriever, + tokeniser=get_tokeniser(), + env=env, ) previous_history = [ diff --git a/django_app/Dockerfile b/django_app/Dockerfile index d9836846a..01771bc04 100644 --- a/django_app/Dockerfile +++ b/django_app/Dockerfile @@ -18,7 +18,7 @@ FROM python:3.12-slim AS poetry-packages RUN apt-get update && apt-get install --yes build-essential > /dev/null -RUN pip install poetry poetry-plugin-bundle +RUN pip install poetry poetry-plugin-bundle==1.3.0 WORKDIR /src @@ -26,7 +26,7 @@ COPY django_app/pyproject.toml . COPY django_app/poetry.lock . # do this so that poetry bundle can run without the project - can't pass --no-root to bundle -#RUN touch README.md +# RUN touch README.md RUN poetry install RUN poetry bundle venv ./venv diff --git a/django_app/frontend/src/styles.scss b/django_app/frontend/src/styles.scss index 3e96c0ea1..842027ad8 100644 --- a/django_app/frontend/src/styles.scss +++ b/django_app/frontend/src/styles.scss @@ -2,7 +2,7 @@ $govuk-assets-path: "govuk-assets/"; $govuk-font-family: sans-serif; $redbox-red: #9c0000; /*old:#A50202*/ -@import "./node_modules/govuk-frontend/dist/govuk/all"; +@import "../node_modules/govuk-frontend/dist/govuk/all"; body { background-color: white; diff --git a/django_app/poetry.lock b/django_app/poetry.lock index c0a0bf517..5b1b38700 100644 --- a/django_app/poetry.lock +++ b/django_app/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. 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"82bbb8e750d6c3636c24f993b1b8ddc9d0c63aad1b05f10dd1405d90cf8f8d88" +content-hash = "473c442b841410b4e083b07b3454128eb0199f100b558ab0ae766ca9ced571eb" diff --git a/django_app/pyproject.toml b/django_app/pyproject.toml index 0d0c4adef..492690080 100644 --- a/django_app/pyproject.toml +++ b/django_app/pyproject.toml @@ -37,6 +37,8 @@ django-gov-notify = "^0.5.0" websockets = "^12.0" django-import-export = "^4.0" dataclasses-json = ">=0.6.7" +django-staff-sso-client = "^4.3.0" +twisted = "24.7.0rc1" [tool.poetry.group.dev.dependencies] pytest = "^8.1.1" diff --git a/django_app/redbox_app/jinja2.py b/django_app/redbox_app/jinja2.py index 30f00cb02..a1d2d5a05 100644 --- a/django_app/redbox_app/jinja2.py +++ b/django_app/redbox_app/jinja2.py @@ -66,7 +66,6 @@ def to_user_timezone(value): user_tz = pytz.timezone("Europe/London") return value.astimezone(user_tz).strftime("%H:%M %d/%m/%Y") - def environment(**options): extra_options = {} env = jinja2.Environment( # nosec: B701 # noqa: S701 @@ -77,6 +76,7 @@ def environment(**options): **extra_options, }, ) + env.filters.update( { "static": static, @@ -86,6 +86,7 @@ def environment(**options): "to_user_timezone": to_user_timezone, "environment": settings.ENVIRONMENT.value, "security": settings.MAX_SECURITY_CLASSIFICATION.value, + "repo_owner": settings.REPO_OWNER, } ) env.globals.update( @@ -97,6 +98,10 @@ def environment(**options): "to_user_timezone": to_user_timezone, "environment": settings.ENVIRONMENT.value, "security": settings.MAX_SECURITY_CLASSIFICATION.value, + "repo_owner": settings.REPO_OWNER, + "analytics_tag": settings.ANALYTICS_TAG, + "analytics_link": settings.ANALYTICS_LINK, + } ) return env diff --git a/django_app/redbox_app/redbox_core/admin.py b/django_app/redbox_app/redbox_core/admin.py index 494fc7822..961809bd5 100644 --- a/django_app/redbox_app/redbox_core/admin.py +++ b/django_app/redbox_app/redbox_core/admin.py @@ -3,15 +3,17 @@ from django.conf import settings from django.contrib import admin +from django.contrib.auth import get_user_model from django.db.models import QuerySet from django.http import HttpResponse from import_export.admin import ImportMixin -from requests.exceptions import RequestException - from redbox_app.redbox_core.client import CoreApiClient +from requests.exceptions import RequestException from . import models +User = get_user_model() + logger = logging.getLogger(__name__) core_api = CoreApiClient(host=settings.CORE_API_HOST, port=settings.CORE_API_PORT) @@ -43,11 +45,11 @@ class UserAdmin(ImportMixin, admin.ModelAdmin): date_hierarchy = "last_login" @admin.display(ordering="ai_experience", description="AI Experience") - def get_ai(self, obj: models.User): + def get_ai(self, obj: User): return obj.ai_experience class Meta: - model = models.User + model = User fields = ["email"] import_id_fields = ["email"] @@ -69,7 +71,9 @@ def reupload(self, request, queryset): # noqa:ARG002 logger.info("Re-uploading file to core-api: %s", file) core_api.reingest_file(file.core_file_uuid, file.user) except RequestException as e: - logger.exception("Error re-uploading File model object %s.", file, exc_info=e) + logger.exception( + "Error re-uploading File model object %s.", file, exc_info=e + ) file.status = models.StatusEnum.errored file.save() else: @@ -78,7 +82,13 @@ def reupload(self, request, queryset): # noqa:ARG002 logger.info("Successfully reuploaded file %s.", file) - list_display = ["original_file_name", "user", "status", "created_at", "last_referenced"] + list_display = [ + "original_file_name", + "user", + "status", + "created_at", + "last_referenced", + ] list_filter = ["user", "status"] date_hierarchy = "created_at" actions = ["reupload"] @@ -112,9 +122,15 @@ class ChatMessageInline(admin.StackedInline): class ChatHistoryAdmin(admin.ModelAdmin): def export_as_csv(self, request, queryset: QuerySet): # noqa:ARG002 - history_field_names: list[str] = [field.name for field in models.ChatHistory._meta.fields] # noqa:SLF001 - message_field_names: list[str] = [field.name for field in models.ChatMessage._meta.fields] # noqa:SLF001 - rating_field_names: list[str] = [field.name for field in models.ChatMessageRating._meta.fields] # noqa:SLF001 + history_field_names: list[str] = [ + field.name for field in models.ChatHistory._meta.fields + ] # noqa:SLF001 + message_field_names: list[str] = [ + field.name for field in models.ChatMessage._meta.fields + ] # noqa:SLF001 + rating_field_names: list[str] = [ + field.name for field in models.ChatMessageRating._meta.fields + ] # noqa:SLF001 response = HttpResponse(content_type="text/csv") response["Content-Disposition"] = "attachment; filename=chathistory.csv" @@ -131,13 +147,21 @@ def export_as_csv(self, request, queryset: QuerySet): # noqa:ARG002 chat_message_rating: models.ChatMessageRating for chat_history in queryset: for chat_message in chat_history.chatmessage_set.all(): - row = [getattr(chat_history, field) for field in history_field_names] + [ - getattr(chat_message, field) for field in message_field_names - ] + row = [ + getattr(chat_history, field) for field in history_field_names + ] + [getattr(chat_message, field) for field in message_field_names] if hasattr(chat_message, "chatmessagerating"): chat_message_rating = chat_message.chatmessagerating - row += [getattr(chat_message_rating, field) for field in rating_field_names] - row += [", ".join(c.text for c in chat_message_rating.chatmessageratingchip_set.all())] + row += [ + getattr(chat_message_rating, field) + for field in rating_field_names + ] + row += [ + ", ".join( + c.text + for c in chat_message_rating.chatmessageratingchip_set.all() + ) + ] writer.writerow(row) return response @@ -149,6 +173,7 @@ def export_as_csv(self, request, queryset: QuerySet): # noqa:ARG002 list_filter = ["users"] date_hierarchy = "created_at" actions = ["export_as_csv"] + ordering = ["-created_at"] class CitationAdmin(admin.ModelAdmin): @@ -160,7 +185,7 @@ def get_user(self, obj): return obj.chat_message.chat_history.users -admin.site.register(models.User, UserAdmin) +admin.site.register(User, UserAdmin) admin.site.register(models.File, FileAdmin) admin.site.register(models.ChatHistory, ChatHistoryAdmin) admin.site.register(models.ChatMessage, ChatMessageAdmin) diff --git a/django_app/redbox_app/redbox_core/auth_views.py b/django_app/redbox_app/redbox_core/auth_views.py index 5c47e1af5..eef0718ab 100644 --- a/django_app/redbox_app/redbox_core/auth_views.py +++ b/django_app/redbox_app/redbox_core/auth_views.py @@ -1,13 +1,15 @@ import logging -from django.contrib.auth import logout +from django.conf import settings +from django.contrib.auth import get_user_model, logout from django.http import HttpRequest from django.shortcuts import redirect, render from magic_link.models import MagicLink +from redbox_app.redbox_core import email_handler +from redbox_app.redbox_core.forms import SignInForm from requests import HTTPError -from redbox_app.redbox_core import email_handler, models -from redbox_app.redbox_core.forms import SignInForm +User = get_user_model() logger = logging.getLogger(__name__) @@ -15,17 +17,21 @@ def sign_in_view(request: HttpRequest): if request.user.is_authenticated: return redirect("homepage") + if settings.LOGIN_METHOD == "sso": + return redirect("/auth/login/") if request.method == "POST": form = SignInForm(request.POST) if form.is_valid(): email = form.cleaned_data["email"].lower() try: - user = models.User.objects.get(email=email) - link = MagicLink.objects.create(user=user, redirect_to="/check-demographics") + user = User.objects.get(email=email) + link = MagicLink.objects.create( + user=user, redirect_to="/check-demographics" + ) full_link = request.build_absolute_uri(link.get_absolute_url()) email_handler.send_magic_link_email(full_link, email) - except models.User.DoesNotExist as e: + except User.DoesNotExist as e: logger.debug("User with email %s not found", email, exc_info=e) except HTTPError as e: logger.exception("failed to send link to %s", email, exc_info=e) diff --git a/django_app/redbox_app/redbox_core/client.py b/django_app/redbox_app/redbox_core/client.py index c590bce93..220d3b9d1 100644 --- a/django_app/redbox_app/redbox_core/client.py +++ b/django_app/redbox_app/redbox_core/client.py @@ -8,9 +8,10 @@ from botocore.exceptions import ClientError from dataclasses_json import Undefined, dataclass_json from django.conf import settings +from django.contrib.auth import get_user_model from yarl import URL -from redbox_app.redbox_core.models import User +User = get_user_model() logger = logging.getLogger(__name__) @@ -34,7 +35,9 @@ def s3_client(): try: client.create_bucket( Bucket=settings.BUCKET_NAME, - CreateBucketConfiguration={"LocationConstraint": settings.AWS_S3_REGION_NAME}, + CreateBucketConfiguration={ + "LocationConstraint": settings.AWS_S3_REGION_NAME + }, ) except ClientError as e: if e.response["Error"]["Code"] != "BucketAlreadyOwnedByYou": @@ -59,9 +62,16 @@ class CoreChatResponse: @dataclass_json(undefined=Undefined.EXCLUDE) @dataclass(frozen=True) class FileStatus: - processing_status: ( - Literal["uploaded", "parsing", "chunking", "embedding", "indexing", "complete", "unknown", "errored"] | None - ) + processing_status: Literal[ + "uploaded", + "parsing", + "chunking", + "embedding", + "indexing", + "complete", + "unknown", + "errored", + ] @dataclass_json(undefined=Undefined.EXCLUDE) @@ -83,13 +93,19 @@ def url(self) -> URL: def upload_file(self, name: str, user: User) -> FileOperation: response = requests.post( - self.url / "file", json={"key": name}, headers={"Authorization": user.get_bearer_token()}, timeout=30 + self.url / "file", + json={"key": name}, + headers={"Authorization": user.get_bearer_token()}, + timeout=30, ) response.raise_for_status() return FileOperation.schema().loads(response.content) def rag_chat( - self, message_history: list[dict[str, str]], selected_files: list[dict[str, str]], user: User + self, + message_history: list[dict[str, str]], + selected_files: list[dict[str, str]], + user: User, ) -> CoreChatResponse: response = requests.post( self.url / "chat/rag", @@ -105,18 +121,24 @@ def rag_chat( def get_file_status(self, file_id: UUID, user: User) -> FileStatus: url = self.url / "file" / str(file_id) / "status" - response = requests.get(url, headers={"Authorization": user.get_bearer_token()}, timeout=60) + response = requests.get( + url, headers={"Authorization": user.get_bearer_token()}, timeout=60 + ) response.raise_for_status() return FileStatus.schema().loads(response.content) def delete_file(self, file_id: UUID, user: User) -> FileOperation: url = self.url / "file" / str(file_id) - response = requests.delete(url, headers={"Authorization": user.get_bearer_token()}, timeout=60) + response = requests.delete( + url, headers={"Authorization": user.get_bearer_token()}, timeout=60 + ) response.raise_for_status() return FileOperation.schema().loads(response.content) def reingest_file(self, file_id: UUID, user: User) -> FileOperation: url = self.url / "file" / str(file_id) - response = requests.put(url, headers={"Authorization": user.get_bearer_token()}, timeout=60) + response = requests.put( + url, headers={"Authorization": user.get_bearer_token()}, timeout=60 + ) response.raise_for_status() return FileOperation.schema().loads(response.content) diff --git a/django_app/redbox_app/redbox_core/forms.py b/django_app/redbox_app/redbox_core/forms.py index 87bcda0dc..f4d0e9031 100644 --- a/django_app/redbox_app/redbox_core/forms.py +++ b/django_app/redbox_app/redbox_core/forms.py @@ -2,8 +2,9 @@ from typing import ClassVar from django import forms +from django.contrib.auth import get_user_model -from redbox_app.redbox_core.models import User +User = get_user_model() class SignInForm(forms.Form): diff --git a/django_app/redbox_app/redbox_core/info_views.py b/django_app/redbox_app/redbox_core/info_views.py index 9c7efc107..8d204e59d 100644 --- a/django_app/redbox_app/redbox_core/info_views.py +++ b/django_app/redbox_app/redbox_core/info_views.py @@ -10,14 +10,20 @@ @require_http_methods(["GET"]) def privacy_notice_view(request): + if settings.REPO_OWNER.lower() == "uktrade": + return render(request, "privacy-notice-uktrade.html", {}) return render(request, "privacy-notice.html", {}) @require_http_methods(["GET"]) def support_view(request): + if settings.REPO_OWNER.lower() == "uktrade": + return render(request, "support-uktrade.html", {"contact_email": settings.CONTACT_EMAIL}) return render(request, "support.html", {"contact_email": settings.CONTACT_EMAIL}) @require_http_methods(["GET"]) def accessibility_statement_view(request): + if settings.REPO_OWNER.lower() == "uktrade": + return render(request, "accessibility-statement-uktrade.html", {"contact_email": settings.CONTACT_EMAIL}) return render(request, "accessibility-statement.html", {"contact_email": settings.CONTACT_EMAIL}) diff --git a/django_app/redbox_app/redbox_core/management/commands/show_magiclink_url.py b/django_app/redbox_app/redbox_core/management/commands/show_magiclink_url.py index 51638a389..1ad1df74a 100644 --- a/django_app/redbox_app/redbox_core/management/commands/show_magiclink_url.py +++ b/django_app/redbox_app/redbox_core/management/commands/show_magiclink_url.py @@ -1,11 +1,12 @@ import logging from argparse import ArgumentParser +from django.contrib.auth import get_user_model from django.core.management import BaseCommand, CommandError from django.db.models import Max from magic_link.models import MagicLink -from redbox_app.redbox_core.models import User +User = get_user_model() logger = logging.getLogger(__name__) @@ -27,7 +28,9 @@ def handle(self, **options): raise CommandError(message) from e try: - latest = MagicLink.objects.filter(user=user).aggregate(Max("created_at"))["created_at__max"] + latest = MagicLink.objects.filter(user=user).aggregate(Max("created_at"))[ + "created_at__max" + ] logger.debug("latest: %s", latest) link: MagicLink = MagicLink.objects.get(user=user, created_at=latest) except MagicLink.DoesNotExist as e: diff --git a/django_app/redbox_app/redbox_core/migrations/0025_alter_user_managers_remove_user_invite_accepted_at_and_more.py b/django_app/redbox_app/redbox_core/migrations/0025_alter_user_managers_remove_user_invite_accepted_at_and_more.py new file mode 100644 index 000000000..7ad103e1d --- /dev/null +++ b/django_app/redbox_app/redbox_core/migrations/0025_alter_user_managers_remove_user_invite_accepted_at_and_more.py @@ -0,0 +1,120 @@ +# Generated by Django 4.2.15 on 2024-10-21 15:13 + +from django.db import migrations, models +import django.utils.timezone +import redbox_app.redbox_core.models + + +class Migration(migrations.Migration): + + dependencies = [ + ("auth", "0012_alter_user_first_name_max_length"), + ("redbox_core", "0024_user_ai_experience_user_name"), + ] + + operations = [ + migrations.AlterModelManagers( + name="user", + managers=[ + ("objects", redbox_app.redbox_core.models.SSOUserManager()), + ], + ), + migrations.RemoveField( + model_name="user", + name="invite_accepted_at", + ), + migrations.RemoveField( + model_name="user", + name="invited_at", + ), + migrations.RemoveField( + model_name="user", + name="last_token_sent_at", + ), + migrations.RemoveField( + model_name="user", + name="verified", + ), + migrations.AddField( + model_name="user", + name="username", + field=models.EmailField( + default="default@default.co.uk", max_length=254, unique=True + ), + ), + migrations.AlterField( + model_name="user", + name="ai_experience", + field=models.CharField(blank=True, max_length=25, null=True), + ), + migrations.AlterField( + model_name="user", + name="date_joined", + field=models.DateTimeField(default=django.utils.timezone.now), + ), + migrations.AlterField( + model_name="user", + name="email", + field=models.EmailField(max_length=254, unique=True), + ), + migrations.AlterField( + model_name="user", + name="first_name", + field=models.CharField(max_length=48), + ), + migrations.AlterField( + model_name="user", + name="grade", + field=models.CharField(blank=True, max_length=3, null=True), + ), + migrations.AlterField( + model_name="user", + name="groups", + field=models.ManyToManyField( + blank=True, + related_name="sso_user_set", + to="auth.group", + verbose_name="groups", + ), + ), + migrations.AlterField( + model_name="user", + name="is_active", + field=models.BooleanField(default=True), + ), + migrations.AlterField( + model_name="user", + name="is_staff", + field=models.BooleanField(default=False), + ), + migrations.AlterField( + model_name="user", + name="is_superuser", + field=models.BooleanField(default=False), + ), + migrations.AlterField( + model_name="user", + name="last_name", + field=models.CharField(max_length=48), + ), + migrations.AlterField( + model_name="user", + name="password", + field=models.CharField(default="fakepassword"), + ), + migrations.AlterField( + model_name="user", + name="profession", + field=models.CharField(blank=True, max_length=4, null=True), + ), + migrations.AlterField( + model_name="user", + name="user_permissions", + field=models.ManyToManyField( + blank=True, + related_name="sso_user_set", + to="auth.permission", + verbose_name="user permissions", + ), + ), + ] \ No newline at end of file diff --git a/django_app/redbox_app/redbox_core/models.py b/django_app/redbox_app/redbox_core/models.py index 3d0f9295a..a4f2b46bc 100644 --- a/django_app/redbox_app/redbox_core/models.py +++ b/django_app/redbox_app/redbox_core/models.py @@ -6,6 +6,8 @@ import boto3 from botocore.config import Config from django.conf import settings +from django.contrib.auth.base_user import BaseUserManager as BaseSSOUserManager +from django.contrib.auth.models import AbstractBaseUser, Group, PermissionsMixin from django.contrib.postgres.fields import ArrayField from django.core import validators from django.db import models @@ -47,85 +49,74 @@ def __str__(self) -> str: # pragma: no cover return f"{self.name}" -class User(BaseUser, UUIDPrimaryKeyBase): - class UserGrade(models.TextChoices): - AA = "AA", _("AA") - AO = "AO", _("AO") - DEPUTY_DIRECTOR = "DD", _("Deputy Director") - DIRECTOR = "D", _("Director") - DIRECTOR_GENERAL = "DG", _("Director General") - EO = "EO", _("EO") - G6 = "G6", _("G6") - G7 = "G7", _("G7") - HEO = "HEO", _("HEO") - PS = "PS", _("Permanent Secretary") - SEO = "SEO", _("SEO") - OT = "OT", _("Other") - - class Profession(models.TextChoices): - AN = "AN", _("Analysis") - CM = "CMC", _("Commercial") - COM = "COM", _("Communications") - CFIN = "CFIN", _("Corporate finance") - CF = "CF", _("Counter fraud") - DDT = "DDT", _("Digital, data and technology") - EC = "EC", _("Economics") - FIN = "FIN", _("Finance") - FEDG = "FEDG", _("Fraud, error, debts and grants") - HR = "HR", _("Human resources") - IA = "IA", _("Intelligence analysis") - IAUD = "IAUD", _("Internal audit") - IT = "IT", _("International trade") - KIM = "KIM", _("Knowledge and information management") - LG = "LG", _("Legal") - MD = "MD", _("Medical") - OP = "OP", _("Occupational psychology") - OD = "OD", _("Operational delivery") - OR = "OR", _("Operational research") - PL = "PL", _("Planning") - PI = "PI", _("Planning inspection") - POL = "POL", _("Policy") - PD = "PD", _("Project delivery") - PR = "PR", _("Property") - SE = "SE", _("Science and engineering") - SC = "SC", _("Security") - SR = "SR", _("Social research") - ST = "ST", _("Statistics") - TX = "TX", _("Tax") - VET = "VET", _("Veterinary") - OT = "OT", _("Other") - - class AIExperienceLevel(models.TextChoices): - CURIOUS_NEWCOMER = "Curious Newcomer", _("I haven't used Generative AI tools") - CAUTIOUS_EXPLORER = "Cautious Explorer", _("I have a little experience using Generative AI tools") - ENTHUSIASTIC_EXPERIMENTER = ( - "Enthusiastic Experimenter", - _("I occasionally use Generative AI tools but am still experimenting with their capabilities"), - ) - EXPERIENCED_NAVIGATOR = ( - "Experienced Navigator", - _("I use Generative AI tools regularly and have a good understanding of their strengths and limitations"), - ) - AI_ALCHEMIST = ( - "AI Alchemist", - _( - "I have extensive experience with Generative AI tools and can leverage them effectively in various " - "contexts" - ), - ) - - username = None - verified = models.BooleanField(default=False, blank=True, null=True) - invited_at = models.DateTimeField(default=None, blank=True, null=True) - invite_accepted_at = models.DateTimeField(default=None, blank=True, null=True) - last_token_sent_at = models.DateTimeField(editable=False, blank=True, null=True) - password = models.CharField("password", max_length=128, blank=True, null=True) - business_unit = models.ForeignKey(BusinessUnit, null=True, blank=True, on_delete=models.SET_NULL) - grade = models.CharField(null=True, blank=True, max_length=3, choices=UserGrade) +class SSOUserManager(BaseSSOUserManager): + + use_in_migrations = True + + def _create_user(self, username, password, **extra_fields): + """Create and save a User with the given email and password.""" + if not username: + raise ValueError("The given email must be set") + # email = self.normalize_email(email) + User = self.model(email=username, **extra_fields) + User.set_password(password) + User.save(using=self._db) + return User + + def create_user(self, username, password=None, **extra_fields): + """Create and save a regular User with the given email and password.""" + extra_fields.setdefault("is_staff", False) + extra_fields.setdefault("is_superuser", False) + return self._create_user(username, password, **extra_fields) + + def create_superuser(self, username, password=None, **extra_fields): + """Create and save a SuperUser with the given email and password.""" + extra_fields.setdefault("is_staff", True) + extra_fields.setdefault("is_superuser", True) + + if extra_fields.get("is_staff") is not True: + raise ValueError("Superuser must have is_staff=True.") + if extra_fields.get("is_superuser") is not True: + raise ValueError("Superuser must have is_superuser=True.") + + return self._create_user(username, password, **extra_fields) + + +class User(AbstractBaseUser, PermissionsMixin, UUIDPrimaryKeyBase): + username = models.EmailField(unique=True, default="default@default.co.uk") + password = models.CharField(default="fakepassword") + email = models.EmailField(unique=True) + first_name = models.CharField(max_length=48) + last_name = models.CharField(max_length=48) + is_staff = models.BooleanField(default=False) + is_active = models.BooleanField(default=True) + is_superuser = models.BooleanField(default=False) + date_joined = models.DateTimeField(default=timezone.now) + business_unit = models.ForeignKey( + BusinessUnit, null=True, blank=True, on_delete=models.SET_NULL + ) + grade = models.CharField(null=True, blank=True, max_length=3) + profession = models.CharField(null=True, blank=True, max_length=4) name = models.CharField(null=True, blank=True) - ai_experience = models.CharField(null=True, blank=True, max_length=25, choices=AIExperienceLevel) - profession = models.CharField(null=True, blank=True, max_length=4, choices=Profession) - objects = BaseUserManager() + ai_experience = models.CharField( + null=True, + blank=True, + max_length=25, + ) + # To avoid clashing with User Model + user_permissions = models.ManyToManyField( + "auth.Permission", + verbose_name="user permissions", + blank=True, + related_name="sso_user_set", + ) + groups = models.ManyToManyField( + Group, verbose_name="groups", blank=True, related_name="sso_user_set" + ) + + USERNAME_FIELD = "username" + REQUIRED_FIELDS = [] + objects = SSOUserManager() def __str__(self) -> str: # pragma: no cover return f"{self.email}" @@ -141,6 +132,112 @@ def get_bearer_token(self) -> str: return f"Bearer {bearer_token}" +# class User(BaseUser, UUIDPrimaryKeyBase): +# class UserGrade(models.TextChoices): +# AA = "AA", _("AA") +# AO = "AO", _("AO") +# DEPUTY_DIRECTOR = "DD", _("Deputy Director") +# DIRECTOR = "D", _("Director") +# DIRECTOR_GENERAL = "DG", _("Director General") +# EO = "EO", _("EO") +# G6 = "G6", _("G6") +# G7 = "G7", _("G7") +# HEO = "HEO", _("HEO") +# PS = "PS", _("Permanent Secretary") +# SEO = "SEO", _("SEO") +# OT = "OT", _("Other") + +# class Profession(models.TextChoices): +# AN = "AN", _("Analysis") +# CM = "CMC", _("Commercial") +# COM = "COM", _("Communications") +# CFIN = "CFIN", _("Corporate finance") +# CF = "CF", _("Counter fraud") +# DDT = "DDT", _("Digital, data and technology") +# EC = "EC", _("Economics") +# FIN = "FIN", _("Finance") +# FEDG = "FEDG", _("Fraud, error, debts and grants") +# HR = "HR", _("Human resources") +# IA = "IA", _("Intelligence analysis") +# IAUD = "IAUD", _("Internal audit") +# IT = "IT", _("International trade") +# KIM = "KIM", _("Knowledge and information management") +# LG = "LG", _("Legal") +# MD = "MD", _("Medical") +# OP = "OP", _("Occupational psychology") +# OD = "OD", _("Operational delivery") +# OR = "OR", _("Operational research") +# PL = "PL", _("Planning") +# PI = "PI", _("Planning inspection") +# POL = "POL", _("Policy") +# PD = "PD", _("Project delivery") +# PR = "PR", _("Property") +# SE = "SE", _("Science and engineering") +# SC = "SC", _("Security") +# SR = "SR", _("Social research") +# ST = "ST", _("Statistics") +# TX = "TX", _("Tax") +# VET = "VET", _("Veterinary") +# OT = "OT", _("Other") + +# class AIExperienceLevel(models.TextChoices): +# CURIOUS_NEWCOMER = "Curious Newcomer", _("I haven't used Generative AI tools") +# CAUTIOUS_EXPLORER = "Cautious Explorer", _( +# "I have a little experience using Generative AI tools" +# ) +# ENTHUSIASTIC_EXPERIMENTER = ( +# "Enthusiastic Experimenter", +# _( +# "I occasionally use Generative AI tools but am still experimenting with their capabilities" +# ), +# ) +# EXPERIENCED_NAVIGATOR = ( +# "Experienced Navigator", +# _( +# "I use Generative AI tools regularly and have a good understanding of their strengths and limitations" +# ), +# ) +# AI_ALCHEMIST = ( +# "AI Alchemist", +# _( +# "I have extensive experience with Generative AI tools and can leverage them effectively in various " +# "contexts" +# ), +# ) + +# username = None +# verified = models.BooleanField(default=False, blank=True, null=True) +# invited_at = models.DateTimeField(default=None, blank=True, null=True) +# invite_accepted_at = models.DateTimeField(default=None, blank=True, null=True) +# last_token_sent_at = models.DateTimeField(editable=False, blank=True, null=True) +# password = models.CharField("password", max_length=128, blank=True, null=True) +# business_unit = models.ForeignKey( +# BusinessUnit, null=True, blank=True, on_delete=models.SET_NULL +# ) +# grade = models.CharField(null=True, blank=True, max_length=3, choices=UserGrade) +# name = models.CharField(null=True, blank=True) +# ai_experience = models.CharField( +# null=True, blank=True, max_length=25, choices=AIExperienceLevel +# ) +# profession = models.CharField( +# null=True, blank=True, max_length=4, choices=Profession +# ) +# objects = BaseUserManager() + +# def __str__(self) -> str: # pragma: no cover +# return f"{self.email}" + +# def save(self, *args, **kwargs): +# self.email = self.email.lower() +# super().save(*args, **kwargs) + +# def get_bearer_token(self) -> str: +# """the bearer token expected by the core-api""" +# user_uuid = str(self.id) +# bearer_token = jwt.encode({"user_uuid": user_uuid}, key=settings.SECRET_KEY) +# return f"Bearer {bearer_token}" + + class StatusEnum(models.TextChoices): uploaded = "uploaded" parsing = "parsing" @@ -214,7 +311,9 @@ def url(self) -> URL | None: return URL(url) if not self.original_file: - logger.error("attempt to access non-existent file %s", self.pk, stack_info=True) + logger.error( + "attempt to access non-existent file %s", self.pk, stack_info=True + ) return None return URL(self.original_file.url) @@ -225,7 +324,9 @@ def name(self) -> str: try: return self.original_file_name or self.original_file.name except ValueError as e: - logger.exception("attempt to access non-existent file %s", self.pk, exc_info=e) + logger.exception( + "attempt to access non-existent file %s", self.pk, exc_info=e + ) @property def unique_name(self) -> str: @@ -260,7 +361,9 @@ class Meta: def __str__(self) -> str: # pragma: no cover return f"{self.name} - {self.users}" - def save(self, force_insert=False, force_update=False, using=None, update_fields=None): + def save( + self, force_insert=False, force_update=False, using=None, update_fields=None + ): self.name = sanitise_string(self.name) super().save(force_insert, force_update, using, update_fields) @@ -276,13 +379,18 @@ class Citation(UUIDPrimaryKeyBase, TimeStampedModel): chat_message = models.ForeignKey("ChatMessage", on_delete=models.CASCADE) text = models.TextField(null=True, blank=True) page_numbers = ArrayField( - models.PositiveIntegerField(), null=True, blank=True, help_text="location of citation in document" + models.PositiveIntegerField(), + null=True, + blank=True, + help_text="location of citation in document", ) def __str__(self): return f"{self.file}: {self.text or ''}" - def save(self, force_insert=False, force_update=False, using=None, update_fields=None): + def save( + self, force_insert=False, force_update=False, using=None, update_fields=None + ): self.text = sanitise_string(self.text) super().save(force_insert, force_update, using, update_fields) @@ -292,26 +400,36 @@ class ChatMessage(UUIDPrimaryKeyBase, TimeStampedModel): text = models.TextField(max_length=32768, null=False, blank=False) role = models.CharField(choices=ChatRoleEnum.choices, null=False, blank=False) route = models.CharField(max_length=25, null=True, blank=True) - selected_files = models.ManyToManyField(File, related_name="+", symmetrical=False, blank=True) + selected_files = models.ManyToManyField( + File, related_name="+", symmetrical=False, blank=True + ) source_files = models.ManyToManyField(File, through=Citation) def __str__(self) -> str: # pragma: no cover return f"{self.text} - {self.role}" - def save(self, force_insert=False, force_update=False, using=None, update_fields=None): + def save( + self, force_insert=False, force_update=False, using=None, update_fields=None + ): self.text = sanitise_string(self.text) super().save(force_insert, force_update, using, update_fields) class ChatMessageRating(TimeStampedModel): - chat_message = models.OneToOneField(ChatMessage, on_delete=models.CASCADE, primary_key=True) - rating = models.PositiveIntegerField(validators=[validators.MinValueValidator(1), validators.MaxValueValidator(5)]) + chat_message = models.OneToOneField( + ChatMessage, on_delete=models.CASCADE, primary_key=True + ) + rating = models.PositiveIntegerField( + validators=[validators.MinValueValidator(1), validators.MaxValueValidator(5)] + ) text = models.TextField(blank=True, null=True) def __str__(self) -> str: # pragma: no cover return f"{self.chat_message} - {self.rating} - {self.text}" - def save(self, force_insert=False, force_update=False, using=None, update_fields=None): + def save( + self, force_insert=False, force_update=False, using=None, update_fields=None + ): self.text = sanitise_string(self.text) super().save(force_insert, force_update, using, update_fields) diff --git a/django_app/redbox_app/redbox_core/views/__init__.py b/django_app/redbox_app/redbox_core/views/__init__.py index e5748d474..229182461 100644 --- a/django_app/redbox_app/redbox_core/views/__init__.py +++ b/django_app/redbox_app/redbox_core/views/__init__.py @@ -7,7 +7,7 @@ file_status_api_view, remove_doc_view, ) -from redbox_app.redbox_core.views.misc_views import health, homepage_view +from redbox_app.redbox_core.views.misc_views import health, homepage_view, faq_view from redbox_app.redbox_core.views.ratings_views import RatingsView __all__ = [ @@ -24,4 +24,5 @@ "homepage_view", "post_message", "remove_doc_view", + "faq_view", ] diff --git a/django_app/redbox_app/redbox_core/views/demographics_views.py b/django_app/redbox_app/redbox_core/views/demographics_views.py index 992cb7e50..b68552dc5 100644 --- a/django_app/redbox_app/redbox_core/views/demographics_views.py +++ b/django_app/redbox_app/redbox_core/views/demographics_views.py @@ -1,5 +1,6 @@ import logging +from django.contrib.auth import get_user_model from django.contrib.auth.decorators import login_required from django.contrib.auth.mixins import LoginRequiredMixin from django.http import HttpRequest, HttpResponse @@ -7,9 +8,9 @@ from django.utils.decorators import method_decorator from django.views import View from django.views.generic import UpdateView - from redbox_app.redbox_core.forms import DemographicsForm -from redbox_app.redbox_core.models import User + +User = get_user_model() logger = logging.getLogger(__name__) @@ -18,7 +19,15 @@ class CheckDemographicsView(View): @method_decorator(login_required) def get(self, request: HttpRequest) -> HttpResponse: user: User = request.user - if all([user.name, user.ai_experience, user.grade, user.business_unit, user.profession]): + if all( + [ + user.name, + user.ai_experience, + user.grade, + user.business_unit, + user.profession, + ] + ): return redirect("chats") else: return redirect("demographics") diff --git a/django_app/redbox_app/redbox_core/views/misc_views.py b/django_app/redbox_app/redbox_core/views/misc_views.py index fa1d38c9f..b28bdacff 100644 --- a/django_app/redbox_app/redbox_core/views/misc_views.py +++ b/django_app/redbox_app/redbox_core/views/misc_views.py @@ -20,3 +20,10 @@ def homepage_view(request): def health(_request: HttpRequest) -> HttpResponse: """this required by ECS Fargate""" return HttpResponse(status=200) + +def faq_view(request): + return render( + request, + template_name="faq.html", + context={"request": request}, + ) \ No newline at end of file diff --git a/django_app/redbox_app/setting_enums.py b/django_app/redbox_app/setting_enums.py index 95c11f2b5..1e2d1664d 100644 --- a/django_app/redbox_app/setting_enums.py +++ b/django_app/redbox_app/setting_enums.py @@ -20,11 +20,30 @@ def is_local(self) -> bool: def uses_minio(self) -> bool: return self.is_test - LOCAL = ("LOCAL", True, ["localhost", "127.0.0.1", "0.0.0.0", *ADDITIONAL_HOSTS]) # noqa: S104 nosec: B104: Not in prod - INTEGRATION = ("INTEGRATION", True, ["localhost", "127.0.0.1", "0.0.0.0", *ADDITIONAL_HOSTS]) # noqa: S104 nosec: B104: Not in prod + LOCAL = ( + "LOCAL", + True, + ["localhost", "127.0.0.1", "0.0.0.0", *ADDITIONAL_HOSTS], + ) # noqa: S104 nosec: B104: Not in prod + INTEGRATION = ( + "INTEGRATION", + True, + ["localhost", "127.0.0.1", "0.0.0.0", *ADDITIONAL_HOSTS], + ) # noqa: S104 nosec: B104: Not in prod DEV = ("DEV", False, ["redbox-dev.ai.cabinetoffice.gov.uk", *ADDITIONAL_HOSTS]) - PREPROD = ("PREPROD", False, ["redbox-preprod.ai.cabinetoffice.gov.uk", *ADDITIONAL_HOSTS]) + DEVELOPMENT = ("DEVELOPMENT", False, [*ADDITIONAL_HOSTS]) + SANDBOX = ("SANDBOX", False, [*ADDITIONAL_HOSTS]) + PREPROD = ( + "PREPROD", + False, + ["redbox-preprod.ai.cabinetoffice.gov.uk", *ADDITIONAL_HOSTS], + ) PROD = ("PROD", False, ["redbox.ai.cabinetoffice.gov.uk", *ADDITIONAL_HOSTS]) + PRODUCTION = ( + "PRODUCTION", + False, + ["redbox-trial.uktrade.digital", *ADDITIONAL_HOSTS], + ) class Classification(StrEnum): diff --git a/django_app/redbox_app/settings.py b/django_app/redbox_app/settings.py index 07397e1b3..42d0eca40 100644 --- a/django_app/redbox_app/settings.py +++ b/django_app/redbox_app/settings.py @@ -5,13 +5,13 @@ import environ import sentry_sdk +from django.urls import reverse_lazy from dotenv import load_dotenv from import_export.formats.base_formats import CSV +from redbox_app.setting_enums import Classification, Environment from sentry_sdk.integrations.django import DjangoIntegration from storages.backends import s3boto3 -from redbox_app.setting_enums import Classification, Environment - load_dotenv() env = environ.Env() @@ -19,6 +19,14 @@ SECRET_KEY = env.str("DJANGO_SECRET_KEY") ENVIRONMENT = Environment[env.str("ENVIRONMENT").upper()] WEBSOCKET_SCHEME = "ws" if ENVIRONMENT.is_test else "wss" +LOGIN_METHOD = env.str("LOGIN_METHOD", "magic_link") + +# TODO remove and use, additional hosts variable +if env.str("HOSTS", ""): + env_hosts = env.str("HOSTS", "").split(",") +else: + env_hosts = ENVIRONMENT.hosts + # SECURITY WARNING: don't run with debug turned on in production! DEBUG = env.bool("DEBUG") @@ -63,6 +71,12 @@ "import_export", ] +AUTH_USER_MODEL = "redbox_core.User" + +if LOGIN_METHOD == "sso": + INSTALLED_APPS.append("authbroker_client") + # AUTH_USER_MODEL = "redbox_core.SSOUser" + MIDDLEWARE = [ "django.middleware.security.SecurityMiddleware", "whitenoise.middleware.WhiteNoiseMiddleware", @@ -112,6 +126,9 @@ "django.contrib.auth.backends.ModelBackend", ] +if LOGIN_METHOD == "sso": + AUTHENTICATION_BACKENDS.append("authbroker_client.backends.AuthbrokerBackend") + AUTH_PASSWORD_VALIDATORS = [ { "NAME": "django.contrib.auth.password_validation.UserAttributeSimilarityValidator", @@ -137,10 +154,7 @@ USE_TZ = True DEFAULT_AUTO_FIELD = "django.db.models.BigAutoField" SITE_ID = 1 -AUTH_USER_MODEL = "redbox_core.User" ACCOUNT_EMAIL_VERIFICATION = "none" -LOGIN_REDIRECT_URL = "homepage" -LOGIN_URL = "sign-in" # CSP settings https://content-security-policy.com/ # https://django-csp.readthedocs.io/ @@ -148,6 +162,9 @@ "'self'", "s3.amazonaws.com", "plausible.io", + "https://www.google-analytics.com/", + "https://region1.google-analytics.com/", + "https://www.googletagmanager.com/", ) CSP_SCRIPT_SRC = ( "'self'", @@ -155,6 +172,9 @@ "plausible.io", "eu.i.posthog.com", "eu-assets.i.posthog.com", + "https://tagmanager.google.com/", + "https://www.googletagmanager.com/", + "ajax.googleapis.com/", ) CSP_OBJECT_SRC = ("'none'",) CSP_REQUIRE_TRUSTED_TYPES_FOR = ("'script'",) @@ -162,14 +182,21 @@ "'self'", "s3.amazonaws.com", ) -CSP_STYLE_SRC = ("'self'",) +CSP_INCLUDE_NONCE_IN = ("script-src",) +CSP_STYLE_SRC = ( + "'self'", + "https://tagmanager.google.com/", +) CSP_FRAME_ANCESTORS = ("'none'",) CSP_CONNECT_SRC = [ "'self'", - f"wss://{ENVIRONMENT.hosts[0]}/ws/chat/", + f"wss://{env_hosts[0]}/ws/chat/", "plausible.io", "eu.i.posthog.com", "eu-assets.i.posthog.com", + "https://www.google-analytics.com/", + "https://region1.google-analytics.com/", + "https://www.googletagmanager.com/", ] # https://pypi.org/project/django-permissions-policy/ @@ -193,7 +220,6 @@ SESSION_EXPIRE_AT_BROWSER_CLOSE = True SESSION_COOKIE_HTTPONLY = True SESSION_COOKIE_AGE = 60 * 60 * 24 -SESSION_COOKIE_SAMESITE = "Strict" SESSION_ENGINE = "django.contrib.sessions.backends.db" LOG_ROOT = "." @@ -232,10 +258,13 @@ SESSION_COOKIE_SECURE = True if ENVIRONMENT.is_test: - ALLOWED_HOSTS = ENVIRONMENT.hosts + ALLOWED_HOSTS = env_hosts else: LOCALHOST = socket.gethostbyname(socket.gethostname()) - ALLOWED_HOSTS = [LOCALHOST, *ENVIRONMENT.hosts] + ALLOWED_HOSTS = [ + LOCALHOST, + *env_hosts, + ] if not ENVIRONMENT.is_local: SENTRY_DSN = env.str("SENTRY_DSN", None) @@ -267,7 +296,9 @@ LOGGING = { "version": 1, "disable_existing_loggers": False, - "formatters": {"verbose": {"format": "%(asctime)s %(levelname)s %(module)s: %(message)s"}}, + "formatters": { + "verbose": {"format": "%(asctime)s %(levelname)s %(module)s: %(message)s"} + }, "handlers": { "file": { "level": LOG_LEVEL, @@ -308,7 +339,9 @@ elif EMAIL_BACKEND_TYPE == "GOVUKNOTIFY": EMAIL_BACKEND = "django_gov_notify.backends.NotifyEmailBackend" GOVUK_NOTIFY_API_KEY = env.str("GOVUK_NOTIFY_API_KEY") - GOVUK_NOTIFY_PLAIN_EMAIL_TEMPLATE_ID = env.str("GOVUK_NOTIFY_PLAIN_EMAIL_TEMPLATE_ID") + GOVUK_NOTIFY_PLAIN_EMAIL_TEMPLATE_ID = env.str( + "GOVUK_NOTIFY_PLAIN_EMAIL_TEMPLATE_ID" + ) else: message = f"Unknown EMAIL_BACKEND_TYPE of {EMAIL_BACKEND_TYPE}" raise ValueError(message) @@ -333,3 +366,21 @@ FILE_EXPIRY_IN_SECONDS = env.int("FILE_EXPIRY_IN_DAYS") * 24 * 60 * 60 SUPERUSER_EMAIL = env.str("SUPERUSER_EMAIL", None) MAX_SECURITY_CLASSIFICATION = Classification[env.str("MAX_SECURITY_CLASSIFICATION")] + +if LOGIN_METHOD == "sso": + AUTHBROKER_URL = env.str("AUTHBROKER_URL") + AUTHBROKER_CLIENT_ID = env.str("AUTHBROKER_CLIENT_ID") + AUTHBROKER_CLIENT_SECRET = env.str("AUTHBROKER_CLIENT_SECRET") + LOGIN_URL = reverse_lazy("authbroker_client:login") + LOGIN_REDIRECT_URL = reverse_lazy("homepage") +elif LOGIN_METHOD == "magic_link": + SESSION_COOKIE_SAMESITE = "Strict" + LOGIN_REDIRECT_URL = "homepage" + LOGIN_URL = "sign-in" +else: + LOGIN_REDIRECT_URL = "homepage" + LOGIN_URL = "sign-in" + +REPO_OWNER = env.str("REPO_OWNER", "i-dot-ai") +ANALYTICS_TAG = env.str("ANALYTICS_TAG", " ") +ANALYTICS_LINK = env.str("ANALYTICS_LINK", " ") diff --git a/django_app/redbox_app/templates/accessibility-statement-uktrade.html b/django_app/redbox_app/templates/accessibility-statement-uktrade.html new file mode 100644 index 000000000..f7594f893 --- /dev/null +++ b/django_app/redbox_app/templates/accessibility-statement-uktrade.html @@ -0,0 +1,43 @@ +{% extends "base.html" %} + +{% set pageTitle = "Accessibility statement" %} + +{% block content %} + +
+

Accessibility statement for Redbox Copilot

+ +

This accessibility statement applies to Redbox Copilot.

+

This website is run by the Department for Business and Trade. Whilst the website is currently only being trialed, overtime we may want as many people as possible to be able to use this website. For example, that means you should be able to:

+
    +
  • change colours, contrast levels and fonts
  • +
  • zoom in up to 300% without the text spilling off the screen
  • +
  • navigate most of the website using just a keyboard
  • +
  • navigate most of the website using speech recognition software
  • +
  • listen to most of the website using a screen reader (including the most recent versions of JAWS, NVDA and VoiceOver)
  • +
+

We've also made the website text as simple as possible to understand.

+

AbilityNet has advice on making your device easier to use if you have a disability.

+ +

Feedback and contact information

+

If you need information on this website in a different format like accessible PDF, large print, easy read, audio recording or braille, email {{contact_email}}

+

We'll consider your request and get back to you in 14 days.

+ +

Reporting accessibility problems with this website

+

We're always looking to improve the accessibility of this website. If you find any problems not listed on this page or think we're not meeting accessibility requirements, contact the team at {{contact_email}}.

+ +

Enforcement procedure

+

The Equality and Human Rights Commission (EHRC) is responsible for enforcing the Public Sector Bodies (Websites and Mobile Applications) (No. 2) Accessibility Regulations 2018 (the "accessibility regulations"). If you're not happy with how we respond to your complaint, contact the Equality Advisory and Support Service (EASS).

+ +

Technical information about this website's accessibility

+

The Department for Business and Trade is committed to making its website accessible, in accordance with the Public Sector Bodies (Websites and Mobile Applications) (No. 2) Accessibility Regulations 2018.

+ +

Compliance status

+

This website is fully compliant with the Web Content Accessibility Guidelines version 2.2 AA standard.

+ +

Preparation of this accessibility statement

+

This statement was prepared on 27th February 2024. It was last reviewed on 21st June 2024.

+ +
+ +{% endblock %} \ No newline at end of file diff --git a/django_app/redbox_app/templates/accessibility-statement.html b/django_app/redbox_app/templates/accessibility-statement.html index 2512a1dc8..064b3df1a 100644 --- a/django_app/redbox_app/templates/accessibility-statement.html +++ b/django_app/redbox_app/templates/accessibility-statement.html @@ -43,4 +43,4 @@

Preparation of this accessibility statement

-{% endblock %} +{% endblock %} \ No newline at end of file diff --git a/django_app/redbox_app/templates/base.html b/django_app/redbox_app/templates/base.html index 294813a85..306eeee22 100644 --- a/django_app/redbox_app/templates/base.html +++ b/django_app/redbox_app/templates/base.html @@ -1,4 +1,5 @@ {% from "macros/iai-top-nav.html" import iaiTopNav %} +{% set cspNonce=request.csp_nonce %} @@ -27,6 +28,20 @@ data-domain="redbox.ai.cabinetoffice.gov.uk" src="https://plausible.io/js/script.pageview-props.tagged-events.outbound-links.file-downloads.js"> {% endif %} + {% if environment | lower == "production" and repo_owner | lower == "uktrade" %} + + + + + +{% endif %} + {# Temporarily removed due to this adding inline styles, which our CSP doesn't allow {% if environment | lower == "preprod" %} @@ -38,6 +53,12 @@ + + + Skip to main content {% if environment | lower != "prod" %} @@ -51,7 +72,6 @@ {% set menu_items = [ {"text": "Documents", "href": url('documents')}, {"text": "Chats", "href": url('chats')}, - {"text": "My details", "href": url('demographics')}, {"text": "Sign out", "href": url('signed-out')} ] %} {% else %} @@ -109,7 +129,7 @@ - + diff --git a/django_app/redbox_app/templates/chats.html b/django_app/redbox_app/templates/chats.html index 4786ccdd9..d613c9d32 100644 --- a/django_app/redbox_app/templates/chats.html +++ b/django_app/redbox_app/templates/chats.html @@ -19,7 +19,21 @@

Chats

- + New chat @@ -126,6 +140,9 @@
+

+ Need help? Read through our Advanced Prompt FAQ section. +

Redbox can make mistakes. You must check for accuracy before using the output.

You can use up to, and including, official sensitive documents.

diff --git a/django_app/redbox_app/templates/documents.html b/django_app/redbox_app/templates/documents.html index 98fe9edea..d1dbf4c3c 100644 --- a/django_app/redbox_app/templates/documents.html +++ b/django_app/redbox_app/templates/documents.html @@ -1,95 +1,83 @@ -{% set pageTitle = "Documents" %} -{% extends "base.html" %} -{% from "macros/govuk-button.html" import govukButton %} -{% from "macros/iai-doc-list.html" import iaiDocList %} - -{% block content %} +{% set pageTitle = "Documents" %} {% extends "base.html" %} {% from +"macros/govuk-button.html" import govukButton %} {% from +"macros/iai-doc-list.html" import iaiDocList %} {% block content %}
-
- {% if ingest_errors %} -
-
-

- There was a problem uploading some documents -

-
-
    - {% for error in ingest_errors %} -
  • {{ error }}
  • - {% endfor %} -
-
+
+
+

+ There was a problem uploading some documents +

+
+
    + {% for error in ingest_errors %} +
  • {{ error }}
  • + {% endfor %} +
+
{% endif %}

Your documents

Manage documents to use with your Redbox.

-

Redbox is a service that lets you securely use a Large Language Model (LLM) with or without documents up to OFFICIAL SENSITIVE.

+

+ Redbox is a service that lets you securely use a Large Language Model + (LLM) with or without documents up to OFFICIAL. +

This means you can:

  • Chat directly with an LLM (currently GPT-4o)
  • -
  • Upload OFFICIAL and OFFICIAL SENSITIVE documents that do not include personal data.
  • +
  • + Upload OFFICIAL documents that do not include + personal data. +
  • Summarise or ask questions of these documents.
{#
- -

Documents will be stored for up to 30 days.

+ +

+ Documents will be stored for up to 30 days. +

- #} - - {# New Docs list #} - {% if completed_files or processing_files %} -

Ready to use

-

These documents are ready to chat with. Documents are deleted after 30 days

- - {{ iaiDocList( - docs = completed_files, - type = "complete" - ) }} - - {% endif %} - - {% if processing_files %} -

Processing

-

These documents will not be included in chat responses

- {{ iaiDocList( - docs = processing_files, - type = "processing" - ) }} - {% endif %} + #} {# New Docs list #} {% if completed_files or processing_files %} +

Ready to use

+

+ These documents are ready to chat with. Documents are deleted after 30 + days +

+ + {{ iaiDocList( docs = completed_files, type = "complete" ) }} + + {% endif %} {% if processing_files %} +

Processing

+

These documents will not be included in chat responses

+ {{ iaiDocList( docs = processing_files, type = "processing" ) }} {% endif + %}
- {% if completed_files or processing_files %} - {{ govukButton( - text="Start chat", - href=url('chats') - ) }} - {{ govukButton( - text="Add documents", - href=url('upload'), - classes="govuk-button--secondary" - ) }} - {% else %} - {{ govukButton( - text="Add documents", - href=url('upload') - ) }} - {% endif %} + {% if completed_files or processing_files %} {{ govukButton( text="Start + chat", href=url('chats') ) }} {{ govukButton( text="Add documents", + href=url('upload'), classes="govuk-button--secondary" ) }} {% else %} {{ + govukButton( text="Add documents", href=url('upload') ) }} {% endif %}
-
-{% endblock %} \ No newline at end of file +{% endblock %} diff --git a/django_app/redbox_app/templates/faq.html b/django_app/redbox_app/templates/faq.html new file mode 100644 index 000000000..a8f5fed26 --- /dev/null +++ b/django_app/redbox_app/templates/faq.html @@ -0,0 +1,155 @@ +{% set pageTitle = "FAQs" %} +{% extends "base.html" %} +{% from "macros/govuk-button.html" import govukButton %} + +{% block content %} + +
+

Advanced Prompt FAQs

+
+
+
+

+ + How can I improve the quality of Redbox's responses? + +

+
+
+

+ Be clear and specific in your instructions. Treat Redbox as a capable but uninformed colleague. Provide context about your task and function/profession-specific terminology. Use examples when helpful, but avoid overly constraining the model. +

+
+
+
+
+

+ + Should I use formal language when prompting Redbox? + +

+
+
+

+ While proper grammar isn't strictly necessary, clear communication is key. Focus on conveying your instructions and context accurately rather than worrying about perfect formatting. +

+
+
+
+
+

+ + How can I get Redbox to provide more detailed analysis of uploaded documents? + +

+
+
+

+ Ask Redbox to "think step-by-step" or break down its analysis process. You can also request it to consider multiple perspectives or potential implications of the document's content. +

+
+
+
+
+

+ + What should I do if Redbox's initial response isn't quite what I need? + +

+
+
+

+ Iterate on your prompt. Ask Redbox to explain its understanding of your request, then clarify or refine your instructions based on its response. Don't hesitate to have a back-and-forth dialogue to hone in on the desired output. +

+
+
+
+
+

+ + How can I use Redbox for more complex tasks like policy analysis? + +

+
+
+

+ Break down complex tasks into smaller steps. Ask Redbox to outline a process, then guide it through each step. Provide relevant background information and specify the type of analysis you need (e.g., pros/cons, historical context, potential outcomes). +

+
+
+
+
+

+ + Can I use Redbox to help draft prompts for more specialised tasks? + +

+
+
+

+ Yes! Ask Redbox to help you formulate prompts for specific tasks. Describe your goal and any relevant constraints, then ask it to generate a detailed prompt you can refine. +

+
+
+
+
+

+ + How can I ensure Redbox's summaries capture the most relevant information? + +

+
+
+

+ Specify your summarisation criteria clearly. For example, "@Summarise this report, focusing on budget implications and policy recommendations. Highlight any areas of uncertainty or debate." +

+
+
+
+
+

+ + Are there techniques to improve Redbox's accuracy on factual queries? + +

+
+
+

+ For fact-based questions, ask Redbox to cite specific sections of the uploaded documents by using the @search feature. You can also request it to express its confidence level and explain its reasoning. +

+
+
+
+
+

+ + How can I use Redbox more effectively for comparative analysis of multiple documents? + +

+
+
+

+ Clearly outline the criteria for comparison. Ask Redbox to create a structured format (e.g., a table or bullet-point list) to present the comparative analysis, making it easier to spot key differences and similarities. +

+
+
+
+
+

+ + What should I do if I suspect Redbox has made a mistake? + +

+
+
+

+ Ask Redbox to double-check its work and explain its reasoning. You can also rephrase your question or provide additional context to see if it leads to a different result. Always verify important information against original sources. +

+
+
+
+
+ + + +{% endblock %} \ No newline at end of file diff --git a/django_app/redbox_app/templates/homepage.html b/django_app/redbox_app/templates/homepage.html index df071c32d..5945f1754 100644 --- a/django_app/redbox_app/templates/homepage.html +++ b/django_app/redbox_app/templates/homepage.html @@ -1,15 +1,19 @@ {% set pageTitle = "" %} {% extends "base.html" %} {% from "macros/govuk-button.html" import govukButton %} - {% block content %}
+ {% if repo_owner | lower == "uktrade" %} +

Use Artificial Intelligence (AI) to interact with Official level documents from your own document set

+

You may summarise documents, ask questions related to one or more documents, and chat directly with the AI to assist you in your work. You can use up to, and including, Official documents.

+ {% else %}

Ask any question of documents in your Redbox

Use Artificial Intelligence (AI) to get insights from your personal document set. You can use up to, and including, {{ security }} documents.

+ {% endif %}
@@ -27,15 +30,208 @@

Ask any question of documen
-
- +

Upload

Easily upload a wide range of document formats into your personal Redbox.

+
+

Chat

You can communicate with your Redbox and ask questions of the documents contained within. Use Redbox to unlock deeper insights in the various documents you have uploaded, pulling together summaries of individual or combined documents.

+
+

Secure

Redbox is built in our secure and private cloud which enables you to upload, up to, and including, {{ security }} documents in your Redbox.

+
+
+
+
+
+
+
+

Frequently Asked Questions

+

Responses to these FAQs about Redbox were generated using Redbox.

+
+
+
+
+
+

+ + What is Redbox? + +

+
+
+

+ Redbox is a new kind of tool for civil servants which uses Large Language Models (LLMs) to process text-based information. + Redbox is currently only available to a small number of DBT staff as the department assesses its usefulness. +

+

+ What makes Redbox particularly useful is that it can look at any documents you upload and work with you on them. +

+
+
+
+
+

+ + What is Generative AI? + +

+
+
+

+ Generative AI is a fairly new kind of algorithmic machine learning based on 'neural architecture' This architecture allows + machine learning models (called large language models in this case) to be trained on large amounts of information, and then + be able to converse with a user by 'generating' answers to questions or assisting with text-based processing tasks. +

+
+
+
+
+

+ + Do I need technical skills to use Redbox? + +

+
+
+

+ Not at all, the interface for Redbox comprises a document upload feature and a chat feature. No technical skills are required, + but an understanding of what large language models are and what they can do makes an enormous different to users when interacting + with them. The principal skill to use Redbox well comes down to a skill called 'prompt engineering', which is just natural language + you type in and get the tool to respond. There are some great Gov.uk short courses on generative AI you may consider taking: + See the courses here. +

+
+
+
+
+

+ + What can I and can’t I share with Redbox? + +

+
+
+

+ For the moment, you can share text and documents up to the {{ security }} classification level. Take care to check before sharing + text or documents with Redbox that they do not exceed this designation. +

+

+ Alongside this, do not share Personal Data with Redbox, for more information on what constitutes personal data, watch this short + video. If + you are unsure whether you are processing personal data, you should contact the Data Protection Team + to check. +

+
+
+
+
+

+ + What is a ‘hallucination’? + +

+
+
+

+ In the context of LLMs, a hallucination refers to the generation of information that is incorrect, nonsensical, or entirely fabricated, despite + being presented in a credible manner. This can happen when the model produces details, facts, or asserts that are not grounded in reality or the + data it was trained on, potentialliy leading to misinformation or misunderstanding. +

+
+
+
+
+

+ + What are the main things Redbox can do? + +

+
+
+

+ Redbox allows you to chat to a LLM, summarise and interact with documents (up to around 140 pages), and even search within documents. Here are some + helpful insights into those: +

+

Chat

+

+ By using @chat at the beginning of any message you send, you will speak directly with the LLM, gaining access to its 'world model', which is everything + it was trained on. +

+
+

+ Top tip: when you chat with the LLM, all the contents of your current chat are considered by the LLM, and you can switch between chat and other interaction + types (more below) in the same chat. +

+
+

Summarise

+

+ By using @summarise at the beginning of any message you send, and ensuring that a document is selected in the left hand panel, you can get the LLM to + look at an entire document and summarise it for you. +

+
+

+ Top tip: like using @chat, using the @summarise feature is very flexible, and you can instruct the LLM to summarise from a particular point of view, or + follow a particular kind of format in its response. Here's an example prompt - imagine you have selected an HR Policy document on taking leave at DBT:

+ @summarise this HR policy from the perspective of a line manager, as my line report has asked to take extended leave and I want to know what steps I need to take.

+ In this example, Redbox will take those instructions into account and generate a response that best fits your requirements. +

+
+

Search

+

+ By using @search at the beginning of any message you send, with any number of documents selected in the side panel, Redbox will search within them + for results that relate to the question or instruction given. +

+
+

+ Top tip: Search is great at finding exact parts of documents which relate to a given theme or topic, and the results will also show you the exact + text it found from the documents os you can be sure of accuracy. +

+
+
+
+
+
+

+ + What can’t Redbox do? + +

+
+
+

+ Redbox is limited to its own training data and document processing, as it cannot currently search the internet, making it inadequate for getting reliable real-time information. +

+

+ Redbox is not well-suited for tasks that involve precise numerical calculations, counting, or data tabulation, such as those often required in spreadsheets or financial analyses. They + may generate incorrect numerical results or misinterpret mathematical expressions, leading to unrealiable outputs. Additionally, Redbox struggles with organising and + manipulating structured data effectively, as it is primarily designed for language generation rather than data processing. +

+

+ This can make Redbox inadequate for tasks requiring accurate data comparison, aggregation, and spreadsheet functions, where human oversight and intervention remain essential + for ensuring accuracy and reliability. +

+
+
+
+
+

+ + Why can Redbox get things wrong? + +

+
+
+

+ Redbox can get things wrong for several reasons, primarily due to its reliance on patterns found in the training data of the LLM rather than true understanding or + reasoning. Redbox may generate inaccurate information because they lack context awareness, leading to misinterpretations, especially in complex or nuanced scenarios. + Additionally, biases inherent in the LLM training data can result in the propagation of errors, stereotypes, or nonsensical outputs, further compromising the accuracy of Redbox' responses. + For more information, read the FAQ section on 'hallucinations' above. +

+
diff --git a/django_app/redbox_app/templates/macros/chat-macros.html b/django_app/redbox_app/templates/macros/chat-macros.html index f8d03d7b9..aa0144bdf 100644 --- a/django_app/redbox_app/templates/macros/chat-macros.html +++ b/django_app/redbox_app/templates/macros/chat-macros.html @@ -14,11 +14,14 @@

  • I can @chat about general questions, not related to your documents
  • -
  • I can @chat/documents over selected documents and +
  • I can @summarise over selected documents and do Q&A on them, extract information, summarise and more!
  • I can help you @search over selected documents and do Q&A on them, with citations
  • -
+
  • I can help you @coach by analysing your current chat window and + giving targeted advice on how to use Redbox most effectively. I can see how you are using tools like @summarise, + @search, and @chat and give feedback to help you get more out of Redbox.
  • +

    diff --git a/django_app/redbox_app/templates/privacy-notice-uktrade.html b/django_app/redbox_app/templates/privacy-notice-uktrade.html new file mode 100644 index 000000000..bd46307a3 --- /dev/null +++ b/django_app/redbox_app/templates/privacy-notice-uktrade.html @@ -0,0 +1,68 @@ +{% extends "base.html" %} + +{% set pageTitle = "Privacy notice" %} + + +{% block content %} + +
    +

    Privacy notice for Redbox Copilot

    +
    +

    This notice sets out how we will use your personal data, and your rights. It is made under Articles 13 and/or 14 of the UK General Data Protection Regulation (UK GDPR).

    + +

    Your data

    +

    Purpose

    +

    The purposes for which we are processing your personal data are:

    +
      +
    • enabling login to the platform
    • +
    • chat history and log information will be anonymised and analysis of this will be used to improve the service
    • +
    + +

    The data

    +

    We will process the following personal data:

    +
      +
    • organisational email address
    • +
    • chat history
    • +
    + + +

    Legal basis of processing

    +

    The legal basis for processing your personal data is:

    +
      +
    • data subject consents
    • +
    + +

    Recipients

    +

    Your personal data will be shared by us with analysts/researchers within the Department for Business and Trade if you have consented to be contacted further about Redbox Copilot.

    +

    As your personal data will be stored on our IT infrastructure it will also be shared with our data processors who provide email, and document management and storage services.

    + +

    Retention

    +

    Your personal data will be retained for 12 months after account inactivity. At that point we will delete your email address, depersonalising the data we hold. If you have provided us consent to contact you, we will retain your email until and continue sharing it with evaluators so they can conduct research on the long term impact of Redbox Copilot.

    + +

    Your rights

    +

    You have the right to request information about how your personal data are processed, and to request a copy of that personal data.

    +

    You have the right to request that any inaccuracies in your personal data are rectified without delay.

    +

    You have the right to request that any incomplete personal data are completed, including by means of a supplementary statement.

    +

    You have the right to request that your personal data are erased if there is no longer a justification for them to be processed.

    +

    You have the right in certain circumstances (for example, where accuracy is contested) to request that the processing of your personal data is restricted.

    +

    You have the right to object to the processing of your personal data where it is processed for direct marketing purposes.

    +

    You have the right to withdraw consent to the processing of your personal data at any time.

    + +

    International transfers

    +

    As your personal data is stored on our IT infrastructure, and shared with our data processors, it may be transferred and stored securely outside the UK. Where that is the case it will be subject to equivalent legal protection through an adequacy decision or reliance on Standard Contractual Clauses.

    + +

    Complaints

    +

    If you consider that your personal data has been misused or mishandled, you may make a complaint to the Information Commissioner, who is an independent regulator. The Information Commissioner can be contacted at: Information Commissioner's Office, Wycliffe House, Water Lane, Wilmslow, Cheshire, SK9 5AF, or 0303 123 1113, or icocasework@ico.org.uk. Any complaint to the Information Commissioner is without prejudice to your right to seek redress through the courts.

    + +

    Contact details

    +

    If you have any questions about this privacy notice or how we handle your personal information, you can also write to us at: {{contact_email}}

    +

    The Department for Business and Trade are registered as a Data Controller under the General Data Protection Regulation and Data Protection Act 2018, and are based at: Department for Business and Trade, Old Admiralty Building, Admiralty Place, London, SW1A 2DY

    + + +

    Changes to this notice

    +

    We may change this privacy notice. When we make changes to this notice, the "last updated" date at the bottom of this page will also change. Any changes to this privacy notice will apply to you and your data immediately. If these changes affect how your personal data is processed, the Department for Business and Trade will take reasonable steps to make sure you know.

    +

    Last updated: 21st June 2024

    +
    +
    + +{% endblock %} \ No newline at end of file diff --git a/django_app/redbox_app/templates/privacy-notice.html b/django_app/redbox_app/templates/privacy-notice.html index 14657b0f3..bd9fbe136 100644 --- a/django_app/redbox_app/templates/privacy-notice.html +++ b/django_app/redbox_app/templates/privacy-notice.html @@ -17,13 +17,14 @@

    Purpose

    The purposes for which we are processing your personal data are:

    • enabling login to the platform
    • -
    • information will be aggregated and used for anonymous further analysis
    • +
    • chat history and log information will be anonymised and analysis of this will be used to improve the service

    The data

    We will process the following personal data:

    • organisational email address
    • +
    • chat history
    @@ -67,4 +68,4 @@

    Changes to this notice

    -{% endblock %} +{% endblock %} \ No newline at end of file diff --git a/django_app/redbox_app/templates/support-uktrade.html b/django_app/redbox_app/templates/support-uktrade.html new file mode 100644 index 000000000..b7b67b740 --- /dev/null +++ b/django_app/redbox_app/templates/support-uktrade.html @@ -0,0 +1,59 @@ +{% extends "base.html" %} + +{% set pageTitle = "Support" %} + +{% block content %} + +
    + +

    Support

    + + {# +

    User guide

    + +

    You can download the User Guide (PDF, 1.1MB), a step-by-step guide to signing in and using your account.

    + +

    Accessing Redbox Copilot

    +

    You can access your Redbox Copilot account by visiting . You'll need to type your work email address in the box provided and select the submit button.

    + #} + +

    Signing in

    +

    If you're having trouble signing in to the Redbox Copilot platform, check that:

    + +
      +
    • you're using your work email address
    • +
    • you've typed your work email address correctly
    • +
    + +

    You should check your junk or spam folder for an email from Redbox Copilot.

    + +

    If you're still having trouble then you can contact us using the details at the bottom of this page.

    + + {# +

    Email address not allowed

    +

    If you see the error message: "Currently you need a Civil Service email address to register", contact us using the details at the bottom of this page and include the name of your organisation.

    + +

    Can’t find my government department

    +

    If you can’t find the department you belong to when you are signing in for the first time then go to the bottom of the dropdown list and if you are:

    +
      +
    • a civil servant then choose: Other - Civil Servant
    • +
    • a public servant then choose: Other - Public Servant
    • +
    • not sure then choose: Other
    • +
    + +

    Can’t find my grade

    +

    If the grades shown do not fit with your role then either choose the one that is equivalent to your role or select ‘Other’.

    + #} + +

    Bug or site vulnerability

    +

    If you've found an issue or a vulnerability, contact us using the details at the bottom of this page including as much information as possible.

    + +

    Website not loading

    +

    If the website is not loading, please close the browser tab and try again later.

    + +

    Contact us

    +

    If you've read the information above but haven't been able to find a solution to your problem, please email us at {{contact_email}} including as much information as possible.

    + +
    + +{% endblock %} \ No newline at end of file diff --git a/django_app/redbox_app/templates/support.html b/django_app/redbox_app/templates/support.html index acbf4d170..74ef590dc 100644 --- a/django_app/redbox_app/templates/support.html +++ b/django_app/redbox_app/templates/support.html @@ -53,9 +53,9 @@

    Website not loading

    If the website is not loading, please close the browser tab and try again later.

    Contact us

    -

    If you've read the information above but haven't been able to find a solution to your problem, please email us at {{contact_email}} including as much information as possible.

    +

    If you've read the information above but haven't been able to find a solution to your problem, please email us at {{contact_email}} including as much information as possible, including the current version number, which is {{ version }}.

    -{% endblock %} +{% endblock %} \ No newline at end of file diff --git a/django_app/redbox_app/urls.py b/django_app/redbox_app/urls.py index 0d5af0c56..bcf517e69 100644 --- a/django_app/redbox_app/urls.py +++ b/django_app/redbox_app/urls.py @@ -17,6 +17,9 @@ path("signed-out/", auth_views.signed_out_view, name="signed-out"), ] +if settings.LOGIN_METHOD == "sso": + auth_urlpatterns.append(path("auth/", include("authbroker_client.urls"))) + info_urlpatterns = [ path("privacy-notice/", info_views.privacy_notice_view, name="privacy-notice"), path( @@ -36,9 +39,13 @@ chat_urlpatterns = [ path("chats//", views.ChatsView.as_view(), name="chats"), path("chats/", views.ChatsView.as_view(), name="chats"), - path("chat//title/", views.ChatsTitleView.as_view(), name="chat-titles"), + path( + "chat//title/", views.ChatsTitleView.as_view(), name="chat-titles" + ), path("post-message/", views.post_message, name="post-message"), - path("citations//", views.CitationsView.as_view(), name="citations"), + path( + "citations//", views.CitationsView.as_view(), name="citations" + ), path("ratings//", views.RatingsView.as_view(), name="ratings"), ] @@ -51,13 +58,25 @@ path("", views.homepage_view, name="homepage"), path("health/", views.health, name="health"), path("file-status/", views.file_status_api_view, name="file-status"), - path("check-demographics/", views.CheckDemographicsView.as_view(), name="check-demographics"), + path( + "check-demographics/", + views.CheckDemographicsView.as_view(), + name="check-demographics", + ), path("demographics/", views.DemographicsView.as_view(), name="demographics"), + path("faq/", views.faq_view, name="faq"), ] urlpatterns = ( - info_urlpatterns + other_urlpatterns + auth_urlpatterns + chat_urlpatterns + file_urlpatterns + admin_urlpatterns + info_urlpatterns + + other_urlpatterns + + auth_urlpatterns + + chat_urlpatterns + + file_urlpatterns + + admin_urlpatterns ) if settings.DEBUG: - urlpatterns = urlpatterns + static(settings.STATIC_URL, document_root=settings.STATIC_ROOT) + urlpatterns = urlpatterns + static( + settings.STATIC_URL, document_root=settings.STATIC_ROOT + ) diff --git a/django_app/start.sh b/django_app/start.sh index b4219c68e..4c29439cd 100644 --- a/django_app/start.sh +++ b/django_app/start.sh @@ -1,5 +1,6 @@ #!/bin/sh +venv/bin/django-admin makemigrations venv/bin/django-admin migrate venv/bin/django-admin collectstatic --noinput venv/bin/django-admin compress --force --engine jinja2 diff --git a/django_app/tests/views/test_misc_views.py b/django_app/tests/views/test_misc_views.py index fc6ca6ffa..ba8eaebd7 100644 --- a/django_app/tests/views/test_misc_views.py +++ b/django_app/tests/views/test_misc_views.py @@ -2,9 +2,10 @@ from http import HTTPStatus import pytest +from django.contrib.auth import get_user_model from django.test import Client -from redbox_app.redbox_core.models import User +User = get_user_model() logger = logging.getLogger(__name__) diff --git a/docker-compose.yml b/docker-compose.yml index 9fe715ec7..289d13f90 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -1,3 +1,5 @@ +version: '3' + services: django-app: image: django-app:latest @@ -26,6 +28,8 @@ services: timeout: 30s retries: 24 start_period: 60s + + db: image: postgres:13 env_file: @@ -42,6 +46,7 @@ services: timeout: 30s retries: 24 start_period: 30s + requirements: image: python:3.12 profiles: @@ -82,6 +87,7 @@ services: networks: - redbox-app-network restart: unless-stopped + core-api: image: redbox-core-api:latest build: @@ -116,6 +122,7 @@ services: timeout: 30s retries: 24 start_period: 60s + minio: image: minio/minio expose: @@ -127,7 +134,8 @@ services: environment: - MINIO_ACCESS_KEY=minioadmin - MINIO_SECRET_KEY=minioadmin - command: server --console-address ":9001" /data + entrypoint: sh + command: -c "mkdir -p /data/redbox-storage-dev && minio server --console-address ":9001" /data" networks: - redbox-app-network volumes: @@ -139,6 +147,7 @@ services: timeout: 30s retries: 24 start_period: 30s + elasticsearch: image: elasticsearch:8.12.0 volumes: @@ -164,6 +173,7 @@ services: timeout: 30s retries: 24 start_period: 60s + kibana: image: kibana:8.12.0 ports: @@ -180,6 +190,7 @@ services: elasticsearch: condition: service_healthy restart: unless-stopped + redis: image: redis:7-alpine restart: always @@ -222,6 +233,7 @@ services: timeout: 20m retries: 5 + networks: redbox-app-network: driver: bridge @@ -233,4 +245,4 @@ volumes: mlflow-data: redis: driver: local - redis-insight: + redis-insight: \ No newline at end of file diff --git a/docs/contributing.md b/docs/contributing.md index 7551816cf..fc2ce799f 100644 --- a/docs/contributing.md +++ b/docs/contributing.md @@ -5,12 +5,14 @@ First off, thanks for taking the time to contribute! ❤️ All types of contributions are encouraged and valued. See the [Table of Contents](#table-of-contents) for different ways to help and details about how this project handles them. Please make sure to read the relevant section before making your contribution. It will make it a lot easier for us maintainers and smooth out the experience for all involved. The community looks forward to your contributions. 🎉 > And if you like the project, but just don't have time to contribute, that's fine. There are other easy ways to support the project and show your appreciation, which we would also be very happy about: +> > - Star the project > - Tweet about it > - Refer this project in your project's readme > - Mention the project at local meetups and tell your friends/colleagues + ## Table of Contents - [Contributing to Redbox](#contributing-to-redbox) @@ -25,7 +27,6 @@ All types of contributions are encouraged and valued. See the [Table of Contents - [Commit Messages](#commit-messages) - [Attribution](#attribution) - ## Code of Conduct This project and everyone participating in it is governed by the @@ -33,7 +34,6 @@ This project and everyone participating in it is governed by the By participating, you are expected to uphold this code. Please report unacceptable behavior to i-dot-ai-enquiries@cabinetoffice.gov.uk - ## I Have a Question > If you want to ask a question, we assume that you have read the available [Documentation](https://github.com/i-dot-ai/redbox/tree/main/docs). @@ -51,8 +51,21 @@ We will then take care of the issue as soon as possible. ## I Want To Contribute > ### Legal Notice +> > When contributing to this project, you must agree that you have authored 100% of the content, that you have the necessary rights to the content and that the content you contribute may be provided under the project license. +### Branch strategy + +To maintain a well-organised and efficient workflow, we must adhere to a specific branching strategy. + +- The **main** branch should be reserved exclusively for production-ready code that has previously been tested in [the sandbox environment](https://redbox-sandbox.uktrade.digital/) +- The **dev** branch is intended for changes that need to be tested in [the sandbox environment](https://redbox-sandbox.uktrade.digital/) + +### Pull requests + +- Each feature, bug fix or task should be on a **dedicated feature branch**, following [this convention](https://dev.to/varbsan/a-simplified-convention-for-naming-branches-and-commits-in-git-il4) +- The **main** and **dev** branches are protected. This means all Pull Requests must be approved by at least one reviewer before merging. + ### Reporting Bugs #### Before Submitting a Bug Report @@ -71,24 +84,25 @@ A good bug report shouldn't leave others needing to chase you up for more inform - Can you reliably reproduce the issue? And can you also reproduce it with older versions? + #### How Do I Submit a Good Bug Report? > You must never report security related issues, vulnerabilities or bugs including sensitive information to the issue tracker, or elsewhere in public. Instead sensitive bugs must be sent by email to <>. + We use GitHub issues to track bugs and errors. If you run into an issue with the project: - Open an [Issue](https://github.com/i-dot-ai/redbox/issues/new). (Since we can't be sure at this point whether it is a bug or not, we ask you not to talk about a bug yet and not to label the issue.) - Explain the behavior you would expect and the actual behavior. -- Please provide as much context as possible and describe the *reproduction steps* that someone else can follow to recreate the issue on their own. This usually includes your code. For good bug reports you should isolate the problem and create a reduced test case. +- Please provide as much context as possible and describe the _reproduction steps_ that someone else can follow to recreate the issue on their own. This usually includes your code. For good bug reports you should isolate the problem and create a reduced test case. - Provide the information you collected in the previous section. Once it's filed: - The project team will label the issue accordingly. - A team member will try to reproduce the issue with your provided steps. If there are no reproduction steps or no obvious way to reproduce the issue, the team will ask you for those steps and mark the issue as `needs-repro`. Bugs with the `needs-repro` tag will not be addressed until they are reproduced. -- If the team is able to reproduce the issue, it will be marked `needs-fix`, as well as possibly other tags (such as `critical`), and the issue will be left to be [implemented by someone](#your-first-code-contribution). - +- If the team is able to reproduce the issue, it will be marked `needs-fix`, as well as possibly other tags (such as `critical`), and the issue will be left to be implemented by someone. ### Suggesting Enhancements @@ -102,6 +116,7 @@ This section guides you through submitting an enhancement suggestion for Redbox, - Find out whether your idea fits with the scope and aims of the project. It's up to you to make a strong case to convince the project's developers of the merits of this feature. Keep in mind that we want features that will be useful to the majority of our users and not just a small subset. If you're just targeting a minority of users, consider writing an add-on/plugin library. + #### How Do I Submit a Good Enhancement Suggestion? Enhancement suggestions are tracked as [GitHub issues](https://github.com/i-dot-ai/redbox/issues). @@ -109,11 +124,11 @@ Enhancement suggestions are tracked as [GitHub issues](https://github.com/i-dot- - Use a **clear and descriptive title** for the issue to identify the suggestion. - Provide a **step-by-step description of the suggested enhancement** in as many details as possible. - **Describe the current behavior** and **explain which behavior you expected to see instead** and why. At this point you can also tell which alternatives do not work for you. -- You may want to **include screenshots and animated GIFs** which help you demonstrate the steps or point out the part which the suggestion is related to. You can use [this tool](https://www.cockos.com/licecap/) to record GIFs on macOS and Windows, and [this tool](https://github.com/colinkeenan/silentcast) or [this tool](https://github.com/GNOME/byzanz) on Linux. +- You may want to **include screenshots and animated GIFs** which help you demonstrate the steps or point out the part which the suggestion is related to. You can use [this tool](https://www.cockos.com/licecap/) to record GIFs on macOS and Windows, and [this tool](https://github.com/colinkeenan/silentcast) or [this tool](https://github.com/rhcarvalho/byzanz-gui) on Linux. - **Explain why this enhancement would be useful** to most Redbox users. You may also want to point out the other projects that solved it better and which could serve as inspiration. - ## Styleguides + ### Commit Messages - Keep the subject line short, clear and descriptive. @@ -122,4 +137,5 @@ Enhancement suggestions are tracked as [GitHub issues](https://github.com/i-dot- - Limit the subject line to 50 characters. ## Attribution + This guide is based on the **contributing-gen**. [Make your own](https://github.com/bttger/contributing-gen)! diff --git a/download_embedder.py b/download_embedder.py new file mode 100644 index 000000000..9e8cad814 --- /dev/null +++ b/download_embedder.py @@ -0,0 +1,29 @@ +import argparse +import logging + +from redbox.embeddings.model import SentenceTransformerDB + +logging.basicConfig(level=logging.INFO) +log = logging.getLogger() + + +def download(): + parser = argparse.ArgumentParser( + description="Download Sentence Transformer Embedder" + ) + parser.add_argument( + "--embedding_model", + type=str, + required=True, + help="Name of the Sentence Transformer Model", + ) + + args = parser.parse_args() + + log.info("💾 Downloading Sentence Transformer Embedder: %s", args.embedding_model) + SentenceTransformerDB(args.embedding_model) + log.info("✅ Downloaded Sentence Transformer Embedder: %s", args.embedding_model) + + +if __name__ == "__main__": + download() diff --git a/mlflow/requirements.txt b/mlflow/requirements.txt index 27042bd37..e81719405 100644 --- a/mlflow/requirements.txt +++ b/mlflow/requirements.txt @@ -1,11 +1,11 @@ mlflow==2.14.3 pydantic==1.10.15 -starlette==0.38.1 +starlette==0.40.0 limits==3.13.0 watchfiles==0.22.0 fastapi==0.111.1 slowapi==0.1.9 uvicorn==0.30.3 -aiohttp===3.9.5 +aiohttp===3.10.2 gunicorn==22.0.0 boto3==1.34.148 \ No newline at end of file diff --git a/notebooks/__init__.py b/notebooks/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/notebooks/chat_log_eda.ipynb b/notebooks/chat_log_eda.ipynb new file mode 100644 index 000000000..b8a52eee5 --- /dev/null +++ b/notebooks/chat_log_eda.ipynb @@ -0,0 +1,475 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chat History Inital EDA (17/07/24)\n", + "\n", + "**Intro** \n", + "\n", + "* This notebook is exploring the chat history logs for Redbox pulled on 17th July. \n", + "\n", + "* Populated columns of this data are 'id', 'created_at', 'modified_at', 'name', 'users', 'id.1', 'created_at.1', 'modified_at.1', 'chat_history', 'text', 'role'.\n", + "\n", + "* Aim of this work is to discover how users are using Redbox and find any other features that may be important to log.\n", + "\n", + "* **IMPORTANT** - For this to work you must save your chathistory.csv dump in notebooks/evaluation/data/chat_histories\n", + "\n", + "**Next Steps** \n", + "\n", + "* Want to get the actual Redbox route so we don't have to rely on inferred data\n", + "\n", + "* *Real* patterns hard to find due to limited data\n", + "\n", + "* Could be interested in how different LLM change user behaviour - would need to store this\n", + "\n", + "* Limitations right now are that we can't really tell whether a chat is successful (e.g. User gets intended output from Redbox). Creating a UI and measuring this would mean we could identify differences in user behaviour that lead to useful/unuseful outputs.\n", + "\n", + "* For RAG use, I'd be interested to see what documents work well or not. Maybe we don't want to store the whole document but maybe the size, type etc. Pairing this with a success variable could lead to interesting insights. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "chathistory_df = pd.read_csv('evaluation/data/chat_histories/chathistory.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "chathistory_df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "chathistory_df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "chathistory_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "chathistory_df['role'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Ensuring date columns are stored as datetimes\n", + "date_cols = ['created_at', 'modified_at','created_at.1', 'modified_at.1']\n", + "for i in date_cols:\n", + " chathistory_df[i] = pd.to_datetime(chathistory_df[i])\n", + "chathistory_df.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Top users (inc. over time)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Pulling out user input rows only\n", + "# Identifying top users of redbox\n", + "user_inputs_df = chathistory_df[(chathistory_df['role']=='user')]\n", + "user_inputs_df['users'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# How has total redbox usage changed overtime\n", + "user_inputs_df.groupby(chathistory_df['modified_at'].rename('Days').dt.day).size().plot(ylabel='No. of prompts', title='Total Redbox Usage Over Time')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This shows a general increase in usage of redbox. \n", + "\n", + "Now, interested in increase/decrease by user." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import seaborn as sns\n", + "\n", + "# Plotting how each users usage has changed over time\n", + "pivot_user_time = user_inputs_df[['modified_at', 'users']].groupby(pd.Grouper(key='modified_at', axis=0, freq='2D', sort=True)).value_counts().reset_index(name='count').pivot(index='modified_at', columns='users', values='count')\n", + "fig = sns.lineplot(data=pivot_user_time, markers=True)\n", + "fig.set_xlabel('Date')\n", + "fig.set_ylabel('No. of Prompts')\n", + "fig.tick_params(axis='x', rotation=90)\n", + "sns.move_legend(fig, \"upper left\", bbox_to_anchor=(1, 1), title='Users')\n", + "fig.set_title('Line graph to show number of prompts for each user over time')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Maybe not the best plot to use due to null entries, however, I think it shows trends in users usage and shows those users which have only used Redbox once.\n", + "\n", + "Next, interested to see whether users that aren't using it as frequently are using certain routes of Redbox to those who are, for example. \n", + "\n", + "Routes column is not currently populated so I have inferred this by identifying whether the user has used @chat, @summarise or otherwise." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Popularity of User Routes (inc. process map)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Creating the inferred routes from the text column \n", + "def routes(row):\n", + " if '@chat' in row['text']:\n", + " val = 'chat'\n", + " elif '@summarise' in row['text']:\n", + " val = 'summarise'\n", + " else:\n", + " val = 'rag'\n", + " return val\n", + "\n", + "user_inputs_df['infer_route'] = user_inputs_df.apply(routes, axis=1)\n", + "user_inputs_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Creating df of each users most popular prompt route\n", + "popular_routes = user_inputs_df[['users', 'infer_route']].value_counts().reset_index(name='count')\n", + "popular_routes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# sns.barplot(popular_routes['infer_route'].value_counts().reset_index(name='count'), x='infer_route', y='count', hue='infer_route', title='Most popular Routes')\n", + "popular_routes[['infer_route', 'count']].groupby('infer_route').sum().reset_index()\n", + "fig = sns.barplot(popular_routes[['infer_route', 'count']].groupby('infer_route').sum().reset_index(), x='infer_route', y='count', hue='infer_route')\n", + "fig.set_xlabel('Inferred Route')\n", + "fig.set_title('Bar plot to show most popular routes')\n", + "fig.set_ylabel('No. of Prompts')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = sns.barplot(data=popular_routes, x='users', y='count', hue='infer_route')\n", + "fig.tick_params(axis='x', rotation=90)\n", + "fig.set_xlabel('Users')\n", + "fig.set_title('Bar plot to show most popular routes by user')\n", + "fig.set_ylabel('No. of Prompts')\n", + "fig.legend(title='Inferred Route')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# See if theres a change in popular routes over time\n", + "route_over_time = user_inputs_df[['modified_at', 'infer_route']].groupby(pd.Grouper(key='modified_at', axis=0, freq='2D', sort=True)).value_counts().reset_index(name='count')\n", + "route_over_time" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = sns.lineplot(data=route_over_time, markers=True, x='modified_at', y='count', hue='infer_route')\n", + "fig.set_ylabel('No. of Prompts')\n", + "fig.set_xlabel('Date')\n", + "fig.tick_params(axis='x', rotation=90)\n", + "fig.legend(title='Inferred Route')\n", + "sns.move_legend(fig, \"upper left\", bbox_to_anchor=(1, 1))\n", + "fig.set_title('Line graph to show number of inputs for each prompt type over time')\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Shows that over the last couple of days, the chat function has overtaken summarise in terms of popularity. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Process Map \n", + "\n", + "Interested to see if theres any distinctive patterns between users, whether that effects their use of Redbox. It would be interesting to compare successful and unsuccessful routes if we measure this somehow so we can provide users with better training." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pm4py.objects.conversion.log import converter as log_conversion_factory\n", + "from pm4py.algo.discovery.heuristics import algorithm as heuristics_miner\n", + "from pm4py.visualization.heuristics_net import visualizer as hn_visualizer\n", + "\n", + "def create_hnet_vis(event_df):\n", + "\n", + " # Convert DataFrame to pm4py event log format\n", + " event_df.columns = ['case:concept:name', 'time:timestamp', 'concept:name']\n", + " log = log_conversion_factory.apply(event_df)\n", + "\n", + " # Discover Heuristic Net\n", + " heu_net = heuristics_miner.apply_heu(log)\n", + "\n", + " # Visualize Heuristic Net\n", + " gviz = hn_visualizer.apply(heu_net)\n", + " hn_visualizer.view(gviz)\n", + "\n", + "\n", + "route_map_df = user_inputs_df[['id', 'modified_at', 'infer_route']]\n", + "create_hnet_vis(route_map_df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Creating a process map for each user.\n", + "for i in user_inputs_df['users'].unique():\n", + " user_input_df_filter = user_inputs_df[user_inputs_df['users']==i]\n", + " route_map_df = user_input_df_filter[['id', 'modified_at', 'infer_route']]\n", + " print(i)\n", + " create_hnet_vis(route_map_df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prompt detail effects\n", + "\n", + "I'm assuming that each chat ends 'successfully' (quite flawed) but want to see whether the level of detail (currently measuring this by word length but could use more advanced metrics) effects how many prompts a user needs to try before they are done with Redbox. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Creating column for number of words\n", + "user_inputs_df['no_input_words'] = user_inputs_df[\"text\"].apply(lambda n: len(n.split()))\n", + "user_inputs_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = sns.displot(user_inputs_df.no_input_words)\n", + "fig.set_axis_labels(x_var='No. of words in prompt', y_var='Count')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Seems to be an anomaly over 1400 words, removing this to see distribution of rest." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sns.histplot(user_inputs_df[user_inputs_df['no_input_words']<700]['no_input_words'])\n", + "fig.set_axis_labels(x_var='No. of words in prompt', y_var='Count')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# How does this vary between users\n", + "fig = sns.displot(\n", + " user_inputs_df[user_inputs_df['no_input_words']<700],\n", + " x=\"no_input_words\", \n", + " col=\"users\", col_wrap=4, height=4,\n", + ")\n", + "fig.set_axis_labels(x_var='No. of words in prompt', y_var='Count')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# How does this vary between users and routes\n", + "fig = sns.displot(\n", + " user_inputs_df[user_inputs_df['no_input_words']<700],\n", + " x=\"no_input_words\", hue='infer_route',\n", + " col=\"users\", col_wrap=4, height=4,\n", + ")\n", + "fig.set_axis_labels(x_var='No. of words in prompt', y_var='Count')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "no_inputs_df = user_inputs_df[[\"id\", \"users\"]].groupby(\"id\").value_counts().reset_index(name='no_inputs')\n", + "no_inputs_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mean_inputs_df = user_inputs_df[[\"id\", \"users\", \"no_input_words\"]].groupby(by=['id', 'users']).agg({'no_input_words': 'mean'}).rename(columns={'no_input_words':'mean_input_words'}).reset_index()\n", + "mean_inputs_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "compare_inputs_words_df = no_inputs_df.merge(mean_inputs_df, left_on=['id', 'users'], right_on=['id', 'users'])\n", + "\n", + "# Remove anomaly to see if pattern more clearly - maybe should have been done before calculating the mean\n", + "fig = sns.scatterplot(\n", + " data=compare_inputs_words_df[compare_inputs_words_df['mean_input_words']<200],\n", + " x='no_inputs',\n", + " y='mean_input_words',\n", + " hue='users')\n", + "fig.set_xlabel('No. of prompts')\n", + "fig.set_ylabel('Mean length of prompt')\n", + "fig.set_title('Scatter plot comparing number of prompts with the length of prompt for each user session')\n", + "sns.move_legend(fig, \"upper left\", bbox_to_anchor=(1, 1))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# A very weak negative col, potentially showing the more detailed input a user gives the less inputs they have to use in a session\n", + "compare_inputs_words_df[compare_inputs_words_df['mean_input_words']<200][['mean_input_words', 'no_inputs']].corr()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "compare_inputs_words_df[compare_inputs_words_df['mean_input_words']<200].groupby('users')[['mean_input_words', 'no_inputs']].corr()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "redbox-MGCo560N-py3.11", + "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": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/evaluation/__init__.py b/notebooks/evaluation/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/notebooks/evaluation/analysis_of_chat_history.py b/notebooks/evaluation/analysis_of_chat_history.py new file mode 100644 index 000000000..edb81a9f4 --- /dev/null +++ b/notebooks/evaluation/analysis_of_chat_history.py @@ -0,0 +1,493 @@ +import inspect +import os +import re +import textwrap +from collections import Counter +from pathlib import Path + +import matplotlib as mpl +import matplotlib.pyplot as plt +import pandas as pd +import seaborn as sns +from bertopic import BERTopic +from django.db.models import F, Prefetch +from wordcloud import STOPWORDS, WordCloud + +# packages = [ +# "django-environ", +# "django-use-email-as-username", +# "psycopg2-binary", +# "django-magic-link", +# "django-single-session", +# "django-compressor", +# "django-import-export", +# "django-storages", +# "daphne", +# "django-allauth", +# ] +# import pip + +# for package in packages: +# pip.main(["install", package]) + + +# django.setup() + +# from redbox_app.redbox_core.models import ChatHistory, ChatMessage + + +class ChatHistoryAnalysis: + def __init__(self) -> None: + root = Path(__file__).parents[2] + self.evaluation_dir = root / "notebooks/evaluation" + results_dir = f"{self.evaluation_dir}/results" + self.visualisation_dir = f"{results_dir}/visualisations/" + self.table_dir = f"{results_dir}/table/" + os.makedirs(results_dir, exist_ok=True) + os.makedirs(self.visualisation_dir, exist_ok=True) + os.makedirs(self.table_dir, exist_ok=True) + + # Need to inversley comment out lines for live data + # self.chat_logs = self.fetch_chat_history() + self.chat_logs = pd.read_csv("notebooks/evaluation/data/chat_histories/chathistory.csv") + + # This column dictionary will change keys depending on names given in data frame structure + COLUMNS_DICT = { + "modified_at": "created_at", + "id": "message_id", + "users": "user_email", + "role": "role", + "text": "text", + } + + # Specifying team members to remove from data + TEAM_EMAILS = ["alfie.dennen@trade.gov.uk", "isobel.daley@trade.gov.uk"] + + # Select specific columns and converting to readable timestamp + self.chat_logs = self.chat_logs[list(COLUMNS_DICT.keys())] + # Note I tried the following with .rename() and it did not work. + self.chat_logs.columns = [COLUMNS_DICT[col] if col in COLUMNS_DICT else col for col in self.chat_logs.columns] + + # Some of this code will change when running live. + # self.chat_logs = self.chat_logs[['created_at', 'users_id', 'user_email', 'chat_history', 'text', 'role', 'message_id']] + self.chat_logs["created_at"] = pd.to_datetime(self.chat_logs["created_at"]) + # Remove users that are redbox team + self.chat_logs = self.chat_logs[~self.chat_logs["user_email"].isin(TEAM_EMAILS)] + + self.ai_responses = self.chat_logs[self.chat_logs["role"] == "ai"] + self.user_responses = self.chat_logs[self.chat_logs["role"] == "user"] + + self.chat_logs["tokens"] = self.chat_logs["text"].apply(self.preprocess_text) + self.ai_responses["tokens"] = self.ai_responses["text"].apply(self.preprocess_text) + self.user_responses["tokens"] = self.user_responses["text"].apply(self.preprocess_text) + + self.user_responses["route"] = self.user_responses["text"].apply( + lambda row: "summarise" + if row.startswith("@summarise") + else ("chat" if row.startswith("@chat") else "no_route") + ) + + self.topic_model = None + self.topic_model_over_time = None + + self.figsize = (10, 5) + mpl.rcParams.update({"font.size": 10}) + mpl.rcParams.update({"font.family": "Arial"}) + + def anonymise_users(self): + """ + If toggle switched in streamlit app then users are anonymised. + """ + chat_log_users = self.chat_logs["user_email"].unique() + n_users = len(chat_log_users) + anon_users = [] + for i in range(1, n_users + 1): + anon_users.append(f"User {i}") + anon_user_dict = dict(zip(chat_log_users, anon_users, strict=False)) + self.chat_logs["user_email"] = self.chat_logs["user_email"].map(anon_user_dict) + self.ai_responses["user_email"] = self.ai_responses["user_email"].map(anon_user_dict) + self.user_responses["user_email"] = self.user_responses["user_email"].map(anon_user_dict) + + def fetch_chat_history(self, limit=100): + chat_messages = ChatMessage.objects.all() + chat_history_objects = ( + ChatHistory.objects.prefetch_related(Prefetch("messages", queryset=chat_messages)) + .annotate(user_email=F("users__email")) + .values( + "created_at", + "users_id", + "user_email", + chat_history=F("id"), + )[:limit] + ) + + results = [] + for chat_history in chat_history_objects: + messages = ChatMessage.objects.filter(chat_history=chat_history["chat_history"]).values( + "text", "role", "id" + ) + for message in messages: + result = { + "created_at": chat_history["created_at"], + "users_id": chat_history["users_id"], + "user_email": chat_history["user_email"], + "chat_history": chat_history["chat_history"], + "text": message["text"], + "role": message["role"], + "message_id": message["id"], + } + results.append(result) + + df = pd.DataFrame(results) + return df + + def preprocess_text(self, text): + tokens = text.split() + tokens = [word.lower() for word in tokens if word.isalpha()] + return tokens + + def process_user_names(self, user_names_column: pd.Series) -> pd.Series: + """ + Takes a pandas column and returns a tidied pandas column. + Creates dictionary of key value pairs for the tidy name. + """ + unique_user_email = user_names_column.unique() + unique_user_names = [user_email.split("@")[0].replace(".", " ").title() for user_email in unique_user_email] + user_name_dict = dict(zip(unique_user_email, unique_user_names, strict=False)) + new_user_names_column = user_names_column.map(user_name_dict) + return new_user_names_column + + def get_user_frequency(self) -> pd.DataFrame: + """ + Creates a data frame of total inputs by user. + """ + user_counts = self.user_responses["user_email"].value_counts().reset_index(name="values") + user_counts["user_email"] = self.process_user_names(user_counts.user_email) + return user_counts + + def plot_user_frequency(self): + """ + Generates a bar plot of total inputs per user. + """ + user_counts = self.get_user_frequency() + plt.figure(figsize=self.figsize) + sns.barplot(data=user_counts, x="user_email", y="values", palette="viridis") + plt.title("Unique Users by Total Number of Messages") + xlabels = user_counts.user_email + xlabels_new = ["\n".join(textwrap.wrap(name, width=10)) for name in xlabels] + plt.xticks(range(len(xlabels_new)), xlabels_new) + plt.xlabel("Users") + plt.ylabel("Total No. of Prompts") + + def get_redbox_traffic(self) -> pd.DataFrame: + """ + Returns a dataframe of redbox usage by time. + """ + redbox_traffic_df = ( + self.user_responses["created_at"].groupby(by=self.user_responses["created_at"].dt.date).count() + ) + + return redbox_traffic_df + + def plot_redbox_traffic(self): + """ + Generates a line plot of redbox usage over time + """ + redbox_traffic_df = self.get_redbox_traffic() + plt.figure(figsize=self.figsize) + redbox_traffic_df.plot(kind="line") + plt.title("Usage of Redbox AI Over Time") + plt.xlabel("Date") + plt.ylabel("Number of Prompts") + + def get_redbox_traffic_by_user(self) -> pd.DataFrame: + """ + Returns a dataframe of redbox usage by user over time. + """ + user_responses = self.user_responses + user_responses["user_email"] = self.process_user_names(user_responses["user_email"]) + redbox_traffic_by_user_df = ( + user_responses.groupby([user_responses["created_at"].dt.date, "user_email"]).size().unstack(fill_value=0) + ) + + return redbox_traffic_by_user_df + + def plot_redbox_traffic_by_user(self): + """ + Generates a plot of redbox usage by user over time + """ + redbox_traffic_by_user_df = self.get_redbox_traffic_by_user() + plt.figure(figsize=self.figsize) + fig = sns.lineplot(data=redbox_traffic_by_user_df, markers=True) + fig.set_xlabel("Date") + fig.set_ylabel("No. of Prompts") + fig.set_title("Usage of Redbox by User over Time") + sns.move_legend(fig, "upper left", bbox_to_anchor=(1, 1), title="Users") + + def get_user_word_frequency(self) -> pd.DataFrame: + """ + Returns a dataframe with word frequency, removing stopwords. + """ + all_tokens = [token for tokens in self.user_responses["tokens"] for token in tokens] + stopwords_removed_from_all_tokens = [word for word in all_tokens if word not in STOPWORDS] + word_freq = Counter(stopwords_removed_from_all_tokens) + return word_freq + + def get_ai_word_frequency(self) -> pd.DataFrame: + """ + Returns a dataframe with word frequency, removing stopwords. + """ + ai_word_freq = Counter( + [token for tokens in self.ai_responses["tokens"] for token in tokens if token not in STOPWORDS] + ) + return ai_word_freq + + def plot_user_wordcloud(self): + """ + Creates wordcloud of user prompts. + """ + word_freq = self.get_user_word_frequency() + # TODO: assess value + wordcloud = WordCloud(width=800, height=400, background_color="white").generate_from_frequencies(word_freq) + plt.figure(figsize=self.figsize) + plt.imshow(wordcloud, interpolation="bilinear") + plt.axis("off") + plt.title("Most Frequent Words") + + def plot_top_user_word_frequency(self): + """ + Creates bar plot of most common user words. + """ + word_freq = self.get_user_word_frequency() + most_common_words = word_freq.most_common( + 20 # TODO: determine how many common words we want and the right vis. for this + ) + words, counts = zip(*most_common_words, strict=False) + plt.figure(figsize=self.figsize) + sns.barplot(x=list(counts), y=list(words), palette="viridis") + plt.title("Top 20 Most Frequent Words") + plt.xlabel("Frequency") + plt.ylabel("Words") + + def plot_ai_wordcloud(self): + """ + Creates wordcloud of AI outputs. + """ + ai_word_freq = self.get_ai_word_frequency() + # TODO: Assess value + ai_wordcloud = WordCloud(width=800, height=400, background_color="white").generate_from_frequencies( + ai_word_freq + ) + plt.figure(figsize=self.figsize) + plt.imshow(ai_wordcloud, interpolation="bilinear") + plt.axis("off") + plt.title("Most Frequent Words in AI Responses") + + def plot_top_ai_word_frequency(self): + """ + Creates bar plot of most common user words. + """ + ai_word_freq = self.get_ai_word_frequency() + + most_common_words = ai_word_freq.most_common( + 20 + ) # TODO: determine how many common words we want and the right vis. for this + + words, counts = zip(*most_common_words, strict=False) + plt.figure(figsize=self.figsize) + sns.barplot(x=list(counts), y=list(words), palette="viridis") + plt.title("Top 20 Most Frequent Words") + plt.xlabel("Frequency") + plt.ylabel("Words") + + def get_ai_response_pattern(self): + """ + Returns a dataframe of ai repsonse patterns. + """ + + def clean_text( + text, + ): + # was including asterisks giving useless info to the graph I'm still not + # entirely convinced on the benefit of this analysis + return re.sub("[!@#$*]", "", text).strip() + + self.ai_responses["clean_text"] = self.ai_responses["text"].apply(clean_text) + ai_response_patterns_df = ( + self.ai_responses["clean_text"].apply(lambda x: " ".join(x.split()[:2])).value_counts().head(10) + ) + return ai_response_patterns_df + + def plot_ai_response_pattern(self): + """ + Bar plot of most common ai response patterns. + """ + ai_response_pattern_df = self.get_ai_response_pattern() + plt.figure(figsize=self.figsize) + sns.barplot(x=ai_response_pattern_df.values, y=ai_response_pattern_df.index, palette="magma") + plt.title("Common Patterns in AI Responses") + plt.xlabel("Frequency") + plt.ylabel("Patterns") + + def get_routes(self) -> pd.DataFrame: + """ + Returns dataframe of common user routes. + """ + # TODO: Some users repeat the same message several times (drop?) + # df = df.drop_duplicates(['text']) + user_responses = self.user_responses + user_responses["user_email"] = self.process_user_names(user_responses["user_email"]) + user_routes_df = user_responses.groupby(["user_email"])["route"].value_counts().unstack() + return user_routes_df + + def plot_user_routes(self): + """ + Creates bar plot of user routes taken. + """ + user_routes_df = self.get_routes() + plt.figure(figsize=self.figsize) + user_routes_df.plot( + kind="bar", color={"chat": "teal", "no_route": "yellowgreen", "summarise": "gold"}, figsize=self.figsize + ) + plt.xticks(rotation=0) + plt.xlabel("Users") + plt.ylabel("Number of routes taken") + plt.title("Routes taken per user") + + def get_route_transitions(self) -> pd.DataFrame: + """ + Returns a dataframe of route transitions. + """ + # TODO: Check this works with the groupby ID and time order of events + def route_transitions(df): + df["next_route"] = df["route"].shift(1) + df["transition"] = df.apply( + lambda row: f"{row['route']} to {row['next_route']}" if pd.notna(row["next_route"]) else None, axis=1 + ) + return df + + # Get route transitions and counts per user session (is 'id' appropriate?) + df_transitions = self.user_responses.groupby("message_id").apply(route_transitions).reset_index(drop=True) + + route_transitions_df = df_transitions["transition"].value_counts().reset_index() + return route_transitions_df + + def plot_route_transitions(self): + """ + Bar plot of common route transition. + """ + route_transitions_df = self.get_route_transitions() + sns.barplot(x=route_transitions_df["transition"], y=route_transitions_df["count"], palette="viridis") + plt.xticks(rotation=45, ha="right") + plt.title("Number of different route transitions") + plt.xlabel("Route transition") + plt.ylabel("Number of route transitions") + + def get_topics(self): + """ + Aims to answer: Are users asking about common topics? + Removes stopwords and fits a simple topic model. + """ + STOPWORDS.add("@chat") + STOPWORDS.add("@summarise") + + text_without_stopwords = self.user_responses["text"].apply( + lambda row: " ".join([word for word in row.split() if word not in (STOPWORDS)]) + ) + created_at = self.user_responses["created_at"].to_list() + + topic_model = BERTopic(verbose=True) + topic_model.fit_transform(text_without_stopwords) + topics_over_time = topic_model.topics_over_time(text_without_stopwords, created_at) + + self.topic_model = topic_model + self.topics_over_time = topics_over_time + + def plot_topics(self): + return self.topic_model.visualize_topics(width=800, height=500) + + def plot_hierarchy(self): + return self.topic_model.visualize_hierarchy(width=800, height=400) + + def plot_barchart(self): + return self.topic_model.visualize_barchart(width=800, height=400) + + def plot_topics_over_time(self): + return self.topic_model.visualize_topics_over_time( + self.topics_over_time, top_n_topics=5, normalize_frequency=True + ) + + def get_prompt_lengths(self) -> pd.DataFrame: + """ + Adds the prompt lengths to the user prompt column + """ + user_responses_df = self.user_responses + user_responses_df["no_input_words"] = user_responses_df["text"].apply(lambda n: len(n.split())) + return user_responses_df + + def filter_prompt_lengths(self, outlier_max: int): + """ + Creates option to filter for outliers + """ + user_responses_df = self.get_prompt_lengths() + user_responses_df = user_responses_df[user_responses_df["no_input_words"] < outlier_max] + return user_responses_df + + def plot_prompt_lengths(self, outlier_max: int): + """ + How does prompt length vary? + """ + user_responses_df = self.filter_prompt_lengths(outlier_max) + fig = sns.displot(user_responses_df["no_input_words"]) + fig.set_axis_labels(x_var="No. of words in prompt", y_var="Count") + + def get_prompt_length_vs_chat_length(self, outlier_max: int) -> pd.DataFrame: + """ + Returns a dataframe comparing average prompt length with chat length. + """ + user_responses_df = self.filter_prompt_lengths(outlier_max) + mean_inputs_df = ( + user_responses_df[["message_id", "user_email", "no_input_words"]] + .groupby(by=["message_id", "user_email"]) + .agg({"no_input_words": "mean"}) + .rename(columns={"no_input_words": "mean_input_words"}) + .reset_index() + ) + no_inputs_df = ( + user_responses_df[["message_id", "user_email"]] + .groupby("message_id") + .value_counts() + .reset_index(name="no_inputs") + ) + compare_inputs_words_df = no_inputs_df.merge( + mean_inputs_df, left_on=["message_id", "user_email"], right_on=["message_id", "user_email"] + ) + + return compare_inputs_words_df + + def plot_prompt_length_vs_chat_legnth(self, outlier_max: int): + """ + Creates a scatterplot of prompt length vs chat legnth. + """ + compare_inputs_words_df = self.get_prompt_length_vs_chat_length(outlier_max=outlier_max) + compare_inputs_words_df["user_email"] = self.process_user_names(compare_inputs_words_df.user_email) + fig = sns.scatterplot(data=compare_inputs_words_df, x="no_inputs", y="mean_input_words", hue="user_email") + fig.set_xlabel("No. of prompts") + fig.set_ylabel("Mean length of prompt") + fig.set_title("Scatter plot comparing number of prompts with the length of prompt for each user session") + sns.move_legend(fig, "upper left", bbox_to_anchor=(1, 1), title="Users") + + +def main(): + chat_history_analysis = ChatHistoryAnalysis() + attrs = (getattr(chat_history_analysis, name) for name in dir(chat_history_analysis)) + methods = filter(inspect.ismethod, attrs) + for method in methods: + try: + method() + except TypeError: + pass + + +if __name__ == "__main__": + main() diff --git a/notebooks/evaluation/app.py b/notebooks/evaluation/app.py new file mode 100644 index 000000000..1d5868d33 --- /dev/null +++ b/notebooks/evaluation/app.py @@ -0,0 +1,56 @@ +import streamlit as st +from analysis_of_chat_history import ChatHistoryAnalysis + +cha = ChatHistoryAnalysis() + +st.set_page_config(page_title="Redbox Chat Analysis", layout="centered") +st.set_option("deprecation.showPyplotGlobalUse", False) +st.title("Redbox Chat History Dashboard") + +on = st.toggle("Anonymise Users") +if on: + cha.anonymise_users() + +user_usage_tab, word_freq_tab, route_tab, topic_tab, prompt_complex = st.tabs( + [ + "Redbox User Usage", + "Word frequency", + "Route Analysis", + "Topic modelling", + "Prompt Complexity", + ] +) + +with user_usage_tab: + st.pyplot(cha.plot_user_frequency()) + st.pyplot(cha.plot_redbox_traffic()) + st.pyplot(cha.plot_redbox_traffic_by_user()) + +with word_freq_tab: + st.subheader("User") + st.pyplot(cha.plot_user_wordcloud()) + st.pyplot(cha.plot_top_user_word_frequency()) + st.subheader("AI") + st.pyplot(cha.plot_ai_wordcloud()) + st.pyplot(cha.plot_top_ai_word_frequency()) + st.pyplot(cha.plot_ai_response_pattern()) + +with route_tab: + st.pyplot(cha.plot_user_routes()) + st.pyplot(cha.plot_route_transitions()) + +with topic_tab: + with st.spinner("Fitting topic model..."): + cha.get_topics() + + st.plotly_chart(cha.plot_topics()) + st.plotly_chart(cha.plot_topics_over_time()) + st.plotly_chart(cha.plot_barchart()) + st.plotly_chart(cha.plot_hierarchy()) + +with prompt_complex: + # Adding slider for prompt legnth + max_outlier = cha.get_prompt_lengths()["no_input_words"].max() + 10 + outlier = st.slider("Please use the slicer if you wish to remove outliers.", 0, max_outlier, max_outlier) + st.pyplot(cha.plot_prompt_lengths(outlier_max=outlier)) + st.pyplot(cha.plot_prompt_length_vs_chat_legnth(outlier_max=outlier)) diff --git a/notebooks/evaluation/rag_create_evaluation_dataset.ipynb b/notebooks/evaluation/rag_create_evaluation_dataset.ipynb index 0c5894563..541987510 100644 --- a/notebooks/evaluation/rag_create_evaluation_dataset.ipynb +++ b/notebooks/evaluation/rag_create_evaluation_dataset.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -32,10 +32,10 @@ "from ragas.testset.generator import TestsetGenerator\n", "from ragas.testset.evolutions import simple, reasoning, multi_context\n", "\n", + "\n", "from redbox.model_db import SentenceTransformerDB\n", "from redbox.models import Settings\n", "from redbox.models.settings import ElasticLocalSettings\n", - "from redbox.parsing import chunk_file\n", "from redbox.models.file import File, Chunk\n", "\n", "from mypy_boto3_s3.client import S3Client\n", @@ -190,7 +190,7 @@ "metadata": {}, "outputs": [], "source": [ - "DATA_VERSION = \"0.1.0\"" + "DATA_VERSION = \"\"" ] }, { @@ -223,8 +223,9 @@ "metadata": {}, "outputs": [], "source": [ - "ROOT = Path.cwd().parents[1]\n", + "ROOT = Path.cwd()\n", "EVALUATION_DIR = ROOT / \"notebooks/evaluation\"\n", + "EVALUATION_DIR = ROOT\n", "\n", "V_ROOT = EVALUATION_DIR / f\"data/{DATA_VERSION}\"\n", "V_RAW = V_ROOT / \"raw\"\n", @@ -497,7 +498,7 @@ "metadata": {}, "outputs": [], "source": [ - "testset_df_renamed.head()" + "# testset_df_renamed.head()" ] }, { @@ -533,69 +534,78 @@ "metadata": {}, "outputs": [], "source": [ - "def save_chunks_to_jsonl(chunks: t.Iterable[Chunk], file_path: Path) -> None:\n", - " with jsonlines.open(file_path, mode=\"w\") as writer:\n", - " for chunk in chunks:\n", - " writer.write(chunk.model_dump_json())\n", + "import glob\n", + "import sys\n", + "from worker.src.loader import UnstructuredDocumentLoader\n", + "import json\n", + "\n", + "def chunk_files(\n", + " file_path: str\n", + " ) -> t.Iterable[Document]:\n", + " \n", + " # Not sure if keys, buckets, and uuids required for evaluation. Appears to work without.\n", + " my_file = File(\n", + " uuid= USER_UUID,\n", + " key = 'a',\n", + " bucket ='a',\n", + " creator_user_uuid=USER_UUID,\n", + " )\n", "\n", + " raw_files = glob.glob(str(V_RAW / \"*.pdf\"))\n", + " \n", + " # Chunk\n", + " for file_path in raw_files:\n", + " with open(file_path, \"rb\") as f:\n", + " loader = UnstructuredDocumentLoader(\n", + " file=my_file,\n", + " file_bytes=f,\n", + " env=ENV\n", + " )\n", "\n", - "def embed_file(\n", - " file_path: Path,\n", - " data_version: str,\n", - " bucket_name: str,\n", - " model: SentenceTransformerDB,\n", - " user_uuid: UUID = USER_UUID,\n", - " s3_client: S3Client = S3_CLIENT,\n", - ") -> list[Chunk]:\n", - " key = f\"{data_version}/{file_path.name}\"\n", - " file = File(key=key, bucket=bucket_name, creator_user_uuid=user_uuid)\n", + " chunks = list(loader.lazy_load())\n", "\n", - " # Upload to bucket\n", - " with open(file_path, \"rb\") as f:\n", - " s3_client.upload_fileobj(f, bucket_name, key)\n", + " print(f'Number of chunks: {len(chunks)}')\n", "\n", - " # Chunk\n", - " chunks = chunk_file(file=file, s3_client=s3_client)\n", + " # Write\n", + " with jsonlines.open(V_CHUNKS / 'documents.jsonl', mode=\"w\") as writer:\n", + " for chunk in chunks:\n", + " writer.write(chunk.page_content)\n", + "\n", + " return chunks\n", + "\n", + "\n", + "def embed_chunks(\n", + " chunks: t.Iterable[Document],\n", + " model: SentenceTransformerDB\n", + " ) -> None:\n", "\n", - " # Embed\n", - " embeddings = [embedding.embedding for embedding in model.embed_sentences([chunk.text for chunk in chunks]).data]\n", + " # Embed chunks\n", + " embeddings = [embedding.embedding for embedding in model(embedding_model_name = MODEL).embed_sentences([chunk.page_content for chunk in chunks]).data]\n", "\n", - " # Merge\n", + " # Merge embeddings with chunk metadata\n", " chunks_embedded = []\n", " for chunk, embedding in zip(chunks, embeddings, strict=True):\n", - " chunk_embedded = Chunk(\n", - " uuid=chunk.uuid,\n", - " created_datetime=chunk.created_datetime,\n", - " creator_user_uuid=chunk.creator_user_uuid,\n", - " parent_file_uuid=chunk.parent_file_uuid,\n", - " index=chunk.index,\n", - " text=chunk.text,\n", - " metadata=chunk.metadata,\n", - " embedding=embedding,\n", - " )\n", + "\n", + " chunk_embedded = dict(chunk)\n", + " chunk_embedded['text'] = chunk_embedded.pop('page_content')\n", + " chunk_embedded['metadata']['creator_user_uuid'] = str(chunk_embedded['metadata']['creator_user_uuid'])\n", + " chunk_embedded['metadata']['parent_file_uuid'] = str(chunk_embedded['metadata']['parent_file_uuid'])\n", + " chunk_embedded['metadata']['created_datetime'] = str(chunk_embedded['metadata']['created_datetime'])\n", + " chunk_embedded['embedding'] = embedding\n", + " chunk_embedded['uuid'] = None\n", + " \n", " chunks_embedded.append(chunk_embedded)\n", "\n", - " return chunks_embedded" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "all_embedded_files = []\n", + " # Write\n", + " with jsonlines.open(V_EMBEDDINGS / f'{MODEL}.jsonl', mode=\"w\") as writer:\n", + " for chunk_embedded in chunks_embedded:\n", + " writer.write(json.dumps(chunk_embedded))\n", + "\n", + " return chunks_embedded\n", "\n", - "for file_path in V_RAW.glob(\"*.*\"):\n", - " file_chunks_embedded = embed_file(\n", - " file_path=file_path,\n", - " data_version=DATA_VERSION,\n", - " bucket_name=ENV.bucket_name,\n", - " model=SentenceTransformerDB(embedding_model_name=MODEL),\n", - " )\n", - " all_embedded_files += file_chunks_embedded\n", "\n", - "save_chunks_to_jsonl(chunks=all_embedded_files, file_path=V_EMBEDDINGS / f\"{MODEL}.jsonl\")" + "file_chunks = chunk_files(file_path = V_RAW)\n", + "emebedded_chunks = embed_chunks(chunks = file_chunks, model = SentenceTransformerDB)" ] }, { @@ -694,7 +704,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.8" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/notebooks/evaluation/rag_experiments.py b/notebooks/evaluation/rag_experiments.py new file mode 100644 index 000000000..7486dd263 --- /dev/null +++ b/notebooks/evaluation/rag_experiments.py @@ -0,0 +1,534 @@ +import json +import sys +from dataclasses import asdict +from operator import itemgetter +from pathlib import Path +from typing import Annotated + +import matplotlib.pyplot as plt +import click +import jsonlines +from onnx import EXPERIMENTAL +import pandas as pd +import seaborn as sns +from deepeval import evaluate +from deepeval.dataset import EvaluationDataset +from deepeval.metrics import ( + AnswerRelevancyMetric, + ContextualPrecisionMetric, + ContextualRecallMetric, + ContextualRelevancyMetric, + FaithfulnessMetric, + HallucinationMetric, +) +from dotenv import find_dotenv, load_dotenv +from elasticsearch import Elasticsearch +from elasticsearch.helpers import bulk, scan +from fastapi import Depends +from langchain.globals import set_verbose +from langchain.schema import StrOutputParser +from langchain_community.chat_models import ChatLiteLLM +from langchain_core.retrievers import BaseRetriever +from langchain_core.runnables import ConfigurableField, Runnable, RunnableLambda, RunnablePassthrough +from langchain_core.vectorstores import VectorStoreRetriever +from scipy import stats +from tiktoken import Encoding + +# Temp hack - there is an issue with the importing of redbox-core +sys.path.append(str(Path(__file__).parents[2])) + +from core_api.src import dependencies +from core_api.src.dependencies import get_tokeniser +from core_api.src.format import format_documents +from core_api.src.retriever import ParameterisedElasticsearchRetriever +from core_api.src.runnables import make_chat_prompt_from_messages_runnable +from redbox.models import ChatRoute, Settings +from redbox.models.chain import ChainInput +from redbox.models.file import UUID +from redbox.models.settings import ElasticLocalSettings + +set_verbose(False) + + +_ = load_dotenv(find_dotenv()) + + +class GetExperimentResults: + """ + Class to handle experiment results retrieval and processing. + + Attributes: + data_version (str): Data version to be used in experiment + benchmark (bool): Benchmark stated or not + V_EMBEDDINGS (str): Embeddings version + V_ROOT (path): Path to where the root of experiment data lies + V_SYNTHETIC (path): Path to the synthetic data + V_RESULTS (path): Path to the results from data processing + MODEL (str): Embedding model + ES_CLIENT (func): Elastic search client from settings + INDEX (str): Index for the json lines file + experiment_name (str): Name of the experiment + """ + + def __init__(self): + self.data_version = None + self.benchmark = None + self.V_EMBEDDINGS = "" + self.V_ROOT = None + self.V_SYNTHETIC = None + self.V_RESULTS = None + self.MODEL = None + self.ES_CLIENT = None + self.INDEX = None + self.experiment_name = None + self.retrieval_system_prompt = None + self.retrieval_question_prompt = None + self.eval_results = None + self.ENV = Settings(minio_host="localhost", elastic=ElasticLocalSettings(host="localhost")) + self.LLM = ChatLiteLLM( + model="gpt-4o", + streaming=True, + ) + self.FILE_UUIDS = None + self.USER_UUID = UUID("aaaaaaaa-aaaa-aaaa-aaaa-aaaaaaaaaaaa") + self.experiment_file_name = None + self.experiment_parameters = None + + def set_data_version(self, data_version): + """ + This function sets the necessary environment variables depending on your data version. + It assumes you have a versioned evaluation folder in your repository e.g. notebooks/evaluation/data/0.2.0 + This should be copied from the Redbox shared Google Drive. + This folder contains the raw files, QA sets, embeddings etc. + """ + self.data_version = data_version + root = Path(__file__).parents[2] + evaluation_dir = root / "notebooks/evaluation" + self.V_ROOT = evaluation_dir / f"data/{self.data_version}" + self.V_SYNTHETIC = self.V_ROOT / "synthetic" + self.V_RESULTS = self.V_ROOT / "results" + self.V_EMBEDDINGS = self.V_ROOT / "embeddings" + self.MODEL = self.ENV.embedding_model + self.INDEX = f"{self.data_version}-{self.MODEL}".lower() + self.ES_CLIENT = self.ENV.elasticsearch_client() + + def load_chunks_from_jsonl_to_index(self) -> set: + """ + This function takes the versioned embeddings (e.g. from notebooks/evaluation/data/0.2.0/embeddings) + and loads them to ElasticSearch. + """ + file_uuids = set() + file_path = self.V_EMBEDDINGS / f"{self.MODEL}.jsonl" + + with jsonlines.open(file_path, mode="r") as reader: + for chunk_raw in reader: + chunk = json.loads(chunk_raw) + self.ES_CLIENT.index( + index=self.INDEX, + id=chunk["uuid"], + body=chunk, + ) + + file_uuids.add(chunk["metadata"]["parent_file_uuid"]) + self.FILE_UUIDS = file_uuids + return file_uuids + + def clear_index(self) -> None: + """ + This function clears the indexes from ElasticSearch. + """ + if not self.ES_CLIENT.indices.exists(index=self.INDEX): + return None + + documents = list(scan(self.ES_CLIENT, index=self.INDEX, query={"query": {"match_all": {}}})) + bulk_data = [{"_op_type": "delete", "_index": doc["_index"], "_id": doc["_id"]} for doc in documents] + if bulk_data: + return bulk(self.ES_CLIENT, bulk_data, request_timeout=300) + return None + + def get_parameterised_retriever( + self, es: Annotated[Elasticsearch, Depends(dependencies.get_elasticsearch_client)] + ) -> BaseRetriever: + """ + 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": self.ENV.ai.rag_k, + "num_candidates": self.ENV.ai.rag_num_candidates, + "match_boost": 1, + "knn_boost": 1, + "similarity_threshold": 0, + } + + return ParameterisedElasticsearchRetriever( + es_client=es, + index_name=self.INDEX, + params=default_params, + embedding_model=dependencies.get_embedding_model(self.ENV), + ).configurable_fields( + params=ConfigurableField( + id="params", + name="Retriever parameters", + description="A dictionary of parameters to use for the retriever.", + ) + ) + + def build_retrieval_chain( + self, + 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: + """ + This is an adaptation of core_api.src.build_chains.build_retrieval_chain. + Function experiements with different retrieval_system_prompt and retrieval_question_prompt. + """ + return ( + RunnablePassthrough.assign(documents=retriever) + | RunnablePassthrough.assign( + formatted_documents=(RunnablePassthrough() | itemgetter("documents") | format_documents) + ) + | { + "response": make_chat_prompt_from_messages_runnable( + system_prompt=str(self.retrieval_system_prompt), + question_prompt=str(self.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 get_rag_results( + self, + question, + ) -> dict: + """ + Get Redbox response for a given question. + """ + retriever = self.get_parameterised_retriever(es=self.ES_CLIENT) + + chain = self.build_retrieval_chain(llm=self.LLM, retriever=retriever, tokeniser=get_tokeniser(), env=self.ENV) + + response = chain.invoke( + input=ChainInput( + question=question, + chat_history=[{"text": "", "role": "user"}], + file_uuids=list(self.FILE_UUIDS), + user_uuid=self.USER_UUID, + ).model_dump() + ) + + filtered_chunks = [] + + for chunk in response["source_documents"]: + dict_chunk = dict(chunk) + filtered_chunk = { + "page_content": dict_chunk["page_content"], + } + filtered_chunks.append(filtered_chunk) + + return {"output_text": response["response"], "source_documents": filtered_chunks} + + def write_rag_results(self) -> None: + """ + Format and write Redbox responses to evaluation dataset. + """ + + synthetic_df = pd.read_csv(f"{self.V_SYNTHETIC}/ragas_synthetic_data.csv") + inputs = synthetic_df["input"].tolist() + + df_function = synthetic_df.copy() + + actual_output = [] + retrieval_context = [] + + for question in inputs: + data = self.get_rag_results(question=question) + actual_output.append(data["output_text"]) + retrieval_context.append(data["source_documents"]) + + df_function["actual_output"] = actual_output + df_function["retrieval_context"] = retrieval_context + + df_function_clean = df_function.dropna() + df_function_clean.to_csv( + f"{self.V_SYNTHETIC}/{self.experiment_name}_complete_ragas_synthetic_data.csv", index=False + ) + + def do_evaluation(self) -> None: + """ + Calculate evaluation metrics for a synthetic RAGAS dataset, aggregate results + and write as CSV. + """ + + dataset = EvaluationDataset() + dataset.add_test_cases_from_csv_file( + file_path=f"{self.V_SYNTHETIC}/{self.experiment_name}_complete_ragas_synthetic_data.csv", + input_col_name="input", + actual_output_col_name="actual_output", + expected_output_col_name="expected_output", + context_col_name="context", + context_col_delimiter=";", + retrieval_context_col_name="retrieval_context", + retrieval_context_col_delimiter=";", + ) + + # Instantiate retrieval metrics + contextual_precision = ContextualPrecisionMetric( + threshold=0.5, # default is 0.5 + model="gpt-4o", + include_reason=True, + ) + + contextual_recall = ContextualRecallMetric( + threshold=0.5, # default is 0.5 + model="gpt-4o", + include_reason=True, + ) + + contextual_relevancy = ContextualRelevancyMetric( + threshold=0.5, # default is 0.5 + model="gpt-4o", + include_reason=True, + ) + + # Instantiate generation metrics + answer_relevancy = AnswerRelevancyMetric( + threshold=0.5, # default is 0.5 + model="gpt-4o", + include_reason=True, + ) + + faithfulness = FaithfulnessMetric( + threshold=0.5, # default is 0.5 + model="gpt-4o", + include_reason=True, + ) + + hallucination = HallucinationMetric( + threshold=0.5, # default is 0.5 + model="gpt-4o", + include_reason=True, + ) + + self.eval_results = evaluate( + test_cases=dataset, + metrics=[ + contextual_precision, + contextual_recall, + contextual_relevancy, + answer_relevancy, + faithfulness, + hallucination, + ], + ) + + return self.eval_results + + def write_evaluation_results(self) -> None: + """ + This function writes the evaluation results to a csv, identifiable by experiment_name. + """ + metric_type = { + "metric_name": [ + "Contextual Precision", + "Contextual Recall", + "Contextual Relevancy", + "Answer Relevancy", + "Faithfulness", + "Hallucination", + ], + "metric_type": ["retrieval", "retrieval", "retrieval", "generation", "generation", "generation"], + } + + evaluation = ( + pd.DataFrame.from_records(asdict(result) for result in self.eval_results) + .explode("metrics_metadata") + .reset_index(drop=True) + .assign( + metric_name=lambda df: df.metrics_metadata.apply(getattr, args=["metric"]), + score=lambda df: df.metrics_metadata.apply(getattr, args=["score"]), + reason=lambda df: df.metrics_metadata.apply(getattr, args=["reason"]), + ) + .merge(pd.DataFrame(metric_type), on="metric_name") + .drop(columns=["success", "metrics_metadata"]) + ) + + evaluation.to_csv(f"{self.V_RESULTS}/{self.experiment_name}_eval_results.csv", index=False) + evaluation.head() + + def load_experiment_param_data( + self, + experiment_file_name=None, + benchmark=None, + ): + """ + This function loads an csv of experiments to try unless benchmark is specified; + in this case it will take the core_api retrieval_system_prompt and retrieval_question_prompt. + """ + + if benchmark: + self.benchmark = benchmark + self.experiment_file_name = "benchmark" + benchmark_df = pd.DataFrame() + benchmark_df["experiment_name"] = ["benchmark"] + benchmark_df["retrieval_system_prompt"] = [self.ENV.ai.retrieval_system_prompt] + benchmark_df["retrieval_question_prompt"] = [self.ENV.ai.retrieval_question_prompt] + self.experiment_parameters = benchmark_df + else: + self.experiment_file_name = experiment_file_name + self.experiment_parameters = pd.read_csv( + f"notebooks/evaluation/data/experiment_parameters/{self.experiment_file_name}.csv" + ) + + def loop_through_experiements(self): + """ + This function calls the other functions to run and write the different experiments. + """ + for _index, row in self.experiment_parameters.iterrows(): + self.experiment_name = row["experiment_name"] + self.retrieval_system_prompt = (row["retrieval_system_prompt"],) + self.retrieval_question_prompt = row["retrieval_question_prompt"] + + self.write_rag_results() + self.do_evaluation() + self.write_evaluation_results() + + def empirical_ci_and_pass_rate(self, df: pd.DataFrame) -> pd.DataFrame: + """ + Calculate confidence intervals and pass rates for aggregated metrics. + """ + + def pass_rate(x): + return pd.Series({'pass_rate' : (round((x >= 0.5).mean(), 4)) * 100}) + + df_grouped = ( + df.groupby(["experiment_name", "metric_name"])["score"] + .agg(["mean", "sem", "min", "max", "count", pass_rate]) + .reset_index() + ) + + # Pass-rate minus 1 for hallucination as lower values are better + df_grouped.loc[df_grouped['metric_name'] == 'Hallucination', 'pass_rate'] = 100 - df_grouped.loc[df_grouped['metric_name'] == 'Hallucination', 'pass_rate'] + + ci = stats.t.interval( + confidence=0.95, df=df_grouped["count"] - 1, loc=df_grouped["mean"], scale=df_grouped["sem"] + ) + + df_grouped["ci_low"] = ci[0] + df_grouped["ci_high"] = ci[1] + + # Sort metric names by input dataframe order + df_grouped['metric_name'] = pd.Categorical(df_grouped['metric_name'], + df['metric_name'].unique()) + + df_grouped.sort_values(['experiment_name', 'metric_name'], inplace = True) + + return df_grouped + + def create_visualisation_plus_grouped_results(self): + """ + This function uses the stored experiment result to save the aggregated metics using empirical_ci_and_pass_rate(). + It also saves a barplot (here confidence intervals are calculated by bootstrapping). + """ + experiments = [] + experiment_names = self.experiment_parameters["experiment_name"] + for experiment_name in experiment_names: + experiment = pd.read_csv(f"{self.V_RESULTS}/{self.experiment_name}_eval_results.csv") + experiment["experiment_name"] = experiment_name + experiments.append(experiment) + + experiments_df = pd.concat(experiments) + + experiment_metrics = self.empirical_ci_and_pass_rate(experiments_df) + experiment_metrics.to_csv(f"{self.V_RESULTS}/{self.experiment_file_name}_eval_results_full.csv") + + # Plot + fig, ax = plt.subplots(nrows = 2, figsize = (6, 8)) + + sns.barplot(experiments_df, x="score", y="metric_name", hue='experiment_name', errorbar=('ci', 95), ax = ax[0]) + ax[0].set(xlabel = 'Mean score', ylabel = 'Metric name', xlim = (0, 1)) + ax[0].legend(title = '', fontsize = 9, loc = 'upper right') + + sns.barplot(experiment_metrics, x="pass_rate", y="metric_name", hue="experiment_name", ax = ax[1]) + ax[1].set(xlabel = 'Pass rate (%)', ylabel = 'Metric name', xlim = (0, 100)) + ax[1].legend(title = '', fontsize = 9, loc = 'upper right') + + plt.tight_layout() + + plt.savefig(f"{self.V_RESULTS}/{self.experiment_file_name}_eval_barplots.png") + + + +class Mutex(click.Option): + def __init__(self, *args, **kwargs): + self.not_required_if = kwargs.pop("not_required_if") + self.required_if_not_set = kwargs.pop("required_if_not_set", True) + + if not self.not_required_if: + msg = "'not_required_if' parameter required" + raise ValueError(msg) + + kwargs["help"] = ( + kwargs.get("help", "") + " Option is mutually exclusive with " + ", ".join(self.not_required_if) + "." + ).strip() + super().__init__(*args, **kwargs) + + def handle_parse_result(self, ctx, opts, args): + current_opt = self.name in opts + for mutex_opt in self.not_required_if: + if mutex_opt in opts: + if current_opt: + msg = f"Illegal usage: '{self.name}' is mutually exclusive with {mutex_opt}." + raise click.UsageError(msg) + self.prompt = None + + if not current_opt and self.required_if_not_set and not any(opt in opts for opt in self.not_required_if): + msg = f"Illegal usage: Either '{self.name}' or one of {', '.join(self.not_required_if)} must be provided." + raise click.UsageError(msg) + return super().handle_parse_result(ctx, opts, args) + + +@click.command() +@click.option( + "--data_version", + required=True, + type=str, + help="Specify the data version you want to use.", +) +@click.option( + "--experiment_file_name", + cls=Mutex, + not_required_if=["benchmark"], + required_if_not_set=True, + type=str, + help="Specify the experiment data file name you want to use. (CSV)", +) +@click.option( + "--benchmark", + "-b", + cls=Mutex, + not_required_if=["experiment_file_name"], + required_if_not_set=True, + is_flag=True, + help="Use the baseline rag function to get benchmarking results.", +) +def main(data_version, experiment_file_name, benchmark): + get_experiment_results = GetExperimentResults() + get_experiment_results.set_data_version(data_version) + get_experiment_results.load_experiment_param_data(experiment_file_name=experiment_file_name, benchmark=benchmark) + get_experiment_results.load_chunks_from_jsonl_to_index() + get_experiment_results.loop_through_experiements() + get_experiment_results.create_visualisation_plus_grouped_results() + get_experiment_results.clear_index() + + +if __name__ == "__main__": + main() diff --git a/notebooks/evaluation/tests/test_rag_experiments.py b/notebooks/evaluation/tests/test_rag_experiments.py new file mode 100644 index 000000000..9a6034994 --- /dev/null +++ b/notebooks/evaluation/tests/test_rag_experiments.py @@ -0,0 +1,212 @@ +import json +import sys +from pathlib import Path +from unittest import mock + +import pytest + +# Hack - wasn't picking up imports properly +sys.path.append(str(Path(__file__).parents[2])) +import tempfile +import unittest +from pathlib import Path +from unittest.mock import MagicMock, patch + +import pandas as pd +from elasticsearch import Elasticsearch +from elasticsearch.helpers import scan +from evaluation.rag_experiments import GetExperimentResults + +from redbox.models import Settings + + +@pytest.fixture() +def env(): + return Settings() + + +@pytest.fixture() +def es_client(): + with ( + mock.patch("elasticsearch.Elasticsearch.search") as mocked_search, + mock.patch("elasticsearch.client.IndicesClient.create") as mocked_index_create, + mock.patch("elasticsearch.Elasticsearch.index") as mocked_index, + mock.patch("elasticsearch.helpers.scan") as mocked_scan, + mock.patch("elasticsearch.Elasticsearch.delete_by_query") as mocked_delete_by_query, + ): + mocked_search.return_value = {"hits": {"hits": [{"_id": "1", "_source": {"field": "value"}}]}} + mocked_index_create.return_value = {"acknowledged": True} + mocked_index.return_value = {"result": "created"} + mocked_delete_by_query.return_value = {"deleted": 1} + mocked_scan.return_value = iter( + [{"_id": "1", "_source": {"field": "value"}}, {"_id": "2", "_source": {"field": "value"}}] + ) + + client = Elasticsearch(hosts=["http://localhost:9200"]) + client.indices = mock.Mock() + client.indices.create = mocked_index_create + client.search = mocked_search + client.index = mocked_index + client.delete_by_query = mocked_delete_by_query + + yield client, mocked_search, mocked_index_create, mocked_index, mocked_scan, mocked_delete_by_query + + +@pytest.mark.usefixtures("es_client") +class TestGetExperimentResults(unittest.TestCase): + @pytest.fixture(autouse=True) + def inject_fixtures(self, es_client): + ( + self.es_client, + self.mocked_search, + self.mocked_index_create, + self.mocked_index, + self.mocked_scan, + self.mocked_delete_by_query, + ) = es_client + + return es_client + + @classmethod + def setUpClass(cls): + super().setUpClass() + + def setUp(self): + self.get_experiment_results = GetExperimentResults() + self.get_experiment_results.set_data_version("0.2.3") + + self.get_experiment_results.ENV = MagicMock() + + self.get_experiment_results.ES_CLIENT = self.es_client + + self.temp_dir = tempfile.TemporaryDirectory() + self.get_experiment_results.V_RESULTS = self.temp_dir.name + + self.mock_synthetic_path = "/mock/synthetic" + self.mock_complete_ragas_file = "mock_complete_ragas_synthetic_data.csv" + self.get_experiment_results.synthetic_data_dir = self.mock_synthetic_path + + def tearDown(self): + self.temp_dir.cleanup() + + @patch("jsonlines.open") + def test_load_chunks_from_jsonl_to_index(self, mock_jsonlines_open): + mock_jsonlines_open.return_value.__enter__.return_value = iter( + [json.dumps({"uuid": "1234", "parent_file_uuid": "abcd", "data": "test data"})] + ) + + file_uuids = self.get_experiment_results.load_chunks_from_jsonl_to_index() + assert "abcd" in file_uuids + self.get_experiment_results.ES_CLIENT.index.assert_called_once() + + @patch("jsonlines.open") + def test_load_chunks_from_empty_jsonl(self, mock_jsonlines_open): + mock_jsonlines_open.return_value.__enter__.return_value = iter([]) + + result = self.get_experiment_results.load_chunks_from_jsonl_to_index() + assert result == set() + + def test_load_experiment_param_data(self): + with patch("pandas.read_csv") as mock_read_csv: + mock_read_csv.return_value = pd.DataFrame( + { + "experiment_name": ["test_experiment"], + "retrieval_system_prompt": ["test_prompt"], + "retrieval_question_prompt": ["test_question_prompt"], + } + ) + + self.get_experiment_results.load_experiment_param_data("test_file") + assert self.get_experiment_results.experiment_parameters["experiment_name"][0] == "test_experiment" + + @patch("evaluation.rag_experiments.GetExperimentResults.get_rag_results") + @patch("pandas.DataFrame.to_csv") + def test_write_rag_results(self, mock_to_csv, mock_get_rag_results): + mock_get_rag_results.return_value = { + "output_text": "test_output", + "source_documents": [{"page_content": "test_content"}], + } + + with patch("pandas.read_csv") as mock_read_csv: + mock_read_csv.return_value = pd.DataFrame({"input": ["test_input"]}) + self.get_experiment_results.write_rag_results() + mock_to_csv.assert_called_once() + + @patch("evaluation.rag_experiments.pd.read_csv") + @patch("evaluation.rag_experiments.evaluate") + def test_do_evaluation(self, mock_evaluate, mock_read_csv): + # Setting up the mock return value for read_csv + mock_data = pd.DataFrame({"column1": [1, 2, 3], "column2": ["a", "b", "c"]}) + mock_read_csv.return_value = mock_data + + mock_eval_result = pd.DataFrame( + { + "input": ["input1", "input2", "input3"], + "actual_output": ["output1", "output2", "output3"], + "expected_output": ["expected1", "expected2", "expected3"], + "context": [["context1"], ["context2"], ["context3"]], + "retrieval_context": [["retrieval1"], ["retrieval2"], ["retrieval3"]], + "additional_metadata": [None, None, None], + "comments": [None, None, None], + } + ) + mock_evaluate.return_value = mock_eval_result + + result = self.get_experiment_results.do_evaluation() + + assert result is not None + assert isinstance(result, pd.DataFrame) + assert result.shape == (3, 7) + + @patch("seaborn.barplot") + @patch("pandas.concat") + @patch("pandas.read_csv") + def test_create_visualisation_plus_grouped_results(self, mock_read_csv, mock_concat, mock_barplot): # noqa: ARG002 + mock_read_csv.return_value = pd.DataFrame( + {"experiment_name": ["test_experiment"], "score": [0.5], "metric_name": ["test_metric"]} + ) + mock_concat.return_value = mock_read_csv.return_value + + self.get_experiment_results.experiment_parameters = {"experiment_name": ["test_experiment"]} + self.get_experiment_results.create_visualisation_plus_grouped_results() + + @patch("pandas.read_csv") + def test_create_visualisation_empty_data(self, mock_read_csv): + mock_read_csv.return_value = pd.DataFrame() + self.get_experiment_results.experiment_parameters = {"experiment_name": []} + + with pytest.raises(ValueError): # noqa: PT011 + self.get_experiment_results.create_visualisation_plus_grouped_results() + + @patch("jsonlines.open") + def test_clear_index(self, mock_jsonlines_open): + with patch("pandas.read_csv") as mock_read_csv: + mock_read_csv.return_value = pd.DataFrame( + { + "experiment_name": ["test_experiment"], + "retrieval_system_prompt": ["test_prompt"], + "retrieval_question_prompt": ["test_question_prompt"], + } + ) + + self.get_experiment_results.load_experiment_param_data("test_file") + + mock_jsonlines_open.return_value.__enter__.return_value = iter( + [json.dumps({"uuid": "1234", "parent_file_uuid": "abcd", "data": "test data"})] + ) + + self.get_experiment_results.load_chunks_from_jsonl_to_index() + + self.get_experiment_results.ES_CLIENT.index.assert_called_once() + + self.get_experiment_results.clear_index() + documents_after_clear = list( + scan(self.get_experiment_results.ES_CLIENT, query={"query": {"match_all": {}}}) + ) + assert len(documents_after_clear) == 0 + + self.get_experiment_results.clear_index() + + +if __name__ == "__main__": + unittest.main() diff --git a/notebooks/poetry.lock b/notebooks/poetry.lock index 2c055e16e..103a48bd8 100644 --- a/notebooks/poetry.lock +++ b/notebooks/poetry.lock @@ -1,91 +1,103 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand. 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+[package.dependencies] +mypy-extensions = ">=0.3.0" +typing-extensions = ">=3.7.4" + [[package]] name = "urllib3" version = "2.2.2" @@ -2278,4 +3243,4 @@ multidict = ">=4.0" [metadata] lock-version = "2.0" python-versions = ">=3.12,<3.13" -content-hash = "b066c89453b92a7159c08ac685bc4f80bc09b49d59fbe48c10e1aa39b06ae8ae" +content-hash = "559e89e8daa9f26cf46ed9d581ce955d9adf3170e41a360ea3d1a080ddcd3dc3" diff --git a/redbox-core/pyproject.toml b/redbox-core/pyproject.toml index 17a313173..e7753ca13 100644 --- a/redbox-core/pyproject.toml +++ b/redbox-core/pyproject.toml @@ -22,6 +22,10 @@ pydantic-settings = "^2.3.4" langchain-elasticsearch = "^0.2.2" pytest-dotenv = "^0.5.2" kneed = "^0.8.5" +sentence-transformers = "^3.0.1" +langchain-community = "^0.2.12" +cryptography = "43.0.1" +aiohttp = "3.10.2" [tool.poetry.group.dev.dependencies] diff --git a/redbox-core/redbox/api/callbacks.py b/redbox-core/redbox/api/callbacks.py index 3bee54479..087ccbfa2 100644 --- a/redbox-core/redbox/api/callbacks.py +++ b/redbox-core/redbox/api/callbacks.py @@ -9,9 +9,28 @@ class LoggerCallbackHandler(BaseCallbackHandler): def __init__(self, logger: Logger): self.logger: Logger = logger + def redact_secrets(self, data: dict[str, Any]) -> dict[str, Any]: + """Redact sensitive information such as API keys.""" + repr_str = data['repr'] + + start_token = "openai_api_key='" + end_token = "'," + + start_index = repr_str.find(start_token) + len(start_token) + end_index = repr_str.find(end_token, start_index) + + current_openai_api_key = repr_str[start_index:end_index] + + new_repr_str = repr_str.replace(f"openai_api_key='{current_openai_api_key}'", "openai_api_key=''") + + data['repr'] = new_repr_str + + return data + def on_llm_start(self, serialized: dict[str, Any], prompts: list[str], **kwargs: Any) -> None: # noqa:ARG002 """Run when LLM starts running.""" - self.logger.info("LLM start: %s, prompts: %s", serialized, prompts) + redacted_serialized = self.redact_secrets(serialized) + self.logger.info("LLM start: %s, prompts: %s", redacted_serialized, prompts) def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None: # noqa:ARG002 """Run when LLM ends running.""" diff --git a/redbox-core/redbox/embeddings/__init__.py b/redbox-core/redbox/embeddings/__init__.py index 24020edad..0f38fc65c 100644 --- a/redbox-core/redbox/embeddings/__init__.py +++ b/redbox-core/redbox/embeddings/__init__.py @@ -1,7 +1,8 @@ +from langchain_community.embeddings import SentenceTransformerEmbeddings from langchain_core.embeddings import Embeddings +from redbox.models.settings import Settings from .langchain import get_azure_embeddings, get_openai_embeddings -from redbox.models.settings import Settings def get_embeddings(env: Settings) -> Embeddings: @@ -9,5 +10,9 @@ def get_embeddings(env: Settings) -> Embeddings: return get_azure_embeddings(env) elif env.embedding_backend == "openai": return get_openai_embeddings(env) + elif env.embedding_backend == "sentencetransformers": + return SentenceTransformerEmbeddings( + model_name=env.embedding_openai_model, + ) else: raise Exception("No configured embedding model") diff --git a/redbox-core/redbox/embeddings/model.py b/redbox-core/redbox/embeddings/model.py new file mode 100644 index 000000000..54c1c1bea --- /dev/null +++ b/redbox-core/redbox/embeddings/model.py @@ -0,0 +1,43 @@ +import logging +from pathlib import Path +from uuid import uuid4 + +from redbox.models.embedding import Embedding, EmbeddingModelInfo, EmbeddingResponse +from sentence_transformers import SentenceTransformer + +MODEL_PATH = str(Path(__file__).parents[1] / "models") + +log = logging.getLogger() +log.setLevel(logging.INFO) + + +class SentenceTransformerDB(SentenceTransformer): + def __init__(self, embedding_model_name: str): + super().__init__(embedding_model_name, cache_folder=MODEL_PATH) + self.embedding_model_name = embedding_model_name + + def embed_sentences(self, sentences: list[str]) -> EmbeddingResponse: + embeddings = self.encode(sentences) + + reformatted_embeddings = [ + Embedding( + object="embedding", + index=i, + embedding=list(embedding), + ) + for i, embedding in enumerate(embeddings) + ] + + return EmbeddingResponse( + object="list", + data=reformatted_embeddings, + embedding_id=str(uuid4()), + embedding_model=self.embedding_model_name, + embedding_model_info=self.get_embedding_model_info(), + ) + + def get_embedding_model_info(self) -> EmbeddingModelInfo: + return EmbeddingModelInfo( + embedding_model=self.embedding_model_name, + vector_size=self.get_sentence_embedding_dimension(), + ) diff --git a/redbox-core/redbox/models/chat.py b/redbox-core/redbox/models/chat.py index 85fdd98bb..dcaa73e9a 100644 --- a/redbox-core/redbox/models/chat.py +++ b/redbox-core/redbox/models/chat.py @@ -72,7 +72,7 @@ class ChatRoute(StrEnum): summarise = "summarise" map_reduce_summarise = "summarise/documents/large" chat = "chat" - chat_with_docs = "chat/documents" + chat_with_docs = "summarise" chat_with_docs_map_reduce = "chat/documents/large" diff --git a/redbox-core/redbox/models/settings.py b/redbox-core/redbox/models/settings.py index daf9c6763..1b043b59f 100644 --- a/redbox-core/redbox/models/settings.py +++ b/redbox-core/redbox/models/settings.py @@ -11,9 +11,7 @@ log = logging.getLogger() -CHAT_SYSTEM_PROMPT = ( - "You are an AI assistant called Redbox tasked with answering questions and providing information objectively." -) +CHAT_SYSTEM_PROMPT = "You are an AI assistant called Redbox tasked with answering questions and providing information objectively." CHAT_WITH_DOCS_SYSTEM_PROMPT = "You are an AI assistant called Redbox tasked with answering questions on user provided documents and providing information objectively." @@ -27,6 +25,56 @@ "4) Maintain the original context and meaning.\n" ) +COACH_SYSTEM_PROMPT = ( + "You are part of Redbox, an advanced language model service created to support UK government civil servants " + "in their daily knowledge work. Redbox features three primary routes:\n" + "@summarise: This route helps users distill large amounts of text into concise summaries. " + "Users can enhance their results by providing context for specific summarization needs.\n" + "@search: A route that assists users in finding semantically related content across documents. " + "Crafting effective search queries is crucial, as users may need guidance on how to frame their searches effectively.\n" + "@chat: This direct route allows users to interact casually with the LLM, " + "facilitating conversational exchanges based on the user's context.\n" + "In this capacity, you function as a new route: @coach. Your role involves analyzing chat transcripts provided " + "by users and offering constructive guidance on how they can better utilize the three core routes described above." + "Your advising style should remain friendly and approachable. Aim for a maximum of three pieces of advice. " + "If you can’t generate at least one actionable suggestion, feel free to say, " + "“Sorry, I have no advice for you at the moment.”\n" + "Your objectives include:\n" + "1) Identifying issues within user prompts, such as a lack of clarity, insufficient context, " + "or the improper use of core routes.\n" + "2) Offering specific guidance for improvement, emphasizing clarity and focus to strengthen user prompts.\n" + "3) Providing actionable suggestions that address the noted issues, without generating sample prompts for users to replicate.\n" + "---\n" + "Embedded Guidance:\n" + "- Emphasize clarity, specificity, and brevity in user instructions.\n" + "- Recommend including relevant context or examples when necessary, balancing detail with flexibility.\n" + "- Suggest alternative, more suitable routes if the user has chosen the wrong one.\n" + "- Clarify that perfection in grammar is unnecessary; the focus should be on clear communication.\n" + "- Encourage simplifying complex queries into manageable components.\n" + "- For queries seeking factual information, prompt users to ask for verification or specifics.\n" + "- Foster an environment of iterative questioning to refine the results based on responses.\n" + "- Remind users they can request explanations for the confidence levels and reasoning behind the search results.\n" + "- When comparing documents, stress the importance of specifying criteria and suggest organizing output in structured formats.\n" + "- If users feel outputs are incorrect, encourage them to rephrase questions or add context, " + "and remind them to verify important information against original sources.\n" + "---\n" + "Example Interactions with Coach Responses:\n" + "Example 1:\n" + "User Input:@summarise\n" + "Coach Response:\n To effectively use @summarise, consider providing specific content or context for summarization. " + "This helps generate a more useful and tailored summary for your needs.\n" + "Example 2:\n" + "User Input:\nsummarise artificial intelligence trends and search for recent articles\n" + "Coach Response:It's great that you're using @summarise and @search! For more accurate results, " + "you might want to use @chat for ongoing dialogue about the trends or specific articles. " + "This can lead to richer insights and better-targeted information.\n" + "Example 3:\n" + "User Input:@summarise latest AI ethics, search new guidelines, and @chat about potential implementation challenges\n" + "Coach Response:\nYou’ve structured your request well by using all three methods; however, to optimize this, " + "consider clearly defining what specific aspects of AI ethics or guidelines you’re curious about. " + "For example, specifying particular ethical concerns can guide a more effective dialogue and yield better results.\n" +) + RETRIEVAL_SYSTEM_PROMPT = ( "Given the following conversation and extracted parts of a long document and a question, create a final answer. \n" "If you don't know the answer, just say that you don't know. Don't try to make up an answer. " @@ -79,19 +127,29 @@ CHAT_QUESTION_PROMPT = "{question}\n=========\n Response: " -CHAT_WITH_DOCS_QUESTION_PROMPT = "Question: {question}. \n\n Documents: \n\n {formatted_documents} \n\n Answer: " +CHAT_WITH_DOCS_QUESTION_PROMPT = ( + "Question: {question}. \n\n Documents: \n\n {formatted_documents} \n\n Answer: " +) -CHAT_WITH_DOCS_REDUCE_QUESTION_PROMPT = "Question: {question}. \n\n Documents: \n\n {summaries} \n\n Answer: " +CHAT_WITH_DOCS_REDUCE_QUESTION_PROMPT = ( + "Question: {question}. \n\n Documents: \n\n {summaries} \n\n Answer: " +) -RETRIEVAL_QUESTION_PROMPT = "{question} \n=========\n{formatted_documents}\n=========\nFINAL ANSWER: " +RETRIEVAL_QUESTION_PROMPT = ( + "{question} \n=========\n{formatted_documents}\n=========\nFINAL ANSWER: " +) -SUMMARISATION_QUESTION_PROMPT = "Question: {question}. \n\n Documents: \n\n {documents} \n\n Answer: " +SUMMARISATION_QUESTION_PROMPT = ( + "Question: {question}. \n\n Documents: \n\n {documents} \n\n Answer: " +) MAP_QUESTION_PROMPT = "Question: {question}. " 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 {summaries} \n\n Answer: " +) CONDENSE_QUESTION_PROMPT = "{question}\n=========\n Standalone question: " @@ -108,6 +166,7 @@ class AISettings(BaseModel): elbow_filter_enabled: bool = False chat_system_prompt: str = CHAT_SYSTEM_PROMPT chat_question_prompt: str = CHAT_QUESTION_PROMPT + coach_system_prompt: str = COACH_SYSTEM_PROMPT stuff_chunk_context_ratio: float = 0.75 chat_with_docs_system_prompt: str = CHAT_WITH_DOCS_SYSTEM_PROMPT chat_with_docs_question_prompt: str = CHAT_WITH_DOCS_QUESTION_PROMPT @@ -163,6 +222,7 @@ class Settings(BaseSettings): anthropic_api_key: str | None = None openai_api_key: str = "NotAKey" + openai_model: str | None = None azure_openai_api_key: str = "NotAKey" azure_openai_endpoint: str | None = None @@ -172,7 +232,7 @@ 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", "sentencetransformers"] = "azure" embedding_max_retries: int = 10 embedding_retry_min_seconds: int = 10 embedding_retry_max_seconds: int = 120 @@ -187,7 +247,7 @@ class Settings(BaseSettings): partition_strategy: Literal["auto", "fast", "ocr_only", "hi_res"] = "fast" clustering_strategy: Literal["full"] | None = None - elastic: ElasticCloudSettings | ElasticLocalSettings = ElasticLocalSettings() + elastic: ElasticLocalSettings = ElasticLocalSettings() elastic_root_index: str = "redbox-data" kibana_system_password: str = "redboxpass" @@ -225,7 +285,9 @@ class Settings(BaseSettings): dev_mode: bool = False superuser_email: str | None = None - model_config = SettingsConfigDict(env_file=".env", env_nested_delimiter="__", extra="allow", frozen=True) + model_config = SettingsConfigDict( + env_file=".env", env_nested_delimiter="__", extra="allow", frozen=True + ) @lru_cache(1) def elasticsearch_client(self) -> Elasticsearch: @@ -247,7 +309,9 @@ def elasticsearch_client(self) -> Elasticsearch: log.info("Cloud ID = %s", self.elastic.cloud_id) log.info("Elastic Cloud API Key = %s", self.elastic.api_key) - return Elasticsearch(cloud_id=self.elastic.cloud_id, api_key=self.elastic.api_key) + return Elasticsearch( + cloud_id=self.elastic.cloud_id, api_key=self.elastic.api_key + ) def s3_client(self): if self.object_store == "minio": diff --git a/worker/Dockerfile b/worker/Dockerfile index 698269630..36656c3e2 100644 --- a/worker/Dockerfile +++ b/worker/Dockerfile @@ -10,16 +10,19 @@ RUN pip install poetry ADD redbox-core/ /app/redbox-core ADD worker/ /app/worker ADD README.md /app/README.md +ADD download_embedder.py /app/ WORKDIR /app/worker RUN poetry install +RUN poetry run python ../download_embedder.py --embedding_model "all-mpnet-base-v2" + # Downlaod 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