forked from phoenix/litellm-mirror
LiteLLM Minor Fixes & Improvements (10/17/2024) (#6293)
* fix(ui_sso.py): fix faulty admin only check Fixes https://github.com/BerriAI/litellm/issues/6286 * refactor(sso_helper_utils.py): refactor /sso/callback to use helper utils, covered by unit testing Prevent future regressions * feat(prompt_factory): support 'ensure_alternating_roles' param Closes https://github.com/BerriAI/litellm/issues/6257 * fix(proxy/utils.py): add dailytagspend to expected views * feat(auth_utils.py): support setting regex for clientside auth credentials Fixes https://github.com/BerriAI/litellm/issues/6203 * build(cookbook): add tutorial for mlflow + langchain + litellm proxy tracing * feat(argilla.py): add argilla logging integration Closes https://github.com/BerriAI/litellm/issues/6201 * fix: fix linting errors * fix: fix ruff error * test: fix test * fix: update vertex ai assumption - parts not always guaranteed (#6296) * docs(configs.md): add argila env var to docs
This commit is contained in:
Krish Dholakia
GitHub
parent
5e381caf75
commit
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23 changed files with 1388 additions and 43 deletions
312
cookbook/mlflow_langchain_tracing_litellm_proxy.ipynb
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cookbook/mlflow_langchain_tracing_litellm_proxy.ipynb
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{
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Databricks Notebook with MLFlow AutoLogging for LiteLLM Proxy calls\n"
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]
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},
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{
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"cell_type": "code",
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"title": ""
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}
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},
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"outputs": [],
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"source": [
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"%pip install -U -qqqq databricks-agents mlflow langchain==0.3.1 langchain-core==0.3.6 "
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]
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},
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{
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"title": ""
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}
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},
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"outputs": [],
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"source": [
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"%pip install \"langchain-openai<=0.3.1\""
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]
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},
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{
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"cell_type": "code",
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"title": ""
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}
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},
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"outputs": [],
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"source": [
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"# Before logging this chain using the driver notebook, you must comment out this line.\n",
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"dbutils.library.restartPython() "
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"title": ""
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}
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},
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"outputs": [],
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"source": [
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"import mlflow\n",
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"from operator import itemgetter\n",
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"from langchain_core.output_parsers import StrOutputParser\n",
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"from langchain_core.prompts import PromptTemplate\n",
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"from langchain_core.runnables import RunnableLambda\n",
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"from langchain_databricks import ChatDatabricks\n",
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"from langchain_openai import ChatOpenAI"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 0,
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"title": ""
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}
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},
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"outputs": [],
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"source": [
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"import mlflow\n",
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"mlflow.langchain.autolog()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 0,
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"metadata": {
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"title": ""
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}
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},
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"outputs": [],
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"source": [
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"# These helper functions parse the `messages` array.\n",
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"\n",
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"# Return the string contents of the most recent message from the user\n",
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"def extract_user_query_string(chat_messages_array):\n",
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" return chat_messages_array[-1][\"content\"]\n",
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"\n",
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"\n",
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"# Return the chat history, which is is everything before the last question\n",
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"def extract_chat_history(chat_messages_array):\n",
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" return chat_messages_array[:-1]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 0,
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}
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},
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"outputs": [],
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"source": [
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"model = ChatOpenAI(\n",
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" openai_api_base=\"LITELLM_PROXY_BASE_URL\", # e.g.: http://0.0.0.0:4000\n",
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" model = \"gpt-3.5-turbo\", # LITELLM 'model_name'\n",
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" temperature=0.1, \n",
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" api_key=\"LITELLM_PROXY_API_KEY\" # e.g.: \"sk-1234\"\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"title": ""
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}
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},
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"outputs": [],
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"source": [
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"############\n",
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"# Prompt Template for generation\n",
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"############\n",
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"prompt = PromptTemplate(\n",
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" template=\"You are a hello world bot. Respond with a reply to the user's question that is fun and interesting to the user. User's question: {question}\",\n",
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" input_variables=[\"question\"],\n",
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")\n",
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"\n",
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"############\n",
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"# FM for generation\n",
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"# ChatDatabricks accepts any /llm/v1/chat model serving endpoint\n",
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"############\n",
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"model = ChatDatabricks(\n",
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" endpoint=\"databricks-dbrx-instruct\",\n",
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" extra_params={\"temperature\": 0.01, \"max_tokens\": 500},\n",
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")\n",
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"\n",
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"\n",
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"############\n",
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"# Simple chain\n",
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"############\n",
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"# The framework requires the chain to return a string value.\n",
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"chain = (\n",
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" {\n",
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" \"question\": itemgetter(\"messages\")\n",
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" | RunnableLambda(extract_user_query_string),\n",
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" \"chat_history\": itemgetter(\"messages\") | RunnableLambda(extract_chat_history),\n",
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" }\n",
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" | prompt\n",
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" | model\n",
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" | StrOutputParser()\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'Hello there! I\\'m here to help with your questions. Regarding your query about \"rag,\" it\\'s not something typically associated with a \"hello world\" bot, but I\\'m happy to explain!\\n\\nRAG, or Remote Angular GUI, is a tool that allows you to create and manage Angular applications remotely. It\\'s a way to develop and test Angular components and applications without needing to set up a local development environment. This can be particularly useful for teams working on distributed systems or for developers who prefer to work in a cloud-based environment.\\n\\nI hope this explanation of RAG has been helpful and interesting! If you have any other questions or need further clarification, feel free to ask.'"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"application/databricks.mlflow.trace": "\"tr-ea2226413395413ba2cf52cffc523502\"",
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"text/plain": [
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"Trace(request_id=tr-ea2226413395413ba2cf52cffc523502)"
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]
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# This is the same input your chain's REST API will accept.\n",
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"question = {\n",
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" \"messages\": [\n",
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" {\n",
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" \"role\": \"user\",\n",
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" \"content\": \"what is rag?\",\n",
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" },\n",
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" ]\n",
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"}\n",
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"\n",
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"chain.invoke(question)"
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]
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},
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{
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"cell_type": "code",
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}
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},
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"outputs": [],
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"source": [
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"mlflow.models.set_model(model=model)"
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]
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}
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],
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