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## Description Related to https://github.com/mlflow/mlflow/pull/10420. MLflow AI gateway will be deprecated and replaced by the `mlflow.deployments` module. Happy to split this PR if it's too large. ``` pip install git+https://github.com/langchain-ai/langchain.git@refs/pull/13699/merge#subdirectory=libs/langchain ``` ## Dependencies Install mlflow from https://github.com/mlflow/mlflow/pull/10420: ``` pip install git+https://github.com/mlflow/mlflow.git@refs/pull/10420/merge ``` ## Testing plan The following code works fine on local and databricks: <details><summary>Click</summary> <p> ```python """ Setup ----- mlflow deployments start-server --config-path examples/gateway/openai/config.yaml databricks secrets create-scope <scope> databricks secrets put-secret <scope> openai-api-key --string-value $OPENAI_API_KEY Run --- python /path/to/this/file.py secrets/<scope>/openai-api-key """ from langchain.chat_models import ChatMlflow, ChatDatabricks from langchain.embeddings import MlflowEmbeddings, DatabricksEmbeddings from langchain.llms import Databricks, Mlflow from langchain.schema.messages import HumanMessage from langchain.chains.loading import load_chain from mlflow.deployments import get_deploy_client import uuid import sys import tempfile from langchain.chains import LLMChain from langchain.prompts import PromptTemplate ############################### # MLflow ############################### chat = ChatMlflow( target_uri="http://127.0.0.1:5000", endpoint="chat", params={"temperature": 0.1} ) print(chat([HumanMessage(content="hello")])) embeddings = MlflowEmbeddings(target_uri="http://127.0.0.1:5000", endpoint="embeddings") print(embeddings.embed_query("hello")[:3]) print(embeddings.embed_documents(["hello", "world"])[0][:3]) llm = Mlflow( target_uri="http://127.0.0.1:5000", endpoint="completions", params={"temperature": 0.1}, ) print(llm("I am")) llm_chain = LLMChain( llm=llm, prompt=PromptTemplate( input_variables=["adjective"], template="Tell me a {adjective} joke", ), ) print(llm_chain.run(adjective="funny")) # serialization/deserialization with tempfile.TemporaryDirectory() as tmpdir: print(tmpdir) path = f"{tmpdir}/llm.yaml" llm_chain.save(path) loaded_chain = load_chain(path) print(loaded_chain("funny")) ############################### # Databricks ############################### secret = sys.argv[1] client = get_deploy_client("databricks") # External - chat name = f"chat-{uuid.uuid4()}" client.create_endpoint( name=name, config={ "served_entities": [ { "name": "test", "external_model": { "name": "gpt-4", "provider": "openai", "task": "llm/v1/chat", "openai_config": { "openai_api_key": "{{" + secret + "}}", }, }, } ], }, ) try: chat = ChatDatabricks( target_uri="databricks", endpoint=name, params={"temperature": 0.1} ) print(chat([HumanMessage(content="hello")])) finally: client.delete_endpoint(endpoint=name) # External - embeddings name = f"embeddings-{uuid.uuid4()}" client.create_endpoint( name=name, config={ "served_entities": [ { "name": "test", "external_model": { "name": "text-embedding-ada-002", "provider": "openai", "task": "llm/v1/embeddings", "openai_config": { "openai_api_key": "{{" + secret + "}}", }, }, } ], }, ) try: embeddings = DatabricksEmbeddings(target_uri="databricks", endpoint=name) print(embeddings.embed_query("hello")[:3]) print(embeddings.embed_documents(["hello", "world"])[0][:3]) finally: client.delete_endpoint(endpoint=name) # External - completions name = f"completions-{uuid.uuid4()}" client.create_endpoint( name=name, config={ "served_entities": [ { "name": "test", "external_model": { "name": "gpt-3.5-turbo-instruct", "provider": "openai", "task": "llm/v1/completions", "openai_config": { "openai_api_key": "{{" + secret + "}}", }, }, } ], }, ) try: llm = Databricks( endpoint_name=name, model_kwargs={"temperature": 0.1}, ) print(llm("I am")) finally: client.delete_endpoint(endpoint=name) # Foundation model - chat chat = ChatDatabricks( endpoint="databricks-llama-2-70b-chat", params={"temperature": 0.1} ) print(chat([HumanMessage(content="hello")])) # Foundation model - embeddings embeddings = DatabricksEmbeddings(endpoint="databricks-bge-large-en") print(embeddings.embed_query("hello")[:3]) # Foundation model - completions llm = Databricks( endpoint_name="databricks-mpt-7b-instruct", model_kwargs={"temperature": 0.1} ) print(llm("hello")) llm_chain = LLMChain( llm=llm, prompt=PromptTemplate( input_variables=["adjective"], template="Tell me a {adjective} joke", ), ) print(llm_chain.run(adjective="funny")) # serialization/deserialization with tempfile.TemporaryDirectory() as tmpdir: print(tmpdir) path = f"{tmpdir}/llm.yaml" llm_chain.save(path) loaded_chain = load_chain(path) print(loaded_chain("funny")) ``` Output: ``` content='Hello! How can I assist you today?' [-0.025058426, -0.01938856, -0.027781019] [-0.025058426, -0.01938856, -0.027781019] sorry, but I cannot continue the sentence as it is incomplete. Can you please provide more information or context? Sure, here's a classic one for you: Why don't scientists trust atoms? Because they make up everything! /var/folders/dz/cd_nvlf14g9g__n3ph0d_0pm0000gp/T/tmpx_4no6ad {'adjective': 'funny', 'text': "Sure, here's a classic one for you:\n\nWhy don't scientists trust atoms?\n\nBecause they make up everything!"} content='Hello! How can I assist you today?' [-0.025058426, -0.01938856, -0.027781019] [-0.025058426, -0.01938856, -0.027781019] a 23 year old female and I am currently studying for my master's degree content="\nHello! It's nice to meet you. Is there something I can help you with or would you like to chat for a bit?" [0.051055908203125, 0.007221221923828125, 0.003879547119140625] [0.051055908203125, 0.007221221923828125, 0.003879547119140625] hello back Well, I don't really know many jokes, but I do know this funny story... /var/folders/dz/cd_nvlf14g9g__n3ph0d_0pm0000gp/T/tmp7_ds72ex {'adjective': 'funny', 'text': " Well, I don't really know many jokes, but I do know this funny story..."} ``` </p> </details> The existing workflow doesn't break: <details><summary>click</summary> <p> ```python import uuid import mlflow from mlflow.models import ModelSignature from mlflow.types.schema import ColSpec, Schema class MyModel(mlflow.pyfunc.PythonModel): def predict(self, context, model_input): return str(uuid.uuid4()) with mlflow.start_run(): mlflow.pyfunc.log_model( "model", python_model=MyModel(), pip_requirements=["mlflow==2.8.1", "cloudpickle<3"], signature=ModelSignature( inputs=Schema( [ ColSpec("string", "prompt"), ColSpec("string", "stop"), ] ), outputs=Schema( [ ColSpec(name=None, type="string"), ] ), ), registered_model_name=f"lang-{uuid.uuid4()}", ) # Manually create a serving endpoint with the registered model and run from langchain.llms import Databricks llm = Databricks(endpoint_name="<name>") llm("hello") # 9d0b2491-3d13-487c-bc02-1287f06ecae7 ``` </p> </details> ## Follow-up tasks (This PR is too large. I'll file a separate one for follow-up tasks.) - Update `docs/docs/integrations/providers/mlflow_ai_gateway.mdx` and `docs/docs/integrations/providers/databricks.md`. --------- Signed-off-by: harupy <17039389+harupy@users.noreply.github.com> Co-authored-by: Bagatur <baskaryan@gmail.com> |
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|---|---|---|
| .. | ||
| __init__.py | ||
| test_anthropic.py | ||
| test_azureml_endpoint.py | ||
| test_azureopenai.py | ||
| test_baichuan.py | ||
| test_base.py | ||
| test_bedrock.py | ||
| test_ernie.py | ||
| test_fireworks.py | ||
| test_google_palm.py | ||
| test_hunyuan.py | ||
| test_imports.py | ||
| test_javelin_ai_gateway.py | ||
| test_openai.py | ||