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⚡ Building applications with LLMs through composability ⚡
0d08a692a3
## 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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🦜️🔗 LangChain
⚡ Building applications with LLMs through composability ⚡
Looking for the JS/TS library? Check out LangChain.js.
To help you ship LangChain apps to production faster, check out LangSmith. LangSmith is a unified developer platform for building, testing, and monitoring LLM applications. Fill out this form to get off the waitlist or speak with our sales team.
Quick Install
With pip:
pip install langchain
With conda:
conda install langchain -c conda-forge
🤔 What is LangChain?
LangChain is a framework for developing applications powered by language models. It enables applications that:
- Are context-aware: connect a language model to sources of context (prompt instructions, few shot examples, content to ground its response in, etc.)
- Reason: rely on a language model to reason (about how to answer based on provided context, what actions to take, etc.)
This framework consists of several parts.
- LangChain Libraries: The Python and JavaScript libraries. Contains interfaces and integrations for a myriad of components, a basic run time for combining these components into chains and agents, and off-the-shelf implementations of chains and agents.
- LangChain Templates: A collection of easily deployable reference architectures for a wide variety of tasks.
- LangServe: A library for deploying LangChain chains as a REST API.
- LangSmith: A developer platform that lets you debug, test, evaluate, and monitor chains built on any LLM framework and seamlessly integrates with LangChain.
This repo contains the langchain (here), langchain-experimental (here), and langchain-cli (here) Python packages, as well as LangChain Templates.
🧱 What can you build with LangChain?
❓ Retrieval augmented generation
- Documentation
- End-to-end Example: Chat LangChain and repo
💬 Analyzing structured data
- Documentation
- End-to-end Example: SQL Llama2 Template
🤖 Chatbots
- Documentation
- End-to-end Example: Web LangChain (web researcher chatbot) and repo
And much more! Head to the Use cases section of the docs for more.
🚀 How does LangChain help?
The main value props of the LangChain libraries are:
- Components: composable tools and integrations for working with language models. Components are modular and easy-to-use, whether you are using the rest of the LangChain framework or not
- Off-the-shelf chains: built-in assemblages of components for accomplishing higher-level tasks
Off-the-shelf chains make it easy to get started. Components make it easy to customize existing chains and build new ones.
Components fall into the following modules:
📃 Model I/O:
This includes prompt management, prompt optimization, a generic interface for all LLMs, and common utilities for working with LLMs.
📚 Retrieval:
Data Augmented Generation involves specific types of chains that first interact with an external data source to fetch data for use in the generation step. Examples include summarization of long pieces of text and question/answering over specific data sources.
🤖 Agents:
Agents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end-to-end agents.
📖 Documentation
Please see here for full documentation, which includes:
- Getting started: installation, setting up the environment, simple examples
- Overview of the interfaces, modules and integrations
- Use case walkthroughs and best practice guides
- LangSmith, LangServe, and LangChain Template overviews
- Reference: full API docs
💁 Contributing
As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
For detailed information on how to contribute, see here.