c6b27b3692
_Thank you to the LangChain team for the great project and in advance for your review. Let me know if I can provide any other additional information or do things differently in the future to make your lives easier 🙏 _ @hwchase17 please let me know if you're not the right person to review 😄 This PR enables LangChain to access the Konko API via the chat_models API wrapper. Konko API is a fully managed API designed to help application developers: 1. Select the right LLM(s) for their application 2. Prototype with various open-source and proprietary LLMs 3. Move to production in-line with their security, privacy, throughput, latency SLAs without infrastructure set-up or administration using Konko AI's SOC 2 compliant infrastructure _Note on integration tests:_ We added 14 integration tests. They will all fail unless you export the right API keys. 13 will pass with a KONKO_API_KEY provided and the other one will pass with a OPENAI_API_KEY provided. When both are provided, all 14 integration tests pass. If you would like to test this yourself, please let me know and I can provide some temporary keys. ### Installation and Setup 1. **First you'll need an API key** 2. **Install Konko AI's Python SDK** 1. Enable a Python3.8+ environment `pip install konko` 3. **Set API Keys** **Option 1:** Set Environment Variables You can set environment variables for 1. KONKO_API_KEY (Required) 2. OPENAI_API_KEY (Optional) In your current shell session, use the export command: `export KONKO_API_KEY={your_KONKO_API_KEY_here}` `export OPENAI_API_KEY={your_OPENAI_API_KEY_here} #Optional` Alternatively, you can add the above lines directly to your shell startup script (such as .bashrc or .bash_profile for Bash shell and .zshrc for Zsh shell) to have them set automatically every time a new shell session starts. **Option 2:** Set API Keys Programmatically If you prefer to set your API keys directly within your Python script or Jupyter notebook, you can use the following commands: ```python konko.set_api_key('your_KONKO_API_KEY_here') konko.set_openai_api_key('your_OPENAI_API_KEY_here') # Optional ``` ### Calling a model Find a model on the [[Konko Introduction page](https://docs.konko.ai/docs#available-models)](https://docs.konko.ai/docs#available-models) For example, for this [[LLama 2 model](https://docs.konko.ai/docs/meta-llama-2-13b-chat)](https://docs.konko.ai/docs/meta-llama-2-13b-chat). The model id would be: `"meta-llama/Llama-2-13b-chat-hf"` Another way to find the list of models running on the Konko instance is through this [[endpoint](https://docs.konko.ai/reference/listmodels)](https://docs.konko.ai/reference/listmodels). From here, we can initialize our model: ```python chat_instance = ChatKonko(max_tokens=10, model = 'meta-llama/Llama-2-13b-chat-hf') ``` And run it: ```python msg = HumanMessage(content="Hi") chat_response = chat_instance([msg]) ```
🦜️🔗 LangChain
⚡ Building applications with LLMs through composability ⚡
Looking for the JS/TS version? Check out LangChain.js.
Production Support: As you move your LangChains into production, we'd love to offer more hands-on support. Fill out this form to share more about what you're building, and our team will get in touch.
🚨Breaking Changes for select chains (SQLDatabase) on 7/28/23
In an effort to make langchain leaner and safer, we are moving select chains to langchain_experimental.
This migration has already started, but we are remaining backwards compatible until 7/28.
On that date, we will remove functionality from langchain.
Read more about the motivation and the progress here.
Read how to migrate your code here.
Quick Install
pip install langchain
or
pip install langsmith && conda install langchain -c conda-forge
🤔 What is this?
Large language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. However, using these LLMs in isolation is often insufficient for creating a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge.
This library aims to assist in the development of those types of applications. Common examples of these applications include:
❓ Question Answering over specific documents
- Documentation
- End-to-end Example: Question Answering over Notion Database
💬 Chatbots
- Documentation
- End-to-end Example: Chat-LangChain
🤖 Agents
- Documentation
- End-to-end Example: GPT+WolframAlpha
📖 Documentation
Please see here for full documentation on:
- Getting started (installation, setting up the environment, simple examples)
- How-To examples (demos, integrations, helper functions)
- Reference (full API docs)
- Resources (high-level explanation of core concepts)
🚀 What can this help with?
There are six main areas that LangChain is designed to help with. These are, in increasing order of complexity:
📃 LLMs and Prompts:
This includes prompt management, prompt optimization, a generic interface for all LLMs, and common utilities for working with LLMs.
🔗 Chains:
Chains go beyond a single LLM call and involve sequences of calls (whether to an LLM or a different utility). LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications.
📚 Data Augmented Generation:
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.
🧠 Memory:
Memory refers to persisting state between calls of a chain/agent. LangChain provides a standard interface for memory, a collection of memory implementations, and examples of chains/agents that use memory.
🧐 Evaluation:
[BETA] Generative models are notoriously hard to evaluate with traditional metrics. One new way of evaluating them is using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this.
For more information on these concepts, please see our full documentation.
💁 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.