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## Description ### Issue This pull request addresses a lingering issue identified in PR #7070. In that previous pull request, an attempt was made to address the problem of empty embeddings when using the `OpenAIEmbeddings` class. While PR #7070 introduced a mechanism to retry requests for embeddings, it didn't fully resolve the issue as empty embeddings still occasionally persisted. ### Problem In certain specific use cases, empty embeddings can be encountered when requesting data from the OpenAI API. In some cases, these empty embeddings can be skipped or removed without affecting the functionality of the application. However, they might not always be resolved through retries, and their presence can adversely affect the functionality of applications relying on the `OpenAIEmbeddings` class. ### Solution To provide a more robust solution for handling empty embeddings, we propose the introduction of an optional parameter, `skip_empty`, in the `OpenAIEmbeddings` class. When set to `True`, this parameter will enable the behavior of automatically skipping empty embeddings, ensuring that problematic empty embeddings do not disrupt the processing flow. The developer will be able to optionally toggle this behavior if needed without disrupting the application flow. ## Changes Made - Added an optional parameter, `skip_empty`, to the `OpenAIEmbeddings` class. - When `skip_empty` is set to `True`, empty embeddings are automatically skipped without causing errors or disruptions. ### Example Usage ```python from openai.embeddings import OpenAIEmbeddings # Initialize the OpenAIEmbeddings class with skip_empty=True embeddings = OpenAIEmbeddings(api_key="your_api_key", skip_empty=True) # Request embeddings, empty embeddings are automatically skipped. docs is a variable containing the already splitted text. results = embeddings.embed_documents(docs) # Process results without interruption from empty embeddings ``` |
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🦜️🔗 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.