langchain openai embeddings的进一步展开说明

In this multi-part series, the author explores various modules and use cases of LangChain and documents their journey via Python notebooks on GitHub. The previous post covered LangChain Models, and this post will focus on Embeddings. The author invites readers to follow along and fork the repository or use individual notebooks on Google Colab. The author also expresses gratitude for the clarity offered by the official LangChain documentation, as much of the code is borrowed or influenced by it.

Getting Started with LangChain
Before diving into the topic of Embeddings, the author provides some instructions on getting started with LangChain. They mention that LangChain can be easily installed with pip from PyPi. However, the dependencies, such as model providers and data stores, should be installed separately based on the user’s specific needs. The author explains that LangChain supports several model providers but in this tutorial, they will only focus on OpenAI, unless explicitly stated otherwise. The OpenAI API key needs to be set via the OPENAI_API_KEY environment variable, or directly inside the notebook or Python code. The author advises against accidentally committing the API key to GitHub and recommends setting the key via the environment variable for production use.

LangChain: Text Embeddings
The author then introduces the concept of text embeddings. They explain that embeddings are a measure of the relatedness of text strings and are represented as a vector of floating point numbers. The distance between two vectors measures their relatedness, with a shorter distance indicating higher relatedness. The author highlights that embeddings are useful for various use cases such as text classification, search, clustering, recommendations, anomaly detection, and diversity measurement.

The LangChain Embedding class serves as an interface for embedding providers like OpenAI, Cohere, and HuggingFace. The base class exposes two methods: `embed_query` for a single document and `embed_documents` for multiple documents.

The author demonstrates the usage of the OpenAI Embeddings wrapper and showcases a few basic operations. They mention that OpenAI offers several first-generation embedding models, but for this tutorial, they will use the default second-generation model, text-embedding-ada-002, with the cl100k_base encoding scheme. They provide code examples that show how to retrieve OpenAI text embeddings for both single and multiple text inputs.

The author also mentions the availability of a FakeEmbeddings class, which allows users to test their pipeline without making actual calls to the embedding providers.

Limitations and Solutions
The author brings up the fact that OpenAI embedding models have a maximum context length, and exceeding this limit will result in an error. To overcome this limitation, the author suggests either truncating the input text length or chunking the text and embedding each chunk individually. They provide examples of how to truncate the input text length and mention the importance of tokenizing the input text with tiktoken before truncating it. However, they note that the `embed_query` and `embed_documents` methods only support string input at the moment, so the tokens need to be re-converted to a string before embedding.

Recommended Use Cases and Vector Databases
The author mentions that the OpenAI Cookbook offers sample code for several use cases that can be used in conjunction with LangChain. They highlight use cases such as text classification, question-answering, recommendations, semantic text search, sentiment analysis with zero-shot classification, and more.

Additionally, the author discusses the use of vector databases to efficiently search over many vectors, instead of repeatedly calling OpenAI embedding models. They suggest using databases like Chroma, Weaviate, Pinecone, Qdrant, Milvus, and others for this purpose. The author notes that they will cover vector databases in the post on Indexes in a future installment.

Conclusion
In conclusion, this post focused on LangChain Text Embeddings and explored the usage of the OpenAI Embeddings wrapper. The author provided examples and discussed methods to deal with limitations imposed by maximum context length. They also mentioned the availability of a FakeEmbeddings class for testing purposes. The author hinted at upcoming posts in the series, including coverage of LangChain Prompts and the use of vector databases. Readers are encouraged to follow along and check out the LangChain repository for more information.

langchain openai embeddings的常见问答Q&A