ZhipuAIEmbeddings
This will help you get started with ZhipuAI embedding models using LangChain. For detailed documentation onZhipuAIEmbeddings features and configuration options, please refer to theAPI reference.
Overview
Integration details
| Provider | Package |
|---|---|
| ZhipuAI | langchain-community |
Setup
To access ZhipuAI embedding models you'll need to create a/an ZhipuAI account, get an API key, and install thezhipuai integration package.
Credentials
Head tohttps://bigmodel.cn/ to sign up to ZhipuAI and generate an API key. Once you've done this set the ZHIPUAI_API_KEY environment variable:
import getpass
import os
ifnot os.getenv("ZHIPUAI_API_KEY"):
os.environ["ZHIPUAI_API_KEY"]= getpass.getpass("Enter your ZhipuAI API key: ")
To enable automated tracing of your model calls, set yourLangSmith API key:
# os.environ["LANGSMITH_TRACING"] = "true"
# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
Installation
The LangChain ZhipuAI integration lives in thezhipuai package:
%pip install-qU zhipuai
Note: you may need to restart the kernel to use updated packages.
Instantiation
Now we can instantiate our model object and generate chat completions:
from langchain_community.embeddingsimport ZhipuAIEmbeddings
embeddings= ZhipuAIEmbeddings(
model="embedding-3",
# With the `embedding-3` class
# of models, you can specify the size
# of the embeddings you want returned.
# dimensions=1024
)
Indexing and Retrieval
Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see ourRAG tutorials.
Below, see how to index and retrieve data using theembeddings object we initialized above. In this example, we will index and retrieve a sample document in theInMemoryVectorStore.
# Create a vector store with a sample text
from langchain_core.vectorstoresimport InMemoryVectorStore
text="LangChain is the framework for building context-aware reasoning applications"
vectorstore= InMemoryVectorStore.from_texts(
[text],
embedding=embeddings,
)
# Use the vectorstore as a retriever
retriever= vectorstore.as_retriever()
# Retrieve the most similar text
retrieved_documents= retriever.invoke("What is LangChain?")
# show the retrieved document's content
retrieved_documents[0].page_content
'LangChain is the framework for building context-aware reasoning applications'
Direct Usage
Under the hood, the vectorstore and retriever implementations are callingembeddings.embed_documents(...) andembeddings.embed_query(...) to create embeddings for the text(s) used infrom_texts and retrievalinvoke operations, respectively.
You can directly call these methods to get embeddings for your own use cases.
Embed single texts
You can embed single texts or documents withembed_query:
single_vector= embeddings.embed_query(text)
print(str(single_vector)[:100])# Show the first 100 characters of the vector
[-0.022979736, 0.007785797, 0.04598999, 0.012741089, -0.01689148, 0.008277893, 0.016464233, 0.009246
Embed multiple texts
You can embed multiple texts withembed_documents:
text2=(
"LangGraph is a library for building stateful, multi-actor applications with LLMs"
)
two_vectors= embeddings.embed_documents([text, text2])
for vectorin two_vectors:
print(str(vector)[:100])# Show the first 100 characters of the vector
[-0.022979736, 0.007785797, 0.04598999, 0.012741089, -0.01689148, 0.008277893, 0.016464233, 0.009246
[-0.02330017, -0.013916016, 0.00022411346, 0.017196655, -0.034240723, 0.011131287, 0.011497498, -0.0
API Reference
For detailed documentation onZhipuAIEmbeddings features and configuration options, please refer to theAPI reference.
Related
- Embedding modelconceptual guide
- Embedding modelhow-to guides