print(embedding_functions.DefaultEmbeddingFunction) 如果上述代码没有抛出异常,并且能够正确打印出函数名,那么说明embedding_functions已经成功导入。 如果导入失败,提供错误排查建议: 如果在尝试导入embedding_functions时遇到任何异常(如ModuleNotFoundError),那么可能是因为chromadb库没有正确安装,或者安装的版本不包含util...
openai_ef = embedding_functions.OpenAIEmbeddingFunction( api_key="YOUR_API_KEY", model_name="text-embedding-ada-002" ) 其他包括Cohere,HuggingFace等。 自定义Embeddings算法 你甚至可以使用自己的本地Embeddings算法,Chroma留有扩展点: from chromadb import Documents, EmbeddingFunction, Embeddings class MyEmb...
class ClientAPI(BaseAPI, ABC): tenant: str database: str @abstractmethod def get_collection( self, name: str, # 集合的名称 embedding_function: Optional[ EmbeddingFunction[Embeddable] ] = ef.DefaultEmbeddingFunction(), # 嵌入函数,可选,用于将文档嵌入向量空间,默认为默认嵌入函数 data_loader: Opti...
vectordb = Chroma(persist_directory=persist_directory, embedding_function=embeddings) qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", vectorstore=vectordb) Run Code Online (Sandbox Code Playgroud) 但该from_chain_type()函数不接受vectorstore数据库作为输入,因此这不起作用。python...
packagetech.amikos;importtech.amikos.chromadb.*;importtech.amikos.chromadb.Collection;importtech.amikos.chromadb.embeddings.DefaultEmbeddingFunction;importjava.util.*;publicclassMain{publicstaticvoidmain(String[]args) {try{Clientclient=newClient(System.getenv("CHROMA_URL"));client.reset();EmbeddingFunc...
get_collection(name="my_collection", embedding_function=emb_fn) 如果您已经自己生成了嵌入(embeddings),您可以直接将它们加载进去: collection.add( embeddings=[[1.2, 2.3, 4.5], [6.7, 8.2, 9.2]], documents=["This is a document", "This is another document"], metadatas=[{"source": "my_...
You can also use a different model by specifying the model id or path as the second argument to thepipelinefunction. For example: // Use a different model for sentiment-analysisletpipe=awaitpipeline('sentiment-analysis','Xenova/bert-base-multilingual-uncased-sentiment'); ...
This function accepts the path where you’ll store the embeddings, the name of the collection to create, the name of the embedding function to use, the data to store in the collection, and the name of the distance function to use. You then instantiate a PersistentClient() object, create...
Instead of using the default embedding model, we will load the embedding already created directly into the collections. We will use theget_or_create_collection()function to create a new collection called "Students2". This function is different fromcreate_collection(). It will get a collection ...
Chromadb + Langchain with SentenceTransformerEmbeddingFunction抛出sqlite3 >= 3.35.0错误，尽管sqlite3...