chroma来进行向量存储了.,这样得到一个vector_store,向量存储的变量,然后,再用,StorageContext这个存储容器,指定,我们的 vector_store这个变量,存储.然后再去使用VectorStoreIndex去创建index索引,指定storage_context. 然后就可以使用index索引进行指定similarity_top_k=2返回相关度最高的前两个值.并且返回vector_retriever...
将Chroma分配为StorageContext中的vector_store 使用该StorageContext初始化你的VectorStoreIndex 下面展示了如何实现这一点,并偷偷看了一眼如何实际查询数据: import chromadb fromllama_index.core import VectorStoreIndex, SimpleDirectoryReader from llama_index.vector_stores.chroma import ChromaVectorStore from llama_...
写代码之前,需要首先安装 LlamaIndex 中的 chromadb。 pip install -U llama-index-vector-stores-chroma -i https://pypi.tuna.tsinghua.edu.cn/simple (2)创建一个chromadb 数据库的实例 db = chromadb.PersistentClient(path="D:\\GitHub\\LEARN_LLM\\LlamaIndex\\vector_store\\chroma_db") (3)创建 ...
found 0.4.1 with grep in /nix/store/i3vg34qd6sh9d5hcw730ff2fdfndp4q5-python3.12-llama-index-vector-stores-chroma-0.4.1 found 0.4.1 in filename of file in /nix/store/i3vg34qd6sh9d5hcw730ff2fdfndp4q5-python3.12-llama-index-vector-stores-chroma-0.4.1 Rebuild report(if merged into mast...
pip install llama-index-vector-stores-chroma 要了解所有可用集成,请访问 LlamaHub。 然后,在代码中使用它: python import chromadb from llama_index.vector_stores.chroma import ChromaVectorStore from llama_index.core import StorageContext chroma_client = chromadb.PersistentClient() chroma_collection = chro...
chromadb这个向量数据库. from llama_index.core import VectorStoreIndex, KeywordTableIndex, SimpleDirectoryReader from llama_index.vector_stores.qdrant import QdrantVectorStore from llama_index.core.node_parser import SentenceSplitter from llama_index.core.ingestion import IngestionPipeline ...
下面我们将把ChromaVectorStore和默认的SimplePropertyGraphStore结合起来使用。 %pipinstallllama-index-vector-stores-chroma 指定chroma存储向量 fromllama_index.core.graph_storesimportSimplePropertyGraphStorefromllama_index.vector_stores.chromaimportChromaVectorStoreimportchromadbclient=chromadb.PersistentClient("./chro...
fromllama_index.coreimportVectorStoreIndex, SimpleDirectoryReader fromllama_index.vector_stores.chromaimportChromaVectorStore fromllama_index.coreimportStorageContext fromllama_index.coreimportVectorStoreIndex, get_response_synthesizer fromllama_index.core.retrieversimportVectorIndexRetriever ...
(docs)from langchain_community.embeddings.fastembed import FastEmbedEmbeddingsfrom langchain_community.vectorstores import Chromavectorstore = Chroma.from_documents(documents=splits, embedding=FastEmbedEmbeddings())retriever = vectorstore.as_retriever()from langchain import hub# pip install langchainhub...
from langchain_community.vectorstores import Chroma vectorstore = Chroma.from_documents(documents=splits, embedding=FastEmbedEmbeddings()) retriever = vectorstore.as_retriever() from langchain import hub # pip install langchainhub prompt = hub.pull("rlm/rag-prompt") ...