使用Ollama与Qdrant

Ollama 为特定应用提供专门的嵌入。Ollama 支持多种嵌入模型，使得可以构建检索增强生成（RAG）应用，这些应用将文本提示与现有文档或特定领域的其他数据结合起来。

安装

您可以使用以下 pip 命令安装所需的包：

pip install ollama qdrant-client

集成示例

以下代码假设Ollama可以通过端口11434访问，Qdrant可以通过端口6334访问。

from qdrant_client import QdrantClient, models
import ollama

COLLECTION_NAME = "NicheApplications"

# Initialize Ollama client
oclient = ollama.Client(host="localhost")

# Initialize Qdrant client
qclient = QdrantClient(host="localhost", port=6333)

# Text to embed
text = "Ollama excels in niche applications with specific embeddings"

# Generate embeddings
response = oclient.embeddings(model="llama3.2", prompt=text)
embeddings = response["embedding"]

# Create a collection if it doesn't already exist
if not qclient.collection_exists(COLLECTION_NAME):
    qclient.create_collection(
        collection_name=COLLECTION_NAME,
        vectors_config=models.VectorParams(
            size=len(embeddings), distance=models.Distance.COSINE
        ),
    )

# Upload the vectors to the collection along with the original text as payload
qclient.upsert(
    collection_name=COLLECTION_NAME,
    points=[models.PointStruct(id=1, vector=embeddings, payload={"text": text})],
)

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