mirror of
https://github.com/qdrant/fastembed.git
synced 2026-07-23 11:20:51 -05:00
docs: Updated README.md (#550)
This commit is contained in:
40
README.md
40
README.md
@@ -246,36 +246,36 @@ pip install qdrant-client[fastembed-gpu]
|
||||
You might have to use quotes ```pip install 'qdrant-client[fastembed]'``` on zsh.
|
||||
|
||||
```python
|
||||
from qdrant_client import QdrantClient
|
||||
from qdrant_client import QdrantClient, models
|
||||
|
||||
# Initialize the client
|
||||
client = QdrantClient("localhost", port=6333) # For production
|
||||
# client = QdrantClient(":memory:") # For small experiments
|
||||
# client = QdrantClient(":memory:") # For experimentation
|
||||
|
||||
# Prepare your documents, metadata, and IDs
|
||||
docs = ["Qdrant has Langchain integrations", "Qdrant also has Llama Index integrations"]
|
||||
metadata = [
|
||||
{"source": "Langchain-docs"},
|
||||
{"source": "Llama-index-docs"},
|
||||
model_name = "sentence-transformers/all-MiniLM-L6-v2"
|
||||
payload = [
|
||||
{"document": "Qdrant has Langchain integrations", "source": "Langchain-docs", },
|
||||
{"document": "Qdrant also has Llama Index integrations", "source": "LlamaIndex-docs"},
|
||||
]
|
||||
docs = [models.Document(text=data["document"], model=model_name) for data in payload]
|
||||
ids = [42, 2]
|
||||
|
||||
# If you want to change the model:
|
||||
# client.set_model("sentence-transformers/all-MiniLM-L6-v2")
|
||||
# List of supported models: https://qdrant.github.io/fastembed/examples/Supported_Models
|
||||
|
||||
# Use the new add() instead of upsert()
|
||||
# This internally calls embed() of the configured embedding model
|
||||
client.add(
|
||||
collection_name="demo_collection",
|
||||
documents=docs,
|
||||
metadata=metadata,
|
||||
ids=ids
|
||||
client.create_collection(
|
||||
"demo_collection",
|
||||
vectors_config=models.VectorParams(
|
||||
size=client.get_embedding_size(model_name), distance=models.Distance.COSINE)
|
||||
)
|
||||
|
||||
search_result = client.query(
|
||||
client.upload_collection(
|
||||
collection_name="demo_collection",
|
||||
query_text="This is a query document"
|
||||
vectors=docs,
|
||||
ids=ids,
|
||||
payload=payload,
|
||||
)
|
||||
|
||||
search_result = client.query_points(
|
||||
collection_name="demo_collection",
|
||||
query=models.Document(text="This is a query document", model=model_name)
|
||||
).points
|
||||
print(search_result)
|
||||
```
|
||||
|
||||
Reference in New Issue
Block a user