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Update llama-index-multitenancy.md to work with llama-index v0.10 (#626)

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Starting v0.10 [llama-index](https://github.com/run-llama/llama_index/releases/tag/v0.10.1) is split between llama_index and llama_index.core libraries. This commit updates documentation to work with llama-index v0.10 and higher. Also this commit adds note on how to run it with local llamaIndex model (like llama.cpp)

Signed-off-by: Pavel Abramov <uncle.decart@gmail.com>
This commit is contained in:
Pavel Abramov
2024-03-21 10:01:21 +01:00
committed by GitHub
parent a21aa0c2ab
commit a10062e6d0
1 changed files with 10 additions and 7 deletions
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qdrant-landing/content/documentation/tutorials/llama-index-multitenancy.md
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@@ -43,7 +43,8 @@ of `QdrantClient`.
```python
from qdrant_client import QdrantClient
from llama_index.vector_stores import QdrantVectorStore
from llama_index.vector_stores.qdrant import QdrantVectorStore
client = QdrantClient("http://localhost:6333")
@@ -62,19 +63,21 @@ LlamaIndex application. We can also use it to set up an embedding model - in our
set up
```python
from llama_index import ServiceContext
from llama_index.core import ServiceContext
service_context = ServiceContext.from_defaults(
embed_model="local:BAAI/bge-small-en-v1.5",
)
```
*Note*, in case you are using Large Language Model different from OpenAI's ChatGPT, you should specify
`llm` parameter for `ServiceContext`.
We can also control how our documents are split into chunks, or nodes using LLamaIndex's terminology.
The `SimpleNodeParser` splits documents into fixed length chunks with an overlap. The defaults are
reasonable, but we can also adjust them if we want to. Both values are defined in tokens.
```python
from llama_index.node_parser import SimpleNodeParser
from llama_index.core.node_parser import SimpleNodeParser
node_parser = SimpleNodeParser.from_defaults(chunk_size=512, chunk_overlap=32)
```
@@ -97,7 +100,7 @@ The last missing piece, before we can start indexing, is the `VectorStoreIndex`.
`ServiceContext` to be initialized.
```python
from llama_index import VectorStoreIndex
from llama_index.core import VectorStoreIndex
index = VectorStoreIndex.from_vector_store(
vector_store=vector_store, service_context=service_context
@@ -112,7 +115,7 @@ some documents manually and insert them into Qdrant collection. Our documents ar
a single metadata attribute - a library name they belong to.
```python
from llama_index.schema import Document
from llama_index.core.schema import Document
documents = [
Document(
@@ -180,7 +183,7 @@ relevant nodes for a given query. Our `VectorStoreIndex` can be used as a retrie
constraints - in our case value of the `library` metadata attribute.
```python
from llama_index.vector_stores.types import MetadataFilters, ExactMatchFilter
from llama_index.core.vector_stores.types import MetadataFilters, ExactMatchFilter
qdrant_retriever = index.as_retriever(
filters=MetadataFilters(
@@ -224,4 +227,4 @@ for node in nodes_with_scores:
```
The results returned by both retrievers are different, due to the different constraints, so we implemented
a real multitenant search application!
a real multitenant search application!