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docs: llama-index integration (#1347)
Updated api refrence and usage for llama index integration.
This commit is contained in:
Raghav Dixit
GitHub
@@ -124,7 +124,9 @@ nav:
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- LangChain:
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- LangChain 🔗: integrations/langchain.md
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- LangChain JS/TS 🔗: https://js.langchain.com/docs/integrations/vectorstores/lancedb
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- LlamaIndex 🦙: https://docs.llamaindex.ai/en/stable/examples/vector_stores/LanceDBIndexDemo/
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- LlamaIndex 🦙:
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- LlamaIndex docs: integrations/llamaIndex.md
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- LlamaIndex demo: https://docs.llamaindex.ai/en/stable/examples/vector_stores/LanceDBIndexDemo/
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- Pydantic: python/pydantic.md
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- Voxel51: integrations/voxel51.md
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- PromptTools: integrations/prompttools.md
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139
docs/src/integrations/llamaIndex.md
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139
docs/src/integrations/llamaIndex.md
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# Llama-Index
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## Quick start
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You would need to install the integration via `pip install llama-index-vector-stores-lancedb` in order to use it. You can run the below script to try it out :
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```python
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import logging
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import sys
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# Uncomment to see debug logs
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# logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
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# logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
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from llama_index.core import SimpleDirectoryReader, Document, StorageContext
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from llama_index.core import VectorStoreIndex
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from llama_index.vector_stores.lancedb import LanceDBVectorStore
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import textwrap
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import openai
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openai.api_key = "sk-..."
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documents = SimpleDirectoryReader("./data/your-data-dir/").load_data()
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print("Document ID:", documents[0].doc_id, "Document Hash:", documents[0].hash)
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## For LanceDB cloud :
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# vector_store = LanceDBVectorStore(
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# uri="db://db_name", # your remote DB URI
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# api_key="sk_..", # lancedb cloud api key
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# region="your-region" # the region you configured
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# ...
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# )
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vector_store = LanceDBVectorStore(
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uri="./lancedb", mode="overwrite", query_type="vector"
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)
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storage_context = StorageContext.from_defaults(vector_store=vector_store)
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index = VectorStoreIndex.from_documents(
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documents, storage_context=storage_context
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)
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lance_filter = "metadata.file_name = 'paul_graham_essay.txt' "
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retriever = index.as_retriever(vector_store_kwargs={"where": lance_filter})
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response = retriever.retrieve("What did the author do growing up?")
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```
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### Filtering
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For metadata filtering, you can use a Lance SQL-like string filter as demonstrated in the example above. Additionally, you can also filter using the `MetadataFilters` class from LlamaIndex:
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```python
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from llama_index.core.vector_stores import (
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MetadataFilters,
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FilterOperator,
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FilterCondition,
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MetadataFilter,
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)
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query_filters = MetadataFilters(
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filters=[
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MetadataFilter(
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key="creation_date", operator=FilterOperator.EQ, value="2024-05-23"
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),
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MetadataFilter(
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key="file_size", value=75040, operator=FilterOperator.GT
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),
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],
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condition=FilterCondition.AND,
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)
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```
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### Hybrid Search
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For complete documentation, refer [here](https://lancedb.github.io/lancedb/hybrid_search/hybrid_search/). This example uses the `colbert` reranker. Make sure to install necessary dependencies for the reranker you choose.
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```python
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from lancedb.rerankers import ColbertReranker
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reranker = ColbertReranker()
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vector_store._add_reranker(reranker)
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query_engine = index.as_query_engine(
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filters=query_filters,
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vector_store_kwargs={
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"query_type": "hybrid",
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}
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)
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response = query_engine.query("How much did Viaweb charge per month?")
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```
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In the above snippet, you can change/specify query_type again when creating the engine/retriever.
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## API reference
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The exhaustive list of parameters for `LanceDBVectorStore` vector store are :
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- `connection`: Optional, `lancedb.db.LanceDBConnection` connection object to use. If not provided, a new connection will be created.
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- `uri`: Optional[str], the uri of your database. Defaults to `"/tmp/lancedb"`.
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- `table_name` : Optional[str], Name of your table in the database. Defaults to `"vectors"`.
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- `table`: Optional[Any], `lancedb.db.LanceTable` object to be passed. Defaults to `None`.
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- `vector_column_name`: Optional[Any], Column name to use for vector's in the table. Defaults to `'vector'`.
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- `doc_id_key`: Optional[str], Column name to use for document id's in the table. Defaults to `'doc_id'`.
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- `text_key`: Optional[str], Column name to use for text in the table. Defaults to `'text'`.
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- `api_key`: Optional[str], API key to use for LanceDB cloud database. Defaults to `None`.
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- `region`: Optional[str], Region to use for LanceDB cloud database. Only for LanceDB Cloud, defaults to `None`.
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- `nprobes` : Optional[int], Set the number of probes to use. Only applicable if ANN index is created on the table else its ignored. Defaults to `20`.
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- `refine_factor` : Optional[int], Refine the results by reading extra elements and re-ranking them in memory. Defaults to `None`.
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- `reranker`: Optional[Any], The reranker to use for LanceDB.
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Defaults to `None`.
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- `overfetch_factor`: Optional[int], The factor by which to fetch more results.
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Defaults to `1`.
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- `mode`: Optional[str], The mode to use for LanceDB.
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Defaults to `"overwrite"`.
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- `query_type`:Optional[str], The type of query to use for LanceDB.
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Defaults to `"vector"`.
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### Methods
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- __from_table(cls, table: lancedb.db.LanceTable) -> `LanceDBVectorStore`__ : (class method) Creates instance from lancedb table.
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- **_add_reranker(self, reranker: lancedb.rerankers.Reranker) -> `None`** : Add a reranker to an existing vector store.
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- Usage :
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```python
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from lancedb.rerankers import ColbertReranker
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reranker = ColbertReranker()
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vector_store._add_reranker(reranker)
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```
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- **_table_exists(self, tbl_name: `Optional[str]` = `None`) -> `bool`** : Returns `True` if `tbl_name` exists in database.
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- __create_index(
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self, scalar: `Optional[bool]` = False, col_name: `Optional[str]` = None, num_partitions: `Optional[int]` = 256, num_sub_vectors: `Optional[int]` = 96, index_cache_size: `Optional[int]` = None, metric: `Optional[str]` = "L2",
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) -> `None`__ : Creates a scalar(for non-vector cols) or a vector index on a table.
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Make sure your vector column has enough data before creating an index on it.
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- __add(self, nodes: `List[BaseNode]`, **add_kwargs: `Any`, ) -> `List[str]`__ :
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adds Nodes to the table
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- **delete(self, ref_doc_id: `str`) -> `None`**: Delete nodes using with node_ids.
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- **delete_nodes(self, node_ids: `List[str]`) -> `None`** : Delete nodes using with node_ids.
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- __query(
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self,
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query: `VectorStoreQuery`,
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**kwargs: `Any`,
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) -> `VectorStoreQueryResult`__:
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Query index(`VectorStoreIndex`) for top k most similar nodes. Accepts llamaIndex `VectorStoreQuery` object.
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@@ -7,8 +7,7 @@ excluded_globs = [
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"../src/fts.md",
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"../src/embedding.md",
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"../src/examples/*.md",
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"../src/integrations/voxel51.md",
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"../src/integrations/langchain.md",
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"../src/integrations/*.md",
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"../src/guides/tables.md",
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"../src/python/duckdb.md",
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"../src/embeddings/*.md",
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