等待集群准备就绪可供使用。

### Creating the Search Index
Currently, the Search index needs to be created from the Couchbase Capella or Server UI or using the REST interface.

Let us define a Search index with the name `vector-index` on the `testing` bucket

For this example, let us use the Import Index feature on the Search Service on the UI.

We are defining an index on the testing bucket’s `_default` scope on the `_default` collection with the vector field set to `embedding` with 1536 dimensions and the text field set to text. We are also indexing and storing all the fields under metadata in the document as a dynamic mapping to account for varying document structures. The similarity metric is set to `dot_product`.


#### How to Import an Index to the Full Text Search service?

- [Couchbase Server](https://docs.couchbase.com/server/current/search/import-search-index.html)
    - Click on Search -> Add Index -> Import
    - Copy the following Index definition in the Import screen
    - Click on Create Index to create the index.


- [Couchbase Capella](https://docs.couchbase.com/cloud/search/import-search-index.html)
    - Copy the index definition to a new file `index.json`
    - Import the file in Capella using the instructions in the documentation.
    - Click on Create Index to create the index.

#### Index Definition

We will now set the bucket, scope, and collection names in the Couchbase cluster that we want to use for Vector Search.

For this example, we are using the default scope & collections.


```python
BUCKET_NAME = "testing"
SCOPE_NAME = "_default"
COLLECTION_NAME = "_default"
SEARCH_INDEX_NAME = "vector-index"

# Import required packages
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core import StorageContext
from llama_index.core import Settings
from llama_index.vector_stores.couchbase import CouchbaseSearchVectorStore

import os
import getpass

os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")

OpenAI API Key: ········

import logging
import sys

logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

!mkdir -p 'data/paul_graham/'
!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'

--2024-04-09 23:31:46--  https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 2606:50c0:8000::154, 2606:50c0:8001::154, 2606:50c0:8003::154, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|2606:50c0:8000::154|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 75042 (73K) [text/plain]
Saving to: ‘data/paul_graham/paul_graham_essay.txt’

data/paul_graham/pa 100%[===================>]  73.28K  --.-KB/s    in 0.008s

2024-04-09 23:31:46 (8.97 MB/s) - ‘data/paul_graham/paul_graham_essay.txt’ saved [75042/75042]

# load documents
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()

vector_store = CouchbaseSearchVectorStore(
    cluster=cluster,
    bucket_name=BUCKET_NAME,
    scope_name=SCOPE_NAME,
    collection_name=COLLECTION_NAME,
    index_name=SEARCH_INDEX_NAME,
)

storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context
)

INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"

query_engine = index.as_query_engine()
response = query_engine.query("What were his investments in Y Combinator?")
print(response)

INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
His investments in Y Combinator were $6k per founder, totaling $12k in the typical two-founder case, in return for 6% equity.

from llama_index.core.schema import TextNode

nodes = [
    TextNode(
        text="The Shawshank Redemption",
        metadata={
            "author": "Stephen King",
            "theme": "Friendship",
        },
    ),
    TextNode(
        text="The Godfather",
        metadata={
            "director": "Francis Ford Coppola",
            "theme": "Mafia",
        },
    ),
    TextNode(
        text="Inception",
        metadata={
            "director": "Christopher Nolan",
        },
    ),
]
vector_store.add(nodes)

['5abb42cf-7312-46eb-859e-60df4f92842a',
 'b90525f4-38bf-453c-a51a-5f0718bccc98',
 '22f732d0-da17-4bad-b3cd-b54e2102367a']

# Metadata filter
from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters

filters = MetadataFilters(
    filters=[ExactMatchFilter(key="theme", value="Mafia")]
)

retriever = index.as_retriever(filters=filters)

retriever.retrieve("What is inception about?")

INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"





[NodeWithScore(node=TextNode(id_='b90525f4-38bf-453c-a51a-5f0718bccc98', embedding=None, metadata={'director': 'Francis Ford Coppola', 'theme': 'Mafia'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='The Godfather', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n'), score=0.3068528194400547)]

def custom_query(query, query_str):
    print("custom query", query)
    return query


query_engine = index.as_query_engine(
    vector_store_kwargs={
        "cb_search_options": {
            "query": {"match": "growing up", "field": "text"}
        },
        "custom_query": custom_query,
    }
)
response = query_engine.query("what were his investments in Y Combinator?")
print(response)

INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
His investments in Y Combinator were based on a combination of the deal he did with Julian ($10k for 10%) and what Robert said MIT grad students got for the summer ($6k). He invested $6k per founder, which in the typical two-founder case was $12k, in return for 6%.