index in the code snippet you provided is an instance of the VectorStoreIndex class from the LlamaIndex library. This class is used to create and manage an index of vectors, which can be used for efficient similarity search and retrieval of data.from llama_index import load_index_from_storage, StorageContext # rebuild storage context storage_context = StorageContext.from_defaults(persist_dir="storage") # load index index = load_index_from_storage(storage_context)
load_index_from_storage is a function that loads an index from a storage context. The storage context is created with the StorageContext.from_defaults method, which sets up the storage context with default settings and a specified persist_dir.index.index in an API call, you would typically need to serialize it into a format that can be transmitted over the network, such as JSON. However, the VectorStoreIndex object from LlamaIndex is a complex object that includes methods and may not be directly serializable.index on the server side. For example, you could create an endpoint that accepts a query, uses the index to find relevant results, and then returns these results in the API response.from flask import Flask, request, jsonify
from llama_index import load_index_from_storage, StorageContext
app = Flask(__name__)
# Load the index
storage_context = StorageContext.from_defaults(persist_dir="storage")
index = load_index_from_storage(storage_context)
@app.route('/query', methods=['POST'])
def query_index():
# Get the query from the request
query = request.json.get('query')
# Use the index to find relevant results
results = index.query(query)
# Return the results in the response
return jsonify(results)/query endpoint with a JSON body that includes the query. The endpoint uses the index to find relevant results and returns these in the response.index has a query method that accepts a string and returns results. The actual method you need to use may be different depending on the specifics of the VectorStoreIndex class.VectorStoreIndex class, you can refer to the LlamaIndex documentation (). For more information on creating APIs with Flask, you can refer to the Flask documentation ().VectorStoreIndex and store these in a dictionary or similar data structure. Then, in your API endpoints, you could accept an additional parameter specifying which project to use.from flask import Flask, request, jsonify
from llama_index import load_index_from_storage, StorageContext
app = Flask(__name__)
# Load the indexes for each project
indexes = {
'project1': load_index_from_storage(StorageContext.from_defaults(persist_dir="storage1")),
'project2': load_index_from_storage(StorageContext.from_defaults(persist_dir="storage2")),
# Add more projects as needed
}
@app.route('/query', methods=['POST'])
def query_index():
# Get the project and query from the request
project = request.json.get('project')
query = request.json.get('query')
# Get the index for the specified project
index = indexes.get(project)
if index is None:
return jsonify({'error': 'Invalid project'}), 400
# Use the index to find relevant results
results = index.query(query)
# Return the results in the response
return jsonify(results)/query endpoint with a JSON body that includes the project and the query. The endpoint uses the appropriate index to find relevant results and returns these in the response.index has a query method that accepts a string and returns results. The actual method you need to use may be different depending on the specifics of the VectorStoreIndex class.VectorStoreIndex class, you can refer to the LlamaIndex documentation (). For more information on creating APIs with Flask, you can refer to the Flask documentation ().load_index_from_storage function. from llama_index import load_index_from_storage, StorageContext # rebuild storage context storage_context = StorageContext.from_defaults(persist_dir="storage") # load index index = load_index_from_storage(storage_context)
load_index_from_storage is a function that loads an index from a storage context. The storage context is created with the StorageContext.from_defaults method, which sets up the storage context with default settings and a specified persist_dir.VectorStoreIndex class and the load_index_from_storage function, you can refer to the LlamaIndex documentation ().