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create_vector_index.py
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create_vector_index.py
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import requests
import openai
import os
import faiss
import json
import numpy as np
from dotenv import load_dotenv
from markdownify import markdownify
load_dotenv()
client = openai.OpenAI()
# Directory where to save vector index
VECTOR_DIRECTORY = "./vector-database"
# Get documentation from OBP
obp_base_url = "https://apisandbox.openbankproject.com"
obp_version = "v5.1.0"
# Get the _static_ swagger docs, we may want to change this if we give this to a bank that has lots of dynamic endpoints
swagger_url = "{}/obp/v5.1.0/resource-docs/{}/swagger?content=static".format(obp_base_url, obp_version)
swagger_response = requests.get(swagger_url)
swagger_json = swagger_response.json()
# glossary
glossary_url = "{}/obp/{}/api/glossary".format(obp_base_url, obp_version)
glossary_response = requests.get(glossary_url)
glossary_json = glossary_response.json()
def resolve_reference(ref, definitions, resolved={}):
"""
Resolves a $ref to its definition, avoiding circular references.
Parameters:
ref (str): The reference to be resolved.
definitions (dict): A dictionary containing the definitions.
resolved (dict, optional): A dictionary containing the resolved references. Defaults to an empty dictionary.
Returns:
dict: The resolved definition.
"""
ref_name = ref.split('/')[-1]
if ref_name in resolved:
return resolved[ref_name]
if ref_name in definitions.keys():
definition = definitions[ref_name]
else:
definition = {}
resolved[ref_name] = definition
properties = definition.get('properties', {})
resolved_properties = resolve_properties(properties, definitions, resolved)
return {**definition, 'properties': resolved_properties}
def resolve_properties(properties, definitions, resolved):
"""
Resolves nested references in properties, avoiding circular references.
"""
resolved_properties = {}
for prop_name, prop_details in properties.items():
if '$ref' in prop_details:
resolved_properties[prop_name] = resolve_reference(prop_details['$ref'], definitions, resolved)
elif prop_details.get('type') == 'array' and 'items' in prop_details and '$ref' in prop_details['items']:
resolved_properties[prop_name] = {
"type": "array",
"items": resolve_reference(prop_details['items']['$ref'], definitions, resolved)
}
else:
resolved_properties[prop_name] = prop_details
return resolved_properties
def parse_swagger(swagger_json):
"""
Parses a Swagger JSON file and extracts information about the endpoints.
Args:
swagger_json (dict): The Swagger JSON object.
Returns:
list: A list of dictionaries, where each dictionary represents an endpoint and its details.
"""
paths = swagger_json['paths']
definitions = swagger_json['definitions']
endpoints = []
for path, methods in paths.items():
for method, details in methods.items():
endpoint_info = {
'path': path,
'method': method,
'summary': details.get('summary'),
'description': markdownify(details.get('description', '')),
'responses': [],
'parameters': {}
}
if ('parameters' in details) & (details['parameters'] != []):
endpoint_info["parameters"] = {
"type": "object",
"properties": {},
"required": [],
}
for param in details['parameters']:
if param['in'] == 'body' and '$ref' in param['schema']:
ref = param['schema']['$ref']
definition = resolve_reference(ref, definitions)
endpoint_info['parameters']['required'].extend(definition.get('required', []))
endpoint_info['parameters']['properties'].update(
resolve_properties(definition.get('properties', {}), definitions, {})
)
elif param['in'] == 'body' and '$ref' not in param['schema']:
# Right now, if the parameter does not have a reference (i.e. something that points to a swagger definition) we skip it
endpoint_info["parameters"] = param["schema"]
elif param['in'] == 'path':
endpoint_info['parameters']['required'].append(param['name'])
endpoint_info['parameters']['properties'][param['name']] = {
"type": param['type'],
"in": "path",
"description": param.get('description', '')
}
elif param['in'] == 'query':
endpoint_info['parameters']['properties'][param['name']] = {
"type": param['type'],
"description": param.get('description', '')
}
if param.get('required', False):
endpoint_info['parameters']['required'].append(param['name'])
if 'responses' in details:
for code, response in details['responses'].items():
if "schema" in response.keys() and ("$ref" in response['schema']):
ref = response['schema']['$ref']
definition_name = ref.split('/')[-1]
definition = resolve_reference(ref, definitions)
response_resolved = {
"code": code,
"body": resolve_properties(definition.get('properties', {}), definitions, {})
}
endpoint_info["responses"].append(response_resolved)
endpoints.append(endpoint_info)
return endpoints
endpoints = parse_swagger(swagger_json)
def parse_glossary(glossary_json):
"""
Parses the glossary JSON and extracts the title and description of each glossary item.
Args:
glossary_json (dict): The glossary JSON containing the glossary items.
Returns:
list: A list of dictionaries, where each dictionary represents a parsed glossary item
with 'title' and 'description' keys.
"""
glossary_items = glossary_json['glossary_items']
parsed_items = []
for item in glossary_items:
title = item.get('title', 'No title')
description_info = item.get('description', {})
# Get markdown description or else return no description
description = description_info.get('markdown', 'No description')
# do not add descriptions if they are empty
if description == "":
continue
parsed_items.append({
'title': title,
'description': description
})
return parsed_items
glossary_items = parse_glossary(glossary_json)
# Create vector embeddings
def get_embeddings(texts):
response = client.embeddings.create(
model="text-embedding-3-small",
input=texts
)
return [e.embedding for e in response.data]
def create_and_save_embedding_faiss(formatted_texts: list, json_metadata: list, filename: str):
"""
Creates and saves text embeddings and metadata for a given list of texts.
Args:
formatted_texts (list): A formatted list of texts for creating embeddings.
json_metadata (list): A list of dictionaries to pass as JSON metadata. Each dictionary represents metadata for a text.
filename (str): A prefix to attach to the saved index and metadata files.
Returns:
None
Raises:
None
Example usage:
create_and_save_embedding_faiss(formatted_texts, json_metadata, filename)
"""
embeddings = get_embeddings(formatted_texts)
# Convert embeddings to a numpy array
embeddings_np = np.array(embeddings).astype('float32')
# Create a FAISS index
index = faiss.IndexFlatL2(embeddings_np.shape[1]) # L2 distance index
index.add(embeddings_np)
# Optionally, save the index to disk for later use
faiss.write_index(index, f"{filename}_index.faiss")
# Save metadata for retrieval
with open(f"{filename}_metadata.json", 'w') as f:
json.dump(json_metadata, f)
glossary_texts = [f"{g['title']} - {g['description']}" for g in glossary_items]
if not os.path.isdir(VECTOR_DIRECTORY):
try:
os.mkdir(VECTOR_DIRECTORY)
except Exception as err:
print(f"Error creating directory {VECTOR_DIRECTORY}: {err}")
create_and_save_embedding_faiss(glossary_texts, glossary_items, os.path.join(VECTOR_DIRECTORY, "glossary"))
endpoint_texts = [f"{e['method'].upper()} {e['path']} - {e['description']}" for e in endpoints]
create_and_save_embedding_faiss(endpoint_texts, endpoints, os.path.join(VECTOR_DIRECTORY, "endpoint"))