@ -1,3 +1,4 @@
""" Vector storage using Firecrawl and Redis """
import re import re
from urllib . parse import urlparse , urljoin from urllib . parse import urlparse , urljoin
import redis import redis
@ -5,11 +6,12 @@ from firecrawl import FirecrawlApp
from redisvl . utils . vectorize import HFTextVectorizer from redisvl . utils . vectorize import HFTextVectorizer
from redisvl . index import SearchIndex from redisvl . index import SearchIndex
from redisvl . schema import IndexSchema from redisvl . schema import IndexSchema
from redisvl . query import VectorQuery from redisvl . query . filter import Tag
from redisvl . query import VectorQuery , FilterQuery
class RedisVectorStorage : class RedisVectorStorage :
""" Provides vector storage database operations using Redis """
""" Provides vector storage database operations using Redis """
def __init__ ( self , context : str = " swarms " , use_gpu = False :
def __init__ ( self , context : str = " swarms " , use_gpu = False , overwrite = False ):
self . use_gpu = use_gpu
self . use_gpu = use_gpu
self . context = context
self . context = context
# Initialize the FirecrawlApp with your API key
# Initialize the FirecrawlApp with your API key
@ -28,7 +30,6 @@ class RedisVectorStorage:
schema = IndexSchema . from_dict ( {
schema = IndexSchema . from_dict ( {
" index " : {
" index " : {
" name " : index_name ,
" name " : index_name ,
" prefix " : " chunk "
} ,
} ,
" fields " : [
" fields " : [
{
{
@ -54,28 +55,37 @@ class RedisVectorStorage:
" algorithm " : " hnsw " ,
" algorithm " : " hnsw " ,
" datatype " : " float32 "
" datatype " : " float32 "
}
}
} ,
{
" name " : " source_url " ,
" type " : " text " ,
" attrs " : {
" sortable " : True
}
}
}
]
]
} )
} )
self . schema = schema
self . schema = schema
self . index = SearchIndex ( self . schema , self . redis_client )
self . index = SearchIndex ( self . schema , self . redis_client )
self . index . create ( )
self . index . create ( overwrite = overwrite , drop = overwrite )
# Function to extract Markdown links
def extract_markdown_links ( self , markdown_text ) :
def extract_markdown_links ( self , markdown_text ) :
pattern = r ' \ [([^ \ ]]+) \ ] \ (([^)]+) \ ) '
""" Extract Markdown links from the given markdown text """
pattern = r ' \ [([^ \ ]]+) \ ] \ (([^)]+?)(?: \ s+ " [^ " ]* " )? \ ) '
links = re . findall ( pattern , markdown_text )
links = re . findall ( pattern , markdown_text )
urls = [ link [ 1 ] for link in links ]
urls = [ link [ 1 ] for link in links ]
return urls
return urls
# Function to check if a URL is internal to the initial domain
def is_internal_link ( self , url : str , base_domain : str ) :
def is_internal_link ( self , url , base_domain ) :
""" Check if a URL is internal to the initial domain """
if ( url == ' \\ ' or url . startswith ( " mailto " ) ) :
return False
parsed_url = urlparse ( url )
parsed_url = urlparse ( url )
return parsed_url . netloc == ' ' or parsed_url . netloc == base_domain
return parsed_url . netloc == ' ' or parsed_url . netloc == base_domain
# Function to split markdown content into chunks of max 5000 characters at natural breakpoints
def split_markdown_content ( self , markdown_text , max_length = 5000 ) :
def split_markdown_content ( self , markdown_text , max_length = 5000 ) :
""" Split markdown content into chunks of max 5000 characters at natural breakpoints """
paragraphs = markdown_text . split ( ' \n \n ' ) # Split by paragraphs
paragraphs = markdown_text . split ( ' \n \n ' ) # Split by paragraphs
chunks = [ ]
chunks = [ ]
current_chunk = ' '
current_chunk = ' '
@ -104,71 +114,102 @@ class RedisVectorStorage:
return chunks
return chunks
# Function to store chunks and their embeddings in Redis
def store_chunks_in_redis ( self , url , chunks ) :
def store_chunks_in_redis ( self , url , chunks ) :
""" Store chunks and their embeddings in Redis """
parsed_url = urlparse ( url )
trimmed_url = parsed_url . netloc + parsed_url . path # Remove scheme (http:// or https://)
data = [ ]
data = [ ]
for i , chunk in enumerate ( chunks ) :
for i , chunk in enumerate ( chunks ) :
embedding = self . vectorizer . embed ( chunk , input_type = " search_document " , as_buffer = True )
embedding = self . vectorizer . embed ( chunk , input_type = " search_document " , as_buffer = True )
data . append ( {
data . append ( {
" id " : f " { } ::chunk:: { i + 1 } " ,
" id " : f " { trimmed_ url} ::chunk:: { i + 1 } " ,
" content " : chunk ,
" content " : chunk ,
" content_embedding " : embedding
" content_embedding " : embedding ,
" source_url " : trimmed_url
} )
} )
self . index . load ( data )
self . index . load ( data )
print ( f " Stored { len ( chunks ) } chunks for URL { url } in Redis. " )
print ( f " Stored { len ( chunks ) } chunks for URL { url } in Redis. " )
# Function to recursively crawl a URL and its Markdown links
def crawl_iterative ( self , start_url , base_domain ) :
def crawl_recursive ( self , url , base_domain , visited = None ) :
""" Iteratively crawl a URL and its Markdown links """
if visited is None :
visited = set ( )
visited = set ( )
stack = [ start_url ]
if url in visited :
while stack :
return
url = stack . pop ( )
visited . add ( url )
if url in visited :
continue
# Check if the URL has already been processed
if self . index . exists ( f " { url } ::chunk::1 " ) :
parsed_url = urlparse ( url )
print ( f " URL { url } has already been processed. Skipping. " )
trimmed_url = parsed_url . netloc + parsed_url . path # Remove scheme (http:// or https://)
return
# Check if the URL has already been processed
print ( f " Crawling URL: { url } " )
t = Tag ( " id " ) == f " { trimmed_url } ::chunk::1 " # Use the original URL format
params = {
# Use a simple filter query instead of a vector query
' pageOptions ' : {
filter_query = FilterQuery ( filter_expression = t )
' onlyMainContent ' : False ,
results = self . index . query ( filter_query )
' fetchPageContent ' : True ,
if results :
' includeHTML ' : True ,
print ( f " URL { url } has already been processed. Skipping. " )
visited . add ( url )
continue
print ( f " Crawling URL: { url } " )
params = {
' pageOptions ' : {
' onlyMainContent ' : False ,
' fetchPageContent ' : True ,
' includeHTML ' : False ,
}
}
}
}
crawl_result = self . app . crawl_url ( url , params = params , wait_until_done = True )
for result in crawl_result :
print ( " Content: \n \n " )
markdown_content = result [ " markdown " ]
# Split markdown content into natural chunks
chunks = self . split_markdown_content ( markdown_content )
# Store the chunks and their embeddings in Redis
self . store_chunks_in_redis ( url , chunks )
links = self . extract_markdown_links ( markdown_content )
crawl_result = [ ]
print ( " Extracted Links: " , links )
if self . is_internal_link ( url , base_domain ) and not url in visited :
crawl_result . append ( self . app . scrape_url ( url , params = params ) )
visited . add ( url )
for result in crawl_result :
markdown_content = result [ " markdown " ]
result_url = result [ " metadata " ] [ " sourceURL " ]
print ( " Markdown sourceURL: " + result_url )
# print("Content:\n\n")
# print(markdown_content)
# print("\n\n")
# Split markdown content into natural chunks
chunks = self . split_markdown_content ( markdown_content )
# Store the chunks and their embeddings in Redis
self . store_chunks_in_redis ( result_url , chunks )
links = self . extract_markdown_links ( markdown_content )
print ( " Extracted Links: " , links )
# print("Extracted Links:", links)
for link in links :
absolute_link = urljoin ( result_url , link )
if self . is_internal_link ( absolute_link , base_domain ) :
if absolute_link not in visited :
stack . append ( absolute_link )
print ( " Appended link: " + absolute_link )
else :
visited . add ( absolute_link )
for link in links :
def crawl ( self , crawl_url : str ) :
absolute_link = urljoin ( url , link )
""" Start the iterative crawling from the initial URL """
if self . is_internal_link ( absolute_link , base_domain ) :
base_domain = urlparse ( crawl_url ) . netloc
self . crawl_recursive ( absolute_link , base_domain , visited )
self . crawl_iterative ( crawl_url , base_domain )
# Function to embed a string and perform a Redis vector database query
def embed ( self , query : str , num_results : int = 3 ) :
def embed ( self , query : str , num_results : int = 3 ) :
""" Embed a string and perform a Redis vector database query """
query_embedding = self . vectorizer . embed ( query )
query_embedding = self . vectorizer . embed ( query )
vector_query = VectorQuery (
vector_query = VectorQuery (
vector = query_embedding ,
vector = query_embedding ,
vector_field_name = " content_embedding " ,
vector_field_name = " content_embedding " ,
num_results = num_results ,
num_results = num_results ,
return_fields = [ " id " , " content " ] ,
return_fields = [ " id " , " content " , " source_url " ],
return_score = True
return_score = True
)
)
@ -176,11 +217,11 @@ class RedisVectorStorage:
results = self . index . query ( vector_query )
results = self . index . query ( vector_query )
return results
return results
def crawl ( self , crawl_url : str ) :
# Start the recursive crawling from the initial URL
base_domain = urlparse ( crawl_url ) . netloc
self . crawl_recursive ( crawl_url , base_domain )
if __name__ == " __main__ " : if __name__ == " __main__ " :
storage = RedisVectorStorage ( )
storage = RedisVectorStorage ( overwrite = False )
storage . crawl ( " https://docs.swarms.world/en/latest/ " )
storage . crawl ( " https://docs.swarms.world/en/latest/ " )
responses = storage . embed ( " What is Swarms, and how do I install swarms? " , 5 )
for response in responses :
encoded_id = response [ ' id ' ] # Access the 'id' field directly
source_url = response [ ' source_url ' ]
print ( f " Decoded ID: { encoded_id } , Source URL: { source_url } " )
Richard Anthony Hein
8 months ago