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import os
from dotenv import load_dotenv
from mistralai import Mistral
import numpy as np
import time
import chromadb
from chromadb.config import Settings
import json
import hashlib
load_dotenv()
MISTRAL_API_KEY = os.getenv("MISTRAL_API_KEY")
COLLECTION_NAME = "webpages_collection"
PERSIST_DIRECTORY = "./chroma_db"
def vectorize(input_texts, batch_size=5):
"""
Get the text embeddings for the given inputs using Mistral API.
"""
try:
client = Mistral(api_key=MISTRAL_API_KEY)
except Exception as e:
print(f"Error initializing Mistral client: {e}")
return []
embeddings = []
for i in range(0, len(input_texts), batch_size):
batch = input_texts[i:i + batch_size]
while True:
try:
embeddings_batch_response = client.embeddings.create(
model="mistral-embed",
inputs=batch
)
time.sleep(1)
embeddings.extend([data.embedding for data in embeddings_batch_response.data])
break
except Exception as e:
if "rate limit exceeded" in str(e).lower():
print("Rate limit exceeded. Retrying after 10 seconds...")
time.sleep(10)
else:
print(f"Error in embedding batch: {e}")
raise
return embeddings
def chunk_content(markdown_content, chunk_size=2048):
"""
Vectorizes the given markdown content into chunks of specified size without cutting sentences.
"""
def find_sentence_end(text, start):
"""Find the nearest sentence end from the start index."""
punctuations = {'.', '!', '?'}
end = start
while end < len(text) and text[end] not in punctuations:
end += 1
while end < len(text) and text[end] in punctuations:
end += 1
while end > start and text[end - 1] not in punctuations:
end -= 1
return end
chunks = []
start = 0
while start < len(markdown_content):
end = min(start + chunk_size, len(markdown_content))
end = find_sentence_end(markdown_content, end)
chunks.append(markdown_content[start:end].strip())
start = end
return chunks
def generate_chunk_id(chunk):
"""Generate a unique ID for a chunk using SHA-256 hash."""
return hashlib.sha256(chunk.encode('utf-8')).hexdigest()
def load_in_vector_db(markdown_content, metadatas=None, collection_name=COLLECTION_NAME):
"""
Load the text embeddings into a ChromaDB collection for efficient similarity search.
"""
try:
client = chromadb.PersistentClient(path=PERSIST_DIRECTORY)
except Exception as e:
print(f"Error initializing ChromaDB client: {e}")
return
try:
if collection_name not in [col.name for col in client.list_collections()]:
collection = client.create_collection(collection_name)
else:
collection = client.get_collection(collection_name)
except Exception as e:
print(f"Error accessing collection: {e}")
return
try:
existing_items = collection.get()
except Exception as e:
print(f"Error retrieving existing items: {e}")
return
existing_ids = set()
if 'ids' in existing_items:
existing_ids.update(existing_items['ids'])
chunks = chunk_content(markdown_content)
text_to_vectorize = []
for chunk in chunks:
chunk_id = generate_chunk_id(chunk)
if chunk_id not in existing_ids:
text_to_vectorize.append(chunk)
print(f"New chunks to vectorize: {len(text_to_vectorize)}")
if text_to_vectorize:
embeddings = vectorize(text_to_vectorize)
for embedding, chunk in zip(embeddings, text_to_vectorize):
chunk_id = generate_chunk_id(chunk)
if chunk_id not in existing_ids:
try:
collection.add(
embeddings=[embedding],
documents=[chunk],
metadatas=[metadatas],
ids=[chunk_id]
)
existing_ids.add(chunk_id)
except Exception as e:
print(f"Error adding embedding to collection: {e}")
def retrieve_from_database(query, collection_name=COLLECTION_NAME, n_results=5, distance_threshold=None):
"""
Retrieve the most similar documents from the vector store based on the query.
"""
try:
client = chromadb.PersistentClient(path=PERSIST_DIRECTORY)
collection = client.get_collection(collection_name)
except Exception as e:
print(f"Error accessing collection: {e}")
return
try:
query_embeddings = vectorize([query])
except Exception as e:
print(f"Error vectorizing query: {e}")
return
try:
raw_results = collection.query(
query_embeddings=query_embeddings,
n_results=n_results,
include=["documents", "metadatas", "distances"]
)
except Exception as e:
print(f"Error querying collection: {e}")
return
if distance_threshold is not None:
filtered_results = {
"ids": [],
"distances": [],
"metadatas": [],
"documents": []
}
for i, distance in enumerate(raw_results['distances'][0]):
if distance <= distance_threshold:
filtered_results['ids'].append(raw_results['ids'][0][i])
filtered_results['distances'].append(distance)
filtered_results['metadatas'].append(raw_results['metadatas'][0][i])
filtered_results['documents'].append(raw_results['documents'][0][i])
results = filtered_results
if len(results['documents']) == 0:
return "No relevant data found in the knowledge database. Have you checked any webpages? If so, please try to find more relevant data."
else:
return results
else:
return raw_results
def search_documents(collection_name=COLLECTION_NAME, query=None, query_embedding=None, metadata_filter=None, n_results=10):
"""
Search for documents in a ChromaDB collection.
:param collection_name: The name of the collection to search within.
:param query: The text query to search for (optional).
:param query_embedding: The embedding query to search for (optional).
:param metadata_filter: A filter to apply to the metadata (optional).
:param n_results: The number of results to return (default is 10).
:return: The search results.
"""
client = chromadb.PersistentClient(path=PERSIST_DIRECTORY)
collection = client.get_collection(collection_name)
if query:
query_embedding = vectorize([query])[0]
if query_embedding:
results = collection.query(query_embeddings=[query_embedding], n_results=n_results, where=metadata_filter)
else:
results = collection.get(where=metadata_filter, limit=n_results)
return results
def delete_documents(collection_name=COLLECTION_NAME, ids=None):
"""
Delete documents from a ChromaDB collection based on their IDs.
:param collection_name: The name of the collection.
:param ids: A list of IDs of the documents to delete.
"""
client = chromadb.PersistentClient(path=PERSIST_DIRECTORY)
collection = client.get_collection(collection_name)
collection.delete(ids=ids)
print(f"Documents with IDs {ids} have been deleted from the collection {collection_name}.")
def delete_collection(collection_name=COLLECTION_NAME):
"""
Delete a ChromaDB collection.
:param collection_name: The name of the collection to delete.
"""
client = chromadb.PersistentClient(path=PERSIST_DIRECTORY)
client.delete_collection(collection_name)
print(f"Collection {collection_name} has been deleted.") |