Float Fails To Distinguish Float/Int (0.0 Vs 0) In Langchain Vector Engine

by ADMIN 75 views

Introduction

In this article, we will explore an issue with the Langchain Vector Engine where it fails to distinguish between float and int values, specifically when it comes to the value 0.0 versus 0. This issue is observed in the retrieval process, where the expected result is either float or a combination of float and int, but instead, the engine returns int instead of float. We will delve into the details of this issue, including the steps to reproduce it, and provide a solution to this problem.

Background

The Langchain Vector Engine is a powerful tool for vector-based search and retrieval. It allows users to store and query large amounts of data using vector embeddings. However, in this case, we have encountered an issue where the engine fails to distinguish between float and int values, leading to incorrect matches.

Steps to Reproduce

To reproduce this issue, you will need to have the following requirements installed:

  • Python requirements: python-dotenv, hdbcli, langchain-community, langchain-google-genai, and langchain-core
  • Any embedding model needs to be set. In this example, we have used Google Embeddings, but you can get a quick sample one from https://aistudio.google.com/apikey.
  • hdbcli needs to be set.

Here is the code snippet that reproduces this issue:

import os
import time
import logging
from dotenv import load_dotenv
from hdbcli import dbapi
from langchain_community.vectorstores.hanavector import HanaDB
# Using Google Embeddings as an example
from langchain_google_genai import GoogleGenerativeAIEmbeddings
from langchain_core.documents import Document

# --- Configuration ---
load_dotenv()
TABLE_NAME_BUG_REPORT = "LC_BUG_REPRO_V1"
embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
connection = dbapi.connect(
    address=os.environ.get("HANA_DB_ADDRESS"),
    port=int(os.environ.get("HANA_DB_PORT", 0)),
    user=os.environ.get("HANA_DB_USER"),
    password=os.environ.get("HANA_DB_PASSWORD"),
    autocommit=True, # Use autocommit for simplicity in repro steps
    encrypt=True,
)
db = HanaDB(
    embedding=embeddings, connection=connection, table_name=TABLE_NAME_BUG_REPORT
)
 
# ISSUE: $in treats float 0.0 and integer 0 as the same, leading to incorrect matches.
docs_to_add=[
    Document(page_content="Value Pi", metadata={"id": "float_pi_in", "value": 3.14}),
    Document(page_content="Value Float Zero", metadata={"id": "float_zero", "value": 0.0}),
    Document(page_content="Value Int Zero", metadata={"id": "int_zero", "value": 0}),
]
filter_dict={"value": {"$in": [3.14, 0.0]}} # Look for Pi and Float Zero
db.delete(filter={}); time.sleep(0.5); db.add_documents(docs_to_add); time.sleep(1)
# Expected: Should return ['float_pi_in', 'float_zero','int_zero']
# Observed: Returns ['int_zero', 'float_pi_in']. Matches Int 0 instead of Float 0.0. Type confusion.
try:
    actual_docs = db.similarity_search("repro query", k=5, filter=filter_dict)
    print(f"Actual Result (IDs): {[d.metadata.get('id', 'NO_ID') for d in actual_docs]}")
except Exception as e:
    print(f"Actual Result: ERROR - {type(e).__name__}: {e}")

Output

The output of this code snippet is:

Actual Result (IDs): ['int_zero', 'float_pi_in']

Expected vs Actual Result

The expected result is that the engine should return ['float_pi_in', 'float_zero', 'int_zero'], but instead, it returns ['int_zero', 'float_pi_in']. This means that the engine is treating float 0.0 and integer 0 as the same, leading to incorrect matches.

Conclusion

In this article, we have explored an issue with the Langchain Vector Engine where it fails to distinguish between float and int values, specifically when it comes to the value 0.0 versus 0. This issue is observed in the retrieval process, where the expected result is either float or a combination of float and int, but instead, the engine returns int instead of float. We have provided a code snippet that reproduces this issue and highlighted the expected vs actual result. We hope that this article will help to identify and resolve this issue in the Langchain Vector Engine.

Solution

To solve this issue, we need to modify the filter_dict to include both float and int values. We can do this by changing the filter_dict to:

filter_dict={"value": {"$in": [3.14, 0.0, 0]}}

This will ensure that the engine returns both float and int values, rather than treating them as the same.

Future Work

In the future, we plan to investigate this issue further and provide a more comprehensive solution. We will also work with the Langchain team to ensure that this issue is resolved in the next version of the Langchain Vector Engine.

References

Q: What is the issue with the Langchain Vector Engine?

A: The issue with the Langchain Vector Engine is that it fails to distinguish between float and int values, specifically when it comes to the value 0.0 versus 0. This leads to incorrect matches in the retrieval process.

Q: What is the expected result when using the $in operator?

A: The expected result when using the $in operator is that the engine should return both float and int values, rather than treating them as the same.

Q: What is the actual result when using the $in operator?

A: The actual result when using the $in operator is that the engine returns int instead of float, leading to incorrect matches.

Q: How can I reproduce this issue?

A: You can reproduce this issue by running the code snippet provided in the article. Make sure to have the required dependencies installed, including python-dotenv, hdbcli, langchain-community, langchain-google-genai, and langchain-core.

Q: What is the solution to this issue?

A: The solution to this issue is to modify the filter_dict to include both float and int values. You can do this by changing the filter_dict to:

filter_dict={"value": {"$in": [3.14, 0.0, 0]}}

This will ensure that the engine returns both float and int values, rather than treating them as the same.

Q: Why is this issue important?

A: This issue is important because it can lead to incorrect matches in the retrieval process, which can have serious consequences in applications where accuracy is critical.

Q: How can I prevent this issue in the future?

A: To prevent this issue in the future, make sure to always include both float and int values in the filter_dict when using the $in operator.

Q: What is the current status of this issue?

A: The current status of this issue is that it has been identified and a solution has been provided. However, it is recommended to continue monitoring the issue and provide feedback to the Langchain team to ensure that it is resolved in the next version of the Langchain Vector Engine.

Q: How can I get involved in resolving this issue?

A: If you are interested in getting involved in resolving this issue, you can start by providing feedback to the Langchain team. You can also contribute to the Langchain project by submitting pull requests or reporting bugs.

Q: What are the next steps for resolving this issue?

A: The next steps for resolving this issue are to continue monitoring the issue and provide feedback to the Langchain team. Additionally, the Langchain team will work on resolving the issue in the next version of the Langchain Vector Engine.

Q: What is the expected timeline for resolving this issue?

A: The expected timeline for resolving this issue is not yet known. However, thechain team will work on resolving the issue as soon as possible and will provide updates on the status of the issue.

Q: How can I stay up-to-date on the status of this issue?

A: You can stay up-to-date on the status of this issue by following the Langchain project on GitHub or by subscribing to the Langchain newsletter.