langchain_semantic_search
Semantic search for Google Drive files using GPT3, LangChain, and Python
GraphCanon updated 1w · GitHub synced 1w
Decision brief
Builds a FAISS index for semantic search over Google Drive files using LangChain, GPT3, Jupyter Notebook.
Good fit when
- Need semantic search capabilities specifically for your own documents in Google Drive
- Want to leverage the combination of Faiss indexing and GPT3's text analysis on private documents
Avoid when
- Seeking a solution that supports large-scale, real-time or non-Google Drive document collections
- Require a fully integrated end-to-end service without configuration for drive paths
Observed Jul 12, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (1285d since push)
- As of 1w
- Provenance
- Not a fork · Personal account
- As of 1w
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/venuv/langchain_semantic_searchSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A Jupyter notebook to enable building a FAISS index from documents stored in Google Drive and performing semantic searches over these documents.
Capability facts
- Languages
- jupyter notebook
Source: github.language · Aug 15, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 15, 2026)
## Search and indexing your own Google Drive Files using GPT3, LangChain, and Python.Source link
Source: README excerpt (regex_v1, Aug 15, 2026)
## Search and indexing your own Google Drive Files using GPT3, LangChain, and Python.Source link
Source: README excerpt (regex_v1, Aug 15, 2026)
rch. Details of this flowchart are described in https://medium.com/@venuv62/can-chatgpt-be-your-bff-code-companion-4375fd73ec3a.Source link
Tags
README
Search and indexing your own Google Drive Files using GPT3, LangChain, and Python.
The jupyter notebook included here (langchain_semantic_search.ipynb) will enable you to build a FAISS index on your document corpus of interest, and search it using semantic search. Details of this flowchart are described in https://medium.com/@venuv62/can-chatgpt-be-your-bff-code-companion-4375fd73ec3a.
I've provided a test directory of Neuromodulation papers if you want to as a sample Drive folder to test against - https://drive.google.com/drive/folders/1eIBnSO7MVOW9-BKPCJhs7JuBDRyXPOFC?usp=sharing. Since the code needs a Google Drive directory path (not an https URL) to work with, you will have to :
- copy the contents of this directory into a GDrive subdirectory of your own
- set the gdrive_path variable in the jupyter notebook appropriately
- set the question within print_answer to 'is sleep a health epidemic' for instance, which should give you a non-null answer
I will be working on a few enhancements to speed up the indexing (perhaps using a Vectorstore) and to optimize the query cost (using ideas from https://gpt-index.readthedocs.io/en/latest/how_to/cost_analysis.html)
For agents
This page has a .md twin and JSON over the API.