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Archive Agent

An intelligent file indexer with powerful AI search (RAG engine), automatic OCR, and a seamless MCP interface.

GitHub Release GitHub License

Archive Agent brings RAG to your command line and connects to your tools via MCP — it's not a chatbot.

Find what you need with natural language

Unlock your documents with semantic AI search & query
Files are split using semantic chunking with context headers and committed to a local database.
RAG engine¹ uses reranking and expanding of retrieved chunks

¹ Retrieval Augmented Generation is the method of matching pre-made snippets of information to a query.

Natively index your documents on-device

Includes local AI file system indexer
Natively ingests PDFs, images, Markdown, plaintext, and more…
Selects and tracks files using patterns like ~/Documents/*.pdf
Transcribes images using automatic OCR (experimental) and entity extraction
Changes are automatically synced to a local Qdrant vector database.

Your AI, Your Choice

Supports many AI providers and MCP
OpenAI or compatible API ¹ for best performance
Ollama and LM Studio for best privacy (local LLM)
Integrates with your workflow** via a built-in MCP server.

¹ Includes xAI / Grok and Claude OpenAI compatible APIs. Simply adjust the URL settings and overwrite OPENAI_API_KEY.

Scalable Performance

Fully resumable parallel processing
Processes multiple files at once using optimized multi-threading.
Uses AI cache and generous request retry logic for all network requests.
Leverages AI structured output with high-quality prompts and schemas.

Architecture

(If you can't see the diagram below, view it on Mermaid.live)

graph LR

  %% Ingestion Pipeline
  subgraph Ingestion
    A[<b>Track and Commit Files</b><br>Supports Profiles] --> B[<b>Ingest Files</b><br>Automatic OCR Strategy<br>Image Entity Extraction]
    B --> C[<b>Semantic Chunking</b><br>with Context Headers<br>Lines per Block: 100<br>Words per Chunk: 200]
    C --> D[<b>Embed Chunks</b><br>Model: text-embedding-3-large<br>Vector Size: 3072]
    D --> E[<b>Store Chunks</b><br>Local Qdrant database]
  end

  %% Query Pipeline
  subgraph Query
    F[<b>Ask Question</b>] --> G[<b>Embed Question</b><br>Model: text-embedding-3-large]
    G --> H[<b>Retrieve Nearest Chunks</b><br>Score Min: 0.1<br>Chunks Max: 40]
    E --> H
    H --> I[<b>Rerank by Relevance</b><br>Chunks Max: 30]
    I --> J[<b>Expand Context</b><br>Chunks Radius: 2]
    J --> K[<b>Generate Answer</b>]
    K --> L[<b>Get Answer</b><br>in CLI, GUI, MCP]
  end

Documentation

Archive Agent

Supported OS

Archive Agent has been tested with these configurations:

Ubuntu 24.04 (PC x64)
Ubuntu 22.04 (PC x64)

If you've successfully installed and tested Archive Agent with a different setup, please let me know and I'll add it here!

Install Archive Agent

Please install these requirements before proceeding:

Docker (for running Qdrant server)
Python >= 3.10 (core runtime) (usually already installed)

Ubuntu / Linux Mint

This installation method should work on any Linux distribution derived from Ubuntu (e.g. Linux Mint).

To install Archive Agent in the current directory of your choice, run this once:

git clone https://github.com/shredEngineer/Archive-Agent
cd Archive-Agent
chmod +x install.sh
./install.sh

The install.sh script will execute the following steps:

Download and install uv (used for Python environment management)
Install the custom Python environment
Install the spaCy model for natural language processing (pre-chunking)
Install pandoc (used for document parsing)
Download and install the Qdrant docker image with persistent storage and auto-restart
Install a global archive-agent command for the current user

Archive Agent is now installed!

👉 Please complete the AI provider setup next.
(Afterward, you'll be ready to Run Archive Agent!)

AI provider setup

Archive Agent lets you choose between different AI providers:

Remote APIs (higher performance and cost, less privacy):
- OpenAI: Requires an OpenAI API key.
Local APIs (lower performance and cost, best privacy):
- Ollama: Requires Ollama running locally.
- LM Studio: Requires LM Studio running locally.

💡 Good to know: You will be prompted to choose an AI provider at startup; see: Run Archive Agent.

📌 Note: You can customize the specific models used by the AI provider in the Archive Agent settings. However, you cannot change the AI provider of an existing profile, as the embeddings will be incompatible; to choose a different AI provider, create a new profile instead.

OpenAI provider setup

If the OpenAI provider is selected, Archive Agent requires the OpenAI API key.

To export your OpenAI API key, replace sk-... with your actual key and run this once:

echo "export OPENAI_API_KEY='sk-...'" >> ~/.bashrc && source ~/.bashrc

This will persist the export for the current user.

💡 Good to know: OpenAI won't use your data for training.

Ollama provider setup

If the Ollama provider is selected, Archive Agent requires Ollama running at http://localhost:11434.

How to install Ollama.

With the default Archive Agent Settings, these Ollama models are expected to be installed:

ollama pull llama3.1:8b             # for chunk/rerank/query
ollama pull llava:7b-v1.6           # for vision
ollama pull nomic-embed-text:v1.5   # for embed

💡 Good to know: Ollama also works without a GPU. At least 32 GiB RAM is recommended for smooth performance.

LM Studio provider setup

If the LM Studio provider is selected, Archive Agent requires LM Studio running at http://localhost:1234.

How to install LM Studio.

With the default Archive Agent Settings, these LM Studio models are expected to be installed:

meta-llama-3.1-8b-instruct              # for chunk/rerank/query
llava-v1.5-7b                           # for vision
text-embedding-nomic-embed-text-v1.5    # for embed

💡 Good to know: LM Studio also works without a GPU. At least 32 GiB RAM is recommended for smooth performance.

Which files are processed

Archive Agent currently supports these file types:

Text:
- Plaintext: .txt, .md, .markdown
- Documents:
  - ASCII documents: .html, .htm (images not supported)
  - Binary documents: .odt, .docx (including images)
- PDF documents: .pdf (including images; see OCR strategies)
Images: .jpg, .jpeg, .png, .gif, .webp, .bmp

📌 Note: Images in HTML documents are currently not supported.

📌 Note: Legacy .doc files are currently not supported.

📌 Note: Unsupported files are tracked but not processed.

How files are processed

Ultimately, Archive Agent decodes everything to text like this:

Plaintext files are decoded to UTF-8.
Documents are converted to plaintext, images are extracted.
PDF documents are decoded according to the OCR strategy.
Images are decoded to text using AI vision.
- Uses OCR, entity extraction, or both combined (default).
- The vision model will reject unintelligible images.
- Entity extraction extracts structured information from images.
- Structured information is formatted as image description.

See Archive Agent settings: image_ocr, image_entity_extract

Archive Agent processes files with optimized performance:

Surgical Synchronization:
- PDF analyzing phase is serialized (due to PyMuPDF threading limitations).
- All other phases (vision, chunking, embedding) run in parallel for maximum performance.
Vision operations are parallelized across images and pages within and across files.
Embedding operations are parallelized across text chunks and files.
Smart chunking uses sequential processing due to carry mechanism dependencies.

See Archive Agent settings: max_workers_ingest, max_workers_vision, max_workers_embed

OCR strategies

For PDF documents, there are different OCR strategies supported by Archive Agent:

strict OCR strategy (recommended):
- PDF OCR text layer is ignored.
- PDF pages are treated as images and processed with OCR only.
- Expensive and slow, but more accurate.
relaxed OCR strategy:
- PDF OCR text layer is extracted.
- PDF foreground images are decoded with OCR, but background images are ignored.
- Cheap and fast, but less accurate.
auto OCR strategy:
- Attempts to select the best OCR strategy for each page, based on the number of characters extracted from the PDF OCR text layer, if any.
- Decides based on ocr_auto_threshold, the minimum number of characters for auto OCR strategy to resolve to relaxed instead of strict.
- Trade-off between cost, speed, and accuracy.

⚠️ Warning: The auto OCR strategy is still experimental. PDF documents often contain small/scattered images related to page style/layout which cause overhead while contributing little information or even cluttering the result.

💡 Good to know: You will be prompted to choose an OCR strategy at startup (see Run Archive Agent).

How smart chunking works

Archive Agent processes decoded text like this:

Decoded text is sanitized and split into sentences.
Sentences are grouped into reasonably-sized blocks.
Each block is split into smaller chunks using an AI model.
- Block boundaries are handled gracefully (last chunk carries over).
Each chunk is prefixed with a context header (improves search).
Each chunk is turned into a vector using AI embeddings.
Each vector is turned into a point with file metadata.
Each point is stored in the Qdrant database.

See Archive Agent settings: chunk_lines_block, chunk_words_target

💡 Good to know: This smart chunking improves the accuracy and effectiveness of the retrieval.

📌 Note: In rare cases where a chunk exceeds the embedding model's token limit (typically 8192 tokens), Archive Agent automatically truncates it as a last resort with progressive 10% reductions (up to 10 attempts) until it fits.

📌 Note: Splitting into sentences may take some time for huge documents. There is currently no possibility to show the progress of this step.

How chunk references work

To ensure that every chunk can be traced back to its origin, Archive Agent maps the text contents of each chunk to the corresponding line numbers or page numbers of the source file.

Line-based files (e.g., .txt) use the range of line numbers as reference.
Page-based files (e.g., .pdf) use the range of page numbers as reference.

📌 Note: References are only approximate due to paragraph/sentence splitting/joining in the chunking process.

How chunks are retrieved

Archive Agent retrieves chunks related to your question like this:

The question is turned into a vector using AI embeddings.
Points with similar vectors are retrieved from the Qdrant database.
Only chunks of points with sufficient score are kept.
If retrieve_knee_enable is enabled, an adaptive cutoff trims low-relevance chunks when there is a clear score drop-off.

See Archive Agent settings: retrieve_score_min, retrieve_chunks_max, retrieve_knee_enable, retrieve_knee_sensitivity, retrieve_knee_min_chunks

📌 Note: Adaptive cutoff uses the Kneedle algorithm on the sorted similarity scores. Set a higher retrieve_knee_sensitivity to make the cutoff more conservative, and use retrieve_knee_min_chunks to enforce a minimum floor.

💡 Tuning tips:

If retrieval feels too short or misses context, increase retrieve_knee_min_chunks (e.g., 3–5) or raise retrieve_knee_sensitivity.
If retrieval feels too long or noisy, lower retrieve_knee_sensitivity slightly (e.g., 0.8–1.0) or reduce retrieve_knee_min_chunks.
If you want to disable the adaptive cutoff entirely, set retrieve_knee_enable to false.

How chunks are reranked and expanded

Archive Agent filters the retrieved chunks .

Retrieved chunks are reranked by relevance to your question.
Only the top relevant chunks are kept (the other chunks are discarded).
Each selected chunk is expanded to get a larger context from the relevant documents.

See Archive Agent settings: rerank_chunks_max, expand_chunks_radius

How answers are generated

Archive Agent answers your question using the reranked and expanded chunks like this:

The LLM receives the chunks as context to the question.
LLM's answer is returned as structured output and formatted.

💡 Good to know: Archive Agent uses an answer template that aims to be universally helpful.

How files are selected for tracking

Archive Agent uses patterns to select your files:

Patterns can be actual file paths.
Patterns can be paths containing wildcards that resolve to actual file paths.
💡 Patterns must be specified as (or resolve to) absolute paths, e.g. /home/user/Documents/*.txt (or ~/Documents/*.txt).
💡 Use the wildcard * to match any file in the given directory.
💡 Use the wildcard ** to match any files and zero or more directories, subdirectories, and symbolic links to directories.

There are included patterns and excluded patterns:

The set of resolved excluded files is removed from the set of resolved included files.
Only the remaining set of files (included but not excluded) is tracked by Archive Agent.
Hidden files are always ignored!

This approach gives you the best control over the specific files or file types to track.

Run Archive Agent

💡 Good to know: At startup, you will be prompted to choose the following:

Profile name
AI provider (see AI Provider Setup)
OCR strategy (see OCR strategies)

Screenshot of command-line interface (CLI):

Quickstart on the command line (CLI)

For example, to track your documents and images, run this:

archive-agent include "~/Documents/**" "~/Images/**"
archive-agent update

To start the GUI, run this:

archive-agent

Or, to ask questions from the command line:

archive-agent query "Which files mention donuts?"

CLI command reference

See list of commands

To see the list of supported commands, run this:

archive-agent

Create or switch profile

To switch to a new or existing profile, run this:

archive-agent switch "My Other Profile"

📌 Note: Always use quotes for the profile name argument, or skip it to get an interactive prompt.

💡 Good to know: Profiles are useful to manage independent Qdrant collections (see Qdrant database) and Archive Agent settings.

Open current profile config in nano

To open the current profile's config (JSON) in the nano editor, run this:

archive-agent config

See Archive Agent settings for details.

Add included patterns

To add one or more included patterns, run this:

archive-agent include "~/Documents/*.txt"

📌 Note: Always use quotes for the pattern argument (to prevent your shell's wildcard expansion), or skip it to get an interactive prompt.

Add excluded patterns

To add one or more excluded patterns, run this:

archive-agent exclude "~/Documents/*.txt"

📌 Note: Always use quotes for the pattern argument (to prevent your shell's wildcard expansion), or skip it to get an interactive prompt.

Remove included / excluded patterns

To remove one or more previously included / excluded patterns, run this:

archive-agent remove "~/Documents/*.txt"

📌 Note: Always use quotes for the pattern argument (to prevent your shell's wildcard expansion), or skip it to get an interactive prompt.

List included / excluded patterns

To see the list of included / excluded patterns, run this:

archive-agent patterns

Resolve patterns and track files

To resolve all patterns and track changes to your files, run this:

archive-agent track

List tracked files

To see the list of tracked files, run this:

archive-agent list

📌 Note: Don't forget to track your files first.

List changed files

To see the list of changed files, run this:

archive-agent diff

📌 Note: Don't forget to track your files first.

Commit changed files to database

To sync changes to your files with the Qdrant database, run this:

archive-agent commit

To see additional information (vision, chunking, embedding), pass the --verbose option.

To bypass the AI cache (vision, chunking, embedding) for this commit, pass the --nocache option.

To automatically confirm deleting untracked files from the database, pass the --confirm-delete option.

💡 Good to know: Changes are triggered by:

File added
File removed
File changed:
- Different file size
- Different modification date

The Qdrant database is updated after all files have been ingested.

📌 Note: Don't forget to track your files first.

Combined track and commit

To track and then commit in one go, run this:

archive-agent update

To see additional information (vision, chunking, embedding), pass the --verbose option.

To bypass the AI cache (vision, chunking, embedding) for this commit, pass the --nocache option.

To automatically confirm deleting untracked files from the database, pass the --confirm-delete option.

Search your files

archive-agent search "Which files mention donuts?"

Lists files relevant to the question.

📌 Note: Always use quotes for the question argument, or skip it to get an interactive prompt.

To see additional information (embedding, retrieval, reranking), pass the --verbose option.

To bypass the AI cache (embedding, reranking) for this search, pass the --nocache option.

Query your files

archive-agent query "Which files mention donuts?"

Answers your question using RAG.

📌 Note: Always use quotes for the question argument, or skip it to get an interactive prompt.

To see additional information (embedding, retrieval, reranking, querying), pass the --verbose option.

To bypass the AI cache (embedding, reranking) for this query, pass the --nocache option.

To save the query results to a JSON file, run either:

--to-json with a specific filename:

archive-agent query "Which files mention donuts?" --to-json answer.json

--to-json-auto [DIR] to auto-generate a clean filename from the question (max 160 chars, truncated with [...] if needed) and write to directory DIR if provided (defaults to current directory .; creates directories in path if not existing):
```
archive-agent query "Which files mention donuts?" --to-json-auto Output/
# Creates: Output/Which_files_mention_donuts_.json
```

📌 Note: As of Archive Agent v12.2.0, a corresponding Markdown file (.md) containing the answer is also created when using the --to-json or --to-json-auto options. (There is currently no way to opt out of this.)

Launch Archive Agent GUI

To launch the Archive Agent GUI in your browser, run this:

archive-agent gui

To see additional information (embedding, retrieval, reranking, querying), pass the --verbose option.

To bypass the AI cache (embedding, reranking) for this query, pass the --nocache option.

To save the query results to JSON files, run this:

--to-json-auto [DIR] to auto-generate clean filenames from the questions (max 160 chars, truncated with [...] if needed) and write to directory DIR if provided (defaults to current directory .; creates directories in path if not existing):
```
archive-agent gui --to-json-auto Output/
```

📌 Note: As of Archive Agent v12.2.0, corresponding Markdown files (.md) containing the answers are also created when using the --to-json-auto option. (There is currently no way to opt out of this.)

📌 Note: Press CTRL+C in the console to close the GUI server.

Start MCP Server

To start the Archive Agent MCP server, run this:

archive-agent mcp

To see additional information (embedding, retrieval, reranking, querying), pass the --verbose option.

To bypass the AI cache (embedding, reranking) for this query, pass the --nocache option.

To save the query results to JSON files, run this:

--to-json-auto [DIR] to auto-generate clean filenames from the questions (max 160 chars, truncated with [...] if needed) and write to directory DIR if provided (defaults to current directory .; creates directories in path if not existing):
```
archive-agent mcp --to-json-auto Output/
```

📌 Note: Press CTRL+C in the console to close the MCP server.

💡 Good to know: Use these MCP configurations to let your IDE or AI extension automate Archive Agent:

MCP Tools

Archive Agent exposes these tools via MCP:

MCP tool	Equivalent CLI command(s)	Argument(s)	Implementation	Description
`get_patterns`	`patterns`	None	Synchronous	Get the list of included / excluded patterns.
`get_files_tracked`	`track` and then `list`	None	Synchronous	Get the list of tracked files.
`get_files_changed`	`track` and then `diff`	None	Synchronous	Get the list of changed files.
`get_search_result`	`search`	`question`	Asynchronous	Get the list of files relevant to the question.
`get_answer_rag`	`query`	`question`	Asynchronous	Get answer to question using RAG.

📌 Note: These commands are read-only, preventing the AI from changing your Qdrant database.

💡 Good to know: Just type #get_answer_rag (e.g.) in your IDE or AI extension to call the tool directly.

💡 Good to know: The #get_answer_rag output follows the QuerySchema format defined in AiQuery.py.

Update Archive Agent

This step is not immediately needed if you just installed Archive Agent. However, to get the latest features, you should update your installation regularly.

To update your Archive Agent installation, run this in the installation directory:

./update.sh

📌 Note: If updating doesn't work, try removing the installation directory and then Install Archive Agent again. Your config and data are safely stored in another place; see Archive Agent settings and Qdrant database for details.

💡 Good to know: To also update the Qdrant docker image, run this:

sudo ./manage-qdrant.sh update

Archive Agent settings

Archive Agent settings are organized as profile folders in ~/.archive-agent-settings/.

E.g., the default profile is located in ~/.archive-agent-settings/default/.

The currently used profile is stored in ~/.archive-agent-settings/profile.json.

📌 Note: To delete a profile, simply delete the profile folder. This will not delete the Qdrant collection (see Qdrant database).

Profile configuration

The profile configuration is contained in the profile folder as config.json.

💡 Good to know: Use the config CLI command to open the current profile's config (JSON) in the nano editor (see Open current profile config in nano).

💡 Good to know: Use the switch CLI command to switch to a new or existing profile (see Create or switch profile).

Key	Description
`config_version`	Config version
`mcp_server_host`	MCP server host (default `http://127.0.0.1`; set to `http://0.0.0.0` to expose in LAN)
`mcp_server_port`	MCP server port (default `8008`)
`ocr_strategy`	OCR strategy in `DecoderSettings.py`
`ocr_auto_threshold`	Minimum number of characters for `auto` OCR strategy to resolve to `relaxed` instead of `strict`
`image_ocr`	Image handling: `true` enables OCR, `false` disables it.
`image_entity_extract`	Image handling: `true` enables entity extraction, `false` disables it.
`chunk_lines_block`	Number of lines per block for chunking
`chunk_words_target`	Target number of words per chunk
`qdrant_server_url`	URL of the Qdrant server
`qdrant_collection`	Name of the Qdrant collection
`retrieve_score_min`	Minimum similarity score of retrieved chunks (`0`...`1`)
`retrieve_chunks_max`	Maximum number of retrieved chunks
`retrieve_knee_enable`	Adaptive cutoff for retrieval (`true` enables knee-based cutoff, `false` disables it)
`retrieve_knee_sensitivity`	Knee detection sensitivity (Kneedle `S` parameter; higher = more conservative)
`retrieve_knee_min_chunks`	Minimum number of chunks to keep when adaptive cutoff is applied
`rerank_chunks_max`	Number of top chunks to keep after reranking
`expand_chunks_radius`	Number of preceding and following chunks to prepend and append to each reranked chunk
`max_workers_ingest`	Maximum number of files to process in parallel, creating one thread for each file
`max_workers_vision`	Maxmimum number of parallel vision requests per file, creating one thread per request
`max_workers_embed`	Maxmimum number of parallel embedding requests per file, creating one thread per request
`ai_provider`	AI provider in `ai_provider_registry.py`
`ai_server_url`	AI server URL
`ai_model_chunk`	AI model used for chunking
`ai_model_embed`	AI model used for embedding
`ai_model_rerank`	AI model used for reranking
`ai_model_query`	AI model used for queries
`ai_model_vision`	AI model used for vision (`""` disables vision)
`ai_vector_size`	Vector size of embeddings (used for Qdrant collection)
`ai_temperature_query`	Temperature of the query model (ignored for GPT-5)

📌 Note: When using GPT-5 (default as of Archive Agent v14.0.0), ai_temperature_query is ignored. GPT-5 reasoning effort and verbosity are currently not available in the configuration, but may be customized directly inside OpenAiProvider.py.

📌 Note: Since max_workers_vision and max_workers_embed requests are processed in parallel per file, and max_workers_ingest files are processed in parallel, the total number of requests multiplies quickly. Adjust according to your system resources and in alignment with your AI provider's rate limits.

Watchlist

The profile watchlist is contained in the profile folder as watchlist.json.

The watchlist is managed by these commands only:

include / exclude / remove
track / commit / update

AI cache

Each profile folder also contains an ai_cache folder.

The AI cache ensures that, in a given profile:

The same image is only OCR-ed once.
The same text is only chunked once.
The same text is only embedded once.
The same combination of chunks is only reranked once.

This way, Archive Agent can quickly resume where it left off if a commit was interrupted.

To bypass the AI cache for a single commit, pass the --nocache option to the commit or update command (see Commit changed files to database and Combined track and commit).

💡 Good to know: Queries are never cached, so you always get a fresh answer.

📌 Note: To clear the entire AI cache, simply delete the profile's cache folder.

📌 Technical Note: Archive Agent keys the cache using a composite hash made from the text/image bytes, and of the AI model names for chunking, embedding, reranking, and vision. Cache keys are deterministic and change generated whenever you change the chunking, embedding or vision AI model names. Since cache entries are retained forever, switching back to a prior combination of AI model names will again access the "old" keys.

Qdrant database

The Qdrant database is stored in ~/.archive-agent-qdrant-storage/.

📌 Note: This folder is created by the Qdrant Docker image running as root.

💡 Good to know: Visit your Qdrant dashboard to manage collections and snapshots.

Developer's guide

Archive Agent was written from scratch for educational purposes (on either end of the software).

💡 Good to know: Tracking the test_data/ gets you started with some kind of test data.

Important modules

To get started, check out these epic modules:

Files are processed in archive_agent/data/FileData.py
The app context is initialized in archive_agent/core/ContextManager.py
The default config is defined in archive_agent/config/ConfigManager.py
The CLI commands are defined in archive_agent/__main__.py
The commit logic is implemented in archive_agent/core/CommitManager.py
The CLI verbosity is handled in archive_agent/util/CliManager.py
The GUI is implemented in archive_agent/core/GuiManager.py
The AI API prompts for chunking, embedding, vision, and querying are defined in archive_agent/ai/AiManager.py
The AI provider registry is located in archive_agent/ai_provider/ai_provider_registry.py

If you miss something or spot bad patterns, feel free to contribute and refactor!

Network and Retry Handling

Archive Agent implements comprehensive retry logic with exponential backoff to handle transient failures:

AI Provider Operations: 10 retries with exponential backoff (max 60s delay) for network timeouts and API errors.
Database Operations: 10 retries with exponential backoff (max 10s delay) for Qdrant connection issues .
Schema Validation: Additional 10 linear retry attempts for AI response parsing failures with cache invalidation.
Dual-Layer Strategy: Network-level retries handle infrastructure failures, while schema-level retries handle AI response quality issues.

This robust retry system ensures reliable operation even with unstable network conditions or intermittent service issues.

Code testing and analysis

To run unit tests, check types, and check style, run this:

./audit.sh

Run Qdrant with in-memory storage

To run Qdrant with in-memory storage (e.g., in OpenAI Codex environment where Docker is not available), export this environment variable before running install.sh and archive-agent:

export ARCHIVE_AGENT_QDRANT_IN_MEMORY=1

The environment variable is checked by install.sh to skip manage-qdrant.sh.
The environment variable is checked by QdrantManager.py to ignore server URL and use in-memory storage instead.

📌 Note: Qdrant in-memory storage is volatile (not persisted to disk).

Tools

Rename file paths in chunk metadata

To bulk-rename file paths in chunk metadata in the currently active Qdrant collection, run this:

cd tools/

./qdrant-rename-paths-in-chunk-metadata.py
# OR
uv run python qdrant-rename-paths-in-chunk-metadata.py

Useful after moving files or renaming folders when you don't want to run the update command again.

📌 Note:

This tool modifies the Qdrant database directly — ensure you have backups if working with critical data.
This tool will not update the tracked files. You need to update your watchlist (see Archive Agent settings) using manual search and replace.

Remove file paths from context headers

Archive Agent < v11.0.0 included file paths in the chunk context headers; this was a bad design decision that led to skewed retrieval.

To bulk-remove all file paths in context headers in the currently active Qdrant collection, run this:

cd tools/

./qdrant-remove-paths-from-chunk-headers.py
# OR
uv run python qdrant-remove-paths-from-chunk-headers.py

📌 Note:

This tool modifies the Qdrant database directly — ensure you have backups if working with critical data.

Known issues

Licensed under GNU GPL v3.0

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

See LICENSE for details.

Collaborators welcome

You are invited to contribute to this open source project! Feel free to file issues and submit pull requests anytime.

Learn about Archive Agent

(Tap to open external link to YouTube playlist)

Archive Agent Logo

Archive Agent

An intelligent file indexer with powerful AI search (RAG engine), automatic OCR, and a seamless MCP interface.

GitHub Release GitHub License

Archive Agent brings RAG to your command line and connects to your tools via MCP — it's not a chatbot.

Find what you need with natural language

Unlock your documents with semantic AI search & query
Files are split using semantic chunking with context headers and committed to a local database.
RAG engine¹ uses reranking and expanding of retrieved chunks

¹ Retrieval Augmented Generation is the method of matching pre-made snippets of information to a query.

Natively index your documents on-device

Includes local AI file system indexer
Natively ingests PDFs, images, Markdown, plaintext, and more…
Selects and tracks files using patterns like ~/Documents/*.pdf
Transcribes images using automatic OCR (experimental) and entity extraction
Changes are automatically synced to a local Qdrant vector database.

Your AI, Your Choice

Supports many AI providers and MCP
OpenAI or compatible API ¹ for best performance
Ollama and LM Studio for best privacy (local LLM)
Integrates with your workflow** via a built-in MCP server.

¹ Includes xAI / Grok and Claude OpenAI compatible APIs. Simply adjust the URL settings and overwrite OPENAI_API_KEY.

Scalable Performance

Fully resumable parallel processing
Processes multiple files at once using optimized multi-threading.
Uses AI cache and generous request retry logic for all network requests.
Leverages AI structured output with high-quality prompts and schemas.

Architecture

(If you can't see the diagram below, view it on Mermaid.live)

graph LR

  %% Ingestion Pipeline
  subgraph Ingestion
    A[<b>Track and Commit Files</b><br>Supports Profiles] --> B[<b>Ingest Files</b><br>Automatic OCR Strategy<br>Image Entity Extraction]
    B --> C[<b>Semantic Chunking</b><br>with Context Headers<br>Lines per Block: 100<br>Words per Chunk: 200]
    C --> D[<b>Embed Chunks</b><br>Model: text-embedding-3-large<br>Vector Size: 3072]
    D --> E[<b>Store Chunks</b><br>Local Qdrant database]
  end

  %% Query Pipeline
  subgraph Query
    F[<b>Ask Question</b>] --> G[<b>Embed Question</b><br>Model: text-embedding-3-large]
    G --> H[<b>Retrieve Nearest Chunks</b><br>Score Min: 0.1<br>Chunks Max: 40]
    E --> H
    H --> I[<b>Rerank by Relevance</b><br>Chunks Max: 30]
    I --> J[<b>Expand Context</b><br>Chunks Radius: 2]
    J --> K[<b>Generate Answer</b>]
    K --> L[<b>Get Answer</b><br>in CLI, GUI, MCP]
  end

Just getting started?

👉 Install Archive Agent on Linux
👉 Run Archive Agent
👉 MCP Tools
👉 Update Archive Agent

Documentation

Archive Agent

Supported OS

Archive Agent has been tested with these configurations:

Ubuntu 24.04 (PC x64)
Ubuntu 22.04 (PC x64)

If you've successfully installed and tested Archive Agent with a different setup, please let me know and I'll add it here!

Install Archive Agent

Please install these requirements before proceeding:

Docker (for running Qdrant server)
Python >= 3.10 (core runtime) (usually already installed)

Ubuntu / Linux Mint

This installation method should work on any Linux distribution derived from Ubuntu (e.g. Linux Mint).

To install Archive Agent in the current directory of your choice, run this once:

git clone https://github.com/shredEngineer/Archive-Agent
cd Archive-Agent
chmod +x install.sh
./install.sh

The install.sh script will execute the following steps:

Download and install uv (used for Python environment management)
Install the custom Python environment
Install the spaCy model for natural language processing (pre-chunking)
Install pandoc (used for document parsing)
Download and install the Qdrant docker image with persistent storage and auto-restart
Install a global archive-agent command for the current user

Archive Agent is now installed!

👉 Please complete the AI provider setup next.
(Afterward, you'll be ready to Run Archive Agent!)

AI provider setup

Archive Agent lets you choose between different AI providers:

Remote APIs (higher performance and cost, less privacy):
- OpenAI: Requires an OpenAI API key.
Local APIs (lower performance and cost, best privacy):
- Ollama: Requires Ollama running locally.
- LM Studio: Requires LM Studio running locally.

💡 Good to know: You will be prompted to choose an AI provider at startup; see: Run Archive Agent.

OpenAI provider setup

If the OpenAI provider is selected, Archive Agent requires the OpenAI API key.

To export your OpenAI API key, replace sk-... with your actual key and run this once:

echo "export OPENAI_API_KEY='sk-...'" >> ~/.bashrc && source ~/.bashrc

This will persist the export for the current user.

💡 Good to know: OpenAI won't use your data for training.

Ollama provider setup

If the Ollama provider is selected, Archive Agent requires Ollama running at http://localhost:11434.

How to install Ollama.

With the default Archive Agent Settings, these Ollama models are expected to be installed:

ollama pull llama3.1:8b             # for chunk/rerank/query
ollama pull llava:7b-v1.6           # for vision
ollama pull nomic-embed-text:v1.5   # for embed

💡 Good to know: Ollama also works without a GPU. At least 32 GiB RAM is recommended for smooth performance.

LM Studio provider setup

If the LM Studio provider is selected, Archive Agent requires LM Studio running at http://localhost:1234.

How to install LM Studio.

With the default Archive Agent Settings, these LM Studio models are expected to be installed:

meta-llama-3.1-8b-instruct              # for chunk/rerank/query
llava-v1.5-7b                           # for vision
text-embedding-nomic-embed-text-v1.5    # for embed

💡 Good to know: LM Studio also works without a GPU. At least 32 GiB RAM is recommended for smooth performance.

Which files are processed

Archive Agent currently supports these file types:

Text:
- Plaintext: .txt, .md, .markdown
- Documents:
  - ASCII documents: .html, .htm (images not supported)
  - Binary documents: .odt, .docx (including images)
- PDF documents: .pdf (including images; see OCR strategies)
Images: .jpg, .jpeg, .png, .gif, .webp, .bmp

📌 Note: Images in HTML documents are currently not supported.

📌 Note: Legacy .doc files are currently not supported.

📌 Note: Unsupported files are tracked but not processed.

How files are processed

Ultimately, Archive Agent decodes everything to text like this:

Plaintext files are decoded to UTF-8.
Documents are converted to plaintext, images are extracted.
PDF documents are decoded according to the OCR strategy.
Images are decoded to text using AI vision.
- Uses OCR, entity extraction, or both combined (default).
- The vision model will reject unintelligible images.
- Entity extraction extracts structured information from images.
- Structured information is formatted as image description.

See Archive Agent settings: image_ocr, image_entity_extract

Archive Agent processes files with optimized performance:

Surgical Synchronization:
- PDF analyzing phase is serialized (due to PyMuPDF threading limitations).
- All other phases (vision, chunking, embedding) run in parallel for maximum performance.
Vision operations are parallelized across images and pages within and across files.
Embedding operations are parallelized across text chunks and files.
Smart chunking uses sequential processing due to carry mechanism dependencies.

See Archive Agent settings: max_workers_ingest, max_workers_vision, max_workers_embed

OCR strategies

For PDF documents, there are different OCR strategies supported by Archive Agent:

strict OCR strategy (recommended):
- PDF OCR text layer is ignored.
- PDF pages are treated as images and processed with OCR only.
- Expensive and slow, but more accurate.
relaxed OCR strategy:
- PDF OCR text layer is extracted.
- PDF foreground images are decoded with OCR, but background images are ignored.
- Cheap and fast, but less accurate.
auto OCR strategy:
- Attempts to select the best OCR strategy for each page, based on the number of characters extracted from the PDF OCR text layer, if any.
- Decides based on ocr_auto_threshold, the minimum number of characters for auto OCR strategy to resolve to relaxed instead of strict.
- Trade-off between cost, speed, and accuracy.

💡 Good to know: You will be prompted to choose an OCR strategy at startup (see Run Archive Agent).

How smart chunking works

Archive Agent processes decoded text like this:

Decoded text is sanitized and split into sentences.
Sentences are grouped into reasonably-sized blocks.
Each block is split into smaller chunks using an AI model.
- Block boundaries are handled gracefully (last chunk carries over).
Each chunk is prefixed with a context header (improves search).
Each chunk is turned into a vector using AI embeddings.
Each vector is turned into a point with file metadata.
Each point is stored in the Qdrant database.

See Archive Agent settings: chunk_lines_block, chunk_words_target

💡 Good to know: This smart chunking improves the accuracy and effectiveness of the retrieval.

📌 Note: Splitting into sentences may take some time for huge documents. There is currently no possibility to show the progress of this step.

How chunk references work

To ensure that every chunk can be traced back to its origin, Archive Agent maps the text contents of each chunk to the corresponding line numbers or page numbers of the source file.

Line-based files (e.g., .txt) use the range of line numbers as reference.
Page-based files (e.g., .pdf) use the range of page numbers as reference.

📌 Note: References are only approximate due to paragraph/sentence splitting/joining in the chunking process.

How chunks are retrieved

Archive Agent retrieves chunks related to your question like this:

The question is turned into a vector using AI embeddings.
Points with similar vectors are retrieved from the Qdrant database.
Only chunks of points with sufficient score are kept.
If retrieve_knee_enable is enabled, an adaptive cutoff trims low-relevance chunks when there is a clear score drop-off.

See Archive Agent settings: retrieve_score_min, retrieve_chunks_max, retrieve_knee_enable, retrieve_knee_sensitivity, retrieve_knee_min_chunks

💡 Tuning tips:

If retrieval feels too short or misses context, increase retrieve_knee_min_chunks (e.g., 3–5) or raise retrieve_knee_sensitivity.
If retrieval feels too long or noisy, lower retrieve_knee_sensitivity slightly (e.g., 0.8–1.0) or reduce retrieve_knee_min_chunks.
If you want to disable the adaptive cutoff entirely, set retrieve_knee_enable to false.

How chunks are reranked and expanded

Archive Agent filters the retrieved chunks .

Retrieved chunks are reranked by relevance to your question.
Only the top relevant chunks are kept (the other chunks are discarded).
Each selected chunk is expanded to get a larger context from the relevant documents.

See Archive Agent settings: rerank_chunks_max, expand_chunks_radius

How answers are generated

Archive Agent answers your question using the reranked and expanded chunks like this:

The LLM receives the chunks as context to the question.
LLM's answer is returned as structured output and formatted.

💡 Good to know: Archive Agent uses an answer template that aims to be universally helpful.

How files are selected for tracking

Archive Agent uses patterns to select your files:

Patterns can be actual file paths.
Patterns can be paths containing wildcards that resolve to actual file paths.
💡 Patterns must be specified as (or resolve to) absolute paths, e.g. /home/user/Documents/*.txt (or ~/Documents/*.txt).
💡 Use the wildcard * to match any file in the given directory.
💡 Use the wildcard ** to match any files and zero or more directories, subdirectories, and symbolic links to directories.

There are included patterns and excluded patterns:

The set of resolved excluded files is removed from the set of resolved included files.
Only the remaining set of files (included but not excluded) is tracked by Archive Agent.
Hidden files are always ignored!

This approach gives you the best control over the specific files or file types to track.

Run Archive Agent

💡 Good to know: At startup, you will be prompted to choose the following:

Profile name
AI provider (see AI Provider Setup)
OCR strategy (see OCR strategies)

Screenshot of command-line interface (CLI):

Quickstart on the command line (CLI)

For example, to track your documents and images, run this:

archive-agent include "~/Documents/**" "~/Images/**"
archive-agent update

To start the GUI, run this:

archive-agent

Or, to ask questions from the command line:

archive-agent query "Which files mention donuts?"

CLI command reference

See list of commands

To see the list of supported commands, run this:

archive-agent

Create or switch profile

To switch to a new or existing profile, run this:

archive-agent switch "My Other Profile"

📌 Note: Always use quotes for the profile name argument, or skip it to get an interactive prompt.

💡 Good to know: Profiles are useful to manage independent Qdrant collections (see Qdrant database) and Archive Agent settings.

Open current profile config in nano

To open the current profile's config (JSON) in the nano editor, run this:

archive-agent config

See Archive Agent settings for details.

Add included patterns

To add one or more included patterns, run this:

archive-agent include "~/Documents/*.txt"

📌 Note: Always use quotes for the pattern argument (to prevent your shell's wildcard expansion), or skip it to get an interactive prompt.

Add excluded patterns

To add one or more excluded patterns, run this:

archive-agent exclude "~/Documents/*.txt"

📌 Note: Always use quotes for the pattern argument (to prevent your shell's wildcard expansion), or skip it to get an interactive prompt.

Remove included / excluded patterns

To remove one or more previously included / excluded patterns, run this:

archive-agent remove "~/Documents/*.txt"

📌 Note: Always use quotes for the pattern argument (to prevent your shell's wildcard expansion), or skip it to get an interactive prompt.

List included / excluded patterns

To see the list of included / excluded patterns, run this:

archive-agent patterns

Resolve patterns and track files

To resolve all patterns and track changes to your files, run this:

archive-agent track

List tracked files

To see the list of tracked files, run this:

archive-agent list

📌 Note: Don't forget to track your files first.

List changed files

To see the list of changed files, run this:

archive-agent diff

📌 Note: Don't forget to track your files first.

Commit changed files to database

To sync changes to your files with the Qdrant database, run this:

archive-agent commit

To see additional information (vision, chunking, embedding), pass the --verbose option.

To bypass the AI cache (vision, chunking, embedding) for this commit, pass the --nocache option.

To automatically confirm deleting untracked files from the database, pass the --confirm-delete option.

💡 Good to know: Changes are triggered by:

File added
File removed
File changed:
- Different file size
- Different modification date

The Qdrant database is updated after all files have been ingested.

📌 Note: Don't forget to track your files first.

Combined track and commit

To track and then commit in one go, run this:

archive-agent update

To see additional information (vision, chunking, embedding), pass the --verbose option.

To bypass the AI cache (vision, chunking, embedding) for this commit, pass the --nocache option.

To automatically confirm deleting untracked files from the database, pass the --confirm-delete option.

Search your files

archive-agent search "Which files mention donuts?"

Lists files relevant to the question.

📌 Note: Always use quotes for the question argument, or skip it to get an interactive prompt.

To see additional information (embedding, retrieval, reranking), pass the --verbose option.

To bypass the AI cache (embedding, reranking) for this search, pass the --nocache option.

Query your files

archive-agent query "Which files mention donuts?"

Answers your question using RAG.

📌 Note: Always use quotes for the question argument, or skip it to get an interactive prompt.

To see additional information (embedding, retrieval, reranking, querying), pass the --verbose option.

To bypass the AI cache (embedding, reranking) for this query, pass the --nocache option.

To save the query results to a JSON file, run either:

--to-json with a specific filename:

archive-agent query "Which files mention donuts?" --to-json answer.json

--to-json-auto [DIR] to auto-generate a clean filename from the question (max 160 chars, truncated with [...] if needed) and write to directory DIR if provided (defaults to current directory .; creates directories in path if not existing):
```
archive-agent query "Which files mention donuts?" --to-json-auto Output/
# Creates: Output/Which_files_mention_donuts_.json
```

Launch Archive Agent GUI

To launch the Archive Agent GUI in your browser, run this:

archive-agent gui

To see additional information (embedding, retrieval, reranking, querying), pass the --verbose option.

To bypass the AI cache (embedding, reranking) for this query, pass the --nocache option.

To save the query results to JSON files, run this:

--to-json-auto [DIR] to auto-generate clean filenames from the questions (max 160 chars, truncated with [...] if needed) and write to directory DIR if provided (defaults to current directory .; creates directories in path if not existing):
```
archive-agent gui --to-json-auto Output/
```

📌 Note: Press CTRL+C in the console to close the GUI server.

Start MCP Server

To start the Archive Agent MCP server, run this:

archive-agent mcp

To see additional information (embedding, retrieval, reranking, querying), pass the --verbose option.

To bypass the AI cache (embedding, reranking) for this query, pass the --nocache option.

To save the query results to JSON files, run this:

--to-json-auto [DIR] to auto-generate clean filenames from the questions (max 160 chars, truncated with [...] if needed) and write to directory DIR if provided (defaults to current directory .; creates directories in path if not existing):
```
archive-agent mcp --to-json-auto Output/
```

📌 Note: Press CTRL+C in the console to close the MCP server.

💡 Good to know: Use these MCP configurations to let your IDE or AI extension automate Archive Agent:

MCP Tools

Archive Agent exposes these tools via MCP:

MCP tool	Equivalent CLI command(s)	Argument(s)	Implementation	Description
`get_patterns`	`patterns`	None	Synchronous	Get the list of included / excluded patterns.
`get_files_tracked`	`track` and then `list`	None	Synchronous	Get the list of tracked files.
`get_files_changed`	`track` and then `diff`	None	Synchronous	Get the list of changed files.
`get_search_result`	`search`	`question`	Asynchronous	Get the list of files relevant to the question.
`get_answer_rag`	`query`	`question`	Asynchronous	Get answer to question using RAG.

📌 Note: These commands are read-only, preventing the AI from changing your Qdrant database.

💡 Good to know: Just type #get_answer_rag (e.g.) in your IDE or AI extension to call the tool directly.

💡 Good to know: The #get_answer_rag output follows the QuerySchema format defined in AiQuery.py.

Update Archive Agent

This step is not immediately needed if you just installed Archive Agent. However, to get the latest features, you should update your installation regularly.

To update your Archive Agent installation, run this in the installation directory:

./update.sh

💡 Good to know: To also update the Qdrant docker image, run this:

sudo ./manage-qdrant.sh update

Archive Agent settings

Archive Agent settings are organized as profile folders in ~/.archive-agent-settings/.

E.g., the default profile is located in ~/.archive-agent-settings/default/.

The currently used profile is stored in ~/.archive-agent-settings/profile.json.

📌 Note: To delete a profile, simply delete the profile folder. This will not delete the Qdrant collection (see Qdrant database).

Profile configuration

The profile configuration is contained in the profile folder as config.json.

💡 Good to know: Use the config CLI command to open the current profile's config (JSON) in the nano editor (see Open current profile config in nano).

💡 Good to know: Use the switch CLI command to switch to a new or existing profile (see Create or switch profile).

Key	Description
`config_version`	Config version
`mcp_server_host`	MCP server host (default `http://127.0.0.1`; set to `http://0.0.0.0` to expose in LAN)
`mcp_server_port`	MCP server port (default `8008`)
`ocr_strategy`	OCR strategy in `DecoderSettings.py`
`ocr_auto_threshold`	Minimum number of characters for `auto` OCR strategy to resolve to `relaxed` instead of `strict`
`image_ocr`	Image handling: `true` enables OCR, `false` disables it.
`image_entity_extract`	Image handling: `true` enables entity extraction, `false` disables it.
`chunk_lines_block`	Number of lines per block for chunking
`chunk_words_target`	Target number of words per chunk
`qdrant_server_url`	URL of the Qdrant server
`qdrant_collection`	Name of the Qdrant collection
`retrieve_score_min`	Minimum similarity score of retrieved chunks (`0`...`1`)
`retrieve_chunks_max`	Maximum number of retrieved chunks
`retrieve_knee_enable`	Adaptive cutoff for retrieval (`true` enables knee-based cutoff, `false` disables it)
`retrieve_knee_sensitivity`	Knee detection sensitivity (Kneedle `S` parameter; higher = more conservative)
`retrieve_knee_min_chunks`	Minimum number of chunks to keep when adaptive cutoff is applied
`rerank_chunks_max`	Number of top chunks to keep after reranking
`expand_chunks_radius`	Number of preceding and following chunks to prepend and append to each reranked chunk
`max_workers_ingest`	Maximum number of files to process in parallel, creating one thread for each file
`max_workers_vision`	Maxmimum number of parallel vision requests per file, creating one thread per request
`max_workers_embed`	Maxmimum number of parallel embedding requests per file, creating one thread per request
`ai_provider`	AI provider in `ai_provider_registry.py`
`ai_server_url`	AI server URL
`ai_model_chunk`	AI model used for chunking
`ai_model_embed`	AI model used for embedding
`ai_model_rerank`	AI model used for reranking
`ai_model_query`	AI model used for queries
`ai_model_vision`	AI model used for vision (`""` disables vision)
`ai_vector_size`	Vector size of embeddings (used for Qdrant collection)
`ai_temperature_query`	Temperature of the query model (ignored for GPT-5)

Watchlist

The profile watchlist is contained in the profile folder as watchlist.json.

The watchlist is managed by these commands only:

include / exclude / remove
track / commit / update

AI cache

Each profile folder also contains an ai_cache folder.

The AI cache ensures that, in a given profile:

The same image is only OCR-ed once.
The same text is only chunked once.
The same text is only embedded once.
The same combination of chunks is only reranked once.

This way, Archive Agent can quickly resume where it left off if a commit was interrupted.

To bypass the AI cache for a single commit, pass the --nocache option to the commit or update command (see Commit changed files to database and Combined track and commit).

💡 Good to know: Queries are never cached, so you always get a fresh answer.

📌 Note: To clear the entire AI cache, simply delete the profile's cache folder.

Qdrant database

The Qdrant database is stored in ~/.archive-agent-qdrant-storage/.

📌 Note: This folder is created by the Qdrant Docker image running as root.

💡 Good to know: Visit your Qdrant dashboard to manage collections and snapshots.

Developer's guide

Archive Agent was written from scratch for educational purposes (on either end of the software).

💡 Good to know: Tracking the test_data/ gets you started with some kind of test data.

Important modules

To get started, check out these epic modules:

Files are processed in archive_agent/data/FileData.py
The app context is initialized in archive_agent/core/ContextManager.py
The default config is defined in archive_agent/config/ConfigManager.py
The CLI commands are defined in archive_agent/__main__.py
The commit logic is implemented in archive_agent/core/CommitManager.py
The CLI verbosity is handled in archive_agent/util/CliManager.py
The GUI is implemented in archive_agent/core/GuiManager.py
The AI API prompts for chunking, embedding, vision, and querying are defined in archive_agent/ai/AiManager.py
The AI provider registry is located in archive_agent/ai_provider/ai_provider_registry.py

If you miss something or spot bad patterns, feel free to contribute and refactor!

Network and Retry Handling

Archive Agent implements comprehensive retry logic with exponential backoff to handle transient failures:

AI Provider Operations: 10 retries with exponential backoff (max 60s delay) for network timeouts and API errors.
Database Operations: 10 retries with exponential backoff (max 10s delay) for Qdrant connection issues .
Schema Validation: Additional 10 linear retry attempts for AI response parsing failures with cache invalidation.
Dual-Layer Strategy: Network-level retries handle infrastructure failures, while schema-level retries handle AI response quality issues.

This robust retry system ensures reliable operation even with unstable network conditions or intermittent service issues.

Code testing and analysis

To run unit tests, check types, and check style, run this:

./audit.sh

Run Qdrant with in-memory storage

To run Qdrant with in-memory storage (e.g., in OpenAI Codex environment where Docker is not available), export this environment variable before running install.sh and archive-agent:

export ARCHIVE_AGENT_QDRANT_IN_MEMORY=1

The environment variable is checked by install.sh to skip manage-qdrant.sh.
The environment variable is checked by QdrantManager.py to ignore server URL and use in-memory storage instead.

📌 Note: Qdrant in-memory storage is volatile (not persisted to disk).

Tools

Rename file paths in chunk metadata

To bulk-rename file paths in chunk metadata in the currently active Qdrant collection, run this:

cd tools/

./qdrant-rename-paths-in-chunk-metadata.py
# OR
uv run python qdrant-rename-paths-in-chunk-metadata.py

Useful after moving files or renaming folders when you don't want to run the update command again.

📌 Note:

This tool modifies the Qdrant database directly — ensure you have backups if working with critical data.
This tool will not update the tracked files. You need to update your watchlist (see Archive Agent settings) using manual search and replace.

Remove file paths from context headers

Archive Agent < v11.0.0 included file paths in the chunk context headers; this was a bad design decision that led to skewed retrieval.

To bulk-remove all file paths in context headers in the currently active Qdrant collection, run this:

cd tools/

./qdrant-remove-paths-from-chunk-headers.py
# OR
uv run python qdrant-remove-paths-from-chunk-headers.py

📌 Note:

This tool modifies the Qdrant database directly — ensure you have backups if working with critical data.

Known issues

Licensed under GNU GPL v3.0

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

See LICENSE for details.

Collaborators welcome

You are invited to contribute to this open source project! Feel free to file issues and submit pull requests anytime.

Learn about Archive Agent

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