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one_fm/docsagent/third_party/g2docsmd-html/patches/001-initial-changes-for-docs-agent.patch create mode 100644 one_fm/docsagent/vector_stores/chroma/be016215-254c-4fbf-9ebf-447892d6de7a/data_level0.bin create mode 100644 one_fm/docsagent/vector_stores/chroma/be016215-254c-4fbf-9ebf-447892d6de7a/header.bin create mode 100644 one_fm/docsagent/vector_stores/chroma/be016215-254c-4fbf-9ebf-447892d6de7a/length.bin create mode 100644 one_fm/docsagent/vector_stores/chroma/be016215-254c-4fbf-9ebf-447892d6de7a/link_lists.bin create mode 100644 one_fm/docsagent/vector_stores/chroma/chroma.sqlite3 diff --git a/one_fm/docsagent/CONTRIBUTING.md b/one_fm/docsagent/CONTRIBUTING.md new file mode 100644 index 0000000000..8956a61b82 --- /dev/null +++ b/one_fm/docsagent/CONTRIBUTING.md @@ -0,0 +1,32 @@ +# How to Contribute + +We would love to accept your patches and contributions to this project. + +## Before you begin + +### Sign our Contributor License Agreement + +Contributions to this project must be 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. \ No newline at end of file diff --git a/one_fm/docsagent/README.md b/one_fm/docsagent/README.md new file mode 100644 index 0000000000..3a916e9d64 --- /dev/null +++ b/one_fm/docsagent/README.md @@ -0,0 +1,460 @@ +# Docs Agent + +The Docs Agent project explores applications and use cases that involve using a large +corpus of documentation as a knowledge source for AI language models. + +Docs Agent provides a set of easy-to-use self-service tools designed to give you and +your team access to Google's [Gemini API][genai-doc-site] for learning, experimentation, +and project deployment. + +## Docs Agent web app + +Docs Agent uses a technique known as **Retrieval Augmented Generation (RAG)**, which +allows you to bring your own documents as knowledge sources to AI language models. +This approach helps the AI language models to generate relevant and accurate responses +that are grounded in the information that you provide and control. + +![Docs Agent architecture](docs/images/docs-agent-architecture-01.png) + +**Figure 1**. Docs Agent uses a vector database to retrieve context for augmenting prompts. + +The Docs Agent chatbot web app is designed to be easily set up and configured in a Linux +environment. If you want to set up and launch the Docs Agent chat app on your host machine, +check out the [Set up Docs Agent][set-up-docs-agent] section below. + +## Docs Agent tasks + +Docs Agent's `agent runtask` command allows you to run pre-defined chains of prompts, +which are referred to as **tasks**. These tasks simplify complex interactions by defining +a series of steps that the Docs Agent will execute. The tasks are defined in `.yaml` files +stored in the [`tasks`][tasks-dir] directory of your Docs Agent project. The tasks are +designed to be reusable and can be used to automate common workflows, such as generating +release notes, updating documentation, or analyzing complex information. + +A task file example: + +```yaml +tasks: + - name: "ExtractWorkflows" + model: "models/gemini-1.5-flash-latest" + description: "An agent that extracts workflows from a source doc." + steps: + - prompt: "Summarize the contents of this document in a concise and informative manner. Focus on the key procedures, steps, or workflows described." + flags: + file: "" + default_input: "./README.md" + - prompt: "Identify and list all key workflows described in the document. Provide a brief description for each workflow, highlighting its purpose and key steps." + - prompt: "Identify all command lines used in the workflows described in the document. Focus on command lines that are essential for executing the workflow steps." + - prompt: "For each identified command line, provide a detailed description of its function and purpose. Include specific examples of its usage, showcasing how it is integrated within the workflows." +``` + +To set up and run the `agent runtask` command, see [Set up Docs Agent CLI][cli-readme]. + +## Summary of features + +The list below summarizes the tasks and features supported by Docs Agent: + +- **Process Markdown**: Split Markdown files into small plain text chunks. (See + [Docs Agent chunking process][chunking-process].) +- **Generate embeddings**: Use an embedding model to process text chunks into embeddings + and store them in a vector database. +- **Perform semantic search**: Compare embeddings in a vector database to retrieve + chunks that are most relevant to user questions. +- **Add context to a user question**: Add chunks returned from a semantic search as + [context][prompt-structure] to a prompt. +- **Fact-check responses**: This [experimental feature][fact-check-section] composes + a follow-up prompt and asks the language model to “fact-check” its own previous response. +- **Generate related questions**: In addition to answering a question, Docs Agent can + [suggest related questions][related-questions-section] based on the context of the + question. +- **Return URLs of source documents**: URLs are stored as chunks' metadata. This enables + Docs Agent to return the URLs of the source documents. +- **Collect feedback from users**: Docs Agent's web app has buttons that allow users + to [like responses][like-generated-responses] or [submit rewrites][submit-a-rewrite]. +- **Convert Google Docs, PDF, and Gmail into Markdown files**: This feature uses + [Apps Script][apps-script-readme] to convert Google Docs, PDF, and Gmail into + Markdown files, which then can be used as input datasets for Docs Agent. +- **Run benchmark test**: Docs Agent can [run benchmark test][benchmark-test] to measure + and compare the quality of text chunks, embeddings, and AI-generated responses. +- **Use the Semantic Retrieval API and AQA model**: Docs Agent can use Gemini's + [Semantic Retrieval API][semantic-api] to upload source documents to online corpora + and use the [AQA model][aqa-model] for answering questions. +- **Manage online corpora using the Docs Agent CLI**: The [Docs Agent CLI][cli-reference] + lets you create, update and delete online corpora using the Semantic Retrieval AI. +- **Prevent duplicate chunks and delete obsolete chunks in databases**: Docs Agent + uses [metadata in chunks][chunking-process] to prevent uploading duplicate chunks + and delete obsolete chunks that are no longer present in the source. +- **Run the Docs Agent CLI from anywhere in a terminal**: + [Set up the Docs Agent CLI][cli-readme] to make requests to the Gemini models + from anywhere in a terminal. +- **Support the Gemini 1.5 models**: Docs Agent works with the Gemini 1.5 models, + `gemini-1.5-pro`, `gemini-1.5-flash`, and `text-embedding-004`. The new + [`full`][new-15-mode] web app mode uses all three Gemini models to their strength: + AQA (`aqa`), Gemini 1.0 Pro (`gemini-pro`), and Gemini 1.5 Pro (`gemini-1.5-pro`). +- **Complete a task using the Docs Agent CLI**: The `agent runtask` command allows you + to run pre-defined chains of prompts, which are referred to as tasks. These tasks + simplify complex interactions by defining a series of steps that the Docs Agent will + execute. The tasks are defined in .yaml files stored in the [`tasks`][tasks-dir] + directory of your Docs Agent project. To run a task in this directory, for example: + + ```sh + agent runtask --task DraftReleaseNotes + ``` + +For more information on Docs Agent's architecture and features, +see the [Docs Agent concepts][docs-agent-concepts] page. + +![Docs Agent chat app](docs/images/docs-agent-chat-app-screenshot-01.png) + +**Figure 2**. A screenshot of the Docs Agent chat app launched using Flutter docs. + +## Set up Docs Agent + +**Note**: For instructions on the Docs Agent CLI setup, see the +[`README.md`][cli-readme] file in the `docs_agent/interfaces` directory. + +This section provides instructions on how to set up and launch the Docs Agent +chatbot web app on a Linux host machine. + +### 1. Prerequisites + +Setting up Docs Agent requires the following prerequisite items: + +- A Linux host machine + +- A [Google Cloud][google-cloud] project with the setup below: + + - An API key enabled with the Generative Language API (that is, + the [Gemini API][genai-doc-site]) + + - (**Optional**) [Authenticated OAuth client credentials][oauth-client] + stored on the host machine + +### 2 Update your host machine's environment + +Update your host machine's environment to prepare for the Docs Agent setup: + +1. Update the Linux package repositories on the host machine: + + ``` + sudo apt update + ``` + +2. Install the following dependencies: + + ``` + sudo apt install git pipx python3-venv + ``` + +3. Install `poetry`: + + ``` + pipx install poetry + ``` + +4. To add `$HOME/.local/bin` to your `PATH` variable, run the following + command: + + ``` + pipx ensurepath + ``` + +5. To set the Google API key as a environment variable, add the following + line to your `$HOME/.bashrc` file: + + ``` + export GOOGLE_API_KEY= + ``` + + Replace `` with the API key to the + [Gemini API][genai-doc-site]. + +6. Update your environment: + + ``` + source ~/.bashrc + ``` + +### 3. (Optional) Authorize credentials for Docs Agent + +**This step is needed only if you plan to use [Gemini's AQA model][aqa-model-concept].** + +Authorize Google Cloud credentials on your host machine: + +1. Download the `client_secret.json` file from your + [Google Cloud project][authorize-credentials]. + +2. Copy the `client_secret.json` file to your host machine. + +3. Install the Google Cloud SDK on your host machine: + + ``` + sudo apt install google-cloud-sdk + ``` + +4. To authenticate credentials, run the following command in the directory of + the host machine where the `client_secret.json` file is located: + + ``` + gcloud auth application-default login --client-id-file=client_secret.json --scopes='https://www.googleapis.com/auth/cloud-platform,https://www.googleapis.com/auth/generative-language.retriever' + ``` + + This command opens a browser and asks to log in using your Google account. + +5. Follow the instructions on the browser and click **Allow** to authenticate. + + This saves the authenticated credentials for Docs Agent + (`application_default_credentials.json`) in the `$HOME/.config/gcloud/` + directory of your host machine. + +### 4. Clone the Docs Agent project + +**Note**: This guide assumes that you're creating a new project directory +from your `$HOME` directory. + +Clone the Docs Agent project and install dependencies: + +1. Clone the following repo: + + ``` + git clone https://github.com/google/generative-ai-docs.git + ``` + +2. Go to the Docs Agent project directory: + + ``` + cd generative-ai-docs/examples/gemini/python/docs-agent + ``` + +3. Install dependencies using `poetry`: + + ``` + poetry install + ``` + +4. Enter the `poetry` shell environment: + + ``` + poetry shell + ``` + + **Important**: From this point, all `agent` command lines below need to + run in this `poetry shell` environment. + +### 5. Edit the Docs Agent configuration file + +This guide uses the [open source Flutter documents][flutter-docs-src] as an example dataset, +which are the source Markdown files for the [Flutter website][flutter-docs-site]. + +To complete this setup walkthrough, run the command below to download the open source +Flutter documents somewhere on your host machine (for instance, in your `$HOME` directory): + +``` +git clone --recurse-submodules https://github.com/flutter/website.git +``` + +Update settings in the Docs Agent project to use your custom dataset: + +1. Go to the Docs Agent project home directory, for example: + + ``` + cd $HOME/generative-ai-docs/examples/gemini/python/docs-agent + ``` + +2. Open the [`config.yaml`][config-yaml] file using a text editor, for example: + + ``` + nano config.yaml + ``` + +3. Edit the file to update the `product_name` field, for example: + + ``` + product_name: "Flutter" + ``` + + This product name is displayed on the Docs Agent chat app UI. + +4. Under the `inputs` field, define the following entries to specify the directories + that contain your source Markdown files. + + - `path`: The directory where the source Markdown files are stored. + - `url_prefix`: The prefix used to create URLs for the source Markdown files. + + **Important**: If URLs do not exist for your Markdown files, you still need to + provide a placeholder string in the `url_prefix` field. + + The example below shows the entries for the Flutter documents downloaded in the + `$HOME/website` directory): + + ``` + inputs: + - path: "/usr/local/home/user01/website/src/content" + url_prefix: "https://docs.flutter.dev" + ``` + + You can also provide multiple input directories (`path` and `url_prefix` sets) under + the `inputs` field, for example: + + ``` + inputs: + - path: "/usr/local/home/user01/website/src/content/ui" + url_prefix: "https://docs.flutter.dev/ui" + - path: "/usr/local/home/user01/website/src/content/tools" + url_prefix: "https://docs.flutter.dev/tools" + ``` + +6. If you want to use the `gemini-pro` model with a local vector database setup + (`chroma`), use the following settings: + + ``` + models: + - language_model: "models/gemini-pro" + ... + db_type: "chroma" + ``` + + (**Optional**) Or if you want to use the Gemini AQA model and populate + a corpus online via the [Semantic Retrieval API][semantic-api], use the + following settings (and update the `corpus_name` field): + + ``` + models: + - language_model: "models/aqa" + ... + db_type: "google_semantic_retriever" + db_configs: + ... + - db_type: "google_semantic_retriever" + corpus_name: "corpora/flutter-dev" + ``` + +7. Save the `config.yaml` file and exit the text editor. + + +### 6. Populate a new vector database + +The Docs Agent CLI can help you chunk documents, generate embeddings extract metadata, +and populate a vector database from Markdown files and more. + +**Note**: The `agent` commands below need to run within the `poetry shell` environment. + +To populate a new vector database: + +1. Go to the Docs Agent project home directory, for example: + + ``` + cd $HOME/generative-ai-docs/examples/gemini/python/docs-agent + ``` + +2. Process Markdown files into small text chunks: + + ``` + agent chunk + ``` + + The command takes documents under the `inputs` fields (specified in your + `config.yaml` file), splits the documents into small text chunk files, and + stores them in the `output_path` direcoty. + +3. Create and populate a new vector database: + + ``` + agent populate + ``` + + This command takes the plain text files in the `output_path` directory + and creates a new Chroma collection in the `vector_stores/` directory. + +### 7. Launch the Docs Agent chat app + +Docs Agent's Flask-based chat app lets users interact with the Docs Agent service through +a web browser. + +**Note**: The `agent chatbot` command needs to run within the `poetry shell` environment. + +To start the Docs Agent chat app: + +1. Go to the Docs Agent project home directory, for example: + + ``` + cd $HOME/generative-ai-docs/examples/gemini/python/docs-agent + ``` + +2. Launch the Docs Agent chat app: + + ``` + agent chatbot + ``` + + The Docs Agent chat app runs on port 5000 by default. If you have an application + already running on port 5000 on your host machine, you can use the `--port` flag to + specify a different port (for example, `agent chatbot --port 5050`). + + **Note**: If this `agent chatbot` command fails to run, check the `HOSTNAME` environment + variable on your host machine (for example, `echo $HOSTNAME`). If this variable is unset, + try setting it to `localhost` by running `export HOSTNAME=localhost` + + Once the app starts running, this command prints output similar to the following: + + ``` + $ agent chatbot + Launching the chatbot UI. + * Serving Flask app 'docs_agent.interfaces.chatbot' + * Debug mode: on + INFO:werkzeug:WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead. + * Running on http://example.com:5000 + INFO:werkzeug:Press CTRL+C to quit + INFO:werkzeug: * Restarting with stat + Launching the chatbot UI. + WARNING:werkzeug: * Debugger is active! + INFO:werkzeug: * Debugger PIN: 391-260-142 + ``` + + Notice the line that shows the URL of this server (`http://example.com:5000` + in the example above). + +3. Open the URL above on a browser. + + Now, users can start asking questions related to your product. + +**The Docs Agent chat app is all set!** + +## Contributors + +Nick Van der Auwermeulen (`@nickvander`), Rundong Du (`@rundong08`), +Meggin Kearney (`@Meggin`), and Kyo Lee (`@kyolee415`). + + + +[contribute-to-docs-agent]: #contribute-to-docs-agent +[set-up-docs-agent]: #set-up-docs-agent +[preprocess-dir]: ./docs_agent/preprocess/ +[populate-vector-database]: ./docs_agent/preprocess/populate_vector_database.py +[fact-check-section]: ./docs/concepts.md#using-a-language-model-to-fact_check-its-own-response +[related-questions-section]: ./docs/concepts.md#using-a-language-model-to-suggest-related-questions +[submit-a-rewrite]: ./docs/concepts.md#enabling-users-to-submit-a-rewrite-of-a-generated-response +[like-generated-responses]: ./docs/concepts.md#enabling-users-to-like-generated-responses +[populate-db-steps]: #populate-a-new-vector-database-from-markdown-files +[start-the-app-steps]: #start-the-docs-agent-chat-app +[genai-doc-site]: https://ai.google.dev/docs/gemini_api_overview +[chroma-docs]: https://docs.trychroma.com/ +[flutter-docs-src]: https://github.com/flutter/website/tree/main/src +[flutter-docs-site]: https://docs.flutter.dev/ +[apps-script-readme]: ./apps_script/README.md +[scripts-readme]: ./docs_agent/preprocess/README.md +[config-yaml]: config.yaml +[benchmark-test]: ./docs_agent/benchmarks/README.md +[semantic-api]: https://ai.google.dev/docs/semantic_retriever +[aqa-model]: https://ai.google.dev/models/gemini#model_variations +[authorize-credentials]: https://ai.google.dev/docs/oauth_quickstart#authorize-credentials +[aqa-model-concept]: ./docs/concepts.md#using-the-semantic-retrieval-api-and-aqa-model +[prompt-structure]: ./docs/concepts.md#structure-of-a-prompt-to-a-language-model +[docs-agent-concepts]: ./docs/concepts.md +[google-cloud]: https://console.cloud.google.com/ +[oauth-client]: https://ai.google.dev/docs/oauth_quickstart#set-cloud +[cli-readme]: docs_agent/interfaces/README.md +[cli-reference]: docs/cli-reference.md +[chunking-process]: docs/chunking-process.md +[new-15-mode]: docs/config-reference.md#app_mode +[tasks-dir]: tasks/ diff --git a/one_fm/docsagent/apps_script/README.md b/one_fm/docsagent/apps_script/README.md new file mode 100644 index 0000000000..d333f36a85 --- /dev/null +++ b/one_fm/docsagent/apps_script/README.md @@ -0,0 +1,165 @@ +# Convert Google Docs, PDF, and Gmail to Markdown files + +The collection of scripts in this `apps_script` directory allows you to convert +the contents of Google Drive folders and Gmail to Markdown files that are +compatible with Docs Agent. + +The steps are: + +1. [Prepare a Google Drive folder](#1_prepare-a-google-driver-folder). +2. [Mount Google Drive on your host machine](#2_mount-google-drive-on-your-host-machine). +3. [Create an Apps Script project](#3_create-an-apps-script-project). +4. [Edit and run main.gs on Apps Script](#4_edit-and-run-main_gs-on-apps-script). +5. [Update config.yaml to include the mounted directory](#5_update-config_yaml-to-include-the-mounted-directory). + +## 1. Prepare a Google Drive folder + +First, create a new folder in Google Drive and add your Google Docs (which will be +used as source documents to Docs Agent) to the folder. + +Do the following: + +1. Browser to https://drive.google.com/. +1. Click **+ New** on the top left corner. +1. Click **New folder**. +1. Name your new folder (for example, `my source Google Docs`). +1. To enter the newly created folder, double click the folder. +1. Add (or move) your source Google Docs to this new folder. + +## 2. Mount Google Drive on your host machine + +Mount your Google Drive to your host machine, so that it becomes easy to access the +folders in Google Drive from your host machine (later in step 5). + +There are a variety of methods and tools available online that enable this setup +(for example, see [`google-drive-ocamlfuse`][google-drive-ocamlfuse] for Linux machines). + +## 3. Create an Apps Script project + +Create a new Apps Script project and copy all the `.gs` scripts in this +`apps_script` directory to your new Apps Script project. + +Do the following: + +1. Browse to https://script.google.com/. +1. Click **New Project**. +1. At the top of the page, click **Untitled Project** and enter a meaningful + title (for example, `gDocs to Docs Agent`). +1. Click the **+** icon next to **Files**. +1. Click **Script**. +1. Name the new script to be one of the `.gs` files in this `apps_script` directory + (for example, `drive_to_markdown`). +1. Copy the content of the `.gs` file to the new script on your Apps Script project. +1. To save, click the "Save project" icon in the toolbar. +1. Repeat the steps until all the `.gs` files are copied to your Apps Script project. +1. Click the **+** icon next to **Services**. +1. Scroll down and click **Drive API**. +1. Select **v2**. +1. Click **Add**. + +You are now ready to edit the parameters on the `main.gs` file to select a folder +in Google Drive and export emails from Gmail. + +![Apps Script project](../docs/images/apps-script-screenshot-01.png) + +**Figure 1**. A screenshot of an example Apps Script project. + +## 4. Edit and run main.gs on Apps Script + +Edit the `main.gs` file on your Apps Script project to select which functions +(features) you want to run. + +Do the following: + +1. Browse to your project on https://script.google.com/. + +1. Open the `main.gs` file. + +1. In the `main` function, comment out any functions that you don't want to run + (see Figure 1): + + * `convertDriveFolderToMDForDocsAgent(folderInput)`: This function converts + the contents of a Google Drive folder to Markdown files (currently only Google + Docs and PDF). Make sure to specify a valid Google Drive folder in the `folderInput` + variable. Use the name of the folder created in **step 1** above, for example: + + ``` + var folderInput = "my source Google Docs" + function main() { + convertDriveFolderToMDForDocsAgent(folderInput); + //exportEmailsToMarkdown(SEARCH_QUERY, folderOutput); + } + ``` + + * `exportEmailsToMarkdown(SEARCH_QUERY, folderOutput)`: This function converts + the emails returned from a Gmail search query into Markdown files. Make sure to + specify a search query in the `SEARCH_QUERY` variable. You can test this search + query directly in the Gmail search bar. Also, specify an output directory for the + resulting emails. + +1. To save, click the "Save project" icon in the toolbar. + +1. Click the "Run" icon in the toolbar. + + When this script runs successfully, the Execution log panel prints output similar + to the following: + + ``` + 9:55:59 PM Notice Execution completed + ``` + + Also, the script creates a new folder in your Google Drive and stores the converted + Markdown files in this folder. The name of this new folder has `-output` as a postfix. + For example, with the folder name `my source Google Docs`, the name of the new folder + is `my source Google Docs-output`. + + With Google Drive mounted on your host machine in step 2, you can now directly access + this folder from the host machine, for example: + + ``` + user@hostname:~/DriveFileStream/My Drive/my source Google Docs-output$ ls + Copy_of_My_Google_Docs_To_Be_Converted.md + ``` + +## 5. Update config.yaml to include the mounted directory + +Once you have your Google Drive mounted on the host machine, you can now +specify one of its folders as an input source directory for Docs Agent. + +Do the following: + +1. In the Docs Agent project, open the [`config.yaml`][config-yaml] file + with a text editor. + +1. Specify your mounted Google Drive folder as an `input` group, for example: + + ``` + input: + - path: "/usr/local/home/user01/DriveFileStream/My Drive/my source Google Docs-output" + url_prefix: "docs.google.com" + ``` + + You **must** specify a value to the `url_prefix` field, such as `docs.google.com`. + Currently this value is used to generate hashes for the content. + +1. (**Optional**) Add an additional Google Drive folder for your exported emails, + for example: + + ``` + input: + - path: "/usr/local/home/user01/DriveFileStream/My Drive/my source Google Docs-output" + url_prefix: "docs.google.com" + - path: "/usr/local/home/user01/DriveFileStream/My Drive/psa-output" + url_prefix: "mail.google.com" + ``` + +1. Save the changes in the `config.yaml` file. + +You're all set with a new documentation source for Docs Agent. You can now follow the +instructions in the project's main [`README`][main-readme] file to launch the Docs Agent app. + + + +[config-yaml]: ../config.yaml +[main-readme]: ../README.md +[google-drive-ocamlfuse]: https://github.com/astrada/google-drive-ocamlfuse diff --git a/one_fm/docsagent/apps_script/drive_to_markdown.gs b/one_fm/docsagent/apps_script/drive_to_markdown.gs new file mode 100644 index 0000000000..b19d33d846 --- /dev/null +++ b/one_fm/docsagent/apps_script/drive_to_markdown.gs @@ -0,0 +1,240 @@ +/** + * Copyright 2023 Google LLC + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +function convertDriveFolderToMDForDocsAgent(folderName, outputFolderName=""){ + gdoc_count = 0; + pdf_count = 0; + new_file_count = 0; + updated_file_count = 0; + unchanged_file_count = 0; + gdoc_count, pdf_count, new_file_count, updated_file_count, unchanged_file_count = convertDriveFolder(folderName, outputFolderName=outputFolderName) + let conversion_count = pdf_count + gdoc_count + let file_count = new_file_count + updated_file_count + unchanged_file_count + Logger.log("Converted a total of: " + gdoc_count + " Google Doc files."); + Logger.log("Converted a total of: " + pdf_count + " PDF files."); + Logger.log("Converted a grand total of: " + conversion_count + " files."); + Logger.log("New files: " + new_file_count) + Logger.log("Updated a total of: " + updated_file_count + " files.") + Logger.log("Files that haven't changed: " + unchanged_file_count); + Logger.log("Input directory had a total of: " + file_count + " files.") +} + +function convertDriveFolder(folderName, outputFolderName="", indexFile="") { + + //Checks if input folder exists or exits + if(folderExistsInput(folderName)){ + var file_count = 0; + var folders = DriveApp.getFoldersByName(folderName); + if (outputFolderName=="") { + var folderOutput = folderName + "-output"; + var output_file_name = folderName + "-index"; + } + else { + var folderOutput = outputFolderName + "-output"; + var output_file_name = outputFolderName + "-index"; + } + Logger.log("Output directory: "+ folderOutput); + folderExistsOrCreate(folderOutput); + var folderOutputObj = DriveApp.getFoldersByName(folderOutput); + if (folderOutputObj.hasNext()){ + var folderOutputName = folderOutputObj.next(); + } + if (indexFile=="") { + var sheet = checkIndexOutputOrCreate(output_file_name, folderOutputName); + var timeZone = Session.getScriptTimeZone(); + var date = Utilities.formatDate(new Date(), timeZone, "MM-dd-yyyy HH:mm:ss z"); + sheet.appendRow(["Created: ", date]) + sheet.appendRow(["Name","ID", "URL", "Markdown ID", "Markdown Output", "Date Created", "Last Updated", "Type", "Folder", "MD5 hash", "Status"]); + } + else { + var sheet = indexFile + } + // var sheet_id = sheet.getId(); + var foldersnext = folders.next(); + var myfiles = foldersnext.getFiles(); + var new_file_count = 0; + var unchanged_file_count = 0; + var updated_file_count = 0; + var gdoc_count = 0; + var pdf_count = 0; + var start_data_row = 2; + var status = "New content"; + + while (myfiles.hasNext()) { + var myfile = myfiles.next(); + var ftype = myfile.getMimeType(); + // If this is a shorcut, retrieve the target file + if (ftype == "application/vnd.google-apps.shortcut") { + var fid = myfile.getTargetId(); + var myfile = DriveApp.getFileById(fid); + var ftype = myfile.getMimeType(); + } + else{ + var fid = myfile.getId(); + } + if (ftype == "application/vnd.google-apps.folder") { + var folder = DriveApp.getFolderById(fid); + Logger.log("Sub-directory: " + folder); + sub_gdoc_count = 0; + sub_pdf_count = 0; + sub_new_file_count = 0; + sub_updated_file_count = 0; + sub_unchanged_file_count = 0; + sub_gdoc_count, sub_pdf_count, sub_new_file_count, sub_updated_file_count, sub_unchanged_file_count = convertDriveFolder(folder, outputFolderName=foldersnext, indexFile=sheet); + gdoc_count += sub_gdoc_count; + pdf_count += sub_pdf_count; + new_file_count += sub_new_file_count; + updated_file_count += sub_updated_file_count; + unchanged_file_count += sub_unchanged_file_count; + continue; + } + var fname = sanitizeFileName(myfile.getName()); + var fdate = myfile.getLastUpdated(); + var furl = myfile.getUrl(); + var fcreate = myfile.getDateCreated(); + + //Function returns an array, assign each array value to seperate variables + var backup_results = returnBackupHash(sheet, "Backup", fid, start_data_row, 1, 9, 3); + if (backup_results != undefined && backup_results[0] != "no_results") { + var backup_fid = backup_results[0]; + var md5_backup = backup_results[1]; + var mdoutput_backup_id = backup_results[2]; + } + if (ftype == "application/vnd.google-apps.document") { + Logger.log("File: " + fname + " is a Google doc."); + let gdoc = DocumentApp.openById(fid); + let gdoc_blob = gdoc.getBody().getText(); + var md5_hash = Utilities.computeDigest(Utilities.DigestAlgorithm.MD5,gdoc_blob, + Utilities.Charset.US_ASCII); + var hash_str = byteToStr(md5_hash); + if (backup_fid == fid && hash_str == md5_backup) { + Logger.log("File is unchanged. Skipping conversion."); + if (mdoutput_backup_id){ + var saved_file = DriveApp.getFileById(mdoutput_backup_id); + var saved_file_id = saved_file.getId(); + } + status = "Unchanged content"; + unchanged_file_count += 1; + var convert_file = false; + } + else if (backup_fid == fid && hash_str != md5_backup){ + status = "Updated content"; + updated_file_count += 1; + var convert_file = true; + } + else { + status = "New content"; + new_file_count += 1; + var convert_file = true; + } + if (convert_file){ + var frontmatter = "---" + "\n"; + frontmatter += "title: \"" + fname + "\"\n"; + frontmatter += "type: \"" + ftype + "\"\n"; + frontmatter += "id: \"" + fid + "\"\n"; + frontmatter += "created: \"" + fcreate + "\"\n"; + frontmatter += "updated: \"" + fdate + "\"\n"; + frontmatter += "URL: \"" + furl + "\"\n"; + frontmatter += "---" + "\n\n"; + var saved_file = convertDocumentToMarkdown(gdoc, folderOutputName, frontmatter); + var saved_file_id = saved_file.getId(); + Logger.log("Finished converting file: " + fname + " to markdown."); + Logger.log("Markdown file: " + saved_file); + status = "New content"; + gdoc_count += 1; + } + file_count += 1; + } + if (ftype == "application/pdf") { + // Converts PDFs - First to a temporary Google Doc and then use convertDocumentToMarkdown to convert to markdown with frontmatter + Logger.log("File: " + fname + " is a PDF."); + let pdfBlob = DriveApp.getFileById(fid).getBlob(); + let pdfblobText = pdfBlob.getDataAsString(); + var md5_hash = Utilities.computeDigest(Utilities.DigestAlgorithm.MD5,pdfblobText, + Utilities.Charset.US_ASCII); + var hash_str = byteToStr(md5_hash); + if (backup_fid == fid && hash_str == md5_backup) { + Logger.log("File is unchanged. Skipping conversion."); + if (mdoutput_backup_id){ + var saved_file = DriveApp.getFileById(mdoutput_backup_id); + var saved_file_id = saved_file.getId(); + } + status = "Unchanged content"; + unchanged_file_count += 1; + var convert_file = false; + } + else if (backup_fid == fid && hash_str != md5_backup){ + status = "Updated content"; + updated_file_count += 1; + var convert_file = true; + } + else { + status = "New content"; + new_file_count += 1; + var convert_file = true; + } + if (convert_file){ + let temp_doc_name = pdfBlob.getName() + "-temp"; + let temp_doc = {title: temp_doc_name, mimeType: pdfBlob.getContentType(), parents: [{id: folderOutputName.getId()}]} + let options = {ocr: true}; + let output = Drive.Files.insert(temp_doc, pdfBlob, options); + let output_id = output.getId(); + let gdoc = DocumentApp.openById(output_id); + var frontmatter = "---" + "\n"; + frontmatter += "title: \"" + fname + "\"\n"; + frontmatter += "type: \"" + ftype + "\"\n"; + frontmatter += "id: \"" + fid + "\"\n"; + frontmatter += "created: \"" + fcreate + "\"\n"; + frontmatter += "updated: \"" + fdate + "\"\n"; + frontmatter += "URL: \"" + furl + "\"\n"; + frontmatter += "---" + "\n\n"; + var saved_file = convertDocumentToMarkdown(gdoc, folderOutputName, frontmatter); + var saved_file_id = saved_file.getId(); + Logger.log("Finished converting file: "+ fname + " to markdown."); + Logger.log("Markdown file: " + saved_file); + Logger.log("Clearing temporary gdoc" ); + let output_file = DriveApp.getFileById(output_id); + output_file.setTrashed(true); + status = "New content"; + pdf_count += 1; + } + file_count += 1; + } + let md_chip = createRichText(saved_file); + let original_chip = createRichText(myfile); + let folder_chip = createRichText(foldersnext); + metadata = [ + fname, + fid, + "original_chip", + saved_file_id, + "md_chip", + fcreate, + fdate, + ftype, + "folder_chip", + hash_str, + status, + ]; + sheet.appendRow(metadata); + // Return final row to inserRichText into correct rows + row_number = sheet.getLastRow(); + insertRichText(sheet, original_chip, "C", row_number); + insertRichText(sheet, md_chip, "E", row_number); + insertRichText(sheet, folder_chip, "I", row_number); + } + } + return gdoc_count, pdf_count, new_file_count, updated_file_count, unchanged_file_count +} diff --git a/one_fm/docsagent/apps_script/exportmd.gs b/one_fm/docsagent/apps_script/exportmd.gs new file mode 100644 index 0000000000..40a4461ffc --- /dev/null +++ b/one_fm/docsagent/apps_script/exportmd.gs @@ -0,0 +1,1310 @@ +/** + * Copyright 2023 Google LLC + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/* Original script is from: +https://github.com/lmmx/gdocs2md-html/blob/master/exportmd.gs +and commit: 0d86cfa +Parsing from mangini/gdocs2md. +Modified by clearf to add files to the google directory structure. +Modified by lmmx to write Markdown, going back to HTML-incorporation. + +Usage: + NB: don't use on top-level doc (in root Drive folder) See comment in setupScript function. + Adding this script to your doc: + - Tools > Script Manager > New + - Select "Blank Project", then paste this code in and save. + Running the script: + - Tools > Script Manager + - Select "convertDocumentToMarkdown" function. + - Click Run button. + - Converted doc will be added to a "Markdown" folder in the source document's directories. + - Images will be added to a subfolder of the "Markdown" folder. +*/ + +function onInstall(e) { + onOpen(e); +} + +function onOpen() { + // Add a menu with some items, some separators, and a sub-menu. + setupScript(); +// In future: +// DocumentApp.getUi().createAddonMenu(); + DocumentApp.getUi().createMenu('Markdown') + .addItem('View as markdown', 'markdownPopup') + .addSubMenu(DocumentApp.getUi().createMenu('Export \u2192 markdown') + .addItem('Export to local file', 'convertSingleDoc') + .addItem('Export entire folder to local file', 'convertFolder') + .addItem('Customise markdown conversion', 'changeDefaults')) + .addSeparator() + .addSubMenu(DocumentApp.getUi().createMenu('Toggle comment visibility') + .addItem('Image source URLs', 'toggleImageSourceStatus') + .addItem('All comments', 'toggleCommentStatus')) + .addItem("Add comment", 'addCommentDummy') + .addToUi(); +} + +function changeDefaults() { + var ui = DocumentApp.getUi(); + var default_settings = '{ use your imagination... }'; + var greeting = ui.alert('This should be set up to display defaults from variables passed to getDocComments etc., e.g. something like:\n\nDefault settings are:' + + '\ncomments - not checking deleted comments.\nDocument - this document (alternatively specify a document ID).' + + '\n\nClick OK to edit these, or cancel.', + ui.ButtonSet.OK_CANCEL); + ui.alert("There's not really need for this yet, so this won't proceed, regardless of what you just pressed."); + return; + + // Future: + if (greeting == ui.Button.CANCEL) { + ui.alert("Alright, never mind!"); + return; + } + // otherwise user clicked OK + // user clicked OK, to proceed with editing these defaults. Ask case by case whether to edit + + var response = ui.prompt('What is x (default y)?', ui.ButtonSet.YES_NO_CANCEL); + + // Example code from docs at https://developers.google.com/apps-script/reference/base/button-set + // Process the user's response. + if (response.getSelectedButton() == ui.Button.YES) { + Logger.log('The user\'s name is %s.', response.getResponseText()); + } else if (response.getSelectedButton() == ui.Button.NO) { + Logger.log('The user didn\'t want to provide a name.'); + } else { + Logger.log('The user clicked the close button in the dialog\'s title bar.'); + } +} + +function setupScript() { + var script_properties = PropertiesService.getScriptProperties(); + script_properties.setProperty("user_email", Drive.About.get().user.emailAddress); + + // manual way to do the following: + // script_properties.setProperty("folder_id", "INSERT_FOLDER_ID_HERE"); + // script_properties.setProperty("document_id", "INSERT_FILE_ID_HERE"); + + var doc_id = DocumentApp.getActiveDocument().getId(); + script_properties.setProperty("document_id", doc_id); + var doc_parents = DriveApp.getFileById(doc_id).getParents(); + var folders = doc_parents; + while (folders.hasNext()) { + var folder = folders.next(); + var folder_id = folder.getId(); + } + script_properties.setProperty("folder_id", folder_id); + script_properties.setProperty("image_folder_prefix", ""); // add if modifying image location +} + +function addCommentDummy() { + // Dummy function to be switched during development for addComment + DocumentApp.getUi() + .alert('Cancelling comment entry', + "There's not currently a readable anchor for Google Docs - you need to write your own!" + + + "\n\nThe infrastructure for using such an anchoring schema is sketched out in" + + " the exportmd.gs script's addComment function, for an anchor defined in anchor_props" + + + "\n\nSee github.com/lmmx/devnotes/wiki/Custom-Google-Docs-comment-anchoring-schema", + DocumentApp.getUi().ButtonSet.OK + ); + return; +} + +function addComment() { + + var doc_id = PropertiesService.getScriptProperties().getProperty('document_id'); + var user_email = PropertiesService.getScriptProperties().getProperty('email'); +/* Drive.Comments.insert({content: "hello world", + context: { + type: 'text/html', + value: 'hinges' + } + }, document_id); */ + var revision_list = Drive.Revisions.list(doc_id).items; + var recent_revision_id = revision_list[revision_list.length - 1].id; + var anchor_props = { + revision_id: recent_revision_id, + starting_offset: '', + offset_length: '', + total_chars: '' + } + insertComment(doc_id, 'hinges', 'Hello world!', my_email, anchor_props); +} + +function insertComment(fileId, selected_text, content, user_email, anchor_props) { + + // NB Deal with handling missing args + + /* + anchor_props is an object with 4 properties: + - revision_id, + - starting_offset, + - offset_length, + - total_chars + */ + + var context = Drive.newCommentContext(); + context.value = selected_text; + context.type = 'text/html'; + var comment = Drive.newComment(); + comment.kind = 'drive#comment'; + var author = Drive.newUser(); + author.kind = 'drive#user'; + author.displayName = user_email; + author.isAuthenticatedUser = true; + comment.author = author; + comment.content = type; + comment.context = context; + comment.status = 'open'; + comment.anchor = "{'r':" + + anchor_props.revision_id + + ",'a':[{'txt':{'o':" + + anchor_props.starting_offset + + ",'l':" + + anchor_props.offset_length + + ",'ml':" + + anchor_props.total_chars + + "}}]}"; + comment.fileId = fileId; + Drive.Comments.insert(comment, fileId); +} + +function decodeScriptSwitches(optional_storage_name) { + var property_name = (typeof(optional_storage_name) == 'string') ? optional_storage_name : 'switch_settings'; + var script_properties = PropertiesService.getScriptProperties(); + return script_properties + .getProperty(property_name) + .replace(/{|}/g,'') // Get the statements out of brackets... + .replace(',', ';'); // ...swap the separator for a semi-colon... + // ...evaluate the stored object string as statements upon string return and voila, switches interpreted +} + + +function getDocComments(comment_list_settings) { + var possible_settings = ['images', 'include_deleted']; + + // switches are processed and set on a script-wide property called "comment_switches" + var property_name = 'comment_switches'; + switchHandler(comment_list_settings, possible_settings, property_name); + + var script_properties = PropertiesService.getScriptProperties(); + var comment_switches = decodeScriptSwitches(property_name); + eval(comment_switches); + + var document_id = script_properties.getProperty("document_id"); + var comments_list = Drive.Comments.list(document_id, + {includeDeleted: include_deleted, + maxResults: 100 }); // 0 to 100, default 20 + // See https://developers.google.com/drive/v2/reference/comments/list for all options + var comment_array = []; + var image_sources = []; + // To collect all comments' image URLs to match against inlineImage class elements LINK_URL attribute + + for (var i = 0; i < comments_list.items.length; i++) { + var comment = comments_list.items[i]; + var comment_text = comment.content; + var comment_status = comment.status; + /* + images is a generic parameter passed in as a switch to + return image URL-containing comments only. + + If the parameter is provided, it's no longer undefined. + */ + var img_url_regex = /(https?:\/\/.+?\.(png|gif|jpe?g))/; + var has_img_url = img_url_regex.test(comment_text); + + if (images && !has_img_url) continue; // no image URL, don't store comment + if (has_img_url) image_sources.push(RegExp.$1); + comment_array.push(comment); + } + script_properties.setProperty('image_source_URLs', image_sources) + return comment_array; +} + +function isValidAttrib(attribute) { // Sanity check function, called per element in array + + // Possible list of attributes to check against (leaving out unchanging ones like kind) + possible_attrs = [ + 'selfLink', + 'commentId', + 'createdDate', + 'modifiedDate', + 'author', + 'htmlContent', + 'content', + 'deleted', + 'status', + 'context', + 'anchor', + 'fileId', + 'fileTitle', + 'replies', + 'author' + ]; + + // Check if attribute(s) provided can be used to match/filter comments: + + if (typeof(attribute) == 'string' || typeof(attribute) == 'object') { + // Either a string/object (1-tuple) + + // Generated with Javascript, gist: https://gist.github.com/lmmx/451b301e1d78ed2c10b4 + + // Return false from the function if any of the attributes specified are not in the above list + + // If an object, the name is the key, otherwise it's just the string + if (attribute.constructor === Object) { + var att_keys = []; + for (var att_key in attribute) { + if (attribute.hasOwnProperty(att_key)) { + att_keys.push(att_key); + } + } + for (var n=0; n < att_keys.length; n++) { + var attribute_name = att_keys[n]; + var is_valid_attrib = (possible_attrs.indexOf(attribute_name) > -1); + + // The attribute needs to be one of the possible attributes listed above, match its given value(s), + // else returning false will throw an error from onAttribError when within getCommentAttributes + return is_valid_attrib; + } + } else if (typeof(attribute) == 'string') { + var attribute_name = attribute; + var is_valid_attrib = (possible_attrs.indexOf(attribute_name) > -1); + return is_valid_attrib; + // Otherwise is a valid (string) attribute + } else if (attribute.constructor === Array) { + return false; // Again, if within getCommentAttributes this will cause an error - shouldn't pass an array + } else { + // Wouldn't expect this to happen, so give a custom error message + Logger.log('Unknown type (assumed impossible) passed to isValidAttrib: ', attribute, attribute.constructor); + throw new TypeError('Unknown passed to isValidAttrib - this should be receiving 1-tuples only, see logs for details.'); + } + } else return false; // Neither string/object / array of strings &/or objects - not a valid attribute +} + +function getCommentAttributes(attributes, comment_list_settings) { + + // A filter function built on Comments.list, for a given list of attributes + // Objects' values are ignored here, only their property titles are used to filter comments. + + + /* + - attributes: array of attributes to filter/match on + - comment_list_settings: (optional) object with properties corresponding to switches in getDocComments + + This function outputs an array of the same length as the comment list, containing + values for all fields matched/filtered on. + */ + + + /* + * All possible comment attributes are listed at: + * https://developers.google.com/drive/v2/reference/comments#properties + */ + + // Firstly, describe the type in a message to be thrown in case of TypeError: + + var attrib_def_message = "'attributes' should be a string (the attribute to get for each comment), " + + "an object (a key-value pair for attribute and desired value), " + + "or an array of objects (each with key-value pairs)"; + + function onAttribError(message) { + Logger.log(message); + throw new TypeError(message); + } + + // If (optional) comment_list_settings isn't set, make a getDocComments call with switches left blank. + if (typeof(comment_list_settings) == 'undefined') var comment_list_settings = {}; + if (typeof(attributes) == 'undefined') onAttribError(attrib_def_message); // no variables specified + + if (isValidAttrib(attributes)) { // This will be true if there's only one attribute, not provided in an array + + /* + Make a 1-tuple (array of 1) from either an object or a string, + i.e. a single attribute, with or without a defined value respectively. + */ + + var attributes = Array(attributes); + + } else if (attributes.constructor === Array) { + + // Check each item in the array is a valid attribute specification + for (var l = 0; l < attributes.length; l++) { + if (! isValidAttrib(attributes[l]) ) { + onAttribError('Error in attribute ' + + (l+1) + ' of ' + attributes.length + + '\n\n' + + attrib_def_message); + } + } + + } else { // Neither attribute nor array of attributes + throw new TypeError(attrib_def_message); + } + + // Attributes now holds an array of string and/or objects specifying a comment match and/or filter query + + var comment_list = getDocComments(comment_list_settings); + var comment_attrib_lists = []; + for (var i in comment_list) { + var comment = comment_list[i]; + var comment_attrib_list = []; + for (var j in attributes) { + var comment_attribute = comment_list[i][attributes[j]]; + comment_attrib_list.push(comment_attribute); + } + comment_attrib_lists.push(comment_attrib_list); + } + // The array comment_attrib_lists is now full of the requested attributes, + // of length equal to that of attributes + return comment_attrib_lists; +} + +// Example function to use getCommentAttributes: + +function filterComments(attributes, comment_list_settings) { + var comment_attributes = getCommentAttributes(attributes, comment_list_settings); + var m = attribs.indexOf('commentId') // no need to keep track of commentID array position + comm_attribs.map(function(attrib_pair) { + if (attrib_pair[1]); + }) +} + +function toggleCommentStatus(comment_switches){ + // Technically just image URL-containing comments, not sources just yet + var attribs = ['commentId', 'status']; + var comm_attribs = getCommentAttributes(attribs, comment_switches); + var rearrangement = []; + comm_attribs.map( + function(attrib_pair) { // for every comment return with the images_only / images: true comments.list setting, + switch (attrib_pair[1]){ // check the status of each + case 'open': + rearrangement.push([attrib_pair[0],'resolved']); + break; + case 'resolved': + rearrangement.push([attrib_pair[0],'open']); + break; + } + } + ); + var script_properties = PropertiesService.getScriptProperties(); + var doc_id = script_properties.getProperty("document_id"); + rearrangement.map( + function(new_attrib_pair) { // for every comment ID with flipped status + Drive.Comments.patch('{"status": "' + + new_attrib_pair[1] + + '"}', doc_id, new_attrib_pair[0]) + } + ); + return; +} + +function toggleImageSourceStatus(){ + toggleCommentStatus({images: true}); +} + +function flipResolved() { + // Flip the status of resolved comments to open, and open comments to resolved (respectful = true) + // I.e. make resolved URL-containing comments visible, without losing track of normal comments' status + + // To force all comments' statuses to switch between resolved and open en masse set respectful to false + + var switch_settings = {}; + switch_settings.respectful = true; + switch_settings.images_only = false; // If true, only switch status of comments with an image URL + switch_settings.switch_deleted_comments = false; // If true, also switch status of deleted comments + + var comments_list = getDocComments( + { images: switch_settings.images_only, + include_deleted: switch_settings.switch_deleted_comments }); + + // Note: these parameters are unnecessary if both false (in their absence assumed false) + // but included for ease of later reuse + + if (switch_settings.respectful) { + // flip between + } else { + // flip all based on status of first in list + } +} + +function markdownPopup() { + var css_style = ''; + + // The above was written with js since doesn't work: + // https://gist.github.com/lmmx/ec084fc351528395f2bb + + var mdstring = stringMiddleMan(); + + var htmlstring = + '' + + css_style + + '
'; + + var html5 = HtmlService.createHtmlOutput(htmlstring) + .setSandboxMode(HtmlService.SandboxMode.IFRAME) + .setWidth(800) + .setHeight(500); + + DocumentApp.getUi() + .showModalDialog(html5, 'Markdown output'); +} + +function stringMiddleMan() { + var returned_string; + convertSingleDoc({"return_string": true}); // for some reason needs the scope to be already set... + // could probably rework to use mdstring rather than returned_string, cut out middle man function + return this.returned_string; +} + +function convertSingleDoc(optional_switches) { + var script_properties = PropertiesService.getScriptProperties(); + // renew comments list on every export + var doc_comments = getDocComments(); + var image_urls = getDocComments({images: true}); // NB assumed false - any value will do + script_properties.setProperty("comments", doc_comments); + script_properties.setProperty("image_srcs", image_urls); + var folder_id = script_properties.getProperty("folder_id"); + var document_id = script_properties.getProperty("document_id"); + var source_folder = DriveApp.getFolderById(folder_id); + var markdown_folders = source_folder.getFoldersByName("Markdown"); + + var markdown_folder; + if (markdown_folders.hasNext()) { + markdown_folder = markdown_folders.next(); + } else { + // Create a Markdown folder if it doesn't exist. + markdown_folder = source_folder.createFolder("Markdown") + } + + convertDocumentToMarkdown(DocumentApp.openById(document_id), markdown_folder, optional_switches); +} + +function convertFolder() { + var script_properties = PropertiesService.getScriptProperties(); + var folder_id = script_properties.getProperty("folder_id"); + var source_folder = DriveApp.getFolderById(folder_id); + var markdown_folders = source_folder.getFoldersByName("Markdown"); + + + var markdown_folder; + if (markdown_folders.hasNext()) { + markdown_folder = markdown_folders.next(); + } else { + // Create a Markdown folder if it doesn't exist. + markdown_folder = source_folder.createFolder("Markdown"); + } + + // Only try to convert google docs files. + var gdoc_files = source_folder.getFilesByType("application/vnd.google-apps.document"); + + // For every file in this directory + while(gdoc_files.hasNext()) { + var gdoc_file = gdoc_files.next() + + var filename = gdoc_file.getName(); + var md_files = markdown_folder.getFilesByName(filename + ".md"); + var update_file = false; + + if (md_files.hasNext()) { + var md_file = md_files.next(); + + if (md_files.hasNext()){ // There are multiple markdown files; delete and rerun + update_file = true; + } else if (md_file.getLastUpdated() < gdoc_file.getLastUpdated()) { + update_file = true; + } + } else { + // There is no folder and the conversion needs to be rerun + update_file = true; + } + + if (update_file) { + convertDocumentToMarkdown(DocumentApp.openById(gdoc_file.getId()), markdown_folder); + } + } +} + +function switchHandler(input_switches, potential_switches, optional_storage_name) { + + // Firstly, if no input switches were set, make an empty input object + if (typeof(input_switches) == 'undefined') input_switches = {}; + + // Use optional storage name if it's defined (must be a string), else use default variable name "switch_settings" + var property_name = (typeof(optional_storage_name) == 'string') ? optional_storage_name : 'switch_settings'; + + // Make a blank object to be populated and stored as the script-wide property named after property_name + var switch_settings = {}; + + for (var i in potential_switches) { + var potential_switch = potential_switches[i]; + + // If each switch has been set (in input_switches), evaluate it, else assume it's switched off (false): + + if (input_switches.propertyIsEnumerable(potential_switch)) { + + // Evaluates a string representing a statement which sets switch_settings properties from input_switches + // e.g. "switch_settings.images = true" when input_switches = {images: true} + + eval('switch_settings.' + potential_switch + " = " + input_switches[potential_switch]); + + } else { + + // Alternatively, the evaluated statement sets anything absent from the input_switches object as false + // e.g. "switch_settings.images = false" when input_switches = {} and potential_switches = ['images'] + + eval('switch_settings.' + potential_switch + " = false"); + } + } + + PropertiesService.getScriptProperties().setProperty(property_name, switch_settings); + + /* + Looks bad but more sensible than repeatedly checking if arg undefined. + + Sets every variable named in the potential_switches array to false if + it wasn't passed into the input_switches object, otherwise evaluates. + + Any arguments not passed in are false, but so are any explicitly passed in as false: + all parameters are therefore Boolean until otherwise specified. + */ + +} + +function convertDocumentToMarkdown(document, destination_folder, frontmatter_input, optional_switches) { + // if returning a string, force_save_images will make the script continue - experimental + var possible_switches = ['return_string', 'force_save_images']; + var property_name = 'conversion_switches'; + switchHandler(optional_switches, possible_switches, property_name); + + // TODO switch off image storage if force_save_images is true - not necessary for normal behaviour + var script_properties = PropertiesService.getScriptProperties(); + var comment_switches = decodeScriptSwitches(property_name); + eval(comment_switches); + + var image_prefix = script_properties.getProperty("image_folder_prefix"); + var numChildren = document.getActiveSection().getNumChildren(); + if (frontmatter_input != "") { + var text = frontmatter_input; + } + else { + var text = "" + } + var md_filename = sanitizeFileName(document.getName()) + ".md"; + var image_foldername = document.getName()+"_images"; + var inSrc = false; + var inClass = false; + var globalImageCounter = 0; + var globalListCounters = {}; + // edbacher: added a variable for indent in src 
 block. Let style sheet do margin.
+  var srcIndent = "";
+
+  var postHasImages = false;
+
+  var files = [];
+
+  // Walk through all the child elements of the doc.
+  for (var i = 0; i < numChildren; i++) {
+    var child = document.getActiveSection().getChild(i);
+    var result = processParagraph(i, child, inSrc, globalImageCounter, globalListCounters, image_prefix + image_foldername);
+    globalImageCounter += (result && result.images) ? result.images.length : 0;
+    if (result!==null) {
+      if (result.sourceGlossary==="start" && !inSrc) {
+        inSrc=true;
+        text+="
\n";
+      } else if (result.sourceGlossary==="end" && inSrc) {
+        inSrc=false;
+        text+="
\n\n";
+      } else if (result.sourceFigCap==="start" && !inSrc) {
+        inSrc=true;
+        text+="
\n";
+      } else if (result.sourceFigCap==="end" && inSrc) {
+        inSrc=false;
+        text+="
\n\n";
+      } else if (result.source==="start" && !inSrc) {
+        inSrc=true;
+        text+="
\n";
+      } else if (result.source==="end" && inSrc) {
+        inSrc=false;
+        text+="
\n\n";
+      } else if (result.inClass==="start" && !inClass) {
+        inClass=true;
+        text+="
\n";
+      } else if (result.inClass==="end" && inClass) {
+        inClass=false;
+        text+="
\n\n";
+      } else if (inClass) {
+        text+=result.text+"\n\n";
+      } else if (inSrc) {
+        text+=(srcIndent+escapeHTML(result.text)+"\n");
+      } else if (result.text && result.text.length>0) {
+        text+=result.text+"\n\n";
+      }
+
+      if (result.images && result.images.length>0) {
+        for (var j=0; j/g, '>');
+}
+
+function standardQMarks(text) {
+  return text.replace(/\u2018|\u8216|\u2019|\u8217/g,"'").replace(/\u201c|\u8220|\u201d|\u8221/g, '"')
+}
+
+// Process each child element (not just paragraphs).
+function processParagraph(index, element, inSrc, imageCounter, listCounters, image_path) {
+  // First, check for things that require no processing.
+  if (element.getType() === DocumentApp.ElementType.UNSUPPORTED) {
+    return null;
+  }
+  if (element.getNumChildren()==0) {
+    return null;
+  }
+  // Skip on TOC.
+  if (element.getType() === DocumentApp.ElementType.TABLE_OF_CONTENTS) {
+    return {"text": "[[TOC]]"};
+  }
+
+  // Set up for real results.
+  var result = {};
+  var pOut = "";
+  var textElements = [];
+  var imagePrefix = "image_";
+
+  // Handle Table elements. Pretty simple-minded now, but works for simple tables.
+  // Note that Markdown does not process within block-level HTML, so it probably
+  // doesn't make sense to add markup within tables.
+  if (element.getType() === DocumentApp.ElementType.TABLE) {
+    textElements.push("\n");
+    var nCols = element.getChild(0).getNumCells();
+    for (var i = 0; i < element.getNumChildren(); i++) {
+      textElements.push("  \n");
+      // process this row
+      for (var j = 0; j < nCols; j++) {
+        textElements.push("    \n");
+      }
+      textElements.push("  \n");
+    }
+    textElements.push("
" + element.getChild(i).getChild(j).getText() + "
\n");
+  }
+
+  // Need to handle this element type, return null for now
+  if (element.getType() === DocumentApp.ElementType.CODE_SNIPPET) {
+    return null
+  }
+
+  // Process various types (ElementType).
+  for (var i = 0; i < element.getNumChildren(); i++) {
+    var t = element.getChild(i).getType();
+
+    if (t === DocumentApp.ElementType.TABLE_ROW) {
+      // do nothing: already handled TABLE_ROW
+    } else if (t === DocumentApp.ElementType.TEXT) {
+      var txt  = element.getChild(i);
+      pOut += txt.getText();
+      textElements.push(txt);
+    } else if (t === DocumentApp.ElementType.INLINE_IMAGE) {
+      var imglink = element.getChild(i).getLinkUrl();
+      result.images = result.images || [];
+      var blob = element.getChild(i).getBlob()
+      var contentType = blob.getContentType();
+      var extension = "";
+      if (/\/png$/.test(contentType)) {
+        extension = ".png";
+      } else if (/\/gif$/.test(contentType)) {
+        extension = ".gif";
+      } else if (/\/jpe?g$/.test(contentType)) {
+        extension = ".jpg";
+      } else {
+        throw "Unsupported image type: "+contentType;
+      }
+
+      var name = imagePrefix + imageCounter + extension;
+      blob.setName(name);
+
+      imageCounter++;
+      if (!return_string || force_save_images) {
+        textElements.push('![](' + image_path + '/' + name + ')');
+      } else {
+        textElements.push('![](' + imglink + ')');
+      }
+      //result.images.push( {
+      //  "bytes": blob.getBytes(),
+      //  "type": contentType,
+      //  "name": name});
+
+      result.images.push({ "blob" : blob } )
+
+    // Need to fix this case TODO
+    } else if (t === DocumentApp.ElementType.INLINE_DRAWING) {
+
+      imageCounter++;
+      if (!return_string || force_save_images) {
+        textElements.push('![](' + "drawing" + '/' + " name" + ')');
+      } else {
+        textElements.push('![](' + "drawing" + ')');
+      }
+      //result.images.push( {
+      //  "bytes": blob.getBytes(),
+      //  "type": contentType,
+      //  "name": name});
+
+      // result.images.push({ "blob" : blob } )
+
+    }
+     else if (t === DocumentApp.ElementType.PAGE_BREAK) {
+      // ignore
+    } else if (t === DocumentApp.ElementType.HORIZONTAL_RULE) {
+      textElements.push('* * *\n');
+    } else if (t === DocumentApp.ElementType.FOOTNOTE) {
+      textElements.push(' ('+element.getChild(i).getFootnoteContents().getText()+')');
+    // Fixes for new elements
+    } else if (t === DocumentApp.ElementType.EQUATION) {
+      textElements.push(element.getChild(i).getText());
+    } else if (t === DocumentApp.ElementType.DATE) {
+      textElements.push(' ('+element.getChild(i)+')');
+    } else if (t === DocumentApp.ElementType.RICH_LINK) {
+      textElements.push(' ('+element.getChild(i).getUrl()+')');
+    } else if (t === DocumentApp.ElementType.PERSON) {
+      textElements.push(element.getChild(i).getName() + ', ');
+    } else if (t === DocumentApp.ElementType.UNSUPPORTED) {
+      textElements.push('  ');
+    } else {
+      Logger.log("Paragraph "+index+" of type "+element.getType()+" has an unsupported child: "
+      +t+" "+(element.getChild(i)["getText"] ? element.getChild(i).getText():'')+" index="+index);
+    }
+  }
+
+  if (textElements.length==0) {
+    // Isn't result empty now?
+    return result;
+  }
+
+// Fix for unrecognized command getIndentFirstLine
+  var ind_f = 0;
+  var ind_s = 0;
+  var ind_e = 0;
+  if (t === DocumentApp.ElementType.PARAGRAPH) {
+
+    var ind_f = element.getIndentFirstLine();
+    var ind_s = element.getIndentStart();
+    var ind_e = element.getIndentEnd();
+  }
+  var i_fse = [ind_f,ind_s,ind_e];
+  var indents = {};
+  for (indt=0;indt 0) indents[indname] = eval(indname);
+    // lazy test, null (no indent) is not greater than zero, but becomes set if indent 'undone'
+  }
+  var inIndent = (Object.keys(indents).length > 0);
+
+  // evb: Add glossary and figure caption too. (And abbreviations: gloss and fig-cap.)
+  // process source code block:
+  if (/^\s*---\s+gloss\s*$/.test(pOut) || /^\s*---\s+source glossary\s*$/.test(pOut)) {
+    result.sourceGlossary = "start";
+  } else if (/^\s*---\s+fig-cap\s*$/.test(pOut) || /^\s*---\s+source fig-cap\s*$/.test(pOut)) {
+    result.sourceFigCap = "start";
+  } else if (/^\s*---\s+src\s*$/.test(pOut) || /^\s*---\s+source code\s*$/.test(pOut)) {
+    result.source = "start";
+  } else if (/^\s*---\s+class\s+([^ ]+)\s*$/.test(pOut)) {
+    result.inClass = "start";
+    result.className = RegExp.$1.replace(/\./g,' ');
+  } else if (/^\s*---\s*$/.test(pOut)) {
+    result.source = "end";
+    result.sourceGlossary = "end";
+    result.sourceFigCap = "end";
+    result.inClass = "end";
+  } else if (/^\s*---\s+jsperf\s*([^ ]+)\s*$/.test(pOut)) {
+    result.text = '';
+  } else {
+
+    prefix = findPrefix(inSrc, element, listCounters);
+
+    var pOut = "";
+    for (var i=0; i):
+      if (gt === DocumentApp.GlyphType.BULLET
+          || gt === DocumentApp.GlyphType.HOLLOW_BULLET
+          || gt === DocumentApp.GlyphType.SQUARE_BULLET) {
+        prefix += "* ";
+      } else {
+        // Ordered list (
    ):
+        var key = listItem.getListId() + '.' + listItem.getNestingLevel();
+        var counter = listCounters[key] || 0;
+        counter++;
+        listCounters[key] = counter;
+        prefix += counter+". ";
+      }
+    }
+  }
+  return prefix;
+}
+
+function processTextElement(inSrc, txt) {
+  if (typeof(txt) === 'string') {
+    return txt;
+  }
+
+  var pOut = txt.getText();
+  if (! txt.getTextAttributeIndices) {
+    return pOut;
+  }
+
+//  Logger.log("Initial String: " + pOut)
+
+  // CRC introducing reformatted_txt to let us apply rational formatting that we can actually parse
+  var reformatted_txt = txt.copy();
+  reformatted_txt.deleteText(0,pOut.length-1);
+  reformatted_txt = reformatted_txt.setText(pOut);
+
+  var attrs = txt.getTextAttributeIndices();
+  var lastOff = pOut.length;
+  // We will run through this loop multiple times for the things we care about.
+  // Font
+  // URL
+  // Then for alignment
+  // Then for bold
+  // Then for italic.
+
+  // FONTs
+  var lastOff = pOut.length; // loop goes backwards, so this holds
+  for (var i=attrs.length-1; i>=0; i--) {
+    var off=attrs[i];
+    var font=txt.getFontFamily(off)
+     if (font) {
+       while (i>=1 && txt.getFontFamily(attrs[i-1])==font) {
+          // detect fonts that are in multiple pieces because of errors on formatting:
+          i-=1;
+          off=attrs[i];
+       }
+       reformatted_txt.setFontFamily(off, lastOff-1, font);
+     }
+    lastOff=off;
+  }
+
+  // URL
+  // XXX TODO actually convert to URL text here.
+  var lastOff=pOut.length;
+  for (var i=attrs.length-1; i>=0; i--) {
+    var off=attrs[i];
+    var url=txt.getLinkUrl(off);
+     if (url) {
+       while (i>=1 && txt.getLinkUrl(attrs[i-1]) == url) {
+          // detect urls that are in multiple pieces because of errors on formatting:
+          i-=1;
+          off=attrs[i];
+       }
+     reformatted_txt.setLinkUrl(off, lastOff-1, url);
+     }
+    lastOff=off;
+  }
+
+  // alignment
+  var lastOff=pOut.length;
+  for (var i=attrs.length-1; i>=0; i--) {
+    var off=attrs[i];
+    var alignment=txt.getTextAlignment(off);
+     if (alignment) { //
+       while (i>=1 && txt.getTextAlignment(attrs[i-1]) == alignment) {
+          i-=1;
+          off=attrs[i];
+       }
+     reformatted_txt.setTextAlignment(off, lastOff-1, alignment);
+     }
+    lastOff=off;
+  }
+
+  // strike
+  var lastOff=pOut.length;
+  for (var i=attrs.length-1; i>=0; i--) {
+    var off=attrs[i];
+    var strike=txt.isStrikethrough(off);
+     if (strike) {
+       while (i>=1 && txt.isStrikethrough(attrs[i-1])) {
+          i-=1;
+          off=attrs[i];
+       }
+     reformatted_txt.setStrikethrough(off, lastOff-1, strike);
+     }
+    lastOff=off;
+  }
+
+  // bold
+  var lastOff=pOut.length;
+  for (var i=attrs.length-1; i>=0; i--) {
+    var off=attrs[i];
+    var bold=txt.isBold(off);
+     if (bold) {
+       while (i>=1 && txt.isBold(attrs[i-1])) {
+          i-=1;
+          off=attrs[i];
+       }
+     reformatted_txt.setBold(off, lastOff-1, bold);
+     }
+    lastOff=off;
+  }
+
+  // italics
+  var lastOff=pOut.length;
+  for (var i=attrs.length-1; i>=0; i--) {
+    var off=attrs[i];
+    var italic=txt.isItalic(off);
+     if (italic) {
+       while (i>=1 && txt.isItalic(attrs[i-1])) {
+          i-=1;
+          off=attrs[i];
+       }
+     reformatted_txt.setItalic(off, lastOff-1, italic);
+     }
+    lastOff=off;
+  }
+
+
+  var mOut=""; // Modified out string
+  var harmonized_attrs = reformatted_txt.getTextAttributeIndices();
+  reformatted_txt.getTextAttributeIndices(); // @lmmx: is this a typo...?
+  pOut = reformatted_txt.getText();
+
+
+  // Markdown is farily picky about how it will let you intersperse spaces around words and strong/italics chars. This regex (hopefully) clears this up
+  // Match any number of \*, followed by spaces/word boundaries against anything that is not the \*, followed by boundaries, spaces and * again.
+  // Test case at http://jsfiddle.net/ovqLv0s9/2/
+
+  var reAlignStars = /(\*+)(\s*\b)([^\*]+)(\b\s*)(\*+)/g;
+
+  var lastOff=pOut.length;
+  for (var i=harmonized_attrs.length-1; i>=0; i--) {
+    var off=harmonized_attrs[i];
+
+    var raw_text = pOut.substring(off, lastOff)
+
+    var d1 = ""; // @lmmx: build up a modifier prefix
+    var d2 = ""; // @lmmx: ...and suffix
+
+    var end_font;
+
+    var mark_bold = false;
+    var mark_italic = false;
+    var mark_code = false;
+    var mark_sup = false;
+    var mark_sub = false;
+    var mark_strike = false;
+
+    // The end of the text block is a special case.
+    if (lastOff == pOut.length) {
+      end_font = reformatted_txt.getFontFamily(lastOff - 1)
+      if (end_font) {
+        if (!inSrc && end_font===end_font.COURIER_NEW) {
+          mark_code = true;
+        }
+      }
+      if (reformatted_txt.isBold(lastOff -1)) {
+        mark_bold = true;
+      }
+      if (reformatted_txt.isItalic(lastOff - 1)) {
+        // edbacher: changed this to handle bold italic properly.
+        mark_italic = true;
+      }
+      if (reformatted_txt.isStrikethrough(lastOff - 1)) {
+        mark_strike = true;
+      }
+      if (reformatted_txt.getTextAlignment(lastOff - 1)===DocumentApp.TextAlignment.SUPERSCRIPT) {
+        mark_sup = true;
+      }
+      if (reformatted_txt.getTextAlignment(lastOff - 1)===DocumentApp.TextAlignment.SUBSCRIPT) {
+        mark_sub = true;
+      }
+    } else {
+      end_font = reformatted_txt.getFontFamily(lastOff -1 )
+      if (end_font) {
+        if (!inSrc && end_font===end_font.COURIER_NEW && reformatted_txt.getFontFamily(lastOff) != end_font) {
+          mark_code=true;
+        }
+      }
+      if (reformatted_txt.isBold(lastOff - 1) && !reformatted_txt.isBold(lastOff) ) {
+        mark_bold=true;
+      }
+      if (reformatted_txt.isStrikethrough(lastOff - 1) && !reformatted_txt.isStrikethrough(lastOff)) {
+        mark_strike=true;
+      }
+      if (reformatted_txt.isItalic(lastOff - 1) && !reformatted_txt.isItalic(lastOff)) {
+        mark_italic=true;
+      }
+      if (reformatted_txt.getTextAlignment(lastOff - 1)===DocumentApp.TextAlignment.SUPERSCRIPT) {
+        if (reformatted_txt.getTextAlignment(lastOff)!==DocumentApp.TextAlignment.SUPERSCRIPT) {
+          mark_sup = true;
+        }
+      }
+      if (reformatted_txt.getTextAlignment(lastOff - 1)===DocumentApp.TextAlignment.SUBSCRIPT) {
+        if (reformatted_txt.getTextAlignment(lastOff)!==DocumentApp.TextAlignment.SUBSCRIPT) {
+          mark_sub = true;
+        }
+      }
+    }
+
+    if (mark_code) {
+      d2 = '`'; // shouldn't these go last? or will it interfere w/ reAlignStars?
+    }
+    if (mark_bold) {
+      d2 = "**" + d2;
+    }
+    if (mark_italic) {
+      d2 = "*" + d2;
+    }
+    if (mark_strike) {
+      d2 = "" + d2;
+    }
+    if (mark_sup) {
+      d2 = '' + d2;
+    }
+    if (mark_sub) {
+      d2 = '' + d2;
+    }
+
+    mark_bold = mark_italic = mark_code = mark_sup = mark_sub = mark_strike = false;
+
+    var font=reformatted_txt.getFontFamily(off);
+    if (off == 0) {
+      if (font) {
+        if (!inSrc && font===font.COURIER_NEW) {
+          mark_code = true;
+        }
+      }
+      if (reformatted_txt.isBold(off)) {
+        mark_bold = true;
+      }
+      if (reformatted_txt.isItalic(off)) {
+        mark_italic = true;
+      }
+      if (reformatted_txt.isStrikethrough(off)) {
+        mark_strike = true;
+      }
+      if (reformatted_txt.getTextAlignment(off)===DocumentApp.TextAlignment.SUPERSCRIPT) {
+        mark_sup = true;
+      }
+      if (reformatted_txt.getTextAlignment(off)===DocumentApp.TextAlignment.SUBSCRIPT) {
+        mark_sub = true;
+      }
+    } else {
+      if (font) {
+        if (!inSrc && font===font.COURIER_NEW && reformatted_txt.getFontFamily(off - 1) != font) {
+          mark_code=true;
+        }
+      }
+      if (reformatted_txt.isBold(off) && !reformatted_txt.isBold(off -1) ) {
+        mark_bold=true;
+      }
+      if (reformatted_txt.isItalic(off) && !reformatted_txt.isItalic(off - 1)) {
+        mark_italic=true;
+      }
+      if (reformatted_txt.isStrikethrough(off) && !reformatted_txt.isStrikethrough(off - 1)) {
+        mark_strike=true;
+      }
+      if (reformatted_txt.getTextAlignment(off)===DocumentApp.TextAlignment.SUPERSCRIPT) {
+        if (reformatted_txt.getTextAlignment(off - 1)!==DocumentApp.TextAlignment.SUPERSCRIPT) {
+          mark_sup = true;
+        }
+      }
+      if (reformatted_txt.getTextAlignment(off)===DocumentApp.TextAlignment.SUBSCRIPT) {
+        if (reformatted_txt.getTextAlignment(off - 1)!==DocumentApp.TextAlignment.SUBSCRIPT) {
+          mark_sub = true;
+        }
+      }
+    }
+
+
+    if (mark_code) {
+      d1 = '`';
+    }
+
+    if (mark_bold) {
+      d1 = d1 + "**";
+    }
+
+    if (mark_italic) {
+      d1 = d1 + "*";
+    }
+
+    if (mark_sup) {
+      d1 = d1 + '';
+    }
+
+    if (mark_sub) {
+      d1 = d1 + '';
+    }
+
+    if (mark_strike) {
+      d1 = d1 + '';
+    }
+
+    var url=reformatted_txt.getLinkUrl(off);
+    if (url) {
+      mOut = d1 + '['+ raw_text +']('+url+')' + d2 + mOut;
+    } else {
+      var new_text = d1 + raw_text + d2;
+      new_text = new_text.replace(reAlignStars, "$2$1$3$5$4");
+      mOut =  new_text + mOut;
+    }
+
+    lastOff=off;
+//    Logger.log("Modified String: " + mOut)
+  }
+
+  mOut = pOut.substring(0, off) + mOut;
+  return mOut;
+}
\ No newline at end of file
diff --git a/one_fm/docsagent/apps_script/gmail_to_markdown.gs b/one_fm/docsagent/apps_script/gmail_to_markdown.gs
new file mode 100644
index 0000000000..3263ef1004
--- /dev/null
+++ b/one_fm/docsagent/apps_script/gmail_to_markdown.gs
@@ -0,0 +1,137 @@
+/**
+ * Copyright 2023 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+function exportEmailsToMarkdown(search, folderName) {
+  //Checks if input folder exists or exits
+  if(folderExistsOrCreate(folderName)){
+    var output_file_name = folderName + "-index";
+    var folderOutputObj = DriveApp.getFoldersByName(folderName);
+    if (folderOutputObj.hasNext()){
+      var folderOutputName = folderOutputObj.next();
+    }
+    var sheet = checkIndexOutputOrCreate(output_file_name, folderOutputName);
+    console.log(`Searching for: "${search}"`);
+    var start = 0;
+    var max = 500;
+    var threads = GmailApp.search(search, start, max);
+    var threadMax = threads.length;
+    if (threads!=null){
+      console.log(threadMax + " threads found.");
+    } else {
+      console.warn("No threads found with the search criteria");
+      return;
+    }
+    let timeZone = Session.getScriptTimeZone();
+    let created_date = Utilities.formatDate(new Date(), timeZone, "MM-dd-yyyy HH:mm:ss z");
+    sheet.appendRow(["Created: ", created_date])
+    sheet.appendRow(["Date", "From", "Subject", "To", "Markdown ID", "Markdown URL", "Full date", "MD5 hash", "Status"]);
+    var start_data_row = 2;
+    var status = "New content";
+    var newEmails = 0;
+    var unchangedEmails = 0;
+    for (var threadCount in threads) {
+      var msgs = threads[threadCount].getMessages();
+      Logger.log("Processing thread " + threadCount + " of " + threadMax);
+      for (var msgCount in msgs) {
+        var msg = msgs[msgCount];
+        var subject = msg.getSubject().replace(/"/g, "\\\"");;
+        // Removes replies and forwards - Can mostly be noise.
+        if(!subject.toLowerCase().includes("re:") &&
+            !subject.toLowerCase().includes("fwd:") &&
+            !subject.toLowerCase().includes("forwarded message")){
+          // Values to get and store messages
+          var date = msg.getDate();
+          let from_author = msg.getFrom().replace(/"/g, "\\\"");
+          var hash_content = from_author + date + subject;
+          let sanitized_subject = sanitizeString(subject);
+          let date_format = Utilities.formatDate(date, "PST", "MM-dd-yyyy");
+          let to = msg.getTo();
+          let to_array = to.split(", ");
+          for (i in to_array) {
+            to_array[i] = "\"" + to_array[i].replace(/^" "/, "").replace(/"/g, "\\\"") + "\"";
+          }
+          let md5_hash = Utilities.computeDigest(Utilities.DigestAlgorithm.MD5,hash_content,
+                          Utilities.Charset.US_ASCII);
+          let hash_str = byteToStr(md5_hash);
+          //Function returns an array, assign each array value to seperate variables. For emails, only need to retrieve
+          // backup markdown ids
+          var backup_results = returnBackupHash(sheet, "Backup", hash_str, start_data_row, 7, 4, 5);
+          if (backup_results != undefined && backup_results[0] != "no_results") {
+            Logger.log("Email is already in markdown format. Skipping conversion.");
+            var status = "Unchanged content";
+            var markdown_id = backup_results[1];
+            if (markdown_id){
+              var md_file = DriveApp.getFileById(markdown_id);
+            }
+            unchangedEmails += 1;
+          }
+          else {
+            var status = "New content";
+            let message = msg.getPlainBody();
+            let filename = sanitizeFileName(date_format + subject + ".md");
+            // Initialize blank text since file will get updated with URL
+            let email_md = "";
+            // Add count here for emails
+            newEmails += 1;
+            Logger.log("Email number: " + newEmails + "| Saving email to: " + filename);
+            var body = "# " + subject + "\n";
+            // Cleans the reply part of emails
+            body += regexToCleanCharsMD(sanitizeBody(message.replace(/^>/g,""))) + "\n";
+            var destinationFolder = DriveApp.getFoldersByName(folderOutputName).next();
+            // Initialize blank file to retrieve URL which is then added to the frontmatter
+            var destinationFile = destinationFolder.createFile(filename, email_md , MimeType.PLAIN_TEXT);
+            // Create metadata for the object
+            var markdown_id = destinationFile.getId();
+            var md_file = DriveApp.getFileById(markdown_id);
+            let md_url = md_file.getUrl();
+            let frontmatter = "---" + "\n";
+            frontmatter += "title: \"" + sanitized_subject + "\"\n";
+            frontmatter += "type: \"" + "email" + "\"\n";
+            frontmatter += "URL: \"" + md_url + "\"\n";
+            frontmatter += "created: \"" + date + "\"\n";
+            frontmatter += "from: \"" + from_author + "\"\n";
+            frontmatter += "to: \[" + to_array + "\]\n";
+            frontmatter += "---" + "\n\n";
+            email_md = frontmatter + body;
+            var encoded = Utilities.base64Encode(email_md);
+            var byteDataArray = Utilities.base64Decode(encoded);
+            var textAsBlob = Utilities.newBlob(byteDataArray);
+            Drive.Files.update(null,markdown_id, textAsBlob);
+          }
+          let md_chip = createRichText(md_file);
+          metadata = [
+            date_format,
+            from_author,
+            sanitized_subject,
+            to,
+            markdown_id,
+            "md_chip",
+            date,
+            hash_str,
+            status,
+          ];
+          sheet.appendRow(metadata);
+          var emailTotal = newEmails + unchangedEmails;
+          let row_number = emailTotal + start_data_row;
+          insertRichText(sheet, md_chip, "F", row_number);
+        }
+      }
+    }
+    Logger.log("Saved a total of " + newEmails + " new emails.");
+    Logger.log("There is a total of " + unchangedEmails + " unchanged emails.");
+    Logger.log("Grand total of " + emailTotal + " emails.");
+  }
+}
\ No newline at end of file
diff --git a/one_fm/docsagent/apps_script/helper_functions.gs b/one_fm/docsagent/apps_script/helper_functions.gs
new file mode 100644
index 0000000000..3fbf968661
--- /dev/null
+++ b/one_fm/docsagent/apps_script/helper_functions.gs
@@ -0,0 +1,214 @@
+/**
+ * Copyright 2023 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// Checks to see if a folder already exists in the drive
+function folderExists(folderName) {
+  const folderIterator = DriveApp.getRootFolder().getFoldersByName(folderName);
+  if(folderIterator.hasNext()) {
+    return true;
+  }
+  else {
+    return false;
+  }
+}
+
+// Checks to see if a folder already exists in the specified root folder
+function folderExistsInRoot(folderName, rootFolder) {
+  const folderIterator = rootFolder.getFoldersByName(folderName);
+  if(folderIterator.hasNext()) {
+    return true;
+  }
+  else {
+    return false;
+  }
+}
+
+// Checks to see if a folder already exists in the drive and exits if it doesn't. Useful for input directories
+function folderExistsInput(folderName){
+  if (folderExists(folderName)) {
+    Logger.log("Folder exists: "+ folderName);
+    return true;
+  }
+  else {
+    Logger.log("Folder does not exist: "+ folderName + ". Please make sure the directory exists.");
+    return false;
+  }
+}
+
+// Checks to see if folder exists or creates it. Useful for output directories
+function folderExistsOrCreate(folderName){
+  if(folderExists(folderName)) {
+    Logger.log("Folder exists: "+ folderName);
+    return true;
+  }
+  else {
+    Logger.log("Folder does not exist: "+ folderName + ". Creating the directory.");
+    DriveApp.createFolder(folderName);
+    return true;
+  }
+}
+
+// Checks to see if folder exists or creates it. Useful for output directories
+function folderExistsOrCreateSubdir(folderName, rootFolder){
+  if(folderExistsInRoot(folderName, rootFolder)) {
+    Logger.log("Folder exists: "+ folderName);
+    return true;
+  }
+  else {
+    Logger.log("Folder does not exist: "+ folderName + ". Creating the directory.");
+    rootFolder.createFolder(folderName);
+    return true;
+  }
+}
+
+// Checks to see if a file exists in a folder
+function checkFileExists(fileName,folderName){
+  let folder = DriveApp.getFoldersByName(folderName);
+  if(!folder.hasNext()){
+  }
+  else{
+    var file = folder.next().getFilesByName(fileName);
+    if(!file.hasNext()){
+       return true;
+    }
+    else{
+       return false;
+    }
+  }
+}
+
+// Function to check if an index output sheet exists or creates it. Returns the file object
+// Specify the file output name and outputdirectory
+function checkIndexOutputOrCreate(fileName, folderOutput, indexFileID="") {
+  var timeZone = Session.getScriptTimeZone();
+  var date = Utilities.formatDate(new Date(), timeZone, "MM-dd-yyyy hh:mm:ss");
+  let file = {title: fileName, mimeType: MimeType.GOOGLE_SHEETS, parents: [{id: folderOutput.getId()}]}
+  let params = "title='" + fileName + "' and parents in '" + folderOutput.getId() + "'";
+  let file_search = DriveApp.searchFiles(params);
+  if (file_search.hasNext()) {
+    if (indexFileID=="") {
+      var fileId = file_search.next().getId();
+    }
+    else {
+      var fileId = indexFileID;
+    }
+    var sheet = SpreadsheetApp.openById(fileId);
+    Logger.log("File index: " + fileName + " exists.");
+    var sheet_index = sheet.getSheetByName("Index");
+    // Checks to see if this is a sub directory
+    if (sheet.getSheetByName("Backup")) {
+      var sheet_backup = sheet.getSheetByName("Backup");
+      sheet.deleteSheet(sheet_backup);
+    }
+    var sheet_backup = sheet.insertSheet("Backup", 1);
+    var sheet_backup_open = sheet.getSheetByName("Backup");
+    sheet_index.getDataRange().copyTo(sheet_backup_open.getRange(1,1));
+    if (sheet_index != null){
+      sheet.deleteSheet(sheet_index);
+    }
+    sheet.insertSheet("Index", 0);
+    sheet_index = sheet.getSheetByName("Index");
+    sheet_index.addDeveloperMetadata("Date", date);
+    }
+  else {
+    Logger.log("File index: " + fileName + " does not exist.");
+    let output = Drive.Files.insert(file).id;
+    var sheet = SpreadsheetApp.openById(output);
+    var sheet_1 = sheet.getSheetByName("Sheet1");
+    sheet.insertSheet("Index", 0);
+    var sheet_index = sheet.getSheetByName("Index")
+    sheet_index.addDeveloperMetadata("Date", date);
+    sheet.deleteSheet(sheet_1);
+  }
+  return sheet;
+}
+
+// Function to convert byte array into a string
+function byteToStr(byteInput){
+  let signatureStr = '';
+  for (i = 0; i < byteInput.length; i++) {
+    let byte = byteInput[i];
+    if (byte < 0)
+      byte += 256;
+    let byteStr = byte.toString(16);
+    if (byteStr.length == 1) byteStr = '0' + byteStr;
+      signatureStr += byteStr;
+    }
+return signatureStr;
+}
+
+// Function to remove special characters for file names
+function sanitizeFileName(fileName){
+  let clean_filename = fileName.replace(/\[/g, "_").replace(/\]/g, "_").replace(/\(/g, "_").replace(/\)/g, "_").replace(/^_/g, "").replace(/,/g, "_").replace(/ /g, "_").replace(/:/g, "").replace(/`/g, "").replace(/\'/g, "").replace(/&/g, "and").replace(//g, "").replace(/’/g, "");
+return clean_filename;
+}
+
+// Function to remove special characters for file names
+function sanitizeString(string){
+  let clean_string = string.replace(/\[/g, "").replace(/\]/g, "").replace(/\(/g, "").replace(/\)/g, "").replace(/^_/g, "").replace(/,/g, " ").replace(/:/g, "").replace(/`/g, "").replace(/\'/g, "").replace(/&/g, "and").replace(//g, "");
+return clean_string;
+}
+
+function sanitizeBody(string){
+  let clean_body = string.replace(/’/g, "'").replace(/^M/g, "");
+return clean_body;
+}
+
+function regexToCleanCharsMD(string){
+  let clean_string = string.replace(/(\*+)(\s*\b)([^\*]+)(\b\s*)(\*+)/g, "$2$1$3$5$4");
+return clean_string;
+}
+
+// Function to check if a backup sheet exists and return a hash if the file exists
+// Specify the sheet name where the backup is saved, default is "Backup"
+// From your backup sheet specify the column that contains the MD5 hash
+// and the columns for which you return values
+function returnBackupHash(sheet, sheet_name, fid, start_data_row, pos_id, pos_1_col, pos_2_col){
+  if (sheet.getSheetByName(sheet_name)){
+    let backup_sheet = sheet.getSheetByName(sheet_name);
+    if(backup_sheet.getLastRow()> start_data_row){
+      let backup_values = backup_sheet.getDataRange().getValues();
+      for (let row_count = start_data_row; row_count < backup_sheet.getLastRow(); row_count++) {
+        let row_id = backup_values[row_count][pos_id];
+        let pos_1_value = backup_values[row_count][pos_1_col];
+        //Retrieve id of existing markdown conversion
+        let pos_2_value = backup_values[row_count][pos_2_col];
+        if (row_id == fid){
+          var results = [row_id, pos_1_value, pos_2_value];
+          break;
+        }
+        else {
+          var results = ["no_results"];
+        }
+      }
+      return results;
+    }
+  }
+}
+
+// Creates a richText item with item.
+function createRichText (item){
+  let title = item.getName();
+  let url = item.getUrl();
+  let richText = SpreadsheetApp.newRichTextValue().setText(title).setLinkUrl(url).build();
+  return richText;
+}
+
+// Insert a richText item in a specific cell
+function insertRichText (sheetItem, item, column, row){
+  let range = sheetItem.getRange(column + row);
+  range.setRichTextValue(item);
+}
diff --git a/one_fm/docsagent/apps_script/main.gs b/one_fm/docsagent/apps_script/main.gs
new file mode 100644
index 0000000000..2fe88de332
--- /dev/null
+++ b/one_fm/docsagent/apps_script/main.gs
@@ -0,0 +1,27 @@
+/**
+ * Copyright 2023 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// Defines the gmail search query for saving emails to markdown
+var SEARCH_QUERY = 'subject: psa to:my-mailing-list@example.com';
+// Defines the directory to output the emails in markdown format
+var folderOutput = "PSA-output"
+// Defines the directory that has your docs content
+var folderInput = "input-folder"
+
+function main() {
+  convertDriveFolderToMDForDocsAgent(folderInput);
+  exportEmailsToMarkdown(SEARCH_QUERY, folderOutput);
+}
\ No newline at end of file
diff --git a/one_fm/docsagent/config.yaml b/one_fm/docsagent/config.yaml
new file mode 100644
index 0000000000..146ccd8095
--- /dev/null
+++ b/one_fm/docsagent/config.yaml
@@ -0,0 +1,48 @@
+#
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+configs:
+  - product_name: "WIKI"
+    models:
+      - language_model: "models/gemini-1.5-flash-latest"
+        embedding_model: "models/embedding-001"
+        api_endpoint: "generativelanguage.googleapis.com"
+        embedding_api_call_limit: 1400
+        embedding_api_call_period: 60
+    docs_agent_config: "normal"
+    markdown_splitter: "token_splitter"
+    log_level: "NORMAL"
+    db_type: "chroma"
+    db_configs:
+      - db_type: "chroma"
+        vector_db_dir: "vector_stores/chroma"
+        collection_name: "docs_collection"
+    output_path: "data/plain_docs"
+    inputs:
+      - path: "/Users/samdani/Desktop/sample/apps/one_fm/one_fm/docsagent/content"
+        url_prefix: "http://onefm.localhost:8003/app/wiki"
+    conditions:
+      - condition_text: "You are a helpful chatbot answering questions from users.
+        Read the context below first and answer the user's question at the end.
+        In your answer, provide a summary in three or five sentences. (BUT DO NOT USE
+        ANY INFORMATION YOU KNOW ABOUT THE WORLD.)"
+        fact_check_question: "Can you compare the text below to the information
+        provided in this prompt above and write a short message that warns the readers
+        about which part of the text they should consider fact-checking? (Please keep
+        your response concise, focus on only one important item, but DO NOT USE BOLD
+        TEXT IN YOUR RESPONSE.)"
+        model_error_message: "Gemini is not able to answer this question at the moment.
+        Rephrase the question and try asking again."
diff --git a/one_fm/docsagent/docs/chunking-process.md b/one_fm/docsagent/docs/chunking-process.md
new file mode 100644
index 0000000000..e4c054b921
--- /dev/null
+++ b/one_fm/docsagent/docs/chunking-process.md
@@ -0,0 +1,68 @@
+# Docs Agent chunking process
+
+This page describes Docs Agent's chunking process and potential optimizations.
+
+Currently, Docs Agent utilizes Markdown headings (`#`, `##`, and `###`) to
+split documents into smaller, manageable chunks. However, the Docs Agent team
+is actively developing more advanced strategies to improve the quality and
+relevance of these chunks for retrieval.
+
+## Chunking technique
+
+In Retrieval Augmented Generation ([RAG][rag]) based systems, ensuring each
+chunk contains the right information and context is crucial for accurate
+retrieval. The goal of an effective chunking process is to ensure that each
+chunk encapsulates a focused topic, which enhances the accuracy of retrieval
+and ultimately leads to better answers. At the same time, the Docs Agent team
+acknowledges the importance of a flexible approach that allows for
+customization based on specific datasets and use cases.
+
+Key characteristics in Docs Agent’s chunking process include:
+
+- **Docs Agent splits documents based on Markdown headings.** However,
+  this approach has limitations, especially when dealing with large sections.
+- **Docs Agent chunks are smaller than 5000 bytes (characters).** This size
+  limit is set by the embedding model used in generating embeddings.
+- **Docs Agent enhances chunks with additional metadata.** The metadata helps
+  Docs Agent to execute operations efficiently, such as preventing duplicate
+  chunks in databases and deleting obsolete chunks that are  no longer
+  present in the source.
+- **Docs Agent retrieves the top 5 chunks and displays the top chunk's URL.**
+  However, this is adjustable in Docs Agent’s configuration (see the `widget`
+  and `experimental` app modes).
+
+The Docs Agent team continues to explore various optimizations to enhance
+the functionality and effectiveness of the chunking process. These efforts
+include refining the chunking algorithm itself and developing advanced
+post-processing techniques, for instance, reconstructing chunks to original
+documents after retrieval.
+
+Additionally, the team has been exploring methods for co-optimizing content
+structure and chunking strategies, which aims to maximize retrieval
+effectiveness by ensuring the structure of the source document itself
+complements the chunking process.
+
+## Chunks retrieval
+
+Docs Agent employs two distinct approaches for storing and retrieving chunks:
+
+- **The local database approach uses a [Chroma][chroma] vector database.**
+  This approach grants greater control over the chunking and retrieval
+  process. This option is recommended for development and experimental
+  setups.
+- **The online corpus approach uses Gemini’s
+  [Semantic Retrieval API][semantic-retrieval].** This approach provides
+  the advantages of centrally hosted online databases, ensuring
+  accessibility for all users throughout the organization. This approach
+  has some drawbacks, as control is reduced because the API may dictate
+  how chunks are selected and where customization can be applied.
+
+Choosing between these approaches depends on the specific needs of the user’s
+deployment situation, which is to balance control and transparency against
+possible improvements in performance, broader reach and ease of use.
+
+
+
+[rag]: concepts.md
+[chroma]: https://docs.trychroma.com/
+[semantic-retrieval]: https://ai.google.dev/gemini-api/docs/semantic_retrieval
diff --git a/one_fm/docsagent/docs/cli-reference.md b/one_fm/docsagent/docs/cli-reference.md
new file mode 100644
index 0000000000..b6b945740f
--- /dev/null
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@@ -0,0 +1,358 @@
+# Docs Agent CLI reference
+
+This page provides a list of the Docs Agent command lines and their usages
+and examples.
+
+The Docs Agent CLI helps developers to manage the Docs Agent project and
+interact with language models. It can handle various tasks such as
+processing documents, populating vector databases, launching the chatbot,
+running benchmark test, sending prompts to language models, and more.
+
+**Important**: All `agent` commands need to run in the `poetry shell`
+environment.
+
+## Processing documents
+
+### Chunk Markdown files into small text chunks
+
+The command below splits Markdown files (and other source files) into small
+chunks of plain text files:
+
+```sh
+agent chunk
+```
+
+### Populate a vector database using text chunks
+
+The command below populates a vector database using plain text files (created
+by running the `agent chunk` command):
+
+```sh
+agent populate
+```
+
+### Populate a vector database and delete stale text chunks
+
+The command below deletes stale entries in the existing vector database
+before populating it with the new text chunks:
+
+```sh
+agent populate --enable_delete_chunks
+```
+
+### Show the Docs Agent configuration
+
+The command below prints all the fields and values in the current
+[`config.yaml`][config-yaml] file:
+
+```sh
+agent show-config
+```
+
+### Clean up the Docs Agent development environment
+
+The command below deletes development databases specified in the
+`config.yaml` file:
+
+```sh
+agent cleanup-dev
+```
+
+### Write logs to a CSV file
+
+The command below writes the summaries of all captured debugging information
+(in the `logs/debugs` directory) to  a `.csv` file:
+
+```sh
+agent write-logs-to-csv
+```
+
+## Launching the chatbot web app
+
+### Launch the Docs Agent web app
+
+The command below launches Docs Agent's Flask-based chatbot web application:
+
+```sh
+agent chatbot
+```
+
+### Launch the Docs Agent web app using a different port
+
+The command below launches the Docs Agent web app to run on port 5005:
+
+```sh
+agent chatbot --port 5005
+```
+
+### Launch the Docs Agent web app as a widget
+
+The command below launches the Docs Agent web app to use
+a widget-friendly template:
+
+```sh
+agent chatbot --app_mode widget
+```
+
+### Launch the Docs Agent web app in full mode
+
+The command below launches the Docs Agent web app to use
+a special template that uses three Gemini models (AQA, Gemini 1.5,
+and Gemini 1.0):
+
+```sh
+agent chatbot --app_mode full
+```
+
+### Launch the Docs Agent web app with a log view enabled
+
+The command below launches the Docs Agent web app while enabling
+a log view page (which is accessible at `/logs`):
+
+```sh
+agent chatbot --enable_show_logs
+```
+
+## Running benchmark test
+
+### Run the Docs Agent benchmark test
+
+The command below runs benchmark test using the questions and answers listed
+in the [`benchmarks.yaml`][benchmarks-yaml] file:
+
+```sh
+agent benchmark
+```
+
+## Interacting with language models
+
+### Ask a question
+
+The command below reads a question from the arguments, asks the Gemini model,
+and prints its response:
+
+```sh
+agent tellme 
+```
+
+Replace `QUESTION` with a question written in plain English, for example:
+
+```sh
+agent tellme does flutter support material design 3?
+```
+
+**Note**: This `agent tellme` command is used to set up the `gemini` command
+in the [Set up Docs Agent CLI][set-up-docs-agent-cli] guide.
+
+### Ask a question to a specific product
+
+The command below enables you to ask a question to a specific product in your
+Docs Agent setup:
+
+```sh
+agent tellme  --product 
+```
+
+The example below asks the question to the `Flutter` product in your
+Docs Agent setup:
+
+```sh
+agent tellme which modules are available? --product=Flutter
+```
+
+You may also specify multiple products, for example:
+
+```sh
+agent tellme which modules are available? --product=Flutter --product=Angular --product=Android
+```
+
+### Ask for advice
+
+The command below reads a request and a filename from the arguments,
+asks the Gemini model, and prints its response:
+
+```sh
+agent helpme  --file 
+```
+
+Replace `REQUEST` with a prompt and `PATH_TO_FILE` with a file's
+absolure or relative path, for example:
+
+```sh
+agent helpme write comments for this C++ file? --file ../my-project/test.cc
+```
+
+### Ask for advice using RAG
+
+The command below uses a local or online vector database (specified in
+the `config.yaml` file) to retrieve relevant context for the request:
+
+```sh
+agent helpme  --file  --rag
+```
+
+### Ask for advice in a session
+
+The command below starts a new session (`--new`), which tracks responses,
+before running the `agent helpme` command:
+
+```sh
+agent helpme  --file  --new
+```
+
+For example:
+
+```sh
+agent helpme write a draft of all features found in this README file? --file ./README.md --new
+```
+
+After starting a session, use the `--cont` flag to include the previous
+responses as context to the request:
+
+```sh
+agent helpme  --cont
+```
+
+For example:
+
+```sh
+agent helpme write a concept doc that delves into more details of these features? --cont
+```
+
+### Print the context in the current session
+
+The command below prints the questions, files, and responses that
+are being used as context in the current session:
+
+```sh
+agent show-session
+```
+
+### Ask the model to perform the request to each file in a directory
+
+The command below applies the request to each file found in the
+specified directory:
+
+```sh
+agent helpme  --perfile 
+```
+
+For example:
+
+```sh
+agent helpme explain what this file does? --perfile ~/my-project --new
+```
+
+### Ask the model to include all files in a directory as context
+
+The command below includes all files found in the specified directory
+as context to the request:
+
+```sh
+agent helpme  --allfiles 
+```
+
+For example:
+
+```sh
+agent helpme write a concept doc covering all features in this project? --allfiles ~/my-project --new
+```
+
+### Ask the model to run a pre-defined chain of prompts
+
+The command below runs a task (a sequence of prompts) defined in
+a `.yaml` file stored in the [`tasks`][tasks-dir] directory:
+
+```sh
+agent runtask --task 
+```
+
+For example:
+
+```sh
+agent runtask --task DraftReleaseNotes
+```
+
+### View the list of available Docs Agent tasks
+
+To see the list of all tasks available in your project, run
+`agent runtask` without any arguments:
+
+```sh
+agent runtask
+```
+
+### Ask the model to run a task using custom input
+
+If a task script has a `` placeholder, you can provide
+a custom input string to the task:
+
+```sh
+agent runtask --task  --custom_input 
+```
+
+For example:
+
+```sh
+agent runtask --task IndexPageGenerator --custom_input ~/my_example/docs/development/
+```
+
+## Managing online corpora
+
+### List all existing online corpora
+
+The command below prints the list of all existing online corpora created
+using the [Semantic Retrieval API][semantic-api]:
+
+```sh
+agent list-corpora
+```
+
+### Share an online corpora with a user
+
+The command below enables `user01@gmail.com` to read text chunks stored in
+`corpora/example01`:
+
+```sh
+agent share-corpus --name corpora/example01 --user user01@gmail.com --role READER
+```
+
+The command below enables `user01@gmail.com` to read and write to
+`corpora/example01`:
+
+```sh
+agent share-corpus --name corpora/example01 --user user01@gmail.com --role WRITER
+```
+
+### Share an online corpora with everyone
+
+The command below enables `EVERYONE` to read text chunks stored in
+`corpora/example01`:
+
+```sh
+agent open-corpus --name corpora/example01
+```
+
+### Remove a user permission from an online corpora
+
+The command below remove an existing user permission set in `corpora/example01`:
+
+```sh
+agent remove-corpus-permission --name corpora/example01/permissions/123456789123456789
+```
+
+### Delete an online corpora
+
+The command below deletes an online corpus:
+
+```sh
+agent delete-corpus --name corpora/example01
+```
+
+
+
+[config-yaml]: ../config.yaml
+[benchmarks-yaml]: ../docs_agent/benchmarks/benchmarks.yaml
+[set-up-docs-agent-cli]: ../docs_agent/interfaces/README.md
+[semantic-api]: https://ai.google.dev/docs/semantic_retriever
+[tasks-dir]: ../tasks
diff --git a/one_fm/docsagent/docs/concepts.md b/one_fm/docsagent/docs/concepts.md
new file mode 100644
index 0000000000..c8cfb53aca
--- /dev/null
+++ b/one_fm/docsagent/docs/concepts.md
@@ -0,0 +1,376 @@
+# Docs Agent concepts
+
+**Note**: If you want to set up and launch the Docs Agent chat app on your host machine,
+see the [Set up Docs Agent][set-up-docs-agent] section in README.
+
+This page describes the architecture and features of Docs Agent.
+
+## Overview
+
+The Docs Agent chat app is designed to be easily set up and configured in a Linux environment.
+and require that you have access to Google’s [Gemini API][genai-doc-site].
+
+Docs Agent uses a technique known as Retrieval Augmented Generation (RAG), which allows
+you to bring your own documents as knowledge sources to AI language models. This approach
+helps the AI language models to generate relevant and accurate responses that are grounded
+in the information that you provide and control.
+
+![Docs Agent architecture](./images/docs-agent-architecture-01.png)
+
+**Figure 1**. Docs Agent uses a vector database to retrieve context for augmenting prompts.
+
+## Main features
+
+The key features of the Docs Agent chat app are:
+
+- Add contextual information to user questions to augment prompts for AI language models.
+- Process documents into embeddings and store them in a vector database for semnatic retrieval.
+
+![Docs Agent flow](./images/docs-agent-architecture-02.png)
+
+**Figure 2**. A user question is augmented by the Docs Agent server and passed to an LLM.
+
+For the moment, the Docs Agent project focuses on providing Python scripts that make it
+easy to process Markdown files into embeddings. However, there is no hard requirement that the
+source documents must exist in Markdown format. What’s important is that the processed content
+is available as embeddings in the vector database.
+
+### Structure of a prompt to a language model
+
+To enable an LLM to answer questions that are not part of the public knowledge (which the LLM
+is likely trained on), the Docs Agent project applies a mixture of prompt engineering and
+embeddings techniques. That is, we process a set of documents (which contain domain specific
+knowledge) into embeddings and store them in a vector database. This vector database allows
+the Docs Agent server to perform semantic search on stored embeddings to find the most relevant
+content from the source documents given user questions.
+
+Once the most relevant content is returned, the Docs Agent server uses the prompt structure
+shown in Figure 3 to augment the user question with a preset **condition** and a list of
+**context**. (When the Docs Agent server starts, the condition value is read from the
+[`config.yaml`][config-yaml] file.) Then the Docs Agent server sends this prompt to a
+language model using the Gemini API and receives a response generated by the model.
+
+![Docs Agent prompt strcture](./images/docs-agent-prompt-structure-01.png)
+
+**Figure 3**. Prompt structure for augmenting a user question with related context
+(Context source: [eventhorizontelescope.org][context-source-01])
+
+### Processing of Markdown files into embeddings
+
+To process information into embeddings using the Python scripts in the project, the
+information needs to be stored in Markdown format. Once you have a set of Markdown files
+stored in a directory on your host machine,  you can run the
+[`files_to_plain_text.py`][files-to-plain-text] script to process those Markdown
+files into small plain text files – the script splits the content by the top three Markdown
+headers (`#`, `##`, and `###`).
+
+Once Markdown files are processed into small plain text files, you can run the
+[`populate_vector_database.py`][populate-vector-database] script to generate embeddings
+for each text file and store those embeddings into a [Chroma][chroma-docs] vector database
+running on the host machine.
+
+The embeddings in this vector database enable the Docs Agent server to perform semantic search
+and retrieve context related to user questions for augmenting prompts.
+
+For more information on the processing of Markdown files, see the [`README`][scripts-readme]
+file in the `scripts` directory.
+
+![Document to embeddings](./images/docs-agent-embeddings-01.png)
+
+**Figure 4**. A document is split into small semantic chunks, which are then used to generate
+embeddings.
+
+![Markdown to embeddings](./images/docs-agent-embeddings-02.png)
+
+**Figure 5**. A Markdown page is split by headers and processed into embeddings.
+
+## Summary of tasks and features
+
+The following list summarizes the tasks and features of the Docs Agent chat app:
+
+- **Process Markdown**: Split Markdown files into small plain text files. (See the
+  Python scripts in the [`preprocess`][preprocess-dir] directory.)
+- **Generate embeddings**: Use an embedding model to process small plain text files
+  into embeddings, and store them in a vector database. (See the
+  [`populate_vector_database.py`][populate-vector-database] script.)
+- **Perform semantic search**: Compare embeddings in the vector database to retrieve
+  most relevant content given user questions.
+- **Add context to a user question**: Add a list of text chunks returned from
+  a semantic search as context in a prompt.
+- **(Experimental) “Fact-check” responses**: This experimental feature composes
+  a follow-up prompt and asks the language model to “fact-check” its own previous response.
+  (See the [Using a language model to fact-check its own response][fact-check-section]
+  section.)
+- **Generate related questions**: In addition to displaying a response to the user
+  question, the web UI displays 5 questions generated by the language model based on
+  the context of the user question. (See the
+  [Using a language model to suggest related questions][related-questions-section]
+  section.)
+- **Return URLs of documentation sources**: Docs Agent's vector database stores URLs
+  as metadata next to embeddings. Whenever the vector database is used to retrieve
+  text chunks for context, the database can also return the URLs of the sources used
+  to generate the embeddings.
+- **Collect feedback from users**: Docs Agent's chatbot web UI includes buttons that
+  allow users to [like generated responses][like-generated-responses] or
+  [submit rewrites][submit-a-rewrite].
+- **Convert Google Docs, PDF, and Gmail into Markdown files**: This feature uses
+  Apps Script to convert Google Docs, PDF, and Gmail into Markdown files, which then
+  can be used as input datasets for Docs Agent. (See the
+  [`apps_script`][apps-script-readme] directory.)
+- **Run benchmark test to monitor the quality of AI-generated responses**: Using
+  Docs Agent, you can run [benchmark test][benchmark-test] to measure and compare
+  the quality of text chunks, embeddings, and AI-generated responses.
+- **Use the Semantic Retrieval API and AQA model**: You can use Gemini's
+  [Semantic Retrieval API][semantic-api] to upload source documents to an online
+  corpus and use the [AQA model][aqa-model] that is specifically created for answering
+  questions using an online corpus.
+
+## Flow of events
+
+The following events take place in the Docs Agent chat app:
+
+1. The [`files_to_plain_text.py`][files-to-plain-text] script converts input
+   Markdown documents into small plain text files, split by Markdown headings
+   (`#`, `##`, and `###`).
+2. The [`populate_vector_database.py`][populate-vector-database] script generates
+   embeddings from the small plain text files and populates a vector database.
+3. When the [`agent chatbot`] command is run, it starts the Docs Agent server and
+   vector database, which loads generated embeddings and metadata (URLs and filenames)
+   stored in the `vector_store` directory.
+4. When the user asks a question, the Docs Agent server uses the vector database to
+   perform semantic search on embeddings, which represent content in the source
+   documents.
+5. Using this semantic search capability, the Docs Agent server finds a list of
+   text chunks that are most relevant to the user question.
+6. The Docs Agent server adds this list of text chunks as context (plus a condition
+   for responses) to the user question and constructs them into a prompt.
+7. The system sends the prompt to a language model via the Gemini API.
+8. The language model generates a response and the Docs Agent server renders it on
+   the chat UI.
+
+Additional events for [“fact-checking” a generated response][fact-check-section]:
+
+9. The Docs Agent server prepares another prompt that compares the generated response
+   (in step 8) to the context (in step 6) and asks the language model to look for
+   a discrepancy in the response.
+10. The language model generates a response that points out one major discrepancy
+    (if it exists) between its previous response and the context.
+11. The Docs Agent server renders this response on the chat UI as a call-out note.
+12. The Docs Agent server passes this second response to the vector database to
+    perform semantic search.
+13. The vector database returns a list of relevant content (that is closely related
+    to the second response).
+14. The Docs Agent server renders the top URL of this list on the chat UI and
+    suggests that the user checks out this URL for fact-checking.
+
+Additional events for
+[suggesting 5 questions related to the user question][related-questions-section]:
+
+15. The Docs Agent server prepares another prompt that asks the language model to
+    generate 5 questions based on the context (in step 6).
+16. The language model generates a response that contains a list of questions related
+    to the context.
+17. The Docs Agent server renders the questions on the chat UI.
+
+## Supplementary features
+
+This section describes additional features implemented on the Docs Agent chat app for
+enhancing the usability of the Q&A experience powered by generative AI.
+
+![Docs Agent UI](./images/docs-agent-ui-screenshot-01.png)
+
+**Figure 6**. A screenshot of the Docs Agent chat UI showing the sections generated by
+three distinct prompts.
+
+### Using a language model to fact-check its own response
+
+In addition to using the prompt structure above (shown in Figure 3), we‘re currently
+experimenting with the following prompt setup for “fact-checking” responses generated
+by the language model:
+
+- Condition:
+
+  ```
+  You are a helpful chatbot answering questions from users. Read the following context
+  first and answer the question at the end:
+  ```
+
+- Context:
+
+  ```
+  
+  ```
+
+- Additional condition (for fact-checking):
+
+  ```
+  Can you compare the text below to the information provided in this prompt above
+  and write a short message that warns the readers about which part of the text they
+  should consider fact-checking? (Please keep your response concise and focus on only
+  one important item.)"
+  ```
+
+- Previously generated response
+
+  ```
+  Text: 
+  ```
+
+This "fact-checking" prompt returns a response similar to the following example:
+
+```
+The text states that Flutter chose to use Dart because it is a fast, productive, object-oriented
+language that is well-suited for building user interfaces. However, the context provided in the
+prompt states that Flutter chose Dart because it is a fast, productive language that is well-suited
+for Flutter's problem domain: creating visual user experiences. Therefore, readers should consider
+fact-checking the claim that Dart is well-suited for building user interfaces.
+```
+
+After the second response, notice that the Docs Agent chat UI also suggests a URL to visit for
+fact-checking  (see Figure 6), which looks similar to the following example:
+
+```
+To verify this information, please check out:
+
+https://docs.flutter.dev/resources/faq
+```
+
+To identify this URL, the Docs Agent server takes the second response (which is the paragraph that
+begins with “The text states that ...” in the example above) and uses it to query the vector
+database. Once the vector database returns a list of the most relevant content to this response,
+the UI only displays the top URL to the user.
+
+Keep in mind that this "fact-checking" prompt setup is currently considered **experimental**
+because we‘ve seen cases where a language model would end up adding incorrect information into its
+second response as well. However, we saw that adding this second response (which brings attention
+to the language model’s possible hallucinations) seems to improve the usability of the system since it
+serves as a reminder to the users that the language model‘s response is far from being perfect, which
+helps encourage the users to take more steps to validate generated responses for themselves.
+
+### Using a language model to suggest related questions
+
+The project‘s latest web UI includes the “Related questions” section, which displays five
+questions that are related to the user question (see Figure 6). These five questions are also
+generated by a language model (via the Gemini API). Using the list of contents returned from the vector
+database as context, the system prepares another prompt asking the language model to generate five
+questions from the included context.
+
+The following is the exact structure of this prompt:
+
+- Condition:
+
+  ```
+  Read the context below and answer the question at the end:
+  ```
+
+- Context:
+
+  ```
+  
+  ```
+
+- Question:
+
+  ```
+  What are 5 questions developers might ask after reading the context?
+  ```
+
+### Enabling users to submit a rewrite of a generated response
+
+The project‘s latest web UI includes the **Rewrite this response** button at the bottom of
+the panel (see Figure 6). When this button is clicked, a widget opens up, expanding the
+main UI panel, and reveals a textarea containing the generated response to the user's question.
+The user is then allowed to edit this response in the textarea and click the **Submit** button
+to submit the updated response to the system.
+
+The system stores the submitted response as a Markdown file in the project's local `rewrites`
+directory. The user may re-click the **Submit** button to update the submitted rewrite multiple
+times.
+
+### Enabling users to like generated responses
+
+The project's latest web UI includes the **Like this response** button at the bottom of the panel
+(see Figure 6). When this button is clicked, the server logs the event of "like" for the response.
+However, clicking the **Liked** button again will reset the button. Then the server logs this reset
+event of "like" for the response.
+
+The user may click this like button multiple times to toggle the state of the like button. But when
+examining the logs, only the final state of the like button will be considered for the response.
+
+### Using Google Docs, PDF, or Gmail as input sources
+
+The project includes Apps Script files that allow you to convert various sources of content
+(including Google Docs and PDF) from your Google Drive and Gmail into Markdown files. You can then
+use these Markdown files as additional input sources for Docs Agent. For more information, see the
+[`README`][apps-script-readme] file in the `apps_script` directory.
+
+![Docs Agent pre-processing flow](./images/docs-agent-pre-processing-01.png)
+
+**Figure 7**. Docs Agent's pre-processing flow for various doc types.
+
+### Using the Semantic Retrieval API and AQA model
+
+Docs Agent provides options to use Gemini's [Semantic Retrieval API][semantic-api] for storing text
+chunks in Google Cloud's online storage (and using this online storage for context retrieval),
+in combination with using the [AQA model][aqa-model] for question-answering.
+
+To use the Semantic Retrieval API, update the `config.yaml` file to the following settings:
+
+```
+models:
+  - language_model: "models/aqa"
+
+...
+
+db_type: "google_semantic_retriever"
+```
+
+The setup above uses both the Semantic Retrieval API and the AQA model.
+
+**Note**: At the moment, when `db_type` is set to `google_semantic_retriever`, running the
+`populate_vector_database.py` script will also create and popluate a local vector database using
+Chroma as well as creating and populating an online corpus using the Semantic Retrieval API.
+
+However, if you want to use only the AQA model without using an online corpus, update the
+`config.yaml` file to the following settings instead:
+
+```
+models:
+  - language_model: "models/aqa"
+
+...
+
+db_type: "chroma"
+```
+
+The setup above uses the AQA model with your local Chroma vector database. For more information,
+see the [More Options: AQA Using Inline Passages][inline-passages] section on the
+_Semantic Retriever Quickstart_ page.
+
+**Note**: To use the Semantic Retrieval API, you need to complete the OAuth setup for your Google
+Cloud project from your host machine. For detailed instructions, see the
+[Authentication with OAuth quickstart][oauth-quickstart] page.
+
+
+
+[set-up-docs-agent]: ../README.md#set-up-docs-agent
+[files-to-plain-text]: ../docs_agent/preprocess/files_to_plain_text.py
+[populate-vector-database]: ../docs_agent/preprocess/populate_vector_database.py
+[context-source-01]: http://eventhorizontelescope.org
+[fact-check-section]: #using-a-language-model-to-fact_check-its-own-response
+[related-questions-section]: #using-a-language-model-to-suggest-related-questions
+[submit-a-rewrite]: #enabling-users-to-submit-a-rewrite-of-a-generated-response
+[like-generated-responses]: #enabling-users-to-like-generated-responses
+[populate-db-steps]: #populate-a-new-vector-database-from-markdown-files
+[genai-doc-site]: https://ai.google.dev/docs/gemini_api_overview
+[chroma-docs]: https://docs.trychroma.com/
+[apps-script-readme]: ../apps_script/README.md
+[scripts-readme]: ../docs_agent/preprocess/README.md
+[config-yaml]: ../config.yaml
+[benchmark-test]: ../docs_agent/benchmarks/README.md
+[semantic-api]: https://ai.google.dev/docs/semantic_retriever
+[aqa-model]: https://ai.google.dev/models/gemini#model_variations
+[oauth-quickstart]: https://ai.google.dev/docs/oauth_quickstart
+[inline-passages]: https://ai.google.dev/docs/semantic_retriever#more_options_aqa_using_inline_passages
+[authorize-credentials]: https://ai.google.dev/docs/oauth_quickstart#authorize-credentials
+[preprocess-dir]: ../docs_agent/preproces/
diff --git a/one_fm/docsagent/docs/config-reference.md b/one_fm/docsagent/docs/config-reference.md
new file mode 100644
index 0000000000..2cdc306296
--- /dev/null
+++ b/one_fm/docsagent/docs/config-reference.md
@@ -0,0 +1,152 @@
+# Docs Agent configuration reference
+
+This page provides a list of additional options that can be specified
+in the Docs Agent configuration file ([`config.yaml`][config-yaml]).
+
+## Web application options
+
+### app_port
+
+This field sets the port which the Docs Agent web app runs on.
+
+```
+app_port: 5001
+```
+
+By default, the web app is set to use port 5000.
+
+### app_mode
+
+This field controls the user interface mode of the web app.
+
+The options are:
+
+* `widget`: This mode launches a compact widget-style interface, suitable
+  for being embedded within a webpage.
+
+  ```
+  app_mode: "widget"
+  ```
+
+* `full`: This special mode is designed to be used with Gemini 1.5 models.
+
+  ```
+  app_mode: "full"
+  ```
+
+When this field is not specified, the web app is set to use the standard mode.
+
+## User feedback options
+
+### feedback_mode
+
+This field sets the type of feedback mechanism available to users for providing
+the quality or relevance of responses.
+
+The options are:
+
+* `feedback`: This is the default setting.
+
+  ```
+  feedback_mode: "feedback"
+  ```
+
+* `rewrite`: This option provides the "Rewrite this response" button to allows
+  users to suggest alternative responses.
+
+  ```
+  feedback_mode: "rewrite"
+  ```
+
+* `like_and_dislike`: This option provides simple "Like" and "Dislike" buttons.
+
+  ```
+  feedback_mode: "like_and_dislike"
+  ```
+
+## Logging options
+
+### log_level
+
+This field controls the level of detail captured in the logs generated by Docs
+Agent.
+
+Setting it to `VERBOSE` provides more comprehensive logging information:
+
+```
+log_level: "VERBOSE"
+```
+
+This field is set to `NORMAL` by default.
+
+### enable_show_logs
+
+Setting this field to `"True"` allows logs to be displayed on a web browser
+(which is accessible at `/logs`):
+
+```
+enable_show_logs: "True"
+```
+
+### enable_logs_to_markdown
+
+Setting this field to `"True"` saves the generated answers as Markdown pages
+on the host machine:
+
+```
+enable_logs_to_markdown: "True"
+```
+
+### enable_logs_for_debugging
+
+Setting this field to `"True"` generates detailed logs for debugging purposes:
+
+```
+enable_logs_for_debugging: "True"
+```
+
+## Database management options
+
+### enable_delete_chunks
+
+Setting this field to `"True"` enables the ability to delete outdated, stale
+text chunks from the vector databases:
+
+```
+enable_delete_chunks: "True"
+```
+
+## Secondary database configuration
+
+Docs Agent allows for the use of a secondary database alongside the primary one
+for providing additional context from a different source.
+
+### secondary_db_type
+
+This field specifies the type of secondary database to be used:
+
+```
+secondary_db_type: "google_semantic_retrieval"
+```
+
+or
+
+```
+secondary_db_type: "chroma"
+```
+
+When `chroma` is specified, the collection in the `vector_stores/chroma`
+directory is used as the secondary database.
+
+### secondary_corpus_name
+
+This field defines the name of the corpus for the secondary database,
+for example:
+
+```
+secondary_corpus_name: "corpora/my-example-corpus"
+```
+
+
+
+[config-yaml]: ../config.yaml
diff --git a/one_fm/docsagent/docs/images/apps-script-screenshot-01.png b/one_fm/docsagent/docs/images/apps-script-screenshot-01.png
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