diff --git a/CODEOWNERS b/CODEOWNERS index bf4611978..3b81ece5f 100644 --- a/CODEOWNERS +++ b/CODEOWNERS @@ -6,5 +6,6 @@ demos/palm/web/travel-planner @lyleaf @jayjicheng demos/palm/web/list-it @mrayinteractive @aaron-wade demos/palm/web/quick-prompt @mrayinteractive @aaron-wade demos/palm/web/textfx @mrayinteractive @aaron-wade +demos/palm/python/docs-agent @nickvander @rundong08 @Meggin @kyolee415 site/ @google/generative-ai-site-team diff --git a/demos/palm/python/DELETEME b/demos/palm/python/DELETEME deleted file mode 100644 index 2517fab01..000000000 --- a/demos/palm/python/DELETEME +++ /dev/null @@ -1 +0,0 @@ -Placeholder to reserve directory structure until files are added. diff --git a/demos/palm/python/docs-agent/CONTRIBUTING.md b/demos/palm/python/docs-agent/CONTRIBUTING.md new file mode 100644 index 000000000..8956a61b8 --- /dev/null +++ b/demos/palm/python/docs-agent/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 accompanied by a +[Contributor License Agreement](https://cla.developers.google.com/about) (CLA). +You (or your employer) retain the copyright to your contribution; this simply +gives us permission to use and redistribute your contributions as part of the +project. + +If you or your current employer have already signed the Google CLA (even if it +was for a different project), you probably don't need to do it again. + +Visit to see your current agreements or to +sign a new one. + +### Review our Community Guidelines + +This project follows [Google's Open Source Community +Guidelines](https://opensource.google/conduct/). + +## Contribution process + +### Code Reviews + +All submissions, including submissions by project members, require review. 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Docs Agent uses a vector database to retrieve context for augmenting prompts. + +## Main features + +The key features of the Docs Agent sample app are: + +- Add context to user questions to augment their prompts to an LLM. +- Process documents into embeddings and store them in a vector database for context retrieval. + +![Docs Agent flow](docs/images/docs-agent-architecture-02.png) + +**Figure 2**. A user question is augmented by the Docs Agent server and passed to an LLM. + +**Note**: 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 PaLM 2 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 +[`condition.txt`][condition-txt] file.) Then the Docs Agent server sends this prompt to a +PaLM 2 model using the PaLM API and receives a response generated by the model. + +![Docs Agent prompt strcture](docs/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 +[`markdown_to_plain_text.py`][markdown-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. + +![Document to embeddings](docs/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](docs/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 sample app: + +- **Process Markdown**: Split Markdown files into small plain text files. (See the + [`markdown_to_plain_text.py`][markdown-to-plain-text] script.) +- **Generate embeddings**: Use small plain text files to generate embeddings, processed by + an embedding model (`embedding-gecko-001`), and store them in a local Chroma vector + database. (See the [`populate_vector_database.py`][populate-vector-database] script.) +- **Semantic search using embeddings**: Compare embeddings in the vector database for most + relevant content given user questions (which are also processed into embeddings using + the same `embedding-gecko-001` model). +- **Add context to a user question in a prompt**: Add the list of content returned from + the semantic search as context to the user question and send the prompt to a PaLM 2 + model using the PaLM API. +- **(Experimental) “Fact-check” responses**: This experimental feature composes a + follow-up prompt and asks the PaLM 2 model to “fact-check” its own previous response. + (See the [Using a PaLM 2 model to fact-check its own response][fact-check-section] section.) +- **Generate 5 related questions**: In addition to displaying a response to the user + question, the web UI displays five questions generated by the PaLM 2 model based on + the context of the user question. (See the + [Using a PaLM 2 model to suggest related questions][related-questions-section] section.) +- **Display URLs of knowledge sources**: The vector database stores URLs as metadata for + embeddings. Whenever the vector database is used to retrieve context (for instance, to + provide context to user questions), the database can also return the URLs of the sources + that were originally used to generate the embeddings. +- **Submit rewrites and likes**: The web UI includes the buttons at the bottom of the + display that allow users to like generated responses or submit rewrites of + the responses. (See the + [Enabling users to submit a rewrite of a generated response][submit-a-rewrite] and + [Enabling users to like generated responses][like-generate-responses] sections.) + +## Flow of events + +The following events take place in the Docs Agent sample app: + +1. The [`markdown_to_plain_text.py`][markdown-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 [`chatbot/launch.sh`][launch-script] script 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 PaLM 2 model via the PaLM API. +8. The PaLM 2 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 PaLM model to look for + a discrepancy in the response. +10. The PaLM 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 PaLM model to + generate 5 questions based on the context (in step 6). +16. The PaLM 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 sample app for +enhancing the usability of the Q&A experience powered by generative AI. + +![Docs Agent UI](docs/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 PaLM 2 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 PaLM 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): + + ``` + Compare the following body of text to the context provided in this prompt and write + a short message that warns the readers about which part of the text below they + should consider fact-checking for themselves? (please keep your response concise and + mention only one important point): + ``` + +- Previously generated response + + ``` + + ``` + +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 PaLM 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 PaLM model’s possible hallucinations) seems to improve the usability of the system since it +serves as a reminder to the users that the PaLM 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 PaLM 2 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 PaLM model (via the PaLM API). Using the list of contents returned from the vector +database as context, the system prepares another prompt asking the PaLM model to generate five +questions from the included context. + +The following is the exact structure of this prompt: + +- Condition: + + ``` + You are a helpful chatbot answering questions from users. Read the following context first + 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. + +## Issues identified + +The following issues have been identified and need to be worked on: + +- **Logical content chunking**: When splitting documents, content is divided into chunks only by the + current 1500-character limit. This approach splits large docs into small chunks, which results in + losing context, especially in large how-to guides with a long sequence of instructions. **[Done]** +- **Clean plain text for embeddings**: The current Markdown processing method doesn’t fully filter + out all Markdown and HTML syntax, which seems to have a negative influence on embeddings. +- **Database support for embeddings**: The system needs a proper database setup for faster lookup + and for enabling us to store metadata (such as URLs) next to embeddings. **[Done]** +- **Better prompting**: We haven’t widely explored all best practices in prompting. Also consider + supporting dynamic prompting given user questions. +- **Real-world feedback**: We need to set up a feedback loop to collect real-world user + interactions, including example prompts and responses, and start using them as part of embeddings. + +## Set up Docs Agent + +This section provides instructions on how to set up the Docs Agent project on a Linux host machine. + +### 1. Prerequisites + +1. Update the Linux package repositories on the host machine: + + ```posix-terminal + sudo apt update + ``` + +2. Install the following dependencies: + + ```posix-terminal + sudo apt install git pip python3-venv + ``` + +3. Install `poetry`: + + ```posix-terminal + curl -sSL https://install.python-poetry.org | python3 - + ``` + + **Important**: Make sure that `$HOME/.local/bin` is in your `PATH` variable. + +4. Set the following environment variable: + + ```posix-terminal + export PYTHON_KEYRING_BACKEND=keyring.backends.null.Keyring + ``` + + This is a [known issue][poetry-known-issue] in `poetry`. + +5. Set the PaLM API key as a environment variable: + + ``` + export PALM_API_KEY= + ``` + + Replace `` with the API key to + [Generative Language API][genai-doc-site]. + + **Tip**: To avoid repeating these `export` lines, add them to your + `$HOME/.bashrc` file. + +### 2. Clone this project repository and install dependencies + +**Note**: This guide assumes that you're cloning the `generative-ai-docs` repository +from your `$HOME` directory. + +1. Clone the `generative-ai-docs` repository, for example: + + ```posix-terminal + git clone https://github.com/google/generative-ai-docs + ``` + +2. Go to the Docs Agent project directory: + + ```posix-terminal + cd ./generative-ai-docs/demos/palm/python/docs-agent + ``` + +4. Install dependencies using `poetry`: + + ```posix-terminal + poetry install + ``` + + This may take some time to complete. + +5. Enter the `poetry` shell environment: + + ```posix-terminal + poetry shell + ``` + + **Important**: From this point, all command lines in the sections below need to run + in this `poetry shell` environment. + + +Now, the next step is to populate a vector database with your own documents. See the +[Populate a new vector database from Markdown files][populate-db-steps] section below. + +## Populate a new vector database from Markdown files + +This section provides instructions on how to bring your own set of documents and create and +populate a vector database (`vector_stores/chroma`) on your host machine. The Python scripts +in the project's `scripts` directory can help you populate documents, embeddings and metadata +from Markdown files (`.md`). + +This section 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 download +the open source Flutter documents on your host machine, run the following command: + +``` +git clone --recurse-submodules https://github.com/flutter/website.git +``` + +**Note**: The Flutter documents are used in this section as an example dataset only. The +Python scripts below are designed to work with any documents in the standard Markdown format. + +### 1. Convert Markdown files to plain text files + +Before generating embeddings, you need to process Markdown files into small chunks of +plain text files. + +To convert Markdown files to plain text files: + +1. Go to the Docs Agent project directory, for example: + + ``` + cd $HOME/generative-ai-docs/demos/palm/python/docs-agent + ``` + +2. Open the `config.yaml` file using a text editor, for example: + + ``` + nano config.yaml + ``` + +3. (**Optional**) Edit `output_path` to a directory that will store plain text files, + for example: + + ``` + output_path: "data/plain_docs" + ``` + + The example above creates a new directory named `data/plain_docs` in the current project + directory (which results in `generative-ai-docs/demos/palm/python/docs-agent/data/plain_docs`). + Then the project uses this `output_path` directory to store the plain text files processed + from the input Markdown files. + +4. Under the `input` 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. + If the URLs do not exist for the source files, provide a mock string. + - (**Optional**) `exclude_path`: The sub-directory to be excluded from + the path directory. + + The example below shows the entries for the Flutter documents downloaded on the + host machine (that is, in the `/home/downloads/website` directory): + + ``` + input: + - path: "/home/downloads/website/src" + url_prefix: "https://docs.flutter.dev" + ``` + + You can also provide a number of input directories (`path` and `url_prefix` sets) under + the input field, for example: + + ``` + input: + - path: "/home/downloads/website/src/ui" + url_prefix: "https://docs.flutter.dev/ui" + - path: "/home/downloads/website/src/codelabs" + url_prefix: "https://docs.flutter.dev/codelabs" + ``` + +5. Save the file and exit the text editor. + +6. Run the Python script: + + ``` + python3 scripts/markdown_to_plain_text.py + ``` + + For a large number of Markdown files, it may take a few minutes to process + Markdown files. + +### 2. Populate a new vector database + +Once you have plain text files processed and stored in the `output_path` directory, +you can run the `populat_vector_database.py` script to populate a vector database +with the contents of the plain text files and their embeddings (and metadata). + +**Important**: For a clean setup, if the `vector_stores/chroma` directory already +exists, delete (or move) the `chroma` directory before populating a new vector +database. (Otherwise, new entries will be added to your existing vector database.) +Also, if the Docs Agent chat app is already running using this `chroma` directory, +shut down the app before deleting the directory. + +To populate a new vector database: + +1. Go to the Docs Agent project directory, for example: + + ``` + cd $HOME/generative-ai-docs/demos/palm/python/docs-agent + ``` + +2. Create and populate a new vector database: + + ``` + python3 ./scripts/populate_vector_database.py + ``` + + This script uses the `output_path` directory from the `config.yaml` file to locate + plain text files and creates a new directory at + `generative-ai-docs/demos/palm/python/docs-agent/vector_stores/chroma`, which + contains embeddings and metadata. + +3. To test the new vector database, run the following script: + + ``` + python3 ./scripts/test_vector_database.py + ``` + + **Note**: Adjust `QUESTION` in `scripts/test_vector_database.py` to be suitable for + the content in your database. + +The next step is to launch the Docs Agent chat app to use the new vector database. See +the [Start the Docs Agent chat app][start-the-app-steps] section below. + +## Start the Docs Agent chat app + +**Important**: This section assumes that you've already created a `vector_stores/chroma` +directory, which contains artifacts for the vector database. If you haven't, see the +[Populate a new vector database from Markdown files][populate-db-steps] section above. + +This Flask app lets users interact with the Docs Agent service through a web browser. The +`launch.sh` script deploys the Flask app in a Python virtual environment (`poetry`), +allowing you to easily bring up and destory the Flask app instance. + +### 1. Configure the Docs Agent chat app + +To customize settings in the Docs Agent chat app, do the following: + +1. (**Optional**) Update the `condition.txt` file to provide a more specific prompt condition + for your custom dataset, for example: + + ``` + You are a helpful chatbot answering questions from developers working on Flutter apps. + Read the following context first and answer the question at the end: + ``` + +2. Edit the `config.yaml` file to update the following field: + + ``` + product_name: "My product" + ``` + + Replace `My product` with your product name (which shows up as the main label on the UI), + for example: + + ``` + product_name: "Flutter" + ``` + +### 2. Launch the Docs Agent chat app + +To launch the Docs Agent chat app, do the following: + +1. Go to the Docs Agent project directory, for example: + + ``` + cd $HOME/generative-ai-docs/demos/palm/python/docs-agent + ``` + +2. Launch the Docs Agent chat app: + + ``` + poetry run ./chatbot/launch.sh + ``` + + **Note**: 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 `-p` flag to specify + a different port (for example, `poetry run ./chatbot/launch.sh -p 5050`). + + Once the app starts running, this command prints output similar to the following: + + ``` + $ poetry run ./chatbot/launch.sh + This script starts your flask app in a virtual environment + Installing all dependencies through pip... + Using the local vector database created at /home/alice/generative-ai-docs/demos/palm/python/docs-agent/vector_database + Using embedded DuckDB with persistence: data will be stored in: /home/alice/generative-ai-docs/demos/palm/python/docs-agent/vector_database + * Serving Flask app 'chatbot' + * Debug mode: on + 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 + Press CTRL+C to quit + * Restarting with stat + Using the local vector database created at /home/alice/generative-ai-docs/demos/palm/python/docs-agent/vector_database + Using embedded DuckDB with persistence: data will be stored in: /home/alice/generative-ai-docs/demos/palm/python/docs-agent/vector_database + * Debugger is active! + * Debugger PIN: 129-640-957 + ``` + + 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 the source dataset. + +**The Docs Agent chat app is all set!** + +## Contribute to Docs Agent + +To contribute to the Docs Agent project, do the following: + +1. Visit https://cla.developers.google.com/ to see your current agreements + or to sign a new one. + +2. Fork the [`generative-ai-docs`][gen-ai-docs-repo] repository. + +3. Make changes in your forked reposiotry. + +4. Create a pull request. + +## 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 +[markdown-to-plain-text]: ./scripts/markdown_to_plain_text.py +[populate-vector-database]: ./scripts/populate_vector_database.py +[condition-txt]: ./condition.txt +[context-source-01]: http://eventhorizontelescope.org +[fact-check-section]: #using-a-palm-2-model-to-fact-check-its-own-response +[related-questions-section]: #using-a-palm-2-model-to-suggest-related-questions +[submit-a-rewrite]: #enabling-users-to-submit-a-rewrite-of-a-generated-response +[like-generate-responses]: #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 +[launch-script]: ./chatbot/launch.sh +[genai-doc-site]: https://developers.generativeai.google/products/palm +[chroma-docs]: https://docs.trychroma.com/ +[flutter-docs-src]: https://github.com/flutter/website/tree/main/src +[flutter-docs-site]: https://docs.flutter.dev/ +[poetry-known-issue]: https://github.com/python-poetry/poetry/issues/1917 +[gen-ai-docs-repo]: https://github.com/google/generative-ai-docs diff --git a/demos/palm/python/docs-agent/chatbot/__init__.py b/demos/palm/python/docs-agent/chatbot/__init__.py new file mode 100644 index 000000000..e99469e0c --- /dev/null +++ b/demos/palm/python/docs-agent/chatbot/__init__.py @@ -0,0 +1,21 @@ +# +# 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. +# + +from flask import Flask +from chatbot import chatui + +app = Flask(__name__) +app.register_blueprint(chatui.bp) diff --git a/demos/palm/python/docs-agent/chatbot/chatui.py b/demos/palm/python/docs-agent/chatbot/chatui.py new file mode 100644 index 000000000..c0f0a3322 --- /dev/null +++ b/demos/palm/python/docs-agent/chatbot/chatui.py @@ -0,0 +1,286 @@ +# +# 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. +# + +"""Chatbot web service for Docs Agent""" + +from flask import ( + Blueprint, + render_template, + request, + redirect, + url_for, + json, +) +import markdown +from bs4 import BeautifulSoup +import urllib +import os +from datetime import datetime +from pytz import timezone +import pytz +import uuid +from scripts import read_config + +from chroma import Format +from docs_agent import DocsAgent + + +# Read the configuration file +config = read_config.ReadConfig() +# Create the 'rewrites' directory if it does not exist. +rewrites_dir = "rewrites" +is_exist = os.path.exists(rewrites_dir) +if not is_exist: + os.makedirs(rewrites_dir) + +product = config.returnConfigValue("product_name") +bp = Blueprint("chatui", __name__) +docs_agent = DocsAgent() + + +@bp.route("/", methods=["GET", "POST"]) +def index(): + server_url = request.url_root.replace("http", "https") + return render_template("chatui/index.html", product=product, server_url=server_url) + + +@bp.route("/like", methods=["GET", "POST"]) +def like(): + if request.method == "POST": + json_data = json.loads(request.data) + is_like = json_data.get("like") + uuid_found = json_data.get("uuid") + log_like(is_like, str(uuid_found).strip()) + return "OK" + else: + return redirect(url_for("chatui.index")) + + +@bp.route("/rewrite", methods=["GET", "POST"]) +def rewrite(): + if request.method == "POST": + json_data = json.loads(request.data) + user_id = json_data.get("user_id") + question_captured = json_data.get("question") + original_response = json_data.get("original_response") + rewrite_captured = json_data.get("rewrite") + date_format = "%m%d%Y-%H%M%S" + date = datetime.now(tz=pytz.utc) + date = date.astimezone(timezone("US/Pacific")) + print("[" + date.strftime(date_format) + "] A user has submitted a rewrite.") + print("Submitted by: " + user_id + "\n") + print("# " + question_captured.strip() + "\n") + print("## Original response\n") + print(original_response.strip() + "\n") + print("## Rewrite\n") + print(rewrite_captured + "\n") + filename = ( + rewrites_dir + + "/" + + question_captured.strip() + .replace(" ", "-") + .replace("?", "") + .replace("'", "") + .lower() + + "-" + + date.strftime(date_format) + + ".md" + ) + with open(filename, "w", encoding="utf-8") as file: + file.write("Submitted by: " + user_id + "\n\n") + file.write("# " + question_captured.strip() + "\n\n") + file.write("## Original response\n\n") + file.write(original_response.strip() + "\n\n") + file.write("## Rewrite\n\n") + file.write(rewrite_captured + "\n") + file.close() + return "OK" + else: + return redirect(url_for("chatui.index")) + + +@bp.route("/result", methods=["GET", "POST"]) +def result(): + if request.method == "POST": + uuid_value = uuid.uuid1() + question_captured = request.form["question"] + query_result = docs_agent.query_vector_store(question_captured) + context = markdown.markdown(query_result.fetch_formatted(Format.CONTEXT)) + context_with_prefix = docs_agent.add_instruction_to_context(context) + response_in_markdown = docs_agent.ask_text_model_with_context( + context_with_prefix, question_captured + ) + if response_in_markdown is None: + response_in_markdown = ( + "The PaLM API is not able to answer this question at the moment. " + "Try to rephrase the question and ask again." + ) + response_in_html = markdown.markdown(response_in_markdown) + metadatas = markdown.markdown( + query_result.fetch_formatted(Format.CLICKABLE_URL) + ) + fact_checked_answer_in_markdown = docs_agent.ask_text_model_to_fact_check( + context_with_prefix, response_in_markdown + ) + if fact_checked_answer_in_markdown is None: + fact_checked_answer_in_markdown = ( + "The PaLM API is not able to answer this question at the moment. " + "Try to rephrase the question and ask again." + ) + fact_checked_answer_in_html = markdown.markdown(fact_checked_answer_in_markdown) + new_question = ( + "What are 5 questions developers might ask after reading the context?" + ) + related_questions = markdown.markdown( + docs_agent.ask_text_model_with_context(response_in_markdown, new_question) + ) + soup = BeautifulSoup(related_questions, "html.parser") + for item in soup.find_all("li"): + if item.string is not None: + link = soup.new_tag( + "a", + href=url_for( + "chatui.question", ask=urllib.parse.quote_plus(item.string) + ), + ) + link.string = item.string + item.string = "" + item.append(link) + related_questions = soup + fact_link = markdown.markdown( + query_result.fetch_nearest_formatted(Format.CLICKABLE_URL) + ) + server_url = request.url_root.replace("http", "https") + # Log the question and response to the log file. + log_question(uuid_value, question_captured, response_in_markdown) + return render_template( + "chatui/index.html", + question=question_captured, + context=context, + context_with_prefix=context_with_prefix, + response_in_markdown=response_in_markdown, + response_in_html=response_in_html, + product=product, + metadatas=metadatas, + fact_checked_answer=fact_checked_answer_in_html, + fact_link=fact_link, + related_questions=related_questions, + server_url=server_url, + uuid=uuid_value, + ) + else: + return redirect(url_for("chatui.index")) + + +@bp.route("/question/", methods=["GET", "POST"]) +def question(ask): + if request.method == "GET": + uuid_value = uuid.uuid1() + question_captured = urllib.parse.unquote_plus(ask) + query_result = docs_agent.query_vector_store(question_captured) + context = markdown.markdown(query_result.fetch_formatted(Format.CONTEXT)) + context_with_prefix = docs_agent.add_instruction_to_context(context) + response_in_markdown = docs_agent.ask_text_model_with_context( + context_with_prefix, question_captured + ) + if response_in_markdown is None: + response_in_markdown = ( + "The PaLM API is not able to answer this question at the moment. " + "Try to rephrase the question and ask again." + ) + response_in_html = markdown.markdown(response_in_markdown) + metadatas = markdown.markdown( + query_result.fetch_formatted(Format.CLICKABLE_URL) + ) + fact_checked_answer_in_markdown = docs_agent.ask_text_model_to_fact_check( + context_with_prefix, response_in_markdown + ) + if fact_checked_answer_in_markdown is None: + fact_checked_answer_in_markdown = ( + "The PaLM API is not able to answer this question at the moment. " + "Try to rephrase the question and ask again." + ) + fact_checked_answer_in_html = markdown.markdown(fact_checked_answer_in_markdown) + new_question = ( + "What are 5 questions developers might ask after reading the context?" + ) + related_questions = markdown.markdown( + docs_agent.ask_text_model_with_context(response_in_markdown, new_question) + ) + soup = BeautifulSoup(related_questions, "html.parser") + for item in soup.find_all("li"): + if item.string is not None: + link = soup.new_tag( + "a", + href=url_for( + "chatui.question", ask=urllib.parse.quote_plus(item.string) + ), + ) + link.string = item.string + item.string = "" + item.append(link) + related_questions = soup + fact_link = markdown.markdown( + query_result.fetch_nearest_formatted(Format.CLICKABLE_URL) + ) + server_url = request.url_root.replace("http", "https") + # Log the question and response to the log file. + log_question(uuid_value, question_captured, response_in_markdown) + return render_template( + "chatui/index.html", + question=question_captured, + context=context, + context_with_prefix=context_with_prefix, + response_in_markdown=response_in_markdown, + response_in_html=response_in_html, + product=product, + metadatas=metadatas, + fact_checked_answer=fact_checked_answer_in_html, + fact_link=fact_link, + related_questions=related_questions, + server_url=server_url, + uuid=uuid_value, + ) + else: + return redirect(url_for("chatui.index")) + + +# Log the question and response to the server's log file. +def log_question(uid, user_question, response): + date_format = "%m/%d/%Y %H:%M:%S %Z" + date = datetime.now(tz=pytz.utc) + date = date.astimezone(timezone("US/Pacific")) + print("UID: " + str(uid)) + print("Question: " + user_question.strip() + "\n") + print("Response:") + print(response.strip() + "\n") + with open("chatui_logs.txt", "a", encoding="utf-8") as log_file: + log_file.write("[" + date.strftime(date_format) + "][UID " + str(uid) + "]\n") + log_file.write("# " + user_question.strip() + "\n\n") + log_file.write(response.strip() + "\n\n") + log_file.close() + + +def log_like(is_like, uid): + date_format = "%m/%d/%Y %H:%M:%S %Z" + date = datetime.now(tz=pytz.utc) + date = date.astimezone(timezone("US/Pacific")) + print("UID: " + str(uid)) + print("Like: " + str(is_like)) + with open("chatui_logs.txt", "a", encoding="utf-8") as log_file: + log_file.write("[" + date.strftime(date_format) + "][UID " + str(uid) + "]\n") + log_file.write("Like: " + str(is_like) + "\n\n") + log_file.close() diff --git a/demos/palm/python/docs-agent/chatbot/launch.sh b/demos/palm/python/docs-agent/chatbot/launch.sh new file mode 100755 index 000000000..ae94cd222 --- /dev/null +++ b/demos/palm/python/docs-agent/chatbot/launch.sh @@ -0,0 +1,38 @@ +#!/bin/bash + +# +# 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. +# + +# Default values +port=5000 +name='chatbot' +# Specify port number with -p argument `launch.sh -p 5555` +while getopts "n:p:h" opt; do + case $opt in + p) port="${OPTARG}";; + h) echo "Usage: $0 [-p port]"; exit 1;; + \?) echo "Invalid option: -$OPTARG"; exit 1;; + esac +done +# Define your hostname +if [[ -z "$HOSTNAME" ]]; then + export HOSTNAME="localhost" +fi +export FLASK_PORT=$port +export FLASK_APP=$name +export FLASK_DEBUG=true + +flask run --host=$HOSTNAME --port=$FLASK_PORT diff --git a/demos/palm/python/docs-agent/chatbot/static/css/chatbox.css b/demos/palm/python/docs-agent/chatbot/static/css/chatbox.css new file mode 120000 index 000000000..3d344232e --- /dev/null +++ b/demos/palm/python/docs-agent/chatbot/static/css/chatbox.css @@ -0,0 +1 @@ +../../../third_party/css/chatbox.css \ No newline at end of file diff --git a/demos/palm/python/docs-agent/chatbot/static/css/style.css b/demos/palm/python/docs-agent/chatbot/static/css/style.css new file mode 100644 index 000000000..066e442f0 --- /dev/null +++ b/demos/palm/python/docs-agent/chatbot/static/css/style.css @@ -0,0 +1,324 @@ +/** + * 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. + */ + +/* ======= General style for HTML elements ======= */ + +body { + font: 16px/1.5em Overpass, "Open Sans", Helvetica, sans-serif; + color: #333; + font-weight: 300; + max-width: 960px; + margin: auto; + background-color: #d9d9d9; + padding-top: 15px; + padding-bottom: 15px; +} + +a { + color: #22578c; +} + +p { + margin: 0 0 1em; + line-height: 130%; +} + +h1 { + margin: 0 0 0.5em; + font-weight: 500; + font-size: 2.0em; + margin-left: 0.8em; + margin-top: 0.3em; +} + +h2 { + margin: 0; + margin-top: 17px; + margin-bottom: 15px; +} + +h3 { + margin: 0; + margin-top: 10px; + margin-bottom: 10px; +} + +h4 { + color: #505050; + margin: 0; + margin-top: 3px; + margin-bottom: 10px; +} + +li { + margin: 0 0 0.3em; +} + +/* ======= Style layout by ID ======= */ + +#callout-box { + margin: auto; + max-width: 800px; + font: 13px arial, sans-serif; + background-color: white; + border-style: solid; + border-width: 1px; + padding: 10px 25px; + box-shadow: 5px 5px 5px grey; + border-radius: 15px; +} + +/* ======= Style by class ======= */ + +.hidden { + display: none; +} + +.disable { + display: none; +} + +.header-wrapper { + display: flex; +} + +.loading { + font: 15px arial, sans-serif; + width: 100%; + margin-left: 12px; + color: #505050; + padding: 2px; +} + +.notselected { + background-color: #303936e6; + padding-top: 3px; + padding-bottom: 5px; +} + +.notselected:hover { + background-color: #121a17e6; + cursor:pointer; +} + +.selected { + background-color: #1e6a9c; + padding-top: 7px; + padding-bottom: 7px; +} + +.selected:hover { + background-color: #0a619a; + cursor:pointer; +} + +.rewrite { + padding: 15px; + border: 2px solid #000; + margin-top: 6px; + border-radius: 15px; +} + +.question, .response, .response-text, .fact-checked-text, .related-questions { + max-width: 700px; + margin-left: 3px; +} + +.full-response { + max-width: 700px; + margin-left: 10px; +} + +/* ======= Style buttons by ID ======= */ + +#rewrite-button { + border: 0; + background-color: #cf633ff2; + color: #fff; + padding: 7px; + border-radius: 5px; + cursor:pointer; +} + +#rewrite-button:hover { + background: #ce3705f2; + cursor:pointer; +} + +#like-button { + border: 0; + color: #fff; + padding-left: 7px; + padding-right: 7px; + border-radius: 5px; + cursor:pointer; +} + +#submit-button { + border: 0; + background: none; + background-color: #CF5C3F; + color: #fff; + padding: 7px; + border-radius: 5px; + cursor:pointer; +} + +#submit-button:hover { + background: #ce3705f2; + cursor:pointer; +} + +#submit-result { + color: #027f02d6; +} + +#edit-text-area { + font: 13px/1.5em Overpass, "Open Sans", Helvetica, sans-serif; + max-height: 500px; + max-width: 650px; + height: 300px; + width: 650px; + padding: 8px; +} + +#rewrite-question-header { + margin: 0; + margin-bottom: 5px; +} + +#rewrite-response-header { + margin: 0; + margin-top: 10px; + margin-bottom: 5px; +} + +#user-id { + margin: 0; + margin-top: 10px; + margin-bottom: 15px; +} + +#fact-check-url { + margin: 0 0 0.7em; +} + +/* ======= Search Box ======= */ + +.search { + border: 2px solid #CF5C3F; + overflow: auto; + max-width: 700px; + margin-top: 15px; + margin-left: 10px; + margin-bottom: 10px; + border-radius: 5px; +} + +.search input[type="text"] { + border: 0; + width: 91%; + padding: 10px; +} + +.search input[type="text"]:focus { + outline: 0; +} + +.search input[type="submit"] { + border: 0; + background: none; + background-color: #CF5C3F; + color: #fff; + float: right; + padding: 10px; + -moz-border-radius-top-right: 5px; + -webkit-border-radius-top-right: 5px; + -moz-border-radius-bottom-right: 5px; + -webkit-border-radius-bottom-right: 5px; + cursor:pointer; +} + +/* ======= Accordion ======= */ + +.accordion { + max-width: 65em; + margin-bottom: 1em; +} + +.accordion > input[type="checkbox"] { + position: absolute; + left: -100vw; +} + +.accordion .content { + overflow-y: hidden; + height: 0; + transition: height 0.3s ease; +} + +.accordion .reference-content { + font-size: 13px; +} + +.accordion > input[type="checkbox"]:checked ~ .content { + height: auto; + overflow: visible; + padding: 15px; + border: 2px solid #000; + margin-top: 6px; + border-radius: 15px; +} + +.accordion .handle { + margin: 0; + font-size: 1.125em; + line-height: 1.2em; +} + +.accordion label { + display: block; + font-weight: normal; + border: 2px solid #000; + padding: 12px; + background: #4490b8ab; + border-radius: 15px; +} + +.accordion label:hover, +.accordion label:focus { + background: #d9d9d9; + cursor:pointer; +} + +.accordion .handle label::before { + font-family: fontawesome, sans-serif; + display: inline-block; + content: "\2964"; + margin-right: 10px; + font-size: .58em; + line-height: 1.556em; + vertical-align: middle; +} + +.accordion > input[type="checkbox"]:checked ~ .handle label::before { + content: "\2965"; +} + +.accordion p:last-child { + margin-bottom: 0; +} + diff --git a/demos/palm/python/docs-agent/chatbot/static/images/favicon.png b/demos/palm/python/docs-agent/chatbot/static/images/favicon.png new file mode 100644 index 000000000..dd83d19ce Binary files /dev/null and b/demos/palm/python/docs-agent/chatbot/static/images/favicon.png differ diff --git a/demos/palm/python/docs-agent/chatbot/static/javascript/app.js b/demos/palm/python/docs-agent/chatbot/static/javascript/app.js new file mode 100644 index 000000000..0903d0961 --- /dev/null +++ b/demos/palm/python/docs-agent/chatbot/static/javascript/app.js @@ -0,0 +1,146 @@ +/** + * 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. + */ + +// Display the "loading" message when a question is entered and submitted. +let askButton = document.getElementById('ask-button'); +let loadingDiv = document.getElementById('loading-div'); + +if (askButton != null){ + askButton.addEventListener('click',function (){ + console.log("here"); + if (loadingDiv.classList.contains("hidden")){ + loadingDiv.classList.remove("hidden"); + console.log("there"); + } + }); +} + +// Toggle the hidden class on the `rewrite-box` div. +let rewriteButton = document.getElementById('rewrite-button'); + +if (rewriteButton != null){ + rewriteButton.addEventListener('click',function (){ + let rewriteBox = document.getElementById('rewrite-box'); + if (rewriteBox.classList.contains("hidden")){ + rewriteBox.classList.remove("hidden"); + // Trigger a focus event on the textarea + let element = document.getElementById('edit-text-area'); + element.dispatchEvent(new Event("focus")); + }else{ + rewriteBox.classList.add("hidden"); + } + }); +} + +// Toggle the selected class on the `like this response` button. +let likeButton = document.getElementById('like-button'); + +if (likeButton != null){ + likeButton.addEventListener('click',function (){ + if (likeButton.classList.contains("notselected")) { + this.classList.remove("notselected"); + this.classList.add("selected"); + this.value = "Liked" + let uuidBox = document.getElementById('uuid-box'); + let uuid = "Unknown"; + if (uuidBox != null){ + uuid = uuidBox.textContent; + } + let xhr = new XMLHttpRequest(); + // The value of `urlLike` is specified in the html template, + // which is set by the Flask server. + // See chatbot/templates/chatui/base.html + xhr.open("POST", urlLike, true); + xhr.setRequestHeader("Accept", "application/json"); + xhr.setRequestHeader("Content-Type", "application/json"); + let data = JSON.stringify({"like": true, "uuid": uuid}); + xhr.send(data); + }else{ + this.classList.remove("selected"); + this.classList.add("notselected"); + this.value = 'Like this response \uD83D\uDC4D'; + let uuidBox = document.getElementById('uuid-box'); + let uuid = "Unknown"; + if (uuidBox != null){ + uuid = uuidBox.textContent; + } + let xhr = new XMLHttpRequest(); + // The value of `urlLike` is specified in the html template, + // which is set by the Flask server. + // See chatbot/templates/chatui/base.html + xhr.open("POST", urlLike, true); + xhr.setRequestHeader("Accept", "application/json"); + xhr.setRequestHeader("Content-Type", "application/json"); + let data = JSON.stringify({"like": false, "uuid": uuid}); + xhr.send(data); + } + }); +} + +// Adjust the size of the `edit-text-area` textarea. +let rewriteTextArea = document.getElementById('edit-text-area'); + +if (rewriteTextArea != null){ + rewriteTextArea.addEventListener('focus', resize_textarea); + rewriteTextArea.addEventListener('input', resize_textarea); +} + +function resize_textarea(){ + this.style.height = "5px"; + this.style.width = "650px"; + this.style.height = (this.scrollHeight)+"px"; + let rewriteSubmitButton = document.getElementById('submit-button'); + if (rewriteSubmitButton != null){ + if (rewriteSubmitButton.classList.contains("disable")){ + rewriteSubmitButton.classList.remove("disable"); + let submitResult = document.getElementById('submit-result'); + submitResult.textContent = "Click to re-submit updated rewrite."; + } + } +} + +// Make a rewrite POST call. +let rewriteSubmitButton = document.getElementById('submit-button'); + +if (rewriteSubmitButton != null){ + rewriteSubmitButton.addEventListener('click',function (){ + let xhr = new XMLHttpRequest(); + // The value of `urlRewrite` below is specified in the html template, + // which is set by the Flask server. + // See chatbot/templates/chatui/base.html + xhr.open("POST", urlRewrite, true); + xhr.setRequestHeader("Accept", "application/json"); + xhr.setRequestHeader("Content-Type", "application/json"); + let rewriteQuestion = document.getElementById('rewrite-question-span'); + let rewriteOriginalResponse = document.getElementById('rewrite-original-response-span'); + let rewriteTextArea = document.getElementById('edit-text-area'); + let userIDInput = document.getElementById('user-id'); + let userID = userIDInput.value; + if (userID == "") { + userID = "anonymous"; + } + let data = JSON.stringify({ + "user_id": userID, + "question": rewriteQuestion.textContent, + "original_response": rewriteOriginalResponse.textContent, + "rewrite": rewriteTextArea.value}); + xhr.send(data); + + let submitResult = document.getElementById('submit-result'); + submitResult.textContent = "Rewrite has been submitted. Thank you!"; + rewriteSubmitButton.classList.add("disable"); + }, false); +} diff --git a/demos/palm/python/docs-agent/chatbot/templates/chatui/base.html b/demos/palm/python/docs-agent/chatbot/templates/chatui/base.html new file mode 100644 index 000000000..7fee30eb6 --- /dev/null +++ b/demos/palm/python/docs-agent/chatbot/templates/chatui/base.html @@ -0,0 +1,25 @@ + + + + + + + + {{ product }} ChatBot + + +
+

{{ product }} Docs Agent

+
+
+ {% block content %} {% endblock %} +
+
+
+ + + + diff --git a/demos/palm/python/docs-agent/chatbot/templates/chatui/index.html b/demos/palm/python/docs-agent/chatbot/templates/chatui/index.html new file mode 100644 index 000000000..44e3e83e0 --- /dev/null +++ b/demos/palm/python/docs-agent/chatbot/templates/chatui/index.html @@ -0,0 +1,18 @@ +{% extends 'chatui/base.html' %} + +{% block content %} + + + {% if response_in_html %} +
+ {% include 'chatui/result.html' %} +
+ {% endif %} +{% endblock %} diff --git a/demos/palm/python/docs-agent/chatbot/templates/chatui/result.html b/demos/palm/python/docs-agent/chatbot/templates/chatui/result.html new file mode 100644 index 000000000..3dd0446fc --- /dev/null +++ b/demos/palm/python/docs-agent/chatbot/templates/chatui/result.html @@ -0,0 +1,58 @@ +
+

Question

+

{{ question | replace("+", " ") | replace("%3F", "?")}}

+
+
+

PaLM's answer

+ + {{ response_in_html | safe }} + +

Important:

+ {{ fact_checked_answer | safe }} +

To verify this information, please check out:

+ {{ fact_link | safe }} +
+ +
+ +

+ +

+
+ {{ context | safe }} + +

Reference:

+ {{ metadatas | safe }} +
+
+
+
+ + +
+ + diff --git a/demos/palm/python/docs-agent/chroma.py b/demos/palm/python/docs-agent/chroma.py new file mode 100644 index 000000000..403ce462b --- /dev/null +++ b/demos/palm/python/docs-agent/chroma.py @@ -0,0 +1,195 @@ +# +# 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. +# + +"""Chroma wrapper""" + +from enum import auto, Enum +import os +import string + +from absl import logging +import chromadb +from chromadb.config import Settings +from chromadb.utils import embedding_functions +from chromadb.api.models import Collection +from chromadb.api.types import QueryResult + +from palm import PaLM + + +class Error(Exception): + """Base error class for chroma""" + + +class ChromaEmbeddingModelNotSupportedError(Error, RuntimeError): + """Raised if the embedding model specified by a collection is not supported.""" + + +class Chroma: + """Chroma wrapper""" + + def __init__(self, chroma_dir) -> None: + self.client = chromadb.PersistentClient(path=chroma_dir) + + def list_collections(self): + return self.client.list_collections() + + def get_collection(self, name, embedding_function=None): + if embedding_function is not None: + return ChromaCollection( + self.client.get_collection(name, embedding_function=embedding_function), + embedding_function, + ) + # Read embedding meta information from the collection + collection = self.client.get_collection(name, lambda x: None) + embedding_model = None + if collection.metadata: + embedding_model = collection.metadata.get("embedding_model", None) + + if embedding_model == "local/all-mpnet-base-v2": + base_dir = os.path.dirname(os.path.abspath(__file__)) + local_model_dir = os.path.join(base_dir, "models/all-mpnet-base-v2") + embedding_function = ( + embedding_functions.SentenceTransformerEmbeddingFunction( + model_name=local_model_dir + ) + ) + elif embedding_model is None or embedding_model == "palm/embedding-gecko-001": + if embedding_model is None: + logging.warning( + "Embedding model is not stored in the metadata of " + "the collection %s. Using PaLM as default.", + name, + ) + palm = PaLM(embed_model="models/embedding-gecko-001", find_models=False) + # We can not redefine embedding_function with def and + # have to assign a lambda to it + # pylint: disable-next=unnecessary-lambda-assignment + embedding_function = lambda texts: [palm.embed(text) for text in texts] + + else: + raise ChromaEmbeddingModelNotSupportedError( + f"Embedding model {embedding_model} specified by collection {name} " + "is not supported." + ) + + return ChromaCollection( + self.client.get_collection(name, embedding_function=embedding_function), + embedding_function, + ) + + +class Format(Enum): + CONTEXT = auto() + URL = auto() + CLICKABLE_URL = auto() + + +class ChromaQueryResultItem: + """Chroma query result item wrapper + + Chroma query result has the following type: + ``` + class QueryResult(TypedDict): + ids: List[IDs] + embeddings: Optional[List[List[Embedding]]] + documents: Optional[List[List[Document]]] + metadatas: Optional[List[List[Metadata]]] + distances: Optional[List[List[float]]] + ``` + Since the Chroma's query support multiple texts as input, the outer list + corresponds to each of the input text. The inner list corresponds to + the nearest k documents for a specific input text. Since we always only + provide one input text to the query call, our access pattern to the + query result will look like `query_result["documents"][0][i]`, where index + 0 stands for the result for the first (and the only) input text, and index + i stands for the i-th nearest document. + """ + + templates_with_ref_index = { + Format.CONTEXT: "$document **[${ref_index}]**", + Format.URL: "**[${ref_index}]** $url ($distance)", + Format.CLICKABLE_URL: '**[${ref_index}]** $url ($distance)', + } + + templates_without_ref_index = { + Format.CONTEXT: "$document", + Format.URL: "$url", + Format.CLICKABLE_URL: '$url', + } + + def __init__(self, result: QueryResult, index: int) -> None: + self.document = result["documents"][0][index] + self.metadata = result["metadatas"][0][index] + self.distance = result["distances"][0][index] + + def format(self, format_type: Format, ref_index: int = None): + d = { + "document": self.document, + "ref_index": ref_index, + "url": self.metadata.get("url", None), + "distance": self.distance, + } + if ref_index is None: + template = self.templates_without_ref_index[format_type] + else: + template = self.templates_with_ref_index[format_type] + + return string.Template(template).substitute(d) + + +class ChromaQueryResult: + """Chroma query result wrapper""" + + def __init__(self, result: QueryResult) -> None: + self.result = result + + def __len__(self): + return len(self.result["documents"][0]) + + def fetch(self, distance_threshold=float("inf")): + for i in range(len(self)): + item = ChromaQueryResultItem(self.result, i) + if item.distance < distance_threshold: + yield item + + def fetch_formatted(self, format_type: Format, distance_threshold=float("inf")): + return "\n\n".join( + item.format(format_type, i + 1) + for i, item in enumerate(self.fetch(distance_threshold=distance_threshold)) + ) + + def fetch_nearest(self): + return ChromaQueryResultItem(self.result, 0) + + def fetch_nearest_formatted(self, format_type: Format): + return self.fetch_nearest().format(format_type) + + +class ChromaCollection: + """Chroma collection wrapper""" + + def __init__(self, collection: Collection, embedding_function) -> None: + self.collection = collection + self.embedding_function = embedding_function + + def query(self, text: str, top_k: int = 1): + return ChromaQueryResult( + self.collection.query(query_texts=[text], n_results=top_k) + ) + + def embed(self, text: str): + return self.embedding_function(text) diff --git a/demos/palm/python/docs-agent/condition.txt b/demos/palm/python/docs-agent/condition.txt new file mode 100644 index 000000000..f28fbd493 --- /dev/null +++ b/demos/palm/python/docs-agent/condition.txt @@ -0,0 +1,2 @@ +You are a helpful chatbot answering questions from users. Read the following context first +and answer the question at the end: diff --git a/demos/palm/python/docs-agent/config.yaml b/demos/palm/python/docs-agent/config.yaml new file mode 100644 index 000000000..13ee5b35b --- /dev/null +++ b/demos/palm/python/docs-agent/config.yaml @@ -0,0 +1,49 @@ +# +# 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. +# + +# Configuration file for Docs Agent + +# `product_name` is the name of your product that appears on the chatbot UI. +product_name: "My product" + +# `output_path` is the directory where the plain text files will be saved +# after Markdown files are processed by `markdown-to-plain-text.py`. It is +# relative to the Doc Agent repo folder. +output_path: "data/plain_docs" + +# `vector_db_dir` is the directory that stores artifacts for the Chroma vector +# database. +vector_db_dir: "vector_stores/chroma" + +# `collection_name` is the name used by the Chroma vector database to identify +# your dataset collection. +collection_name: "docs_collection" + +# You can list a number of input sources under the `input` field: +# `path` (Required): The directory where the source Markdown files are +# stored. +# `url_prefix` (Required): The prefix of the URL used to create URLs for the +# source files. If URLs don't exist for the source +# files, you still need to provide a mock string. +# `exclude_path` (Optional): The sub-directory to be excluded from the `path` +# directory when processing source files. +input: + - path: "data/example/markdown-src-01" + url_prefix: "https://example.com/markdown-src-01" + - path: "data/example/markdown-src-02" + url_prefix: "https://example.com/makrdown-src-02" + exclude_path: "/reference/changelogs/" + diff --git a/demos/palm/python/docs-agent/docs/images/docs-agent-architecture-01.png b/demos/palm/python/docs-agent/docs/images/docs-agent-architecture-01.png new file mode 100644 index 000000000..21912a876 Binary files /dev/null and b/demos/palm/python/docs-agent/docs/images/docs-agent-architecture-01.png differ diff --git a/demos/palm/python/docs-agent/docs/images/docs-agent-architecture-02.png b/demos/palm/python/docs-agent/docs/images/docs-agent-architecture-02.png new file mode 100644 index 000000000..9eff8da5f Binary files /dev/null and b/demos/palm/python/docs-agent/docs/images/docs-agent-architecture-02.png differ diff --git a/demos/palm/python/docs-agent/docs/images/docs-agent-embeddings-01.png b/demos/palm/python/docs-agent/docs/images/docs-agent-embeddings-01.png new file mode 100644 index 000000000..79df22fb6 Binary files /dev/null and b/demos/palm/python/docs-agent/docs/images/docs-agent-embeddings-01.png differ diff --git a/demos/palm/python/docs-agent/docs/images/docs-agent-embeddings-02.png b/demos/palm/python/docs-agent/docs/images/docs-agent-embeddings-02.png new file mode 100644 index 000000000..6be46d0af Binary files /dev/null and b/demos/palm/python/docs-agent/docs/images/docs-agent-embeddings-02.png differ diff --git a/demos/palm/python/docs-agent/docs/images/docs-agent-prompt-structure-01.png b/demos/palm/python/docs-agent/docs/images/docs-agent-prompt-structure-01.png new file mode 100644 index 000000000..64119ac07 Binary files /dev/null and b/demos/palm/python/docs-agent/docs/images/docs-agent-prompt-structure-01.png differ diff --git a/demos/palm/python/docs-agent/docs/images/docs-agent-ui-screenshot-01.png b/demos/palm/python/docs-agent/docs/images/docs-agent-ui-screenshot-01.png new file mode 100644 index 000000000..d680cae39 Binary files /dev/null and b/demos/palm/python/docs-agent/docs/images/docs-agent-ui-screenshot-01.png differ diff --git a/demos/palm/python/docs-agent/docs_agent.py b/demos/palm/python/docs-agent/docs_agent.py new file mode 100644 index 000000000..dde2bb19f --- /dev/null +++ b/demos/palm/python/docs-agent/docs_agent.py @@ -0,0 +1,151 @@ +# +# 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. +# + +"""Docs Agent""" + +import os +import sys + +from absl import logging +import google.api_core + +from chroma import Chroma +from palm import PaLM + +from scripts import read_config + +### Set up the PaLM API key from the environment ### +API_KEY = os.getenv("PALM_API_KEY") +if API_KEY is None: + sys.exit("Please set the environment variable PALM_API_KEY to be your API key.") + +### Select your PaLM API endpoint ### +PALM_API_ENDPOINT = "generativelanguage.googleapis.com" + +palm = PaLM(api_key=API_KEY, api_endpoint=PALM_API_ENDPOINT) + +BASE_DIR = os.path.dirname(os.path.abspath(__file__)) + +### Set up the path to the chroma vector database ### +LOCAL_VECTOR_DB_DIR = os.path.join(BASE_DIR, "vector_stores/chroma") +COLLECTION_NAME = "docs_collection" + +IS_CONFIG_FILE = True +if IS_CONFIG_FILE: + config_values = read_config.ReadConfig() + LOCAL_VECTOR_DB_DIR = config_values.returnConfigValue("vector_db_dir") + COLLECTION_NAME = config_values.returnConfigValue("collection_name") + +### Set up the path to the `condition.txt` file that holds custom condition text. ### +CONDITION_FILE = os.path.join(BASE_DIR, "condition.txt") + +### Select the number of contents to be used for providing context +NUM_RETURNS = 5 + + +class DocsAgent: + """DocsAgent class""" + + prompt_condition = ( + "Answer the question below as truthfully as possible, " + "and if you're unsure of the answer, say \"Sorry, I don't know.\"" + ) + text_model_error_response = ( + "I'm a large language model. " + "I'm currently not able to help you with that question. " + "You may rephrase your question with more specifics and try again." + ) + chat_model_error_response = ( + "I'm a large language model. " + "I'm currently not able to help you with that question." + ) + palm_none_response = ( + "PaLM is not able to answer this question at the moment. " + "You may rephrase the question and ask again." + ) + + def __init__(self): + # Initialize the Chroma vector database + logging.info( + "Using the local vector database created at %s", LOCAL_VECTOR_DB_DIR + ) + self.chroma = Chroma(LOCAL_VECTOR_DB_DIR) + self.collection = self.chroma.get_collection(COLLECTION_NAME) + # Update PaLM's condition string + self.update_condition_from_file() + + # Use this method for talking to PaLM (Text) + def ask_text_model_with_context(self, context, question): + new_prompt = f"{context}\nQuestion: {question}" + try: + response = palm.generate_text( + prompt=new_prompt, + max_output_tokens=800, + candidate_count=1, + temperature=0.0, + ) + except google.api_core.exceptions.InvalidArgument: + return self.text_model_error_response + if response.result is None: + print("Block reason: " + str(response.filters)) + print("Safety feedback: " + str(response.safety_feedback)) + return self.palm_none_response + return response.result + + # Use this method for talking to PaLM (Chat) + def ask_chat_model_with_context(self, context, question): + try: + response = palm.chat( + context=context, + messages=question, + temperature=0.05, + ) + except google.api_core.exceptions.InvalidArgument: + return self.chat_model_error_response + + if response.last is None: + return self.palm_none_response + return response.last + + # Use this method for asking PaLM (Text) for fact-checking + def ask_text_model_to_fact_check(self, context, prev_response): + question = ( + "Can you compare the following body of text " + "to the context provided in this prompt and " + "write a short message that warns the readers about " + "which part of the text below they should consider " + "fact-checking for themselves? " + "(please keep your response concise and mention " + "only one important point):\n\n" + ) + question += prev_response + return self.ask_text_model_with_context(context, question) + + # Query the local Chroma vector database using the user question + def query_vector_store(self, question): + return self.collection.query(question, NUM_RETURNS) + + # Add specific instruction as a prefix to the context + def add_instruction_to_context(self, context): + new_context = "" + new_context += self.prompt_condition + "\n" + context + return new_context + + # Update the condition string for PaLM from the `condition.txt` file + def update_condition_from_file(self): + with open(CONDITION_FILE, "r", encoding="utf-8") as text_file: + self.prompt_condition = text_file.read() + text_file.close() diff --git a/demos/palm/python/docs-agent/hello_world.py b/demos/palm/python/docs-agent/hello_world.py new file mode 100644 index 000000000..b8a1ae240 --- /dev/null +++ b/demos/palm/python/docs-agent/hello_world.py @@ -0,0 +1,60 @@ +# +# 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. +# + +"""Hello World for Docs Agent""" + +from chroma import Format +from docs_agent import DocsAgent + +# This `hello_world` script contains the minimal set of function calls +# needed to use Docs Agent. +# +# Note: This script assumes that the vector database has already been populated. +# +# To run this script: +# $ python3 hello_world.py +# + +# Initialize Docs Agent. +print("STATE: Initializing Docs Agent.") +docs_agent = DocsAgent() + +# This question is used for testing. +question = "What are some differences between apples and oranges?" + +# Print the question. +print("\nQuestion: " + question) + +# Pass the question to the vector database and get a list of the most relevant content. +result = docs_agent.query_vector_store(question) +context = result.fetch_formatted(Format.CONTEXT) + +# Add instruction (see `condition.txt`) as a prefix to the context. +context_with_prefix = docs_agent.add_instruction_to_context(context) + +print("\nSending the prompt to PaLM 2...") + +# Pass the context and question to PaLM 2's `text-bison-001` model. +response_text = docs_agent.ask_text_model_with_context(context_with_prefix, question) +print("\n[Text answer]:") +print(response_text) + +# Pass the context and question to PaLM 2's `chat-bison-001` model. +response_chat = docs_agent.ask_chat_model_with_context(context_with_prefix, question) +print("\n[Chat answer]:") +print(response_chat) + +print("") diff --git a/demos/palm/python/docs-agent/palm.py b/demos/palm/python/docs-agent/palm.py new file mode 100644 index 000000000..448001b38 --- /dev/null +++ b/demos/palm/python/docs-agent/palm.py @@ -0,0 +1,153 @@ +# +# 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. +# + +"""Rate limited PaLM wrapper""" + +import os + +import google.generativeai +from ratelimit import limits, sleep_and_retry +from typing import List + + +class Error(Exception): + """Base error class for palm""" + + +class PaLMNoAPIKeyError(Error, RuntimeError): + """Raised if no API key is provided nor found in environment variable.""" + + def __init__(self) -> None: + super().__init__( + "PaLM API key is not provided " + "or set in the environment variable PALM_API_KEY" + ) + + +class PaLMUnsupportedModelError(Error, RuntimeError): + """Raised if a specified model is not supported by the endpoint.""" + + def __init__(self, model, api_endpoint) -> None: + super().__init__( + f"The specified model {model} is not supported " + f"on the API endpoint {api_endpoint}" + ) + + +class PaLMNoModelError(Error, RuntimeError): + """Raised if calling an API but the corresponding model is not specified.""" + + def __init__(self, func_name, attr) -> None: + super().__init__( + f"Cannot call PaLM.{func_name}() since PaLM.{attr} is not set " + "during class initialization." + ) + + +class PaLM: + """Rate limited PaLM wrapper + + This class exposes PaLM's chat, text, and embedding API, but with a rate limit. + Besides the rate limit, the `chat` and `generate_text` method has the same name and + behavior as `google.generativeai.chat` and `google.generativeai.generate_text`, + respectively. The `embed` method is different from + `google.generativeai.generate_embeddings` since `embed` returns List[float] + while `google.generativeai.generate_embeddings` returns a dict. And that's why it + has a different name. + """ + + DEFAULT_ENDPOINT = "generativelanguage.googleapis.com" # Prod endpoint + MINUTE = 60 # seconds in a minute + MAX_EMBED_PER_MINUTE = 1500 + MAX_MESSAGE_PER_MINUTE = 30 + MAX_TEXT_PER_MINUTE = 30 + + def __init__( + self, + api_key=None, + api_endpoint=DEFAULT_ENDPOINT, + chat_model=None, + text_model=None, + embed_model=None, + find_models=True, + ) -> None: + if api_key is None: + api_key = os.getenv("PALM_API_KEY") + if api_key is None: + raise PaLMNoAPIKeyError() + google.generativeai.configure( + api_key=api_key, client_options={"api_endpoint": api_endpoint} + ) + self.api_endpoint = api_endpoint + self.chat_model = chat_model + self.text_model = text_model + self.embed_model = embed_model + + # Check whether the specified models are supported + supported_models = set( + model.name for model in google.generativeai.list_models() + ) + for model in (chat_model, text_model, embed_model): + if model and model not in supported_models: + raise PaLMUnsupportedModelError(model, api_endpoint) + + # Check whether we need to find available models + if (not find_models) or ( + chat_model is not None + and text_model is not None + and embed_model is not None + ): + return + # Find available models + for model in google.generativeai.list_models(): + if ( + self.chat_model is None + and "generateMessage" in model.supported_generation_methods + ): + self.chat_model = model.name + if ( + self.text_model is None + and "generateText" in model.supported_generation_methods + ): + self.text_model = model.name + if ( + self.embed_model is None + and "embedText" in model.supported_generation_methods + ): + self.embed_model = model.name + + @sleep_and_retry + @limits(calls=MAX_MESSAGE_PER_MINUTE, period=MINUTE) + def chat(self, *args, **kwargs): + if self.chat_model is None: + raise PaLMNoModelError(func_name="chat", attr="chat_model") + return google.generativeai.chat(*args, model=self.chat_model, **kwargs) + + @sleep_and_retry + @limits(calls=MAX_TEXT_PER_MINUTE, period=MINUTE) + def generate_text(self, *args, **kwargs): + if self.text_model is None: + raise PaLMNoModelError(func_name="generate_text", attr="text_model") + return google.generativeai.generate_text(*args, model=self.text_model, **kwargs) + + @sleep_and_retry + @limits(calls=MAX_EMBED_PER_MINUTE, period=MINUTE) + def embed(self, text: str) -> List[float]: + if self.embed_model is None: + raise PaLMNoModelError(func_name="embed", attr="embed_model") + return google.generativeai.generate_embeddings( + model=self.embed_model, text=text + )["embedding"] diff --git a/demos/palm/python/docs-agent/poetry.lock b/demos/palm/python/docs-agent/poetry.lock new file mode 100644 index 000000000..6220b567d --- /dev/null +++ b/demos/palm/python/docs-agent/poetry.lock @@ -0,0 +1,4417 @@ +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. + +[[package]] +name = "absl-py" +version = "1.4.0" +description = "Abseil Python Common 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Available formats are text, parseable, colorized, msvs +# (visual studio) and html. You can also give a reporter class, eg +# mypackage.mymodule.MyReporterClass. +output-format=text + +# Tells whether to display a full report or only the messages +reports=no + +# Python expression which should return a note less than 10 (10 is the highest +# note). You have access to the variables errors warning, statement which +# respectively contain the number of errors / warnings messages and the total +# number of statements analyzed. This is used by the global evaluation report +# (RP0004). +evaluation=10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10) + +# Template used to display messages. This is a python new-style format string +# used to format the message information. See doc for all details +#msg-template= + + +[BASIC] + +# Good variable names which should always be accepted, separated by a comma +good-names=main,_ + +# Bad variable names which should always be refused, separated by a comma +bad-names= + +# Colon-delimited sets of names that determine each other's naming style when +# the name regexes allow several styles. +name-group= + +# Include a hint for the correct naming format with invalid-name +include-naming-hint=no + +# List of decorators that produce properties, such as abc.abstractproperty. Add +# to this list to register other decorators that produce valid properties. +property-classes=abc.abstractproperty,cached_property.cached_property,cached_property.threaded_cached_property,cached_property.cached_property_with_ttl,cached_property.threaded_cached_property_with_ttl + +# Regular expression matching correct function names +function-rgx=^(?:(?PsetUp|tearDown|setUpModule|tearDownModule)|(?P_?[A-Z][a-zA-Z0-9]*)|(?P_?[a-z][a-z0-9_]*))$ + +# Regular expression matching correct variable names +variable-rgx=^[a-z][a-z0-9_]*$ + +# Regular expression matching correct constant names +const-rgx=^(_?[A-Z][A-Z0-9_]*|__[a-z0-9_]+__|_?[a-z][a-z0-9_]*)$ + +# Regular expression matching correct attribute names +attr-rgx=^_{0,2}[a-z][a-z0-9_]*$ + +# Regular expression matching correct argument names +argument-rgx=^[a-z][a-z0-9_]*$ + +# Regular expression matching correct class attribute names +class-attribute-rgx=^(_?[A-Z][A-Z0-9_]*|__[a-z0-9_]+__|_?[a-z][a-z0-9_]*)$ + +# Regular expression matching correct inline iteration names +inlinevar-rgx=^[a-z][a-z0-9_]*$ + +# Regular expression matching correct class names +class-rgx=^_?[A-Z][a-zA-Z0-9]*$ + +# Regular expression matching correct module names +module-rgx=^(_?[a-z][a-z0-9_]*|__init__)$ + +# Regular expression matching correct method names +method-rgx=(?x)^(?:(?P_[a-z0-9_]+__|runTest|setUp|tearDown|setUpTestCase|tearDownTestCase|setupSelf|tearDownClass|setUpClass|(test|assert)_*[A-Z0-9][a-zA-Z0-9_]*|next)|(?P_{0,2}[A-Z][a-zA-Z0-9_]*)|(?P_{0,2}[a-z][a-z0-9_]*))$ + +# Regular expression which should only match function or class names that do +# not require a docstring. +no-docstring-rgx=(__.*__|main|test.*|.*test|.*Test)$ + +# Minimum line length for functions/classes that require docstrings, shorter +# ones are exempt. +docstring-min-length=10 + + +[TYPECHECK] + +# List of decorators that produce context managers, such as +# contextlib.contextmanager. Add to this list to register other decorators that +# produce valid context managers. +contextmanager-decorators=contextlib.contextmanager,contextlib2.contextmanager + +# Tells whether missing members accessed in mixin class should be ignored. A +# mixin class is detected if its name ends with "mixin" (case insensitive). +ignore-mixin-members=yes + +# List of module names for which member attributes should not be checked +# (useful for modules/projects where namespaces are manipulated during runtime +# and thus existing member attributes cannot be deduced by static analysis. It +# supports qualified module names, as well as Unix pattern matching. +ignored-modules= + +# List of class names for which member attributes should not be checked (useful +# for classes with dynamically set attributes). This supports the use of +# qualified names. +ignored-classes=optparse.Values,thread._local,_thread._local + +# List of members which are set dynamically and missed by pylint inference +# system, and so shouldn't trigger E1101 when accessed. Python regular +# expressions are accepted. +generated-members= + + +[FORMAT] + +# Maximum number of characters on a single line. +max-line-length=88 + +# TODO(https://github.com/PyCQA/pylint/issues/3352): Direct pylint to exempt +# lines made too long by directives to pytype. + +# Regexp for a line that is allowed to be longer than the limit. +ignore-long-lines=(?x)( + ^\s*(\#\ )??$| + ^\s*(from\s+\S+\s+)?import\s+.+$) + +# Allow the body of an if to be on the same line as the test if there is no +# else. +single-line-if-stmt=yes + +# Maximum number of lines in a module +max-module-lines=99999 + +# String used as indentation unit. The internal Google style guide mandates 2 +# spaces. Google's externaly-published style guide says 4, consistent with +# PEP 8. Here, we use 2 spaces, for conformity with many open-sourced Google +# projects (like TensorFlow). +indent-string=' ' + +# Number of spaces of indent required inside a hanging or continued line. +indent-after-paren=4 + +# Expected format of line ending, e.g. empty (any line ending), LF or CRLF. +expected-line-ending-format= + + +[MISCELLANEOUS] + +# List of note tags to take in consideration, separated by a comma. +notes=TODO + + +[STRING] + +# This flag controls whether inconsistent-quotes generates a warning when the +# character used as a quote delimiter is used inconsistently within a module. +check-quote-consistency=yes + + +[VARIABLES] + +# Tells whether we should check for unused import in __init__ files. +init-import=no + +# A regular expression matching the name of dummy variables (i.e. expectedly +# not used). +dummy-variables-rgx=^\*{0,2}(_$|unused_|dummy_) + +# List of additional names supposed to be defined in builtins. Remember that +# you should avoid to define new builtins when possible. +additional-builtins= + +# List of strings which can identify a callback function by name. A callback +# name must start or end with one of those strings. +callbacks=cb_,_cb + +# List of qualified module names which can have objects that can redefine +# builtins. +redefining-builtins-modules=six,six.moves,past.builtins,future.builtins,functools + + +[LOGGING] + +# Logging modules to check that the string format arguments are in logging +# function parameter format +logging-modules=logging,absl.logging,tensorflow.io.logging + + +[SIMILARITIES] + +# Minimum lines number of a similarity. +min-similarity-lines=4 + +# Ignore comments when computing similarities. +ignore-comments=yes + +# Ignore docstrings when computing similarities. +ignore-docstrings=yes + +# Ignore imports when computing similarities. +ignore-imports=no + + +[SPELLING] + +# Spelling dictionary name. Available dictionaries: none. To make it working +# install python-enchant package. +spelling-dict= + +# List of comma separated words that should not be checked. +spelling-ignore-words= + +# A path to a file that contains private dictionary; one word per line. +spelling-private-dict-file= + +# Tells whether to store unknown words to indicated private dictionary in +# --spelling-private-dict-file option instead of raising a message. +spelling-store-unknown-words=no + + +[IMPORTS] + +# Deprecated modules which should not be used, separated by a comma +deprecated-modules=regsub, + TERMIOS, + Bastion, + rexec, + sets + +# Create a graph of every (i.e. internal and external) dependencies in the +# given file (report RP0402 must not be disabled) +import-graph= + +# Create a graph of external dependencies in the given file (report RP0402 must +# not be disabled) +ext-import-graph= + +# Create a graph of internal dependencies in the given file (report RP0402 must +# not be disabled) +int-import-graph= + +# Force import order to recognize a module as part of the standard +# compatibility libraries. +known-standard-library= + +# Force import order to recognize a module as part of a third party library. +known-third-party=enchant, absl + +# Analyse import fallback blocks. This can be used to support both Python 2 and +# 3 compatible code, which means that the block might have code that exists +# only in one or another interpreter, leading to false positives when analysed. +analyse-fallback-blocks=no + + +[CLASSES] + +# List of method names used to declare (i.e. assign) instance attributes. +defining-attr-methods=__init__, + __new__, + setUp + +# List of member names, which should be excluded from the protected access +# warning. +exclude-protected=_asdict, + _fields, + _replace, + _source, + _make + +# List of valid names for the first argument in a class method. +valid-classmethod-first-arg=cls, + class_ + +# List of valid names for the first argument in a metaclass class method. +valid-metaclass-classmethod-first-arg=mcs + + +[EXCEPTIONS] + +# Exceptions that will emit a warning when being caught. Defaults to +# "Exception" +overgeneral-exceptions=builtins.StandardError, + builtins.Exception, + builtins.BaseException diff --git a/demos/palm/python/docs-agent/pyproject.toml b/demos/palm/python/docs-agent/pyproject.toml new file mode 100644 index 000000000..60ac1fbbf --- /dev/null +++ b/demos/palm/python/docs-agent/pyproject.toml @@ -0,0 +1,43 @@ +[tool.poetry] +name = "docs-agent" +version = "0.1.1" +description = "" +authors = ["Docs Agent contributors"] +readme = "README.md" + +[tool.poetry.dependencies] +python = ">=3.10,<3.12" +rich = "^13.3.5" +Markdown = "^3.4.3" +beautifulsoup4 = "^4.12.2" +protobuf = ">=3.20" +chromadb = "==0.4.13" +sentence-transformers = "^2.2.2" +ratelimit = "^2.2.1" +absl-py = "^1.4.0" +python-frontmatter = "^1.0.0" +flatdict = "^4.0.1" +google-generativeai = "^0.1.0" +grpcio = "^1.57.0" +grpcio-tools = "^1.57.0" +uuid = "^1.30" +pytz = ">=2020.1" + +[tool.poetry.group.dev.dependencies] +ipython = "^8.13.2" +pylint = "^2.17.4" +black = "^23.3.0" + +[tool.poetry.group.internal] +optional = true + +[tool.poetry.group.internal.dependencies] +seqio = "^0.0.16" + +[tool.poetry.group.chatbot.dependencies] +flask = "^2.3.2" +flask-cors = "^3.0.10" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/demos/palm/python/docs-agent/run_console.py b/demos/palm/python/docs-agent/run_console.py new file mode 100644 index 000000000..3041aad21 --- /dev/null +++ b/demos/palm/python/docs-agent/run_console.py @@ -0,0 +1,72 @@ +# +# 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. +# + +"""Run the Docs Agent console in the terminal""" + +from absl import logging +from rich.console import Console +from rich.markdown import Markdown +from rich.panel import Panel +import sys + +from chroma import Format +from docs_agent import DocsAgent + +# Set logging level to WARNING or above to disable progress bar +logging.set_verbosity(logging.WARNING) + +# Initialize Rich console +ai_console = Console(width=160) +ai_console.rule("Fold") + +# Initialize Docs Agent +ai_console.print("STATE: Initializing Docs Agent.") +docs_agent = DocsAgent() +ai_console.print("\nHello! I'm PaLM 2.\n") + +# First question (for quick testing) +question = "What are some differences between apples and oranges?" + +# User input loop +while True: + # Get context from the vector database + result = docs_agent.query_vector_store(question) + context = result.fetch_formatted(Format.CONTEXT) + # Add instruction to the context + context_with_prefix = docs_agent.add_instruction_to_context(context) + # Print the context + ai_console.print(Panel.fit(Markdown("\nContext: " + context_with_prefix))) + # Get URLs of the context from the vector database + metadatas = result.fetch_formatted(Format.URL) + ai_console.print(Panel.fit(Markdown(metadatas))) + # Print the question + ai_console.print(Panel.fit("Question: " + question)) + ai_console.print("\nPaLM 2:") + # Pass the context and question to PaLM 2 (Text) + response_text = docs_agent.ask_text_model_with_context(context_with_prefix, question) + ai_console.print("\n[Text answer]:") + ai_console.print(Panel.fit(Markdown(response_text))) + # Pass the context and question to PaLM 2 (Chat) + response_chat = docs_agent.ask_chat_model_with_context(context_with_prefix, question) + ai_console.print("\n[Chat answer]:") + ai_console.print(Panel.fit(Markdown(response_chat))) + # Keep asking questions to PaLM 2 + ai_console.print("\n######## Ask PaLM 2 ########") + question = input("How can I help?\n> ") + ai_console.print("") + if question.startswith("exit"): + ai_console.print("Goodbye!") + sys.exit() diff --git a/demos/palm/python/docs-agent/scripts/markdown_to_plain_text.py b/demos/palm/python/docs-agent/scripts/markdown_to_plain_text.py new file mode 100644 index 000000000..8f1585dd7 --- /dev/null +++ b/demos/palm/python/docs-agent/scripts/markdown_to_plain_text.py @@ -0,0 +1,419 @@ +# +# 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. +# + +"""Process Markdown files into plain text""" + +from markdown import markdown +from bs4 import BeautifulSoup +import os +import re +import json +import frontmatter +import read_config +import uuid + +### Makrdown splitter ### +# By default, use the custom Markdown splitter +# (instead of splitting Markdown by CHUNK_SIZE limit.) +USE_CUSTOM_MARKDOWN_SPLITTER = True + +#### Chunk size #### +# By default, split Markdown files into 3000 chracters per chunk. +CHUNK_SIZE = 3000 + +#### Input variables for the script #### +# +# Note: The hardcoded values below are overwritten +# if the `input-values.yaml` file is found. +# +# MY_INPUT_PATH: An array of directories that contain source Markdown files. +# URL_PREFIX: An array of prefixes to be used to create URLs for source Markdown files. +# MY_OUTPUT_PATH: The target directory where processed plain text files will be stored. + +MY_INPUT_PATH = ["data/raw/markdown-src-01", "data/raw/markdown-src-02"] +URL_PREFIX = [ + "https://my-example.com/markdown-src-01", + "https://my-example.com/markdown-src-02", +] +MY_OUTPUT_PATH = "data/plain_docs" + +#### Read the `input-values-yaml` file #### +# At a minimum, INPUT_YAML must configure the following values: +# output_path: The target directory where processed plain text files will be stored. +# input: +# - path: A directory that contains source Markdown files. +# url_prefix: A prefix to be used to create URLs for the source Markdown files. +IS_CONFIG_FILE = True +if IS_CONFIG_FILE: + config = read_config.ReadConfig() + MY_OUTPUT_PATH = config.returnConfigValue("output_path") + input_len = config.returnInputCount() + +print("Started the markdown-to-plain-text.py script.") + +BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) + + +def resolve_path(rel_or_abs_path: str, base_dir=BASE_DIR): + path = rel_or_abs_path.strip() + if path.startswith("/"): + return path + else: + return os.path.join(base_dir, path) + + +MY_OUTPUT_PATH = resolve_path(MY_OUTPUT_PATH) +os.makedirs(MY_OUTPUT_PATH, exist_ok=True) + +print("Set the output directory: " + MY_OUTPUT_PATH) +print("Processing files from " + str(input_len) + " sources.") + + +# This function converts a Markdown string to plain text. +def markdown_to_text(markdown_string): + # Remove lines in Markdown + markdown_string = re.sub(r"<\!--(.*?)-->", "", markdown_string) + + # md -> html -> text since BeautifulSoup can extract text cleanly + html = markdown(markdown_string) + + # Extract text + soup = BeautifulSoup(html, "html.parser") + text = "".join(soup.findAll(string=True)) + + # Remove [][] in Markdown + text = re.sub(r"\[(.*?)\]\[(.*?)\]", "\\1", text) + + # Remove {: } in devsite Markdown + text = re.sub(r"\{:(.*?)\}", "", text) + + # Remove {. } in g3doc Markdown + text = re.sub(r"\{.(.*?)\}", "", text) + + # Remove a single line `sh` in g3doc Markdown + text = re.sub(r"(?m)^sh$", "", text) + + # Remove a single line ````sh` in g3doc Markdown + # text = re.sub(r'(?m)^```sh$', '', text) + + # Remove code snippets + text = re.sub(r"
(.*?)
", "\\1", text) + text = re.sub(r"(.*?)", "\\1", text) + text = re.sub(r"(?m)(.*?)", "\\1", text) + text = re.sub( + r"(^|)(Important|Note|Caution|Tip|Warning|Important|Key Point|Key Term):\s?", + "", + text, + ) + text = re.sub( + r"(^|)(Objective|Success|Beta|Preview|Deprecated):\s?", + "", + text, + ) + text = re.sub(r"(Project|Book):(.*)\n", "", text) + text = text.strip() + "\n" + return text + + +# Function to verify that include exists and exports its content +def read_markdown(file): + try: + with open(file, "r", encoding="utf-8") as mdfile: + output = mdfile.read() + return output + except FileNotFoundError: + print("[FileNotFound] Missing the include file: " + file) + + +# This function converts Markdown page (#), section (##), and subsection (###) +# headings into plain English. +def process_page_and_section_titles(markdown_text): + updated_markdown = "" + page_title = "" + section_title = "" + subsection_title = "" + new_line = "" + metadata = {} + # Processes the frontmatter in a markdown file + data = frontmatter.loads(markdown_text) + if "title" in data: + page_title = data["title"] + markdown_text = data.content + metadata = data.metadata + if "URL" in data: + final_url = data["URL"] + metadata["URL"] = final_url + for line in markdown_text.split("\n"): + new_line = "" + skip_this_line = False + if line.startswith("#"): + match = re.search(r"^(\#*)\s+(.*)$", line) + heading = "" + captured_title = "" + if match: + heading = match[1] + # Remove {: } in devsite Markdown + captured_title = re.sub(r"\{:(.*?)\}", "", match[2]) + # Special case of RFC pages. + if re.search(r"^\{\{\s+(.*)\.(.*)\s+\}\}$", captured_title): + heading = "" + page_title = "RFC" + skip_this_line = True + + # Detect Markdown heading levels + if heading == "#": + page_title = captured_title.strip() + metadata["title"] = page_title + subsection_title = "" + section_title = "" + elif heading == "##": + section_title = captured_title.strip() + subsection_title = "" + elif heading == "###": + subsection_title = captured_title.strip() + + # Convert Markdown headings into plain English + # (but keep `#` for the `process_document_into_sections()` + # function to detect these headings for splitting). + if page_title: + new_line = ( + '# The "' + + page_title + + '" page contains the following content:\n\n' + ) + + if section_title: + new_line = ( + '# The "' + + page_title + + '" page has the "' + + section_title + + '" section that contains the following content:\n' + ) + + if subsection_title: + new_line = ( + '# On the "' + + page_title + + '" page, the "' + + section_title + + '" section has the "' + + subsection_title + + '" subsection that contains the following content:\n' + ) + + if skip_this_line is False: + if new_line: + updated_markdown += new_line + "\n" + else: + updated_markdown += line + "\n" + return updated_markdown, metadata + + +# This function replaces Markdown's includes sections with content. +def process_includes(markdown_text, root): + updated_markdown = "" + for line in markdown_text.split("\n"): + new_line = "" + # Replaces Markdown includes with content + if line.startswith("<<"): + include_match = re.search("^<<(.*?)>>", line) + if include_match: + include_file = os.path.abspath(root + "/" + include_match[1]) + new_line = read_markdown(include_file) + if new_line: + updated_markdown += new_line + "\n" + else: + updated_markdown += line + "\n" + return updated_markdown + + +# This function divides Markdown content into sections and +# returns an array containing these sections. +# But this function requires pre-processed Markdown headings from +# the `process_page_and_section_titles()` function, which simplifies +# three levels of Markdown headings (#, ##, and ###) into just a single #. +def process_document_into_sections(markdown_text): + sections = [] + buffer = "" + first_section = True + for line in markdown_text.split("\n"): + if line.startswith("#"): + match = re.search(r"^(\#*)\s+(.*)$", line) + heading = "" + if match: + heading = match[1] + if heading == "#": + if first_section is True: + # Ignore the first detection of `#`. + first_section = False + else: + # When a new `#` is detected, store the text in `buffer` into + # an array entry and clear the buffer for the next section. + sections.append(buffer) + buffer = "" + buffer += line + "\n" + # Add the last section on the page. + sections.append(buffer) + return sections + + +# This function processes Markdown files in the `input_path` directory +# into plain text files. +def process_markdown_files_from_source(configs, inputpath, counter, excludepath): + f_count = 0 + for root, dirs, files in os.walk(resolve_path(inputpath)): + if IS_CONFIG_FILE: + if "exclude_path" in configs[counter]: + dirs[:] = [d for d in dirs if d not in excludepath] + if "url_prefix" in configs[counter]: + namespace_uuid = uuid.uuid3( + uuid.NAMESPACE_DNS, configs[counter]["url_prefix"] + ) + for file in files: + f_count += 1 + # Process only Markdown files + if file.endswith(".md"): + with open(os.path.join(root, file), "r", encoding="utf-8") as auto: + # Construct a new sub-directory for storing output plain text files + new_path = MY_OUTPUT_PATH + re.sub( + resolve_path(inputpath), "", os.path.join(root, "") + ) + is_exist = os.path.exists(new_path) + if not is_exist: + os.makedirs(new_path) + # Grab the filename without the .md extension + new_filename = os.path.join(new_path, file) + # Add filename to a list + file_slash = "/" + file + relative_path = os.path.relpath(root + file_slash, inputpath) + file_index.append(relative_path) + match = re.search(r"(.*)\.md$", new_filename) + new_filename_no_ext = match[1] + # Read the input Markdown content + to_file = auto.read() + # Reformat the page and section titles + to_file, metadata = process_page_and_section_titles(to_file) + # Process includes lines in Markdown + to_file = process_includes(to_file, root) + doc = [] + if USE_CUSTOM_MARKDOWN_SPLITTER is True: + # Use a custom splitter to split into small chunks + docs = process_document_into_sections(to_file) + else: + # Use the Markdown splitter to split into small chunks + docs = markdown_splitter.create_documents([to_file]) + i = 0 + for doc in docs: + # Clean up Makrdown and HTML syntax + if USE_CUSTOM_MARKDOWN_SPLITTER is True: + content = markdown_to_text(doc) + else: + content = markdown_to_text(doc.page_content) + # Save clean plain text to a new filename appended with an index + filename_to_save = new_filename_no_ext + "_" + str(i) + ".md" + # Generate UUID for each plain text chunk and collect its metadata, + # which will be written to the top-level `file_index.json` file. + md_hash = uuid.uuid3(namespace_uuid, content) + uuid_file = uuid.uuid3(namespace_uuid, filename_to_save) + if bool(metadata): + full_file_metadata[filename_to_save] = { + "UUID": str(uuid_file), + "source": input_path, + "source_file": relative_path, + "source_id": counter, + "URL": url_pre, + "md_hash": str(md_hash), + "metadata": metadata, + } + else: + full_file_metadata[filename_to_save] = { + "UUID": str(uuid_file), + "source": input_path, + "source_file": relative_path, + "source_id": counter, + "URL": url_pre, + "md_hash": str(md_hash), + } + with open(filename_to_save, "w", encoding="utf-8") as new_file: + new_file.write(content) + new_file.close() + i = i + 1 + auto.close() + print("Processed " + str(f_count) + " Markdown files from the source: " + inputpath) + return f_count + + +# Write the recorded input variables into a file: `file_index.json` +def save_file_index_json(src_file_index): + json_out_file = MY_OUTPUT_PATH + "/file_index.json" + with open(json_out_file, "w", encoding="utf-8") as outfile: + json.dump(src_file_index, outfile) + print( + "Created " + json_out_file + " to store the complete list of processed files." + ) + + +#### Main #### +source_file_index = {} +input_counter = 0 +total_file_count = 0 + +# Main for-loop +for input_counter in range(input_len): + full_file_metadata = {} + file_index = [] + exclude = [] + # Process `input-values.yaml` into input variables. + if IS_CONFIG_FILE: + # Reads all the input values defined in the configuration file + config_values = config.returnConfigValue("input") + if "path" in config_values[input_counter]: + input_path = config_values[input_counter]["path"] + if "url_prefix" in config_values[input_counter]: + url_pre = config_values[input_counter]["url_prefix"] + if "exclude_path" in config_values[input_counter]: + exclude = config_values[input_counter]["exclude_path"] + else: + input_path = MY_INPUT_PATH[input_counter] + url_pre = URL_PREFIX[input_counter] + + # Process Markdown files in the `input` path + file_count = process_markdown_files_from_source( + config_values, input_path, input_counter, exclude + ) + if not input_path.endswith("/"): + input_path = input_path + "/" + input_path = resolve_path(input_path) + # Record the input variables used in this path. + file_list = {} + for file in file_index: + file_obj = {file: {"source": input_path, "URL": url_pre}} + file_list[file] = file_obj + source_file_index[input_counter] = full_file_metadata + input_counter += 1 + total_file_count += file_count + +# Write the recorded input variables into `file_index.json`. +save_file_index_json(source_file_index) + +print( + "Processed a total of " + + str(total_file_count) + + " Markdown files from " + + str(input_counter) + + " sources." +) diff --git a/demos/palm/python/docs-agent/scripts/populate_vector_database.py b/demos/palm/python/docs-agent/scripts/populate_vector_database.py new file mode 100644 index 000000000..51eeb0ce8 --- /dev/null +++ b/demos/palm/python/docs-agent/scripts/populate_vector_database.py @@ -0,0 +1,307 @@ +# +# 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. +# + +"""Populate the vector database with embeddings generated from text chunks""" + +import os +import sys +import re +import json +import chromadb +import flatdict +import uuid +from chromadb.config import Settings +from chromadb.utils import embedding_functions +from chromadb.api.types import Documents, Embeddings +import google.generativeai as palm +from ratelimit import limits, sleep_and_retry +import read_config + +### Notes on how to use this script ### +# +# Prerequisites: +# - Have plain text files stored in the PLAIN_TEXT_DIR directory +# (see `markdown_to_plain_text.py`) +# +# Do the following: +# 1. If you are not using a `input-values.yaml` file, +# edit PLAIN_TEXT_DIR in this script (see below). +# 2. Run: +# $ python3 ./scripts/populate-vector-database.py +# +# To test, run: +# $ python3 ./script/test-vector-database.py +# + +BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +### Select the input directory of plain text files, this will be overridden by +### `input-values.yaml` +### Set up the path to the local LLM ### +LOCAL_VECTOR_DB_DIR = os.path.join(BASE_DIR, "vector_stores/chroma") +COLLECTION_NAME = "docs_collection" + +IS_CONFIG_FILE = True +if IS_CONFIG_FILE: + config_values = read_config.ReadConfig() + PLAIN_TEXT_DIR = config_values.returnConfigValue("output_path") + input_len = config_values.returnInputCount() + LOCAL_VECTOR_DB_DIR = config_values.returnConfigValue("vector_db_dir") + COLLECTION_NAME = config_values.returnConfigValue("collection_name") + +### Select the file index that is generated with your plain text files, same directory +INPUT_FILE_INDEX = "file_index.json" + +# Select the type of embeddings to use, PALM or LOCAL +EMBEDDINGS_TYPE = "PALM" + +### Set up the PaLM API key from the environment ### +API_KEY = os.getenv("PALM_API_KEY") +if API_KEY is None: + sys.exit("Please set the environment variable PALM_API_KEY to be your API key.") + +# PaLM API call limit to 300 per minute +API_CALLS = 280 +API_CALL_PERIOD = 60 + +# Enable relative directories. +if not BASE_DIR.endswith("/"): + BASE_DIR = BASE_DIR + "/" + +if not PLAIN_TEXT_DIR.endswith("/"): + PLAIN_TEXT_DIR = PLAIN_TEXT_DIR + "/" + +FULL_BASE_DIR = BASE_DIR + PLAIN_TEXT_DIR +print("Plain text directory: " + FULL_BASE_DIR + "\n") + +FULL_INDEX_PATH = PLAIN_TEXT_DIR + INPUT_FILE_INDEX +try: + with open(FULL_INDEX_PATH, "r", encoding="utf-8") as index_file: + index = json.load(index_file) +except FileNotFoundError: + msg = "The file " + FULL_INDEX_PATH + "does not exist." + +if EMBEDDINGS_TYPE == "PALM": + palm.configure(api_key=API_KEY) + # This returns models/embedding-gecko-001" + models = [ + m for m in palm.list_models() if "embedText" in m.supported_generation_methods + ] + # MODEL = "models/embedding-gecko-001" + MODEL = models[0] +elif EMBEDDINGS_TYPE == "LOCAL": + MODEL = os.path.join(BASE_DIR, "models/all-mpnet-base-v2") + emb_fn = embedding_functions.SentenceTransformerEmbeddingFunction(model_name=MODEL) +else: + MODEL = os.path.join(BASE_DIR, "models/all-mpnet-base-v2") + emb_fn = embedding_functions.SentenceTransformerEmbeddingFunction(model_name=MODEL) + +chroma_client = chromadb.PersistentClient(path=LOCAL_VECTOR_DB_DIR) + +# Create embed function for PaLM +# API call limit to 5 qps +@sleep_and_retry +@limits(calls=API_CALLS, period=API_CALL_PERIOD) +def embed_function(texts: Documents) -> Embeddings: + # Embed the documents using any supported method + return [ + palm.generate_embeddings(model=MODEL, text=text)["embedding"] for text in texts + ] + + +if EMBEDDINGS_TYPE == "PALM": + collection = chroma_client.get_or_create_collection( + name=COLLECTION_NAME, embedding_function=embed_function + ) +elif EMBEDDINGS_TYPE == "LOCAL": + collection = chroma_client.get_or_create_collection( + name=COLLECTION_NAME, embedding_function=emb_fn + ) +else: + collection = chroma_client.get_or_create_collection( + name=COLLECTION_NAME, embedding_function=emb_fn + ) + +documents = [] +metadatas = [] +ids = [] +i = 0 +updated_count = 0 +new_count = 0 +unchanged_count = 0 + +# Read plain text files (.md) from the PLAIN_TEXT_DIR dir and +# add their content to the vector database. +# Embeddings are generated automatically as they are added to the database. +for root, dirs, files in os.walk(PLAIN_TEXT_DIR): + for file in files: + file_update = False + # Persists every nth time and if there was an actual update or added file. + # However, we don't need to persist, which takes time, if there are no updates. + if i % 100 == 0 and file_update == True: + chroma_client.persist() + if file.endswith(".md"): + with open(os.path.join(root, file), "r", encoding="utf-8") as auto: + print("Process an entry into the database: " + str(i)) + print("Opening a file: " + file) + # Extract the original filename used (without a file extension) + match = re.search(r"(.*)\.md$", file) + filename_no_ext = match[1] + toFile = auto.read() + # Contruct the URL + match2 = re.search(r"(.*)_\d*$", filename_no_ext) + filename_for_url = match2[1] + clean_filename = re.sub(PLAIN_TEXT_DIR, "", os.path.join(root, "")) + url = clean_filename + filename_for_url + ".md" + url_path = "" + md_hash = "" + uuid_file = "" + # Build the full filename to match entries in file_index.json + # Using the full path avoids mismatches + full_file_name = FULL_BASE_DIR + clean_filename + file + metadata_dict_extra = {} + # Flag to see if there is a predefined URL from frontmatter + final_url = False + # Reads the metadata associated with files + for key in index: + if full_file_name in index[key]: + if ( + "URL" in index[key][full_file_name] + and "source_id" in index[key][full_file_name] + ): + # This ensures the URL is retrived from the correct file. + # Avoids issues with common file names such as README.md + if int(key) == index[key][full_file_name]["source_id"]: + if index[key][full_file_name]["URL"]: + url_path = index[key][full_file_name]["URL"] + else: + print("No valid URL value for: " + file) + # If metadata exists, add these to a dictionary that is then + # merged with other metadata values + if "metadata" in index[key][full_file_name]: + # Save and flatten dictionary + metadata_dict_extra = flatdict.FlatterDict( + index[key][full_file_name]["metadata"], delimiter="_" + ) + metadata_dict_extra = dict(metadata_dict_extra) + # Extracts user specified URL + if "URL" in metadata_dict_extra: + final_url = True + final_url_value = metadata_dict_extra["URL"] + else: + metadata_dict_extra = {} + if "UUID" in index[key][full_file_name]: + uuid_file = index[key][full_file_name]["UUID"] + if "md_hash" in index[key][full_file_name]: + md_hash = str(index[key][full_file_name]["md_hash"]) + # Add a trailing "/" to the url path in case the configuration file + # didn't have it. + # Do not add slashes to PSAs. + if ( + not url_path.endswith("/") + and not url_path.startswith("PSA") + and not url.startswith("/") + ): + url_path = url_path + "/" + url = url_path + url + # Remove .md at the end of URLs by default. + match3 = re.search(r"(.*)\.md$", url) + url = match3[1] + # Replaces the URL if it comes from frontmatter + if (final_url): + url = final_url_value + # Creates a dictionary with basic metadata values + # (i.e. source, URL, and md_hash) + metadata_dict_main = { + "source": filename_no_ext, + "url": url, + "md_hash": md_hash, + } + # Merges dictionaries with main metadata and additional metadata + metadata_dict_final = metadata_dict_main | metadata_dict_extra + str_uuid_file = str(uuid_file) + print("UUID: " + str_uuid_file) + print("Markdown hash: " + str(md_hash)) + print("URL: " + url) + if toFile and toFile.strip(): + # Skip if the file size is larger than 10000 bytes (API limit) + filesize = len(toFile) + if filesize < 10000: + if md_hash != "" and str_uuid_file != "": + query = {} + # The query looks for the UUID, which is unique and + # compares to see if the hash has changed + query = collection.get( + include=["metadatas"], + ids=str_uuid_file, + where={"md_hash": {"$ne": md_hash}}, + ) + # Extract any id whose content may have changed + id_to_remove = query["ids"] + if id_to_remove != []: + print("Out of date content.") + # Delete the existing entry + collection.delete(ids=id_to_remove) + # Add a new entry + collection.add( + documents=toFile, + metadatas=metadata_dict_final, + ids=str_uuid_file, + ) + print("Updated.") + updated_count += 1 + file_update = True + else: + query_2 = collection.get( + include=["metadatas"], + ids=str_uuid_file, + where={"md_hash": {"$eq": md_hash}}, + ) + id_up_to_date = query_2["ids"] + if id_up_to_date != []: + print("Up to date content.") + unchanged_count += 1 + else: + collection.add( + documents=toFile, + metadatas=metadata_dict_final, + ids=str_uuid_file, + ) + print("Added content.") + new_count += 1 + file_update = True + i += 1 + else: + print( + "[Warning] Skipped " + + file + + " because the file size is too large!" + ) + else: + print("[Warning] Empty file!") + print("") + auto.close() +# results = collection.query( +# query_texts=["What are some differences between apples and oranges?"], +# n_results=3, +# ) +# print("\nTesting:") +# print(results) + +print("") +print("Total number of entries: " + str(i)) +print("New entries: " + str(new_count)) +print("Updated entries: " + str(updated_count)) +print("Unchanged entries: " + str(unchanged_count)) diff --git a/demos/palm/python/docs-agent/scripts/read_config.py b/demos/palm/python/docs-agent/scripts/read_config.py new file mode 100644 index 000000000..852ab65e2 --- /dev/null +++ b/demos/palm/python/docs-agent/scripts/read_config.py @@ -0,0 +1,96 @@ +# +# 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. +# + +"""Read the configuration file to import user settings""" + +import os +import sys +import yaml + +# The configuration file config.yaml exists in the root of the project +BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +INPUT_YAML = os.path.join(BASE_DIR, "config.yaml") +### Set up the path to the local LLM ### +LOCAL_VECTOR_DB_DIR = os.path.join(BASE_DIR, "vector_stores/chroma") + +# Define the required keys to run scripts and chatbot +required_keys = ["output_path", "input", "product_name", "vector_db_dir"] +# Define any supported optional keys to run scripts and chatbot +optional_keys = [] +# Define any required keys that define the properties of input paths +required_input_keys = ["path", "url_prefix"] +# Define any optional keys that define the properties of input paths +optional_input__keys = ["md_extension", "exlude_path"] + + +class ReadConfig: + # Tries to ingest the configuration file and validate its keys + def __init__(self): + try: + with open(INPUT_YAML, "r", encoding="utf-8") as inp_yaml: + self.config_values = yaml.safe_load(inp_yaml) + self.IS_CONFIG_FILE = True + print("Configuration defined in: " + INPUT_YAML) + # Check that the required keys exist + self.validateKeys() + except FileNotFoundError: + print("The file " + INPUT_YAML + " does not exist.") + # Exits the scripts if there is no valid config file + return sys.exit(1) + + # Function to return the full configuration file + def returnFullConfig(self): + return self.config_values + + # Function to return the path of the configuration file + def returnConfigFile(self): + configFilePath = BASE_DIR + INPUT_YAML + return configFilePath + + # Function to count the quantity of input paths + def returnInputCount(self): + count = len(self.returnConfigValue("input")) + return count + + # Validates that a configuratioon file contains the required or optional keys + def validateKeys(self): + for key in required_keys: + if key in self.config_values: + # Validates lists such as input with their respective keys + if key == "input": + count = 0 + for input in self.config_values["input"]: + count += 1 + for required_key in required_input_keys: + if required_key not in input: + print( + "Missing input configuration key: " + + required_key + + " from input source " + + str(count) + ) + else: + print("Missing required configuration key: " + key) + for key in optional_keys: + if key not in self.config_values: + print("Missing optional configuration key: " + key) + + # Checks if a key exists and returns its value + def returnConfigValue(self, key): + if key in self.config_values: + return self.config_values[key] + else: + print("Error: " + key + " does not exist in the " + INPUT_YAML + " file.") diff --git a/demos/palm/python/docs-agent/scripts/test_vector_database.py b/demos/palm/python/docs-agent/scripts/test_vector_database.py new file mode 100644 index 000000000..075bda6e3 --- /dev/null +++ b/demos/palm/python/docs-agent/scripts/test_vector_database.py @@ -0,0 +1,125 @@ +# +# 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. +# + +"""Test the vector database""" + +import os +import sys +import google.generativeai as palm +import chromadb +from chromadb.config import Settings +from chromadb.utils import embedding_functions +from chromadb.api.types import Document, Embedding, Documents, Embeddings +from rich.console import Console +from rich.markdown import Markdown +from rich.panel import Panel +from ratelimit import limits, sleep_and_retry +import read_config + +BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) + +# Set the directory path to locate the Chroma vector database +LOCAL_VECTOR_DB_DIR = os.path.join(BASE_DIR, "vector_stores/chroma") +COLLECTION_NAME = "docs_collection" + +IS_CONFIG_FILE = True +if IS_CONFIG_FILE: + config_values = read_config.ReadConfig() + LOCAL_VECTOR_DB_DIR = config_values.returnConfigValue("vector_db_dir") + COLLECTION_NAME = config_values.returnConfigValue("collection_name") + +# Set a test question +QUESTION = "What are some differences between apples and oranges?" +NUM_RETURNS = 5 + +# Set up the PaLM API key from the environment +API_KEY = os.getenv("PALM_API_KEY") +if API_KEY is None: + sys.exit("Please set the environment variable PALM_API_KEY to be your API key.") + +# Select your PaLM API endpoint +PALM_API_ENDPOINT = "generativelanguage.googleapis.com" +palm.configure(api_key=API_KEY, client_options={"api_endpoint": PALM_API_ENDPOINT}) + +# Set up the path to the local LLM +# This value is used only when `EMBEDDINGS_TYPE` is set to `LOCAL` +LOCAL_LLM = os.path.join(BASE_DIR, "models/all-mpnet-base-v2") + +# Use the PaLM API for generating embeddings by default +EMBEDDINGS_TYPE = "PALM" + +# PaLM API call limit to 300 per minute +API_CALLS = 280 +API_CALL_PERIOD = 60 + + +# Create embed function for PaLM +# API call limit to 5 qps +@sleep_and_retry +@limits(calls=API_CALLS, period=API_CALL_PERIOD) +def embed_palm_api_call(text: Document) -> Embedding: + return palm.generate_embeddings(model=PALM_EMBEDDING_MODEL, text=text)["embedding"] + + +def embed_palm(texts: Documents) -> Embeddings: + # Embed the documents using any supported method + return [embed_palm_api_call(text) for text in texts] + + +# Initialize Rich console +ai_console = Console(width=160) +ai_console.rule("Fold") + +chroma_client = chromadb.PersistentClient(path=LOCAL_VECTOR_DB_DIR) + +if EMBEDDINGS_TYPE == "PALM": + PALM_EMBEDDING_MODEL = "models/embedding-gecko-001" + emb_fn = embed_palm +elif EMBEDDINGS_TYPE == "LOCAL": + emb_fn = embedding_functions.SentenceTransformerEmbeddingFunction( + model_name=LOCAL_LLM + ) +else: + emb_fn = embedding_functions.SentenceTransformerEmbeddingFunction( + model_name=LOCAL_LLM + ) + +collection = chroma_client.get_collection( + name=COLLECTION_NAME, embedding_function=emb_fn +) + +results = collection.query(query_texts=[QUESTION], n_results=NUM_RETURNS) + +print("") +ai_console.print(Panel.fit(Markdown("Question: " + QUESTION))) +print("Results:") +print(results) +print("") + +i = 0 +for document in results["documents"]: + for content in document: + print("Content " + str(i) + ": ") + ai_console.print(Panel.fit(Markdown(content))) + source = results["metadatas"][0][i] + this_id = results["ids"][0][i] + distance = results["distances"][0][i] + print(" source: " + source["source"]) + print(" URL: " + source["url"]) + print(" ID: " + this_id) + print(" Distance: " + str(distance)) + print("") + i += 1 diff --git a/demos/palm/python/docs-agent/setup.py b/demos/palm/python/docs-agent/setup.py new file mode 100644 index 000000000..1d9261f80 --- /dev/null +++ b/demos/palm/python/docs-agent/setup.py @@ -0,0 +1,24 @@ +# +# 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. +# + +"""For setting up local packages in the project.""" +from setuptools import setup + +setup( + name="docs_agent", + packages=["chatbot", "docs_agent"], + install_requires=["flask"], +) diff --git a/demos/palm/python/docs-agent/third_party/css/chatbox.css b/demos/palm/python/docs-agent/third_party/css/chatbox.css new file mode 100644 index 000000000..e7d562c98 --- /dev/null +++ b/demos/palm/python/docs-agent/third_party/css/chatbox.css @@ -0,0 +1,42 @@ +/** + * Copyright (c) 2023 by Landgreen (https://codepen.io/lilgreenland/pen/pyVvqB) + * + * Permission is hereby granted, free of charge, to any person obtaining a copy of + * this software and associated documentation files (the "Software"), to deal in + * the Software without restriction, including without limitation the rights to + * use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies + * of the Software, and to permit persons to whom the Software is furnished to do + * so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + */ + +#body-box { + margin: auto; + max-width: 800px; + font: 15px arial, sans-serif; + background-color: white; + border-style: solid; + border-width: 1px; + padding: 20px 25px 25px; + box-shadow: 5px 5px 5px grey; + border-radius: 15px; +} + +#chat-border { + border-style: solid; + background-color: #f6f9f6; + border-width: 3px; + margin: 20px; + padding: 10px 20px 15px 15px; + border-radius: 15px; +} + diff --git a/demos/palm/python/docs-agent/vector_stores/.gitkeep b/demos/palm/python/docs-agent/vector_stores/.gitkeep new file mode 100644 index 000000000..e69de29bb diff --git a/demos/palm/web/quick-prompt/package-lock.json b/demos/palm/web/quick-prompt/package-lock.json index c5db3cb46..d695ab416 100644 --- a/demos/palm/web/quick-prompt/package-lock.json +++ b/demos/palm/web/quick-prompt/package-lock.json @@ -1318,9 +1318,9 @@ } }, "node_modules/@grpc/grpc-js/node_modules/protobufjs": { - 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