From a09a7da5944b429bef87129d7f53a5bad7218ce4 Mon Sep 17 00:00:00 2001 From: Matteo Cargnelutti Date: Mon, 23 Oct 2023 17:11:18 -0400 Subject: [PATCH] v0.0.1 --- .env.example | 59 + .gitignore | 12 + .vscode/extensions.json | 7 + .vscode/settings.json | 14 + LICENSE | 21 + README.md | 200 + banner.png | Bin 0 -> 43744 bytes poetry.lock | 4545 +++++++++++++++++++ pyproject.toml | 39 + screenshots.webp | Bin 0 -> 520518 bytes setup.cfg | 3 + warc/.keep | 0 warc_gpt/__init__.py | 42 + warc_gpt/commands/__init__.py | 2 + warc_gpt/commands/ingest.py | 215 + warc_gpt/commands/visualize.py | 135 + warc_gpt/static/images/lil.svg | 171 + warc_gpt/static/images/logo-title-white.svg | 35 + warc_gpt/static/index.css | 396 ++ warc_gpt/static/index.js | 313 ++ warc_gpt/templates/index.html | 78 + warc_gpt/utils/__init__.py | 2 + warc_gpt/utils/check_env.py | 32 + warc_gpt/utils/list_available_models.py | 49 + warc_gpt/views/__init__.py | 2 + warc_gpt/views/api.py | 306 ++ warc_gpt/views/ui.py | 21 + wsgi.py | 5 + 28 files changed, 6704 insertions(+) create mode 100644 .env.example create mode 100644 .gitignore create mode 100644 .vscode/extensions.json create mode 100644 .vscode/settings.json create mode 100644 LICENSE create mode 100644 README.md create mode 100644 banner.png create mode 100644 poetry.lock create mode 100644 pyproject.toml create mode 100644 screenshots.webp create mode 100644 setup.cfg create mode 100644 warc/.keep create mode 100644 warc_gpt/__init__.py create mode 100644 warc_gpt/commands/__init__.py create mode 100644 warc_gpt/commands/ingest.py create mode 100644 warc_gpt/commands/visualize.py create mode 100644 warc_gpt/static/images/lil.svg create mode 100644 warc_gpt/static/images/logo-title-white.svg create mode 100644 warc_gpt/static/index.css create mode 100644 warc_gpt/static/index.js create mode 100644 warc_gpt/templates/index.html create mode 100644 warc_gpt/utils/__init__.py create mode 100644 warc_gpt/utils/check_env.py create mode 100644 warc_gpt/utils/list_available_models.py create mode 100644 warc_gpt/views/__init__.py create mode 100644 warc_gpt/views/api.py create mode 100644 warc_gpt/views/ui.py create mode 100644 wsgi.py diff --git a/.env.example b/.env.example new file mode 100644 index 0000000..470f8f7 --- /dev/null +++ b/.env.example @@ -0,0 +1,59 @@ +#------------------------------------------------------------------------------- +# LLM APIs settings +#------------------------------------------------------------------------------- +# NOTE: WARC-GPT needs to be able to interact with at least 1 LLM API. +OLLAMA_API_URL="http://localhost:11434" # Allows for running models locally. See: https://ollama.ai/ +#OPENAI_API_KEY="" +#OPENAI_ORG_ID="" + +#ANTHROPIC_API_KEY="" + +#COHERE_API_KEY="" + +#PERPLEXITYAI_API_KEY="" + +#------------------------------------------------------------------------------- +# Prompts +#------------------------------------------------------------------------------- +RAG_PROMPT=" +Here is context: +{context} +---------------- +Context comes from web pages that were captured as part of a web archives collection. +When possible, use context to answer the question asked by the user. +If you don't know the answer, just say that you don't know, don't try to make up an answer. +Ignore context if it is empty or irrelevant. +When possible and relevant, use context to cite or direct quote your sources. + +Question: {question} + +Helpful answer:" +# Inspired by LangChain's default prompt. {context} and {question} are reserved keywords. +# NOTE: An earlier vection of WARC-GPT used ALL CAPS CONTEXT, and QUESTION keywords. + +#------------------------------------------------------------------------------- +# Paths +#------------------------------------------------------------------------------- +WARC_FOLDER_PATH="./warc" +VISUALIZATIONS_FOLDER_PATH="./visualizations" +VECTOR_SEARCH_PATH="./chromadb" + +#------------------------------------------------------------------------------- +# Vector Store / Search settings +#------------------------------------------------------------------------------- +VECTOR_SEARCH_COLLECTION_NAME="collection" + +VECTOR_SEARCH_SENTENCE_TRANSFORMER_MODEL="intfloat/e5-large-v2" # Can be any Sentence-Transformers compatible model available on Hugging Face +VECTOR_SEARCH_SENTENCE_TRANSFORMER_DEVICE="cpu" +VECTOR_SEARCH_DISTANCE_FUNCTION="cosine" # https://docs.trychroma.com/usage-guide#changing-the-distance-function +VECTOR_SEARCH_NORMALIZE_EMBEDDINGS="true" +VECTOR_SEARCH_CHUNK_PREFIX="passage: " # Can be used to add prefix to text embeddings stored in vector store +VECTOR_SEARCH_QUERY_PREFIX="query: " # Can be used to add prefix to text embeddings used for semantic search + +VECTOR_SEARCH_TEXT_SPLITTER_CHUNK_OVERLAP=25 # Determines, for a given chunk of text, how many tokens must overlap with adjacent chunks. +VECTOR_SEARCH_SEARCH_N_RESULTS=4 # How many entries should the vector search return? + +#------------------------------------------------------------------------------- +# Hugging Face's tokenizer settings +#------------------------------------------------------------------------------- +TOKENIZERS_PARALLELISM="false" diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..06c043d --- /dev/null +++ b/.gitignore @@ -0,0 +1,12 @@ +.env +*.pyc +*.warc +*.warc.gz +.DS_Store +chromadb/ +warcs/ +visualizations/ +test.py +TODO.md +runs/ +_*/ \ No newline at end of file diff --git a/.vscode/extensions.json b/.vscode/extensions.json new file mode 100644 index 0000000..87f61ef --- /dev/null +++ b/.vscode/extensions.json @@ -0,0 +1,7 @@ +{ + "recommendations": [ + "ms-python.black-formatter", + "ms-python.flake8", + "tobermory.es6-string-html" + ] +} \ No newline at end of file diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 0000000..0b329dc --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,14 @@ +{ + "editor.formatOnSave": true, + "python.formatting.provider": "black", + "files.associations": { + "*.html": "html", + "*.js": "javascript" + }, + "[html]": { + "editor.formatOnSave": false + }, + "[javascript]": { + "editor.formatOnSave": false + } +} diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..05d13df --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2024 Harvard Library Innovation Laboratory + +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. \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..1c3e9a0 --- /dev/null +++ b/README.md @@ -0,0 +1,200 @@ +# WARC-GPT + +**WARC + AI:** Experimental Retrieval Augmented Generation Pipeline for Web Archive Collections. + +More info: +- "WARC-GPT: An Open-Source Tool for Exploring Web Archives Using AI". Feb 12 2024 - _lil.law.harvard.edu_ + +![](screenshots.webp) + + +--- + +## Summary +- [Features](#features) +- [Installation](#installation) +- [Configuring the application](#configuring-the-application) +- [Ingesting WARCs ](#ingesting-warcs) +- [Interacting with the Web UI](#interacting-with-the-web-ui) +- [Interacting with the API](#interacting-with-the-api) +- [Visualizing Embeddings](#visualizing-embeddings) +- [Disclaimer](#disclaimer) + +--- + +## Features +- Retrieval Augmented Generation pipeline for WARC files +- Highly customizable, can interact with many different LLMs, providers and embedding models +- REST API +- Web UI +- Embeddings visualization + +[☝️ Summary](#summary) + +--- + +## Installation +WARC-GPT requires the following machine-level dependencies to be installed. + +- [Python 3.11+](https://python.org) +- [Python Poetry](https://python-poetry.org/) + +Use the following commands to clone the project and instal its dependencies: + +```bash +git clone https://github.com/harvard-lil/warc-gpt.git +poetry env use 3.11 +poetry install +``` + +[☝️ Summary](#summary) + +--- + +## Configuring the application + +This program uses environment variables to handle settings. Copy `.env.example` into a new `.env` file and edit it as needed. + +```bash +cp .env.example .env +``` + +By default, `.env` is configured: +- To use [intfloat/e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) as an embedding model +- To connect to a local instance of [Ollama](https://ollama.ai) for inference +- To use a basic, fairly neutral retrieval prompt + +Edit this file as needed to adjust settings, replace the embedding model, retrieval prompt, or to connect WARC-GPT to [Open AI](https://platform.openai.com/docs/introduction), [Anthropic](https://docs.anthropic.com/claude/reference/getting-started-with-the-api), [Cohere](https://docs.cohere.com/docs) or [Perplexity AI](https://docs.perplexity.ai/). + +[☝️ Summary](#summary) + +--- + +## Ingesting WARCs + +Place the WARC files you would to explore with WARC-GPT under `./warc` and run the following command to: +- Extract text from all the `text/html` and `application/pdf` response records present in the WARC files. +- Generate text embeddings for this text. WARC-GPT will automatically split text based on the embedding model's context window. +- Store these embeddings in a vector store, so it can be used as WARC-GPT's knowledge base. + +```bash +poetry run flask ingest +``` + +**Note:** Running `ingest` clears the `./chromadb` folder. + +[☝️ Summary](#summary) + +--- + +## Starting the server + +The following command will start WARC-GPT's server on port `5000`. + +```bash +poetry run flask run +# Not: Use --port to use a different port +``` + +[☝️ Summary](#summary) + +--- + +## Interacting with the WEB UI + +Once the server is started, the application's web UI should be available on `http://localhost:5000`. + +Unless the **Disable RAG** option is turned on, the system will try to find relevant excerpts in its knowledge base - populated ahead of time using WARC files and the `ingest` command - to answer the questions it is asked. + +The interface also automatically handles a basic chat history, allowing for few-shots / chain-of-thoughts prompting. + +[☝️ Summary](#summary) + +--- + +## Interacting with the API + +### [GET] /api/models +Returns a list of available models as JSON. + +### [POST] /api/completion +For a given message, retrieves relevant context from the knowledge base and use an LLM to generate a text completion. + +
+Accepts JSON body with the following properties: + +- `model`: One of the models `/api/models` lists (required) +- `message`: User prompt (required) +- `temperature`: Defaults to 0.0 (required) +- `max_tokens`: If provided, caps number of tokens that will be generated in response. +- `no_rag`: If set and true, the API will not try to retrieve context. +- `rag_prompt_override`: If provided, will be used in replacement of the predefined RAG prompt. {context} and {question} placeholders will be automatically replaced. +- `history`: A list of chat completion objects representing the chat history. Each object must contain "user" and "content". + +
+ +
+Returns a JSON object containing the following properties: + +- `id_exchange`: Unique identifier for this completion +- `response`: Text of the response generated by the LLM +- `response_info`: An object containg technical information about the response that was generated + - `response_info.completion_tokens`: Number of tokens generated by the LLM. + - `response_info.prompt_tokens`: Number of tokens passed to the LLM. + - `response_info.total_tokens`: Total number of tokens +- `request_info`: An object containing information about the request given to the chatbot + - `request_info.message`: Same as input `message` + - `request_info.message_plus_prompt`: If RAG is enabled, presents the message alongside the context and retrieval prompt, as it was given to the LLM. + - `request_info.max_tokens`: Same as input `max_tokens`, if provided. + - `request_info.model`: Same as input `model`. + - `request_info.no_rag`: Same as input `no_rag`. + - `request_info.temperature`: Same as input `temperature`. +- `context`: Array of objects, elements pulled from the vector store. + - `context[].warc_filename`: Filename of the WARC from which that excerpt is from. + - `context[].warc_record_content_type`: Can start with either `text/html` or `application/pdf`. + - `context[].warc_record_id`: Individual identifier of the WARC record within the WARC file. + - `context[].warc_record_date`: Date at which the WARC record was created. + - `context[].warc_record_target_uri`: Filename of the WARC from which that excerpt is from. + - `context[].warc_record_text`: Text excerpt. +- `history`: Array of chat history objects (Open AI format). Does not contain full context as a tokens-saving measure. + +
+ +[☝️ Summary](#summary) + +--- + +## Visualizing embeddings + +WARC-GPT allows for generating basic interactive [T-SNE](https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding) 2D scatter plots of the vector stores it generates. + +Use the `visualize` command to do so: + +```bash +poetry run flask visualize +``` + +`visualize` takes a `--questions` option which allows to place questions on the plot: + +```bash +poetry run flask visualize --questions="Who am I?;Who are you?" +``` + +[☝️ Summary](#summary) + +--- + +## Disclaimer + +The Library Innovation Lab is an organization based at the Harvard Law School Library. We are a cross-functional group of software developers, librarians, lawyers, and researchers doing work at the edges of technology and digital information. + +Our work is rooted in library principles including longevity, authenticity, reliability, and privacy. Any work that we produce takes these principles as a primary lens. However due to the nature of exploration and a desire to prototype our work with real users, we do not guarantee service or performance at the level of a production-grade platform for all of our releases. This includes WARC-GPT, which is an experimental boilerplate released under [MIT License](LICENSE). + +Successful experimentation hinges on user feedback, so we encourage anyone interested in trying out our work to do so. It is all open-source and available on Github. + +**Please keep in mind:** +- We are an innovation lab leveraging our resources and flexibility to conduct explorations for a broader field. Projects may be eventually passed off to another group, take a totally unexpected turn, or be sunset completely. +- While we always have priorities set around security and privacy each of those topics is complex in its own right and often requires grand scale work. Experiments can sometimes initially prioritize closed-loop feedback over broader questions of security. We will always disclose when this is the case. +- There are some experiments that are destined to become mainstays in our established platforms and tools. We will also disclose when that’s the case. + +[☝️ Summary](#summary) diff --git a/banner.png b/banner.png new file mode 100644 index 0000000000000000000000000000000000000000..3972aeb214983895ceb9d1dedd9bb2e953a090f7 GIT binary patch literal 43744 zcmeFYg;$i{_Xau`Ac7zuA*rNvOM?nXgLF62-3`)>h;%6_-QC^Y-Q68S+%tas-F5$l zyS}s5ti?Obd)|Ha+41aW?+N@ODfHqw?sEtP@?w|;r$E2Wx2jEP1az^?a45=&5!(-<@8%iFQ% z`JAzJXQ=Tkq?B}FUM9`WDeXDl!KDQ#1zvBi9(jY<{hZrJX&z` zY}C-tnP!Ch6aO_G-Rfo5VX=?h*6pZlKwjWo73VP`pt?1bIHsiEOlHw^)FpzxTN6*G$ zzKC*+XHWfuyAEZhqfV@3&t0c7S%_?8_&KT&T`g&XgGi%;Nc^PhPpS_SjeyalFk?jO zX_dc6*KzRLk8b%#B%P0_zP#mdKjORMQDx?1-6r>4`LG#m<`DvER{tekx__uUYn}-C zX0Rk`a!qab=0e0dKSMCgbHQwBr6!}>rD+|7;VNCsZ%r6Z<=&OhkuS}n=h0o|hs$`b z$_)-lB2SinDyPTz_>a<__Gnx>Iw(Tt?)Qxb4iP4Ltq0FOLCwIq!0U(!2|#Kgt`Kvh za!oYw38JO2vJC`6M*@4pIcD(LgAWmGMI;0fw%{<)A2YZ!WrP1gUfT*P+45VMnSC|4 zh45Q{)wcbr_r}4<*6@vxh{PAgZz$Lh$Qy`=z(+a9nZ0?3p9;QlP0J2`OrDRC-$>wm z$a&NLM_AhXiC9U{ltcecj(=O3gmfB(otXV3sd0#ir1%8sXWhv^ZGHrdj8myv&0ZtT 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zrqba~51k#=IPVwsCwcbd8TS2`GWPsMfhCy$rUmuGm7z7C%W7YoMEKpq?}wdtnD!P5 zv-0b+vk5Z4UdQ!00CQt7srIx3;^1!1x(a-W=!p)p7VrAiX8lq5pizy=%-gnGBEAU{ zz}$iVZw_sB{@j>WZF%?0TTUQumR-3|_M7+mf@}I)lgj?>U#nunNtDwob3TrAm{RAi z!!`nN6=~Uy{pU>3!|i2-m0)CtM}za9`@8dRj#OEcSx+fE6bzaAyhqD7Gn=Poesq8W zs^%p1E+ZTIvd&bqjMBY2l3xsN)L8uA2a5f=u?d3V9S$Qi-qzm=cr3KjmjGukh4$VG z*aBy&r-|)u+s(8F9;^UZ2A`)R@A8YmqA{puoe8L*3jat#y6TxM`CLC9foxH`{_6k$ literal 0 HcmV?d00001 diff --git a/setup.cfg b/setup.cfg new file mode 100644 index 0000000..5e49371 --- /dev/null +++ b/setup.cfg @@ -0,0 +1,3 @@ +[flake8] +max-line-length = 100 +extend-ignore = F401 diff --git a/warc/.keep b/warc/.keep new file mode 100644 index 0000000..e69de29 diff --git a/warc_gpt/__init__.py b/warc_gpt/__init__.py new file mode 100644 index 0000000..30bc370 --- /dev/null +++ b/warc_gpt/__init__.py @@ -0,0 +1,42 @@ +""" +`warc_gpt` module: REST API and CLI commands for the `warc-gpt` pipeline. +""" +import os + +from dotenv import load_dotenv +from flask import Flask + +from warc_gpt import utils + + +load_dotenv() + +WARC_RECORD_DATA = { + "warc_filename": "", + "warc_record_id": "", + "warc_record_date": None, + "warc_record_target_uri": None, + "warc_record_content_type": None, + "warc_record_text": "", +} +""" Shape of the data we extract and store from WARC records. """ + + +def create_app(): + """ + App factory (https://flask.palletsprojects.com/en/2.3.x/patterns/appfactories/) + """ + app = Flask(__name__) + + # Note: Every module in this app assumes the app context is available and initialized. + with app.app_context(): + utils.check_env() + + os.makedirs(os.environ["WARC_FOLDER_PATH"], exist_ok=True) + os.makedirs(os.environ["VISUALIZATIONS_FOLDER_PATH"], exist_ok=True) + os.makedirs(os.environ["VECTOR_SEARCH_PATH"], exist_ok=True) + + from warc_gpt import commands + from warc_gpt import views + + return app diff --git a/warc_gpt/commands/__init__.py b/warc_gpt/commands/__init__.py new file mode 100644 index 0000000..96951d6 --- /dev/null +++ b/warc_gpt/commands/__init__.py @@ -0,0 +1,2 @@ +from .ingest import ingest +from .visualize import visualize diff --git a/warc_gpt/commands/ingest.py b/warc_gpt/commands/ingest.py new file mode 100644 index 0000000..dad6802 --- /dev/null +++ b/warc_gpt/commands/ingest.py @@ -0,0 +1,215 @@ +""" +`commands.ingest` module: Controller for the `ingest` CLI command. +""" +import os +import glob +import traceback +import io +from shutil import rmtree + +import click +import chromadb +from bs4 import BeautifulSoup +from bs4 import Comment as HTMLComment +from PyPDF2 import PdfReader +from sentence_transformers import SentenceTransformer +from warcio.archiveiterator import ArchiveIterator +from langchain.text_splitter import SentenceTransformersTokenTextSplitter +from flask import current_app + +from warc_gpt import WARC_RECORD_DATA + + +@current_app.cli.command("ingest") +def ingest() -> None: + """ + Generates sentence embeddings and metadata for a set of WARCs and saves them in a vector store. + + See: options in .env.example + """ + environ = os.environ + + normalize_embeddings = environ["VECTOR_SEARCH_NORMALIZE_EMBEDDINGS"] == "true" + chunk_prefix = environ["VECTOR_SEARCH_CHUNK_PREFIX"] + + warc_files = [] + embedding_model = None + chroma_client = None + chroma_collection = None + total_records = 0 + total_embeddings = 0 + + # Cleanup + rmtree(environ["VECTOR_SEARCH_PATH"], ignore_errors=True) + os.makedirs(environ["VECTOR_SEARCH_PATH"], exist_ok=True) + + # List WARC files to process + warc_files += glob.glob(environ["WARC_FOLDER_PATH"] + "/*.warc", recursive=True) + warc_files += glob.glob(environ["WARC_FOLDER_PATH"] + "/*.warc.gz", recursive=True) + warc_files.sort() + + if not warc_files: + click.echo("No WARC files to ingest.") + exit(1) + + click.echo(f"{len(warc_files)} WARC files to ingest.") + + # Init embedding model + embedding_model = SentenceTransformer( + environ["VECTOR_SEARCH_SENTENCE_TRANSFORMER_MODEL"], + device=environ["VECTOR_SEARCH_SENTENCE_TRANSFORMER_DEVICE"], + ) + + # Init text splitter function + text_splitter = SentenceTransformersTokenTextSplitter( + model_name=environ["VECTOR_SEARCH_SENTENCE_TRANSFORMER_MODEL"], + chunk_overlap=int(environ["VECTOR_SEARCH_TEXT_SPLITTER_CHUNK_OVERLAP"]), + tokens_per_chunk=embedding_model[0].max_seq_length, + ) # Note: The text splitter adjusts its cut-off based on the models' max_seq_length + + # Init vector store + chroma_client = chromadb.PersistentClient( + path=environ["VECTOR_SEARCH_PATH"], + settings=chromadb.Settings(anonymized_telemetry=False), + ) + + chroma_collection = chroma_client.create_collection( + name=environ["VECTOR_SEARCH_COLLECTION_NAME"], + metadata={"hnsw:space": environ["VECTOR_SEARCH_DISTANCE_FUNCTION"]}, + ) + + # + # For each WARC: + # - Extract text from text/html and application/pdf records + # - Split and generate embeddings for said text + # - Save in vector store + # + for warc_file in warc_files: + click.echo(f"🗜️ Ingesting HTML and PDF records from {warc_file}") + + with open(warc_file, "rb") as stream: + for record in ArchiveIterator(stream): + record_data = dict(WARC_RECORD_DATA) + + if record.rec_type != "response": + continue + + # Extract metadata + rec_headers = record.rec_headers + http_headers = record.http_headers + record_data["warc_filename"] = os.path.basename(warc_file) + record_data["warc_record_id"] = rec_headers.get_header("WARC-Record-ID") + record_data["warc_record_date"] = rec_headers.get_header("WARC-Date") + record_data["warc_record_target_uri"] = rec_headers.get_header("WARC-Target-URI") + record_data["warc_record_content_type"] = http_headers.get_header("Content-Type") + record_data["warc_record_text"] = "" + + # Skip incomplete records + if ( + not record_data["warc_record_id"] + or not record_data["warc_record_date"] + or not record_data["warc_record_target_uri"] + or not record_data["warc_record_content_type"] + ): + continue + + # Skip records that are not HTTP 2XX + if http_headers.get_statuscode().startswith("2") is not True: + continue + + # + # Extract text from text/html + # + if record_data["warc_record_content_type"].startswith("text/html"): + try: + response_as_text = record.content_stream().read().decode("utf-8") + + soup = BeautifulSoup(response_as_text, "html.parser") + + # Skip documents with no body tag + if not soup.body or len(soup.body) < 1: + continue + + all_text = soup.body.findAll(string=True) + + for text in all_text: + if text.parent.name in ["script", "style"]: # No + + + + +

+
+
+ +
+ + +
+
+ + + + + +

Edit RAG Prompt

+ +

Retrieval Prompt

+ + + +
+ + + diff --git a/warc_gpt/utils/__init__.py b/warc_gpt/utils/__init__.py new file mode 100644 index 0000000..542dbb5 --- /dev/null +++ b/warc_gpt/utils/__init__.py @@ -0,0 +1,2 @@ +from .check_env import check_env +from .list_available_models import list_available_models diff --git a/warc_gpt/utils/check_env.py b/warc_gpt/utils/check_env.py new file mode 100644 index 0000000..457c3f7 --- /dev/null +++ b/warc_gpt/utils/check_env.py @@ -0,0 +1,32 @@ +""" +`utils.check_env` module: Checks that required env variables are available. +""" +import os + + +def check_env() -> bool: + """ + Checks that required env variables are available. + Throws if properties are missing or unusable. + """ + environ = os.environ + + for prop in [ + "WARC_FOLDER_PATH", + "VISUALIZATIONS_FOLDER_PATH", + "VECTOR_SEARCH_PATH", + "VECTOR_SEARCH_COLLECTION_NAME", + "VECTOR_SEARCH_SENTENCE_TRANSFORMER_MODEL", + "VECTOR_SEARCH_DISTANCE_FUNCTION", + "VECTOR_SEARCH_NORMALIZE_EMBEDDINGS", + "VECTOR_SEARCH_CHUNK_PREFIX", + "VECTOR_SEARCH_QUERY_PREFIX", + "VECTOR_SEARCH_SENTENCE_TRANSFORMER_DEVICE", + "VECTOR_SEARCH_TEXT_SPLITTER_CHUNK_OVERLAP", + "VECTOR_SEARCH_SEARCH_N_RESULTS", + "RAG_PROMPT", + ]: + if prop not in environ: + raise Exception(f"env var {prop} must be defined.") + + return True diff --git a/warc_gpt/utils/list_available_models.py b/warc_gpt/utils/list_available_models.py new file mode 100644 index 0000000..d82909a --- /dev/null +++ b/warc_gpt/utils/list_available_models.py @@ -0,0 +1,49 @@ +""" +`utils.list_available_models` module: Checks what models LiteLLM has access to. +""" +import os + +import litellm +import requests + + +litellm.telemetry = False + + +def list_available_models() -> list: + """ + Returns a list of the models LiteLLM can talk to based on current environment. + More info: + - https://docs.litellm.ai/docs/ + - https://github.com/jmorganca/ollama/blob/main/docs/api.md#list-local-models + """ + models = [] + + if os.environ.get("OPENAI_API_KEY"): + for model in litellm.open_ai_chat_completion_models: + if model.startswith("gpt"): + models.append(model) + + if os.environ.get("ANTHROPIC_API_KEY"): + for model in litellm.anthropic_models: + models.append(model) + + if os.environ.get("COHERE_API_KEY"): + for model in litellm.cohere_models: + models.append(model) + + if os.environ.get("PERPLEXITYAI_API_KEY"): + for model in litellm.perplexity_models: + models.append(model) + + # List models available at Ollama endpoint if provided + try: + ollama_api_url = os.environ.get("OLLAMA_API_URL", "http://localhost:11343") + response = requests.get(f"{ollama_api_url}/api/tags", timeout=1).json() + + for model in response["models"]: + models.append(f"ollama/{model['name']}") + except Exception: + pass + + return models diff --git a/warc_gpt/views/__init__.py b/warc_gpt/views/__init__.py new file mode 100644 index 0000000..52714ea --- /dev/null +++ b/warc_gpt/views/__init__.py @@ -0,0 +1,2 @@ +from .api import get_models, post_completion +from .ui import get_root diff --git a/warc_gpt/views/api.py b/warc_gpt/views/api.py new file mode 100644 index 0000000..224cc13 --- /dev/null +++ b/warc_gpt/views/api.py @@ -0,0 +1,306 @@ +""" +`views.api` module: API routes controller. +""" + +import os +import traceback +import uuid + +import chromadb +import litellm +from sentence_transformers import SentenceTransformer +from flask import current_app, jsonify, request, g + +from warc_gpt.utils import list_available_models + +litellm.telemetry = False + + +@current_app.route("/api/models") +def get_models(): + return jsonify(list_available_models()), 200 + + +@current_app.route("/api/completion", methods=["POST"]) +def post_completion(): + """ + [POST] /api/completion + + Accepts JSON body with the following properties: + - "model": One of the models /api/models lists (required) + - "message": User prompt (required) + - "temperature": Defaults to 0.0 + - "max_tokens": If provided, caps number of tokens that will be generated in response. + - "no_rag": If set and true, the API will not try to retrieve context. + - "history": A list of chat completion objects representing the chat history. Each object must contain "user" and "content". + - "rag_prompt_override": If provided, will be used in replacement of the predefined RAG prompt. {context} and {question} placeholders will be automatically replaced. + + Example of a "history" list: + ``` + [ + {"role": "user", "content": "Foo bar"}, + {"role": "assistant", "content": "Bar baz"} + ] + ``` + """ + environ = os.environ + + chroma_client = None + chroma_collection = None + embedding_model = None + + available_models = list_available_models() + + input = request.get_json() + model = None + message = None + temperature = 0.0 + max_tokens = None + no_rag = False + rag_prompt_override = None + + vector_search_results = None + prompt = "" + context = "" + + history = [] # List of chat completion objects keeping track of exchanges (no RAG context) + messages = [] # List of chat completion objects sent to LLM - last "user" entry has RAG context + + normalize_embeddings = environ["VECTOR_SEARCH_NORMALIZE_EMBEDDINGS"] == "true" + query_prefix = environ["VECTOR_SEARCH_QUERY_PREFIX"] + + # + # Check that "model" was provided and is available + # + if "model" not in input: + return jsonify({"error": "No model provided."}), 400 + + if input["model"] not in available_models: + return jsonify({"error": "Requested model is invalid or not available."}), 400 + + model = input["model"] + + # + # Check that "message" was provided + # + if "message" not in input: + return jsonify({"error": "No message provided."}), 400 + + message = str(input["message"]).strip() + + if not message: + return jsonify({"error": "Message cannot be empty."}), 400 + + # + # Validate "temperature" if provided + # + if "temperature" in input: + try: + temperature = float(input["temperature"]) + assert temperature >= 0.0 + except Exception: + return ( + jsonify({"error": "temperature must be a float superior or equal to 0.0."}), + 400, + ) + + # + # Validate "max_tokens" if provided + # + if "max_tokens" in input: + try: + max_tokens = int(input["max_tokens"]) + assert max_tokens > 0 + except Exception: + return (jsonify({"error": "max_tokens must be an int superior to 0."}), 400) + + # + # Validate "no_rag" if provided + # + if "no_rag" in input: + try: + assert isinstance(input["no_rag"], bool) + no_rag = input["no_rag"] + except Exception: + current_app.logger.warn("no_rag parameter was passed but ignored as invalid.") + + # + # Validate "rag_prompt_override" if provided + # + if "rag_prompt_override" in input: + try: + assert isinstance(input["rag_prompt_override"], str) + rag_prompt_override = str(input["rag_prompt_override"]).strip() + except Exception: + current_app.logger.warn( + "rag_prompt_override parameter was passed but ignored as invalid." + ) + + # + # Validate "history" if provided + # + if "history" in input: + try: + for past_message in input["history"]: + assert past_message["role"] + assert past_message["content"] + history.append(past_message) + + except Exception: + return ( + jsonify({"error": "past_messages must be an array of chat completion objects."}), + 400, + ) + + # + # Retrieve context - unless in no_rag mode + # + + # Load vector store client and collection + # Shared at app-level through: + # - g.chroma_client + # - g.chroma_collection + try: + assert not no_rag + + if "chroma_client" in g: + chroma_client = g.chroma_client + else: + chroma_client = chromadb.PersistentClient( + path=environ["VECTOR_SEARCH_PATH"], + settings=chromadb.Settings(anonymized_telemetry=False), + ) + + g.chroma_client = chroma_client + + if "chroma_collection" in g: + chroma_collection = g.chroma_collection + else: + chroma_collection = chroma_client.get_collection( + name=environ["VECTOR_SEARCH_COLLECTION_NAME"] + ) + + g.chroma_collection = chroma_collection + + except AssertionError: + pass # no_rag mode + except Exception: + current_app.logger.error(traceback.format_exc()) + return jsonify({"error": "Could not load vector store."}), 500 + + # Load embedding model (Shared at app-level via g.embedding_model) + try: + assert not no_rag + + if "embedding_model" in g: + embedding_model = g.embedding_model + else: + embedding_model = SentenceTransformer( + environ["VECTOR_SEARCH_SENTENCE_TRANSFORMER_MODEL"], + device=environ["VECTOR_SEARCH_SENTENCE_TRANSFORMER_DEVICE"], + ) + + g.embedding_model = embedding_model + except AssertionError: + pass # no_rag mode + except Exception: + current_app.logger.error(traceback.format_exc()) + return jsonify({"error": "Could not load embedding model."}), 500 + + # Retrieve context chunks + try: + assert not no_rag + + message_embedding = embedding_model.encode( + sentences=f"{query_prefix}{message}", + normalize_embeddings=normalize_embeddings, + ).tolist() + + vector_search_results = chroma_collection.query( + query_embeddings=message_embedding, + n_results=int(environ["VECTOR_SEARCH_SEARCH_N_RESULTS"]), + ) + except AssertionError: + pass # no_rag mode + except Exception: + current_app.logger.error(traceback.format_exc()) + return jsonify({"error": "Could not retrieve context from vector store."}), 500 + + # + # Prepare prompt + # + + # Prepare text version of context that was retrieved + if vector_search_results: + for vector in vector_search_results["metadatas"][0]: + warc = vector["warc_filename"] + uri = vector["warc_record_target_uri"] + date = vector["warc_record_date"] + text = vector["warc_record_text"] + + context += f"Excerpt from web content {uri} captured on {date} as part of the web archive saved in {warc}:\n{text}\n\n" # noqa + + if no_rag: + prompt = message + else: + prompt = rag_prompt_override if rag_prompt_override else environ["RAG_PROMPT"] + prompt = prompt.replace("{context}", context) + prompt = prompt.replace("{question}", message) + prompt = prompt.strip() + + # + # Query LLM + # + try: + messages = list(history) + messages.append({"content": prompt, "role": "user"}) + + # Try adjust messages list to context length (buggy) + try: + new_messages = litellm.utils.trim_messages(messages, model=model) + assert new_messages + messages = new_messages + except Exception: + current_app.logger.warn(f"litellm could not trim messages for {model}") + + response = litellm.completion( + model=model, + messages=messages, + temperature=temperature, + max_tokens=max_tokens, + api_base=os.environ["OLLAMA_API_URL"] if model.startswith("ollama") else None, + ) + + history.append({"content": message, "role": "user"}) + + history.append( + { + "content": response["choices"][0]["message"]["content"], + "role": "assistant", + } + ) + + output = { + "id_exchange": str(uuid.uuid4()), + "response": response["choices"][0]["message"]["content"], + "request_info": { + "model": model, + "message": message, + "temperature": temperature, + "max_tokens": max_tokens, + "no_rag": no_rag, + "message_plus_prompt": prompt, + }, + "response_info": { + "prompt_tokens": response["usage"]["prompt_tokens"], + "completion_tokens": response["usage"]["completion_tokens"], + "total_tokens": response["usage"]["total_tokens"], + }, + "context": vector_search_results["metadatas"][0] if vector_search_results else [], + "history": history, + } + + return (jsonify(output), 200) + except Exception: + current_app.logger.error(traceback.format_exc()) + return jsonify({"error": "Could not query LLM."}), 500 diff --git a/warc_gpt/views/ui.py b/warc_gpt/views/ui.py new file mode 100644 index 0000000..264b70a --- /dev/null +++ b/warc_gpt/views/ui.py @@ -0,0 +1,21 @@ +""" +`views.ui` module: UI routes controller. +""" +import os + +from flask import current_app, render_template + +from warc_gpt.utils import list_available_models + + +@current_app.route("/") +def get_root(): + available_models = list_available_models() + return ( + render_template( + "index.html", + available_models=available_models, + rag_prompt=os.environ["RAG_PROMPT"], + ), + 200, + ) diff --git a/wsgi.py b/wsgi.py new file mode 100644 index 0000000..7958444 --- /dev/null +++ b/wsgi.py @@ -0,0 +1,5 @@ +""" WSGI hook """ +from warc_gpt import create_app + +if __name__ == "__main__": + create_app().run()