diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..578bc0f --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,46 @@ +# Contributing to `conda` + +Do you want to contribute to this project? Make sure to read this guidelines first :) + +## Issue + +**When to do it**: + +- You found bugs but you don't know how to solve them or don't have time/will to do the solve +- You want new features but you don't know how to implement them or don't have time/will to do the implementation + +> ⚠️ _Always check open and closed issues before you submit yours to avoid duplicates_ + +**How to do it**: + +- Open an issue +- Give the issue a meaningful title (short but effective problem description) +- Describe the problem following the issue template + +## Traditional contribution + +**When to do it**: + +- You found bugs and corrected them +- You optimized/improved the code +- You added new features that you think could be useful to others + +**How to do it**: + +1. Fork this repository +2. Commit your changes +3. Submit pull request (make sure to provide a thorough description of the changes) + + +## Showcase your PrAIvateSearch + +**When to do it**: + +- You modified the base application with new features but you don't want/can't merge them with the original PrAIvateSearch + +**How to do it**: + +- Go to [_GitHub Discussions > Show and tell_](https://github.com/AstraBert/PrAIvateSearch/discussions/categories/show-and-tell) page +- Open a new discussion there, describing your PrAIvateSearch application + +### Thanks for contributing! \ No newline at end of file diff --git a/LICENSE b/LICENSE index fb85af0..5b33604 100644 --- a/LICENSE +++ b/LICENSE @@ -1,6 +1,6 @@ MIT License -Copyright (c) 2024 Astra Clelia Bertelli +Copyright (c) 2025 Clelia (Astra) Bertelli Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal diff --git a/README.md b/README.md index 6cd6d47..72f30be 100644 --- a/README.md +++ b/README.md @@ -1,134 +1,74 @@

qdurllm

Search your favorite websites and chat with them, on your desktop🌐

+# Docs in active development!👷‍♀️ -
- GitHub top language - GitHub commit activity - Static Badge - Static Badge - Docker image size - Static Badge -
- Flowchart -

Flowchart for qdurllm

-
-
+They will be soon available on: https://astrabert.github.io/qdurllm/ -**qdurllm** (**Qd**rant **URL**s and **L**arge **L**anguage **M**odels) is a local search engine that lets you select and upload URL content to a vector database: after that, you can search, retrieve and chat with this content. +In the meantime, refer to the **Quickstart guide** in this README! -This is provisioned through a multi-container Docker application, leveraging Qdrant, Langchain, llama.cpp, quantized Gemma and Gradio. +## Quickstart -## Demo! +### 1. Prerequisites -Head over to the [demo space on HuggingFace](https://huggingface.co/spaces/as-cle-bert/qdurllm-demo)🦀 +- [`conda`](https://docs.conda.io/projects/conda/en/latest/user-guide/getting-started.html) package manager +- [`docker`](https://www.docker.com/) and [`docker compose`](https://docs.docker.com/compose/). -## Requirements +### 2. Installation -The only requirement is to have `docker` and `docker-compose`. +> [!IMPORTANT] +> _This is only for the pre-release of `v1.0.0`, i.e. `v1.0.0-rc.0`_ -If you don't have them, make sure to install them [here](https://docs.docker.com/get-docker/). - -## Installation - -You can install the application by cloning the GitHub repository +1. Clone the `january-2025` branch of this GitHub repo: ```bash -git clone https://github.com/AstraBert/qdurllm.git -cd qdurllm +git clone -b january-2025 --single-branch https://github.com/AstraBert/qdurllm.git +cd qdurllm/ ``` -Or you can simply paste the following text into a `compose.yaml` file: - -```yaml -networks: - mynet: - driver: bridge -services: - local-search-application: - image: astrabert/local-search-application - networks: - - mynet - ports: - - "7860:7860" - qdrant: - image: qdrant/qdrant - ports: - - "6333:6333" - volumes: - - "./qdrant_storage:/qdrant/storage" - networks: - - mynet - llama_server: - image: astrabert/llama.cpp-gemma - ports: - - "8000:8000" - networks: - - mynet -``` +2. Create the `conda` environment: -Placing the file in whatever directory you want in your file system. +```bash +conda env create -f environment.yml +``` -Prior to running the application, you can optionally pull all the needed images from Docker hub: +3. Pull `qdrant` from Docker Hub: ```bash docker pull qdrant/qdrant -docker pull astrabert/llama.cpp-gemma -docker pull astrabert/local-search-application ``` -## How does it work? - -When launched (see [Usage](#usage)), the application runs three containers: - -- `qdrant`(port 6333): serves as vector database provider for semantic search-based retrieval -- `llama.cpp-gemma`(port 8000): this is an implementation of a [quantized Gemma model](https://huggingface.co/lmstudio-ai/gemma-2b-it-GGUF) provided by LMStudio and Google, served with `llama.cpp` server. This works for text-generation scopes, enriching the search experience of the user. -- `local-search-application`(port 7860): a Gradio tabbed interface with: - + The possibility to upload one or multiple contents by specifying the URL (thanks to Langchain) - + The possibility to chat with the uploaded URLs thanks to `llama.cpp-gemma` - + The possibility to perform a direct search that leverages double-layered retrieval with `all-MiniLM-L6-v2` (that identifies the 10 best matches) and `sentence-t5-base` (that re-encodes the 10 best matches and extracts the best hit from them) - this is the same RAG implementation used in combination with `llama.cpp-gemma`. Wanna see how double-layered RAG performs compared to single-layered RAG? Head over [here](./double-layered-rag-benchmarks/)! - -> _The overall computational burden is light enough to make the application run not only GPUless, but also with low RAM availability (>=8GB, although it can take up to 10 mins for Gemma to respond on 8GB RAM)._ - -## Usage - -### Run it +### 3. Launching -You can make the application work with the following - really simple - command, which has to be run within the same directory where you stored your `compose.yaml` file: +1. Launch `qdrant` vector database services with `docker compose` (from within the `qdurllm` folder): ```bash -docker compose up -d +docker compose up ``` -If you've already pulled all the images, you'll find the application running at `http://localhost:7860` or `http://0.0.0.0:7860` in less than a minute. +2. Activate the `qdurllm` conda environment you just created: -If you have not pulled the images, you'll have to wait that their installation is complete before actually using the application. - -### Use it - -Once the app is loaded, you'll find a first tab in which you can write the URLs whose content you want to interact with: - -![upload_URLs](./imgs/tutorial1.png) - -Now that your URLs are uploaded, you can either chat with their content through `llama.cpp-gemma`: - -![chat_with_URLs](./imgs/tutorial2.png) +```bash +conda activate qdurllm +``` -> _Note that you can also set parameters like maximum output tokens, temperature, repetition penalty and generation seed_ +3. Go inside the `app` directory and launch the Gradio application: -Or you can use double-layered-retrieval semantic search to query your URL content(s) directly: +```bash +cd app/ +python3 app.py +``` -![direct_search](./imgs/tutorial3.png) +You should see the app running on `http://localhost:7860` once all the models are downloaded from HuggingFace Hub. -## License and rights of usage +## Relies on -The software is (and will always be) open-source, provided under [MIT license](./LICENSE). +- [Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct), with Apache 2.0 license +- [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base), with Apache 2.0 license +- [prithivida/Splade_PP_en_v1](https://huggingface.co/prithivida/Splade_PP_en_v1), with Apache 2.0 license -Anyone can use, modify and redistribute any portion of it, as long as the author, [Astra Clelia Bertelli](https://astrabert.vercel.app) is cited. -## Contributions and funding +## Give feedback! -Contribution are always more than welcome! Feel free to flag issues, open PRs or [contact the author](mailto:astra.bertelli01@universitadipavia.it) to suggest any changes, request features or improve the code. +Comment on the [**discussion thread created for this release**](https://github.com/AstraBert/qdurllm/discussions) with your feedback or create [**issues**](https://github.com/AstraBert/qdurllm/issues) :) -If you found the application useful, please consider [funding it](https://github.com/sponsors/AstraBert) in order to allow improvements! diff --git a/_config.yml b/_config.yml deleted file mode 100644 index 2f7efbe..0000000 --- a/_config.yml +++ /dev/null @@ -1 +0,0 @@ -theme: jekyll-theme-minimal \ No newline at end of file diff --git a/app/__pycache__/loadUrls.cpython-312.pyc b/app/__pycache__/loadUrls.cpython-312.pyc new file mode 100644 index 0000000..7814559 Binary files /dev/null and b/app/__pycache__/loadUrls.cpython-312.pyc differ diff --git a/app/__pycache__/rag.cpython-312.pyc b/app/__pycache__/rag.cpython-312.pyc new file mode 100644 index 0000000..ef340e1 Binary files /dev/null and b/app/__pycache__/rag.cpython-312.pyc differ diff --git a/app/__pycache__/texInference.cpython-312.pyc b/app/__pycache__/texInference.cpython-312.pyc new file mode 100644 index 0000000..4fe3186 Binary files /dev/null and b/app/__pycache__/texInference.cpython-312.pyc differ diff --git a/app/app.py b/app/app.py new file mode 100644 index 0000000..aaf0fff --- /dev/null +++ b/app/app.py @@ -0,0 +1,68 @@ +from rag import client, SemanticCache, NeuralSearcher, dense_encoder, sparse_encoder +from texInference import pipe +from loadUrls import urlload, to_db +import gradio as gr +import time + + +searcher = NeuralSearcher("memory", client, dense_encoder, sparse_encoder) +semantic_cache = SemanticCache(client, dense_encoder, "semantic_cache") + + +def upload2qdrant(url): + global client + documents = urlload(url) + if type(documents) == list: + try: + to_db(documents) + return "URLs successfully uploaded to Qdrant collection!" + except Exception as e: + return f"An error occured: {e}" + else: + return documents + +demo0 = gr.Interface(fn=upload2qdrant, title="Upload URL content to Qdrant", inputs=gr.Textbox(label="URL(s)", info="Add one URL or more (if more, you should provide them comma-separated, like this: URL1,URL2,...,URLn)"), outputs=gr.Textbox(label="Logs")) + + +def reply(message, history, ntokens, rep_pen, temp, topp, systemins): + sr = semantic_cache.search_cache(message) + if sr: + response = sr + this_hist = '' + for c in response: + this_hist+=c + time.sleep(0.001) + yield this_hist + else: + context, url = searcher.search_text(message) + prompt = [{"role": "system", "content": systemins}, {"role": "user", "content": f"This is the context information to reply to my prompt:\n\n{context}"}, {"role": "user", "content": message}] + results = pipe(prompt, temp, topp, ntokens, rep_pen) + results = results.split("<|im_start|>assistant\n")[1] + response = results.replace("<|im_end|>", "") + semantic_cache.upload_to_cache(message, response) + this_hist = '' + for c in response: + this_hist+=c + time.sleep(0.001) + yield this_hist + +def direct_search(input_text): + context, url = searcher.search_text(input_text) + return context, f"Reference website [here]({url})" + +demo2 = gr.Interface(fn=direct_search, inputs=gr.Textbox(label="Search Query", placeholder="Input your search query here...", ), outputs=[gr.Textbox(label="Retrieved Content"), gr.Markdown(label="URL")], title="Search your URLs") + +user_max_new_tokens = gr.Slider(0, 4096, value=512, label="Max new tokens", info="Select max output tokens (higher number of tokens will result in a longer latency)") +user_max_temperature = gr.Slider(0, 1, value=0.1, step=0.1, label="Temperature", info="Select generation temperature") +user_max_rep_pen = gr.Slider(0, 10, value=1.2, step=0.1, label="Repetition penalty", info="Select repetition penalty") +user_top_p = gr.Slider(0.1, 1, value=1, step=0.1, label="top_p", info="Select top_p for the generation") +system_ins = gr.Textbox(label="System Prompt", info="Insert your system prompt here", value="You are an helpful web searching assistant. You reply based on the contextual information you are provided with and on your knowledge.") +additional_accordion = gr.Accordion(label="Parameters to be set before you start chatting", open=True) +demo1 = gr.ChatInterface(fn=reply, title="Chat with your URLs", additional_inputs=[user_max_new_tokens, user_max_temperature, user_max_rep_pen, user_top_p, system_ins], additional_inputs_accordion=additional_accordion) + +my_theme = gr.themes.Soft(primary_hue=gr.themes.colors.rose, secondary_hue=gr.themes.colors.pink) + +demo = gr.TabbedInterface([demo0, demo1, demo2], ["Upload URLs", "Chat with URLs", "Direct Search"], theme=my_theme) + +if __name__ == "__main__": + demo.launch(server_name="0.0.0.0", server_port=7860) \ No newline at end of file diff --git a/app/loadUrls.py b/app/loadUrls.py new file mode 100644 index 0000000..07924ad --- /dev/null +++ b/app/loadUrls.py @@ -0,0 +1,29 @@ +from langchain_community.document_loaders.url import UnstructuredURLLoader +from langchain.text_splitter import CharacterTextSplitter +from rag import upload_text_to_qdrant, client +from typing import List, Dict + +def urlload(urls: str) -> List[Dict[str,str]]: + links = urls.split(",") + try: + loader = UnstructuredURLLoader( + urls=links, method="elements", + strategy="fast" + ) + docs = loader.load() + text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) + pages = text_splitter.split_documents(docs) + contents = [{"text": pages[i].page_content, "url": pages[i].metadata["source"]} for i in range(len(pages))] + return contents + except Exception as e: + return f"An error occurred while parsing the URLs: {e}" + + +def to_db(contents = List[Dict[str, str]]) -> None: + c = 0 + for content in contents: + upload_text_to_qdrant(client, "memory", content, c) + c+=1 + return + + diff --git a/app/rag.py b/app/rag.py new file mode 100644 index 0000000..fe12e2d --- /dev/null +++ b/app/rag.py @@ -0,0 +1,129 @@ +from qdrant_client import QdrantClient, models +from fastembed import SparseTextEmbedding +from sentence_transformers import SentenceTransformer +import torch +import uuid +from typing import List, Dict + +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + +dense_encoder = SentenceTransformer('nomic-ai/modernbert-embed-base').to(device) +sparse_encoder = SparseTextEmbedding(model_name = 'prithivida/Splade_PP_en_v1') + +client = QdrantClient('http://localhost:6333') +client.recreate_collection( + collection_name = 'memory', + vectors_config = {}, + sparse_vectors_config = { + 'sparse-text': models.SparseVectorParams(index = models.SparseIndexParams(on_disk = False)) + }) +client.recreate_collection( + collection_name= 'semantic_cache', + vectors_config = models.VectorParams(size = 768, distance = models.Distance.COSINE) +) + +def reranking(docs: List[str], query: str, dense_encoder: SentenceTransformer): + query = "search_query: " + query + docs = ["search_document: " + doc for doc in docs] + query_vector = dense_encoder.encode(query) + docs_vector = dense_encoder.encode(docs) + similarities = dense_encoder.similarity(docs_vector, query_vector) + sims = [float(sim[0]) for sim in similarities] + text2sims = {docs[i]: sims[i] for i in range(len(sims))} + sorted_items = sorted(text2sims.items(), key=lambda x: x[1], reverse=True) + return sorted_items[0][0] + + +def get_sparse_embedding(text: str, model: SparseTextEmbedding): + embeddings = list(model.embed(text)) + vector = {f"sparse-text": models.SparseVector(indices=embeddings[0].indices, values=embeddings[0].values)} + return vector + + +def get_query_sparse_embedding(text: str, model: SparseTextEmbedding): + embeddings = list(model.embed(text)) + query_vector = models.NamedSparseVector( + name="sparse-text", + vector=models.SparseVector( + indices=embeddings[0].indices, + values=embeddings[0].values, + ), + ) + return query_vector + + +def upload_text_to_qdrant(client: QdrantClient, collection_name: str, docs: dict, point_id_sparse: int): + try: + client.upsert( + collection_name=collection_name, + points=[ + models.PointStruct( + id=point_id_sparse, + vector=get_sparse_embedding(docs["text"], sparse_encoder), + payload=docs, + ) + ], + ) + return True + except Exception as e: + return False + + +class SemanticCache: + def __init__(self, client: QdrantClient, text_encoder: SentenceTransformer, collection_name: str, threshold: float = 0.75): + self.client = client + self.text_encoder = text_encoder + self.collection_name = collection_name + self.threshold = threshold + def upload_to_cache(self, question = str, answer = str): + docs = {"question": question, "answer": answer} + tct = 'search_document: ' + question + point_id = str(uuid.uuid4()) + self.client.upsert( + collection_name=self.collection_name, + points=[ + models.PointStruct( + id=point_id, + vector=self.text_encoder.encode(tct).tolist(), + payload=docs, + ) + ], + ) + def search_cache(self, question: str, limit: int = 5): + question = 'search_query: ' + question + vector = self.text_encoder.encode(question).tolist() + search_result = self.client.search( + collection_name=self.collection_name, + query_vector=vector, + query_filter=None, + limit=limit, + ) + payloads = [hit.payload["answer"] for hit in search_result if hit.score > self.threshold] + if len(payloads) > 0: + return payloads[0] + else: + return "" + + + +class NeuralSearcher: + def __init__(self, text_collection_name: str, client: QdrantClient, dense_encoder: SentenceTransformer , sparse_encoder: SparseTextEmbedding): + self.text_collection_name = text_collection_name + self.dense_encoder = dense_encoder + self.qdrant_client = client + self.sparse_encoder = sparse_encoder + def search_text(self, text: str, limit: int = 5): + search_result_sparse = self.qdrant_client.search( + collection_name=self.text_collection_name, + query_vector=get_query_sparse_embedding(text, self.sparse_encoder), + query_filter=None, + limit=limit, + ) + payloads = [hit.payload["text"] for hit in search_result_sparse] + urls = [hit.payload["url"] for hit in search_result_sparse] + txt2url = {payloads[i]: urls[i] for i in range(len(urls))} + context = reranking(payloads, text, self.dense_encoder) + context = context.replace("search_document: ", "") + return context, txt2url[context] + + diff --git a/app/texInference.py b/app/texInference.py new file mode 100644 index 0000000..bb65986 --- /dev/null +++ b/app/texInference.py @@ -0,0 +1,21 @@ +import warnings +warnings.filterwarnings('ignore') +import torch +from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig +from trl import setup_chat_format +from typing import Dict, List + +model_name = 'Qwen/Qwen2.5-1.5B-Instruct' +quantization_config = BitsAndBytesConfig(load_in_4bit = True, bnb_4bit_compute_dtype = torch.bfloat16, bnb_4bit_use_double_quant = True, bnb_4bit_quant_type = 'nf4') +device = 'cuda' if torch.cuda.is_available() else 'cpu' +quantized_model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype = torch.bfloat16, quantization_config = quantization_config).to(device) +tokenizer = AutoTokenizer.from_pretrained(model_name) +tokenizer.chat_template = None +(quantized_model, tokenizer) = setup_chat_format(model = quantized_model, tokenizer = tokenizer) + +def pipe(prompt: List[Dict[(str, str)]], temperature: float, top_p: float, max_new_tokens: int, repetition_penalty: float) -> str: + tokenized_chat = tokenizer.apply_chat_template(prompt, tokenize = True, add_generation_prompt = True, return_tensors = 'pt').to(device) + outputs = quantized_model.generate(tokenized_chat, max_new_tokens = max_new_tokens, temperature = temperature, top_p = top_p, repetition_penalty = repetition_penalty).to(device) + results = tokenizer.decode(outputs[0]) + return results + diff --git a/application/Dockerfile b/application/Dockerfile deleted file mode 100644 index f9970cf..0000000 --- a/application/Dockerfile +++ /dev/null @@ -1,13 +0,0 @@ -FROM python:3.10.14-slim-bookworm - -WORKDIR /app - -ADD . /app/ - -RUN python3 -m pip cache purge -RUN python3 -m pip install --no-cache-dir -r requirements.txt -RUN python3 -m nltk.downloader all - -EXPOSE 7860 - -ENTRYPOINT [ "python3", "/app/scripts/app.py" ] \ No newline at end of file diff --git a/application/requirements.txt b/application/requirements.txt deleted file mode 100644 index b2c4793..0000000 --- a/application/requirements.txt +++ /dev/null @@ -1,11 +0,0 @@ -requests==2.31.0 -qdrant_client==1.9.0 -nltk==3.8.1 -spacy==3.7.2 -sentence_transformers==3.0.0 -psutil -unstructured -langchain -langchain_core -langchain_community -gradio diff --git a/application/scripts/__pycache__/app.cpython-310.pyc b/application/scripts/__pycache__/app.cpython-310.pyc deleted file mode 100644 index a07c440..0000000 Binary files a/application/scripts/__pycache__/app.cpython-310.pyc and /dev/null differ diff --git a/application/scripts/__pycache__/utils.cpython-310.pyc b/application/scripts/__pycache__/utils.cpython-310.pyc deleted file mode 100644 index c392fde..0000000 Binary files a/application/scripts/__pycache__/utils.cpython-310.pyc and /dev/null differ diff --git a/application/scripts/app.py b/application/scripts/app.py deleted file mode 100644 index 5527960..0000000 --- a/application/scripts/app.py +++ /dev/null @@ -1,105 +0,0 @@ -from qdrant_client import QdrantClient, models -from sentence_transformers import SentenceTransformer -from utils import NeuralSearcher, urlload, upload_to_qdrant_collection, upload_to_qdrant_subcollection -import gradio as gr -import time -import requests - -client = QdrantClient(host="host.docker.internal", port="6333") -encoder = SentenceTransformer(".cache/huggingface/models--sentence-transformers--all-MiniLM-L6-v2/snapshots/8b3219a92973c328a8e22fadcfa821b5dc75636a") -encoder1 = SentenceTransformer(".cache/huggingface/models--sentence-transformers--sentence-t5-base/snapshots/50c53e206f8b01c9621484a3c0aafce4e55efebf") -coll_name = "HTML_collection" -subcoll_name = "Subcollection" - -client.recreate_collection( - collection_name = coll_name, - vectors_config=models.VectorParams( - size = encoder.get_sentence_embedding_dimension(), # Vector size is defined by used model - distance = models.Distance.COSINE, - ), -) - -client.recreate_collection( - collection_name = subcoll_name, - vectors_config=models.VectorParams( - size = encoder1.get_sentence_embedding_dimension(), # Vector size is defined by used model - distance = models.Distance.COSINE, - ), -) - -def call_upload2qdrant(url): - global client, encoder, coll_name - documents = urlload(url) - if type(documents) == list: - try: - upload_to_qdrant_collection(client, coll_name, encoder, documents) - return "Successfully uploaded URL content to Qdrant collection" - except Exception as e: - return f"An error occured: {e}" - else: - return documents - -def reranked_rag(client, encoder0, encoder1, collection0, collection1, message): - txt2txt0 = NeuralSearcher(collection0, client, encoder0) - search_results0 = txt2txt0.search(message, 10) - upload_to_qdrant_subcollection(client, collection1, encoder1, search_results0) - txt2txt1 = NeuralSearcher(collection1, client, encoder1) - search_results1 = txt2txt1.search(message, 1) - return search_results1 - -demo0 = gr.Interface(fn=call_upload2qdrant, title="Upload URL content to Qdrant", inputs=gr.Textbox(label="URL(s)", info="Add one URL or more (if more, you should provide them comma-separated, like this: URL1,URL2,...,URLn)"), outputs=gr.Textbox(label="Logs")) - -def llama_cpp_respond(query, max_new_tokens, temperature, repeat_penalty, seed): - url = "http://host.docker.internal:8000/completion" - headers = { - "Content-Type": "application/json" - } - data = { - "prompt": query, - "n_predict": int(max_new_tokens), - "temperature": temperature, - "repeat_penalty": repeat_penalty, - "seed": int(seed), - } - - response = requests.post(url, headers=headers, json=data) - - a = response.json() - print(a) - return a["content"] - - -def reply(message, history, ntokens, rep_pen, temp, seed): - global client, encoder, encoder1, coll_name, subcoll_name - results = reranked_rag(client, encoder, encoder1, coll_name, subcoll_name, message) - print(results) - prompt = f'Context: {results[0]["text"]}; Request: {message}; Answer to request based on context: ' - response = llama_cpp_respond(prompt, ntokens, temp, rep_pen, seed) - response = response + f' [^Reference]\n\n\n[^Reference]: {results[0]["url"]}' - this_hist = '' - for char in response: - this_hist += char - time.sleep(0.0001) - yield this_hist - -def direct_search(input_text): - global client, encoder, encoder1, coll_name, subcoll_name - results = reranked_rag(client, encoder, encoder1, coll_name, subcoll_name, input_text) - return results[0]["text"], f"Reference website [here]({results[0]['url']})" - -demo2 = gr.Interface(fn=direct_search, inputs=gr.Textbox(label="Search Query", value="Input your search query here...", ), outputs=[gr.Textbox(label="Retrieved Content"), gr.Markdown(label="URL")], title="Search your URLs") - -user_max_new_tokens = gr.Slider(0, 1024, value=512, label="Max new tokens", info="Select max output tokens (higher number of tokens will result in a longer latency)") -user_max_rep_pen = gr.Slider(0, 1, value=0.5, label="Temperature", info="Select generation temperature") -user_max_temperature = gr.Slider(0, 10, value=1.2, label="Repetition penalty", info="Select repetition penalty") -user_seed = gr.Textbox(label="Seed", info="Write generation seed (if you are not familiar with it, leave it as it is)", value="4294967295") -additional_accordion = gr.Accordion(label="Parameters to be set before you start chatting", open=True) -chatbot = gr.Chatbot(height=400) -demo1 = gr.ChatInterface(fn=reply, title="Chat with your URLs", chatbot=chatbot, additional_inputs=[user_max_new_tokens, user_max_rep_pen, user_max_temperature, user_seed], additional_inputs_accordion=additional_accordion) - -my_theme = gr.themes.Soft(primary_hue=gr.themes.colors.rose, secondary_hue=gr.themes.colors.pink) - -demo = gr.TabbedInterface([demo0, demo1, demo2], ["Upload URLs", "Chat with URLs", "Direct Search"], theme=my_theme) - -if __name__ == "__main__": - demo.launch(server_name="0.0.0.0", server_port=7860) \ No newline at end of file diff --git a/application/scripts/utils.py b/application/scripts/utils.py deleted file mode 100644 index 4a7c9cd..0000000 --- a/application/scripts/utils.py +++ /dev/null @@ -1,73 +0,0 @@ -from qdrant_client import models -from langchain_community.document_loaders.url import UnstructuredURLLoader -from langchain.text_splitter import CharacterTextSplitter - - -def urlload(urls): - links = urls.split(",") - try: - loader = UnstructuredURLLoader( - urls=links, method="elements", - strategy="fast" - ) - docs = loader.load() - text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) - pages = text_splitter.split_documents(docs) - contents = [{"text": pages[i].page_content, "url": pages[i].metadata["source"]} for i in range(len(pages))] - return contents - except Exception as e: - return f"An error occurred while parsing the URLs: {e}" - -class NeuralSearcher: - def __init__(self, collection_name, client, model): - self.collection_name = collection_name - # Initialize encoder model - self.model = model - # initialize Qdrant client - self.qdrant_client = client - def search(self, text: str, num_return: int): - # Convert text query into vector - vector = self.model.encode(text).tolist() - - # Use `vector` for search for closest vectors in the collection - search_result = self.qdrant_client.search( - collection_name=self.collection_name, - query_vector=vector, - query_filter=None, # If you don't want any filters for now - limit=num_return, # 5 the most closest results is enough - ) - # `search_result` contains found vector ids with similarity scores along with the stored payload - # In this function you are interested in payload only - payloads = [hit.payload for hit in search_result] - return payloads - - - -def upload_to_qdrant_collection(client, collection_name, encoder, documents): - client.upload_points( - collection_name=collection_name, - points=[ - models.PointStruct( - id=idx, vector=encoder.encode(doc["text"]).tolist(), payload=doc - ) - for idx, doc in enumerate(documents) - ], - ) - -def upload_to_qdrant_subcollection(client, collection_name, encoder, documents): - client.delete_collection(collection_name=collection_name) - client.create_collection(collection_name = collection_name, - vectors_config=models.VectorParams( - size = encoder.get_sentence_embedding_dimension(), # Vector size is defined by used model - distance = models.Distance.COSINE, - ), - ) - client.upload_points( - collection_name=collection_name, - points=[ - models.PointStruct( - id=idx, vector=encoder.encode(doc["text"]).tolist(), payload=doc - ) - for idx, doc in enumerate(documents) - ], - ) diff --git a/compose.yaml b/compose.yaml index 2c7aa0d..5f5184b 100644 --- a/compose.yaml +++ b/compose.yaml @@ -3,12 +3,6 @@ networks: driver: bridge services: - local-search-application: - image: astrabert/local-search-application - networks: - - mynet - ports: - - "7860:7860" qdrant: image: qdrant/qdrant ports: @@ -17,10 +11,4 @@ services: - "./qdrant_storage:/qdrant/storage" networks: - mynet - llama_server: - image: astrabert/llama.cpp-gemma - ports: - - "8000:8000" - networks: - - mynet diff --git a/docs/about.md b/docs/about.md new file mode 100644 index 0000000..ce8d016 --- /dev/null +++ b/docs/about.md @@ -0,0 +1,12 @@ +# About `qdurllm` + +**`qdurllm`** is an open source local Gradio application that leverages: + +- semantic caching +- sparse retrieval + reranking +- LLM inference + +To allow you to upload website pages to a Qdrant database, search them and chat with them. + +> _Powered by [Qdrant](https://qdrant.tech), [FastEmbed](https://pypi.org/project/fastembed/), [SentenceTransformers](https://sbert.net/), [Nomic AI](https://www.nomic.ai/), [HuggingFace](https://huggingface.co), [Gradio](https://gradio.app), [Unstructured.io](https://unstructured.io/) and [LangChain](langchain.com)_ + diff --git a/docs/contributing.md b/docs/contributing.md new file mode 100644 index 0000000..75d5447 --- /dev/null +++ b/docs/contributing.md @@ -0,0 +1,46 @@ +# Contributing to `qdurllm` + +Do you want to contribute to this project? Make sure to read this guidelines first :) + +## Issue + +**When to do it**: + +- You found bugs but you don't know how to solve them or don't have time/will to do the solve +- You want new features but you don't know how to implement them or don't have time/will to do the implementation + +> ⚠️ _Always check open and closed issues before you submit yours to avoid duplicates_ + +**How to do it**: + +- Open an issue +- Give the issue a meaningful title (short but effective problem description) +- Describe the problem following the issue template + +## Traditional contribution + +**When to do it**: + +- You found bugs and corrected them +- You optimized/improved the code +- You added new features that you think could be useful to others + +**How to do it**: + +1. Fork this repository +2. Commit your changes +3. Submit pull request (make sure to provide a thorough description of the changes) + + +## Showcase your PrAIvateSearch + +**When to do it**: + +- You modified the base application with new features but you don't want/can't merge them with the original PrAIvateSearch + +**How to do it**: + +- Go to [_GitHub Discussions > Show and tell_](https://github.com/AstraBert/PrAIvateSearch/discussions/categories/show-and-tell) page +- Open a new discussion there, describing your PrAIvateSearch application + +### Thanks for contributing! \ No newline at end of file diff --git a/docs/index.md b/docs/index.md new file mode 100644 index 0000000..72f30be --- /dev/null +++ b/docs/index.md @@ -0,0 +1,74 @@ +

qdurllm

+

Search your favorite websites and chat with them, on your desktop🌐

+ +# Docs in active development!👷‍♀️ + +They will be soon available on: https://astrabert.github.io/qdurllm/ + +In the meantime, refer to the **Quickstart guide** in this README! + +## Quickstart + +### 1. Prerequisites + +- [`conda`](https://docs.conda.io/projects/conda/en/latest/user-guide/getting-started.html) package manager +- [`docker`](https://www.docker.com/) and [`docker compose`](https://docs.docker.com/compose/). + +### 2. Installation + +> [!IMPORTANT] +> _This is only for the pre-release of `v1.0.0`, i.e. `v1.0.0-rc.0`_ + +1. Clone the `january-2025` branch of this GitHub repo: + +```bash +git clone -b january-2025 --single-branch https://github.com/AstraBert/qdurllm.git +cd qdurllm/ +``` + +2. Create the `conda` environment: + +```bash +conda env create -f environment.yml +``` + +3. Pull `qdrant` from Docker Hub: + +```bash +docker pull qdrant/qdrant +``` + +### 3. Launching + +1. Launch `qdrant` vector database services with `docker compose` (from within the `qdurllm` folder): + +```bash +docker compose up +``` + +2. Activate the `qdurllm` conda environment you just created: + +```bash +conda activate qdurllm +``` + +3. Go inside the `app` directory and launch the Gradio application: + +```bash +cd app/ +python3 app.py +``` + +You should see the app running on `http://localhost:7860` once all the models are downloaded from HuggingFace Hub. + +## Relies on + +- [Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct), with Apache 2.0 license +- [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base), with Apache 2.0 license +- [prithivida/Splade_PP_en_v1](https://huggingface.co/prithivida/Splade_PP_en_v1), with Apache 2.0 license + + +## Give feedback! + +Comment on the [**discussion thread created for this release**](https://github.com/AstraBert/qdurllm/discussions) with your feedback or create [**issues**](https://github.com/AstraBert/qdurllm/issues) :) + diff --git a/double-layered-rag-benchmarks/README.md b/double-layered-rag-benchmarks/README.md deleted file mode 100644 index 6908bf2..0000000 --- a/double-layered-rag-benchmarks/README.md +++ /dev/null @@ -1,55 +0,0 @@ -# Small benchmark test results - -This small benchmark was conceived to test the solidity of the double-layered RAG approach -implemented inside *qdurllm* as a retrieval technique. - -## RAG workflow - -The RAG workflow goes like this: - -- All text batches, obtained by chunking URL contents, are first encoded by `All-MiniLM-L6-v2` and uploaded to a permanent Qdrant collection -- The first retrieval call extract the 10 closest matches to the query in terms of cosine distance, based on `All-MiniLM-L6-v2` search -- The 10 best hits get re-encoded by `sentence-t5-base` and uploaded to a non-permanent Qdrant collection (gets deleted at every round after use) -- `sentence-t5-base` performs a second retrieval call extracting the best match to the original query, and this best match gets returned - -## First test - -Benchmark is based on the content of 4 web pages: - -- https://www.technologyreview.com/2023/12/05/1084417/ais-carbon-footprint-is-bigger-than-you-think/ -- https://semiengineering.com/ai-power-consumption-exploding/ -- https://www.piie.com/blogs/realtime-economics/2024/ais-carbon-footprint-appears-likely-be-alarming -- https://news.climate.columbia.edu/2023/06/09/ais-growing-carbon-footprint/ - -The content of these URLs was chunked up and uploaded to Qdrant collections, and at the same time smaller portions of each chunk (encompassing 10-25% of the text) were used for querying, and the retrieved results compared with the original full text. - -## First results - -The correct/total retrievals ratio for the only `All-MiniLM-L6-v2` is 81.54%, whereas the correct/total retrievals ratio for the previously described double-layered `All-MiniLM-L6-v2` + `sentence-t5-base` goes up 93.85%, equalling the one of `sentence-t5-base` alone. Following a double-layered approach with switched roles for the two encoders yields a correct/total retrievals ratio of 84.62%. - -The advantage of this technique is that it does not require that all the chunks of text are encoded in 768-dimensional vectors (as would happen if we adopted `sentence-t5-base` alone), but this step is done dynamically at each vector call. As you can see, it also definitely improves the performance of the sole `All-MiniLM-L6-v2` by little more than 12%. - -The disadvantage is in the execution time: on a 8GB RAM-12 cores Windows 10 laptop, double-layered RAG takes an average of 8.39 s, against the 0.23 s of the sole `sentence-t5-base`. - -## Second test - -The second benchmark is based on []() dataset, available on HuggingFace. It is a Q&A dataset based on a set of 358 answers (used as content to retrieve) and questions (used as retrieval queries). - -## Second results - -- Avg time for `All-MiniLM-L6-v2`: 0.13889479704117508 +/- 0.018010675079187972 -- Avg time for `sentence-t5-base`: 0.3546350625123871 +/- 0.1378480017367839 -- Avg time for `All-MiniLM-L6-v2` + `sentence-t5-base`: 10.722357098306164 +/- 1.2552639024596886 -- Avg time for `sentence-t5-base` + `All-MiniLM-L6-v2`: 2.722710152020615 +/- 0.30703480688834284 -- Correct/Total retrievals for `All-MiniLM-L6-v2`: 0.41853932584269665 -- Correct/Total retrievals for `sentence-t5-base`: 0.5084269662921348 -- Correct/Total retrievals for `All-MiniLM-L6-v2` + `sentence-t5-base`: 0.5028089887640449 -- Correct/Total retrievals for `sentence-t5-base` + `All-MiniLM-L6-v2`: 0.42134831460674155 - -## Code availability - -The benchmark test code is available [here](./scripts/benchmark_test.py) for the first test and [here](./rageval.ipynb) for the second one. - 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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.5/57.5 kB\u001b[0m \u001b[31m5.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m141.1/141.1 kB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m50.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", - "cudf-cu12 24.4.1 requires protobuf<5,>=3.20, but you have protobuf 5.27.2 which is incompatible.\n", - "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 16.1.0 which is incompatible.\n", - "google-ai-generativelanguage 0.6.4 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.2 which is incompatible.\n", - "google-api-core 2.16.2 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0.dev0,>=3.19.5, but you have protobuf 5.27.2 which is incompatible.\n", - "google-cloud-aiplatform 1.59.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.2 which is incompatible.\n", - "google-cloud-bigquery-connection 1.12.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.2 which is incompatible.\n", - "google-cloud-bigquery-storage 2.25.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.2 which is incompatible.\n", - "google-cloud-bigtable 2.24.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.2 which is incompatible.\n", - "google-cloud-datastore 2.15.2 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.2 which is incompatible.\n", - "google-cloud-firestore 2.11.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.2 which is incompatible.\n", - "google-cloud-functions 1.13.3 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.2 which is incompatible.\n", - "google-cloud-translate 3.11.3 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.2 which is incompatible.\n", - "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\n", - "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 16.1.0 which is incompatible.\n", - "tensorflow 2.15.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 5.27.2 which is incompatible.\n", - "tensorflow-metadata 1.15.0 requires protobuf<4.21,>=3.20.3; python_version < \"3.11\", but you have protobuf 5.27.2 which is incompatible.\u001b[0m\u001b[31m\n", - "\u001b[0m" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "from qdrant_client import models\n", - "from langchain_community.document_loaders.url import UnstructuredURLLoader\n", - "from langchain.text_splitter import CharacterTextSplitter\n", - "\n", - "\n", - "def urlload(urls):\n", - " links = urls.split(\",\")\n", - " try:\n", - " loader = UnstructuredURLLoader(\n", - " urls=links, method=\"elements\",\n", - " strategy=\"fast\"\n", - " )\n", - " docs = loader.load()\n", - " text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n", - " pages = text_splitter.split_documents(docs)\n", - " contents = [{\"text\": pages[i].page_content, \"url\": pages[i].metadata[\"source\"]} for i in range(len(pages))]\n", - " return contents\n", - " except Exception as e:\n", - " return f\"An error occurred while parsing the URLs: {e}\"\n", - "\n", - "class NeuralSearcher:\n", - " def __init__(self, collection_name, client, model):\n", - " self.collection_name = collection_name\n", - " # Initialize encoder model\n", - " self.model = model\n", - " # initialize Qdrant client\n", - " self.qdrant_client = client\n", - " def search(self, text: str, num_return: int):\n", - " # Convert text query into vector\n", - " vector = self.model.encode(text).tolist()\n", - "\n", - " # Use `vector` for search for closest vectors in the collection\n", - " search_result = self.qdrant_client.search(\n", - " collection_name=self.collection_name,\n", - " query_vector=vector,\n", - " query_filter=None, # If you don't want any filters for now\n", - " limit=num_return, # 5 the most closest results is enough\n", - " )\n", - " # `search_result` contains found vector ids with similarity scores along with the stored payload\n", - " # In this function you are interested in payload only\n", - " payloads = [hit.payload for hit in search_result]\n", - " return payloads\n", - "\n", - "\n", - "\n", - "def upload_to_qdrant_collection(client, collection_name, encoder, documents):\n", - " client.upload_points(\n", - " collection_name=collection_name,\n", - " points=[\n", - " models.PointStruct(\n", - " id=idx, vector=encoder.encode(doc).tolist(), payload={\"text\": doc}\n", - " )\n", - " for idx, doc in enumerate(documents)\n", - " ],\n", - " )\n", - "\n", - "def upload_to_qdrant_subcollection(client, collection_name, encoder, documents):\n", - " client.delete_collection(collection_name=collection_name)\n", - " client.create_collection(collection_name = collection_name,\n", - " vectors_config=models.VectorParams(\n", - " size = encoder.get_sentence_embedding_dimension(), # Vector size is defined by used model\n", - " distance = models.Distance.COSINE,\n", - " ),\n", - " )\n", - " client.upload_points(\n", - " collection_name=collection_name,\n", - " points=[\n", - " models.PointStruct(\n", - " id=idx, vector=encoder.encode(doc[\"text\"]).tolist(), payload=doc\n", - " )\n", - " for idx, doc in enumerate(documents)\n", - " ],\n", - " )\n" - ], - "metadata": { - "execution": { - "iopub.status.busy": "2024-07-14T16:42:54.820405Z", - "iopub.execute_input": "2024-07-14T16:42:54.820797Z", - "iopub.status.idle": "2024-07-14T16:42:54.834809Z", - "shell.execute_reply.started": "2024-07-14T16:42:54.820765Z", - "shell.execute_reply": "2024-07-14T16:42:54.833566Z" - }, - "trusted": true, - "id": "Uft6xib-KEKc" - }, - "execution_count": 2, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "from huggingface_hub import login\n", - "from google.colab import userdata\n", - "\n", - "\n", - "token = userdata.get(\"HF_TOKEN\")\n", - "login(token)\n" - ], - "metadata": { - "execution": { - "iopub.status.busy": "2024-07-14T16:22:27.201719Z", - "iopub.execute_input": "2024-07-14T16:22:27.202106Z", - "iopub.status.idle": "2024-07-14T16:22:27.742075Z", - "shell.execute_reply.started": "2024-07-14T16:22:27.202074Z", - "shell.execute_reply": "2024-07-14T16:22:27.741109Z" - }, - "trusted": true, - "id": "s9_FhDt_KEKe", - "outputId": "e75729e9-0f7f-43a6-e151-e3fc44053b5f", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "execution_count": 3, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.\n", - "Token is valid (permission: fineGrained).\n", - "Your token has been saved to /root/.cache/huggingface/token\n", - "Login successful\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "from datasets import load_dataset\n", - "\n", - "dataset = load_dataset(\"Lambent/synthetic-rag-hermes-simple-qa-1st-ic\")" - ], - "metadata": { - "execution": { - "iopub.status.busy": "2024-07-14T16:24:14.518607Z", - "iopub.execute_input": "2024-07-14T16:24:14.519008Z", - "iopub.status.idle": "2024-07-14T16:24:16.594408Z", - "shell.execute_reply.started": "2024-07-14T16:24:14.518974Z", - "shell.execute_reply": "2024-07-14T16:24:16.593271Z" - }, - "trusted": true, - "id": "oJ709D7gKEKf", - "outputId": "e8bf2088-d585-4dc0-a170-976aa0632b8c", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 81, - "referenced_widgets": [ - "c2d078a9db3d461d9d8baad56b9df106", - "8494583c4a034e8fbc5917734b481813", - "a2302f13b73545c8ba2ea735412ffc4c", - "50659106cea94884ab15d14c5d22cd04", - "6d6483b0b7334998b1c988c9d1f28b92", - "de0450493b4948aeab7ee9d5be53c197", - "80dc1e6566db46f1af899879c7fe0f13", - "9ea270401fdc43d9991796ed6e339a33", - "945261cda5b9492f9f0d132c20451303", - "de447adcccab40c0abd63fa42d95a2a5", - "bb91291e147645189592353d4e309a1e", - "8e7db1c64260484b94561455eeeab22f", - "9618c9ed54e94669bbe2884eee581895", - "eeaff9c1de2a44539eafb0363f654a21", - "00b38eebfec94ac591eb561cf6bf15eb", - "23a46b9999b94345b55e3696bbea299d", - "d593e3fc072f4b48a82b33a59a091ab6", - "9f0e7f69bdc44d30b4e2924b7fc4f279", - "31aa3a68477c4fe89017d48486252672", - "96931c04c9f046a6a877dc83958b12e0", - "fdb8cd6c7acf4ab989b96bf8d7a69603", - "06dc3bd2af8447088169eec6e911acb6" - ] - } - }, - "execution_count": 4, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "Downloading data: 0%| | 0.00/2.17M [00:00:20: DeprecationWarning: `recreate_collection` method is deprecated and will be removed in the future. Use `collection_exists` to check collection existence and `create_collection` instead.\n", - " client.recreate_collection(\n", - ":28: DeprecationWarning: `recreate_collection` method is deprecated and will be removed in the future. Use `collection_exists` to check collection existence and `create_collection` instead.\n", - " client.recreate_collection(\n", - ":37: DeprecationWarning: `recreate_collection` method is deprecated and will be removed in the future. Use `collection_exists` to check collection existence and `create_collection` instead.\n", - " client.recreate_collection(\n", - ":45: DeprecationWarning: `recreate_collection` method is deprecated and will be removed in the future. Use `collection_exists` to check collection existence and `create_collection` instead.\n", - " client.recreate_collection(\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "True" - ] - }, - "metadata": {}, - "execution_count": 6 - } - ] - }, - { - "cell_type": "code", - "source": [ - "def call_upload2qdrant(documents, coll_name, encoder):\n", - " global client\n", - " if type(documents) == list:\n", - " try:\n", - " upload_to_qdrant_collection(client, coll_name, encoder, documents)\n", - " return \"Successfully uploaded URL content to Qdrant collection\"\n", - " except Exception as e:\n", - " return f\"An error occured: {e}\"\n", - " else:\n", - " return documents\n", - "\n", - "def reranked_rag(client, encoder0, encoder1, collection0, collection1, message):\n", - " txt2txt0 = NeuralSearcher(collection0, client, encoder0)\n", - " search_results0 = txt2txt0.search(message, 10)\n", - " upload_to_qdrant_subcollection(client, collection1, encoder1, search_results0)\n", - " txt2txt1 = NeuralSearcher(collection1, client, encoder1)\n", - " search_results1 = txt2txt1.search(message, 1)\n", - " return search_results1\n", - "\n", - "def direct_search(input_text):\n", - " global client, encoder, encoder1, coll_name, subcoll_name\n", - " results = reranked_rag(client, encoder, encoder1, coll_name, subcoll_name, input_text)\n", - " return results[0][\"text\"]" - ], - "metadata": { - "execution": { - "iopub.status.busy": "2024-07-14T16:37:15.164697Z", - "iopub.execute_input": "2024-07-14T16:37:15.165448Z", - "iopub.status.idle": "2024-07-14T16:37:15.177743Z", - "shell.execute_reply.started": "2024-07-14T16:37:15.165412Z", - "shell.execute_reply": "2024-07-14T16:37:15.176554Z" - }, - "trusted": true, - "id": "wf45d9LlKEKh" - }, - "execution_count": 7, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "if __name__==\"__main__\":\n", - " docs = answers\n", - "\n", - " print(call_upload2qdrant(docs,coll_name,encoder))\n", - "\n", - " print(call_upload2qdrant(docs,coll_name1,encoder1))\n", - "\n", - " from math import ceil\n", - " import time\n", - "\n", - " newdocs = {}\n", - " for i in range(len(docs)):\n", - " newdocs.update({questions[i]: docs[i]})\n", - "\n", - " print(\"Successful mapping\")\n", - " txt2txt0 = NeuralSearcher(coll_name, client, encoder)\n", - " txt2txt1 = NeuralSearcher(coll_name1, client, encoder1)\n", - "\n", - " times0 = []\n", - " times1 = []\n", - " times01 = []\n", - " times10 = []\n", - " points0 = 0\n", - " points1 = 0\n", - " points01 = 0\n", - " points10 = 0\n", - "\n", - " from statistics import mean, stdev\n", - "\n", - " print(\"Started benchmark\")\n", - " for k in newdocs:\n", - " start0 = time.time()\n", - " results0 = txt2txt0.search(k,1)\n", - " end0 = time.time()\n", - " if results0[0][\"text\"] == newdocs[k]:\n", - " points0+=1\n", - " times0.append(end0-start0)\n", - " start1 = time.time()\n", - " results1 = txt2txt1.search(k,1)\n", - " end1 = time.time()\n", - " if results1[0][\"text\"] == newdocs[k]:\n", - " points1+=1\n", - " times1.append(end1-start1)\n", - " start01 = time.time()\n", - " results01 = reranked_rag(client, encoder, encoder1, coll_name, subcoll_name, k)\n", - " end01 = time.time()\n", - " if results01[0][\"text\"] == newdocs[k]:\n", - " points01+=1\n", - " times01.append(end01-start01)\n", - " start10 = time.time()\n", - " results10 = reranked_rag(client, encoder1, encoder, coll_name1, small_subcoll_name, k)\n", - " end10 = time.time()\n", - " if results10[0][\"text\"] == newdocs[k]:\n", - " points10+=1\n", - " times10.append(end10-start10)\n", - "\n", - " print(f\"Avg time for All-MiniLM-L6-v2: {mean(times0)} +/- {stdev(times0)}\")\n", - " print(f\"Avg time for sentence-t5-base: {mean(times1)} +/- {stdev(times1)}\")\n", - " print(f\"Avg time for All-MiniLM-L6-v2 + sentence-t5-base: {mean(times01)} +/- {stdev(times01)}\")\n", - " print(f\"Avg time for sentence-t5-base + All-MiniLM-L6-v2: {mean(times10)} +/- {stdev(times10)}\")\n", - " print(f\"Correct/Total retrievals for All-MiniLM-L6-v2: {points0/len(newdocs)}\")\n", - " print(f\"Correct/Total retrievals for sentence-t5-base: {points1/len(newdocs)}\")\n", - " print(f\"Correct/Total retrievals for All-MiniLM-L6-v2 + sentence-t5-base: {points01/len(newdocs)}\")\n", - " print(f\"Correct/Total retrievals for sentence-t5-base + All-MiniLM-L6-v2: {points10/len(newdocs)}\")" - ], - "metadata": { - "execution": { - "iopub.status.busy": "2024-07-14T16:43:14.576276Z", - "iopub.execute_input": "2024-07-14T16:43:14.576672Z" - }, - "trusted": true, - "id": "DpNHkwFbKEKi", - "outputId": "18d45d0f-3407-43f0-a343-0cd1a2d9d22b", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "execution_count": 8, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Successfully uploaded URL content to Qdrant collection\n", - "Successfully uploaded URL content to Qdrant collection\n", - "Successful mapping\n", - "Started benchmark\n", - "Avg time for All-MiniLM-L6-v2: 0.13889479704117508 +/- 0.018010675079187972\n", - "Avg time for sentence-t5-base: 0.3546350625123871 +/- 0.1378480017367839\n", - "Avg time for All-MiniLM-L6-v2 + sentence-t5-base: 10.722357098306164 +/- 1.2552639024596886\n", - "Avg time for sentence-t5-base + All-MiniLM-L6-v2: 2.722710152020615 +/- 0.30703480688834284\n", - "Correct/Total retrievals for All-MiniLM-L6-v2: 0.41853932584269665\n", - "Correct/Total retrievals for sentence-t5-base: 0.5084269662921348\n", - "Correct/Total retrievals for All-MiniLM-L6-v2 + sentence-t5-base: 0.5028089887640449\n", - "Correct/Total retrievals for sentence-t5-base + All-MiniLM-L6-v2: 0.42134831460674155\n" - ] - } - ] - } - ] -} diff --git a/double-layered-rag-benchmarks/scripts/benchmark_test.py b/double-layered-rag-benchmarks/scripts/benchmark_test.py deleted file mode 100644 index bcafd49..0000000 --- a/double-layered-rag-benchmarks/scripts/benchmark_test.py +++ /dev/null @@ -1,142 +0,0 @@ -from qdrant_client import QdrantClient, models -from sentence_transformers import SentenceTransformer -from utils import NeuralSearcher, urlload, upload_to_qdrant_collection, upload_to_qdrant_subcollection - -client = QdrantClient("http://localhost:6333") -encoder = SentenceTransformer("all-MiniLM-L6-v2") -encoder1 = SentenceTransformer("sentence-t5-base") -coll_name = "Small_HTML_collection" -coll_name1 = "HTML_collection" -subcoll_name = "Subcollection" -small_subcoll_name = "Small_Subcollection" - - -client.recreate_collection( - collection_name = coll_name, - vectors_config=models.VectorParams( - size = encoder.get_sentence_embedding_dimension(), # Vector size is defined by used model - distance = models.Distance.COSINE, - ), -) - -client.recreate_collection( - collection_name = coll_name1, - vectors_config=models.VectorParams( - size = encoder1.get_sentence_embedding_dimension(), # Vector size is defined by used model - distance = models.Distance.COSINE, - ), -) - - -client.recreate_collection( - collection_name = subcoll_name, - vectors_config=models.VectorParams( - size = encoder1.get_sentence_embedding_dimension(), # Vector size is defined by used model - distance = models.Distance.COSINE, - ), -) - -client.recreate_collection( - collection_name = small_subcoll_name, - vectors_config=models.VectorParams( - size = encoder.get_sentence_embedding_dimension(), # Vector size is defined by used model - distance = models.Distance.COSINE, - ), -) - -def call_upload2qdrant(url, coll_name, encoder): - global client - documents = urlload(url) - if type(documents) == list: - try: - upload_to_qdrant_collection(client, coll_name, encoder, documents) - return "Successfully uploaded URL content to Qdrant collection" - except Exception as e: - return f"An error occured: {e}" - else: - return documents - -def reranked_rag(client, encoder0, encoder1, collection0, collection1, message): - txt2txt0 = NeuralSearcher(collection0, client, encoder0) - search_results0 = txt2txt0.search(message, 10) - upload_to_qdrant_subcollection(client, collection1, encoder1, search_results0) - txt2txt1 = NeuralSearcher(collection1, client, encoder1) - search_results1 = txt2txt1.search(message, 1) - return search_results1 - -def direct_search(input_text): - global client, encoder, encoder1, coll_name, subcoll_name - results = reranked_rag(client, encoder, encoder1, coll_name, subcoll_name, input_text) - return results[0]["text"] - - -if __name__=="__main__": - docs = urlload("https://www.technologyreview.com/2023/12/05/1084417/ais-carbon-footprint-is-bigger-than-you-think/,https://semiengineering.com/ai-power-consumption-exploding/,https://www.piie.com/blogs/realtime-economics/2024/ais-carbon-footprint-appears-likely-be-alarming,https://news.climate.columbia.edu/2023/06/09/ais-growing-carbon-footprint/") - - print(call_upload2qdrant("https://www.technologyreview.com/2023/12/05/1084417/ais-carbon-footprint-is-bigger-than-you-think/,https://semiengineering.com/ai-power-consumption-exploding/,https://www.piie.com/blogs/realtime-economics/2024/ais-carbon-footprint-appears-likely-be-alarming,https://news.climate.columbia.edu/2023/06/09/ais-growing-carbon-footprint/",coll_name,encoder)) - - print(call_upload2qdrant("https://www.technologyreview.com/2023/12/05/1084417/ais-carbon-footprint-is-bigger-than-you-think/,https://semiengineering.com/ai-power-consumption-exploding/,https://www.piie.com/blogs/realtime-economics/2024/ais-carbon-footprint-appears-likely-be-alarming,https://news.climate.columbia.edu/2023/06/09/ais-growing-carbon-footprint/",coll_name1,encoder1)) - - from math import ceil - import time - - newdocs = {} - import random as r - for doc in docs: - text = doc["text"].split(" ") - n = r.randint(ceil(len(text)/10), ceil(len(text)/4)) - newtext = " ".join(text[:n]) - newdocs.update({newtext: doc["text"]}) - - print("Successful fragmentation") - txt2txt0 = NeuralSearcher(coll_name, client, encoder) - txt2txt1 = NeuralSearcher(coll_name1, client, encoder1) - - times0 = [] - times1 = [] - times01 = [] - times10 = [] - points0 = 0 - points1 = 0 - points01 = 0 - points10 = 0 - - from statistics import mean, stdev - - print("Started benchmark") - for k in newdocs: - start0 = time.time() - results0 = txt2txt0.search(k,1) - end0 = time.time() - if results0[0]["text"] == newdocs[k]: - points0+=1 - times0.append(end0-start0) - start1 = time.time() - results1 = txt2txt1.search(k,1) - end1 = time.time() - if results1[0]["text"] == newdocs[k]: - points1+=1 - times1.append(end1-start1) - start01 = time.time() - results01 = reranked_rag(client, encoder, encoder1, coll_name, subcoll_name, k) - end01 = time.time() - if results01[0]["text"] == newdocs[k]: - points01+=1 - times01.append(end01-start01) - start10 = time.time() - results10 = reranked_rag(client, encoder1, encoder, coll_name1, small_subcoll_name, k) - end10 = time.time() - if results10[0]["text"] == newdocs[k]: - points10+=1 - times10.append(end10-start10) - - print(f"Avg time for All-MiniLM-L6-v2: {mean(times0)} +/- {stdev(times0)}") - print(f"Avg time for sentence-t5-base: {mean(times1)} +/- {stdev(times1)}") - print(f"Avg time for All-MiniLM-L6-v2 + sentence-t5-base: {mean(times01)} +/- {stdev(times01)}") - print(f"Avg time for sentence-t5-base + All-MiniLM-L6-v2: {mean(times10)} +/- {stdev(times10)}") - print(f"Correct/Total retrievals for All-MiniLM-L6-v2: {points0/len(newdocs)}") - print(f"Correct/Total retrievals for sentence-t5-base: {points1/len(newdocs)}") - print(f"Correct/Total retrievals for All-MiniLM-L6-v2 + sentence-t5-base: {points01/len(newdocs)}") - print(f"Correct/Total retrievals for sentence-t5-base + All-MiniLM-L6-v2: {points10/len(newdocs)}") - - diff --git a/double-layered-rag-benchmarks/scripts/utils.py b/double-layered-rag-benchmarks/scripts/utils.py deleted file mode 100644 index 4a7c9cd..0000000 --- a/double-layered-rag-benchmarks/scripts/utils.py +++ /dev/null @@ -1,73 +0,0 @@ -from qdrant_client import models -from langchain_community.document_loaders.url import UnstructuredURLLoader -from langchain.text_splitter import CharacterTextSplitter - - -def urlload(urls): - links = urls.split(",") - try: - loader = UnstructuredURLLoader( - urls=links, method="elements", - strategy="fast" - ) - docs = loader.load() - text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) - pages = text_splitter.split_documents(docs) - contents = [{"text": pages[i].page_content, "url": pages[i].metadata["source"]} for i in range(len(pages))] - return contents - except Exception as e: - return f"An error occurred while parsing the URLs: {e}" - -class NeuralSearcher: - def __init__(self, collection_name, client, model): - self.collection_name = collection_name - # Initialize encoder model - self.model = model - # initialize Qdrant client - self.qdrant_client = client - def search(self, text: str, num_return: int): - # Convert text query into vector - vector = self.model.encode(text).tolist() - - # Use `vector` for search for closest vectors in the collection - search_result = self.qdrant_client.search( - collection_name=self.collection_name, - query_vector=vector, - query_filter=None, # If you don't want any filters for now - limit=num_return, # 5 the most closest results is enough - ) - # `search_result` contains found vector ids with similarity scores along with the stored payload - # In this function you are interested in payload only - payloads = [hit.payload for hit in search_result] - return payloads - - - -def upload_to_qdrant_collection(client, collection_name, encoder, documents): - client.upload_points( - collection_name=collection_name, - points=[ - models.PointStruct( - id=idx, vector=encoder.encode(doc["text"]).tolist(), payload=doc - ) - for idx, doc in enumerate(documents) - ], - ) - -def upload_to_qdrant_subcollection(client, collection_name, encoder, documents): - client.delete_collection(collection_name=collection_name) - client.create_collection(collection_name = collection_name, - vectors_config=models.VectorParams( - size = encoder.get_sentence_embedding_dimension(), # Vector size is defined by used model - distance = models.Distance.COSINE, - ), - ) - client.upload_points( - collection_name=collection_name, - points=[ - models.PointStruct( - id=idx, vector=encoder.encode(doc["text"]).tolist(), payload=doc - ) - for idx, doc in enumerate(documents) - ], - ) diff --git a/environment.yml b/environment.yml new file mode 100644 index 0000000..511b4e7 --- /dev/null +++ b/environment.yml @@ -0,0 +1,310 @@ +name: qdurllm +channels: + - pytorch + - nvidia + - conda-forge +dependencies: + - _libgcc_mutex=0.1=conda_forge + - _openmp_mutex=4.5=2_kmp_llvm + - aiohappyeyeballs=2.4.4=pyhd8ed1ab_1 + - aiohttp=3.11.11=py312h178313f_0 + - aiosignal=1.3.2=pyhd8ed1ab_0 + - annotated-types=0.7.0=pyhd8ed1ab_1 + - anyio=4.8.0=pyhd8ed1ab_0 + - async-timeout=4.0.3=pyhd8ed1ab_0 + - attrs=24.3.0=pyh71513ae_0 + - aws-c-auth=0.7.22=h96bc93b_2 + - aws-c-cal=0.6.14=h88a6e22_1 + - aws-c-common=0.9.19=h4ab18f5_0 + - aws-c-compression=0.2.18=h83b837d_6 + - aws-c-event-stream=0.4.2=ha47c788_12 + - aws-c-http=0.8.1=h29d6fba_17 + - aws-c-io=0.14.8=h21d4f22_5 + - aws-c-mqtt=0.10.4=h759edc4_4 + - aws-c-s3=0.5.9=h594631b_3 + - aws-c-sdkutils=0.1.16=h83b837d_2 + - aws-checksums=0.1.18=h83b837d_6 + - aws-crt-cpp=0.26.9=he3a8b3b_0 + - aws-sdk-cpp=1.11.329=hba8bd5f_3 + - backoff=2.2.1=pyhd8ed1ab_1 + - beautifulsoup4=4.12.3=pyha770c72_1 + - blas=2.116=mkl + - blas-devel=3.9.0=16_linux64_mkl + - brotli-python=1.1.0=py312h2ec8cdc_2 + - bzip2=1.0.8=h4bc722e_7 + - c-ares=1.34.4=hb9d3cd8_0 + - ca-certificates=2024.12.14=hbcca054_0 + - certifi=2024.2.2=pyhd8ed1ab_0 + - cffi=1.17.1=py312h06ac9bb_0 + - chardet=5.2.0=py312h7900ff3_2 + - charset-normalizer=3.3.2=pyhd8ed1ab_0 + - click=8.1.7=unix_pyh707e725_1 + - colorama=0.4.6=pyhd8ed1ab_1 + - cryptography=44.0.0=py312hda17c39_0 + - cuda-cudart=12.4.127=0 + - cuda-cupti=12.4.127=0 + - cuda-libraries=12.4.1=0 + - cuda-nvrtc=12.4.127=0 + - cuda-nvtx=12.4.127=0 + - cuda-opencl=12.6.77=0 + - cuda-runtime=12.4.1=0 + - cuda-version=12.6=3 + - dataclasses-json=0.6.7=pyhd8ed1ab_1 + - datasets=3.2.0=pyhd8ed1ab_0 + - deepdiff=8.1.1=pyhd8ed1ab_0 + - dill=0.3.8=pyhd8ed1ab_0 + - emoji=2.14.0=pyhd8ed1ab_1 + - eval-type-backport=0.2.2=pyhd8ed1ab_0 + - eval_type_backport=0.2.2=pyha770c72_0 + - exceptiongroup=1.2.2=pyhd8ed1ab_1 + - ffmpeg=4.3=hf484d3e_0 + - filelock=3.16.1=pyhd8ed1ab_1 + - filetype=1.2.0=pyhd8ed1ab_0 + - freetype=2.12.1=h267a509_2 + - frozenlist=1.5.0=py312h66e93f0_0 + - fsspec=2024.9.0=pyhff2d567_0 + - gflags=2.2.2=h5888daf_1005 + - giflib=5.2.2=hd590300_0 + - glog=0.7.1=hbabe93e_0 + - gmp=6.3.0=hac33072_2 + - gmpy2=2.1.5=py312h7201bc8_3 + - gnutls=3.6.13=h85f3911_1 + - greenlet=3.1.1=py312h2ec8cdc_1 + - h11=0.14.0=pyhd8ed1ab_1 + - h2=4.1.0=pyhd8ed1ab_1 + - hpack=4.0.0=pyhd8ed1ab_1 + - html5lib=1.1=pyhd8ed1ab_2 + - httpcore=1.0.7=pyh29332c3_1 + - httpx=0.28.1=pyhd8ed1ab_0 + - huggingface_hub=0.27.1=pyhd8ed1ab_0 + - hyperframe=6.0.1=pyhd8ed1ab_1 + - icu=73.2=h59595ed_0 + - idna=3.6=pyhd8ed1ab_0 + - jinja2=3.1.5=pyhd8ed1ab_0 + - joblib=1.3.2=pyhd8ed1ab_0 + - jsonpatch=1.33=pyhd8ed1ab_1 + - jsonpath-python=1.0.6=pyhff2d567_2 + - jsonpointer=3.0.0=py312h7900ff3_1 + - keyutils=1.6.1=h166bdaf_0 + - krb5=1.21.3=h659f571_0 + - lame=3.100=h166bdaf_1003 + - langchain=0.3.14=pyhd8ed1ab_0 + - langchain-core=0.3.29=pyhd8ed1ab_0 + - langchain-text-splitters=0.3.5=pyhd8ed1ab_0 + - langdetect=1.0.9=pyhd8ed1ab_1 + - langsmith=0.2.10=pyhd8ed1ab_0 + - lcms2=2.16=hb7c19ff_0 + - ld_impl_linux-64=2.43=h712a8e2_2 + - lerc=4.0.0=h27087fc_0 + - libabseil=20240116.2=cxx17_he02047a_1 + - libarrow=16.1.0=hcb6531f_6_cpu + - libarrow-acero=16.1.0=hac33072_6_cpu + - libarrow-dataset=16.1.0=hac33072_6_cpu + - libarrow-substrait=16.1.0=h7e0c224_6_cpu + - libblas=3.9.0=16_linux64_mkl + - libbrotlicommon=1.1.0=hb9d3cd8_2 + - libbrotlidec=1.1.0=hb9d3cd8_2 + - libbrotlienc=1.1.0=hb9d3cd8_2 + - libcblas=3.9.0=16_linux64_mkl + - libcrc32c=1.1.2=h9c3ff4c_0 + - libcublas=12.4.5.8=0 + - libcufft=11.2.1.3=0 + - libcufile=1.11.1.6=0 + - libcurand=10.3.7.77=0 + - libcurl=8.8.0=hca28451_1 + - libcusolver=11.6.1.9=0 + - libcusparse=12.3.1.170=0 + - libdeflate=1.20=hd590300_0 + - libedit=3.1.20240808=pl5321h7949ede_0 + - libev=4.33=hd590300_2 + - libevent=2.1.12=hf998b51_1 + - libexpat=2.6.4=h5888daf_0 + - libffi=3.4.2=h7f98852_5 + - libgcc=14.2.0=h77fa898_1 + - libgcc-ng=14.2.0=h69a702a_1 + - libgfortran=14.2.0=h69a702a_1 + - libgfortran-ng=14.2.0=h69a702a_1 + - libgfortran5=14.2.0=hd5240d6_1 + - libgoogle-cloud=2.24.0=h2736e30_0 + - libgoogle-cloud-storage=2.24.0=h3d9a0c8_0 + - libgrpc=1.62.2=h15f2491_0 + - libhwloc=2.11.2=default_he43201b_1000 + - libiconv=1.17=hd590300_2 + - libjpeg-turbo=3.0.0=hd590300_1 + - liblapack=3.9.0=16_linux64_mkl + - liblapacke=3.9.0=16_linux64_mkl + - liblzma=5.6.3=hb9d3cd8_1 + - liblzma-devel=5.6.3=hb9d3cd8_1 + - libmagic=5.39=h753d276_1 + - libnghttp2=1.58.0=h47da74e_1 + - libnpp=12.2.5.30=0 + - libnsl=2.0.1=hd590300_0 + - libnvfatbin=12.6.77=0 + - libnvjitlink=12.4.127=0 + - libnvjpeg=12.3.1.117=0 + - libparquet=16.1.0=h6a7eafb_6_cpu + - libpng=1.6.43=h2797004_0 + - libprotobuf=4.25.3=h08a7969_0 + - libre2-11=2023.09.01=h5a48ba9_2 + - libsqlite=3.46.0=hde9e2c9_0 + - libssh2=1.11.0=h0841786_0 + - libstdcxx=14.2.0=hc0a3c3a_1 + - libstdcxx-ng=14.2.0=h4852527_1 + - libthrift=0.19.0=hb90f79a_1 + - libtiff=4.6.0=h1dd3fc0_3 + - libutf8proc=2.8.0=hf23e847_1 + - libuuid=2.38.1=h0b41bf4_0 + - libwebp=1.4.0=h2c329e2_0 + - libwebp-base=1.4.0=hd590300_0 + - libxcb=1.15=h0b41bf4_0 + - libxcrypt=4.4.36=hd590300_1 + - libxml2=2.12.7=hc051c1a_1 + - libxslt=1.1.39=h76b75d6_0 + - libzlib=1.2.13=h4ab18f5_6 + - llvm-openmp=15.0.7=h0cdce71_0 + - lxml=5.2.2=py312hb90d8a5_0 + - lz4-c=1.9.4=hcb278e6_0 + - marshmallow=3.20.2=pyhd8ed1ab_0 + - mkl=2022.1.0=h84fe81f_915 + - mkl-devel=2022.1.0=ha770c72_916 + - mkl-include=2022.1.0=h84fe81f_915 + - mpc=1.3.1=h24ddda3_1 + - mpfr=4.2.1=h90cbb55_3 + - mpmath=1.3.0=pyhd8ed1ab_1 + - multidict=6.1.0=py312h178313f_2 + - multiprocess=0.70.16=py312h66e93f0_1 + - mypy_extensions=1.0.0=pyha770c72_1 + - ncurses=6.5=h2d0b736_2 + - ndjson=0.3.1=pyhd8ed1ab_0 + - nest-asyncio=1.6.0=pyhd8ed1ab_1 + - nettle=3.6=he412f7d_0 + - networkx=3.4.2=pyh267e887_2 + - nltk=3.9.1=pyhd8ed1ab_1 + - numpy=1.26.4=py312heda63a1_0 + - olefile=0.47=pyhd8ed1ab_1 + - openh264=2.1.1=h780b84a_0 + - openjpeg=2.5.2=h488ebb8_0 + - openssl=3.4.0=h7b32b05_1 + - orc=2.0.1=h17fec99_1 + - orderly-set=5.2.3=pyh29332c3_1 + - orjson=3.10.14=py312h12e396e_0 + - packaging=23.2=pyhd8ed1ab_0 + - pandas=2.2.3=py312hf9745cd_1 + - pillow=10.3.0=py312hdcec9eb_0 + - pip=24.3.1=pyh8b19718_2 + - propcache=0.2.1=py312h66e93f0_0 + - psutil=6.1.1=py312h66e93f0_0 + - pthread-stubs=0.4=hb9d3cd8_1002 + - pyarrow=16.1.0=py312h8da182e_1 + - pyarrow-core=16.1.0=py312h5429d62_1_cpu + - pycparser=2.22=pyh29332c3_1 + - pydantic=2.9.2=pyhd8ed1ab_0 + - pydantic-core=2.23.4=py312h12e396e_0 + - pypdf=5.1.0=pyha770c72_1 + - pysocks=1.7.1=pyha55dd90_7 + - python=3.12.3=hab00c5b_0_cpython + - python-dateutil=2.8.2=pyhd8ed1ab_0 + - python-iso639=2024.10.22=pyhff2d567_1 + - python-magic=0.4.27=pyh9ac5cc3_5 + - python-oxmsg=0.0.1=pyhff2d567_1 + - python-tzdata=2024.2=pyhd8ed1ab_1 + - python-xxhash=3.5.0=py312h66e93f0_1 + - python_abi=3.12=5_cp312 + - pytorch=2.5.1=py3.12_cuda12.4_cudnn9.1.0_0 + - pytorch-cuda=12.4=hc786d27_7 + - pytorch-mutex=1.0=cuda + - pytz=2024.1=pyhd8ed1ab_0 + - pyyaml=6.0.2=py312h66e93f0_1 + - rapidfuzz=3.11.0=py312h2ec8cdc_0 + - re2=2023.09.01=h7f4b329_2 + - readline=8.2=h8228510_1 + - regex=2023.12.25=py312h98912ed_0 + - requests=2.32.3=pyhd8ed1ab_1 + - requests-toolbelt=1.0.0=pyhd8ed1ab_1 + - s2n=1.4.15=he19d79f_0 + - safetensors=0.5.2=py312h12e396e_0 + - scikit-learn=1.6.1=py312h7a48858_0 + - scipy=1.15.1=py312h180e4f1_0 + - sentence-transformers=3.3.1=pyhd8ed1ab_1 + - setuptools=75.8.0=pyhff2d567_0 + - six=1.16.0=pyhd8ed1ab_1 + - snappy=1.2.1=h8bd8927_1 + - sniffio=1.3.1=pyhd8ed1ab_1 + - soupsieve=2.5=pyhd8ed1ab_1 + - sqlalchemy=2.0.37=py312h66e93f0_0 + - tabulate=0.9.0=pyhd8ed1ab_2 + - tbb=2021.13.0=hceb3a55_1 + - tenacity=9.0.0=pyhd8ed1ab_1 + - threadpoolctl=3.5.0=pyhc1e730c_0 + - tk=8.6.13=noxft_h4845f30_101 + - tokenizers=0.21.0=py312h8360d73_0 + - torchaudio=2.5.1=py312_cu124 + - torchtriton=3.1.0=py312 + - torchvision=0.20.1=py312_cu124 + - tqdm=4.67.1=pyhd8ed1ab_1 + - transformers=4.48.0=pyhd8ed1ab_0 + - typing-extensions=4.12.2=hd8ed1ab_1 + - typing_extensions=4.12.2=pyha770c72_1 + - typing_inspect=0.9.0=pyhd8ed1ab_1 + - tzdata=2024b=hc8b5060_0 + - unstructured=0.16.12=pyhd8ed1ab_0 + - unstructured-client=0.28.1=pyhd8ed1ab_1 + - urllib3=1.26.18=pyhd8ed1ab_0 + - webencodings=0.5.1=pyhd8ed1ab_3 + - wheel=0.45.1=pyhd8ed1ab_1 + - wrapt=1.17.1=py312h66e93f0_0 + - xorg-libxau=1.0.12=hb9d3cd8_0 + - xorg-libxdmcp=1.1.5=hb9d3cd8_0 + - xxhash=0.8.2=hd590300_0 + - xz=5.6.3=hbcc6ac9_1 + - xz-gpl-tools=5.6.3=hbcc6ac9_1 + - xz-tools=5.6.3=hb9d3cd8_1 + - yaml=0.2.5=h7f98852_2 + - yarl=1.18.3=py312h66e93f0_0 + - zlib=1.2.13=h4ab18f5_6 + - zstandard=0.23.0=py312hef9b889_1 + - zstd=1.5.6=ha6fb4c9_0 + - pip: + - accelerate==1.2.1 + - aiofiles==23.2.1 + - bitsandbytes==0.45.0 + - coloredlogs==15.0.1 + - fastapi==0.115.6 + - fastembed==0.5.0 + - ffmpy==0.5.0 + - flatbuffers==24.12.23 + - gradio==5.12.0 + - gradio-client==1.5.4 + - grpcio==1.69.0 + - grpcio-tools==1.69.0 + - httpx-sse==0.4.0 + - humanfriendly==10.0 + - langchain-community==0.3.14 + - loguru==0.7.3 + - markdown-it-py==3.0.0 + - markupsafe==2.1.5 + - mdurl==0.1.2 + - mmh3==4.1.0 + - onnx==1.17.0 + - onnxruntime==1.20.1 + - portalocker==2.10.1 + - protobuf==5.29.3 + - py-rust-stemmers==0.1.3 + - pydantic-settings==2.7.1 + - pydub==0.25.1 + - pygments==2.19.1 + - python-dotenv==1.0.1 + - python-multipart==0.0.20 + - qdrant-client==1.12.2 + - rich==13.9.4 + - ruff==0.9.1 + - safehttpx==0.1.6 + - semantic-version==2.10.0 + - shellingham==1.5.4 + - starlette==0.41.3 + - sympy==1.13.1 + - tomlkit==0.13.2 + - trl==0.13.0 + - typer==0.15.1 + - uvicorn==0.34.0 + - websockets==14.1 diff --git a/llama.cpp-gemma/Dockerfile b/llama.cpp-gemma/Dockerfile deleted file mode 100644 index 9b72c09..0000000 --- a/llama.cpp-gemma/Dockerfile +++ /dev/null @@ -1,26 +0,0 @@ -FROM ubuntu - -WORKDIR /app - -ADD . /app/ - -RUN apt-get update && apt-get install -y \ - build-essential \ - libpq-dev \ - libffi-dev \ - libssl-dev \ - musl-dev \ - libxml2-dev \ - libxslt1-dev \ - zlib1g-dev \ - curl \ - git \ - && rm -rf /var/lib/apt/lists/* - -RUN /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)" && (echo; echo 'eval "$(/home/linuxbrew/.linuxbrew/bin/brew shellenv)"') >> /root/.bashrc && eval "$(/home/linuxbrew/.linuxbrew/bin/brew shellenv)" && cd && /bin/bash -c "$(source .bashrc)" && cd /app - -RUN /home/linuxbrew/.linuxbrew/bin/brewbrew install llama.cpp - -EXPOSE 8000 - -ENTRYPOINT [ "/home/linuxbrew/.linuxbrew/bin/llama-server", "-m", "/app/.cache/llama.cpp/gemma-2b-it-q4_k_m.gguf", "--port", "8000", "--host", "0.0.0.0", "-n", "1024", "-np", "10"] \ No newline at end of file diff --git a/mkdocs.yaml b/mkdocs.yaml new file mode 100644 index 0000000..42a98ae --- /dev/null +++ b/mkdocs.yaml @@ -0,0 +1,31 @@ +site_name: qdurllm +theme: + name: material + features: + - navigation.instant + - navigation.tracking + - navigation.tabs + - navigation.sections + - navigation.expand + - search.highlight + - search.share + palette: + scheme: default + primary: indigo + accent: indigo +plugins: +- search +- awesome-pages +markdown_extensions: +- pymdownx.highlight +- pymdownx.superfences +- pymdownx.inlinehilite +- pymdownx.snippets +- pymdownx.tasklist +- admonition +- toc +- attr_list +nav: +- Home: index.md +- About: about.md +- Contributing: contributing.md \ No newline at end of file