diff --git a/.gitpod.yml b/.gitpod.yml deleted file mode 100644 index 8c19dc1..0000000 --- a/.gitpod.yml +++ /dev/null @@ -1,10 +0,0 @@ -image: astrabert/searchphi:gitpod - -ports: - - name: streamlit - port: 8501 - onOpen: open-browser - -tasks: - - init: | - docker run -d -p 8501:8501 astrabert/searchphi:gitpod diff --git a/.gradio/flagged/Image Search Query/41ddcc66f19720a7c3bd/Components-of-a-nuclear-power-plant-1400x803.png b/.gradio/flagged/Image Search Query/41ddcc66f19720a7c3bd/Components-of-a-nuclear-power-plant-1400x803.png new file mode 100644 index 0000000..82cf601 Binary files /dev/null and b/.gradio/flagged/Image Search Query/41ddcc66f19720a7c3bd/Components-of-a-nuclear-power-plant-1400x803.png differ diff --git a/.gradio/flagged/dataset1.csv b/.gradio/flagged/dataset1.csv new file mode 100644 index 0000000..d096b08 --- /dev/null +++ b/.gradio/flagged/dataset1.csv @@ -0,0 +1,20 @@ +Search Query,Image Search Query,Maximum Number of Search Results,Enable RAG,Debug,Search Results,timestamp +,.gradio\flagged\Image Search Query\41ddcc66f19720a7c3bd\Components-of-a-nuclear-power-plant-1400x803.png,3,false,true,"### Understanding Nuclear Power Plants and Reactors + +Nuclear power plants generate electricity through controlled nuclear fission in large, specialized facilities. These systems consist of multiple components including nuclear reactors which convert uranium or other fissile materials into usable energy. + +#### Key Components: +- **Reactor Core**: The heart of any nuclear power plant where the actual reaction takes place. + - *Bangombe Deposit*: A notable location for potential future use as an additional source of fuel. + +- **Heat Transfer Fluids**: Various types such as heavy water (deuterium oxide) and light water (ordinary H₂O), depending on specific designs like RBMK reactors mentioned here. + +- **Power Outputs**: Units can produce significant amounts of electrical output; e.g., one reactor might be rated at around 150 MWe (Megawatts Electrical). + +- **Safety Measures & Regulations**: Strict guidelines ensure safety during operation by managing coolant flow rates carefully (""Follow Loads Reasonably Easily Without Burning""). + +- **Fuel Cycle Management**: Ensures smooth transition between different phases of operations using detailed guides available online. + +This type of technology has been extensively studied over decades leading up to today's advanced models. For instance, there have been numerous reports documenting its development process from early stages all the way to current state-of-the-art technologies. + +If interested further details about individual aspects could refer directly to World-Nuclear Organization resources linked within this summary. They provide comprehensive documentation covering everything from theoretical principles down to practical applications across diverse contexts worldwide.",2024-12-10 16:31:14.503594 diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 77a81c6..62ab51e 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,4 +1,4 @@ -# Contributing to SearchPhi +# Contributing to PrAIvateSearch Do you want to contribute to this project? Make sure to read this guidelines first :) @@ -32,15 +32,15 @@ Do you want to contribute to this project? Make sure to read this guidelines fir 3. Submit pull request (make sure to provide a thorough description of the changes) -## Showcase your SearchPhi +## 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 SearchPhi +- 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/SearchPhi/discussions/categories/show-and-tell) page -- Open a new discussion there, describing your SearchPhi application +- 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/Dockerfile.gitpod b/Dockerfile.gitpod deleted file mode 100644 index 4133cb8..0000000 --- a/Dockerfile.gitpod +++ /dev/null @@ -1,8 +0,0 @@ -FROM astrabert/searchphi:gitpod - -RUN apt update && apt install -y docker.io git - -EXPOSE 8501 - -ENTRYPOINT [ "streamlit", "run" ] -CMD [ "app.py" ] \ No newline at end of file diff --git a/README.md b/README.md index c477815..edfd8b6 100644 --- a/README.md +++ b/README.md @@ -1,43 +1,52 @@ -

SearchPhi

-

Open source and AI-powered web search engine🌐

+

PrAIvateSearch

+

Own your AI, search the web with it🌐😎

- GitHub top language - GitHub commit activity - Static Badge - Static Badge - Docker image size - Static Badge
- Logo + Logo
-> **_DOCS TO BE UPDATED SOON!_** -## About SearchPhi +## About PrAIvateSearch -SearchPhi is a Streamlit application that aims to implement similar features to SearchGPT, but in an open-source, local and private way. +PrAIvateSearch is a Gradio application that aims to implement similar features to SearchGPT, but in an open-source, local and private way. + +## Flowchart + +
+ Logo +

Flowchart for PrAIvateSearch

+
+ +The process of creating and the functioning of PrAIvateSearch is explained in this [blog post on HuggingFace](https://huggingface.co/blog/as-cle-bert/build-an-ai-powered-search-engine-from-scratch). ## Installation and usage -### Source code 1. Clone the repository: ```bash -git clone https://github.com/AstraBert/SearchPhi.git -cd SearchPhi +git clone https://github.com/AstraBert/PrAIvateSearch.git +cd PrAIvateSearch +``` + +2. Move `.env.example` to `.env`... + +```bash +mv .env.example .env ``` -2. Create a `model` folder, download [this GGUF file](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf/blob/main/Phi-3-mini-4k-instruct-q4.gguf) and move the GGUF file in the `model` folder: +...and specify PostgreSQL related variables: ```bash -mkdir model -mv /path/to/Downloads/Phi-3-mini-4k-instruct-q4.gguf model/ +# .env file +PG_DB="postgres" +PG_USER="pgql_usr" +PG_PASSWORD="pgql_psw" ``` + 3. Install necessary dependencies: - Linux: ```bash @@ -57,59 +66,32 @@ source c:\path\to\SearchPhi\Scripts\activate # For Git python3 -m pip install -r requirements.txt ``` - -4. Run the application: +4. Start third-party services: ```bash -python3 -m streamlit run app.py +docker compose up -d ``` -You'll see the application on `http://localhost:8501`. - -**PROs**: You can customize the application code (change the GGUF model, change CPU/GPU settings, change generation kwargs, modify the app interface...) - -**CONs**: Longer and more complex installation process - -### Docker - -1. Pull the image +5. Run the application: ```bash -docker pull astrabert/searchphi:latest +python3 scripts/app.py ``` -2. Run the container: - -```bash -docker run -p 8501:8501 astrabert/searchphi:latest -``` - -Shortly after you submit the `docker run` command, the container logs will tell you that the application is up and running on `http://localhost:8501`. - -**PROs**: Shorter and simpler installation process +Once the models will be downloaded and loaded on your hardware, you'll see the application on `http://localhost:7860`. -**CONs**: You cannot customize the application code +**PROs**: You can customize the application code (change the model, change CPU/GPU settings, change generation kwargs, modify the app interface...) -### Run in cloud - -- **GitPod workspaces**: Click [here](https://gitpod.io/#https://github.com/AstraBert/SearchPhi) to open the GitPod workspace - -**PROs**: No local installation and you can exploit better hardwares - -**CONs**: Limited resources +**CONs**: Longer and more complex installation process ### Usage note -> ⚠️ _The Streamlit application was successfully developed and tested on a Windows 10.0.22631 machine, with 32GB RAM, 16 core CPU and Nvidia GEFORCE RTX4050 GPU (6GB, cuda version 12.3), python version 3.11.9_ - -> ⚠️ _The Docker container was successfully tested on a Windows 10.0.22631 machine and on a Ubuntu 22.04.3 machine_ +> ⚠️ _The Gradio application was successfully developed and tested on a Windows 10.0.22631 machine, with 32GB RAM, 16 core CPU and Nvidia GEFORCE RTX4050 GPU (6GB, cuda version 12.3), python version 3.11.9_ Although being at a good stage of development, the application is a `beta` and might still contain bugs and have OS/hardware/python version incompatibilities. ## Demo -You can try out SearchPhi on [this HuggingFace Space](https://huggingface.co/spaces/as-cle-bert/SearchPhi). - Here's a video demo of what it can do: ![Video demo for SearechPhi](./imgs/demo.gif) diff --git a/compose.custom.yaml b/compose.custom.yaml deleted file mode 100644 index 19d97c0..0000000 --- a/compose.custom.yaml +++ /dev/null @@ -1,40 +0,0 @@ -networks: - mynet: - driver: bridge - -services: - db: - image: postgres - restart: always - ports: - - "5432:5432" - networks: - - mynet - environment: - POSTGRES_DB: $PG_DB - POSTGRES_USER: $PG_USER - POSTGRES_PASSWORD: $PG_PASSWORD - volumes: - - pgdata:/var/lib/postgresql/data - - semantic_memory: - image: qdrant/qdrant - restart: always - ports: - - "6333:6333" - - "6334:6334" - networks: - - mynet - volumes: - - "./qdrant_storage:/qdrant/storage" - - adminer: - image: adminer - restart: always - ports: - - "8080:8080" - networks: - - mynet - -volumes: - pgdata: \ No newline at end of file diff --git a/compose.yaml b/compose.yaml index a38fe16..19d97c0 100644 --- a/compose.yaml +++ b/compose.yaml @@ -11,9 +11,9 @@ services: networks: - mynet environment: - POSTGRES_DB: pgql_usr - POSTGRES_USER: pgql_psw - POSTGRES_PASSWORD: pgql_psw + POSTGRES_DB: $PG_DB + POSTGRES_USER: $PG_USER + POSTGRES_PASSWORD: $PG_PASSWORD volumes: - pgdata:/var/lib/postgresql/data @@ -27,15 +27,7 @@ services: - mynet volumes: - "./qdrant_storage:/qdrant/storage" - - praivatesearch: - image: astrabert/praivatesearch:latest - restart: always - ports: - - "7860:7860" - networks: - - mynet - + adminer: image: adminer restart: always diff --git a/imgs/PrAIvateSearch_Flowchart.png b/imgs/PrAIvateSearch_Flowchart.png new file mode 100644 index 0000000..19a60cd Binary files /dev/null and b/imgs/PrAIvateSearch_Flowchart.png differ diff --git a/imgs/PrAIvateSearch_logo.png b/imgs/PrAIvateSearch_logo.png new file mode 100644 index 0000000..42c13c5 Binary files /dev/null and b/imgs/PrAIvateSearch_logo.png differ diff --git a/imgs/SearchPhi_logo.png b/imgs/SearchPhi_logo.png deleted file mode 100644 index 143818c..0000000 Binary files a/imgs/SearchPhi_logo.png and /dev/null differ diff --git a/imgs/demo.gif b/imgs/demo.gif index 75bfac6..6a296c1 100644 Binary files a/imgs/demo.gif and b/imgs/demo.gif differ diff --git a/lib/docker/Dockerfile b/lib/docker/Dockerfile deleted file mode 100644 index b8d019b..0000000 --- a/lib/docker/Dockerfile +++ /dev/null @@ -1,16 +0,0 @@ -FROM python:3.11.9-slim-bookworm - -WORKDIR /app -ADD . /app - -RUN apt update && apt install -y gcc g++ - -RUN python3 -m pip cache purge -RUN python3 -m pip install --no-cache-dir -r requirements.txt -RUN python3 -m nltk.downloader "punkt" -RUN python3 -m nltk.downloader "stopwords" -RUN python3 -m nltk.downloader "punkt_tab" - -EXPOSE 7860 - -CMD ["python3", "/app/app.py"] diff --git a/lib/docker/app.py b/lib/docker/app.py deleted file mode 100644 index c549469..0000000 --- a/lib/docker/app.py +++ /dev/null @@ -1,50 +0,0 @@ -import warnings -warnings.filterwarnings("ignore") - -import gradio as gr -from text_inference import text_inference -from image_gen import caption_image -from PIL import Image -from websearching import web_search, date_for_debug - -def reply(text_input, image_input=None, max_results=5, enable_rag=False, debug = True): - if debug: - print(f"[{date_for_debug()}] Started query processing...") - if image_input is None: - prompt, qdrant_success = web_search(text_input, max_results, enable_rag, debug) - if debug: - print(qdrant_success) - results = text_inference(prompt) - results = results.replace("<|im_end|>","") - if debug: - print(f"[{date_for_debug()}] Finished query processing!") - return results - else: - if text_input: - img = Image.fromarray(image_input) - caption = caption_image(img) - full_query = caption +"\n\n"+text_input - prompt, qdrant_success = web_search(full_query, max_results, enable_rag) - if debug: - print(qdrant_success) - results = text_inference(prompt) - results = results.replace("<|im_end|>","") - if debug: - print(f"[{date_for_debug()}] Finished query processing!") - return results - else: - img = Image.fromarray(image_input) - caption = caption_image(img) - prompt, qdrant_success = web_search(caption, max_results, enable_rag) - if debug: - print(qdrant_success) - results = text_inference(prompt) - results = results.replace("<|im_end|>","") - if debug: - print(f"[{date_for_debug()}] Finished query processing!") - return results - - -iface = gr.Interface(fn=reply, inputs=[gr.Textbox(value="",label="Search Query"), gr.Image(value=None, label="Image Search Query"), gr.Slider(1,10,value=5,label="Maximum Number of Search Results", step=1), gr.Checkbox(value=False, label="Enable RAG"), gr.Checkbox(value=True, label="Debug")], outputs=[gr.Markdown(value="Your output will be generated here", label="Search Results")], title="PrAIvateSearch") - -iface.launch(server_name="0.0.0.0", server_port=7860) \ No newline at end of file diff --git a/lib/docker/image_gen.py b/lib/docker/image_gen.py deleted file mode 100644 index 2a6fd47..0000000 --- a/lib/docker/image_gen.py +++ /dev/null @@ -1,47 +0,0 @@ -import warnings -warnings.filterwarnings("ignore") - -import einops -import timm - -import torch -from transformers import AutoProcessor, AutoModelForCausalLM -from rake_nltk import Metric, Rake - -device = "cuda:0" if torch.cuda.is_available() else "cpu" -torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 - -model = AutoModelForCausalLM.from_pretrained("/app/florence-2/", torch_dtype=torch_dtype, trust_remote_code=True).to(device) -processor = AutoProcessor.from_pretrained("/app/florence-2/", trust_remote_code=True) - -task_prompt = "" -raker = Rake(include_repeated_phrases=False, ranking_metric=Metric.WORD_DEGREE) - -def extract_keywords_from_caption(caption: str) -> str: - raker.extract_keywords_from_text(caption) - keywords = raker.get_ranked_phrases()[:5] - fnl = [] - for keyword in keywords: - if "image" in keyword: - continue - else: - fnl.append(keyword) - return " ".join(fnl) - -def caption_image(image): - global task_prompt - prompt = task_prompt - inputs = processor(text=prompt, images=image, return_tensors="pt").to(device, torch_dtype) - generated_ids = model.generate( - input_ids=inputs["input_ids"], - pixel_values=inputs["pixel_values"], - max_new_tokens=1024, - num_beams=3 - ) - generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0] - - parsed_answer = processor.post_process_generation(generated_text, task=task_prompt, image_size=(image.width, image.height)) - - caption = parsed_answer[""] - search_words = extract_keywords_from_caption(caption) - return search_words \ No newline at end of file diff --git a/lib/docker/memory.py b/lib/docker/memory.py deleted file mode 100644 index e77a719..0000000 --- a/lib/docker/memory.py +++ /dev/null @@ -1,73 +0,0 @@ -from sqlalchemy import MetaData, create_engine, text -from sqlalchemy.orm import sessionmaker -import warnings - -class ErrorOccuredWarning(Warning): - """An error occured but it was handled by try...except""" - -class PGClient: - def __init__(self, connection_string: str): - """ - Initialize a Client instance. - - Args: - connection_string (str): A string representing the database connection information. - - Returns: - None - """ - self.engine = create_engine(connection_string) - self.meta = MetaData(schema="public") - self.Session = sessionmaker(self.engine) - - with self.Session() as sess: - with sess.begin(): - sess.execute(text("create schema if not exists public;")) - def execute_query(self, query): - try: - with self.Session() as sess: - with sess.begin(): - res = sess.execute(text(query)) - return res - except Exception as e: - warnings.warn(f"An error occurred: {e}", ErrorOccuredWarning) - return None - def disconnect(self) -> None: - """ - Disconnect the client from the database. - - Returns: - None - """ - self.engine.dispose() - return - - -class ConversationHistory: - def __init__(self, client: PGClient, user_id: int): - self.client = client - self.user_id = user_id - self.client.execute_query("""DROP TABLE IF EXISTS conversations;""") - self.client.execute_query("""DROP TABLE IF EXISTS messages;""") - self.client.execute_query("""CREATE TABLE conversations ( - id SERIAL PRIMARY KEY, - user_id INTEGER NOT NULL, - created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP - );""") - self.client.execute_query("""CREATE TABLE messages ( - id SERIAL PRIMARY KEY, - conversation_id INTEGER REFERENCES conversations(id), - role VARCHAR(10) NOT NULL, - content TEXT NOT NULL, - timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP - );""") - conv_id = self.client.execute_query(f"INSERT INTO conversations (user_id) VALUES ({self.user_id}) RETURNING id") - conversation_id = conv_id.fetchone()[0] - self.conversation_id = conversation_id - def add_message(self, role, content): - self.client.execute_query(f"INSERT INTO messages (conversation_id, role, content) VALUES ({self.conversation_id}, '{role}', '{content}')") - def get_conversation_history(self): - res = self.client.execute_query(f"SELECT role, content FROM messages WHERE conversation_id = {self.conversation_id} ORDER BY timestamp ASC") - messages = res.fetchall() - return [{"role": role, "content": content} for role, content in messages] - \ No newline at end of file diff --git a/lib/docker/rag.py b/lib/docker/rag.py deleted file mode 100644 index cba2cc6..0000000 --- a/lib/docker/rag.py +++ /dev/null @@ -1,47 +0,0 @@ -from qdrant_client import QdrantClient -from sentence_transformers import SentenceTransformer - -class NeuralSearcher: - """ - A class for performing neural search operations on embedded documents using Qdrant. - - This class enables semantic search over documents by converting text queries into - vectors and finding similar vectors in a Qdrant collection. - - Args: - collection_name (str): Name of the Qdrant collection to search in - client (QdrantClient): Initialized Qdrant client for database operations - model (SentenceTransformer): Model for encoding text into vectors - """ - - def __init__(self, collection_name: str, client: QdrantClient, model: SentenceTransformer): - self.collection_name = collection_name - # Initialize encoder model - self.model = model - # initialize Qdrant client - self.qdrant_client = client - - def search(self, text: str, limit: int = 1): - """ - Perform a neural search for the given text query. - - Args: - text (str): Search query text - limit (int, optional): Maximum number of results to return. Defaults to 1 - - Returns: - list: List of payload objects from the most similar documents found in the collection, - where each payload contains the document text and metadata - """ - # 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=limit, - ) - payloads = [hit.payload for hit in search_result] - return payloads diff --git a/lib/docker/requirements.txt b/lib/docker/requirements.txt deleted file mode 100644 index e3b7d0a..0000000 --- a/lib/docker/requirements.txt +++ /dev/null @@ -1,17 +0,0 @@ -googlesearch-python -nltk -rake_nltk -boilerpy3 -qdrant_client -trl -torch -accelerate -transformers -gradio -einops -timm -pillow -sqlalchemy -sentence_transformers -bitsandbytes -python_dotenv \ No newline at end of file diff --git a/lib/docker/text_inference.py b/lib/docker/text_inference.py deleted file mode 100644 index b6e495d..0000000 --- a/lib/docker/text_inference.py +++ /dev/null @@ -1,59 +0,0 @@ -import warnings -warnings.filterwarnings("ignore") - -import accelerate - -import torch -from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig -from dotenv import load_dotenv -from memory import ConversationHistory, PGClient -import os -import random as r -from trl import setup_chat_format - -load_dotenv() - -model_name = "/app/qwen/" -quantization_config = BitsAndBytesConfig(load_in_4bit=True, - bnb_4bit_compute_dtype=torch.bfloat16, - bnb_4bit_use_double_quant=True, - bnb_4bit_quant_type= "nf4" -) - -quantized_model = AutoModelForCausalLM.from_pretrained(model_name, device_map="cuda:0", torch_dtype=torch.bfloat16,quantization_config=quantization_config) -tokenizer = AutoTokenizer.from_pretrained(model_name) -tokenizer.chat_template = None -quantized_model, tokenizer = setup_chat_format(model=quantized_model, tokenizer=tokenizer) - - - -pg_db = os.getenv("PG_DB") -pg_user = os.getenv("PG_USER") -pg_psw = os.getenv("PG_PASSWORD") - -pg_conn_str = f"postgresql://{pg_user}:{pg_psw}@localhost:5432/{pg_db}" -pg_client = PGClient(pg_conn_str) - -usr_id = r.randint(1,10000) -convo_hist = ConversationHistory(pg_client, usr_id) -convo_hist.add_message(role="system", content="You are a web searching assistant: your task is to create a human-readable content based on a JSON representation of the keywords of several websites related to the search that the user performed and on the context that you are provided with") - -def pipe(prompt: str, temperature: float, top_p: float, max_new_tokens: int, repetition_penalty: float): - tokenized_chat = tokenizer.apply_chat_template(prompt, tokenize=True, add_generation_prompt=True, return_tensors="pt") - outputs = quantized_model.generate(tokenized_chat, max_new_tokens=max_new_tokens, temperature=temperature, top_p=top_p, repetition_penalty=repetition_penalty) - results = tokenizer.decode(outputs[0]) - return results - -def text_inference(message): - convo_hist.add_message(role="user", content=message) - prompt = convo_hist.get_conversation_history() - res = pipe( - prompt, - temperature=0.1, - top_p=1, - max_new_tokens=512, - repetition_penalty=1.2 - ) - ret = res.split("<|im_start|>assistant\n")[1] - convo_hist.add_message(role="assistant", content=ret) - return ret diff --git a/lib/docker/websearching.py b/lib/docker/websearching.py deleted file mode 100644 index 1e4a1c8..0000000 --- a/lib/docker/websearching.py +++ /dev/null @@ -1,117 +0,0 @@ -import warnings -warnings.filterwarnings("ignore") - -from googlesearch import search -from rake_nltk import Rake -from boilerpy3 import extractors -import json -from langchain.text_splitter import CharacterTextSplitter -from qdrant_client import QdrantClient, models -from sentence_transformers import SentenceTransformer -from rag import NeuralSearcher -import random as r -from datetime import datetime -from urllib.parse import urlparse - - - -encoder = SentenceTransformer("/app/labse/") -splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) -extractor = extractors.ArticleExtractor() -collection_name = f"cute_kitty_{r.randint(1,10000)}" -qdrant_client = QdrantClient("http://localhost:6333") -searcher = NeuralSearcher(collection_name, qdrant_client, encoder) -r = Rake() - -qdrant_client.recreate_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, - ), -) - -def extract_corpus(url): - # Parse the URL to get its components - parsed_url = urlparse(url) - # Extract the domain name without subdomains or TLD - domain = parsed_url.netloc.split('.') - # Return the main word (corpus) - if len(domain) > 2: # Handle subdomains - return domain[-2] - return domain[0] - -def upload_to_qdrant(client: QdrantClient, collection_name: str, encoder: SentenceTransformer, text: str): - try: - chunks = splitter.split_text(text) - docs = [] - for chunk in chunks: - docs.append({"text": chunk}) - 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(docs) - ], - ) - return True - except Exception as e: - return False - - -def date_for_debug(): - date = datetime.now() - s = f"{date.year}-{date.month}-{date.day} {date.hour}:{date.minute}:{date.second}" - return s - -# Function to perform web search -def web_search(query, num_results=5, enable_rag=False, debug = True): - global qdrant_client, encoder, collection_name - search_results = [] - for url in search(query, num_results=num_results): - search_results.append(url) - urls = list(set(search_results)) - jsonlike = {} - finalcont = "" - if len(urls) > 0: - for url in urls: - try: - content = extractor.get_content_from_url(url) - r.extract_keywords_from_text(content) - keywords = r.get_ranked_phrases()[:20] - jsonlike.update({url: {"keywords": keywords}}) - finalcont+=content+"\n\n" - except Exception as e: - if debug: - print(f"[{date_for_debug()}] WARNING! {e}") - content = extract_corpus(url) + " " + " ".join(url.split("/")[3:]) - r.extract_keywords_from_text(content) - keywords = r.get_ranked_phrases()[:20] - jsonlike.update({url: {"keywords": keywords}}) - finalcont += content - continue - else: - jsonlike = {"keywords": "THE SEARCH DID NOT PRODUCE MEANINGFUL RESULTS (base the answer on the context, if given)"} - context = "" - if enable_rag: - res = searcher.search(finalcont, 3) - for i in range(len(res)): - context += res[i]["text"]+"\n\n"+"---------------"+"\n\n" - truth = upload_to_qdrant(qdrant_client, collection_name, encoder, finalcont) - jsonstr = json.dumps(jsonlike) - if truth: - if context: - return "KEYWORDS:\n\n"+jsonstr+"\n\nCONTEXT:\n\n"+context, f"[{date_for_debug()}] SUCCESS! Semantic memory successfully updated!" - else: - return "KEYWORDS:\n\n"+jsonstr, f"[{date_for_debug()}] SUCCESS! Semantic memory successfully updated!" - if context: - return "KEYWORDS:\n\n"+jsonstr+"\n\nCONTEXT:\n\n"+context, f"[{date_for_debug()}] WARNING! Something went wrong while updating semantic memory" - return jsonstr, f"[{date_for_debug()}] WARNING! Something went wrong while updating semantic memory" - - - - diff --git a/lib/scripts/app.py b/lib/scripts/app.py index c549469..445705f 100644 --- a/lib/scripts/app.py +++ b/lib/scripts/app.py @@ -14,7 +14,7 @@ def reply(text_input, image_input=None, max_results=5, enable_rag=False, debug = prompt, qdrant_success = web_search(text_input, max_results, enable_rag, debug) if debug: print(qdrant_success) - results = text_inference(prompt) + results = text_inference(prompt, debug) results = results.replace("<|im_end|>","") if debug: print(f"[{date_for_debug()}] Finished query processing!") @@ -27,7 +27,7 @@ def reply(text_input, image_input=None, max_results=5, enable_rag=False, debug = prompt, qdrant_success = web_search(full_query, max_results, enable_rag) if debug: print(qdrant_success) - results = text_inference(prompt) + results = text_inference(prompt, debug) results = results.replace("<|im_end|>","") if debug: print(f"[{date_for_debug()}] Finished query processing!") @@ -38,7 +38,7 @@ def reply(text_input, image_input=None, max_results=5, enable_rag=False, debug = prompt, qdrant_success = web_search(caption, max_results, enable_rag) if debug: print(qdrant_success) - results = text_inference(prompt) + results = text_inference(prompt, debug) results = results.replace("<|im_end|>","") if debug: print(f"[{date_for_debug()}] Finished query processing!") diff --git a/lib/scripts/memory.py b/lib/scripts/memory.py index e77a719..b9ff62f 100644 --- a/lib/scripts/memory.py +++ b/lib/scripts/memory.py @@ -65,6 +65,7 @@ def __init__(self, client: PGClient, user_id: int): conversation_id = conv_id.fetchone()[0] self.conversation_id = conversation_id def add_message(self, role, content): + content = content.replace("'","''") self.client.execute_query(f"INSERT INTO messages (conversation_id, role, content) VALUES ({self.conversation_id}, '{role}', '{content}')") def get_conversation_history(self): res = self.client.execute_query(f"SELECT role, content FROM messages WHERE conversation_id = {self.conversation_id} ORDER BY timestamp ASC") diff --git a/lib/scripts/text_inference.py b/lib/scripts/text_inference.py index 0fd47ec..12e45c3 100644 --- a/lib/scripts/text_inference.py +++ b/lib/scripts/text_inference.py @@ -10,6 +10,7 @@ import os import random as r from trl import setup_chat_format +from websearching import date_for_debug load_dotenv() @@ -44,9 +45,12 @@ def pipe(prompt: str, temperature: float, top_p: float, max_new_tokens: int, rep results = tokenizer.decode(outputs[0]) return results -def text_inference(message): +def text_inference(message, debug): convo_hist.add_message(role="user", content=message) prompt = convo_hist.get_conversation_history() + if debug: + print(f"[{date_for_debug()}] CONVERSATIONAL HISTORY") + print(prompt) res = pipe( prompt, temperature=0.1, diff --git a/lib/scripts/websearching.py b/lib/scripts/websearching.py index cc04e88..c0cf07b 100644 --- a/lib/scripts/websearching.py +++ b/lib/scripts/websearching.py @@ -105,12 +105,12 @@ def web_search(query, num_results=5, enable_rag=False, debug = True): jsonstr = json.dumps(jsonlike) if truth: if context: - return "KEYWORDS:\n\n"+jsonstr+"\n\nCONTEXT:\n\n"+context, f"[{date_for_debug()}] SUCCESS! Semantic memory successfully updated!" + return "QUERY:\n\n"+query+"\n\nKEYWORDS:\n\n"+jsonstr+"\n\nCONTEXT:\n\n"+context, f"[{date_for_debug()}] SUCCESS! Semantic memory successfully updated!" else: - return "KEYWORDS:\n\n"+jsonstr, f"[{date_for_debug()}] SUCCESS! Semantic memory successfully updated!" + return "QUERY:\n\n"+query+"\n\nKEYWORDS:\n\n"+jsonstr, f"[{date_for_debug()}] SUCCESS! Semantic memory successfully updated!" if context: - return "KEYWORDS:\n\n"+jsonstr+"\n\nCONTEXT:\n\n"+context, f"[{date_for_debug()}] WARNING! Something went wrong while updating semantic memory" - return jsonstr, f"[{date_for_debug()}] WARNING! Something went wrong while updating semantic memory" + return "QUERY:\n\n"+query+"\n\nKEYWORDS:\n\n"+jsonstr+"\n\nCONTEXT:\n\n"+context, f"[{date_for_debug()}] WARNING! Something went wrong while updating semantic memory" + return "QUERY:\n\n"+query+"\n\nKEYWORDS:\n\n"+jsonstr, f"[{date_for_debug()}] WARNING! Something went wrong while updating semantic memory" diff --git a/requirements.txt b/requirements.txt index de0e886..e3b7d0a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,7 +1,7 @@ -googlesearch-python==1.2.4 -nltk==3.8.1 -rake_nltk==1.0.6 -boilerpy3==1.0.7 +googlesearch-python +nltk +rake_nltk +boilerpy3 qdrant_client trl torch diff --git a/run_container.sh b/run_container.sh deleted file mode 100644 index ea25e33..0000000 --- a/run_container.sh +++ /dev/null @@ -1 +0,0 @@ -docker run -d -p 8501:8501 astrabert/searchphi:latest