first commit

.gitignore

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+model/
+__pycache__/

Dockerfile

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+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"
+
+EXPOSE 8501
+
+ENTRYPOINT [ "streamlit", "run" ]
+CMD [ "app.py" ]

app.py

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+import streamlit as st
+from websearching import web_search
+from llama_cpp_inf import run_inference_lcpp
+
+def reply(query):
+    jsonstr = web_search(query)
+    results = run_inference_lcpp(jsonstr, query)
+    return results
+
+st.set_page_config(page_title="SearchPhi", page_icon="🔎")
+# Title of the web app
+st.title("SearchPhi🔎")
+st.subheader("With llama.cpp!🦙")
+# Input text box for the search query
+query = st.text_input("Enter search term:")
+
+# Number of results to display
+num_results = st.number_input("Number of results to display:", min_value=1, max_value=5, value=3)
+
+# Button to initiate search
+if st.button("Search"):
+    if query:
+        results = reply(query)
+        st.write(f"**Results for '{query}':**")
+        st.write_stream(results)
+    else:
+        st.write("Please enter a search term.")

llama_cpp_inf.py

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+## Imports
+from llama_cpp import Llama
+import re
+
+## Instantiate model from downloaded file
+llm = Llama(
+    model_path="model/Phi-3-mini-4k-instruct-q4.gguf",
+    n_ctx=4096,  # Context length to use
+    n_threads=14,            # Number of CPU threads to use
+    n_gpu_layers=3        # Number of model layers to offload to GPU
+)
+
+## Generation kwargs
+generation_kwargs = {
+    "max_tokens":1024,
+    "stop":["<|end|>"],
+    "echo":False, # Echo the prompt in the output
+    "top_k":1 # This is essentially greedy decoding, since the model will always return the highest-probability token. Set this value > 1 for sampling decoding
+}
+
+def run_inference_lcpp(jsonstr, user_search):
+    prompt = f"""Instructions for the assistant: Starting from the URLs and the keywords deriving from Google search results and provided to you in JSON format, generate a meaningful summary of the search results that satisfies the user's query.
+    URLs and keywords in JSON format: {jsonstr}.
+    User's query to satisfy: {user_search}"""
+    res = llm(prompt, **generation_kwargs)
+    response = res["choices"][0]["text"]
+    jsondict = eval(jsonstr)
+    addon = "Reference websites:\n- "+ '\n- '.join(list(jsondict.keys()))
+    input_string = response.replace("<|assistant|>", "") + "\n\n" + addon
+    frag_res = re.findall(r'\w+|\s+|[^\w\s]', input_string)
+    for word in frag_res:
+        yield word
+
+if __name__ == "__main__":
+    prompt = """Context: A vector database, vector store or vector search engine is a database that can store vectors (fixed-length lists of numbers) along with other data items. Vector databases typically implement one or more Approximate Nearest Neighbor (ANN) algorithms,[1][2] so that one can search the database with a query vector to retrieve the closest matching database records.
+
+    Vectors are mathematical representations of data in a high-dimensional space. In this space, each dimension corresponds to a feature of the data, with the number of dimensions ranging from a few hundred to tens of thousands, depending on the complexity of the data being represented. A vector's position in this space represents its characteristics. Words, phrases, or entire documents, as well as images, audio, and other types of data, can all be vectorized; Prompt: Describe what is a vector database"""
+    res = llm(prompt, **generation_kwargs) # Res is a dictionary
+
+    ## Unpack and the generated text from the LLM response dictionary and print it
+    print(res["choices"][0]["text"])
+    # res is short for result

requirements.txt

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+llama_cpp_python==0.2.83
+streamlit==1.37.0
+googlesearch-python==1.2.4
+nltk==3.8.1
+rake_nltk==1.0.6
+boilerpy3==1.0.7

websearching.py

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+from googlesearch import search
+from rake_nltk import Rake
+from boilerpy3 import extractors
+import json
+
+extractor = extractors.ArticleExtractor()
+r = Rake()
+
+# Function to perform web search
+def web_search(query, num_results=5):
+    search_results = []
+    for url in search(query, num_results=num_results):
+        search_results.append(url)
+    urls = list(set(search_results))
+    jsonlike = {}
+    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}})
+        except Exception:
+            continue
+    jsonstr = json.dumps(jsonlike)
+    return jsonstr
+
+
+
+
+