google search

1 ActiveLoop Courses/1 LangChain & Vector Databases in Production/7 Making LLMs Interact with the World Using Tools/2 Supercharge Your Blog Posts Automatically with LangChain and Google Search/1_supercharge.py

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+title = "OpenAI CEO: AI regulation ‘is essential’"
+
+text_all = """ Altman highlighted the potential benefits of AI technologies like ChatGPT and Dall-E 2 to help address significant challenges such as climate change and cancer, but he also stressed the need to mitigate the risks associated with increasingly powerful AI models. Altman proposed that governments consider implementing licensing and testing requirements for AI models that surpass a certain threshold of capabilities. He highlighted OpenAI’s commitment to safety and extensive testing before releasing any new systems, emphasising the company’s belief that ensuring the safety of AI is crucial. Senators Josh Hawley and Richard Blumenthal expressed their recognition of the transformative nature of AI and the need to understand its implications for elections, jobs, and security. Blumenthal played an audio introduction using an AI voice cloning software trained on his speeches, demonstrating the potential of the technology. Blumenthal raised concerns about various risks associated with AI, including deepfakes, weaponised disinformation, discrimination, harassment, and impersonation fraud. He also emphasised the potential displacement of workers in the face of a new industrial revolution driven by AI."""
+
+text_to_change = """ Senators Josh Hawley and Richard Blumenthal expressed their recognition of the transformative nature of AI and the need to understand its implications for elections, jobs, and security. Blumenthal played an audio introduction using an AI voice cloning software trained on his speeches, demonstrating the potential of the technology."""
+
+
+
+
+
+
+from langchain_community.chat_models import ChatOpenAI
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+from langchain.prompts.chat import (
+    ChatPromptTemplate,
+    HumanMessagePromptTemplate,
+)
+
+template = """ You are an exceptional copywriter and content creator.
+
+You're reading an article with the following title:
+----------------
+{title}
+----------------
+
+You've just read the following piece of text from that article.
+----------------
+{text_all}
+----------------
+
+Inside that text, there's the following TEXT TO CONSIDER that you want to enrich with new details.
+----------------
+{text_to_change}
+----------------
+
+What are some simple and high-level Google queries that you'd do to search for more info to add to that paragraph?
+Write 3 queries as a bullet point list, prepending each line with -.
+"""
+
+human_message_prompt = HumanMessagePromptTemplate(
+    prompt=PromptTemplate(
+        template=template,
+        input_variables=["text_to_change", "text_all", "title"],
+    )
+)
+chat_prompt_template = ChatPromptTemplate.from_messages([human_message_prompt])
+
+# Before executing the following code, make sure to have
+# your OpenAI key saved in the “OPENAI_API_KEY” environment variable.
+chat = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.9)
+chain = LLMChain(llm=chat, prompt=chat_prompt_template)
+
+response = chain.run({
+    "text_to_change": text_to_change,
+    "text_all": text_all,
+    "title": title
+})
+
+queries = [line[2:] for line in response.split("\n")]
+print(queries)
+
+# Get Search Results
+
+
+
+from langchain.tools import Tool
+from langchain_community.utilities.google_search import GoogleSearchAPIWrapper
+
+# Remember to set the "GOOGLE_CSE_ID" and "GOOGLE_API_KEY" environment variable.
+search = GoogleSearchAPIWrapper()
+TOP_N_RESULTS = 5
+
+def top_n_results(query):
+    return search.results(query, TOP_N_RESULTS)
+
+tool = Tool(
+    name = "Google Search",
+    description="Search Google for recent results.",
+    func=top_n_results
+)
+
+all_results = []
+
+for query in queries:
+    results = tool.run(query)
+    all_results += results
+
+    # Find the Most Relevant Results
+
+    import newspaper
+
+pages_content = []
+
+for result in all_results:
+	try:
+		article = newspaper.Article(result["link"])
+		article.download()
+		article.parse()
+
+		if len(article.text) > 0:
+			pages_content.append({ "url": result["link"], "text": article.text })
+	except:
+		continue
+
+print("Number of pages: ", len(pages_content))
+
+# Now, it is crucial to split the saved contents into smaller chunks 
+# to ensure the articles do not exceed the model’s input length.
+# The code below splits the text by either newline or spaces, 
+# depending on the situation. It makes sure that each chunk has 
+# 3000 characters with 100 overlaps between the chunks.
+
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.docstore.document import Document
+
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=3000, chunk_overlap=100)
+
+docs = []
+for d in pages_content:
+    chunks = text_splitter.split_text(d["text"])
+    for chunk in chunks:
+        new_doc = Document(page_content=chunk, metadata={ "source": d["url"] })
+        docs.append(new_doc)
+
+print("Number of chunks: ", len(docs))
+
+
+
+
+
+from langchain.embeddings import OpenAIEmbeddings
+
+embeddings = OpenAIEmbeddings(model="text-embedding-ada-002")
+
+docs_embeddings = embeddings.embed_documents([doc.page_content for doc in docs])
+query_embedding = embeddings.embed_query(text_to_change)
+
+
+
+
+
+
+
+
+import numpy as np
+from sklearn.metrics.pairwise import cosine_similarity
+
+def get_top_k_indices(list_of_doc_vectors, query_vector, top_k):
+    # convert the lists of vectors to numpy arrays
+    list_of_doc_vectors = np.array(list_of_doc_vectors)
+    query_vector = np.array(query_vector)
+
+    # compute cosine similarities
+    similarities = cosine_similarity(query_vector.reshape(1, -1), list_of_doc_vectors).flatten()
+
+    # sort the vectors based on cosine similarity
+    sorted_indices = np.argsort(similarities)[::-1]
+
+    # retrieve the top K indices from the sorted list
+    top_k_indices = sorted_indices[:top_k]
+
+    return top_k_indices
+
+top_k = 3
+best_indexes = get_top_k_indices(docs_embeddings, query_embedding, top_k)
+best_k_documents = [doc for i, doc in enumerate(docs) if i in best_indexes]
+
+
+
+
+
+
+
+
+
+
+template = """You are an exceptional copywriter and content creator.
+
+You're reading an article with the following title:
+----------------
+{title}
+----------------
+
+You've just read the following piece of text from that article.
+----------------
+{text_all}
+----------------
+
+Inside that text, there's the following TEXT TO CONSIDER that you want to enrich with new details.
+----------------
+{text_to_change}
+----------------
+
+Searching around the web, you've found this ADDITIONAL INFORMATION from distinct articles.
+----------------
+{doc_1}
+----------------
+{doc_2}
+----------------
+{doc_3}
+----------------
+
+Modify the previous TEXT TO CONSIDER by enriching it with information from the previous ADDITIONAL INFORMATION.
+"""
+
+human_message_prompt = HumanMessagePromptTemplate(
+    prompt=PromptTemplate(
+        template=template,
+        input_variables=["text_to_change", "text_all", "title", "doc_1", "doc_2", "doc_3"],
+    )
+)
+chat_prompt_template = ChatPromptTemplate.from_messages([human_message_prompt])
+
+chat = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.9)
+chain = LLMChain(llm=chat, prompt=chat_prompt_template)
+
+response = chain.run({
+    "text_to_change": text_to_change,
+    "text_all": text_all,
+    "title": title,
+    "doc_1": best_k_documents[0].page_content,
+    "doc_2": best_k_documents[1].page_content,
+    "doc_3": best_k_documents[2].page_content
+})
+
+print("Text to Change: ", text_to_change)
+print("Expanded Variation:", response)

langchain/conda_activate.sh

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+conda activate langchain-0.2.3-python-3.12.3

langchain/conda_install.sh

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 conda create -n  langchain-0.2.3-python-3.12.3 python=3.12.3
 
-conda activate langchain-0.2.3-python-3.12.3

langchain/conda_remove.sh

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+conda remove --name langchain-0.2.3-python-3.12.3 --all   

langchain/install_requirements.sh

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+pip install -r requirements.txt

langchain/requirements.txt

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+langchain==0.2.3
+langchain_community==0.2.3