If the text you wish to ask about is longer than the token limit (~4,000 tokens for text-davinci-002/-003 and ~2,000 tokens for earlier models), you can split the text into smaller pieces, rank them by relevance, and then ask your question only using the most-relevant-looking pieces.

prompt = """Answer the question as truthfully as possible, and if you're unsure of the answer, say "Sorry, I don't know".

We can update the initial prompt to tell the model to explicitly make use of the provided text.
prompt = """Answer the question as truthfully as possible using the provided text, and if the answer is not contained within the text below, say "I don't know"

Context:
The men's high jump event at the 2020 Summer Olympics took place between 30 July and 1 August 2021 at the Olympic Stadium.
33 athletes from 24 nations competed; the total possible number depended on how many nations would use universality places 
to enter athletes in addition to the 32 qualifying through mark or ranking (no universality places were used in 2021).
Italian athlete Gianmarco Tamberi along with Qatari athlete Mutaz Essa Barshim emerged as joint winners of the event following
a tie between both of them as they cleared 2.37m. Both Tamberi and Barshim agreed to share the gold medal in a rare instance
where the athletes of different nations had agreed to share the same medal in the history of Olympics. 
Barshim in particular was heard to ask a competition official "Can we have two golds?" in response to being offered a 
'jump off'. Maksim Nedasekau of Belarus took bronze. The medals were the first ever in the men's high jump for Italy and 
Belarus, the first gold in the men's high jump for Italy and Qatar, and the third consecutive medal in the men's high jump
for Qatar (all by Barshim). Barshim became only the second man to earn three medals in high jump, joining Patrik Sjöberg
of Sweden (1984 to 1992).

Q: Who won the 2020 Summer Olympics men's high jump?
A:"""

openai.Completion.create(
    prompt=prompt,
    temperature=0,
    max_tokens=300,
    top_p=1,
    frequency_penalty=0,
    presence_penalty=0,
    model=COMPLETIONS_MODEL
)["choices"][0]["text"].strip(" \n")

What if the provided text is too long to include in prompt? In the remainder of this notebook, we will demonstrate a method for augmenting GPT-3 with a large body of additional contextual information by using document embeddings and retrieval. This method answers queries in two steps: first it retrieves the information relevant to the query, then it writes an answer tailored to the question based on the retrieved information. The first step uses the Embedding API, the second step uses the Completions API.

The steps are:

Preprocess the contextual information by splitting it into chunks and create an embedding vector for each chunk.
On receiving a query, embed the query in the same vector space as the context chunks and find the context embeddings which are most similar to the query.
Prepend the most relevant context embeddings to the query prompt.
Submit the question along with the most relevant context to GPT, and receive an answer which makes use of the provided contextual information.

1) Preprocess the document library, Since this is a small example, we will store and search the embeddings locally. If you have a larger dataset, consider using a vector search engine like Pinecone or Weaviate to power the search. we chose to use Curie embeddings, which are 4096-dimensional embeddings at a very good price and performance point. 
document_embeddings = load_embeddings("https://cdn.openai.com/API/examples/data/olympics_sections_document_embeddings.csv")

# ===== OR, uncomment the below line to recaculate the embeddings from scratch. ========

# document_embeddings = compute_doc_embeddings(df)
# An example embedding:
example_entry = list(document_embeddings.items())[0]
print(f"{example_entry[0]} : {example_entry[1][:5]}... ({len(example_entry[1])} entries)")

2) Find the most similar document embeddings to the question embedding: 
order_document_sections_by_query_similarity("Who won the men's high jump?", document_embeddings)[:5]
def vector_similarity(x: list[float], y: list[float]) -> float:
    """
    Returns the similarity between two vectors.
    
    Because OpenAI Embeddings are normalized to length 1, the cosine similarity is the same as the dot product.
    """
    return np.dot(np.array(x), np.array(y))

def order_document_sections_by_query_similarity(query: str, contexts: dict[(str, str), np.array]) -> list[(float, (str, str))]:
    """
    Find the query embedding for the supplied query, and compare it against all of the pre-calculated document embeddings
    to find the most relevant sections. 
    
    Return the list of document sections, sorted by relevance in descending order.
    """
    query_embedding = get_embedding(query)
    
    document_similarities = sorted([
        (vector_similarity(query_embedding, doc_embedding), doc_index) for doc_index, doc_embedding in contexts.items()
    ], reverse=True)
    
    return document_similarities

3) Add the most relevant document sections to the query prompt
Selected 2 document sections:
("Athletics at the 2020 Summer Olympics – Men's high jump", 'Summary')
("Athletics at the 2020 Summer Olympics – Men's long jump", 'Summary')
===
 Answer the question as truthfully as possible using the provided context, and if the answer is not contained within the text below, say "I don't know."

Context:

* The men's high jump event at the 2020 Summer Olympics took place between 30 July and 1 August 2021 at the Olympic Stadium. 33 athletes from 24 nations competed; the total possible number depended on how many nations would use universality places to enter athletes in addition to the 32 qualifying through mark or ranking (no universality places were used in 2021). Italian athlete Gianmarco Tamberi along with Qatari athlete Mutaz Essa Barshim emerged as joint winners of the event following a tie between both of them as they cleared 2.37m. Both Tamberi and Barshim agreed to share the gold medal in a rare instance where the athletes of different nations had agreed to share the same medal in the history of Olympics. Barshim in particular was heard to ask a competition official "Can we have two golds?" in response to being offered a 'jump off'. Maksim Nedasekau of Belarus took bronze. The medals were the first ever in the men's high jump for Italy and Belarus, the first gold in the men's high jump for Italy and Qatar, and the third consecutive medal in the men's high jump for Qatar (all by Barshim). Barshim became only the second man to earn three medals in high jump, joining Patrik Sjöberg of Sweden (1984 to 1992).
* The men's long jump event at the 2020 Summer Olympics took place between 31 July and 2 August 2021 at the Japan National Stadium. Approximately 35 athletes were expected to compete; the exact number was dependent on how many nations use universality places to enter athletes in addition to the 32 qualifying through time or ranking (1 universality place was used in 2016). 31 athletes from 20 nations competed. Miltiadis Tentoglou won the gold medal, Greece's first medal in the men's long jump. Cuban athletes Juan Miguel Echevarría and Maykel Massó earned silver and bronze, respectively, the nation's first medals in the event since 2008.

 Q: Who won the 2020 Summer Olympics men's high jump?
 A:
We have now obtained the document sections that are most relevant to the question. As a final step, let's put it all together to get an answer to the question.
4) Answer the user's question based on the context.Wow! By combining the Embeddings and Completions APIs, we have created a question-answering model which can answer questions using a large base of additional knowledge. It also understands when it doesn't know the answer!

For this example we have used a dataset of Wikipedia articles, but that dataset could be replaced with books, articles, documentation, service manuals, or much much more. We can't wait to see what you create with GPT-3!

From the text below, extract the following entities in the following format:
Companies: <comma-separated list of companies mentioned>
People & titles: <comma-separated list of people mentioned (with their titles or roles appended in parentheses)>

Text:
"""
In March 1981, United States v. AT&T came to trial under Assistant Attorney General William Baxter. AT&T chairman Charles L. Brown thought the company would be gutted. He realized that AT&T would lose and, in December 1981, resumed negotiations with the Justice Department. Reaching an agreement less than a month later, Brown agreed to divestiture—the best and only realistic alternative. AT&T's decision allowed it to retain its research and manufacturing arms. The decree, titled the Modification of Final Judgment, was an adjustment of the Consent Decree of 14 January 1956. Judge Harold H. Greene was given the authority over the modified decree....

In 1982, the U.S. government announced that AT&T would cease to exist as a monopolistic entity. On 1 January 1984, it was split into seven smaller regional companies, Bell South, Bell Atlantic, NYNEX, American Information Technologies, Southwestern Bell, US West, and Pacific Telesis, to handle regional phone services in the U.S. AT&T retains control of its long distance services, but was no longer protected from competition.
"""