Demo Building Dynamic Context with Custom Data Part 2

# Generate embeddings for each row in the 'text' column
df = df.assign(embedding=df["text"].apply(lambda x: text_embedding(x)))

df.head()

   year_film  year_ceremony  ceremony                    category                     name                      film  winner                                               text                                           embedding
10639      2022            2023        95       actor in a leading role           Austin Butler                    Elvis   False                 Austin Butler got nominated under the category...                [–0.0378, –0.0199, …]
10640      2022            2023        95       actor in a leading role         Colin Farrell       The Banshees of Inisherin   False                 Colin Farrell got nominated under the category...                [–0.0082, –0.0100, …]
…          …               …       …                                 …                          …                         …      …                                               …                                               …

df["text"].iloc[100]
# Output:
# 'Viktor Prášil, Frank Kruse, Markus Stembler, Lars Ginzel and Stefan Korte got nominated under the category, sound, for the film All Quiet on the Western Front but did not win'

import numpy as np

def vector_similarity(vec1, vec2):
    return np.dot(np.squeeze(np.array(vec1)), np.squeeze(np.array(vec2)))

# 1. Embed the query
query = "Who won the Best Picture award?"
query_embedding = text_embedding(query)

# 2. Compute similarity for each row
df["similarity"] = df["embedding"].apply(lambda x: vector_similarity(x, query_embedding))

# 3. Retrieve top 20 matches
top_res = df.nlargest(20, "similarity")

context = "\n".join(top_res["text"])
print(context)

prompt = f"""
From the data provided in three backticks, respond to the question: {query}
```{context}```
"""

result = get_word_completion(prompt)
print(result)
```text

Expected output:

<Callout icon="triangle-alert" color="#FF6B6B">
Keep an eye on token limits when concatenating many text entries into `context`.
</Callout>

## 5. Try a Different Question

For example, “Who is the lyricist for RRR?”:

```python
query = "Who is the lyricist for RRR?"
query_embedding = text_embedding(query)

# Recompute similarities and get top matches
df["similarity"] = df["embedding"].apply(lambda x: vector_similarity(x, query_embedding))
top_res = df.nlargest(20, "similarity")
context = "\n".join(top_res["text"])

# Craft prompt and call API
prompt = f"""
From the data provided in three backticks, respond to the question: {query}
```{context}```
"""

result = get_word_completion(prompt)
print(result)

The lyricist for RRR is Chandra Bose.