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Readme fixes #23

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Readme fixes #23

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16 changes: 8 additions & 8 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -65,7 +65,7 @@ from datetime import datetime, timedelta
Load up your PostgreSQL credentials. Safest way is with a .env file:

``` python
_ = load_dotenv(find_dotenv(), override=True)
_ = load_dotenv(find_dotenv(), override=True)
service_url = os.environ['TIMESCALE_SERVICE_URL']
```

Expand Down Expand Up @@ -477,7 +477,7 @@ tpvec.search([1.0, 9.0], limit=4, filter={ "__start_date": specific_datetime, "_
0.14489260377438218]]

``` python
tpvec.search([1.0, 9.0], limit=4,
tpvec.search([1.0, 9.0], limit=4,
predicates=client.Predicates("__uuid_timestamp", ">", specific_datetime) & client.Predicates("__uuid_timestamp", "<", specific_datetime+timedelta(days=1)))
```

Expand Down Expand Up @@ -826,7 +826,7 @@ Here are resources about using Timescale Vector with LlamaIndex:
search](https://github.com/run-llama/llama-hub/tree/main/llama_hub/llama_packs/timescale_vector_autoretrieval):
This pack demonstrates performing auto-retrieval for hybrid search
based on both similarity and time, using the timescale-vector
(PostgreSQL) vectorstore.
(PostgreSQL) vectorstore.
- [Learn more about Timescale Vector and
LlamaIndex](https://www.timescale.com/blog/timescale-vector-x-llamaindex-making-postgresql-a-better-vector-database-for-ai-applications/)

Expand Down Expand Up @@ -896,8 +896,8 @@ def get_document(blog):
content = f"Author {blog['author']}, title: {blog['title']}, contents:{chunk}"
metadata = {
"id": str(client.uuid_from_time(blog['published_time'])),
"blog_id": blog['id'],
"author": blog['author'],
"blog_id": blog['id'],
"author": blog['author'],
"category": blog['category'],
"published_time": blog['published_time'].isoformat(),
}
Expand All @@ -908,7 +908,7 @@ def embed_and_write(blog_instances, vectorizer):
embedding = OpenAIEmbeddings()
vector_store = TimescaleVector(
collection_name="blog_embedding",
service_url=service_url,
service_url=vectorizer.service_url,
embedding=embedding,
time_partition_interval=timedelta(days=30),
)
Expand All @@ -921,12 +921,12 @@ def embed_and_write(blog_instances, vectorizer):
documents = []
for blog in blog_instances:
# skip blogs that are not published yet, or are deleted (in which case it will be NULL)
if blog['published_time'] != None:
if blog['published_time'] is not None:
documents.extend(get_document(blog))

if len(documents) == 0:
return

texts = [d.page_content for d in documents]
metadatas = [d.metadata for d in documents]
ids = [d.metadata["id"] for d in documents]
Expand Down