Some small cleanup

SilasMarvin · SilasMarvin · commit 048f0789c3ed · 2024-08-21T15:48:02.000-07:00
diff --git a/.gitignore b/.gitignore
@@ -0,0 +1,2 @@
+venv
+lsp-ai-chat.md
diff --git a/README.md b/README.md
@@ -44,7 +44,7 @@ results = Document.vector_search("text_embedding", "some query to search against
 
 ### Example 2: Using mixedbread-ai/mxbai-embed-large-v1
 
-This example shows how to use the `mixedbread-ai/mxbai-embed-large-v1` transformer, which has an embedding size of 512 and requires specific parameters for recall.
+This example shows how to use the `mixedbread-ai/mxbai-embed-large-v1` transformer, which has an embedding size of 1024 and requires specific parameters for recall.
 
 ```python
 from django.db import models
@@ -54,19 +54,19 @@ class Article(Embed):
     content = models.TextField()
     content_embedding = VectorField(
         field_to_embed="content",
-        dimensions=512,
+        dimensions=1024,
         transformer="mixedbread-ai/mxbai-embed-large-v1",
         transformer_recall_parameters={
-            "query": "Represent this sentence for searching relevant passages: "
+            "prompt": "Represent this sentence for searching relevant passages: "
         }
     )
 
 # Searching
-results = Article.vector_search("content_embedding", "search query")
+results = Article.vector_search("content_embedding", "some query to search against")
 ```
 
 Note the differences between the two examples:
-1. The `dimensions` parameter is set to 384 for `intfloat/e5-small-v2` and 512 for `mixedbread-ai/mxbai-embed-large-v1`.
+1. The `dimensions` parameter is set to 384 for `intfloat/e5-small-v2` and 1024 for `mixedbread-ai/mxbai-embed-large-v1`.
 2. The `mixedbread-ai/mxbai-embed-large-v1` transformer requires additional parameters for recall, which are specified in the `transformer_recall_parameters` argument.
 
 Both examples will automatically generate embeddings when instances are saved and allow for vector similarity searches using the `vector_search` method.
diff --git a/src/postgresml_django/main.py b/src/postgresml_django/main.py
@@ -61,7 +61,7 @@ def vector_search(
         # Generate an embedding for the text
         query_embedding = GenerateEmbedding(
             Value(query_text),
-            "intfloat/e5-small-v2",
+            field_instance.transformer,
             field_instance.transformer_recall_parameters,
         )
 

Original file line number	Diff line number	Diff line change
`@@ -61,7 +61,7 @@ def vector_search(`
`61`	`61`	`# Generate an embedding for the text`
`62`	`62`	`query_embedding = GenerateEmbedding(`
`63`	`63`	`Value(query_text),`
`64`		`- "intfloat/e5-small-v2",`
	`64`	`+ field_instance.transformer,`
`65`	`65`	`field_instance.transformer_recall_parameters,`
`66`	`66`	`)`
`67`	`67`