Revert "RAG-pipeline"

This reverts commit cad4205.

README.md

@@ -9,8 +9,7 @@
 </p>
 
 [![Downloads](https://static.pepy.tech/badge/embed-anything)](https://pepy.tech/project/embed-anything)
-[![Multimodal Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1CowJrqZxDDYJzkclI-rbHaZHgL9C6K3p?usp=sharing)
-[![RAG Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://drive.google.com/file/d/1GCSJ7kMwJWT86khVN9JrP_I-v0ipeWvP/view?usp=sharing)
+[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1CowJrqZxDDYJzkclI-rbHaZHgL9C6K3p?usp=sharing)
 [![license]( https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)
 [![license]( https://img.shields.io/badge/Package-PYPI-blue.svg)](https://pypi.org/project/embed-anything/)
 [![license](https://img.shields.io/discord/1213966302046064711?style=flat&logo=discord&link=https%3A%2F%2Fdiscord.gg%2FHGxDZxNt9G)](https://discord.gg/juETVTMdZu)
@@ -33,7 +32,6 @@ We will soon post on how we use Candle to increase the performance and decrease
 
 ## Examples
 1. Image Search: [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1CowJrqZxDDYJzkclI-rbHaZHgL9C6K3p?usp=sharing)
-2. RAG with Pinecone: [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://drive.google.com/file/d/1GCSJ7kMwJWT86khVN9JrP_I-v0ipeWvP/view?usp=sharing)
 
 [Watch the demo](https://youtu.be/HLXIuznnXcI)
 

retrieval.py

@@ -17,42 +17,16 @@
 pc = Pinecone(api_key="")
 index = pc.Index("anything")
 
-for i in range(len(data)):
-    index.upsert(
-        vectors=[{"id": str(i), "values": data[i].embedding, "metadata": {"text": data[i].text}}]
-    )
+# for i in range(len(data)):
+#     index.upsert(
+#         vectors=[{"id": str(i), "values": data[i].embedding, "metadata": {"text": data[i].text}}]
+#     )
 
 
 
 def retrieval(query):
     query_embedding = embed_anything.embed_query(query, embeder="OpenAI")
-    context =  index.query(vector=query_embedding[0].embedding, top_k=2)
-    indices = [int(context.matches[i]['id']) for i in range(len(context.matches))]
-    return indices
+    return index.query(vector=query_embedding[0].embedding, top_k=2)
+index.fetch(["82", "81"])
 
-indices = retrieval(["what is AI?"])
-
-def get_text(indices):
-    return [index.fetch([str(e)])['vectors'][str(e)]['metadata']['text'] for e in indices]
-
-
-
-content = query + " "
-for i in range(min(len(indices), 3)):
-    content += get_text(indices)[i] + " "
-
-
-
-client = OpenAI()
-
-
-
-response = client.chat.completions.create(
-  model="gpt-3.5-turbo-0125",
-  response_format={ "type": "json_object" },
-  messages=[
-    {"role": "system", "content": "You are a helpful assistant designed to output JSON."},
-    {"role": "user", "content": content}
-  ]
-)
-print(response.choices[0].message.content)
+# print(retrieval(["what is AI?"]))