ff

.env

@@ -1,7 +0,0 @@
-RUNPOD_KEY = 80SUW0PDAG3SDFSS2JA0E5QFCIMECU7SCXUTNH3A
-RUNPOD_URL = https://api.runpod.ai/v2/vllm-s4x8uw9y3qv296/openai/v1
-# QDRANT_URL = https://2fee3feb-59b4-43ca-adef-ffaaacd4e0c6.europe-west3-0.gcp.cloud.qdrant.io
-QDRANT_URL = https://7fcf6c04-2f9f-40f3-b26b-c19885487d64.europe-west3-0.gcp.cloud.qdrant.io
-# QDRANT_KEY = X2zkjP-ruS0uvdDEWbj2ZxeMVtmDbG1WXH6lQdGUnOYjT_-ODzqWZQ
-QDRANT_KEY = c-p0ej858dskulydMoLpokCLK2Q2phWXTUHjrKBYYRz3y-g7YSKZ4A
-OPENAI_API_KEY = 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

app.py

@@ -7,6 +7,9 @@ from qdrant_client import QdrantClient
 from qdrant_client.http.models import Distance, VectorParams
 from langchain_qdrant import QdrantVectorStore
 from langchain_openai import OpenAIEmbeddings
+import openai
+
+openai.api_key = os.getenv("OPENAI_API_KEY")
 
 load_dotenv()
 
@@ -17,14 +20,11 @@ model = OpenAI(api_key=RUNPOD_KEY, base_url= RUNPOD_URL)
 QDRANT_URL = os.getenv("QDRANT_URL")
 QDRANT_KEY = os.getenv("QDRANT_KEY")
 
-print(QDRANT_URL)
-print(QDRANT_KEY)
-
 OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
-print(OPENAI_API_KEY)
 
 client = QdrantClient(QDRANT_URL, api_key=QDRANT_KEY)
-collection_name = "search_engine"
+# collection_name = "search_engine"
+collection_name = "week_4_assesment_embeddings"
 
 embeddings = OpenAIEmbeddings(
     model="text-embedding-3-small",
@@ -59,7 +59,7 @@ prompt = PromptTemplate(
 
 def prompt_template(query):
     
-    results = qdrant.similarity_search( query=query, k=3 )
+    results = qdrant.similarity_search( query=query, k= 6)
 
     _ctx = ''
 
@@ -70,6 +70,38 @@ def prompt_template(query):
 
     return _prompt
 
+def prompt_top6(text):
+    # query_embedding = openai.Embedding.create(
+    #     input=text,
+    #     model="text-embedding-3-small"
+    # )['data'][0]['embedding']
+
+    query_embedding = embeddings.embed_query(text)
+
+    search_results = client.search(
+        collection_name="week_4_assesment_embeddings",
+        query_vector=query_embedding,
+        limit=6
+    )
+
+    # search_results = qdrant.similarity_search( query=text, k= 6)
+
+    print(search_results)
+
+
+    chunks = ''
+    for result in search_results:
+    #   print(f"Question: {text}")
+    #   print(f"Answer: {result.payload['answer']}")
+        chunks += f"Chunk: {result.payload['context']}\n"
+        chunks += '-----\n'
+
+    _prompt = prompt.format(instruction=text, input=chunks)
+    
+    return _prompt
+
+
+
 def generate_response(prompt):
         
     response = model.chat.completions.create(
@@ -83,13 +115,19 @@ def generate_response(prompt):
     return response.choices[0].message
 
 def main(query, history):
-    prompt = prompt_template(query)
+    # prompt = prompt_template(query)
+    prompt = prompt_top6(query)
     resault = generate_response(prompt)
 
+    
 
     return resault.content
 
-demo = gr.ChatInterface(fn=main, title = "News GPT")
+examples = [
+    "What is the Berry Export Summary 2028 and what is its purpose?",
+]
+
+demo = gr.ChatInterface(fn=main, title = "Assignment 4 GPT", examples=examples)
 
 
 if __name__ == "__main__":