initial commit

app.py

@@ -88,7 +88,7 @@ async def addText(vectorstorename: str, text: str):
 @app.get("/answerQuery")
 async def answerQuery(query: str, vectorstorename: str, llmModel: str = "llama3-70b-8192"):
     try:
-        response = answerQuery(query=query, vectorstorename=vectorstorename, llmModel=llmModel)
+        response = await answerQuery(query=query, vectorstorename=vectorstorename, llmModel=llmModel)
         output = {
             "output": response
         }

functions.py

@@ -1,6 +1,6 @@
 from langchain_core.runnables import RunnablePassthrough, RunnableLambda
 from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain_community.vectorstores import SupabaseVectorStore
+from langchain_qdrant import QdrantVectorStore
 from langchain_core.prompts.chat import ChatPromptTemplate
 from langchain_core.output_parsers import StrOutputParser
 from langchain_huggingface import HuggingFaceEmbeddings
@@ -67,8 +67,14 @@ def matchPassword(username: str, password: str) -> str:
 
 
 def createTable(tablename: str):
-    pass
-
+    qdrant = QdrantVectorStore.from_documents(
+        [],
+        embeddings,
+        url=os.environ["QDRANT_URL"],
+        prefer_grpc=True,
+        api_key=os.environ["QDRANT_API_KEY"],
+        collection_name=tablename
+    )
 
 def addDocuments(text: str, vectorstorename: str):
     global embeddings
@@ -77,14 +83,11 @@ def addDocuments(text: str, vectorstorename: str):
         chunk_overlap = 200,
         add_start_index = True
     )
-    print(text)
     texts = text_splitter.create_documents([text])
-    print(texts)
-    vectorstore = SupabaseVectorStore(
-        client = client,
+    vectorstore = QdrantVectorStore.from_existing_collection(
         embedding = embeddings,
-        table_name = vectorstorename,
-        query_name = "match_documents",
+        collection_name=vectorstorename,
+        url=os.environ["QDRANT_URL"],
     )
     vectorstore.add_documents(documents = texts)
 
@@ -101,11 +104,10 @@ def answerQuery(query: str, vectorstore: str, llmModel: str = "llama3-70b-8192")
     global prompt 
     global client
     global embeddings
-    vectorstore = SupabaseVectorStore(
-        client = client,
+    vectorstore = QdrantVectorStore.from_existing_collection(
         embedding = embeddings,
-        table_name = vectorstore,
-        query_name = "match_documents",
+        collection_name=vectorstore,
+        url=os.environ["QDRANT_URL"],
     )
     retriever = vectorstore.as_retriever()
     chain = (

requirements.txt

@@ -4,6 +4,7 @@ gradio
 langchain
 langchain-community
 langchain-huggingface
+langchain-qdrant
 langchain-groq
 PyPDF2
 python-dotenv

secrets.env

@@ -1,3 +1,5 @@
 SUPABASE_URL=https://lvuhhlrkcuexzqtsbqyu.supabase.co
 SUPABASE_KEY=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJzdXBhYmFzZSIsInJlZiI6Imx2dWhobHJrY3VleHpxdHNicXl1Iiwicm9sZSI6InNlcnZpY2Vfcm9sZSIsImlhdCI6MTcxNTI0MDIxNCwiZXhwIjoyMDMwODE2MjE0fQ.zrRiN_MQCa6SOpvZFqSqFMUpcduNnt7eQP9sdXMmAF4
-GROQ_API_KEY=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
+GROQ_API_KEY=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
+QDRANT_URL=https://baeef19e-8f9f-4b14-b95f-45946d6fe1e6.us-east4-0.gcp.cloud.qdrant.io:6333
+QDRANT_API_KEY=k0V8kKNulQdRLukhYy03kJcncctoDImbiPHgmvaEEsup8MwTjqgT0w