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UPDATE: Cohere Reranking
Browse files- functions.py +8 -2
- secrets.env +2 -1
functions.py
CHANGED
@@ -1,3 +1,4 @@
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from langchain_core.runnables import RunnablePassthrough, RunnableLambda
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from langchain_text_splitters import RecursiveCharacterTextSplitter
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from langchain_qdrant import QdrantVectorStore
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@@ -7,12 +8,12 @@ from langchain.retrievers import ParentDocumentRetriever
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from langchain.storage import InMemoryStore
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from langchain.docstore.document import Document
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from langchain_huggingface import HuggingFaceEmbeddings
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from supabase.client import create_client
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from qdrant_client import QdrantClient
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from langchain_groq import ChatGroq
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from supabase import create_client
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from dotenv import load_dotenv
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import pandas as pd
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import os
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load_dotenv("secrets.env")
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@@ -159,11 +160,16 @@ def answerQuery(query: str, vectorstore: str, llmModel: str = "llama3-70b-8192")
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vectorstore=vectorstore,
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docstore=store,
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child_splitter=RecursiveCharacterTextSplitter(),
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)
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chain = (
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{"context": retriever | RunnableLambda(format_docs), "question": RunnablePassthrough()}
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| prompt
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| ChatGroq(model = llmModel, temperature = 0.
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| StrOutputParser()
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)
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return {
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from langchain.retrievers.contextual_compression import ContextualCompressionRetriever
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from langchain_core.runnables import RunnablePassthrough, RunnableLambda
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from langchain_text_splitters import RecursiveCharacterTextSplitter
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from langchain_qdrant import QdrantVectorStore
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from langchain.storage import InMemoryStore
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from langchain.docstore.document import Document
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from langchain_huggingface import HuggingFaceEmbeddings
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from langchain_cohere import CohereRerank
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from supabase.client import create_client
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from qdrant_client import QdrantClient
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from langchain_groq import ChatGroq
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from supabase import create_client
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from dotenv import load_dotenv
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import os
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load_dotenv("secrets.env")
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vectorstore=vectorstore,
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docstore=store,
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child_splitter=RecursiveCharacterTextSplitter(),
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)
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compressor = CohereRerank(model="rerank-english-v3.0")
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retriever = ContextualCompressionRetriever(
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base_compressor=compressor, base_retriever=retriever
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)
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chain = (
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{"context": retriever | RunnableLambda(format_docs), "question": RunnablePassthrough()}
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| prompt
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| ChatGroq(model = llmModel, temperature = 0.75, max_tokens = 512)
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| StrOutputParser()
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)
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return {
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secrets.env
CHANGED
@@ -2,4 +2,5 @@ SUPABASE_URL=https://lvuhhlrkcuexzqtsbqyu.supabase.co
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SUPABASE_KEY=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJzdXBhYmFzZSIsInJlZiI6Imx2dWhobHJrY3VleHpxdHNicXl1Iiwicm9sZSI6InNlcnZpY2Vfcm9sZSIsImlhdCI6MTcxNTI0MDIxNCwiZXhwIjoyMDMwODE2MjE0fQ.zrRiN_MQCa6SOpvZFqSqFMUpcduNnt7eQP9sdXMmAF4
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GROQ_API_KEY=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
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QDRANT_URL=https://baeef19e-8f9f-4b14-b95f-45946d6fe1e6.us-east4-0.gcp.cloud.qdrant.io:6333
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QDRANT_API_KEY=k0V8kKNulQdRLukhYy03kJcncctoDImbiPHgmvaEEsup8MwTjqgT0w
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SUPABASE_KEY=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJzdXBhYmFzZSIsInJlZiI6Imx2dWhobHJrY3VleHpxdHNicXl1Iiwicm9sZSI6InNlcnZpY2Vfcm9sZSIsImlhdCI6MTcxNTI0MDIxNCwiZXhwIjoyMDMwODE2MjE0fQ.zrRiN_MQCa6SOpvZFqSqFMUpcduNnt7eQP9sdXMmAF4
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GROQ_API_KEY=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
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QDRANT_URL=https://baeef19e-8f9f-4b14-b95f-45946d6fe1e6.us-east4-0.gcp.cloud.qdrant.io:6333
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QDRANT_API_KEY=k0V8kKNulQdRLukhYy03kJcncctoDImbiPHgmvaEEsup8MwTjqgT0w
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COHERE_API_KEY=lCu3rZEjcUPAt0RsdQpQlGtgYp1uKAmuNIBdjFKq
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