Update app.py
Browse files
app.py
CHANGED
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@@ -1,13 +1,14 @@
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import os
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from typing import List
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from langchain.embeddings
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from langchain.vectorstores.chroma import Chroma
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from langchain.chains import (
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ConversationalRetrievalChain,
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)
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from langchain.
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from langchain.prompts.chat import (
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ChatPromptTemplate,
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SystemMessagePromptTemplate,
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@@ -17,11 +18,12 @@ from langchain.docstore.document import Document
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from langchain.memory import ChatMessageHistory, ConversationBufferMemory
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from langsmith_config import setup_langsmith_config
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import openai
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import chainlit as cl
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-
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setup_langsmith_config()
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text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
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system_template = """Use the following pieces of context to answer the users question.
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@@ -78,7 +80,8 @@ async def on_chat_start():
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metadatas = [{"source": f"{i}-pl"} for i in range(len(texts))]
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# Create a Chroma vector store
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embeddings =
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docsearch = await cl.make_async(Chroma.from_texts)(
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texts, embeddings, metadatas=metadatas
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)
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import os
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from typing import List
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from langchain.embeddings import CohereEmbeddings
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from langchain.vectorstores.chroma import Chroma
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from langchain.chains import (
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ConversationalRetrievalChain,
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)
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from langchain.llms.fireworks import Fireworks
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from langchain.chat_models.fireworks import ChatFireworks
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from langchain.prompts.chat import (
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ChatPromptTemplate,
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SystemMessagePromptTemplate,
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from langchain.memory import ChatMessageHistory, ConversationBufferMemory
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from langsmith_config import setup_langsmith_config
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import openai
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import fireworks.client
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import chainlit as cl
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FIREWORKS_API_KEY = os.getenv("FIREWORKS_API_KEY")
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setup_langsmith_config()
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os.environ["FIREWORKS_API_KEY"] = FIREWORKS_API_KEY
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text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
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system_template = """Use the following pieces of context to answer the users question.
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metadatas = [{"source": f"{i}-pl"} for i in range(len(texts))]
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# Create a Chroma vector store
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embeddings = CohereEmbeddings(cohere_api_key="COHERE_API_KEY")
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docsearch = await cl.make_async(Chroma.from_texts)(
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texts, embeddings, metadatas=metadatas
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)
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