Update LangChain examples

demos/langchain-pdf-chat/app.py

@@ -1,23 +1,20 @@
 from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.vectorstores import Chroma
+from langchain_community.vectorstores import Chroma
 from langchain.chains import RetrievalQA
-from langchain.prompts.chat import (
-    ChatPromptTemplate,
-    SystemMessagePromptTemplate,
-    HumanMessagePromptTemplate,
-)
 import os
 import chainlit as cl
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.llms import DeepSparse
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.llms import DeepSparse
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from io import BytesIO
 import PyPDF2
+from langchain_community.document_loaders import PyPDFLoader
 
 MODEL_PATH = "hf:neuralmagic/mpt-7b-chat-pruned50-quant"
 text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
 embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", model_kwargs={'device': 'cpu'})
-llm = DeepSparse(model=MODEL_PATH)
+llm = DeepSparse(model=MODEL_PATH,streaming=True,generation_config={})
+Loader = PyPDFLoader
 
 @cl.on_chat_start
 async def init():
@@ -37,16 +34,11 @@ async def init():
     if file.type != "application/pdf":
         raise TypeError("Only PDF files are supported")
 
-    pdf_stream = BytesIO(file.content)
-    pdf = PyPDF2.PdfReader(pdf_stream)
-    pdf_text = ""
-    for page in pdf.pages:
-        pdf_text += page.extract_text()
-
-    # texts = text_splitter.create_documents(pdf_text)
-    texts = text_splitter.create_documents([pdf_text])
+    loader = Loader(file.path)
+    documents = loader.load()
+
+    texts = text_splitter.split_documents(documents)
     for i, text in enumerate(texts): text.metadata["source"] = f"{i}-pl"
-
 
     # Create a Chroma vector store
     docsearch = Chroma.from_documents(texts, embeddings)
@@ -69,15 +61,14 @@ async def init():
 
     cl.user_session.set("chain", chain)
 
-
 @cl.on_message
-async def main(message):
+async def main(message: cl.Message):
     chain = cl.user_session.get("chain")  # type: RetrievalQAWithSourcesChain
     cb = cl.AsyncLangchainCallbackHandler(
-        stream_final_answer=True, answer_prefix_tokens=["FINAL", "ANSWER"]
+        stream_final_answer=True, answer_prefix_tokens=["FINAL", "ANSWER"] 
     )
     cb.answer_reached = True
-    res = await chain.acall(message, callbacks=[cb])
+    res = await chain.acall(message.content, callbacks=[cb])
 
     answer = res["result"]
     source_documents = res["source_documents"]
@@ -111,4 +102,4 @@ async def main(message):
     else:
         await cl.Message(content=answer,
                          elements=source_elements
-                        ).send()
+                        ).send()

demos/langchain-pdf-chat/requirements.txt

@@ -1,8 +1,9 @@
 datasets
 transformers
-deepsparse-nightly==1.6.0.20231007
 chainlit
-langchain
 PyPDF2
 sentence_transformers
 chromadb
+langchain-community==0.0.27
+langchain==0.1.11
+deepsparse-nightly==1.7.0.20240304

demos/langchain-video-chat/app.py

@@ -1,16 +1,12 @@
 from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.vectorstores import Chroma
 from langchain.chains import RetrievalQA
-from langchain.prompts.chat import (
-    ChatPromptTemplate,
-    SystemMessagePromptTemplate,
-    HumanMessagePromptTemplate,
-)
+
 import os
 import chainlit as cl
 
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.llms import DeepSparse
+from langchain_community.vectorstores import Chroma
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.llms import DeepSparse
 
 from pytube import YouTube
 import whisper
@@ -19,7 +15,7 @@
 MODEL_PATH = "hf:neuralmagic/mpt-7b-chat-pruned50-quant"
 text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
 embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", model_kwargs={'device': 'cpu'})
-llm = DeepSparse(model=MODEL_PATH)
+llm = DeepSparse(model=MODEL_PATH,generation_config={})
 model = whisper.load_model("base")
 
 def transcribe(youtube_url, model):

demos/langchain-video-chat/requirements.txt

@@ -1,9 +1,10 @@
 datasets
 transformers
-deepsparse-nightly==1.6.0.20231007
 chainlit
-langchain
 sentence_transformers
 chromadb
 git+https://github.com/openai/whisper.git
 pytube
+langchain-community==0.0.27
+langchain==0.1.11
+deepsparse-nightly==1.7.0.20240304

demos/medical-chatbot/ingest.py

@@ -1,6 +1,6 @@
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.vectorstores import FAISS
-from langchain.document_loaders import PyPDFLoader, DirectoryLoader
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain_community.document_loaders import PyPDFLoader, DirectoryLoader
 from langchain.text_splitter import RecursiveCharacterTextSplitter 
 
 DATA_PATH = 'data/'

demos/medical-chatbot/model.py

@@ -1,10 +1,9 @@
-from langchain.document_loaders import PyPDFLoader, DirectoryLoader
 from langchain.prompts import PromptTemplate
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.vectorstores import FAISS
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
 from langchain.chains import RetrievalQA
 import chainlit as cl
-from langchain.llms import DeepSparse
+from langchain_community.llms import DeepSparse
 import os
 
 MODEL_PATH = os.environ.get("MODEL_PATH") or "hf:neuralmagic/mpt-7b-chat-pruned50-quant"

demos/medical-chatbot/requirements.txt

@@ -1,9 +1,10 @@
 pypdf
-langchain
 torch
 transformers
 datasets
 sentence_transformers
 faiss_cpu
 chainlit
 deepsparse-nightly[llm]
+langchain-community==0.0.27
+langchain==0.1.11