Upload appchat.py

appchat.py

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+# -*- coding: utf-8 -*-
+"""chatbot_with_memory (1).ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1sIEqI5-wciuiYOdlEYwBkTPUIlvMEzkF
+"""
+
+!pip install chromadb==0.4.6
+!pip install pydantic==1.10
+!pip install sentence-transformers
+
+!pip install huggingface_hub
+
+!pip install transformers
+
+from langchain.document_loaders import TextLoader  #for textfiles
+from langchain.text_splitter import CharacterTextSplitter #text splitter
+from langchain.embeddings import HuggingFaceEmbeddings #for using HugginFace models
+from langchain.vectorstores import FAISS
+from langchain.chains.question_answering import load_qa_chain
+from langchain.chains.question_answering import load_qa_chain
+from langchain import HuggingFaceHub
+from langchain.document_loaders import UnstructuredPDFLoader  #load pdf
+from langchain.indexes import VectorstoreIndexCreator #vectorize db index with chromadb
+from langchain.chains import RetrievalQA
+from langchain.document_loaders import UnstructuredURLLoader  #load urls into docoument-loader
+from langchain.chains.question_answering import load_qa_chain
+from langchain import HuggingFaceHub
+import os
+huggingfacehub_api_token = os.environ.get("HUGGINGFACEHUB_API_TOKEN")
+
+
+pip install pypdf
+
+from langchain.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+
+#import csvfrom langchain.document_loaders import PyPDFLoader
+# Load the PDF file from current working directory
+loader = PyPDFLoader("/content/Document sans titre (5).pdf")
+# Split the PDF into Pages
+pages = loader.load_and_split()
+
+#import from langchain.text_splitter import RecursiveCharacterTextSplitter
+# Define chunk size, overlap and separators
+text_splitter = RecursiveCharacterTextSplitter(
+    chunk_size= 128,
+    chunk_overlap=64,
+    separators=['\n\n', '\n', '(?=>\. )', ' ', '']
+)
+docs  = text_splitter.split_documents(pages)
+
+from langchain.embeddings import HuggingFaceEmbeddings
+embeddings = HuggingFaceEmbeddings()
+
+pip install faiss-gpu
+
+#Create the vectorized db
+# Vectorstore: https://python.langchain.com/en/latest/modules/indexes/vectorstores.html
+from langchain.vectorstores import FAISS
+db = FAISS.from_documents(docs, embeddings)
+
+llm=HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":1, "max_length":1000000, "max_new_tokens": 500})
+chain = load_qa_chain(llm, chain_type="stuff")
+
+#QUERYING
+query = "quelles sont les villes les facultees de medcine  ?"
+docs = db.similarity_search(query)
+chain.run(input_documents=docs, question=query)
+
+from langchain.chains import RetrievalQA
+qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff",
+retriever=db.as_retriever(search_kwargs={"k": 3}))
+
+query = "donner moi plus  des information sur les facultees de medcine?"
+qa.run(query)
+
+query = "What is the meaning of Descriptive Data Analysis?"
+qa.run(query)#import csv
+
+repo_id = 'google/flan-t5-xxl'  # has 3B parameters: https://huggingface.co/lmsys/fastchat-t5-3b-v1.0
+llm = HuggingFaceHub(huggingfacehub_api_token=os.environ["HUGGINGFACEHUB_API_TOKEN"],
+                     repo_id=repo_id,
+                     model_kwargs={'temperature':0.5, 'max_length':256})
+
+query1 = "Bonjour, je suis zaynab ,j'ai des questions a vous "
+query2 = "j'habite a marrakech. tu sait son pays?"
+query3 = "quel est mon prenom?"
+query4 = "ou j'habite"
+
+pip install langchain --upgrade
+
+from langchain import HuggingFaceHub
+from langchain.chains import ConversationChain
+
+"""### Conversation Buffer memory"""
+
+from langchain.chains.conversation.memory import ConversationBufferMemory
+ # Adjust the import path accordingly
+memory = ConversationBufferMemory()
+conversation_buf = ConversationChain(
+    llm=llm,
+    memory=memory)
+
+print("input: ",query1)
+conversation_buf.predict(input=query1)
+
+print("input: ",query2)
+conversation_buf.predict(input=query2)
+
+memory.load_memory_variables({})
+
+print("input: ",query3)
+conversation_buf.predict(input=query3)
+
+print("input: ",query4)
+conversation_buf.predict(input=query4)
+
+print(memory.buffer)
+
+"""### Conversation Buffer Window Memory"""
+
+from langchain.memory import ConversationBufferWindowMemory
+
+memory2 = ConversationBufferWindowMemory(k=2)
+conversation_buf2 = ConversationChain(
+    llm=llm,
+    memory=memory2
+)
+
+print("input: ",query1)
+conversation_buf2.predict(input=query1)
+
+print("input: ",query2)
+conversation_buf2.predict(input=query2)
+
+print("input: ",query3)
+conversation_buf2.predict(input=query3)
+
+print(memory2.buffer)
+
+"""### Conversation Summary Memory"""
+
+from langchain.memory import ConversationSummaryBufferMemory
+
+memory3 = ConversationSummaryBufferMemory(llm=llm, max_token_limit=80)
+conversation_buf3 = ConversationChain(
+    llm=llm,
+    memory=memory3
+)
+
+print("input: ",query1)
+conversation_buf3.predict(input=query1)
+
+print("input: ",query2)
+conversation_buf3.predict(input=query2)
+
+print("input: ",query3)
+conversation_buf3.predict(input=query3)
+
+memory3.load_memory_variables({})
+
+"""### Chat PDF with Memory
+
+Updated version of Pydantic package (dependency of chromadb) has changed leaving chromadb, incompatible: here are the possible solutions: [import error chromadb](https://github.com/langchain-ai/langchain/issues/1957)  || Install specific versions of chromadb and pydantic while the bug is resolved
+
+![image.png](data:image/png;base64,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
+"""
+
+!pip install pypdf
+
+import langchain
+import chromadb
+
+import os
+import getpass
+
+from langchain.document_loaders import PyPDFLoader  #document loader: https://python.langchain.com/docs/modules/data_connection/document_loaders
+from langchain.text_splitter import RecursiveCharacterTextSplitter  #document transformer: text splitter for chunking
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain import PromptTemplate
+from langchain.vectorstores import Chroma #vector store
+from langchain import HuggingFaceHub  #model hub
+from langchain.chains import RetrievalQA
+
+from langchain.memory import ConversationBufferMemory
+
+#loading the API key
+import getpass
+import os
+os.environ['HUGGING_FACE_HUB_API_KEY'] = getpass.getpass('Hugging face api key:')
+
+path = input("Enter PDF file path: ")#"C:/Users/Sourav/Downloads/pdf"
+loader = PyPDFLoader(path)
+pages = loader.load()
+
+#number of pages
+len(pages)
+
+splitter = RecursiveCharacterTextSplitter(chunk_size=256, chunk_overlap=10)
+docs = splitter.split_documents(pages)
+
+tokens = docs
+num_tokens = len(tokens)
+print("Nombre de jetons :", num_tokens)
+
+for token in tokens:
+    print(token)
+
+embeddings = HuggingFaceEmbeddings()
+doc_search = Chroma.from_documents(docs, embeddings)
+
+print(doc_search)
+
+query = "Quelle sont les Facultees existent ?"
+similar_docs = doc_search.similarity_search(query, k=3)
+
+print(similar_docs)
+
+query = "donner moi des information ecole nationale d'Industrie Minérale ?"
+similar_docs = doc_search.similarity_search(query, k=10)
+
+repo_id = 'google/flan-t5-xxl'  # has 3B parameters: https://huggingface.co/lmsys/fastchat-t5-3b-v1.0
+llm = HuggingFaceHub(huggingfacehub_api_token=os.environ['HUGGING_FACE_HUB_API_KEY'],
+                     repo_id=repo_id,
+                     model_kwargs={'temperature':1, 'max_length':10000000000, "max_tokens":1000000000})
+
+template = """
+Use the following context (delimited by <ctx></ctx>) and the chat history (delimited by <hs></hs>) to answer the question:
+------
+<ctx>
+{context}
+</ctx>
+------
+<hs>
+{history}
+</hs>
+------
+{question}
+Answer:
+"""
+prompt = PromptTemplate(
+    input_variables=["history", "context", "question"],
+    template=template,
+)
+
+memory = ConversationBufferMemory(
+    memory_key="history",
+    input_key="question"
+)
+
+retrieval_chain = RetrievalQA.from_chain_type(llm,
+                                              chain_type='stuff',
+                                              retriever=doc_search.as_retriever(),
+                                              chain_type_kwargs={
+                                                  "prompt": prompt,
+                                                  "memory": memory
+                                              })
+
+query = " donner moi les villes de ces facultees de medcine? "
+retrieval_chain.run(query)
+
+query = "donner moi des information sur  Facultees de medcine  ?"
+retrieval_chain.run(query)
+
+memory.load_memory_variables({})
+