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b6ce469 40e1b4d 7010433 40e1b4d 7010433 40e1b4d 7010433 40e1b4d 7010433 40e1b4d 7010433 40e1b4d 7010433 40e1b4d 7010433 40e1b4d 7010433 40e1b4d 7010433 40e1b4d 7010433 40e1b4d b6ce469 40e1b4d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 | import tiktoken
from langchain.document_loaders import PyMuPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_openai.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores import Qdrant
from langchain_core.prompts import ChatPromptTemplate
from operator import itemgetter
from langchain.schema.runnable import RunnablePassthrough
def tiktoken_len(text):
tokens = tiktoken.encoding_for_model("gpt-3.5-turbo").encode(
text,
)
return len(tokens)
def chunk_documents(docs, tiktoken_len):
text_splitter = RecursiveCharacterTextSplitter(
chunk_size = 200,
chunk_overlap = 0,
length_function = tiktoken_len,
)
split_chunks = text_splitter.split_documents(docs)
print('len(split_chunks) =', len(split_chunks))
return split_chunks
def create_base_chain(openai_chat_model, base_instructions):
human_template = "{question}"
base_prompt = ChatPromptTemplate.from_messages([
("system", base_instructions),
("human", human_template)
])
base_chain = base_prompt | openai_chat_model
print("Created base chain\n")
return base_chain
def create_rag_chain_from_file(openai_chat_model, base_instructions, file_path, file_name):
# Load the documents from a PDF file using PyMuPDFLoader
docs = PyMuPDFLoader(file_path).load()
print("Loaded", len(docs), "documents")
print("First document:\n", docs[0], "\n")
# Create a Qdrant vector store from the split chunks and embedding model, and obtain its retriever
split_chunks = chunk_documents(docs, tiktoken_len)
embedding_model = OpenAIEmbeddings(model="text-embedding-3-small")
qdrant_vectorstore = Qdrant.from_documents(
split_chunks,
embedding_model,
location=":memory:",
collection_name=file_name,
)
qdrant_retriever = qdrant_vectorstore.as_retriever()
print("Created Qdrant vector store from uploaded PDF file =", file_name)
# Define the RAG prompt template
RAG_PROMPT = """
Use the provided context while replying to the user query. Only use the provided context to answer the query.
QUERY:
{question}
CONTEXT:
{context}
"""
RAG_PROMPT = base_instructions + RAG_PROMPT
print("RAG prompt template =", RAG_PROMPT)
rag_prompt = ChatPromptTemplate.from_template(RAG_PROMPT)
# Create the retrieval augmented QA chain using the Qdrant retriever, RAG prompt, and OpenAI chat model
rag_chain = (
{"context": itemgetter("question") | qdrant_retriever, "question": itemgetter("question")}
| RunnablePassthrough.assign(context=itemgetter("context"))
| {"response": rag_prompt | openai_chat_model, "context": itemgetter("context")}
)
print("Created RAG chain from uploaded PDF file =", file_name, "\n")
return rag_chain
|