Spaces:
Runtime error
Runtime error
File size: 4,454 Bytes
7d4f0c3 aeadfac 7d4f0c3 aeadfac 7d4f0c3 aeadfac 7d4f0c3 |
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 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 |
import chainlit as cl
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.document_loaders.csv_loader import CSVLoader
from langchain.embeddings import CacheBackedEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI
from langchain.storage import LocalFileStore
from langchain.prompts.chat import (
ChatPromptTemplate,
SystemMessagePromptTemplate,
HumanMessagePromptTemplate,
)
import chainlit as cl
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
system_template = """
Use the following pieces of context to answer the user's question.
Please respond as if you were Miles Morales from the Spider-Man comics and movies. General speech patterns: Uses contractions often, like "I'm," "can't," and "don't."
Might sprinkle in some Spanish, given his Puerto Rican heritage. References to modern pop culture, music, or tech. Miles is a brave young hero, grappling with his dual
heritage and urban life. He has a passion for music, especially hip-hop, and is also into art, being a graffiti artist himself. He speaks with an urban and youthful tone,
reflecting the voice of modern NYC youth. He might occasionally reference modern pop culture, his friends, or his school life.
If you don't know the answer, just say you're unsure. Don't try to make up an answer.
You can make inferences based on the context as long as it aligns with Miles' personality and experiences.
Example of your interaction:
User: "What did you think of the latest Spider-Man movie?"
MilesBot: "Haha, watching Spider-Man on screen is always surreal for me. But it's cool to see different takes on the web-slinger's story. Always reminds me of the Spider-Verse!"
Example of your response:
```
The answer is foo
```
Begin!
----------------
{context}"""
messages = [
SystemMessagePromptTemplate.from_template(system_template),
HumanMessagePromptTemplate.from_template("{question}"),
]
prompt = ChatPromptTemplate(messages=messages)
chain_type_kwargs = {"prompt": prompt}
@cl.author_rename
def rename(orig_author: str):
rename_dict = {"RetrievalQA": "Searching the Spiderverse"}
return rename_dict.get(orig_author, orig_author)
@cl.on_chat_start
async def init():
msg = cl.Message(content=f"Building Index...")
await msg.send()
# build FAISS index from csv
loader = CSVLoader(file_path="./data/spiderverse.csv", source_column="Review_Url")
data = loader.load()
documents = text_splitter.transform_documents(data)
store = LocalFileStore("./cache/")
core_embeddings_model = OpenAIEmbeddings()
embedder = CacheBackedEmbeddings.from_bytes_store(
core_embeddings_model, store, namespace=core_embeddings_model.model
)
# make async docsearch
docsearch = await cl.make_async(FAISS.from_documents)(documents, embedder)
chain = RetrievalQA.from_chain_type(
ChatOpenAI(model="gpt-4", temperature=0, streaming=True),
chain_type="stuff",
return_source_documents=True,
retriever=docsearch.as_retriever(),
chain_type_kwargs = {"prompt": prompt}
)
msg.content = f"Index built!"
await msg.send()
cl.user_session.set("chain", chain)
@cl.on_message
async def main(message):
chain = cl.user_session.get("chain")
cb = cl.AsyncLangchainCallbackHandler(
stream_final_answer=False, answer_prefix_tokens=["FINAL", "ANSWER"]
)
cb.answer_reached = True
res = await chain.acall(message, callbacks=[cb], )
answer = res["result"]
source_elements = []
visited_sources = set()
# Get the documents from the user session
docs = res["source_documents"]
metadatas = [doc.metadata for doc in docs]
all_sources = [m["source"] for m in metadatas]
for source in all_sources:
if source in visited_sources:
continue
visited_sources.add(source)
# Create the text element referenced in the message
source_elements.append(
cl.Text(content="https://www.imdb.com" + source, name="Review URL")
)
if source_elements:
answer += f"\nSources: {', '.join([e.content.decode('utf-8') for e in source_elements])}"
else:
answer += "\nNo sources found"
await cl.Message(content=answer, elements=source_elements).send()
|