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6f8cf1e068
Got some user feedback that the `implicit` / `explicit` distinction is confusing. Instead I was thinking we would just deprecate the `with_embeddings` API and then organize working with embeddings into 3 buckets: 1. manually generate embeddings 2. use a provided embedding function 3. define your own custom embedding function
120 lines
3.1 KiB
Python
120 lines
3.1 KiB
Python
import pickle
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import re
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import zipfile
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from pathlib import Path
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import requests
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from langchain.chains import RetrievalQA
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from langchain.document_loaders import UnstructuredHTMLLoader
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from langchain.embeddings import OpenAIEmbeddings
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from langchain.llms import OpenAI
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from langchain.vectorstores import LanceDB
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from modal import Image, Secret, Stub, web_endpoint
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import lancedb
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lancedb_image = Image.debian_slim().pip_install(
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"lancedb", "langchain", "openai", "pandas", "tiktoken", "unstructured", "tabulate"
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)
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stub = Stub(
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name="example-langchain-lancedb",
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image=lancedb_image,
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secrets=[Secret.from_name("my-openai-secret")],
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)
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docsearch = None
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docs_path = Path("docs.pkl")
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db_path = Path("lancedb")
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def get_document_title(document):
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m = str(document.metadata["source"])
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title = re.findall("pandas.documentation(.*).html", m)
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if title[0] is not None:
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return title[0]
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return ""
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def download_docs():
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pandas_docs = requests.get(
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"https://eto-public.s3.us-west-2.amazonaws.com/datasets/pandas_docs/pandas.documentation.zip"
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)
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with open(Path("pandas.documentation.zip"), "wb") as f:
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f.write(pandas_docs.content)
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file = zipfile.ZipFile(Path("pandas.documentation.zip"))
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file.extractall(path=Path("pandas_docs"))
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def store_docs():
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docs = []
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if not docs_path.exists():
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for p in Path("pandas_docs/pandas.documentation").rglob("*.html"):
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if p.is_dir():
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continue
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loader = UnstructuredHTMLLoader(p)
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raw_document = loader.load()
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m = {}
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m["title"] = get_document_title(raw_document[0])
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m["version"] = "2.0rc0"
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raw_document[0].metadata = raw_document[0].metadata | m
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raw_document[0].metadata["source"] = str(raw_document[0].metadata["source"])
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docs = docs + raw_document
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with docs_path.open("wb") as fh:
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pickle.dump(docs, fh)
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else:
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with docs_path.open("rb") as fh:
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docs = pickle.load(fh)
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return docs
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def qanda_langchain(query):
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download_docs()
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docs = store_docs()
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text_splitter = RecursiveCharacterTextSplitter(
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chunk_size=1000,
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chunk_overlap=200,
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)
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documents = text_splitter.split_documents(docs)
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embeddings = OpenAIEmbeddings()
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db = lancedb.connect(db_path)
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table = db.create_table(
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"pandas_docs",
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data=[
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{
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"vector": embeddings.embed_query("Hello World"),
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"text": "Hello World",
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"id": "1",
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}
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],
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mode="overwrite",
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)
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docsearch = LanceDB.from_documents(documents, embeddings, connection=table)
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qa = RetrievalQA.from_chain_type(
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llm=OpenAI(), chain_type="stuff", retriever=docsearch.as_retriever()
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)
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return qa.run(query)
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@stub.function()
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@web_endpoint(method="GET")
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def web(query: str):
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answer = qanda_langchain(query)
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return {
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"answer": answer,
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}
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@stub.function()
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def cli(query: str):
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answer = qanda_langchain(query)
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print(answer)
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