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docs: cleanup langchain-chroma example

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mudler 2023-05-13 11:16:56 +02:00
parent de36a48861
commit c3622299ce
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examples/langchain-chroma/query.py
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@ -2,25 +2,14 @@
import os import os
from langchain.vectorstores import Chroma from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings from langchain.embeddings import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter,CharacterTextSplitter
from langchain.llms import OpenAI from langchain.llms import OpenAI
from langchain.chains import VectorDBQA from langchain.chains import VectorDBQA
from langchain.document_loaders import TextLoader
base_path = os.environ.get('OPENAI_API_BASE', 'http://localhost:8080/v1') base_path = os.environ.get('OPENAI_API_BASE', 'http://localhost:8080/v1')
# Load and process the text # Load and process the text
loader = TextLoader('state_of_the_union.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=300, chunk_overlap=70)
texts = text_splitter.split_documents(documents)
# Embed and store the texts
# Supplying a persist_directory will store the embeddings on disk
persist_directory = 'db'
embedding = OpenAIEmbeddings() embedding = OpenAIEmbeddings()
persist_directory = 'db'
# Now we can load the persisted database from disk, and use it as normal. # Now we can load the persisted database from disk, and use it as normal.
vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding) vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding)