update deprecated langchain usages; add python debug config

Signed-off-by: Tyler Gillson <tyler.gillson@gmail.com>

.vscode/launch.json

@@ -1,6 +1,19 @@
 {
     "version": "0.2.0",
     "configurations": [
+        {
+            "name": "Python: Current File",
+            "type": "python",
+            "request": "launch",
+            "program": "${file}",
+            "console": "integratedTerminal",
+            "justMyCode": false,
+            "cwd": "${workspaceFolder}/examples/langchain-chroma",
+            "env": {
+                "OPENAI_API_BASE": "http://localhost:8080/v1",
+                "OPENAI_API_KEY": "abc"
+            }
+        },
         {
             "name": "Launch Go",
             "type": "go",

examples/langchain-chroma/query.py

@@ -2,8 +2,9 @@
 import os
 from langchain.vectorstores import Chroma
 from langchain.embeddings import OpenAIEmbeddings
-from langchain.llms import OpenAI
-from langchain.chains import VectorDBQA
+from langchain.chat_models import ChatOpenAI
+from langchain.chains import RetrievalQA
+from langchain.vectorstores.base import VectorStoreRetriever
 
 base_path = os.environ.get('OPENAI_API_BASE', 'http://localhost:8080/v1')
 
@@ -12,8 +13,10 @@ embedding = OpenAIEmbeddings()
 persist_directory = 'db'
 
 # Now we can load the persisted database from disk, and use it as normal. 
+llm = ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo", openai_api_base=base_path)
 vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding)
-qa = VectorDBQA.from_chain_type(llm=OpenAI(temperature=0, model_name="gpt-3.5-turbo", openai_api_base=base_path), chain_type="stuff", vectorstore=vectordb)
+retriever = VectorStoreRetriever(vectorstore=vectordb)
+qa = RetrievalQA.from_llm(llm=llm, retriever=retriever)
 
 query = "What the president said about taxes ?"
 print(qa.run(query))

examples/langchain-chroma/store.py

@@ -2,9 +2,7 @@
 import os
 from langchain.vectorstores import Chroma
 from langchain.embeddings import OpenAIEmbeddings
-from langchain.text_splitter import RecursiveCharacterTextSplitter,TokenTextSplitter,CharacterTextSplitter
-from langchain.llms import OpenAI
-from langchain.chains import VectorDBQA
+from langchain.text_splitter import CharacterTextSplitter
 from langchain.document_loaders import TextLoader
 
 base_path = os.environ.get('OPENAI_API_BASE', 'http://localhost:8080/v1')
@@ -14,7 +12,6 @@ loader = TextLoader('state_of_the_union.txt')
 documents = loader.load()
 
 text_splitter = CharacterTextSplitter(chunk_size=300, chunk_overlap=70)
-#text_splitter = TokenTextSplitter()
 texts = text_splitter.split_documents(documents)
 
 # Embed and store the texts