Use langchain voyage (#215)

ingest.py

@@ -11,11 +11,12 @@
 from langchain.indexes import SQLRecordManager, index
 from langchain.schema.embeddings import Embeddings
 from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.utils.html import PREFIXES_TO_IGNORE_REGEX, SUFFIXES_TO_IGNORE_REGEX
+from langchain.utils.html import (PREFIXES_TO_IGNORE_REGEX,
+                                  SUFFIXES_TO_IGNORE_REGEX)
 from langchain.vectorstores import Weaviate
+from voyage import VoyageEmbeddings
 
 from constants import WEAVIATE_DOCS_INDEX_NAME
-from voyage import VoyageEmbeddings
 
 logger = logging.getLogger(__name__)
 

main.py

@@ -7,29 +7,27 @@
 
 import langsmith
 import weaviate
-from constants import WEAVIATE_DOCS_INDEX_NAME
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
 from langchain.chat_models import ChatOpenAI
-from langchain.embeddings import OpenAIEmbeddings
-from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder, PromptTemplate
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.embeddings.voyageai import VoyageEmbeddings
+from langchain.prompts import (ChatPromptTemplate, MessagesPlaceholder,
+                               PromptTemplate)
 from langchain.schema import Document
 from langchain.schema.embeddings import Embeddings
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.schema.messages import AIMessage, HumanMessage
 from langchain.schema.output_parser import StrOutputParser
 from langchain.schema.retriever import BaseRetriever
-from langchain.schema.runnable import (
-    Runnable,
-    RunnableBranch,
-    RunnableLambda,
-    RunnableMap,
-)
+from langchain.schema.runnable import (Runnable, RunnableBranch,
+                                       RunnableLambda, RunnableMap)
 from langchain.vectorstores import Weaviate
 from langserve import add_routes
 from langsmith import Client
 from pydantic import BaseModel
-from voyage import VoyageEmbeddings
+
+from constants import WEAVIATE_DOCS_INDEX_NAME
 
 RESPONSE_TEMPLATE = """\
 You are an expert programmer and problem-solver, tasked with answering any question \
@@ -97,8 +95,8 @@ class ChatRequest(BaseModel):
 
 
 def get_embeddings_model() -> Embeddings:
-    if os.environ.get("VOYAGE_AI_URL") and os.environ.get("VOYAGE_AI_MODEL"):
-        return VoyageEmbeddings()
+    if os.environ.get("VOYAGE_API_KEY") and os.environ.get("VOYAGE_AI_MODEL"):
+        return VoyageEmbeddings(model=os.environ["VOYAGE_AI_MODEL"])
     return OpenAIEmbeddings(chunk_size=200)