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feat: openai embeddings integration

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This commit is contained in:
Timothy J. Baek
2024-04-14 19:48:15 -04:00
parent b48e73fa43
commit b1b72441bb
6 changed files with 155 additions and 46 deletions
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6
backend/apps/rag/main.py
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@@ -421,7 +421,7 @@ def store_data_in_vector_db(data, collection_name, overwrite: bool = False) -> b
docs = text_splitter.split_documents(data)
if len(docs) > 0:
log.info("store_data_in_vector_db", "store_docs_in_vector_db")
log.info(f"store_data_in_vector_db {docs}")
return store_docs_in_vector_db(docs, collection_name, overwrite), None
else:
raise ValueError(ERROR_MESSAGES.EMPTY_CONTENT)
@@ -440,7 +440,7 @@ def store_text_in_vector_db(
def store_docs_in_vector_db(docs, collection_name, overwrite: bool = False) -> bool:
log.info("store_docs_in_vector_db", docs, collection_name)
log.info(f"store_docs_in_vector_db {docs} {collection_name}")
texts = [doc.page_content for doc in docs]
metadatas = [doc.metadata for doc in docs]
@@ -468,6 +468,8 @@ def store_docs_in_vector_db(docs, collection_name, overwrite: bool = False) -> b
collection.add(*batch)
else:
collection = CHROMA_CLIENT.create_collection(name=collection_name)
if app.state.RAG_EMBEDDING_ENGINE == "ollama":
embeddings = [
generate_ollama_embeddings(