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fix: integration

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This commit is contained in:
Timothy J. Baek 2024-04-14 18:47:45 -04:00
parent 9cdb5bf9fe
commit 36ce157907
3 changed files with 28 additions and 7 deletions
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27
backend/apps/rag/main.py
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@ -39,7 +39,7 @@ import uuid
import json
from apps.ollama.main import generate_ollama_embeddings
from apps.ollama.main import generate_ollama_embeddings, GenerateEmbeddingsForm
from apps.web.models.documents import (
Documents,
@ -277,7 +277,12 @@ def query_doc_handler(
try:
if app.state.RAG_EMBEDDING_ENGINE == "ollama":
query_embeddings = generate_ollama_embeddings(
{"model": app.state.RAG_EMBEDDING_MODEL, "prompt": form_data.query}
GenerateEmbeddingsForm(
**{
"model": app.state.RAG_EMBEDDING_MODEL,
"prompt": form_data.query,
}
)
)
return query_embeddings_doc(
@ -314,7 +319,12 @@ def query_collection_handler(
try:
if app.state.RAG_EMBEDDING_ENGINE == "ollama":
query_embeddings = generate_ollama_embeddings(
{"model": app.state.RAG_EMBEDDING_MODEL, "prompt": form_data.query}
GenerateEmbeddingsForm(
**{
"model": app.state.RAG_EMBEDDING_MODEL,
"prompt": form_data.query,
}
)
)
return query_embeddings_collection(
@ -373,6 +383,7 @@ 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")
return store_docs_in_vector_db(docs, collection_name, overwrite), None
else:
raise ValueError(ERROR_MESSAGES.EMPTY_CONTENT)
@ -390,9 +401,8 @@ def store_text_in_vector_db(
return store_docs_in_vector_db(docs, collection_name, overwrite)
async def store_docs_in_vector_db(
docs, collection_name, overwrite: bool = False
) -> bool:
def store_docs_in_vector_db(docs, collection_name, overwrite: bool = False) -> bool:
log.info("store_docs_in_vector_db", docs, collection_name)
texts = [doc.page_content for doc in docs]
metadatas = [doc.metadata for doc in docs]
@ -413,13 +423,16 @@ async def store_docs_in_vector_db(
metadatas=metadatas,
embeddings=[
generate_ollama_embeddings(
{"model": RAG_EMBEDDING_MODEL, "prompt": text}
GenerateEmbeddingsForm(
**{"model": RAG_EMBEDDING_MODEL, "prompt": text}
)
)
for text in texts
],
):
collection.add(*batch)
else:
collection = CHROMA_CLIENT.create_collection(
name=collection_name,
embedding_function=app.state.sentence_transformer_ef,