feat: RAG text ingestion(store) api

This commit is contained in:
Timothy J. Baek 2024-03-24 00:40:27 -07:00
parent c2d6d3230b
commit 7e0ea8f77d
2 changed files with 79 additions and 33 deletions

View file

@ -111,39 +111,6 @@ class StoreWebForm(CollectionNameForm):
url: str
def store_data_in_vector_db(data, collection_name, overwrite: bool = False) -> bool:
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=app.state.CHUNK_SIZE, chunk_overlap=app.state.CHUNK_OVERLAP
)
docs = text_splitter.split_documents(data)
texts = [doc.page_content for doc in docs]
metadatas = [doc.metadata for doc in docs]
try:
if overwrite:
for collection in CHROMA_CLIENT.list_collections():
if collection_name == collection.name:
print(f"deleting existing collection {collection_name}")
CHROMA_CLIENT.delete_collection(name=collection_name)
collection = CHROMA_CLIENT.create_collection(
name=collection_name,
embedding_function=app.state.sentence_transformer_ef,
)
collection.add(
documents=texts, metadatas=metadatas, ids=[str(uuid.uuid1()) for _ in texts]
)
return True
except Exception as e:
print(e)
if e.__class__.__name__ == "UniqueConstraintError":
return True
return False
@app.get("/")
async def get_status():
return {
@ -325,6 +292,56 @@ def store_web(form_data: StoreWebForm, user=Depends(get_current_user)):
)
def store_data_in_vector_db(data, collection_name, overwrite: bool = False) -> bool:
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=app.state.CHUNK_SIZE,
chunk_overlap=app.state.CHUNK_OVERLAP,
add_start_index=True,
)
docs = text_splitter.split_documents(data)
return store_docs_in_vector_db(docs, collection_name, overwrite)
def store_text_in_vector_db(
text, name, collection_name, overwrite: bool = False
) -> bool:
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=app.state.CHUNK_SIZE,
chunk_overlap=app.state.CHUNK_OVERLAP,
add_start_index=True,
)
docs = text_splitter.create_documents([text], metadatas=[{"name": name}])
return store_docs_in_vector_db(docs, collection_name, overwrite)
def store_docs_in_vector_db(docs, collection_name, overwrite: bool = False) -> bool:
texts = [doc.page_content for doc in docs]
metadatas = [doc.metadata for doc in docs]
try:
if overwrite:
for collection in CHROMA_CLIENT.list_collections():
if collection_name == collection.name:
print(f"deleting existing collection {collection_name}")
CHROMA_CLIENT.delete_collection(name=collection_name)
collection = CHROMA_CLIENT.create_collection(
name=collection_name,
embedding_function=app.state.sentence_transformer_ef,
)
collection.add(
documents=texts, metadatas=metadatas, ids=[str(uuid.uuid1()) for _ in texts]
)
return True
except Exception as e:
print(e)
if e.__class__.__name__ == "UniqueConstraintError":
return True
return False
def get_loader(filename: str, file_content_type: str, file_path: str):
file_ext = filename.split(".")[-1].lower()
known_type = True
@ -460,6 +477,33 @@ def store_doc(
)
class TextRAGForm(BaseModel):
name: str
content: str
collection_name: Optional[str] = None
@app.post("/text")
def store_text(
form_data: TextRAGForm,
user=Depends(get_current_user),
):
collection_name = form_data.collection_name
if collection_name == None:
collection_name = calculate_sha256_string(form_data.content)
result = store_text_in_vector_db(form_data.content, form_data.name, collection_name)
if result:
return {"status": True, "collection_name": collection_name}
else:
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail=ERROR_MESSAGES.DEFAULT(),
)
@app.get("/scan")
def scan_docs_dir(user=Depends(get_admin_user)):
for path in Path(DOCS_DIR).rglob("./**/*"):

View file

@ -137,6 +137,8 @@ def rag_messages(docs, messages, template, k, embedding_function):
k=k,
embedding_function=embedding_function,
)
elif doc["type"] == "text":
context = doc["content"]
else:
context = query_doc(
collection_name=doc["collection_name"],