EmbedAI
EmbedAI copied to clipboard
SamurAIGPT
500 Internal Server Error
After uploading a document, when asking questions, I get: Error getting data.<!doctype html>
Internal Server Error
The server encountered an internal error and was unable to complete your request. Either the server is overloaded or there is an error in the application.
Traceback (most recent call last): File "/usr/local/lib/python3.10/dist-packages/flask/app.py", line 2190, in wsgi_app response = self.full_dispatch_request() File "/usr/local/lib/python3.10/dist-packages/flask/app.py", line 1486, in full_dispatch_request rv = self.handle_user_exception(e) File "/usr/local/lib/python3.10/dist-packages/flask_cors/extension.py", line 165, in wrapped_function return cors_after_request(app.make_response(f(*args, **kwargs))) File "/usr/local/lib/python3.10/dist-packages/flask/app.py", line 1484, in full_dispatch_request rv = self.dispatch_request() File "/usr/local/lib/python3.10/dist-packages/flask/app.py", line 1469, in dispatch_request return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args) File "/home/it/Scripts/PrivateChatGPT/server/privateGPT.py", line 146, in get_answer res = qa(query) File "/usr/local/lib/python3.10/dist-packages/langchain/chains/base.py", line 140, in call raise e File "/usr/local/lib/python3.10/dist-packages/langchain/chains/base.py", line 134, in call self._call(inputs, run_manager=run_manager) File "/usr/local/lib/python3.10/dist-packages/langchain/chains/retrieval_qa/base.py", line 119, in _call docs = self._get_docs(question) File "/usr/local/lib/python3.10/dist-packages/langchain/chains/retrieval_qa/base.py", line 181, in _get_docs return self.retriever.get_relevant_documents(question) File "/usr/local/lib/python3.10/dist-packages/langchain/vectorstores/base.py", line 377, in get_relevant_documents docs = self.vectorstore.similarity_search(query, **self.search_kwargs) File "/usr/local/lib/python3.10/dist-packages/langchain/vectorstores/chroma.py", line 182, in similarity_search docs_and_scores = self.similarity_search_with_score(query, k, filter=filter) File "/usr/local/lib/python3.10/dist-packages/langchain/vectorstores/chroma.py", line 229, in similarity_search_with_score results = self.__query_collection( File "/usr/local/lib/python3.10/dist-packages/langchain/utils.py", line 52, in wrapper return func(*args, **kwargs) File "/usr/local/lib/python3.10/dist-packages/langchain/vectorstores/chroma.py", line 121, in __query_collection return self._collection.query( File "/usr/local/lib/python3.10/dist-packages/chromadb/api/models/Collection.py", line 227, in query return self._client._query( File "/usr/local/lib/python3.10/dist-packages/chromadb/api/local.py", line 437, in _query uuids, distances = self._db.get_nearest_neighbors( File "/usr/local/lib/python3.10/dist-packages/chromadb/db/clickhouse.py", line 585, in get_nearest_neighbors uuids, distances = index.get_nearest_neighbors(embeddings, n_results, ids) File "/usr/local/lib/python3.10/dist-packages/chromadb/db/index/hnswlib.py", line 240, in get_nearest_neighbors raise NoIndexException( chromadb.errors.NoIndexException: Index not found, please create an instance before querying 192.168.70.36 - - [30/May/2023 08:26:06] "POST /get_answer HTTP/1.1" 500 -
This shows document is uploaded, but you need to click on Ingest button to train the model on your document data
Thank you @Anil-matcha The result is gibberish, I assume we need to ingest more documents?
Loading documents from source_documentsLoaded 3 documents from source_documentsSplit into 96 chunks of text (max. 500 characters each)03@919:D5362GF%0='H(!4H",&-:-69);!=,'A<76H-D&75>"=#.4"@0.912&=8GC5=)"+EG7!))!&60>:,==$'+%'@(3:(,.>3$A+A"C&;"9GH#@):D:>+0A&%EEA@=),=G5H&C0GD5)8&G(A+5A>8*<.@3;<&A9.D4C,)>:+B<@,;G3643EAE6G>-)$27*"6&%79C10=#.H0@7H64H.*33(,2A9G.;:5B2%BD.@9&D0$75-59#>!HB,*D+ Source: source_documents/state_of_the_union.txt
What documents have you uploaded ?
Maybe you can clean the source documents and db folder and do upload your document, ingest and then run the query
What documents have you uploaded ?
Maybe you can clean the source documents and db folder and do upload your document, ingest and then run the query
Removed DB and source documents. I have uploaded PDF, but even tried a simple txt file with 2 sentences and the same output, just gibberish
192.168.70.36 - - [31/May/2023 17:44:11] "GET /download_model HTTP/1.1" 200 - 192.168.70.36 - - [31/May/2023 17:46:18] "POST /upload_doc HTTP/1.1" 200 - Loading documents from source_documents Loaded 1 documents from source_documents Split into 69 chunks of text (max. 500 characters each) Using embedded DuckDB with persistence: data will be stored in: db/ 192.168.70.36 - - [31/May/2023 17:46:29] "GET /ingest HTTP/1.1" 200 - 192.168.70.36 - - [31/May/2023 17:46:50] "OPTIONS /get_answer HTTP/1.1" 200 - Using embedded DuckDB with persistence: data will be stored in: db/ .A>#68*<$DC36G$(3<2H:3E26.H0#F*3@93#H7$,2DCDA(=''G$<F7G&()$34&(<2=;,GGF0))$6>5D,0C('&&.@EG#B'+A4H7!;2G7)C'&*3$-B@D<E""2)*5(***3@@6.1(<);$"*0&5;0