langflow Error building Component Astra DB: Error initializing AstraDBVectorStore: {"errors":[{"message":"Request invalid: field 'command.name' value `null` not valid. Problem: must not be null.","errorCode":"COMMAND_FIELD

Bug Description

This error is happening when I export the flow JSON trying to run it on my application.

Reproduction

from langflow.load import run_flow_from_json TWEAKS = { "ChatInput-UuJgn": {}, "AstraVectorStoreComponent-2mwWS": {}, "ParseData-M3W6Z": {}, "Prompt-dt6pd": {}, "ChatOutput-R5l9b": {}, "SplitText-PbqQD": {}, "File-N0Za6": {}, "OpenAIModel-LYQTz": {}, "Memory-u45lJ": {}, "OpenAIEmbeddings-ZAXxT": {} }

result = run_flow_from_json(flow="Vector_Store_RAG.json", input_value="message", fallback_to_env_vars=True, # False by default tweaks=TWEAKS)

Expected behavior

I would expect to run the flow in my Django external application.

Who can help?

No response

Operating System

Macbook Sonoma 14.2

Langflow Version

1.0.14

Python Version

3.10

Screenshot

No response

Flow File

No response

Aug 16 '24 18:08 Nkurayijahubert

@Nkurayijahubert Hello there! I'm here to help you with any bugs, questions, or contributions. Let's get to the bottom of this together.

To resolve the error "Request invalid: field 'command.name' value null not valid. Problem: must not be null." when trying to build a component for Astra DB, you need to ensure that the command.name field is not null. This typically means that the name field for the component is missing or not set in your flow JSON.

Here is an example of how you can set the name field for the component:

{
  "template": {
    "_type": "Component",
    "name": "your_component_name",  // Ensure this field is set
    "chunk_size": {
      "advanced": true,
      "display_name": "Chunk Size",
      "dynamic": false,
      "info": "",
      "list": false,
      "name": "chunk_size",
      "placeholder": "",
      "required": false,
      "show": true,
      "title_case": false,
      "type": "int",
      "value": 1000
    },
    "client": {
      "advanced": true,
      "display_name": "Client",
      "dynamic": false,
      "info": "",
      "input_types": [
        "Message",
        "str"
      ],
      "list": false,
      "load_from_db": false,
      "name": "client",
      "placeholder": "",
      "required": false,
      "show": true,
      "title_case": false,
      "type": "str",
      "value": ""
    },
    // ... other fields ...
  }
}

Make sure to replace "your_component_name" with the actual name you want to give to your component. This should resolve the error by ensuring that the command.name field is not null ^[1].

To continue talking to Dosu, mention @dosu.

Aug 16 '24 18:08 dosubot[bot]

@dosu do I have to add it manually?

Aug 16 '24 20:08 Nkurayijahubert

Yes, you will need to manually add the name field to your flow JSON for the Astra DB component. Ensure that each component in your JSON has a valid name field set to avoid the error. If you have multiple components, verify that each one has a unique and non-null name field. This should resolve the issue you're encountering.

To continue talking to Dosu, mention @dosu.

Aug 16 '24 20:08 dosubot[bot]

@dosu There many places with "template": {}

Aug 16 '24 20:08 Nkurayijahubert

To ensure the command.name field is not null, you need to add the name field within the template object of the specific component causing the issue. For the Astra DB component, it should look something like this:

{
  "template": {
    "_type": "Component",
    "name": "AstraDBComponent",  // Add this line
    "chunk_size": {
      "advanced": true,
      "display_name": "Chunk Size",
      "dynamic": false,
      "info": "",
      "list": false,
      "name": "chunk_size",
      "placeholder": "",
      "required": false,
      "show": true,
      "title_case": false,
      "type": "int",
      "value": 1000
    },
    "client": {
      "advanced": true,
      "display_name": "Client",
      "dynamic": false,
      "info": "",
      "input_types": [
        "Message",
        "str"
      ],
      "list": false,
      "load_from_db": false,
      "name": "client",
      "placeholder": "",
      "required": false,
      "show": true,
      "title_case": false,
      "type": "str",
      "value": ""
    }
    // ... other fields ...
  }
}

Make sure to add the "name": "AstraDBComponent" line within the template object of the Astra DB component. If you have multiple components, ensure each one has a unique and non-null name field within their respective template objects ^[1].

To continue talking to Dosu, mention @dosu.

Aug 16 '24 20:08 dosubot[bot]

@dosu the error persist

Aug 16 '24 20:08 Nkurayijahubert

Ensure that the name field is correctly set for each component in your flow JSON. Here is a more detailed example for the Astra DB component:

{
  "template": {
    "_type": "Component",
    "name": "AstraDBComponent",  // Ensure this field is set
    "collection_name": {
      "display_name": "Collection Name",
      "info": "The name of the collection within Astra DB where the vectors will be stored.",
      "type": "str",
      "value": ""
    },
    "token": {
      "display_name": "Astra DB Application Token",
      "info": "Authentication token for accessing Astra DB.",
      "type": "str",
      "value": "ASTRA_DB_APPLICATION_TOKEN"
    },
    "api_endpoint": {
      "display_name": "API Endpoint",
      "info": "API endpoint URL for the Astra DB service.",
      "type": "str",
      "value": "ASTRA_DB_API_ENDPOINT"
    },
    "vector_store_inputs": {
      "display_name": "Vector Store Inputs",
      "type": "list",
      "value": []
    },
    "embedding": {
      "display_name": "Embedding",
      "type": "dict",
      "value": {}
    },
    "namespace": {
      "display_name": "Namespace",
      "info": "Optional namespace within Astra DB to use for the collection.",
      "type": "str",
      "value": ""
    },
    "metric": {
      "display_name": "Metric",
      "info": "Optional distance metric for vector comparisons in the vector store.",
      "type": "str",
      "options": ["cosine", "dot_product", "euclidean"],
      "value": "cosine"
    },
    "batch_size": {
      "display_name": "Batch Size",
      "info": "Optional number of data to process in a single batch.",
      "type": "int",
      "value": 100
    },
    "bulk_insert_batch_concurrency": {
      "display_name": "Bulk Insert Batch Concurrency",
      "info": "Optional concurrency level for bulk insert operations.",
      "type": "int",
      "value": 5
    },
    "bulk_insert_overwrite_concurrency": {
      "display_name": "Bulk Insert Overwrite Concurrency",
      "info": "Optional concurrency level for bulk insert operations that overwrite existing data.",
      "type": "int",
      "value": 5
    },
    "bulk_delete_concurrency": {
      "display_name": "Bulk Delete Concurrency",
      "info": "Optional concurrency level for bulk delete operations.",
      "type": "int",
      "value": 5
    },
    "setup_mode": {
      "display_name": "Setup Mode",
      "info": "Configuration mode for setting up the vector store, with options like 'Sync', 'Async', or 'Off'.",
      "type": "str",
      "options": ["Sync", "Async", "Off"],
      "value": "Sync"
    },
    "pre_delete_collection": {
      "display_name": "Pre Delete Collection",
      "info": "Boolean flag to determine whether to delete the collection before creating a new one.",
      "type": "bool",
      "value": false
    },
    "metadata_indexing_include": {
      "display_name": "Metadata Indexing Include",
      "info": "Optional list of metadata fields to include in the indexing.",
      "type": "str",
      "value": ""
    },
    "metadata_indexing_exclude": {
      "display_name": "Metadata Indexing Exclude",
      "info": "Optional list of metadata fields to exclude from the indexing.",
      "type": "str",
      "value": ""
    },
    "collection_indexing_policy": {
      "display_name": "Collection Indexing Policy",
      "info": "Optional dictionary defining the indexing policy for the collection.",
      "type": "str",
      "value": ""
    },
    "add_to_vector_store": {
      "display_name": "Add to Vector Store",
      "info": "If true, the Vector Store Inputs will be added to the Vector Store.",
      "type": "bool",
      "value": true
    },
    "search_input": {
      "display_name": "Search Input",
      "type": "str",
      "value": ""
    },
    "search_type": {
      "display_name": "Search Type",
      "type": "str",
      "options": ["Similarity", "MMR"],
      "value": "Similarity"
    },
    "number_of_results": {
      "display_name": "Number of Results",
      "info": "Number of results to return.",
      "type": "int",
      "value": 4
    }
  }
}

Ensure that the name field is set to a non-null value for the Astra DB component and any other components in your JSON. This should resolve the error ^[1].

To continue talking to Dosu, mention @dosu.

Aug 16 '24 20:08 dosubot[bot]

Hi @Nkurayijahubert, can you please share the exact steps you took?

I just tested that I was able to export the Vector Store RAG example and run it locally on my machine via the run_flow_from_json function. Attached is the json I used, and the code to run it. Plmk if that works for you, then we can figure out if anything is different in your environment.

from langflow.load import run_flow_from_json
TWEAKS = {
  "ChatInput-iaJtf": {},
  "AstraVectorStoreComponent-rd8LL": {
      "token": "<insert token>",
      "api_endpoint": "<insert endpoint>"
      },
  "ParseData-aybR0": {},
  "Prompt-jCJmL": {},
  "ChatOutput-JP4os": {},
  "SplitText-CQxrN": {},
  "File-IlhlA": {},
  "AstraVectorStoreComponent-7hUKp": {},
  "OpenAIEmbeddings-l7Jz2": {},
  "OpenAIEmbeddings-6YrUm": {},
  "OpenAIModel-W43IT": {}
}
# and I just exported `OPENAI_API_KEY` in the terminal I was running this from

result = run_flow_from_json(flow="test_rag.json",
                            input_value="message",
                            fallback_to_env_vars=True, # False by default
                            tweaks=TWEAKS)

print(result)

test_rag.json

Aug 20 '24 06:08 jordanrfrazier

Hi @jordanrfrazier these are the steps I am taking:

flow_file = "vector_store.json"

TWEAKS = { "File-x63LT": {}, "SplitText-fRVmN": {}, "OpenAIEmbeddings-rU5oN": { "openai_api_key": os.getenv("OPENAI_API_KEY"), }, "AstraDB-305Eo": { "api_endpoint": os.getenv("ASTRA_DB_API_ENDPOINT"), "token": os.getenv("ASTRA_DB_APPLICATION_TOKEN"), }, "ChatInput-bLV8x": {}, "ParseData-7oE65": {}, "Prompt-z6UsK": {}, "Memory-re2mi": {}, "OpenAIModel-ul65C": { "api_key": os.getenv("OPENAI_API_KEY"), }, "ChatOutput-4YOYF": {}, }

flow = run_flow_from_json(flow=flow_file, input_value="Hello, what is the capital of France?", fallback_to_env_vars=True, # False by default tweaks=TWEAKS)

Aug 20 '24 06:08 Nkurayijahubert

@Nkurayijahubert can you please attach your flow json file here, and I'll try running and debugging on my end tomorrow

Aug 20 '24 07:08 jordanrfrazier

Here it is @jordanrfrazier

vector_store.json

Aug 20 '24 07:08 Nkurayijahubert

Hi @Nkurayijahubert sorry for the late response. I was able to download and run your given JSON file, though I did re-upload the flow into a locally running langflow in order to verify the ID's of the components were correct for the TWEAKS.

Can you please try to

Run the latest langflow
Upload your json file
Verify your TWEAKS component IDs are correct, taken from the </> API button
Re-download your json and try running locally again.

Aug 26 '24 21:08 jordanrfrazier

hey @Nkurayijahubert Do you need any assistance with this case? If not, please let us know if this issue can be closed.

Sep 02 '24 11:09 carlosrcoelho

@Nkurayijahubert Thank you for your contribution! This issue will be closed. If you have any questions or encounter another problem, please open a new issue and we will be ready to assist you.

Sep 10 '24 20:09 carlosrcoelho

@dosu i'm trying to run a query in langflow and i receive the following error: Error details: ValueError Details: Error initializing AstraDBVectorStore: Server internal error: Missing/Invalid authentication credentials provided for type: astra

Jan 31 '25 15:01 pascal-maker

To resolve the "Server internal error: Missing/Invalid authentication credentials provided for type: astra" error when using AstraDBVectorStore in Langflow, ensure that you have provided a valid authentication token. The token should be set using the SecretStrInput for the token field. Make sure that the ASTRA_DB_APPLICATION_TOKEN environment variable or the input field in your configuration contains a valid Astra DB application token. This token is necessary for authenticating API requests to Astra DB ^[1].

To continue talking to Dosu, mention @dosu.

^{Help Dosu learn! Give it feedback: Great Response | Irrelevant Answer | Incorrect Sources | Too Verbose | Hallucination | Other}

Jan 31 '25 15:01 dosubot[bot]

Langflow query failed: Error running graph: Error building Component Astra DB:

Error initializing AstraDBVectorStore: UNAUTHENTICATED: Invalid token. Client error '401 Unauthorized' for url 'https://5bc16637-aaab-4388-a38c-bd51aa7b7f56-us-east-2.apps.astra.datastax.com/api/json/v1/default_keyspace' For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/401 @dosu

Jan 31 '25 17:01 pascal-maker

Hi @pascal-maker, please make sure that the AstraCS Token you are using has the necessary permissions. You may want to just try creating a token with Administrator User permissions to ensure it is being used correctly.

Jan 31 '25 19:01 jordanrfrazier

To resolve the "UNAUTHENTICATED: Invalid token. Client error '401 Unauthorized'" error when using AstraDBVectorStore in Langflow, ensure that you are using a valid token for authentication. Here are the steps to troubleshoot and fix the issue:

Check Token Validity: Verify that the token you are using is valid and has not expired. Tokens have an expiration time, and using an expired token will result in an unauthorized error.
Include Token in Request: Ensure that the token is included in the request. It can be passed as a query parameter or in the header. The code checks for the token in both places:
- As a query parameter: query_param: str = Security(api_key_query)
- As a header: header_param: str = Security(api_key_header)
Verify Token Details: If the token is included but still results in an error, verify the token details. The code checks for the presence of sub (subject) and type in the token payload. If these are missing, the token is considered invalid.
Check Secret Key: Ensure that the secret key used to decode the token is correctly set in the settings. The secret key is used to decode the JWT token, and if it is not set or incorrect, it will result in a decoding error.
Generate a New Token: If the token is invalid or expired, generate a new token using the create_token function. Ensure that the token is created with the correct user details and expiration time.

By following these steps, you should be able to resolve the "UNAUTHENTICATED: Invalid token" error ^[1]^[2]^[3].

To continue talking to Dosu, mention @dosu.

^{Help Dosu learn! Give it feedback: Great Response | Irrelevant Answer | Incorrect Sources | Too Verbose | Hallucination | Other}

Jan 31 '25 19:01 dosubot[bot]

Error building Component Astra DB: Error initializing AstraDBVectorStore: {"errors":[{"message":"Request invalid: field 'command.name' value `null` not valid. Problem: must not be null.","errorCode":"COMMAND_FIELD_INVALID"}]}

Bug Description

Reproduction

Expected behavior

Who can help?

Operating System

Langflow Version

Python Version

Screenshot

Flow File