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Published 1 week ago • verified publisher icon crewAIInc

[BUG] JSONKnowledgeSource Error Failed to upsert documents: Request URL is missing an 'http://' or 'https://' protocol. in upsert.

Open fzozyurt opened this issue 8 months ago • 4 comments

Description

Hello,

I call Knowledge with JSONKnowledgeSource(file_paths=["log.json"]) in CrewAI, and I use Huggingface as Embedding, but I get an error like the one below. When I use StringKnowledgeSource instead of JSON, it does not give this error and does the operation. I think there is a problem with JSON.

The error I get is: [2025-04-26 14:27:54][ERROR]: Failed to upsert documents: Request URL is missing an 'http://' or 'https://' protocol. in upsert.

Code: ` json_source = JSONKnowledgeSource(file_paths=["log.json"])

crew = Crew( agents=[agent], tasks=[sample_task], process=Process.sequential, embedder={ "provider": "huggingface", "config": {"model": "sentence-transformers/all-MiniLM-L6-v2"} }, knowledge_sources=[json_source] memory=True, verbose=True )`

Steps to Reproduce

When crew runs the kickoff process it gives this error.

Expected behavior

Since I gave Knowledge as a file, I expect it not to ask for a URL starting with http. When I try it with StringKnowledgeSource, I do not get this error. It only gives this error with JSON.

Screenshots/Code snippets

Code: ` json_source = JSONKnowledgeSource(file_paths=["log.json"])

crew = Crew( agents=[agent], tasks=[sample_task], process=Process.sequential, embedder={ "provider": "huggingface", "config": {"model": "sentence-transformers/all-MiniLM-L6-v2"} }, knowledge_sources=[json_source] memory=True, verbose=True crew.kickoff(inputs={"complaint":"Analyze transaction ID 7b68c389-996f-4ec0-a896-0a976dd91460 and see how I can resolve the error here. "}) )`

Operating System

Ubuntu 20.04

Python Version

3.11

crewAI Version

0.108.0

crewAI Tools Version

Virtual Environment

Venv

Evidence

DEBUG:httpx:load_ssl_context verify=True cert=None trust_env=True http2=False DEBUG:httpx:load_verify_locations cafile='/etc/pki/tls/custom-certs/ca-bundle.crt' DEBUG:chromadb.api.segment:Collection knowledge_Log_Analyst already exists, returning existing collection.

[2025-04-26 14:27:54][ERROR]: Failed to upsert documents: Request URL is missing an 'http://' or 'https://' protocol. in upsert.

Possible Solution

None

Additional context

When I add Knowledge as a string it works, but when added as JSON it doesn't work.

fzozyurt avatar May 03 '25 21:05 fzozyurt

try this to use the huggingface models: https://github.com/huggingface/text-embeddings-inference

embedder={
"provider": "huggingface",
"config": {"api_url": "<url_of_your_embedding_model>"}
},

lorenzejay avatar May 04 '25 00:05 lorenzejay

try this to use the huggingface models: https://github.com/huggingface/text-embeddings-inference

embedder={
"provider": "huggingface",
"config": {"api_url": "<url_of_your_embedding_model>"}
},

This is not a way to download and run the model. It is run via Hugginface with the API. Why doesn't it work in JSONKnowledge when it works in StringKnowledge?

Why do I need to use inference when it can be used directly with transformer or sentencetransformer? It is very difficult for me to set up a separate server and cluster. I am sure it is the same for many users.

fzozyurt avatar May 04 '25 10:05 fzozyurt

To add to the discussion, I was able to reproduce the OP's error using both JSONKnowledgeSource and TextFileKnowledgeSource. They both throw the same error the OP mentioned.

To check if this is an issue with Embedchain itself (which CrewAI uses for RAG by default) or if the problem is somewhere in CrewAI (maybe in BaseFileKnowledgeSource or BaseKnowledgeSource?), the example below runs without any errors using Embedchain directly, with the same setup the OP described.

Directory Structure:

crewai_jsnknowledgesrc_hf/
├── knowledge/
│   └── user_info.json
└── test_embedchain.py

File knowledge/user_info.json:

[
  {
    "id": "USR001",
    "name": "Alice Wonderland",
    "email": "[email protected]",
    "city": "New York",
    "hobbies": ["Reading", "Chess", "Hiking"],
    "last_interaction": "2024-03-10",
    "preference": "Prefers email communication"
  },
  {
    "id": "USR002",
    "name": "Bob The Builder",
    "email": "[email protected]",
    "city": "London",
    "hobbies": ["Woodworking", "Gardening"],
    "last_interaction": "2024-03-15",
    "preference": "Likes phone calls for urgent matters"
  },
  {
    "id": "USR003",
    "name": "Charlie Chaplin",
    "email": "[email protected]",
    "city": "Los Angeles",
    "hobbies": ["Filmmaking", "Comedy", "Travel"],
    "last_interaction": "2024-02-28",
    "preference": "Enjoys newsletters"
  },
  {
    "id": "USR004",
    "name": "Diana Prince",
    "email": "[email protected]",
    "city": "Themyscira",
    "hobbies": ["Archaeology", "Combat training", "Diplomacy"],
    "last_interaction": "2024-03-18",
    "preference": "Values honesty and directness"
  }
]

File test_embedchain.py:

from embedchain import App
from pprint import pprint
import os

os.environ["HUGGINGFACE_ACCESS_TOKEN"] = "hf_YOUR_KEY"

embedchain_config = {
    "embedder": {
        "provider": "huggingface",
        "config": {
            "model": "sentence-transformers/all-MiniLM-L6-v2",
        }
    }
}

app = App.from_config(config=embedchain_config)

app.add("knowledge/user_info.json")

result = app.search("Who is Charlie Chaplin?")

pprint(result)

mouramax avatar May 04 '25 13:05 mouramax

This is not a way to download and run the model.

I think what's happening at this moment is OP is trying to download a hf model and run it locally, and crewai takes a hf with API

Can you try with the custom embeddings, I think this should resolve the issue, you can put the hf function inside the function and I think this should run smoothly.

https://docs.crewai.com/concepts/memory#adding-custom-embedding-function

Vidit-Ostwal avatar May 04 '25 14:05 Vidit-Ostwal

Dear @lorenzejay @Vidit-Ostwal @mouramax , I created the code as follows. The knowledge file also contains the necessary knowledge information. However, I still cannot get information from knowledge.

To try this, I customized the code in the lorenzejay/crewai_knowledge_getting_started [https://github.com/lorenzejay/crewai_knowledge_getting_started/tree/main/simple_knowledge_example] repo as follows and added sentande_transformer as a custom.However, the result did not change.

My Customize Code:


from crewai.knowledge.source.json_knowledge_source import JSONKnowledgeSource
from pydantic import BaseModel, Field
from chromadb import Documents, EmbeddingFunction, Embeddings
from sentence_transformers import SentenceTransformer

# Create a custom embedding function
class CustomEmbedder(EmbeddingFunction):
    def __call__(self, input: Documents) -> Embeddings:
        embedder = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
        # generate embeddings
        return embedder.encode(input) # this is a dummy embedding

embeding={
        "provider": "custom",
        "config": {
            "embedder": CustomEmbedder()
        }
    }

json_knowledge_source = JSONKnowledgeSource(
        file_paths=["lorenze.json", "random.json"]
    )

LLM_CONFIGS = {
    "qwen3-235b-a22b":{
        "model": "openrouter/qwen/qwen3-235b-a22b:free",
        "base_url":"https://openrouter.ai/api/v1",
        "api_key":"****"
    },
    "gemini-2.0-flash":{
        "model": "gemini/gemini-2.0-flash",
        "api_key":"****"
    },
    "gemini-2.0-flash":{
        "model": "gemini/gemma-3-27b",
        "api_key":"****"
    }
}

def get_llm(model_name: str = "gemini-2.0-flash"):
    if model_name not in LLM_CONFIGS:
        raise ValueError(f"{model_name} model not found in the list. Available models: {list(LLM_CONFIGS.keys())}")
    
    config = LLM_CONFIGS[model_name]
    return LLM(**config)

research = Agent(
    role="User Understanding Assistant",
    goal="You understand users and their preferences.Your goal is to answer questions about users.",
    backstory="You are a helpful assistant that can answer questions about users.",
    allow_delegation=False,
    verbose=True,
    llm=get_llm()
)

research_task = Task(
    description="Answer questions about users.The question is: {question}",
    expected_output="A susinct answer to the question.",
    agent=research
)

crew=Crew(
    agents=[research],
    tasks=[research_task],
    process=Process.sequential,
    verbose=True,
    llm=get_llm(),
    embedder=embeding,
    knowledge_sources=[json_knowledge_source]
    )

inputs = {"question": "What are the names of the users and what are their roles?"}
crew().kickoff(inputs=inputs)```

fzozyurt avatar May 31 '25 21:05 fzozyurt

Is this throwing any kind of error?

Vidit-Ostwal avatar Jun 01 '25 04:06 Vidit-Ostwal

Is this throwing any kind of error?

No, it does not give an error but it responds without taking into account in the Agent knowledge.

fzozyurt avatar Jun 01 '25 09:06 fzozyurt

Is this throwing any kind of error?

No, it does not give an error but it responds without taking into account in the Agent knowledge.

Can you see any file getting created https://docs.crewai.com/concepts/knowledge#supported-knowledge-parameters

Vidit-Ostwal avatar Jun 01 '25 09:06 Vidit-Ostwal

Is this throwing any kind of error?

No, it does not give an error but it responds without taking into account in the Agent knowledge.

Can you see any file getting created https://docs.crewai.com/concepts/knowledge#supported-knowledge-parameters

Yes, A file appears to have been created as follows.

Knowledge storage location: /opt/app-root/src/.local/share/log_analysis_crew/knowledge

Knowledge storage contents: 📄 chroma.sqlite3 📁 Collection: 3c8ef12a-8080-4dac-a9d4-9eda4a6cf63a/ └── header.bin └── length.bin └── data_level0.bin └── link_lists.bin

fzozyurt avatar Jun 01 '25 13:06 fzozyurt

I think this is an another issues, Can you check this PR this might resolve this issue:

https://github.com/crewAIInc/crewAI/pull/2831

Vidit-Ostwal avatar Jun 02 '25 05:06 Vidit-Ostwal

Can you also try passing the knowledge at the agent level once?

Vidit-Ostwal avatar Jun 02 '25 05:06 Vidit-Ostwal

Facing similar issues but I have configured a custom embedder from base class EmbeddingFunction.

Configuring in Agent like this:

Agent(
  ...
  embedder={
      "provider": "custom",
      "config": {
          "embedding_model": self.embedding,
      },
  }
)

Also, can we have proper documentation for Custom Embedder implementation and Custom Knowledge storage similar to Custom LLM.

lazzyms avatar Jun 02 '25 15:06 lazzyms

Can you also try passing the knowledge at the agent level once?

I have defined the knowledge parameter at the agent but the change is not available.

fzozyurt avatar Jun 02 '25 15:06 fzozyurt

Can you also try passing the knowledge at the agent level once?

I have defined the knowledge parameter at the agent but the change is not available.

change is not availabe? didn't get you.

Vidit-Ostwal avatar Jun 02 '25 15:06 Vidit-Ostwal

Can you also try passing the knowledge at the agent level once?

I have defined the knowledge parameter at the agent but the change is not available.

change is not availabe? didn't get you.

Try the problem in this way has been solved. Thank you for your support.

Code:

json_knowledge_source = JSONKnowledgeSource(
        file_paths=["log.json"]
    )

llm = LLM(
    model="hosted_vllm/deepseek-r1-14b",
    api_key="EMPTY",
    base_url="..."
)
research = Agent(
    role="Query Generator Agent",
    goal="You understand users and their preferences.Your goal is to answer questions about users. You are responsible for creating the most suitable query according to the user's problem.",
    backstory="You are a useful assistant that prepares elasticsearch querry according to the information transmitted by users.",
    allow_delegation=False,
    verbose=True,
    llm=llm,
    embedder=embeding,
    knowledge_sources=[json_knowledge_source]
)

research_task = Task(
    description="Understand the function of the transmitted text and create the appropriate Query. The Question is: {Question}",
    expected_output="Graylog Search query",
    agent=research
)

crew=Crew(
    agents=[research],
    tasks=[research_task],
    process=Process.sequential,
    verbose=True,
    llm=llm,
    embedder=embeding,
    knowledge_sources=[json_knowledge_source]
    )

fzozyurt avatar Jun 02 '25 21:06 fzozyurt

@fzozyurt, knowledge_source and embedder at both agent and crew level?

Vidit-Ostwal avatar Jun 03 '25 04:06 Vidit-Ostwal

@fzozyurt, knowledge_source and embedder at both agent and crew level?

When I do not add to CREW but only add to Agent, I get the following error. For this reason, I added to both sides. There may be a mistake here.

> > > Can you also try passing the knowledge at the agent level once?
> > 
> > 
> > I have defined the knowledge parameter at the agent but the change is not available.
> 
> change is not availabe? didn't get you.

I added Knowledge to the Agent section, but I get the following error.

**
╭──────────────────────────────────────────── Crew Execution Started ─────────────────────────────────────────────╮
│                                                                                                                 │
│  Crew Execution Started                                                                                         │
│  Name: crew                                                                                                     │
│  ID: 61beb795-20ff-4bfb-99ba-49b60d69c068                                                                       │
│                                                                                                                 │
│                                                                                                                 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
🚀 Crew: crew
└── 📋 Task: 5805d013-72be-4f35-98d0-44b7a71edc36
    Assigned to: User Understanding Assistant
    Status: ❌ Failed
    ├── ✅ Knowledge Retrieval Completed
    └── ✅ Knowledge Retrieval Completed
# Agent: User Understanding Assistant
## Task: Answer questions about users.The question is: What are the names of the users and what are their roles?
[1m[95m# Agent:[00m [1m[92mUser Understanding Assistant[00m

[1m[95m# Agent:[00m [1m[92mUser Understanding Assistant[00m
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# Agent: User Understanding Assistant An unknown error occurred. Please check the details below.
 Error details: maximum recursion depth exceeded while calling a Python object
# Agent: User Understanding Assistant
## Task: Answer questions about users.The question is: What are the names of the users and what are their roles?
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 Error details: maximum recursion depth exceeded in __instancecheck__
# Agent: User Understanding Assistant
## Task: Answer questions about users.The question is: What are the names of the users and what are their roles?
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# Agent: User Understanding Assistant An unknown error occurred. Please check the details below.
 Error details: maximum recursion depth exceeded while calling a Python object
╭───────────────────────────────────────────────── Task Failure ──────────────────────────────────────────────────╮
│                                                                                                                 │
│  Task Failed                                                                                                    │
│  Name: 5805d013-72be-4f35-98d0-44b7a71edc36                                                                     │
│  Agent: User Understanding Assistant                                                                            │
│                                                                                                                 │
│                                                                                                                 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭───────────────────────────────────────────────── Crew Failure ──────────────────────────────────────────────────╮
│                                                                                                                 │
│  Crew Execution Failed                                                                                          │
│  Name: crew                                                                                                     │
│  ID: 61beb795-20ff-4bfb-99ba-49b60d69c068                                                                       │
│                                                                                                                 │
│                                                                                                                 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
---------------------------------------------------------------------------
RecursionError                            Traceback (most recent call last)
/usr/local/lib/python3.11/dist-packages/IPython/core/formatters.py in catch_format_error(method, self, *args, **kwargs)
    223     try:
--> 224         r = method(self, *args, **kwargs)
    225     except NotImplementedError:

88 frames
RecursionError: maximum recursion depth exceeded

During handling of the above exception, another exception occurred:

RecursionError                            Traceback (most recent call last)
... last 11 frames repeated, from the frame below ...

RecursionError: maximum recursion depth exceeded while calling a Python object

During handling of the above exception, another exception occurred:

RecursionError                            Traceback (most recent call last)
RecursionError: maximum recursion depth exceeded

During handling of the above exception, another exception occurred:

RecursionError                            Traceback (most recent call last)
... last 11 frames repeated, from the frame below ...

RecursionError: maximum recursion depth exceeded in __instancecheck__

During handling of the above exception, another exception occurred:

RecursionError                            Traceback (most recent call last)
RecursionError: maximum recursion depth exceeded

During handling of the above exception, another exception occurred:

RecursionError                            Traceback (most recent call last)
RecursionError: maximum recursion depth exceeded

During handling of the above exception, another exception occurred:

RecursionError                            Traceback (most recent call last)
... last 11 frames repeated, from the frame below ...

/usr/local/lib/python3.11/dist-packages/rich/console.py in __exit__(self, exc_type, exc_value, traceback)
    862     def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
    863         """Exit buffer context."""
--> 864         self._exit_buffer()
    865 
    866     def begin_capture(self) -> None:

RecursionError: maximum recursion depth exceeded while calling a Python object

**

fzozyurt avatar Jun 03 '25 18:06 fzozyurt

By any chance are you using a .ipynb file for inference?

Vidit-Ostwal avatar Jun 03 '25 18:06 Vidit-Ostwal

By any chance are you using a .ipynb file for inference?

Yes, I am using codes for testing in ipynb. Could this be a problem? I can also try the process normally.

fzozyurt avatar Jun 03 '25 18:06 fzozyurt

I think there was a bug with version 0.121.0 with using of new integration with rich library. Can you upgrade the version or try the inference with the .py files?

Vidit-Ostwal avatar Jun 03 '25 18:06 Vidit-Ostwal

I think there was a bug with version 0.121.0 with using of new integration with rich library. Can you upgrade the version or try the inference with the .py files?

Thank you for your support. Yes, the problem was solved when I tried it in Python.

    json_knowledge_source = JSONKnowledgeSource(
        file_paths=["log.json"]
    )

    @agent
    def researcher(self) -> Agent:
        return Agent(
            config=self.agents_config['researcher'], # type: ignore[index]
            verbose=True,
            llm=llm,
            embedder=embeding,
            knowledge_sources=[self.json_knowledge_source]
        )

    # To learn more about structured task outputs,
    # task dependencies, and task callbacks, check out the documentation:
    # https://docs.crewai.com/concepts/tasks#overview-of-a-task
    @task
    def research_task(self) -> Task:
        return Task(
            config=self.tasks_config['research_task'], 
        )
    @crew
    def crew(self) -> Crew:
        """Creates the ResearchCrew crew"""
        # To learn how to add knowledge sources to your crew, check out the documentation:
        # https://docs.crewai.com/concepts/knowledge#what-is-knowledge

        return Crew(
            agents=self.agents, # Automatically created by the @agent decorator
            tasks=self.tasks, # Automatically created by the @task decorator
            llm=llm,
            process=Process.sequential,
            verbose=True,
        )

fzozyurt avatar Jun 03 '25 20:06 fzozyurt