Hello Team,
I am trying to do this agentic RAG. Always getting an error and not able to succeed. Has anyone tried this in local environment?
I have tried with docker localhost as well. I like to know how to make the vector db in local laptop and refer to it. If anyone tried this so far, pls let me know. Thanks!
@karthidec can you please share your code and the error you are facing?
from phi.agent import AgentKnowledge
from phi.vectordb.pgvector import PgVector
from phi.embedder.google import GeminiEmbedder
from dotenv import find_dotenv
from dotenv import load_dotenv
env_file = find_dotenv(".env")
load_dotenv(env_file)
embeddings = GeminiEmbedder().get_embedding("The quick brown fox jumps over the lazy dog.")
# Print the embeddings and their dimensions
print(f"Embeddings: {embeddings[:5]}")
print(f"Dimensions: {len(embeddings)}")
# Example usage:
knowledge_base = AgentKnowledge(
vector_db=PgVector(
db_url="postgresql+psycopg://ai:ai@localhost:5432/ai",
table_name="gemini_embeddings",
embedder=GeminiEmbedder(),
),
num_documents=2,
)
Error message:
Traceback (most recent call last):
File "/Users/467501/Desktop/GenAI-POC/Phidata/gemini_embedder.py", line 3, in <module>
from phi.embedder.google import GeminiEmbedder
File "/Users/467501/Desktop/GenAI-POC/Phidata/venv/lib/python3.9/site-packages/phi/embedder/google.py", line 14, in <module>
class GeminiEmbedder(Embedder):
File "/Users/467501/Desktop/GenAI-POC/Phidata/venv/lib/python3.9/site-packages/pydantic/_internal/_model_construction.py", line 226, in __new__
complete_model_class(
File "/Users/467501/Desktop/GenAI-POC/Phidata/venv/lib/python3.9/site-packages/pydantic/_internal/_model_construction.py", line 658, in complete_model_class
schema = cls.__get_pydantic_core_schema__(cls, handler)
File "/Users/467501/Desktop/GenAI-POC/Phidata/venv/lib/python3.9/site-packages/pydantic/main.py", line 702, in __get_pydantic_core_schema__
return handler(source)
File "/Users/467501/Desktop/GenAI-POC/Phidata/venv/lib/python3.9/site-packages/pydantic/_internal/_schema_generation_shared.py", line 84, in __call__
schema = self._handler(source_type)
File "/Users/467501/Desktop/GenAI-POC/Phidata/venv/lib/python3.9/site-packages/pydantic/_internal/_generate_schema.py", line 610, in generate_schema
schema = self._generate_schema_inner(obj)
File "/Users/467501/Desktop/GenAI-POC/Phidata/venv/lib/python3.9/site-packages/pydantic/_internal/_generate_schema.py", line 879, in _generate_schema_inner
return self._model_schema(obj)
File "/Users/467501/Desktop/GenAI-POC/Phidata/venv/lib/python3.9/site-packages/pydantic/_internal/_generate_schema.py", line 691, in _model_schema
{k: self._generate_md_field_schema(k, v, decorators) for k, v in fields.items()},
File "/Users/467501/Desktop/GenAI-POC/Phidata/venv/lib/python3.9/site-packages/pydantic/_internal/_generate_schema.py", line 691, in <dictcomp>
{k: self._generate_md_field_schema(k, v, decorators) for k, v in fields.items()},
File "/Users/467501/Desktop/GenAI-POC/Phidata/venv/lib/python3.9/site-packages/pydantic/_internal/_generate_schema.py", line 1071, in _generate_md_field_schema
common_field = self._common_field_schema(name, field_info, decorators)
File "/Users/467501/Desktop/GenAI-POC/Phidata/venv/lib/python3.9/site-packages/pydantic/_internal/_generate_schema.py", line 1263, in _common_field_schema
schema = self._apply_annotations(
File "/Users/467501/Desktop/GenAI-POC/Phidata/venv/lib/python3.9/site-packages/pydantic/_internal/_generate_schema.py", line 2056, in _apply_annotations
schema = get_inner_schema(source_type)
File "/Users/467501/Desktop/GenAI-POC/Phidata/venv/lib/python3.9/site-packages/pydantic/_internal/_schema_generation_shared.py", line 84, in __call__
schema = self._handler(source_type)
File "/Users/467501/Desktop/GenAI-POC/Phidata/venv/lib/python3.9/site-packages/pydantic/_internal/_generate_schema.py", line 2037, in inner_handler
schema = self._generate_schema_inner(obj)
File "/Users/467501/Desktop/GenAI-POC/Phidata/venv/lib/python3.9/site-packages/pydantic/_internal/_generate_schema.py", line 884, in _generate_schema_inner
return self.match_type(obj)
File "/Users/467501/Desktop/GenAI-POC/Phidata/venv/lib/python3.9/site-packages/pydantic/_internal/_generate_schema.py", line 986, in match_type
return self._match_generic_type(obj, origin)
File "/Users/467501/Desktop/GenAI-POC/Phidata/venv/lib/python3.9/site-packages/pydantic/_internal/_generate_schema.py", line 1014, in _match_generic_type
return self._union_schema(obj)
File "/Users/467501/Desktop/GenAI-POC/Phidata/venv/lib/python3.9/site-packages/pydantic/_internal/_generate_schema.py", line 1325, in _union_schema
choices.append(self.generate_schema(arg))
File "/Users/467501/Desktop/GenAI-POC/Phidata/venv/lib/python3.9/site-packages/pydantic/_internal/_generate_schema.py", line 610, in generate_schema
schema = self._generate_schema_inner(obj)
File "/Users/467501/Desktop/GenAI-POC/Phidata/venv/lib/python3.9/site-packages/pydantic/_internal/_generate_schema.py", line 884, in _generate_schema_inner
return self.match_type(obj)
File "/Users/467501/Desktop/GenAI-POC/Phidata/venv/lib/python3.9/site-packages/pydantic/_internal/_generate_schema.py", line 975, in match_type
return self._call_schema(obj)
File "/Users/467501/Desktop/GenAI-POC/Phidata/venv/lib/python3.9/site-packages/pydantic/_internal/_generate_schema.py", line 1818, in _call_schema
type_hints = _typing_extra.get_function_type_hints(function, globalns=globalns, localns=localns)
File "/Users/467501/Desktop/GenAI-POC/Phidata/venv/lib/python3.9/site-packages/pydantic/_internal/_typing_extra.py", line 730, in get_function_type_hints
type_hints[name] = eval_type_backport(value, globalns, localns, type_params)
File "/Users/467501/Desktop/GenAI-POC/Phidata/venv/lib/python3.9/site-packages/pydantic/_internal/_typing_extra.py", line 609, in eval_type_backport
return _eval_type_backport(value, globalns, localns, type_params)
File "/Users/467501/Desktop/GenAI-POC/Phidata/venv/lib/python3.9/site-packages/pydantic/_internal/_typing_extra.py", line 633, in _eval_type_backport
return _eval_type(value, globalns, localns, type_params)
File "/Users/467501/Desktop/GenAI-POC/Phidata/venv/lib/python3.9/site-packages/pydantic/_internal/_typing_extra.py", line 667, in _eval_type
return typing._eval_type( # type: ignore
File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/typing.py", line 290, in _eval_type
return t._evaluate(globalns, localns, recursive_guard)
File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/typing.py", line 546, in _evaluate
eval(self.__forward_code__, globalns, localns),
File "<string>", line 1, in <module>
NameError: name 'model_types' is not defined
Process finished with exit code 1
I am following the below page and have GOOGLE_API_KEY=xxx in .env file. I have psql up and running in my local machine and able to connect.
@karthidec we made a release that fixes this issue. You can update phidata pip install -U phidata and this should work fine now
Thanks for the update!
Now, i am seeing below error.
Embeddings: [0.032336954, -0.03396037, -0.027726775, -0.012424927, -0.011384341]
Dimensions: 768
Traceback (most recent call last):
File "/Users/467501/Desktop/GenAI-POC/Phidata/gemini_embedder.py", line 18, in <module>
vector_db=PgVector(
File "/Users/467501/Desktop/GenAI-POC/Phidata/venv/lib/python3.9/site-packages/phi/vectordb/pgvector/pgvector.py", line 105, in __init__
raise ValueError("Embedder.dimensions must be set.")
ValueError: Embedder.dimensions must be set.
Process finished with exit code 1
@karthidec the Gemini embedders expects dimension param