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[ "hwchase17", "langchain" ]
### Issue you'd like to raise. Hi, I am trying to stream the response from the llm back to the client by using a callback with a custom StreamHandler, but the on_llm_new_token also includes the output from the rephrase_question step. while the final response does not include the rephrased answer. I don't want the rephrased question to be present in the response that is streaming. the StreamHandler class is given below `class StreamHandler(BaseCallbackHandler): def __init__(self): self.text = "" def on_llm_new_token(self, token: str, **kwargs): old_text = self.text self.text += token # Calculate the new content since the last emission new_content = self.text[len(old_text) :] socketio.emit("update_response", {"response": new_content})` The qa-chain is defined as below: qa_chain = ConversationalRetrievalChain.from_llm( llm=chat, retriever=MyVectorStoreRetriever( vectorstore=vectordb, search_type="similarity_score_threshold", search_kwargs={"score_threshold": SIMILARITY_THRESHOLD, "k": 1}, ), return_source_documents=True, rephrase_question=False) response = qa_chain( { "question": user_input, "chat_history":chat_history, },callbacks=[stream_handler] ) ### Suggestion: _No response_
Issue: Rephrased question in included in the on_llm_new_token method while streaming the response from the LLM
https://api.github.com/repos/langchain-ai/langchain/issues/14703/comments
7
2023-12-14T08:47:45Z
2024-06-30T16:03:41Z
https://github.com/langchain-ai/langchain/issues/14703
2,041,224,728
14,703
[ "hwchase17", "langchain" ]
### Issue you'd like to raise. my code: only_recall_inputs = RunnableParallel({ "question": itemgetter('question'), "history": ????????, "docs": itemgetter('question') | retriever, }) just a simple chain I want the "history" part to be [] or '' how to do this? ### Suggestion: _No response_
Issue: How to set the Chain with valid/empty input
https://api.github.com/repos/langchain-ai/langchain/issues/14702/comments
1
2023-12-14T08:45:49Z
2024-03-21T16:06:32Z
https://github.com/langchain-ai/langchain/issues/14702
2,041,221,683
14,702
[ "hwchase17", "langchain" ]
### System Info python 3.10 langchain version:0.0.350 ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction when use elasticsearchStore add_doccuments The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() langchain 0.0.317 is ok upgrade langchain 0.0.350 exist error ### Expected behavior fix it
The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
https://api.github.com/repos/langchain-ai/langchain/issues/14701/comments
2
2023-12-14T08:24:32Z
2024-03-21T16:06:27Z
https://github.com/langchain-ai/langchain/issues/14701
2,041,187,377
14,701
[ "hwchase17", "langchain" ]
### System Info LangChain: 0.0.348 langchain-google-genai: 0.0.3 python: 3.11 os: macOS11.6 ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction langchain_google_genai.chat_models.ChatGoogleGenerativeAIError: Message of 'system' type not supported by Gemini. Please only provide it with Human or AI (user/assistant) messages. ### Expected behavior no error
Gemini not support SystemMessage and raise an error
https://api.github.com/repos/langchain-ai/langchain/issues/14700/comments
8
2023-12-14T07:07:13Z
2024-03-26T16:07:11Z
https://github.com/langchain-ai/langchain/issues/14700
2,041,050,685
14,700
[ "hwchase17", "langchain" ]
### Issue you'd like to raise. what is this issue, and how can i resolve it: ```python os.environ["AZURE_OPENAI_API_KEY"] = AZURE_OPENAI_API_KEY os.environ["AZURE_OPENAI_ENDPOINT"] = AZURE_OPENAI_ENDPOINT os.environ["OPENAI_API_TYPE"] = "azure" os.environ["OPENAI_API_VERSION"] = "2023-05-15" os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY embedding = OpenAIEmbeddings() COLLECTION_NAME = "network_team_documents" CONNECTION_STRING = PGVector.connection_string_from_db_params( driver=os.environ.get(DB_DRIVER, DB_DRIVER), host=os.environ.get(DB_HOST, DB_HOST), port=int(os.environ.get(DB_PORT, DB_PORT)), database=os.environ.get(DB_DB, DB_DB), user=os.environ.get(DB_USER, DB_USER), password=os.environ.get(DB_PASS, DB_PASS), ) store = PGVector( collection_name=COLLECTION_NAME, connection_string=CONNECTION_STRING, embedding_function=embedding, extend_existing=True, ) gpt4 = AzureChatOpenAI( azure_deployment="GPT4", openai_api_version="2023-05-15", ) retriever = store.as_retriever(search_type="similarity", search_kwargs={"k": 10}) qa_chain = RetrievalQA.from_chain_type(llm=gpt4, chain_type="stuff", retriever=retriever, return_source_documents=True) return qa_chain ``` ```python Traceback (most recent call last): File "/opt/network_tool/chatbot/views.py", line 21, in chat chat_object = create_session() File "/opt/network_tool/chatbot/chatbot_functions.py", line 95, in create_session store = PGVector( File "/opt/klevernet_venv/lib/python3.10/site-packages/langchain_community/vectorstores/pgvector.py", line 199, in __init__ self.__post_init__() File "/opt/klevernet_venv/lib/python3.10/site-packages/langchain_community/vectorstores/pgvector.py", line 207, in __post_init__ EmbeddingStore, CollectionStore = _get_embedding_collection_store() File "/opt/klevernet_venv/lib/python3.10/site-packages/langchain_community/vectorstores/pgvector.py", line 66, in _get_embedding_collection_store class CollectionStore(BaseModel): File "/opt/klevernet_venv/lib/python3.10/site-packages/sqlalchemy/orm/decl_api.py", line 195, in __init__ _as_declarative(reg, cls, dict_) File "/opt/klevernet_venv/lib/python3.10/site-packages/sqlalchemy/orm/decl_base.py", line 247, in _as_declarative return _MapperConfig.setup_mapping(registry, cls, dict_, None, {}) File "/opt/klevernet_venv/lib/python3.10/site-packages/sqlalchemy/orm/decl_base.py", line 328, in setup_mapping return _ClassScanMapperConfig( File "/opt/klevernet_venv/lib/python3.10/site-packages/sqlalchemy/orm/decl_base.py", line 578, in __init__ self._setup_table(table) File "/opt/klevernet_venv/lib/python3.10/site-packages/sqlalchemy/orm/decl_base.py", line 1729, in _setup_table table_cls( File "", line 2, in __new__ File "/opt/klevernet_venv/lib/python3.10/site-packages/sqlalchemy/util/deprecations.py", line 281, in warned return fn(*args, **kwargs) # type: ignore[no-any-return] File "/opt/klevernet_venv/lib/python3.10/site-packages/sqlalchemy/sql/schema.py", line 436, in __new__ return cls._new(*args, **kw) File "/opt/klevernet_venv/lib/python3.10/site-packages/sqlalchemy/sql/schema.py", line 468, in _new raise exc.InvalidRequestError( sqlalchemy.exc.InvalidRequestError: Table 'langchain_pg_collection' is already defined for this MetaData instance. Specify 'extend_existing=True' to redefine options and columns on an existing Table object. ``` ### Suggestion: _No response_
sqlalchemy.exc.InvalidRequestError: Table 'langchain_pg_collection' is already defined for this MetaData instance.
https://api.github.com/repos/langchain-ai/langchain/issues/14699/comments
15
2023-12-14T06:51:40Z
2023-12-27T19:12:07Z
https://github.com/langchain-ai/langchain/issues/14699
2,041,031,199
14,699
[ "hwchase17", "langchain" ]
### System Info Langchain version = 0.0.344 Python version = 3.11.5 ### Who can help? @hwchase17 @agola11 ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [X] Agents / Agent Executors - [X] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction Here is my code for connecting to Snowflake database and getting the tables and executing them in langchain SQLagent.. Howver i always get AttributeError: items. from snowflake.sqlalchemy import URL llm=AzureChatOpenAI(temperature=0.0,deployment_name=gpt-4-32k) snowflake_url = URL( account='xxxxx, user='xxxxxx', password='xxxxxx', database='xxxxx', schema='xxxxxx', warehouse='xxxxxxx' ) db = SQLDatabase.from_uri(snowflake_url, sample_rows_in_table_info=1, include_tables=['gc']) # Create the SQLDatabaseToolkit toolkit = SQLDatabaseToolkit(db=db, llm=llm) agent_executor = create_sql_agent( llm=llm, toolkit=toolkit, verbose=True, prefix=MSSQL_AGENT_PREFIX, format_instructions = MSSQL_AGENT_FORMAT_INSTRUCTIONS, agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION, top_k=30, early_stopping_method="generate", handle_parsing_errors = True, ) question = "List top 10 records from gc" response = agent_executor.run(question) Entering new AgentExecutor chain... Action: sql_db_list_tables Action Input: "" Observation: gc Thought:The 'gc' table is available in the database. I should now check the schema of the 'gc' table to understand its structure and the data it contains. Action: sql_db_schema Action Input: "gc" --------------------------------------------------------------------------- KeyError Traceback (most recent call last) File [c:\Anaconda_3\Lib\site-packages\sqlalchemy\sql\base.py:1150](file:///C:/Anaconda_3/Lib/site-packages/sqlalchemy/sql/base.py:1150), in ColumnCollection.__getattr__(self, key) [1149](file:///C:/Anaconda_3/Lib/site-packages/sqlalchemy/sql/base.py:1149) try: -> [1150](file:///C:/Anaconda_3/Lib/site-packages/sqlalchemy/sql/base.py:1150) return self._index[key] [1151](file:///C:/Anaconda_3/Lib/site-packages/sqlalchemy/sql/base.py:1151) except KeyError as err: KeyError: 'items' The above exception was the direct cause of the following exception: AttributeError Traceback (most recent call last) Cell In[66], [line 33](vscode-notebook-cell:?execution_count=66&line=33) [31](vscode-notebook-cell:?execution_count=66&line=31) from langchain.globals import set_debug [32](vscode-notebook-cell:?execution_count=66&line=32) set_debug(False) ---> [33](vscode-notebook-cell:?execution_count=66&line=33) response = agent_executor.run(question) File [~\AppData\Roaming\Python\Python311\site-packages\langchain\chains\base.py:507](https://file+.vscode-resource.vscode-cdn.net/c%3A/Users/W67529/Autogen/~/AppData/Roaming/Python/Python311/site-packages/langchain/chains/base.py:507), in Chain.run(self, callbacks, tags, metadata, *args, **kwargs) [505](https://file+.vscode-resource.vscode-cdn.net/c%3A/Users/W67529/Autogen/~/AppData/Roaming/Python/Python311/site-packages/langchain/chains/base.py:505) if len(args) != 1: [506](https://file+.vscode-resource.vscode-cdn.net/c%3A/Users/W67529/Autogen/~/AppData/Roaming/Python/Python311/site-packages/langchain/chains/base.py:506) raise ValueError("`run` supports only one positional argument.") --> [507](https://file+.vscode-resource.vscode-cdn.net/c%3A/Users/W67529/Autogen/~/AppData/Roaming/Python/Python311/site-packages/langchain/chains/base.py:507) return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[ [508](https://file+.vscode-resource.vscode-cdn.net/c%3A/Users/W67529/Autogen/~/AppData/Roaming/Python/Python311/site-packages/langchain/chains/base.py:508) _output_key [509](https://file+.vscode-resource.vscode-cdn.net/c%3A/Users/W67529/Autogen/~/AppData/Roaming/Python/Python311/site-packages/langchain/chains/base.py:509) ] [511](https://file+.vscode-resource.vscode-cdn.net/c%3A/Users/W67529/Autogen/~/AppData/Roaming/Python/Python311/site-packages/langchain/chains/base.py:511) if kwargs and not args: [512](https://file+.vscode-resource.vscode-cdn.net/c%3A/Users/W67529/Autogen/~/AppData/Roaming/Python/Python311/site-packages/langchain/chains/base.py:512) return self(kwargs, callbacks=callbacks, tags=tags, metadata=metadata)[ ... [201](file:///C:/Anaconda_3/Lib/site-packages/sqlalchemy/util/compat.py:201) # https://cosmicpercolator.com/2016/01/13/exception-leaks-in-python-2-and-3/ [202](file:///C:/Anaconda_3/Lib/site-packages/sqlalchemy/util/compat.py:202) # as the __traceback__ object creates a cycle [203](file:///C:/Anaconda_3/Lib/site-packages/sqlalchemy/util/compat.py:203) del exception, replace_context, from_, with_traceback AttributeError: items ### Expected behavior Should have executed the question.
SQLagent always giving me AttributeError: items for Snowflake tables
https://api.github.com/repos/langchain-ai/langchain/issues/14697/comments
8
2023-12-14T06:29:48Z
2024-03-21T16:06:17Z
https://github.com/langchain-ai/langchain/issues/14697
2,041,006,966
14,697
[ "hwchase17", "langchain" ]
### System Info **Environment Details** **Langchain version 0.0.336 Python 3.9.2rc1** **Error encountered while executing the sample code mentioned in the "Semi_structured_multi_modal_RAG_LLaMA2.ipynb" notebook from the cookbook.** File [c:\Users\PLNAYAK\Documents\Local_LLM_Inference\llms\lib\site-packages\unstructured\file_utils\filetype.py:551](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/unstructured/file_utils/filetype.py:551), in add_metadata_with_filetype.<locals>.decorator.<locals>.wrapper(*args, **kwargs) [549](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/unstructured/file_utils/filetype.py:549) @functools.wraps(func) ... --> [482](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/unstructured_inference/inference/layout.py:482) model = get_model(model_name, **kwargs) [483](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/unstructured_inference/inference/layout.py:483) if isinstance(model, UnstructuredObjectDetectionModel): [484](file:///C:/Users/PLNAYAK/Documents/Local_LLM_Inference/llms/lib/site-packages/unstructured_inference/inference/layout.py:484) detection_model = model **TypeError: get_model() got an unexpected keyword argument 'ocr_languages'** I would appreciate any assistance in resolving this issue. Thank you. ### Who can help? @eyurtsev ### Information - [X] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [X] Document Loaders - [X] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction from typing import Any from pydantic import BaseModel from unstructured.partition.pdf import partition_pdf # Get elements raw_pdf_elements = partition_pdf( filename=path + "Employee-Stock-Option-Plans-ESOP-Best-Practices-2.pdf",# Unstructured first finds embedded image blocks infer_table_structure=True, # Post processing to aggregate text once we have the title max_characters=4000, new_after_n_chars=3800, combine_text_under_n_chars=2000, image_output_dir_path=path, languages=['eng'], ) ### Expected behavior The notebook should run without any issues and produce the expected output as documented in the cookbook
TypeError: get_model() got an unexpected keyword argument 'ocr_languages'
https://api.github.com/repos/langchain-ai/langchain/issues/14696/comments
16
2023-12-14T06:18:44Z
2024-05-21T16:08:06Z
https://github.com/langchain-ai/langchain/issues/14696
2,040,991,652
14,696
[ "hwchase17", "langchain" ]
### System Info ### **SYSTEM INFO** LangChain version : 0.0.345 Python version : 3.9.6 ### **ISSUE** I create this custom function which will throw an error if the vectorestore cannot retrieve any relevant document ``` def check_threshold(inp, vecs): query = inp['question'] threshold = inp['threshold'] d = [doc for doc,score in vecs.similarity_search_with_relevance_scores(query) if score >= threshold] if len(d) < 1: raise Exception("Not found!") return "\n\n".join([x.page_content for x in d]) ``` I want to use another chain if the main chain fails by using `with_fallbacks` function in the main chain ``` main_chain = ({ "context" : lambda x: check_threshold(x, vecs), "question" : lambda x: x['question'] } | prompt | llm | StrOutputParser() ).with_fallbacks([fallback_chain]) ``` In the above code, the fallback_chain never gets triggered. **PS : The above code is just an example, the original code uses more complicated calculations with many exceptions raise in several custom functions. Therefore, It is not feasible to use built-in Python try-except error handler** ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [x] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [X] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [X] Chains - [X] Callbacks/Tracing - [ ] Async ### Reproduction ``` def check_threshold(inp, vecs): query = inp['question'] threshold = inp['threshold'] d = [doc for doc,score in vecs.similarity_search_with_relevance_scores(query) if score >= threshold] if len(d) < 1: raise Exception("Not found!") return "\n\n".join([x.page_content for x in d]) main_chain = ({ "context" : lambda x: check_threshold(x, vecs), "question" : lambda x: x['question'] } | prompt | llm | StrOutputParser() ).with_fallbacks([fallback_chain]) main_chain.invoke({"question":"Hello, good morning"}) ``` ### Expected behavior fallback_chain get triggered whenever the main_chain raise an exception
PYTHON ISSUE : Fallback does not catch exception in custom function using LCEL
https://api.github.com/repos/langchain-ai/langchain/issues/14695/comments
1
2023-12-14T04:54:06Z
2024-03-21T16:06:12Z
https://github.com/langchain-ai/langchain/issues/14695
2,040,901,181
14,695
[ "hwchase17", "langchain" ]
### Issue you'd like to raise. Recently, langchain released the google gemini independent package to connect the google gemini LLM capabilities. This is also the first independent package released by langchain, which is a very big progress and change. But I noticed that the name of this package is langchain-google-genai, which may not be very systematic. Perhaps, we can use python's namespace feature to manage all langchain-related packages. Documentation about namespace package: - https://packaging.python.org/guides/packaging-namespace-packages/ - https://www.python.org/dev/peps/pep-0420/ ### Suggestion: Use the namespace capability to manage and publish all independent packages of langchain. The specific directory structure is as follows: docs: https://packaging.python.org/en/latest/guides/packaging-namespace-packages/ ``` pyproject.toml # AND/OR setup.py, setup.cfg src/ langchain/ # namespace package # No __init__.py here. google-genai/ # Regular import packages have an __init__.py. __init__.py module.py ``` and then, you can use like: ```python import langchain.google-genai from langchain import google-genai # ... code ```
Issue: Use python namespace capabilities to manage standalone packages
https://api.github.com/repos/langchain-ai/langchain/issues/14694/comments
1
2023-12-14T03:49:26Z
2024-03-21T16:06:07Z
https://github.com/langchain-ai/langchain/issues/14694
2,040,852,615
14,694
[ "hwchase17", "langchain" ]
### Feature request With Gemini Pro going GA today (Dec. 13th). When can users of LangChain expect an update to use the new LLM? ### Motivation This will allow users of LangChain to use the latest LLM that Google is providing along with their safety settings. ### Your contribution I can try and help. Happy to contribute where needed
Google Gemini
https://api.github.com/repos/langchain-ai/langchain/issues/14671/comments
9
2023-12-13T19:02:58Z
2024-02-07T23:45:17Z
https://github.com/langchain-ai/langchain/issues/14671
2,040,302,557
14,671
[ "hwchase17", "langchain" ]
### System Info Langchain: 0.0.349 Langchain-community: v0.0.1 Langchain-core: 0.0.13 Python: 3.12 Platform: Mac OS ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [X] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction I have the following BaseModel and BaseTool classes created. ```python class TaskPost(BaseModel): """ TaskPost """ # noqa: E501 due_date: Optional[datetime] = Field(default=None, description="ISO 8601 Due date on the task. REQUIRED for scheduled tasks", alias="dueDate") duration: Optional[TaskDuration] = None status: Optional[StrictStr] = Field(default=None, description="Defaults to workspace default status.") auto_scheduled: Optional[AutoScheduledInfo] = Field(default=None, alias="autoScheduled") name: Annotated[str, Field(min_length=1, strict=True)] = Field(description="Name / title of the task") project_id: Optional[StrictStr] = Field(default=None, alias="projectId") workspace_id: StrictStr = Field(alias="workspaceId") description: Optional[StrictStr] = Field(default=None, description="Input as GitHub Flavored Markdown") priority: Optional[StrictStr] = 'MEDIUM' labels: Optional[List[StrictStr]] = None assignee_id: Optional[StrictStr] = Field(default=None, description="The user id the task should be assigned to", alias="assigneeId") __properties: ClassVar[List[str]] = ["dueDate", "duration", "status", "autoScheduled", "name", "projectId", "workspaceId", "description", "priority", "labels", "assigneeId"] model_config = { "populate_by_name": True, "validate_assignment": True } class CreateTaskTool(BaseTool): name = "create_task" description = ( """Use this to create a new task from all available args that you have. Always make sure date and time inputs are in ISO format""") args_schema: Type[BaseModel] = openapi_client.TaskPost verbose = True ``` The agent will use the alias names instead of the field name. i.e (workspaceId, dueDate) instead of (workspace_id, due_date) ```linux [tool/start] [1:chain:AgentExecutor > 7:tool:create_task] Entering Tool run with input: "{'name': 'Update code', 'workspaceId': 'xxxxxyyyyyzzzzz', 'dueDate': '2023-12-14T00:00:00'}" ``` When the agent calls `_parse_input` function from [langchian_core/tools.py and reaches line 247](https://github.com/langchain-ai/langchain/blob/14bfc5f9f477fcffff3f9aa564a864c5d5cd5300/libs/core/langchain_core/tools.py#L247) the results are filtered out because the results have the field names and the tool_input has the alias names which do not match. ``` CreateTaskTool -> _parse_input -> parse_obj -> result: due_date=datetime.datetime(2023, 12, 14, 0, 0) duration=None status=None auto_scheduled=None name='Update code' project_id=None workspace_id='xxxxxyyyyyzzzzz' description=None priority='MEDIUM' labels=None assignee_id=None CreateTaskTool -> _parse_input -> parse_obj -> result keys: dict_keys(['due_date', 'duration', 'status', 'auto_scheduled', 'name', 'project_id', 'workspace_id', 'description', 'priority', 'labels', 'assignee_id']) CreateTaskTool -> _parse_input -> parse_obj -> tool_input keys: dict_keys(['name', 'workspaceId', 'dueDate']) CreateTaskTool -> _parse_input -> parse_obj -> finalResults: {'name': 'Update code'} ``` ### Expected behavior ``` CreateTaskTool -> _parse_input -> parse_obj -> result keys: dict_keys(['due_date', 'duration', 'status', 'auto_scheduled', 'name', 'project_id', 'workspace_id', 'description', 'priority', 'labels', 'assignee_id']) CreateTaskTool -> _parse_input -> parse_obj -> tool_input keys: dict_keys(['name', 'workspaceId', 'dueDate']) CreateTaskTool -> _parse_input -> finalResults: {'name': 'Update code','workspaceId': 'xxxxxyyyyyzzzzz', 'dueDate': '2023-12-14T00:00:00'} or CreateTaskTool -> _parse_input -> finalResults: {'name': 'Update code','workspace_Id': 'xxxxxyyyyyzzzzz', 'due_date': '2023-12-14T00:00:00'} ```
LangChain Agent bug when parsing tool inputs that use alias field names
https://api.github.com/repos/langchain-ai/langchain/issues/14663/comments
1
2023-12-13T16:51:16Z
2024-03-20T16:06:48Z
https://github.com/langchain-ai/langchain/issues/14663
2,040,113,293
14,663
[ "hwchase17", "langchain" ]
### System Info LangChain version 0.0.348 Python version 3.10 Operating System MacOS Monterey version 12.6 SQLAlchemy version 2.0.23 ### Who can help? @hwchase17 @agola11 ### Information - [ ] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [X] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [X] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction ```# Connect to db # Create an SQLAlchemy engine engine = create_engine("mysql+mysqlconnector://user:pass@host/database") # Test the database connection try: # Connect and execute a simple query with engine.connect() as connection: query = text("SELECT 1") result = connection.execute(query) for row in result: print("Connection successful, got row:", row) except Exception as e: print("Error connecting to database:", e) # Create an instance of SQLDatabase db = SQLDatabase(engine) # using Llama2 llm = LlamaCpp( model_path="/path_to/llama-2-7b.Q4_K_M.gguf", verbose=True, n_ctx=2048) # using Default and Suffix prompt template PROMPT = PromptTemplate( input_variables=["input", "table_info", "dialect", "top_k"], template=_DEFAULT_TEMPLATE + PROMPT_SUFFIX, ) db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True, return_sql=True, prompt=PROMPT) langchain.debug = True response = db_chain.run(formatted_prompt) print(response ### Expected behavior Expected Behavior: Expected to generate SQL queries without errors. Actual Behavior: Received TypeError: wrong type and context window exceedance errors. File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/llama_cpp/llama.py", line 1325, in _create_completion f"Requested tokens ({len(prompt_tokens)}) exceed context window of {llama_cpp.llama_n_ctx(self._ctx)}" File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/llama_cpp/llama_cpp.py", line 612, in llama_n_ctx return _lib.llama_n_ctx(ctx) ctypes.ArgumentError: argument 1: <class 'TypeError'>: wrong type```
SQLDatabaseChain with LlamaCpp Llama2 "Chain Run Errored With Error: ArgumentError: <class 'TypeError'>: wrong type"
https://api.github.com/repos/langchain-ai/langchain/issues/14660/comments
2
2023-12-13T16:38:34Z
2024-03-20T16:06:44Z
https://github.com/langchain-ai/langchain/issues/14660
2,040,091,551
14,660
[ "hwchase17", "langchain" ]
### Issue you'd like to raise. I am attempting to call an instance of ConversationalRetrieverChain with a list of dictionary objects that I've pre-processed with a similarity search and cohere reranker. I've created an extension of BaseRetriever in order to pass my list of dictionary objects to the "retriever=" parameter. However when my extended class instantiates, I get an error saying "seai_retriever object has no field "documents". My code is below. What am I doing wrong? ``` from langchain.schema.retriever import BaseRetriever from langchain.schema.document import Document from langchain.callbacks.manager import CallbackManagerForRetrieverRun from typing import List class seai_retriever(BaseRetriever): def __init__(self, documents): self.documents = documents def retrieve(self, query, top_n=10): retrieved_docs = [doc for doc in self.documents if query.lower() in doc['content'].lower()] retrieved_docs = sorted(retrieved_docs, key=lambda x: x['content'].find(query), reverse=True)[:top_n] return retrieved_docs def _get_relevant_documents(self, query: str, *, run_manager: CallbackManagerForRetrieverRun) -> List[Document]: retrieved_docs = [doc for doc in self.documents if query.lower() in doc['content'].lower()] retrieved_docs = sorted(retrieved_docs, key=lambda x: x['content'].find(query), reverse=True)[:top_n] return retrieved_docs ``` ### Suggestion: _No response_
Getting "object has no field "documents" error with extended BaseRetriever classs
https://api.github.com/repos/langchain-ai/langchain/issues/14659/comments
5
2023-12-13T16:29:16Z
2024-06-01T00:07:37Z
https://github.com/langchain-ai/langchain/issues/14659
2,040,074,533
14,659
[ "hwchase17", "langchain" ]
### System Info Mac Studio M1 Max 32GB macOS 14.1.2 Using rye Python 3.11.6 langchain==0.0.350 langchain-community==0.0.2 langchain-core==0.1.0 ### Who can help? _No response_ ### Information - [x] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [X] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction **Error** ``` File "/venv/lib/python3.11/site-packages/langchain_community/document_loaders/xml.py", line 41, in _get_elements from unstructured.partition.xml import partition_xml ImportError: cannot import name 'partition_xml' from partially initialized module 'unstructured.partition.xml' (most likely due to a circular import) (/venv/lib/python3.11/site-packages/unstructured/partition/xml.py) ``` I tried to load XML document like this link(from langchain document) https://python.langchain.com/docs/integrations/document_loaders/xml ``` from langchain.document_loaders import UnstructuredXMLLoader loader = UnstructuredXMLLoader( "aaa.xml", ) docs = loader.load() docs[0] ``` ### Expected behavior no circular import
UnstructuredXMLLoader import error (circular import)
https://api.github.com/repos/langchain-ai/langchain/issues/14658/comments
1
2023-12-13T16:02:17Z
2024-03-20T16:06:38Z
https://github.com/langchain-ai/langchain/issues/14658
2,040,021,283
14,658
[ "hwchase17", "langchain" ]
### Issue you'd like to raise. Hi, I'm trying to use StreamingStdOutCallbackHandler for a conversation chain. But it prints out the memory after the response. Is there a way to not printing memory without using an agent? ### Suggestion: _No response_
Issue: streaming issues
https://api.github.com/repos/langchain-ai/langchain/issues/14656/comments
2
2023-12-13T15:47:29Z
2024-01-10T03:38:15Z
https://github.com/langchain-ai/langchain/issues/14656
2,039,990,933
14,656
[ "hwchase17", "langchain" ]
### Issue you'd like to raise. I'm currently working on a project where I need to fetch all the sub-URLs from a website using Langchain. How can we achieve this, below is my code ` loader = UnstructuredURLLoader(urls=urls) urlDocument = loader.load() text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=50) texts = text_splitter.split_documents(documents=urlDocument)` ### Suggestion: _No response_
Issue: I'm currently working on a project where I need to fetch all the sub-URLs from a website using Langchain.
https://api.github.com/repos/langchain-ai/langchain/issues/14651/comments
3
2023-12-13T13:27:30Z
2024-03-27T16:08:12Z
https://github.com/langchain-ai/langchain/issues/14651
2,039,714,207
14,651
[ "hwchase17", "langchain" ]
### System Info Langchain Version = 0.0.311 Python Version = 3.9 Tried it on my local system as well on Company's hosted Jupyter Hub as well ### Who can help? @eyurtsev @agola11 ### Information - [X] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [X] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction ``` from langchain.text_splitter import TokenTextSplitter token_splitter_model_name = "gpt-3.5-turbo" SPLIT_CHUNK_SIZE = 1024 CHUNK_OVERLAP = 256 text_splitter = TokenTextSplitter.from_tiktoken_encoder(model_name=token_splitter_model_name, chunk_size=SPLIT_CHUNK_SIZE , chunk_overlap = CHUNK_OVERLAP) blog_content= ' your text here' blog_splits=text_splitter.split_text(blog_content) ``` ### Expected behavior The ways this token text splitter works , isn't how it is intended to work . For Example :- When specified chunk_size = 1024 and overlap = 256 with input text tokens = 991 , it made two chunks ; first = token[0 : 991] second = token[768 : 991] but logically it should work this ways . If input text token >1024 , there should be two chunks . first = token[0 : 1024] second = token[768 : ] and so on .............. If input text token <=1024 , there should be one chunk only . first = token[ 0 : ] More details on this issue raised previously :- https://github.com/langchain-ai/langchain/issues/5897
Bug in Text splitting while using langchain.text_splitter.split_text_on_tokens¶
https://api.github.com/repos/langchain-ai/langchain/issues/14649/comments
3
2023-12-13T10:31:27Z
2024-03-25T16:07:21Z
https://github.com/langchain-ai/langchain/issues/14649
2,039,411,662
14,649
[ "hwchase17", "langchain" ]
### System Info from langchain.document_transformers import DoctranTextTranslator from langchain.schema import Document documents = [Document(page_content=sample_text)] qa_translator = DoctranTextTranslator(language="spanish") translated_document = await qa_translator.atransform_documents(documents) TypeError Traceback (most recent call last) [<ipython-input-18-c526f9c55393>](https://localhost:8080/#) in <cell line: 8>() 6 openai_api_model="gpt-3.5-turbo", language="chinese") 7 ----> 8 translated_document = await qa_translator.atransform_documents(documents) 9 10 [/usr/local/lib/python3.10/dist-packages/langchain_community/document_transformers/doctran_text_translate.py](https://localhost:8080/#) in atransform_documents(self, documents, **kwargs) 61 ] 62 for i, doc in enumerate(doctran_docs): ---> 63 doctran_docs[i] = await doc.translate(language=self.language).execute() 64 return [ 65 Document(page_content=doc.transformed_content, metadata=doc.metadata) TypeError: object Document can't be used in 'await' expression ### Who can help? _No response_ ### Information - [X] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [X] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction test ### Expected behavior test
TypeError: object Document can't be used in 'await' expression
https://api.github.com/repos/langchain-ai/langchain/issues/14645/comments
1
2023-12-13T07:52:41Z
2024-03-20T16:06:28Z
https://github.com/langchain-ai/langchain/issues/14645
2,039,150,807
14,645
[ "hwchase17", "langchain" ]
I have two code. The 1st code is to load PDF documents and use ParentDocumentRetriever to add_documents() and save vectorstore to local disk. The 2nd code is to load vectorstore and call ParentDocumentRetriever.get_relevant_documents The problem is, the ParentDocumentRetriever.get_relevant_documents() got empty result. Any idea?? What is the inMemoryStore for?? Here are the two codes: ### the 1st code: LoadDocumentsAndSaveVectorstore.py ``` loader = DirectoryLoader('data/', glob='**/*.pdf', loader_cls=PyPDFLoader) docs = loader.load() store = InMemoryStore() parent_splitter = RecursiveCharacterTextSplitter(chunk_size=1000) child_splitter = RecursiveCharacterTextSplitter(chunk_size=128, chunk_overlap=64) texts = [""] vectorstore = FAISS.from_texts(texts, embedding_function) retriever = ParentDocumentRetriever( vectorstore=vectorstore, docstore=store, child_splitter=child_splitter, parent_splitter=parent_splitter, ) retriever.add_documents(docs, ids=None) vectorstore.save_local("ppstore") print("save data to ppstore") ``` The 1st code works fine. The 2nd file is to load the vectorstore and query. The strange thing is, the **retriever1.get_relevant_documents(query)** got empty result. ### the 2nd code: load vectorstore and use retriever to get relevant_documents ``` print("load ppstore") store = InMemoryStore() parent_splitter = RecursiveCharacterTextSplitter(chunk_size=1000) child_splitter = RecursiveCharacterTextSplitter(chunk_size=128, chunk_overlap=64) db = FAISS.load_local("ppstore", embedding_function) retriever1 = ParentDocumentRetriever( vectorstore=db, docstore=store, child_splitter=child_splitter, parent_splitter=parent_splitter, ) query = "Please describe Sardina dataset" print("query:", query) sub_docs = db.similarity_search(query) print("=== check small chunk ===") print(sub_docs[0].page_content) print(len(sub_docs[0].page_content)) ## this respond OK, the len is a little bit smaller than 128 retrieved_docs = retriever1.get_relevant_documents(query) ## I got empty result print("=== check larger chunk ===") print(retrieved_docs[0].page_content) print(len(retrieved_docs[0].page_content)) ```
ParentDocumentRetriever.get_relevant_documents() got empty result
https://api.github.com/repos/langchain-ai/langchain/issues/14643/comments
5
2023-12-13T07:31:59Z
2024-01-11T20:34:16Z
https://github.com/langchain-ai/langchain/issues/14643
2,039,122,156
14,643
[ "hwchase17", "langchain" ]
### System Info # Dependency Versions langchain==0.0.349 langchain-community==0.0.1 langchain-core==0.0.13 openai==1.3.8 # Python Version Python 3.11.4 # Redis Stack Version redis-stack-server 6.2.0 ### Who can help? _No response_ ### Information - [X] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [X] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction from langchain.globals import set_llm_cache from langchain.cache import RedisSemanticCache from langchain.embeddings import OpenAIEmbeddings from langchain_community.chat_models import ChatOpenAI from redis import Redis import time llm = ChatOpenAI(openai_api_key='<OPENAI_API_KEY>', model_name='gpt-3.5-turbo') cache = RedisSemanticCache(redis_url="redis://localhost:6379", embedding=OpenAIEmbeddings(openai_api_key='<OPENAI_API_KEY>'), score_threshold=0.95) set_llm_cache(cache) start = time.time() response = llm.predict("""Tell me about USA in only two sentences""") print(time.time()-start) print(response) start = time.time() response = llm.predict("""Tell me about INDIA in only two sentences""") print(time.time()-start) print(response) start = time.time() response = llm.predict("""What is LLMs in the context of GEN AI ?""") print(time.time()-start) print(response) ### Expected behavior As the score_threshold is set to 0.98, I expect all the three prompts to give three different responses. But we are getting one response for all the three prompts. Output from running the script : 4.252941131591797 The United States of America is a federal republic consisting of 50 states, a federal district, five major self-governing territories, and various possessions. It is a diverse and influential country known for its cultural and economic power on the global stage. 0.3903520107269287 The United States of America is a federal republic consisting of 50 states, a federal district, five major self-governing territories, and various possessions. It is a diverse and influential country known for its cultural and economic power on the global stage. 0.6625611782073975 The United States of America is a federal republic consisting of 50 states, a federal district, five major self-governing territories, and various possessions. It is a diverse and influential country known for its cultural and economic power on the global stage.
(RedisSemanticCache + ChatOpenAI + OpenAIEmbeddings) - Not working as expected - Wanted to understand, if I am doing something wrong here.
https://api.github.com/repos/langchain-ai/langchain/issues/14640/comments
2
2023-12-13T06:34:44Z
2024-04-25T16:12:16Z
https://github.com/langchain-ai/langchain/issues/14640
2,039,051,024
14,640
[ "hwchase17", "langchain" ]
### Issue you'd like to raise. Issue 1: I am working on the summarization using the stuff and map-reduce of the Langchain. I have integrated it with the AWS Bedrock's anthropic llm which has a token limit of 100000. It is working fine but when the pdf with 40000 tokens is passed, the bedrock is throwing an error: i) with VPN connected: An error occurred: Error raised by bedrock service: Could not connect to the endpoint URL: "https://bedrock-runtime.us-east-1.amazonaws.com/model/anthropic.claude-v2/invoke". ii) without VPN connected: An error occurred: Error raised by bedrock service: An error occurred (ExpiredTokenException) when calling the InvokeModel operation: The security token included in the request is expired Any reason why this is happening? thanks in advance! Issue 2: 2) map-reduce takes a lot of time to produce the summary for the 40000+ tokens documents when the anthropic threshold is reduced to 40000. Sometimes it is taking a lot of time but erroring out. Any help is appreciated. Thanks in advance! ### Suggestion: _No response_
Issue: bedrock is throwing an error for the langchain stuff method using the anthropic model for the summarization.
https://api.github.com/repos/langchain-ai/langchain/issues/14639/comments
1
2023-12-13T05:48:17Z
2023-12-15T07:47:12Z
https://github.com/langchain-ai/langchain/issues/14639
2,039,002,274
14,639
[ "hwchase17", "langchain" ]
### System Info Hello, after forking and cloning the repo on my machine, I tried to open it using docker and specifically in VS Code with the option to "Reopen in Container". While building, the final command of [dev.Dockerfile](https://github.com/langchain-ai/langchain/blob/ca7da8f7ef9bc7a613ff07279c4603cad5fd175a/libs/langchain/dev.Dockerfile#L44) resulted in the following error: ```logs #0 1.241 Directory ../core does not exist ``` After investigating, I found out that the issue lies in [pyproject.toml](https://github.com/langchain-ai/langchain/blob/ca7da8f7ef9bc7a613ff07279c4603cad5fd175a/libs/langchain/pyproject.toml) which is using relative paths like `../core` and `../community` in some occasions. Additionally, even after replacing `../` with `libs/` (which I am not sure if it breaks something else), the actual `core` and `community` directories are never copied over in [dev.Dockerfile](https://github.com/langchain-ai/langchain/blob/ca7da8f7ef9bc7a613ff07279c4603cad5fd175a/libs/langchain/dev.Dockerfile). These should also be copied in the created docker container, similarly to [line 41](https://github.com/langchain-ai/langchain/blob/ca7da8f7ef9bc7a613ff07279c4603cad5fd175a/libs/langchain/dev.Dockerfile#L41). After making these two changes, the container was successfully built. I'll check out whether the change of paths in pyproject.toml is affecting any other files, and if not I will create a PR for this. ### Who can help? _No response_ ### Information - [X] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction Steps to reproduce: 1. Fork and clone the repo on your machine 2. Open it with VS Code (with Dev Containers extension installed) 3. Run the VS Code command: "Dev Containers: Rebuild Container" ### Expected behavior Build the development docker container without errors
Dockerfile issues when trying to build the repo using .devcontainer
https://api.github.com/repos/langchain-ai/langchain/issues/14631/comments
3
2023-12-12T23:17:45Z
2023-12-28T16:25:05Z
https://github.com/langchain-ai/langchain/issues/14631
2,038,690,605
14,631
[ "hwchase17", "langchain" ]
### Feature request Azure OpenAI now previews the DALLE-3 model. Today, DALLEAPIWrapper only supports the openai API. ### Motivation My customers are using Azure OpenAI and would like to use DALL-E-3 in their solutions. ### Your contribution PR may not be possible but I'd like to help anyway I can.
DALLEAPIWrapper to support Azure OpenAI
https://api.github.com/repos/langchain-ai/langchain/issues/14625/comments
2
2023-12-12T22:22:43Z
2024-03-20T16:06:23Z
https://github.com/langchain-ai/langchain/issues/14625
2,038,636,476
14,625
[ "hwchase17", "langchain" ]
### Feature request Add first-class support for Vertex AI Endpoints in Langchain. This would involve providing a similar interface to the existing SageMakerEndpoint class, allowing users to easily connect to and interact with Vertex AI Endpoints. ### Motivation Although VertexAIModelGarden already exist, there may be instances where users require custom models with unique input and output formats. To address this need, a more versatile class could be developed, upon which VertexAIModelGarden could be built. This would allow for seamless integration of custom models without compromising the functionality of the existing Model Garden class. ### Your contribution The implementation taking inspiration from SageMakerEndpoint if pertinent.
Add support for Vertex AI Endpoint
https://api.github.com/repos/langchain-ai/langchain/issues/14622/comments
1
2023-12-12T20:35:10Z
2024-03-19T16:06:32Z
https://github.com/langchain-ai/langchain/issues/14622
2,038,512,133
14,622
[ "hwchase17", "langchain" ]
### Feature request Hi, it seems the only DocStore available are InMemory or Google. I'd like to submit a feature request for an S3DocStore. ### Motivation Many people have raised issues related to limited DocStore options.
S3DocStore
https://api.github.com/repos/langchain-ai/langchain/issues/14616/comments
3
2023-12-12T18:55:18Z
2024-04-10T16:11:48Z
https://github.com/langchain-ai/langchain/issues/14616
2,038,377,269
14,616
[ "hwchase17", "langchain" ]
### System Info langchain Version: 0.0.348 Python: 3.9.16 Docs suggests to use proxy entry as follows but it does not work: from slacktoolkit import SlackToolkit # Proxy settings proxies = { 'http': 'http://proxy.example.com:8080', 'https': 'https://proxy.example.com:8080' } # Initialize SlackToolkit with proxy slack_toolkit = SlackToolkit(proxies=proxies) ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [x] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction # Proxy settings proxies = { 'http': 'your_proxy', 'https': 'your_proxy' } # Initialize SlackToolkit with proxy toolkit = SlackToolkit(proxies=proxies) tools = toolkit.get_tools() llm = OpenAI(temperature=0) agent = initialize_agent( tools=toolkit.get_tools(), llm=llm, verbose=True, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, ) agent.run("Send a greeting to my coworkers in the #slack1 channel. Your name is chatbot. Set the sender name as chatbot.") ### Expected behavior slack message sent in #slack1 channel
SlackToolkit() does not support proxy configuration
https://api.github.com/repos/langchain-ai/langchain/issues/14608/comments
2
2023-12-12T17:16:53Z
2023-12-15T04:00:14Z
https://github.com/langchain-ai/langchain/issues/14608
2,038,224,081
14,608
[ "hwchase17", "langchain" ]
### System Info Not relevant ### Who can help? @hwchase17 @agola11 ### Information - [X] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [X] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction Use the QA With Sources chain with the default prompt. If the chain type is `stuff` or `map_reduce` the default prompts used are [this](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/chains/qa_with_sources/stuff_prompt.py) and [this](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/chains/qa_with_sources/map_reduce_prompt.py) respectively. These files are massive, and easily adds a 1000+ tokens to every request. In models like PALM2, there's barely any tokens left for other questions. ### Expected behavior These prompts should be much more short, even if as a sample input.
QaWithSources default prompt is massive
https://api.github.com/repos/langchain-ai/langchain/issues/14596/comments
4
2023-12-12T13:40:37Z
2024-03-20T16:06:19Z
https://github.com/langchain-ai/langchain/issues/14596
2,037,780,695
14,596
[ "hwchase17", "langchain" ]
### Feature request As of the current implementation of the QA with sources chain, if `return_source_documents` is set to `True`, [all the sources](https://github.com/langchain-ai/langchain/blob/76905aa043e3e604b5b34faf5e91d0aedb5ed6dd/libs/langchain/langchain/chains/qa_with_sources/base.py#L165) that are retrieved from the vector DB are returned. The `sources` field returns a list of file names that were used by the LLM. I propose we could do something like this 1. Assign each `Document` a unique UUID as the `source` before passing it to the LLM. 2. Once the LLM returns the relevant sources, we can backmap this to the actual `Document`s that were used by the LLM as opposed to getting just the filename. ### Motivation This information seems vastly more useful than the entire response from the vector DB. For our current use cases, we've ended up overriding these functions to add this functionality. ### Your contribution I can raise a PR adding this if this is something that you'd find useful as well. An additional issue to report, the `_split_sources` [function](https://github.com/langchain-ai/langchain/blob/76905aa043e3e604b5b34faf5e91d0aedb5ed6dd/libs/langchain/langchain/chains/qa_with_sources/base.py#L124) splits at the first instance of `SOURCE` which seems to be a bit problematic. I can fix this to split at the last occurence.
Sources returned in QaWithSources can be optimised
https://api.github.com/repos/langchain-ai/langchain/issues/14595/comments
1
2023-12-12T13:30:30Z
2024-03-19T16:06:24Z
https://github.com/langchain-ai/langchain/issues/14595
2,037,760,278
14,595
[ "hwchase17", "langchain" ]
### Feature request How to use custom tracing tool like opentelemetry or tempo ### Motivation If I don't want to use LangSmith ### Your contribution N/A
How to use custom tracing tool like opentelemetry or tempo
https://api.github.com/repos/langchain-ai/langchain/issues/14594/comments
1
2023-12-12T12:34:07Z
2024-03-19T16:06:17Z
https://github.com/langchain-ai/langchain/issues/14594
2,037,660,794
14,594
[ "hwchase17", "langchain" ]
### System Info OS: Apple M1 Max ______________________ Name: langchain Version: 0.0.349 Summary: Building applications with LLMs through composability Home-page: https://github.com/langchain-ai/langchain Author: Author-email: License: MIT Requires: aiohttp, async-timeout, dataclasses-json, jsonpatch, langchain-community, langchain-core, langsmith, numpy, pydantic, PyYAML, requests, SQLAlchemy, tenacity Required-by: ### Who can help? @hwchase17 @agola11 ### Information - [X] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [X] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction Steps to reproduce: I have followed the instructions provided here : https://python.langchain.com/docs/integrations/llms/llamacpp. Though not able inference it correctly. Model path : https://huggingface.co/TheBloke/deepseek-coder-6.7B-instruct-GGUF ``` from langchain.callbacks.manager import CallbackManager from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler from langchain.chains import LLMChain, QAGenerationChain from langchain.llms import LlamaCpp from langchain.prompts import PromptTemplate template = """Question: {question} Answer: Let's work this out in a step by step way to be sure we have the right answer.""" prompt = PromptTemplate(template=template, input_variables=["question"]) callback_manager = CallbackManager([StreamingStdOutCallbackHandler()]) n_gpu_layers = 1 # Change this value based on your model and your GPU VRAM pool. n_batch = 512 # Should be between 1 and n_ctx, consider the amount of VRAM in your GPU. llm = LlamaCpp( model_path="../models/deepcoder-gguf/deepseek-coder-6.7b-instruct.Q2_K.gguf", n_gpu_layers=n_gpu_layers, max_tokens=2000, top_p=1, n_batch=n_batch, callback_manager=callback_manager, f16_kv=True, verbose=True, # Verbose is required to pass to the callback manager ) llm( "Question: Write python program to add two numbers ? Answer:" ) ``` Result: ` < """"""""""""""""""""""/"` Requesting you to look into it. Please let me know in case you need more information. Thank you. I have tried the same model file with **[llama-cpp-python](https://github.com/abetlen/llama-cpp-python)** package and it works as expected. Please find below the code that I have tried: ``` import json import time from llama_cpp import Llama n_gpu_layers = 1 # Change this value based on your model and your GPU VRAM pool. n_batch = 512 llm = Llama(model_path="../models/deepcoder-gguf/deepseek-coder-6.7b-instruct.Q5_K_M.gguf" , chat_format="llama-2", n_gpu_layers=n_gpu_layers,n_batch=n_batch) start_time = time.time() pp = llm.create_chat_completion( messages = [ {"role": "system", "content": "You are an python language assistant."}, { "role": "user", "content": "Write quick sort ." } ]) end_time = time.time() print("execution time:", {end_time - start_time}) print(pp["choices"][0]["message"]["content"]) ``` Output : ``` ## Quick Sort Algorithm in Python Here is a simple implementation of the quicksort algorithm in Python: ```python def partition(arr, low, high): i = (low-1) # index of smaller element pivot = arr[high] # pivot for j in range(low , high): if arr[j] <= pivot: i += 1 arr[i],arr[j] = arr[j],arr[i] arr[i+1],arr[high] = arr[high],arr[i+1] return (i+1) def quickSort(arr, low, high): if low < high: pi = partition(arr,low,high) quickSort(arr, low, pi-1) quickSort(arr, pi+1, high) # Test the code n = int(input("Enter number of elements in array: ")) print("Enter elements: ") arr = [int(input()) for _ in range(n)] quickSort(arr,0,n-1) print ("Sorted array is:") for i in range(n): print("%d" %arr[i]), This code first defines a helper function `partition()` that takes an array and two indices. It then rearranges the elements of the array so that all numbers less than or equal to the pivot are on its left, while all numbers greater than the pivot are on its right. The `quickSort()` function is then defined which recursively applies this partitioning process until the entire array is sorted. The user can input their own list of integers and the program will output a sorted version of that list. [/code] Conclusion In conclusion, Python provides several built-in functions for sorting lists such as `sort()` or `sorted()` but it's also possible to implement quick sort algorithm from scratch using custom function. This can be useful in situations where you need more control over the sorting process or when dealing with complex data structures. ``` ### Expected behavior It should inference the model just like the native [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) package.
Not able to inference deepseek-coder-6.7b-instruct.Q5_K_M.gguf
https://api.github.com/repos/langchain-ai/langchain/issues/14593/comments
6
2023-12-12T11:20:20Z
2024-05-25T13:36:24Z
https://github.com/langchain-ai/langchain/issues/14593
2,037,539,816
14,593
[ "hwchase17", "langchain" ]
### Issue you'd like to raise. i have created a chatbot to chat with the sql database using openai and langchain, but how to store or output data into excel using langchain. I got some idea from chatgpt which i have integrated with my code, but there is an error while importing the modules below is my code import pandas as pd import sqlalchemy as sal import os, sys, openai import constants from langchain.sql_database import SQLDatabase from langchain.llms.openai import OpenAI from langchain_experimental.sql import SQLDatabaseChain from sqlalchemy import create_engine # import ChatOpenAI from langchain.chat_models import ChatOpenAI from typing import List, Optional from langchain.agents.agent_toolkits import SQLDatabaseToolkit from langchain.callbacks.manager import CallbackManagerForToolRun from langchain.chat_models import ChatOpenAI from langchain_experimental.plan_and_execute import ( PlanAndExecute, load_agent_executor, load_chat_planner, ) from langchain.sql_database import SQLDatabase from langchain.text_splitter import TokenTextSplitter from langchain.tools import BaseTool from langchain.tools.sql_database.tool import QuerySQLDataBaseTool from secret_key import openapi_key from langchain import PromptTemplate from langchain.models import ChatGPTClient from langchain.utils import save_conversation os.environ['OPENAI_API_KEY'] = openapi_key def chat(question): from urllib.parse import quote_plus server_name = constants.server_name database_name = constants.database_name username = constants.username password = constants.password encoded_password = quote_plus(password) connection_uri = f"mssql+pyodbc://{username}:{encoded_password}@{server_name}/{database_name}?driver=ODBC+Driver+17+for+SQL+Server" # custom_suffix = """"" # If the SQLResult is empty, the Answer should be "No results found". DO NOT hallucinate an answer if there is no result.""" engine = create_engine(connection_uri) model_name="gpt-3.5-turbo-16k" db = SQLDatabase(engine, view_support=True, include_tables=['EGV_emp_departments_ChatGPT']) # db = SQLDatabase(engine, view_support=True, include_tables=['egv_emp_acadamics_ChatGPT']) llm = ChatOpenAI(temperature=0, verbose=False, model=model_name) db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True) from langchain.prompts import PromptTemplate PROMPT = """ Given an input question, first create a syntactically correct mssql query to run, then look at the results of the query and return the answer. The question: {db_chain.run} """ prompt_template = """" Use the following pieces of context to answer the question at the end. If you don't know the answer, please think rationally and answer from your own knowledge base. Don't consider the table which are not mentioned, if no result is matching with the keyword Please return the answer as invalid question {context} Question: {questions} """ PROMPT = PromptTemplate( template=prompt_template, input_variables=["context", "questions"] ) def split_text(text, chunk_size, chunk_overlap=0): text_splitter = TokenTextSplitter( chunk_size=chunk_size, chunk_overlap=chunk_overlap ) yield from text_splitter.split_text(text) class QuerySQLDatabaseTool2(QuerySQLDataBaseTool): def _run( self, query: str, run_manager: Optional[CallbackManagerForToolRun] = None, ) -> str: result = self.db.run_no_throw(query) return next(split_text(result, chunk_size=14_000)) class SQLDatabaseToolkit2(SQLDatabaseToolkit): def get_tools(self) -> List[BaseTool]: tools = super().get_tools() original_query_tool_description = tools[0].description new_query_tool = QuerySQLDatabaseTool2( db=self.db, description=original_query_tool_description ) tools[0] = new_query_tool return tools return db_chain.run(question) # answer=chat("give the names of employees who have completed PG") answer= chat("give the list of employees joined in january and february of 2023 with Employee ID, Name, Department,Date of join") print(answer) conversation_data= chatgpt.chat(prompt="convert into .csv and .xlsx only if the multiple values are asked in the question, if one a single thing is asked, just give the answer in chatbot no need to save the answer") # conversation_data = chat("convert into .csv and .xlsx only if the multiple values are asked in the question, if one a single thing is asked, just give the answer in chatbot no need to save the answer") save_conversation(conversation_data, "chat_data.csv") df = pd.read_csv("chat_data.csv") path = r"C:\Users\rndbcpsoft\OneDrive\Desktop\test\chat_data.xlsx" df.to_excel(path, index=False) print(f"Conversation data has been saved to '{path}' in Excel format.") ### Suggestion: _No response_
Issue: <How to store/export the output of a chatbot to excel ' prefix>
https://api.github.com/repos/langchain-ai/langchain/issues/14592/comments
5
2023-12-12T11:08:08Z
2024-03-21T16:05:52Z
https://github.com/langchain-ai/langchain/issues/14592
2,037,519,560
14,592
[ "hwchase17", "langchain" ]
### System Info Python 3.9.12, LangChain 0.0.346 ### Who can help? @agola11 @3coins ### Information - [ ] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction When you use caching with LangChain, it does not distinguish different LLM models. For example, the response for LLama2 was used for a prompt for Claude 2. ``` def ask_name(model_id): question = 'what is your name?' bedrock = Bedrock(model_id=model_id, model_kwargs={'temperature': 0.1}) print('me: ' + question) t0 = datetime.datetime.now() print(f'{bedrock.model_id}: ' + bedrock(question).strip()) print('({:.2f} sec)'.format((datetime.datetime.now() - t0).total_seconds())) print() model_ids = ['meta.llama2-70b-chat-v1','anthropic.claude-v2',] for model_id in model_ids: ask_name(model_id) ask_name(model_id) ``` ==> ``` me: what is your name? meta.llama2-70b-chat-v1: Answer: My name is LLaMA, I'm a large language model trained by a team of researcher at Meta AI. (2.24 sec) me: what is your name? meta.llama2-70b-chat-v1: Answer: My name is LLaMA, I'm a large language model trained by a team of researcher at Meta AI. (0.00 sec) me: what is your name? anthropic.claude-v2: Answer: My name is LLaMA, I'm a large language model trained by a team of researcher at Meta AI. (0.00 sec) ``` This is because of https://github.com/langchain-ai/langchain/blob/db6bf8b022c17353b46f97ab3b9f44ff9e88a488/libs/langchain/langchain/llms/bedrock.py#L235 ``` @property def _identifying_params(self) -> Mapping[str, Any]: """Get the identifying parameters.""" _model_kwargs = self.model_kwargs or {} return { **{"model_kwargs": _model_kwargs}, } ``` My current workaround is subclassing the `Bedrock` class: ``` class MyBedrock(Bedrock): @property def _identifying_params(self) -> Mapping[str, Any]: """Get the identifying parameters.""" return { 'model_id': self.model_id, **BedrockBase._identifying_params.__get__(self) } ``` This seems working. ### Expected behavior LLama 2 should replay with "My name is LLaMa..." while Claude 2 should reply with "My name is Claude."
Caching with Bedrock does not distinguish models or params
https://api.github.com/repos/langchain-ai/langchain/issues/14590/comments
1
2023-12-12T10:17:59Z
2024-03-19T16:06:07Z
https://github.com/langchain-ai/langchain/issues/14590
2,037,430,016
14,590
[ "hwchase17", "langchain" ]
### System Info python: 3.11 langchain: 0.0.347-0.0.349 langchain_core: 0.0.12, 0.0.13 ### Who can help? @hwchase17 ### Information - [ ] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction ```python import langchain_core.load class Custom(langchain_core.load.Serializable): @classmethod def is_lc_serializable(cls) -> bool: return True out = langchain_core.load.dumps(Custom()) langchain_core.load.loads( out, valid_namespaces=["langchain", "__main__"], ) ``` ### Expected behavior I'm expecting it's possible to make a custom class serializable, but since langchain_core 0.0.13 the `valid_namespaces` is effectively ignored as it relies on a whitelist of what can be serialized `SERIALIZABLE_MAPPING`. So I get the error: > ValueError: Trying to deserialize something that cannot be deserialized in current version of langchain-core: ('__main__', 'Custom') Triggered in [load.py#L68](https://github.com/langchain-ai/langchain/blob/v0.0.349/libs/core/langchain_core/load/load.py#L68) --- I'm not sure if serialization was ever intended to be part of the public API, but I've found it convenient to be able to make my custom parts of chains also abide by the serialization protocol and still be able to dump/load my chains
Unable to dump/load custom classes since langchain_core 0.0.13
https://api.github.com/repos/langchain-ai/langchain/issues/14589/comments
3
2023-12-12T10:16:56Z
2023-12-13T10:44:16Z
https://github.com/langchain-ai/langchain/issues/14589
2,037,428,125
14,589
[ "hwchase17", "langchain" ]
### System Info Python 3.9.13, LangChain 0.0.347, Windows 10 ### Who can help? @hwchase17 @agola ### Information - [ ] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [X] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [X] Callbacks/Tracing - [ ] Async ### Reproduction Running this snippet: ```python answers = (answer for answer in ["answer1", "answer2"]) class CustomLLM(LLM): def _llm_type(self) -> str: return "custom" def _call(self, prompt: str, **_) -> str: return next(answers) class CustomOutputParser(BaseOutputParser[str]): reject_output: bool = True def parse(self, text: str) -> str: if self.reject_output: self.reject_output = False raise OutputParserException(f"Parsing failed") return text def get_format_instructions(self) -> str: return "format instructions" class CustomCallbackHandler(BaseCallbackHandler): def on_llm_end(self, response: LLMResult, **_): print(f"received LLM response: {response}") llm = CustomLLM() chain = LLMChain( llm=llm, prompt=PromptTemplate.from_template("Testing prompt"), output_key="chain_output", verbose=True, output_parser=OutputFixingParser.from_llm(llm, CustomOutputParser()), ) result = chain({}, callbacks=[CustomCallbackHandler()]) print(f"Chain result is {result}") ``` produces the following output: ``` > Entering new LLMChain chain... Prompt after formatting: Testing prompt received LLM response: generations=[[Generation(text='answer1')]] llm_output=None run=None > Finished chain. Chain result is {'chain_output': 'answer2'} ``` ### Expected behavior The output line `received LLM response: generations=[[Generation(text='answer1')]] llm_output=None run=None` should also appear with `answer2`, because that one is also generated while running the chain that was configured to use the `CustomCallbackHandler`. The callbacks should also be used in the chain that is created inside the OutputFixingParser. In my opinion, the chain doing the retry, should have the overall chain as a parent in the callback methods (`on_chain_start()` an so on).
OutputFixingParser should use callbacks
https://api.github.com/repos/langchain-ai/langchain/issues/14588/comments
1
2023-12-12T09:35:53Z
2024-03-19T16:06:02Z
https://github.com/langchain-ai/langchain/issues/14588
2,037,349,229
14,588
[ "hwchase17", "langchain" ]
### Issue you'd like to raise. below is my code ` confluence_url = config.get("confluence_url", None) username = config.get("username", None) api_key = config.get("api_key", None) space_key = config.get("space_key", None) documents = [] embedding = OpenAIEmbeddings() loader = ConfluenceLoader( url=confluence_url, username=username, api_key=api_key ) for space_key in space_key: documents.extend(loader.load(space_key=space_key,include_attachments=True,limit=100))` The error I am getting: raise HTTPError(http_error_msg, response=self) requests.exceptions.HTTPError: 404 Client Error: Not Found for url: https://api.media.atlassian.com/file/UNKNOWN_MEDIA_ID/binary?token=sometoken&name=Invalid%20file%20id%20-%20UNKNOWN_MEDIA_ID&max-age=2940 ### Suggestion: _No response_
Issue: Getting error while integrating Confluence Spaces including attachments
https://api.github.com/repos/langchain-ai/langchain/issues/14586/comments
3
2023-12-12T09:00:13Z
2024-02-21T11:48:20Z
https://github.com/langchain-ai/langchain/issues/14586
2,037,287,446
14,586
[ "hwchase17", "langchain" ]
### System Info Langchain Version: 0.0.335 Platform: Win11 Python Version: 3.11.5 Hi experts, I'm trying to execute the RAG Search Example on the Langchain Doc: https://python.langchain.com/docs/expression_language/get_started **Here is the code:** from langchain.chat_models import ChatOpenAI from langchain.embeddings import OpenAIEmbeddings from langchain.prompts import ChatPromptTemplate from langchain.vectorstores import DocArrayInMemorySearch from langchain.schema import StrOutputParser from langchain.schema.runnable import RunnableParallel, RunnablePassthrough vectorstore = DocArrayInMemorySearch.from_texts( ["harrison worked at kensho", "bears like to eat honey"], embedding=OpenAIEmbeddings(), ) retriever = vectorstore.as_retriever() template = """Answer the question based only on the following context: {context} Question: {question} """ prompt = ChatPromptTemplate.from_template(template) model = ChatOpenAI() output_parser = StrOutputParser() setup_and_retrieval = RunnableParallel( {"context": retriever, "question": RunnablePassthrough()} ) chain = setup_and_retrieval | prompt | model | output_parser chain.invoke("where did harrison work?") **but the example fails with the ValidationError: 2 validation errors for DocArrayDoc.** **Here is the error details:** C:\Project\pythonProjectAI\.venv\Lib\site-packages\pydantic\_migration.py:283: UserWarning: `pydantic.error_wrappers:ValidationError` has been moved to `pydantic:ValidationError`. warnings.warn(f'`{import_path}` has been moved to `{new_location}`.') Traceback (most recent call last): File "C:\Project\pythonProjectAI\AI.py", line 28, in <module> chain.invoke("where did harrison work?") File "C:\Project\pythonProjectAI\.venv\Lib\site-packages\langchain\schema\runnable\base.py", line 1427, in invoke input = step.invoke( ^^^^^^^^^^^^ File "C:\Project\pythonProjectAI\.venv\Lib\site-packages\langchain\schema\runnable\base.py", line 1938, in invoke output = {key: future.result() for key, future in zip(steps, futures)} ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Project\pythonProjectAI\.venv\Lib\site-packages\langchain\schema\runnable\base.py", line 1938, in <dictcomp> output = {key: future.result() for key, future in zip(steps, futures)} ^^^^^^^^^^^^^^^ File "C:\Program Files\Python\Lib\concurrent\futures\_base.py", line 456, in result return self.__get_result() ^^^^^^^^^^^^^^^^^^^ File "C:\Program Files\Python\Lib\concurrent\futures\_base.py", line 401, in __get_result raise self._exception File "C:\Program Files\Python\Lib\concurrent\futures\thread.py", line 58, in run result = self.fn(*self.args, **self.kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Project\pythonProjectAI\.venv\Lib\site-packages\langchain\schema\retriever.py", line 112, in invoke return self.get_relevant_documents( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Project\pythonProjectAI\.venv\Lib\site-packages\langchain\schema\retriever.py", line 211, in get_relevant_documents raise e File "C:\Project\pythonProjectAI\.venv\Lib\site-packages\langchain\schema\retriever.py", line 204, in get_relevant_documents result = self._get_relevant_documents( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Project\pythonProjectAI\.venv\Lib\site-packages\langchain\schema\vectorstore.py", line 657, in _get_relevant_documents docs = self.vectorstore.similarity_search(query, **self.search_kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Project\pythonProjectAI\.venv\Lib\site-packages\langchain\vectorstores\docarray\base.py", line 127, in similarity_search results = self.similarity_search_with_score(query, k=k, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Project\pythonProjectAI\.venv\Lib\site-packages\langchain\vectorstores\docarray\base.py", line 106, in similarity_search_with_score query_doc = self.doc_cls(embedding=query_embedding) # type: ignore ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Project\pythonProjectAI\.venv\Lib\site-packages\pydantic\main.py", line 164, in __init__ __pydantic_self__.__pydantic_validator__.validate_python(data, self_instance=__pydantic_self__) pydantic_core._pydantic_core.ValidationError: 2 validation errors for DocArrayDoc text Field required [type=missing, input_value={'embedding': [-0.0192381..., 0.010137099064823456]}, input_type=dict] For further information visit https://errors.pydantic.dev/2.5/v/missing metadata Field required [type=missing, input_value={'embedding': [-0.0192381..., 0.010137099064823456]}, input_type=dict] For further information visit https://errors.pydantic.dev/2.5/v/missing ### Who can help? _No response_ ### Information - [X] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [X] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction from langchain.chat_models import ChatOpenAI from langchain.embeddings import OpenAIEmbeddings from langchain.prompts import ChatPromptTemplate from langchain.vectorstores import DocArrayInMemorySearch from langchain.schema import StrOutputParser from langchain.schema.runnable import RunnableParallel, RunnablePassthrough vectorstore = DocArrayInMemorySearch.from_texts( ["harrison worked at kensho", "bears like to eat honey"], embedding=OpenAIEmbeddings(), ) retriever = vectorstore.as_retriever() template = """Answer the question based only on the following context: {context} Question: {question} """ prompt = ChatPromptTemplate.from_template(template) model = ChatOpenAI() output_parser = StrOutputParser() setup_and_retrieval = RunnableParallel( {"context": retriever, "question": RunnablePassthrough()} ) chain = setup_and_retrieval | prompt | model | output_parser chain.invoke("where did harrison work?") ### Expected behavior The example runs well.
ValidationError: 2 validation errors for DocArrayDoc returned when try to execute the RAG Search Example
https://api.github.com/repos/langchain-ai/langchain/issues/14585/comments
19
2023-12-12T08:57:21Z
2024-06-08T16:07:56Z
https://github.com/langchain-ai/langchain/issues/14585
2,037,282,618
14,585
[ "hwchase17", "langchain" ]
https://github.com/langchain-ai/langchain/blob/76905aa043e3e604b5b34faf5e91d0aedb5ed6dd/libs/langchain/langchain/chains/graph_qa/cypher.py#L266C2-L269C25 Hi, I am heavily using GraphCypherQAChain and sometimes cypher_query_corrector returns generated cypher as valid but have some syntax problems or completely it is not a cypher query; and during the execution of the neo4j, it raises errors. Where do you think that these errors should be handled, how to continue the chain execution without any interruption and return a plausible response to the user? @tomasonjo Example: <img width="1104" alt="image" src="https://github.com/langchain-ai/langchain/assets/9192832/0cac12a9-0a4c-4446-af30-2f54ac3290c8"> Thanks!
GraphCypherQAChain Unhandled Exception while running Erroneous Cypher Queries
https://api.github.com/repos/langchain-ai/langchain/issues/14584/comments
1
2023-12-12T08:18:57Z
2024-03-19T16:05:58Z
https://github.com/langchain-ai/langchain/issues/14584
2,037,219,925
14,584
[ "hwchase17", "langchain" ]
### Feature request Currently, SemanticSimilarityExampleSelector only passes `k` as a parameter to vectorstore [see here](https://github.com/langchain-ai/langchain/blob/76905aa043e3e604b5b34faf5e91d0aedb5ed6dd/libs/core/langchain_core/example_selectors/semantic_similarity.py#L55C10-L55C10). vectorstore, depending on the implementation can take multiple other arguments. most notably, `filters` can be passed down [see here](https://github.com/langchain-ai/langchain/blob/76905aa043e3e604b5b34faf5e91d0aedb5ed6dd/libs/community/langchain_community/vectorstores/faiss.py#L505C13-L507C38) ### Motivation Having the ability to filter down examples (on top of similarity search) would be very helpful in controlling the examples that are added to the prompt. This feature provides significant more control over examples selection. ### Your contribution It is very easy to update this. add a new attribute `vectorstore_kwargs` to the class class SemanticSimilarityExampleSelector(BaseExampleSelector, BaseModel): """Example selector that selects examples based on SemanticSimilarity.""" vectorstore: VectorStore """VectorStore than contains information about examples.""" k: int = 4 """Number of examples to select.""" example_keys: Optional[List[str]] = None """Optional keys to filter examples to.""" input_keys: Optional[List[str]] = None """Optional keys to filter input to. If provided, the search is based on the input variables instead of all variables.""" vectorstore_kwargs: Optional[Dict[str, Any]] = None """additional arguments passed to vectorstore for similarity search.""" and then update the `select_examples` function with `example_docs = self.vectorstore.similarity_search(query, k=self.k, **self.vectorstore_kwargs)`
passing down vectorstore additional argument in SemanticSimilarityExampleSelector
https://api.github.com/repos/langchain-ai/langchain/issues/14583/comments
1
2023-12-12T07:18:55Z
2024-03-19T16:05:52Z
https://github.com/langchain-ai/langchain/issues/14583
2,037,136,239
14,583
[ "hwchase17", "langchain" ]
### System Info python = "^3.10" openai = "^1.3.8" langchain = "^0.0.349" ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [X] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction ```` import chromadb from langchain.embeddings import AzureOpenAIEmbeddings from langchain.vectorstores.chroma import Chroma client = chromadb.HttpClient( host=CHROMA_SERVER_HOST, port=CHROMA_SERVER_HTTP_PORT, ) embeddings = AzureOpenAIEmbeddings( openai_api_type=AZURE_OPENAI_API_TYPE, azure_endpoint=AZURE_OPENAI_API_BASE, api_key=AZURE_OPENAI_API_KEY, openai_api_version=AZURE_OPENAI_API_VERSION, azure_deployment=AZURE_EMBEDDING_DEPLOYMENT_NAME, ) vectordb = Chroma( client=client, collection_name=CHROMA_COLLECTION_NAME_FBIG_1000, embedding_function=embeddings, ) ```` ### Expected behavior TypeError: cannot pickle '_thread.RLock' object When I use openai = "0.28.1" it doesn't have the above error
RetrievalQA and AzureOpenAIEmbeddings lead to TypeError: cannot pickle '_thread.lock' object
https://api.github.com/repos/langchain-ai/langchain/issues/14581/comments
15
2023-12-12T06:39:49Z
2024-07-27T16:03:39Z
https://github.com/langchain-ai/langchain/issues/14581
2,037,087,675
14,581
[ "hwchase17", "langchain" ]
### System Info Ubuntu 20.04 CUDA 12.1 NVIDIA RTX 4070 ### Who can help? @hwchase17 @eyurtsev ### Information - [X] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [X] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction ``` from langchain.callbacks.manager import CallbackManager from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler from langchain.chains import LLMChain from langchain.llms import LlamaCpp from langchain.prompts import PromptTemplate template = """Question: {question} Answer: Your Answer""" prompt = PromptTemplate(template=template, input_variables=["question"]) callback_manager = CallbackManager([StreamingStdOutCallbackHandler()]) n_gpu_layers = 80 # Change this value based on your model and your GPU VRAM pool. n_batch = 512 # Should be between 1 and n_ctx, consider the amount of VRAM in your GPU.llm = LlamaCpp(model_path="/home/rtx-4070/Downloads/openorca-platypus2-13b.Q4_K_M.gguf", n_gpu_layers=n_gpu_layers, n_batch=n_batch, callback_manager=callback_manager, verbose=True, n_ctx=2048 ) from langchain_experimental.agents.agent_toolkits import create_csv_agent from langchain.agents.agent_types import AgentType agent = create_csv_agent( llm, "/home/rtx-4070/git_brainyneurals/langchain_local/docs/SPY.csv", verbose=True, agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION, handle_parsing_errors=True ) agent.run("How many rows are there?") ``` I am running opeorca model which I have downloaded from HuggingFace. But I am facing this erros. Could you please help me with related solutions? Or Suggest any other ways or models. Would be grateful. Thanks in advance. ### Expected behavior I want to make work simple csv agent that runs on my RTX 4070 Desktop GPU with any open source models.
An output parsing error occurred. Could not parse LLM output create_csv_agent
https://api.github.com/repos/langchain-ai/langchain/issues/14580/comments
2
2023-12-12T05:58:28Z
2024-03-19T16:05:47Z
https://github.com/langchain-ai/langchain/issues/14580
2,037,040,612
14,580
[ "hwchase17", "langchain" ]
### System Info Lanchain: 0.0.348 Python 3.12.0 ### Who can help? @hwchase17 @agola11 ### Information - [X] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [X] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [X] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction Steps to reproduce the error: 1. Run the [notebook](https://github.com/langchain-ai/langchain/blob/master/docs/docs/integrations/llms/predibase.ipynb) with the latest version of Langchain. The error occurs specficially in 13 `review = overall_chain.run("Tragedy at sunset on the beach")` ### Expected behavior The expected behavior is simply to return the output from the LLM.
Predibase TypeError: LlmMixin.prompt() got an unexpected keyword argument 'model_name'
https://api.github.com/repos/langchain-ai/langchain/issues/14564/comments
1
2023-12-11T23:00:10Z
2024-03-19T16:05:42Z
https://github.com/langchain-ai/langchain/issues/14564
2,036,678,638
14,564
[ "hwchase17", "langchain" ]
### Issue you'd like to raise. Hi, I have this issue when working with more than 1 table using the Llama2 model. Let's say table 1 has values A, B, C, and table 2 has values X, Y, Z. When I run the query, it often gives me an error that it creates the query but assumes that table 1 has values X, Y, Z, and it mixes them up. Any suggestions on how to avoid this? I'm trying to solve it with prompts, but the model seems to ignore instructions in these cases. ### Suggestion: _No response_
Issue: Problem when using model llama2 13b chat to create SQL queries in 2 or more tables, it mixes the columns of the tables
https://api.github.com/repos/langchain-ai/langchain/issues/14553/comments
1
2023-12-11T19:58:47Z
2024-03-18T16:08:58Z
https://github.com/langchain-ai/langchain/issues/14553
2,036,427,335
14,553
[ "hwchase17", "langchain" ]
### System Info Name: langchain Version: 0.0.348 Name: PyGithub Version: 2.1.1 ### Who can help? @hwchase17 @agola11 ``` Traceback (most recent call last): File "/Users/mac/Dev Projects/Chainlit_qa/test.py", line 12, in <module> github = GitHubAPIWrapper( ^^^^^^^^^^^^^^^^^ File "/Users/mac/Dev Projects/Chainlit_qa/myenv/lib/python3.11/site-packages/pydantic/v1/main.py", line 339, in __init__ values, fields_set, validation_error = validate_model(__pydantic_self__.__class__, data) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/mac/Dev Projects/Chainlit_qa/myenv/lib/python3.11/site-packages/pydantic/v1/main.py", line 1102, in validate_model values = validator(cls_, values) ^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/mac/Dev Projects/Chainlit_qa/myenv/lib/python3.11/site-packages/langchain/utilities/github.py", line 69, in validate_environment installation = gi.get_installations()[0] ~~~~~~~~~~~~~~~~~~~~~~^^^ File "/Users/mac/Dev Projects/Chainlit_qa/myenv/lib/python3.11/site-packages/github/PaginatedList.py", line 62, in __getitem__ return self.__elements[index] ~~~~~~~~~~~~~~~^^^^^^^ IndexError: list index out of range ``` ### Information - [X] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [X] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction Run basic python file: ``` from langchain.agents.agent_toolkits.github.toolkit import GitHubToolkit from langchain.utilities.github import GitHubAPIWrapper github = GitHubAPIWrapper() toolkit = GitHubToolkit.from_github_api_wrapper(github) tools = toolkit.get_tools() ``` ### Expected behavior Fetching github repository information
GithubAPIWrapper throws list index out of range error
https://api.github.com/repos/langchain-ai/langchain/issues/14550/comments
8
2023-12-11T18:30:31Z
2023-12-22T09:32:06Z
https://github.com/langchain-ai/langchain/issues/14550
2,036,284,698
14,550
[ "hwchase17", "langchain" ]
### Feature request As of `langchain==0.0.348` in [`ChatVertexAI` here](https://github.com/langchain-ai/langchain/blob/v0.0.348/libs/langchain/langchain/chat_models/vertexai.py#L187-L191): 1. `vertexai.language_models.ChatSession.send_message` returns a `vertexai.language_models.MultiCandidateTextGenerationResponse` response 2. `ChatVertexAI` throws out the response's `grounding_metadata`, only preserving the `text` The request is to not discard this useful metadata ### Motivation I am trying to use the `response.grounding_metadata.citations` in my own code ### Your contribution I will open a PR to make this: ```python generations = [ ChatGeneration( message=AIMessage(content=r.text), generation_info=r.raw_prediction_response, ) for r in response.candidates ] ``` So the raw response remains accessible
Request: `ChatVertexAI` preserving `grounding_metadata`
https://api.github.com/repos/langchain-ai/langchain/issues/14548/comments
1
2023-12-11T18:06:33Z
2024-01-25T04:37:45Z
https://github.com/langchain-ai/langchain/issues/14548
2,036,244,262
14,548
[ "hwchase17", "langchain" ]
### System Info Langchain 0.0.331, macOS Monterey, Python 3.10.9 ### Who can help? _No response_ ### Information - [X] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [X] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction from langchain.document_loaders import UnstructuredHTMLLoader loader = UnstructuredHTMLLoader("https://www.sec.gov/ix?doc=/Archives/edgar/data/40987/000004098720000010/gpc-12312019x10k.htm") documents = loader.load() FileNotFoundError: [Errno 2] No such file or directory: 'https://www.sec.gov/ix?doc=/Archives/edgar/data/40987/000004098720000010/gpc-12312019x10k.htm ### Expected behavior Success loading .htm file
Does HTML Doc Loader accept .htm sites?
https://api.github.com/repos/langchain-ai/langchain/issues/14545/comments
2
2023-12-11T16:25:09Z
2024-04-10T16:15:24Z
https://github.com/langchain-ai/langchain/issues/14545
2,036,050,915
14,545
[ "hwchase17", "langchain" ]
### System Info Langchain version = 0.0.344 Python version = 3.11.5 ### Who can help? @hwchase17 @agola11 ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [X] Agents / Agent Executors - [X] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction here is my code. i am unable to connect to powerbi dataset. from langchain.agents.agent_toolkits import PowerBIToolkit from langchain.utilities.powerbi import PowerBIDataset from azure.identity import ClientSecretCredential from azure.core.credentials import TokenCredential # Create an instance of the language model (llm) toolkit = PowerBIToolkit( powerbi=PowerBIDataset( dataset_id="", table_names=['WinShareTable'], credential=ClientSecretCredential( client_id=", client_secret='', tenant_id="" ) ), llm=llm ) Either getting field "credential" not yet prepared so type is still a ForwardRef, you might need to call PowerBIDataset.update_forward_refs() error or Tokencredentials error ### Expected behavior Should allow to connect to PowerBi datasets
impossible to connect to PowerBI Datasets even after providing all the information
https://api.github.com/repos/langchain-ai/langchain/issues/14538/comments
1
2023-12-11T14:07:35Z
2024-03-18T16:08:54Z
https://github.com/langchain-ai/langchain/issues/14538
2,035,763,317
14,538
[ "hwchase17", "langchain" ]
### Discussed in https://github.com/langchain-ai/langchain/discussions/13245 <div type='discussions-op-text'> <sup>Originally posted by **yallapragada** November 12, 2023</sup> I am testing a simple RAG implementation with Azure Cognitive Search. I am seeing a "cannot import name 'Vector' from azure.search.documents.models" error when I invoke my chain. Origin of my error is line 434 in lanchain/vectorstores/azuresearch.py (from azure.search.documents.models import Vector) this is the relevant code snippet, I get the import error when I execute rag_chain.invoke(question) from langchain.schema.runnable import RunnablePassthrough from langchain.prompts import ChatPromptTemplate from langchain.chat_models.azure_openai import AzureChatOpenAI question = "my question.." -- vector_store is initialized using AzureSearch(), not including that snippet here -- retriever = vector_store.as_retriever() template = ''' Answer the question based on the following context: {context} Question: {question} ''' prompt = ChatPromptTemplate.from_template(template=template) llm = AzureChatOpenAI( deployment_name='MY_DEPLOYMENT_NAME', model_name='MY_MODEL', openai_api_base=MY_AZURE_OPENAI_ENDPOINT, openai_api_key=MY_AZURE_OPENAI_KEY, openai_api_version='2023-05-15', openai_api_type='azure' ) rag_chain = {'context' : retriever, 'question' : RunnablePassthrough} | prompt | llm rag_chain.invoke(question) -------------- my package versions langchain==0.0.331 azure-search-documents==11.4.0b11 azure-core==1.29.5 openai==0.28.1</div>
Stable release 11.4.0 of azure.search.documents.models not compatible with latest langchain version -> class Vector gone
https://api.github.com/repos/langchain-ai/langchain/issues/14534/comments
1
2023-12-11T12:44:45Z
2024-03-18T16:08:49Z
https://github.com/langchain-ai/langchain/issues/14534
2,035,602,125
14,534
[ "hwchase17", "langchain" ]
### Issue with current documentation: Page: https://python.langchain.com/docs/modules/memory/ In dark mode, there is very little contrast between inputs and outputs. Especially for pages imported from Jupyter notebooks, it can be really confusing figuring out which code blocks are safe to test in a `.py` function and which code blocks are intended for use in Jupyter. Adding in explicit contrast or labeling between input and output blocks would be helpful. ![image](https://github.com/langchain-ai/langchain/assets/113563866/ba628424-fcc5-4496-8053-128254e068a7) ### Idea or request for content: _No response_
DOC: Please add stronger contrast or labeling between notebook input and output blocks
https://api.github.com/repos/langchain-ai/langchain/issues/14532/comments
2
2023-12-11T12:20:45Z
2024-04-01T16:05:54Z
https://github.com/langchain-ai/langchain/issues/14532
2,035,556,970
14,532
[ "hwchase17", "langchain" ]
### Feature request Weaviate has released a beta version of their [python client v4](https://weaviate.io/developers/weaviate/client-libraries/python) which seems to be more robust compared to v3. It follows a different structure but allows for more versatility and better error handling when it comes to queries. I think it would be great to add support in langchain for the new client. ### Motivation I was using the v3 client (without langchain) to batch import data into Weaviate. It worked, but it was slower than I expected and also resulted in errors that I was not able to manage easily. The new v4 client supports their new [gRPC API](https://weaviate.io/developers/weaviate/api/grpc) which outperforms the traditional REST API that v3 is using. ### Your contribution I will start creating some custom functions using Weaviate's new client to test its reliability. If I don't encounter any serious errors, I'll try to find the time and create a PR to add support for it in langchain. I think that support for both v3 and v4 should exist, at least until v4 becomes stable.
Support Weaviate client v4
https://api.github.com/repos/langchain-ai/langchain/issues/14531/comments
1
2023-12-11T12:15:23Z
2024-03-12T13:14:58Z
https://github.com/langchain-ai/langchain/issues/14531
2,035,547,273
14,531
[ "hwchase17", "langchain" ]
### System Info langchain==0.0.348 wsl python 3.10.2 ### Who can help? @hwchase17 @agola11 ### Information - [X] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [X] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction I'm looking to run a simple RAG using Qdrant pre instantiated with data (something like the [link from the docs](https://colab.research.google.com/drive/19RxxkZdnq_YqBH5kBV10Rt0Rax-kminD?usp=sharing#scrollTo=fvCHMT73SmKi)) but its giving me the error in the title. I'm posting it here since the error seems to be on langchain side. not qdrant side. Ive tried various ways of using langchain to connect to qdrant but it always ends up with this error. This happens even if i use the deprecated VectorDBQA or RetrievalQA ``` from qdrant_client import QdrantClient from langchain.chat_models import AzureChatOpenAI from langchain.embeddings import HuggingFaceEmbeddings from langchain.vectorstores import Qdrant QDRANT_ENDPOINT = "localhost" MODEL_TO_USE = 'BAAI/bge-base-en-v1.5' collection_name = "collection" client = QdrantClient(QDRANT_ENDPOINT, port=6333, timeout=120) embeddings = HuggingFaceEmbeddings(model_name=MODEL_TO_USE) qdrant = Qdrant(client, collection_name, embeddings) qdrant.similarity_search("middle east") ``` gives ``` >>> qdrant.similarity_search("middle east") Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/user/.local/lib/python3.10/site-packages/langchain/vectorstores/qdrant.py", line 274, in similarity_search results = self.similarity_search_with_score( File "/home/user/.local/lib/python3.10/site-packages/langchain/vectorstores/qdrant.py", line 350, in similarity_search_with_score return self.similarity_search_with_score_by_vector( File "/home/user/.local/lib/python3.10/site-packages/langchain/vectorstores/qdrant.py", line 595, in similarity_search_with_score_by_vector results = self.client.search( File "/home/user/.local/lib/python3.10/site-packages/qdrant_client/qdrant_client.py", line 340, in search return self._client.search( File "/home/user/.local/lib/python3.10/site-packages/qdrant_client/qdrant_remote.py", line 472, in search search_result = self.http.points_api.search_points( File "/home/user/.local/lib/python3.10/site-packages/qdrant_client/http/api/points_api.py", line 1388, in search_points return self._build_for_search_points( File "/home/user/.local/lib/python3.10/site-packages/qdrant_client/http/api/points_api.py", line 636, in _build_for_search_points return self.api_client.request( File "/home/user/.local/lib/python3.10/site-packages/qdrant_client/http/api_client.py", line 74, in request return self.send(request, type_) File "/home/user/.local/lib/python3.10/site-packages/qdrant_client/http/api_client.py", line 97, in send raise UnexpectedResponse.for_response(response) qdrant_client.http.exceptions.UnexpectedResponse: Unexpected Response: 400 (Bad Request) Raw response content: b'{"status":{"error":"Wrong input: Default vector params are not specified in config"},"time":0.000118885}' ``` Using this as a retriever also fails, which is what i need it for. Testing with qdrant alone works. ``` client.query(collection_name,"middle east") # using qdrant itself works ``` ### Expected behavior For the queries to be fetched and not throw this error
b'{"status":{"error":"Wrong input: Default vector params are not specified in config"},"time":0.00036507}'
https://api.github.com/repos/langchain-ai/langchain/issues/14526/comments
1
2023-12-11T10:30:41Z
2024-03-18T16:08:44Z
https://github.com/langchain-ai/langchain/issues/14526
2,035,343,197
14,526
[ "hwchase17", "langchain" ]
### System Info langchain=0.0.348 openai=0.28.1 ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [x] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction model= "chatglm3" llm = ChatOpenAI(model_name=model,openai_api_key=api_key,openai_api_base=api_url) db = SQLDatabase.from_uri("mysql+pymysql://.....") toolkit = SQLDatabaseToolkit(db=db, llm=llm,use_query_checker=True) agent_executor = create_sql_agent( llm=llm,toolkit=toolkit, verbose=True,agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,handle_parsing_errors=False ) content=agent_executor.run("Look at the structure of the table ads_pm_empl_count first, then give the first three rows of data") print(content) <img width="875" alt="联想截图_20231211165355" src="https://github.com/langchain-ai/langchain/assets/73893296/8fcee38e-0235-44c5-8454-6fd368351488"> ### Expected behavior How do I fix this output format problem
When the self-deployed chatglm3 model is invoked based on the openai API specification and create_sql_agent is used to query the first three rows of the data table, the output format is reported to be incorrect. But there are no formatting errors with qwen. How do I fix chatglm3
https://api.github.com/repos/langchain-ai/langchain/issues/14523/comments
1
2023-12-11T08:58:58Z
2024-03-18T16:08:39Z
https://github.com/langchain-ai/langchain/issues/14523
2,035,156,450
14,523
[ "hwchase17", "langchain" ]
### System Info langchain=0.0.348 python=3.9 openai=0.28.1 ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction ![Uploading 联想截图_20231211165355.png…]() model= "chatglm3" #llm = OpenAI(model_name=model,openai_api_key=api_key,openai_api_base=api_url) llm = ChatOpenAI(model_name=model,openai_api_key=api_key,openai_api_base=api_url) db = SQLDatabase.from_uri("mysql+pymysql://。。。。。") toolkit = SQLDatabaseToolkit(db=db, llm=llm,use_query_checker=True) #memory = ConversationBufferMemory(memory_key ="chat_history ") agent_executor = create_sql_agent( llm=llm, toolkit=toolkit, verbose=True, agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION, handle_parsing_errors=False ) # content=agent_executor.output_schema.schema() content=agent_executor.run("Look at the structure of the table ads_pm_empl_count first, then give the first three rows of data") print(content) ### Expected behavior How do I fix this output format problem
1
https://api.github.com/repos/langchain-ai/langchain/issues/14522/comments
1
2023-12-11T08:50:01Z
2023-12-11T09:00:17Z
https://github.com/langchain-ai/langchain/issues/14522
2,035,138,288
14,522
[ "hwchase17", "langchain" ]
### System Info OS == Windows 11 Python == 3.10.11 Langchain == 0.0.348 ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [X] Agents / Agent Executors - [X] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction ```bash Python 3.10.11 (tags/v3.10.11:7d4cc5a, Apr 5 2023, 00:38:17) [MSC v.1929 64 bit (AMD64)] on win32 Type "help", "copyright", "credits" or "license" for more information. >>> from dotenv import load_dotenv >>> load_dotenv() True >>> from langchain.agents import AgentType, initialize_agent >>> from langchain.tools import StructuredTool >>> from langchain.chat_models import ChatOpenAI >>> llm = ChatOpenAI(model='gpt-3.5-turbo') >>> def func0(a: int, b: int) -> int: ... return a+b ... >>> def func1(a: int, b: int, d: dict[str, int]={}) -> int: ... print(d) ... return a+b ... >>> def func2(a: int, b: int, d: dict[str, int]={'c': 0}) -> int: ... print(d) ... return a+b ... >>> tool0 = StructuredTool.from_function(func0, name=func0.__name__, description='a+b') >>> tool1 = StructuredTool.from_function(func1, name=func1.__name__, description='a+b') >>> tool2 = StructuredTool.from_function(func2, name=func2.__name__, description='a+b') >>> agent0 = initialize_agent(agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, tools=[tool0], llm=llm) >>> agent1 = initialize_agent(agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, tools=[tool1], llm=llm) >>> agent2 = initialize_agent(agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, tools=[tool2], llm=llm) >>> agent0.run('hello') 'Hi there! How can I assist you today?' >>> agent0.invoke(dict(input='hello')) {'input': 'hello', 'output': 'Hi there! How can I assist you today?'} >>> agent1.run('hello') Traceback (most recent call last): File "<stdin>", line 1, in <module> File "D:\workspace\agi-baby\venv\lib\site-packages\langchain\chains\base.py", line 507, in run return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[ File "D:\workspace\agi-baby\venv\lib\site-packages\langchain\chains\base.py", line 288, in __call__ inputs = self.prep_inputs(inputs) File "D:\workspace\agi-baby\venv\lib\site-packages\langchain\chains\base.py", line 435, in prep_inputs raise ValueError( ValueError: A single string input was passed in, but this chain expects multiple inputs ({'', 'input'}). When a chain expects multiple inputs, please call it by passing in a dictionary, eg `chain({'foo': 1, 'bar': 2})` >>> agent1.invoke(dict(input='hello')) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "D:\workspace\agi-baby\venv\lib\site-packages\langchain\chains\base.py", line 89, in invoke return self( File "D:\workspace\agi-baby\venv\lib\site-packages\langchain\chains\base.py", line 288, in __call__ inputs = self.prep_inputs(inputs) File "D:\workspace\agi-baby\venv\lib\site-packages\langchain\chains\base.py", line 445, in prep_inputs self._validate_inputs(inputs) File "D:\workspace\agi-baby\venv\lib\site-packages\langchain\chains\base.py", line 197, in _validate_inputs raise ValueError(f"Missing some input keys: {missing_keys}") ValueError: Missing some input keys: {''} >>> agent2.run('hello') Traceback (most recent call last): File "<stdin>", line 1, in <module> File "D:\workspace\agi-baby\venv\lib\site-packages\langchain\chains\base.py", line 507, in run return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[ File "D:\workspace\agi-baby\venv\lib\site-packages\langchain\chains\base.py", line 288, in __call__ inputs = self.prep_inputs(inputs) File "D:\workspace\agi-baby\venv\lib\site-packages\langchain\chains\base.py", line 435, in prep_inputs raise ValueError( ValueError: A single string input was passed in, but this chain expects multiple inputs ({"'c'", 'input'}). When a chain expects multiple inputs, please call it by passing in a dictionary, eg `chain({'foo': 1, 'bar': 2})` >>> agent2.invoke(dict(input='hello')) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "D:\workspace\agi-baby\venv\lib\site-packages\langchain\chains\base.py", line 89, in invoke return self( File "D:\workspace\agi-baby\venv\lib\site-packages\langchain\chains\base.py", line 288, in __call__ inputs = self.prep_inputs(inputs) File "D:\workspace\agi-baby\venv\lib\site-packages\langchain\chains\base.py", line 445, in prep_inputs self._validate_inputs(inputs) File "D:\workspace\agi-baby\venv\lib\site-packages\langchain\chains\base.py", line 197, in _validate_inputs raise ValueError(f"Missing some input keys: {missing_keys}") ValueError: Missing some input keys: {"'c'"} ``` ### Expected behavior Given a `StructuredTool` which has an argument which defaults to a `dict` value, `StructuredChatAgent` with the tool should work. In the above reproduction codes, `agent1` and `agent2` should works as `agent0` works.
[Maybe Bug] `StructuredChatAgent` raises `ValueError` with a `StructuredTool` which has an argument which defaults to a `dict` default value
https://api.github.com/repos/langchain-ai/langchain/issues/14521/comments
3
2023-12-11T08:41:13Z
2024-03-18T16:08:34Z
https://github.com/langchain-ai/langchain/issues/14521
2,035,123,971
14,521
[ "hwchase17", "langchain" ]
### System Info LangChain: 0.0.311 Python: 3.11 OS: macOS 11.6 ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [X] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction Thought: The user is asking for help implementing CRUD operations for a specific table in a MySQL database using the Go language. This is a programming task so I will use the programmer_agent tool to help with this. Action: ``` { "action": "programmer_agent", "action_input": { "task": { "title": "Implement CRUD operations for limited_relationships_config table in MySQL using Go", "description": "Write functions for adding, updating, deleting, and retrieving limited relationships configurations in a MySQL database using Go. The operations will be performed on the `limited_relationships_config` table.", "type": "code" } } } ``` ### Expected behavior Thought: The user is asking for help implementing CRUD operations for a specific table in a MySQL database using the Go language. This is a programming task so I will use the programmer_agent tool to help with this. Action: ``` { "action": "programmer_agent", "action_input": { "task": "Implement CRUD operations for limited_relationships_config table in MySQL using Go" } } ```
StructuredChatAgent would go wrong when the input contain some code such as protobuf.
https://api.github.com/repos/langchain-ai/langchain/issues/14520/comments
3
2023-12-11T07:44:54Z
2024-03-18T16:08:29Z
https://github.com/langchain-ai/langchain/issues/14520
2,035,027,147
14,520
[ "hwchase17", "langchain" ]
### Feature request i want to use gpt2 for text genration & want to control the llm ### Motivation gpt2 is smaller version & its is best for next-word prediction ### Your contribution want to use below code for loading gpt2 model ``` from transformers import AutoModelForCausalLM, AutoTokenizer model_id = 'gpt2' tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) prompt = 'want to do more ' config1 = { 'num_beams': 3, # Number of beams for beam search 'do_sample': True, # Whether to do sampling or not 'temperature': 0.6 # The value used to module the next token probabilities } config2 = { 'penalty_alpha': 0.6, # The penalty alpha for contrastive search 'top_k': 6 # The number of highest probability vocabulary tokens to keep for top-k-filtering } inputs = tokenizer(prompt, return_tensors='pt') output = model.generate(**inputs, **config1) # Here, you should choose config1 or config2 result = tokenizer.decode(output[0], skip_special_tokens=True) print("------->>>>.",result) ```
how to use gpt2 with custom promt?
https://api.github.com/repos/langchain-ai/langchain/issues/14519/comments
1
2023-12-11T07:38:08Z
2024-03-18T16:08:23Z
https://github.com/langchain-ai/langchain/issues/14519
2,035,017,496
14,519
[ "hwchase17", "langchain" ]
### System Info ```langchain = "^0.0.345"``` I want to embed and store multiple documents in PGVector and RAG query the DB. When saving documents, I am specifying a collection_name for each document. (For example, if I have 3 documents, I have 3 collection_names) Is it possible to separate collections like this? Also, is the collection_name required when connecting to PGVector? ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [X] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction db1 = PGVector.from_documents( embedding=embed, documents=documents, collection_name="my doc 1", connection_string=CONNECTION_STRING ) db2 = PGVector.from_documents( embedding=embed, documents=documents, collection_name="my doc 2", connection_string=CONNECTION_STRING ) # after, To query the DB where the document is stored. db_connection = PGVector( embedding=embed, documents=documents, collection_name="my doc 2", # I don't want to specify a collection_name. connection_string=CONNECTION_STRING ) ### Expected behavior I want to connect to the PGVector only the first time, and then use that session to query the collection_name (1 document in my case).
Is 'collection_name' required when initializing 'PGVector'?
https://api.github.com/repos/langchain-ai/langchain/issues/14518/comments
2
2023-12-11T05:36:15Z
2024-03-28T14:13:27Z
https://github.com/langchain-ai/langchain/issues/14518
2,034,867,869
14,518
[ "hwchase17", "langchain" ]
https://github.com/langchain-ai/langchain/blob/c0f4b95aa9961724ab4569049b4c3bc12ebbacfc/libs/langchain/langchain/vectorstores/chroma.py#L742 This function breaks the pattern of how the `embedding_function` is referenced by just calling it `embedding`. Small issue but definitely makes it a bit confusing to navigate without diving into the code/docs. Happy to PR it in if worthwhile
Breaking of pattern in `from_document` function
https://api.github.com/repos/langchain-ai/langchain/issues/14517/comments
1
2023-12-11T05:30:25Z
2024-03-18T16:08:13Z
https://github.com/langchain-ai/langchain/issues/14517
2,034,861,990
14,517
[ "hwchase17", "langchain" ]
### System Info Python 3.10.2 langchain version 0.0.339 WSL ### Who can help? @hwchase17 @agola11 ### Information - [x ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [X] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction I am using Qdrant on docker with information preloaded. However im unable to get it to search ``` MODEL_TO_USE = 'all-mpnet-base-v2' client = QdrantClient(QDRANT_ENDPOINT, port=6333, timeout=120) embeddings = HuggingFaceEmbeddings(model_name=MODEL_TO_USE) qdrant = Qdrant(client, collection_name, embeddings) conversation_chain = ConversationalRetrievalChain.from_llm( llm=llm, retriever=qdrant.as_retriever(search_kwargs={'k': 5}), get_chat_history=lambda h : h, memory=memory ) # im using a streamlit frontend import streamlit as st st.session_state.conversation = conversation_chain # during chat result = st.session_state.conversation({ "question": user_query, "chat_history": st.session_state['chat_history']}) ``` Result would then lead to the error. ### Expected behavior Chat is supposed to work instead of getting ``` File "pydantic/main.py", line 341, in pydantic.main.BaseModel.__init__ pydantic.error_wrappers.ValidationError: 1 validation error for Document page_content none is not an allowed value (type=type_error.none.not_allowed) ```
Qdrant retriever with existing data leads to pydantic.error_wrappers.ValidationError: 1 validation error for Document page_content none is not an allowed value (type=type_error.none.not_allowed)
https://api.github.com/repos/langchain-ai/langchain/issues/14515/comments
16
2023-12-11T02:50:24Z
2024-06-25T10:46:41Z
https://github.com/langchain-ai/langchain/issues/14515
2,034,721,756
14,515
[ "hwchase17", "langchain" ]
### Issue with current documentation: _No response_ ### Idea or request for content: _No response_
DOC: Could load GGUF models from https
https://api.github.com/repos/langchain-ai/langchain/issues/14514/comments
21
2023-12-11T02:45:28Z
2024-03-19T16:05:38Z
https://github.com/langchain-ai/langchain/issues/14514
2,034,717,889
14,514
[ "hwchase17", "langchain" ]
### Feature request Customize how messages are formatted in MessagesPlaceholder Currently, history messages are always format to: ''' Human: ... AI: ... ''' Popular chat fine-tunings use all sorts of different formats. Example: ''' <|im_start|>user ...<|im_end|> <|im_start|>assistant ...<|im_end|> ''' There's currently no way to change how history messages are prompted. ### Motivation I wanted to use langchain to make chatbots. Currently that use case is not well supported. ### Your contribution Here's how I got it to work. I have to manually parse the history in a previous step. <img width="683" alt="image" src="https://github.com/langchain-ai/langchain/assets/2053475/15fe31e2-cc23-47f2-9d0c-d5670f22768a">
Customize how messages are formatted in MessagesPlaceholder
https://api.github.com/repos/langchain-ai/langchain/issues/14513/comments
1
2023-12-10T23:33:17Z
2024-03-17T16:10:46Z
https://github.com/langchain-ai/langchain/issues/14513
2,034,583,189
14,513
[ "hwchase17", "langchain" ]
### System Info 0.0.348, linux , python 3.11 ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [x] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction ``` chain_with_history = RunnableWithMessageHistory( chain, lambda session_id: RedisChatMessageHistory(session_id, url=REDIS_URL, ttl=600), input_messages_key="inputs", history_messages_key="history", ) return chain_with_history response = customer_support_invoke().stream( {"inputs": userChats.inputs, "profile": userChats.profile}, config={"configurable": {"session_id": "123"}}, ) for s in response: print(s.content, end="", flush=True) ``` if I streaming twice , it will raise an error "ValueError: Got unexpected message type: AIMessageChunk " ### Expected behavior streaming correctly
RunnableWithMessageHistory streaming bug
https://api.github.com/repos/langchain-ai/langchain/issues/14511/comments
11
2023-12-10T20:43:15Z
2024-04-05T16:07:05Z
https://github.com/langchain-ai/langchain/issues/14511
2,034,526,238
14,511
[ "hwchase17", "langchain" ]
### Issue with current documentation: import requests from typing import Optional from langchain.tools import StructuredTool ``` python def post_message(url: str, body: dict, parameters: Optional[dict]=None) -> str: """Sends a POST request to the given url with the given body and parameters.""" result = requests.post(url, json=body, params=parameters) return result.text custom_tool = StructuredTool.from_function(post_message) from langchain.agents import initialize_agent, AgentType from langchain.chat_models import ChatOpenAI tools = [custom_tool] # Add any tools here llm = ChatOpenAI(temperature=0) # or any other LLM agent_chain = initialize_agent(tools, llm, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True) ``` How to run this and get the results if I have an api endpoint ### Idea or request for content: _No response_
Help me run this
https://api.github.com/repos/langchain-ai/langchain/issues/14508/comments
8
2023-12-10T16:37:15Z
2023-12-11T04:49:51Z
https://github.com/langchain-ai/langchain/issues/14508
2,034,443,411
14,508
[ "hwchase17", "langchain" ]
### Issue with current documentation: The in-memory cache section of the LLM Caching documentation shows uncached and subsequent cached responses that differ. The later examples show cached responses that are the same as the uncached response, which makes more sense. ### Idea or request for content: The cached response under LLM Caching: In-memory should be "\n\nWhy couldn't the bicycle stand up by itself? It was...two tired!"
DOC: LLM Caching example using in-memory cache is unclear
https://api.github.com/repos/langchain-ai/langchain/issues/14505/comments
1
2023-12-10T15:22:40Z
2024-03-17T16:10:41Z
https://github.com/langchain-ai/langchain/issues/14505
2,034,414,953
14,505
[ "hwchase17", "langchain" ]
### System Info Langchain version: 0.0.348 Python version: 3.10.6 Platform: Linux The issue is that when using Conversational Chain for with Kendra Retriever with memory, on any followup questions it gives this error. My guess is somehow QueryText is getting more than the {question} value asked. ![image](https://github.com/langchain-ai/langchain/assets/147480492/ddf6ce1f-8ca2-45da-842a-06d2232ba6aa) It works for first hit but for any follow up question (while memory is not empty) it gives this error. ### Who can help? @hwchase17 @ago ### Information - [X] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [X] Vector Stores / Retrievers - [X] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction Steps to reproduce error: Create the retriever object: from langchain.retrievers import AmazonKendraRetriever retriever = AmazonKendraRetriever( index_id=kendra_index_id, top_k = 5 ) Then use it in Conversational Chain: chain = ConversationalRetrievalChain.from_llm( llm, retriever=retriever, memory=memory, verbose=True, condense_question_prompt=chat_prompt, combine_docs_chain_kwargs=dict(prompt=rag_prompt)) chain.run({"question":question}) Run at least twice, to get the error. ### Expected behavior There shouldn't be any such error. Question or QueryText is always less than 1000 characters.
AmazonKendraRetriever: Error The provided QueryText has a character count of 1020, which exceeds the limit.
https://api.github.com/repos/langchain-ai/langchain/issues/14494/comments
5
2023-12-09T16:34:11Z
2024-03-17T16:10:37Z
https://github.com/langchain-ai/langchain/issues/14494
2,033,925,929
14,494
[ "hwchase17", "langchain" ]
### Issue you'd like to raise. I tried to work on SQL cutsom prompt, but it didn't work and is still giving the wrong sql queries . Here is the code : def process_user_input(user_input): create_db() input_db = SQLDatabase.from_uri('sqlite:///sample_db_2.sqlite') llm_1 = OpenAI(temperature=0) db_agent = SQLDatabaseChain.from_llm(llm = llm_1, db = input_db, verbose=True,) chain = create_sql_query_chain(ChatOpenAI(temperature=0), input_db) response = chain.invoke({"question": user_input}) ### Suggestion: _No response_
why the sql langchain's custom prompt is not working?
https://api.github.com/repos/langchain-ai/langchain/issues/14487/comments
2
2023-12-09T12:19:40Z
2024-03-17T16:10:32Z
https://github.com/langchain-ai/langchain/issues/14487
2,033,828,081
14,487
[ "hwchase17", "langchain" ]
### System Info I have retriever implementation like this ``` def get_vector_store(options: StoreOptions) -> VectorStore: """Gets the vector store for the given options.""" vector_store: VectorStore embedding = get_embeddings() store_type = os.environ.get("STORE") if store_type == StoreType.QDRANT.value: client = qdrant_client.QdrantClient( url=os.environ["QDRANT_URL"], prefer_grpc=True, api_key=os.getenv("QDRANT_API_KEY", None), ) vector_store = Qdrant( client, collection_name=options.namespace, embeddings=embedding ) # vector_store = Qdrant.from_documents([], embedding, url='http://localhost:6333', collection=options.namespace) else: raise ValueError("Invalid STORE environment variable value") return vector_store ``` get-embeddings.py ``` return OllamaEmbeddings(base_url=f"host.docker.internal:11434", model="mistral") ``` ``` knowledgebase: VectorStore = get_vector_store(StoreOptions("knowledgebase")) async def get_relevant_docs(text: str, bot_id: str) -> Optional[str]: try: kb_retriever = knowledgebase.as_retriever( search_kwargs={ "k": 3, "score_threshold": vs_thresholds.get("kb_score_threshold"), "filter": {"bot_id": bot_id}, }, ) result = kb_retriever.get_relevant_documents(text) if result and len(result) > 0: # Assuming result is a list of objects and each object has a page_content attribute all_page_content = "\n\n".join([item.page_content for item in result]) return all_page_content return None except Exception as e: logger.error( "Error occurred while getting relevant docs", incident="get_relevant_docs", payload=text, error=str(e), ) return None ``` As long as i use chatgpt embeddings and chat models, i get the correct outputs. Once i switch to ollama, none of my retrievers are working. I see the documents being ingested to qdrant, which means embeddings are working, but retrievers fail to retrieve any document ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [X] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction ss ### Expected behavior retrievers should be able to fetch the documents from qdrant irrespective of embedding models being used
Retrievers don't seem to work properly with ollama
https://api.github.com/repos/langchain-ai/langchain/issues/14485/comments
4
2023-12-09T08:06:14Z
2023-12-10T04:17:57Z
https://github.com/langchain-ai/langchain/issues/14485
2,033,718,595
14,485
[ "hwchase17", "langchain" ]
### Feature request It would be nice to write code for any generic LLM to use in a given chain, being able to specify its provider and parameters in a config file outside the source code. Is there a built-in way to do so? What major roadblocks would you see in doing that? ### Motivation Most examples that I see in the docs or over the Internet tend to specify from code whether the LLM to be used in a given chain has to be from openAI, anthropic, LLaMA, etc., resulting in different codebases, while it would be great to write one single unified codebase and compare the performance of different open-source or API LLMs by simply changing one line of config. This would be especially relevant when the specific LLM provider is not set or known from the get-go and multiple ones should be compared to find the most suitable one in terms of perfomance, efficiency and cost for any given LLM-driven application. ### Your contribution I don't have time for a PR now, but since I have been doing a similar thing for our private codebase at the laboratory, combining openAI, anthropic and LLaMA under a single unified spell without using the langchain framework, I may be interested in supporting that in the future as the framework already has lots of interesting features in terms of tooling, memory and storage.
generic codebase?
https://api.github.com/repos/langchain-ai/langchain/issues/14483/comments
7
2023-12-09T07:06:17Z
2024-04-09T16:14:17Z
https://github.com/langchain-ai/langchain/issues/14483
2,033,679,268
14,483
[ "hwchase17", "langchain" ]
### System Info langchain==0.0.335 ### Who can help? @hwchase17 @agol ### Information - [ ] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [X] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction 1. Create new object of `langchain.llms.vertexai.VertexAI` class. 2. Add attribute `request_timeout` with some value. 3. Run the model. Timeout specified is not respected. ### Expected behavior VertexAI request should timeout as per the `request_timeout` specified and then retried as per retry configuration. This works flawlessly for `AzureChatOpenAI`.
VertexAI classs doesn't support request_timeout
https://api.github.com/repos/langchain-ai/langchain/issues/14478/comments
2
2023-12-09T03:28:39Z
2024-03-18T16:08:03Z
https://github.com/langchain-ai/langchain/issues/14478
2,033,581,284
14,478
[ "hwchase17", "langchain" ]
### Issue with current documentation: Hi, I'm trying to understand _**Pinecone.from_documents**_ as shown [here ](https://python.langchain.com/docs/integrations/vectorstores/pinecone). I saw it used the same way in another tutorial as well and it seems like it should be calling OpenAIEmbeddings.embed_documents at some point and then upserting the texts and vectors to the pinecone index, but I can't find the actual python file for the method or any documentation anywhere. I'm trying to adapt it and need to understand how **_from_documents_** works. Specifically I am trying to do the following and am curious if the method could be used in this way My goals: - Use Pinecone.from_documents(documents, embeddings, index_name=index_name, namespace=namespace) in a for loop where in each iteration namespace change and the "chunked" documents are different. Each namespace represents a different chunking strategy for an experiment. Note that I defined embeddings=OpenAIEmbeddingsWrapper(model=embedding_model_name) before the for loop, where OpenAIEmbeddingsWrapper is a wrapper class around the OpenAIEmbeddings object, and embedding_model_name="text-embedding-ada-002". Why I'm asking: - It seems like the number of texts and vectors that I can extract from OpenAIEmbeddingsWrapper (using the OpenAIEmbeddings.embed_documents method) for each namespace doesn't match what's in Pinecone (610 texts/vectors from the method vs 1251 in Pinecone). ### Idea or request for content: Can you share some details about Pinecone.from_documents and if it can be used multiple times to upsert chunk documents onto a pinecone index?
DOC: Need clarity on Pinecone.from_documents and OpenAIEmbeddings.
https://api.github.com/repos/langchain-ai/langchain/issues/14472/comments
7
2023-12-08T22:48:39Z
2024-03-18T16:07:59Z
https://github.com/langchain-ai/langchain/issues/14472
2,033,402,079
14,472
[ "hwchase17", "langchain" ]
### System Info OS: Using docker image amd64/python:3.10-slim Python Version: 3.10.13 Langchain Version: 0.0.336 OpenAI Version: 0.27.7 Tenacity Version: 4.65.0 ### Who can help? @agola11 @hwchase17 ### Information - [X] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [X] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction When I try to use an llm with a custom openai_api_base argument within an agent, the agent appears to be attempting to access the **OpenAI** API endpoint instead of the custom one I have specified. Running: llm = ChatOpenAI(default_headers={"api-key":"**REDACTED**", openai_api_base="**REDACTED**", openai_api_key="none").bind(stop=["\nObservation"]) tools = [] tools.append(Tool.from_function(func=self.get_scores, name="get_scores", description="function to get scores")) prompt = PromptTemplate.from_template("""Answer the following questions as best you can. You have access to the following tools: {tools} Use the following format: Question: the input question you must answer Thought: you should always think about what to do Action: the action to take, should be one of [{tool_names}] Action Input: the input to the action Observation: the result of the action ... (this Thought/Action/Action Input/Observation can repeat N times) Thought: I now know the final answer Final Answer: the final answer to the original input question Begin! Question: {input} Thought:{agent_scratchpad}""") prompt = prompt.partial(tools=render_text_description(tools), tool_names=", ".join([t.name for t in tools]), ) agent = ( { "input": lambda x: x["input"], "agent_scratchpad": lambda x: format_log_to_str(x["intermediate_steps"]), } | prompt | llm | ReActSingleInputOutputParser() ) agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True) output = agent_executor.invoke({"input":"foo"}) yields: `File "/usr/local/lib/python3.10/site-packages/langchain/chains/base.py", line 87, in invoke return self( File "/usr/local/lib/python3.10/site-packages/langchain/chains/base.py", line 310, in __call__ raise e File "/usr/local/lib/python3.10/site-packages/langchain/chains/base.py", line 304, in __call__ self._call(inputs, run_manager=run_manager) File "/usr/local/lib/python3.10/site-packages/langchain/agents/agent.py", line 1245, in _call next_step_output = self._take_next_step( File "/usr/local/lib/python3.10/site-packages/langchain/agents/agent.py", line 1032, in _take_next_step output = self.agent.plan( File "/usr/local/lib/python3.10/site-packages/langchain/agents/agent.py", line 385, in plan output = self.runnable.invoke(inputs, config={"callbacks": callbacks}) File "/usr/local/lib/python3.10/site-packages/langchain/schema/runnable/base.py", line 1427, in invoke input = step.invoke( File "/usr/local/lib/python3.10/site-packages/langchain/schema/runnable/base.py", line 2787, in invoke return self.bound.invoke( File "/usr/local/lib/python3.10/site-packages/langchain/chat_models/base.py", line 142, in invoke self.generate_prompt( File "/usr/local/lib/python3.10/site-packages/langchain/chat_models/base.py", line 459, in generate_prompt return self.generate(prompt_messages, stop=stop, callbacks=callbacks, **kwargs) File "/usr/local/lib/python3.10/site-packages/langchain/chat_models/base.py", line 349, in generate raise e File "/usr/local/lib/python3.10/site-packages/langchain/chat_models/base.py", line 339, in generate self._generate_with_cache( File "/usr/local/lib/python3.10/site-packages/langchain/chat_models/base.py", line 492, in _generate_with_cache return self._generate( File "/usr/local/lib/python3.10/site-packages/langchain/chat_models/openai.py", line 422, in _generate response = self.completion_with_retry( File "/usr/local/lib/python3.10/site-packages/langchain/chat_models/openai.py", line 352, in completion_with_retry return _completion_with_retry(**kwargs) File "/usr/local/lib/python3.10/site-packages/tenacity/__init__.py", line 289, in wrapped_f return self(f, *args, **kw) File "/usr/local/lib/python3.10/site-packages/tenacity/__init__.py", line 379, in __call__ do = self.iter(retry_state=retry_state) File "/usr/local/lib/python3.10/site-packages/tenacity/__init__.py", line 325, in iter raise retry_exc.reraise() File "/usr/local/lib/python3.10/site-packages/tenacity/__init__.py", line 158, in reraise raise self.last_attempt.result() File "/usr/local/lib/python3.10/concurrent/futures/_base.py", line 451, in result return self.__get_result() File "/usr/local/lib/python3.10/concurrent/futures/_base.py", line 403, in __get_result raise self._exception File "/usr/local/lib/python3.10/site-packages/tenacity/__init__.py", line 382, in __call__ result = fn(*args, **kwargs) File "/usr/local/lib/python3.10/site-packages/langchain/chat_models/openai.py", line 350, in _completion_with_retry return self.client.create(**kwargs) File "/usr/local/lib/python3.10/site-packages/openai/api_resources/chat_completion.py", line 25, in create return super().create(*args, **kwargs) File "/usr/local/lib/python3.10/site-packages/openai/api_resources/abstract/engine_api_resource.py", line 153, in create response, _, api_key = requestor.request( File "/usr/local/lib/python3.10/site-packages/openai/api_requestor.py", line 230, in request resp, got_stream = self._interpret_response(result, stream) File "/usr/local/lib/python3.10/site-packages/openai/api_requestor.py", line 624, in _interpret_response self._interpret_response_line( File "/usr/local/lib/python3.10/site-packages/openai/api_requestor.py", line 687, in _interpret_response_line raise self.handle_error_response( File "/usr/local/lib/python3.10/site-packages/openai/api_requestor.py", line 337, in handle_error_response raise error.APIError( openai.error.APIError: Invalid response object from API: '{\n "detail": "No authorization token provided",\n "status": 401,\n "title": "Unauthorized",\n "type": "about:blank"\n}\n' (HTTP response code was 401)` When I change the openai_api_base to something nonsensical, the same error is returned, making me think that it is using OpenAI's API base and not the custom one specified. ### Expected behavior I would expect the agent to work as shown here: https://python.langchain.com/docs/modules/agents/agent_types/react
agent executor not using custom openai_api_base
https://api.github.com/repos/langchain-ai/langchain/issues/14470/comments
10
2023-12-08T21:41:20Z
2024-03-18T16:07:54Z
https://github.com/langchain-ai/langchain/issues/14470
2,033,344,391
14,470
[ "hwchase17", "langchain" ]
### System Info langchain==0.0.346 ### Who can help? @hwch ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [X] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [X] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction https://github.com/langchain-ai/langchain/pull/14266 added a deprecation for `input_variables` argument of `PromptTemplate.from_file`. It was released in 0.0.346. However, https://github.com/langchain-ai/langchain/blob/v0.0.346/libs/langchain/langchain/chains/llm_summarization_checker/base.py#L20-L31 still uses `input_variables` at the module-level. So now this `DeprecationWarning` is emitted simply for importing from LangChain. Can we fix this, so LangChain isn't emitting `DeprecationWarning`s? ### Expected behavior I expect LangChain to not automatically emit `DeprecationWarning`s when importing from it
DeprecationWarning: `input_variables' is deprecated and ignored
https://api.github.com/repos/langchain-ai/langchain/issues/14467/comments
1
2023-12-08T21:10:46Z
2023-12-13T01:43:28Z
https://github.com/langchain-ai/langchain/issues/14467
2,033,316,778
14,467
[ "hwchase17", "langchain" ]
### Issue you'd like to raise. Hi, I'm using the llama2 model for SQL, and I modified it to work directly using LLM. I also added more tables to test the model, and I'm modifying the prompt. When I do this, it generates longer queries based on the question I send, but when the query is very long, it doesn't complete generating everything. I checked the response character count, and on average, it returns around 400+- maximum response characters (I tried increasing and removing the character limit, but it didn't solve the problem). llm = Replicate( model=llama2_13b, input={"temperature": 0.1, "max_length": 2000, Incorrect return type. Retrieve the performance of Miles Norris in his most recent game. ```sql SELECT pg_player_game_stats.Points, pg_player_game_stats.Rebounds, pg_player_game_stats.Assists FROM nba_roster_temp JOIN player_game_stats ON nba_roster_temp."PlayerID" = player_game_stats."PlayerID" WHERE nba_roster As you can see, the SQL query it returns is incomplete, and here I'm also counting the number of characters. number character: 401 Any help you can provide would be appreciated ### Suggestion: _No response_
Issue: llama2-sql for long queries doesn't return the complete query.
https://api.github.com/repos/langchain-ai/langchain/issues/14465/comments
1
2023-12-08T19:31:22Z
2024-03-16T16:14:16Z
https://github.com/langchain-ai/langchain/issues/14465
2,033,203,436
14,465
[ "hwchase17", "langchain" ]
### Issue you'd like to raise. Hi, I'm trying to implement the [web scraping tutorial ](https://python.langchain.com/docs/use_cases/web_scraping#llm-with-function-calling) using ChatOllama instead of ChatOpenAI. This is what I'm trying to do: ``` import pprint from langchain.chains import create_extraction_chain from langchain.document_loaders import AsyncChromiumLoader from langchain.document_transformers import BeautifulSoupTransformer from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.chat_models import ChatOllama def extract(content: str, schema: dict, llm): return create_extraction_chain(schema=schema, llm=llm).run(content) def scrape_with_playwright(urls, schema, llm): loader = AsyncChromiumLoader(urls) docs = loader.load() bs_transformer = BeautifulSoupTransformer() docs_transformed = bs_transformer.transform_documents( docs, tags_to_extract=["span"] ) print("Extracting content with LLM") # Grab the first 1000 tokens of the site splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder( chunk_size=1000, chunk_overlap=0 ) splits = splitter.split_documents(docs_transformed) # Process the first split extracted_content = extract(schema=schema, content=splits[0].page_content, llm=llm) return extracted_content if __name__ == '__main__': llm = ChatOllama(base_url="https://localhost:11434", model="llama2") schema = { "properties": { "news_article_title": {"type": "string"}, "news_article_summary": {"type": "string"}, }, "required": ["news_article_title", "news_article_summary"], } urls = ["https://www.wsj.com"] extracted_content = scrape_with_playwright(urls, schema=schema, llm=llm) pprint.pprint(extracted_content) ``` Instead of the results shown I get this error: `requests.exceptions.SSLError: HTTPSConnectionPool(host='localhost', port=11434): Max retries exceeded with url: /api/generate/ (Caused by SSLError(SSLError(1, '[SSL: WRONG_VERSION_NUMBER] wrong version number (_ssl.c:1006)')))` when the `extract` function is called. Could anyone please help me understand what I'm doing wrong? Thanks! ### Suggestion: _No response_
Web Scraping with ChatOllama gives SSL: WRONG_VERSION_NUMBER
https://api.github.com/repos/langchain-ai/langchain/issues/14450/comments
6
2023-12-08T15:19:32Z
2024-07-03T07:29:16Z
https://github.com/langchain-ai/langchain/issues/14450
2,032,847,275
14,450
[ "hwchase17", "langchain" ]
During the recent initiative to secure API keys with `SecretStr` (https://github.com/langchain-ai/langchain/issues/12165), some implementations and their corresponding tests were implemented with some flaws. More specifically, they were not really masking the API key. For instsance, in `libs/langchain/langchain/chat_models/javelin_ai_gateway.py` we have: ``` @property def _default_params(self) -> Dict[str, Any]: params: Dict[str, Any] = { "gateway_uri": self.gateway_uri, "javelin_api_key": cast(SecretStr, self.javelin_api_key).get_secret_value(), "route": self.route, **(self.params.dict() if self.params else {}), } return params ``` In the above snippet, `self.javelin_api_key` is cast to `SecretStr`, and then immediately `.get_secret_value()` is invoked, essentially retrieving the original string. Note that Javelin chat lacks unit tests. This could be used to handle the case where the API key is `None`, but then it might appear like there's no masking and it's preferable to address the `None` case directly. It's worth noting that this pattern is repeated in tests, such as in `libs/langchain/tests/integration_tests/chat_models/test_baiduqianfan.py`: ``` def test_uses_actual_secret_value_from_secret_str() -> None: """Test that actual secret is retrieved using `.get_secret_value()`.""" chat = QianfanChatEndpoint( qianfan_ak="test-api-key", qianfan_sk="test-secret-key", ) assert cast(SecretStr, chat.qianfan_ak).get_secret_value() == "test-api-key" assert cast(SecretStr, chat.qianfan_sk).get_secret_value() == "test-secret-key" ``` The point of the test would be to assert that the API key is indeed a secret, and not just cast it back and forth. Let me point out that the test suite for baiduqianfan chat does indeed catch whether the API key is indeed masked with a `SecretStr` by capturing the stdout. @eyurtsev @hwchase17 ### Suggestion: PR to fix the issues
Issue: Flawed implementations of SecretStr for API keys
https://api.github.com/repos/langchain-ai/langchain/issues/14445/comments
1
2023-12-08T13:45:01Z
2024-02-02T14:32:30Z
https://github.com/langchain-ai/langchain/issues/14445
2,032,683,865
14,445
[ "hwchase17", "langchain" ]
### System Info npm version: "^0.0.203" MacOS Bun version: 1.0.15+b3bdf22eb ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [X] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [X] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [X] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction The following code will cause this error: ``` import { Pinecone } from '@pinecone-database/pinecone'; import { VectorDBQAChain } from 'langchain/chains'; import { OpenAIEmbeddings } from 'langchain/embeddings/openai'; import { OpenAI } from 'langchain/llms/openai'; import { PineconeStore } from 'langchain/vectorstores/pinecone'; const pinecone = new Pinecone(); const indexKey = process.env.PINECONE_INDEX_KEY; if (!indexKey) { throw new Error('PINECONE_INDEX_KEY is not set.'); } const pineconeIndex = pinecone.Index(indexKey); export async function queryDocuments(query: string, returnSourceDocuments = false) { const vectorStore = await PineconeStore.fromExistingIndex( new OpenAIEmbeddings({ modelName: 'text-embedding-ada-002', }), { pineconeIndex, }, ); const model = new OpenAI({ modelName: 'gpt-4-1106-preview', }); const chain = VectorDBQAChain.fromLLM(model, vectorStore, { k: 5, returnSourceDocuments, }); return await chain.call({ query }); } ``` The embeddings have been created and confirmed to exist in the Pinecone console, e.g.: <img width="1240" alt="Screenshot 2023-12-08 at 13 46 24" src="https://github.com/langchain-ai/langchain/assets/1304307/66c23c7e-916a-461d-b8f6-28a7fa460300"> ### Expected behavior I would expect it to query the vector DB and correctly prompt GPT-4 with the results. But instead, I get the following error: ``` ? Enter your query what is the third wave of dam Creating query for "what is the third wave of dam"... 499 | var _a; 500 | return __generator(this, function (_b) { 501 | switch (_b.label) { 502 | case 0: 503 | _a = this.transformer; 504 | return [4 /*yield*/, this.raw.json()]; ^ SyntaxError: Unexpected end of JSON input at /Users/andy/dev/runestone/node_modules/@pinecone-database/pinecone/dist/pinecone-generated-ts-fetch/runtime.js:504:46 at step (/Users/andy/dev/runestone/node_modules/@pinecone-database/pinecone/dist/pinecone-generated-ts-fetch/runtime.js:72:18) at /Users/andy/dev/runestone/node_modules/@pinecone-database/pinecone/dist/pinecone-generated-ts-fetch/runtime.js:53:53 at /Users/andy/dev/runestone/node_modules/@pinecone-database/pinecone/dist/pinecone-generated-ts-fetch/runtime.js:47:9 at new Promise (:1:21) at /Users/andy/dev/runestone/node_modules/@pinecone-database/pinecone/dist/pinecone-generated-ts-fetch/runtime.js:43:12 at /Users/andy/dev/runestone/node_modules/@pinecone-database/pinecone/dist/pinecone-generated-ts-fetch/runtime.js:498:16 at /Users/andy/dev/runestone/node_modules/@pinecone-database/pinecone/dist/pinecone-generated-ts-fetch/apis/VectorOperationsApi.js:405:46 at step (/Users/andy/dev/runestone/node_modules/@pinecone-database/pinecone/dist/pinecone-generated-ts-fetch/apis/VectorOperationsApi.js:84:18) ```
Unexpected end of JSON
https://api.github.com/repos/langchain-ai/langchain/issues/14443/comments
1
2023-12-08T12:47:37Z
2024-03-18T16:07:49Z
https://github.com/langchain-ai/langchain/issues/14443
2,032,599,462
14,443
[ "hwchase17", "langchain" ]
### Issue you'd like to raise. Hello Team, We are using opensearch vectordb to store the embeddings, then we are using these embeddings to retrieve the similar document during conversastnal retrieval. while checking the settings of the index created by vector_db.add_text(page_content,metadatas). i have seen the number_of_replicas as 5 and shards as 1(this is the default behaviour of vector_db.add_text(page_content,metadats) **I want to pass as number_of_replicas as 1** and shards as 1 whenever the index is created on opensearch. Can you please help me with this, how can i custom pass this replicas value. Also i am adding code for better understanding below In the below code if u see the **langchain vector_db.add_text() in Line 109** creating a index with 5 shards by default. i just want to pass this parameter manually lets say 1 shard. Can u please help me out here. Please let me know if you want any more info to understand my issue. ![langchain_num_shard](https://github.com/langchain-ai/langchain/assets/34799343/453e20f7-15c3-4aa5-a862-398696037b0f) ### Suggestion: _No response_
Issue: Opensearch manually assigned shards and replicas while using vector_db.add_text(page_contents,metatas)
https://api.github.com/repos/langchain-ai/langchain/issues/14442/comments
1
2023-12-08T11:02:46Z
2024-03-16T16:14:06Z
https://github.com/langchain-ai/langchain/issues/14442
2,032,443,629
14,442
[ "hwchase17", "langchain" ]
### Issue you'd like to raise. #prompt template How Can I use Prompt Template in my code below? def chat_langchain(new_project_qa, query, not_uuid): check = query.lower() user_experience_inst = UserExperience.objects.get(not_uuid=not_uuid) # query="If the context is related to hi/hello with or without incorrect spelling then reply Hi! How can I assist you today?,else Search the entire context and provide formal and accurate answer for this query - {}. Explain the relevant information with important points, if necessary.If you don't find answer then give relavant answer else reply with text 'Sorry, I can't find the related information' ".format(check) if check not in ['hi','hello','hey','hui','hiiii','hii','hiii','heyyy'] and not user_experience_inst.custom_prompt: query = "Search the entire context and provide formal and accurate answer for this query - {}. Explain the relevant information with important points, if necessary.If you don't find answer then reply with text 'Sorry, I can't find the related information' otherwise give relavant answer".format(check) elif check not in ['hi','hello','hey','hui','hiiii','hii','hiii','heyyy'] and user_experience_inst.custom_prompt: query = f"{user_experience_inst.custom_prompt} {check}.If you don't find answer then reply with text 'Sorry, I can't find the related information'" else: query="Search the entire context and provide formal and accurate answer for this query - {}. Explain the relevant information with important points, if necessary.".format(check) result = new_project_qa(query) relevant_document = result['source_documents'] if relevant_document: source = relevant_document[0].metadata.get('source', '') # Check if the file extension is ".pdf" file_extension = os.path.splitext(source)[1] if file_extension.lower() == ".pdf": source = os.path.basename(source) # Retrieve the UserExperience instance using the provided not_uuid user_experience_inst = UserExperience.objects.get(not_uuid=not_uuid) bot_ending = user_experience_inst.bot_ending_msg if user_experience_inst.bot_ending_msg is not None else "" # Create the list_json dictionary if bot_ending != '': list_json = { 'bot_message': result['result'] + '\n\n' + str(bot_ending), "citation": source } else: list_json = { 'bot_message': result['result'] + str(bot_ending), "citation": source } else: # Handle the case when relevant_document is empty list_json = { 'bot_message': result['result'], 'citation': '' } # Return the list_json dictionary return list_json ### Suggestion: _No response_
Issue: How Can I use Prompt Template?
https://api.github.com/repos/langchain-ai/langchain/issues/14441/comments
1
2023-12-08T10:24:07Z
2024-03-16T16:14:01Z
https://github.com/langchain-ai/langchain/issues/14441
2,032,387,502
14,441
[ "hwchase17", "langchain" ]
### Feature request When utilizing custom_table_info in the sqldatabase instance while employing the create_sql_agent function, it appears that there is an issue where it disregards the sql_db_schema. Currently, it only utilizes either the custom_table_info or the sql_db_schema. This poses a challenge, especially when crucial information, such as identifying which ID column corresponds to other tables, cannot be specified in the custom table info. There is a need for an option to use both the custom table information and the sql_db_schema table_info={'invoice':" the customer_id in invoice table is referenced to customers table's company_id",} db=SQLDatabase(engine=dbengine,include_tables=["invoice","customer"],custom_table_info=table_info) Invoking: `sql_db_schema` with `customer, invoice` CREATE TABLE customer ( id SERIAL NOT NULL, key VARCHAR NOT NULL, company_id VARCHAR NOT NULL, company_name VARCHAR NOT NULL, ) /* 1 rows from customer table: id key company_id company_name 670 CUST-0ab15 17 Aim Inc */ the customer_id in invoice table is referenced to customers table's company_id, It doesnt have the schema of invoice table so generating wrong sql queries ### Motivation I need both custom_table_info and sql_db_schema to work, as some kinda metadata is needed to specify that is specific to my use cases. ### Your contribution NO
custom_table_info along with sql_db_schema while using create_sql_agent
https://api.github.com/repos/langchain-ai/langchain/issues/14440/comments
1
2023-12-08T10:10:16Z
2024-03-16T16:13:56Z
https://github.com/langchain-ai/langchain/issues/14440
2,032,367,290
14,440
[ "hwchase17", "langchain" ]
### Issue with current documentation: The supabase vectorstore does not support setting the `score_threshold` in `as_retriever` despite being showcased as an option in the vectorestore superclass docstring example. https://github.com/langchain-ai/langchain/blob/a05230a4ba4dee591d3810440ce65e16860956ae/libs/langchain/langchain/vectorstores/supabase.py#L218 https://github.com/langchain-ai/langchain/blob/a05230a4ba4dee591d3810440ce65e16860956ae/libs/core/langchain_core/vectorstores.py#L596 ### Idea or request for content: The VectoreStore superclass of SupabaseVectoreStore contains logic in `similarity_search_by_vector_with_relevance_scores` that could be used in the SupabaseVectorStore subclass to support the `score_threshold` parameter.
DOC: `SupabaseVectorStore` support for similarity `score_threshold` filtering in `as_retriever`
https://api.github.com/repos/langchain-ai/langchain/issues/14438/comments
2
2023-12-08T09:48:57Z
2024-03-17T16:10:06Z
https://github.com/langchain-ai/langchain/issues/14438
2,032,332,864
14,438
[ "hwchase17", "langchain" ]
### System Info Python 3.11 Langchain 0.0.348 ### Who can help? _No response_ ### Information - [x] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [x] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction I am trying DoctranTextTranslator of langchain. However, I got an error message after running below code. Error: doctran_docs[i] = await doc.translate(language=self.language).execute() ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ TypeError: object Document can't be used in 'await' expression ``` from langchain.document_transformers import DoctranTextTranslator from langchain.schema import Document from dotenv import load_dotenv import asyncio load_dotenv() sample_text = """[Generated with ChatGPT] Confidential Document - For Internal Use Only Date: July 1, 2023 Subject: Updates and Discussions on Various Topics Dear Team, I hope this email finds you well. In this document, I would like to provide you with some important updates and discuss various topics that require our attention. Please treat the information contained herein as highly confidential. Security and Privacy Measures As part of our ongoing commitment to ensure the security and privacy of our customers' data, we have implemented robust measures across all our systems. We would like to commend John Doe (email: [email protected]) from the IT department for his diligent work in enhancing our network security. Moving forward, we kindly remind everyone to strictly adhere to our data protection policies and guidelines. Additionally, if you come across any potential security risks or incidents, please report them immediately to our dedicated team at [email protected]. HR Updates and Employee Benefits Recently, we welcomed several new team members who have made significant contributions to their respective departments. I would like to recognize Jane Smith (SSN: 049-45-5928) for her outstanding performance in customer service. Jane has consistently received positive feedback from our clients. Furthermore, please remember that the open enrollment period for our employee benefits program is fast approaching. Should you have any questions or require assistance, please contact our HR representative, Michael Johnson (phone: 418-492-3850, email: [email protected]). Marketing Initiatives and Campaigns Our marketing team has been actively working on developing new strategies to increase brand awareness and drive customer engagement. We would like to thank Sarah Thompson (phone: 415-555-1234) for her exceptional efforts in managing our social media platforms. Sarah has successfully increased our follower base by 20% in the past month alone. Moreover, please mark your calendars for the upcoming product launch event on July 15th. We encourage all team members to attend and support this exciting milestone for our company. Research and Development Projects In our pursuit of innovation, our research and development department has been working tirelessly on various projects. I would like to acknowledge the exceptional work of David Rodriguez (email: [email protected]) in his role as project lead. David's contributions to the development of our cutting-edge technology have been instrumental. Furthermore, we would like to remind everyone to share their ideas and suggestions for potential new projects during our monthly R&D brainstorming session, scheduled for July 10th. Please treat the information in this document with utmost confidentiality and ensure that it is not shared with unauthorized individuals. If you have any questions or concerns regarding the topics discussed, please do not hesitate to reach out to me directly. Thank you for your attention, and let's continue to work together to achieve our goals. Best regards, Jason Fan Cofounder & CEO Psychic [email protected] """ documents = [Document(page_content=sample_text)] qa_translator = DoctranTextTranslator(language="spanish", openai_api_model="gpt-3.5-turbo") async def atransform_documents(docs): return await qa_translator.atransform_documents(docs) translated_document = asyncio.run(atransform_documents(documents)) print(translated_document[0].page_content) ``` ### Expected behavior It should return the translated text.
DoctranTextTranslator Is Not Working
https://api.github.com/repos/langchain-ai/langchain/issues/14437/comments
1
2023-12-08T09:10:36Z
2024-03-16T16:13:46Z
https://github.com/langchain-ai/langchain/issues/14437
2,032,270,431
14,437
[ "hwchase17", "langchain" ]
### System Info langchain version: 0.0.348 for the type hint for es_connection variable in class ElasticsearchChatMessageHistory, module is used as a type @hwchase17 @eyurtsev ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [X] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction import json import logging from time import time from typing import TYPE_CHECKING, Any, Dict, List, Optional from langchain_core.chat_history import BaseChatMessageHistory from langchain_core.messages import ( BaseMessage, message_to_dict, messages_from_dict, ) if TYPE_CHECKING: from elasticsearch import Elasticsearch logger = logging.getLogger(__name__) class ElasticsearchChatMessageHistory(BaseChatMessageHistory): """Chat message history that stores history in Elasticsearch. Args: es_url: URL of the Elasticsearch instance to connect to. es_cloud_id: Cloud ID of the Elasticsearch instance to connect to. es_user: Username to use when connecting to Elasticsearch. es_password: Password to use when connecting to Elasticsearch. es_api_key: API key to use when connecting to Elasticsearch. es_connection: Optional pre-existing Elasticsearch connection. index: Name of the index to use. session_id:Arbitrary key that is used to store the messages of a single chat session. """ def __init__( self, index: str, session_id: str, *, es_connection: Optional["**Elasticsearch**"] = None, es_url: Optional[str] = None, es_cloud_id: Optional[str] = None, es_user: Optional[str] = None, es_api_key: Optional[str] = None, es_password: Optional[str] = None, ): self.index: str = index self.session_id: str = session_id ### Expected behavior import of module elasticsearch is not done properly
import error in elasticsearch memory module: Module cannot be used as a type
https://api.github.com/repos/langchain-ai/langchain/issues/14436/comments
1
2023-12-08T09:09:41Z
2024-03-16T16:13:41Z
https://github.com/langchain-ai/langchain/issues/14436
2,032,269,106
14,436
[ "hwchase17", "langchain" ]
### System Info langchain version:0.0.311 os:macOS 11.6 python: 3.11 ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [X] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction This is code sample of using agent: ``` sys_p = self.create_sys_prompt() tools = [ ProgrammerAgent(self.env), DefaultAgent(), ] agent_obj = StructuredChatAgent.from_llm_and_tools( llm=self.env.llm, tools=tools, prefix=sys_p + PREFIX, verbose=True, ) agent = AgentExecutor.from_agent_and_tools( agent=agent_obj, tools=tools, verbose=True, ) task = f""" Here is the requirement: ```Markdown {requirement} ``` Please Implement the requirement. """ return agent.run(task) ``` this is the code of ProgrammerAgent tool: ``` from typing import Any, Type from langchain.tools import BaseTool from pydantic import Field, BaseModel from enviroment import Environment class ProgramRequirementSchema(BaseModel): task: str = Field(description="Coding task") task_context: str = Field(description="Contextual background information for the task.") project_path: str = Field(description="Project path") class ProgrammerAgent(BaseTool): name: str = "programmer_agent" description: str = """ programmer agent is a agent that write code for a given coding task. """ args_schema: Type[ProgramRequirementSchema] = ProgramRequirementSchema env: Environment = Field(default=None) def __init__(self, env: Environment): super().__init__() self.env = env def _run(self, task: str, task_context: str, project_path: str) -> Any: result = "success" return result ``` And this is the wrong action: ``` Action: ```json { "action": "programmer_agent", "action_input": { "task": { "title": "Implement the requirement", "description": "1. Update the `grpc.proto` file. 2. Design the database and write the create SQL. 3. Implement the database operation interface. 4. Implement the grpc interface." }, "task_context": { "title": "Context", "description": "The project is built with golang, and the database used is relational database. The grpc interface is defined in `grpc.proto`." }, "project_path": "golang.52tt.com/services/tt-rev/offering-room" } } ``` ### Expected behavior the value of action_input should be : ```json { "action": "programmer_agent", "action_input": { "task": "1. Update the `grpc.proto` file. 2. Design the database and write the create SQL. 3. Implement the database operation interface. 4. Implement the grpc interface.", "task_context": "The project is built with golang, and the database used is relational database. The grpc interface is defined in `grpc.proto`.", "project_path": "golang.52tt.com/services/tt-rev/offering-room" } } ```
StructuredChatAgent did not provide the correct action input.
https://api.github.com/repos/langchain-ai/langchain/issues/14434/comments
3
2023-12-08T08:15:05Z
2023-12-08T10:27:18Z
https://github.com/langchain-ai/langchain/issues/14434
2,032,157,152
14,434
[ "hwchase17", "langchain" ]
### System Info Langchain Version: 0.0.346 Python: 3.11.4 ### Who can help? @eyurtsev ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [X] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction ```python from langchain.vectorstores import ElasticsearchStore ``` Results in ``` File "C:\Users\XXX\Desktop\Projects\XXX\api\controllers\Vector.py", line 5, in <module> from langchain.vectorstores import ElasticsearchStore ImportError: cannot import name 'ElasticsearchStore' from 'langchain.vectorstores' ``` or ```python from langchain.vectorstores.elasticsearch import ElasticsearchStore ``` Results in ``` File "C:\Users\XXX\Desktop\Projects\XXX\api\controllers\Vector.py", line 5, in <module> from langchain.vectorstores.elasticsearch import ElasticsearchStore ModuleNotFoundError: No module named 'langchain.vectorstores.elasticsearch' ``` ### Expected behavior I am upgrading from `langchain==0.0.279` to `langchain==0.0.346` and this is the issue that arised. Expected behavior would be import successfully, new langchain version does not seems to be backward compatible for `ElasticsearchStore`
Bug: ImportError for ElasticsearchStore
https://api.github.com/repos/langchain-ai/langchain/issues/14431/comments
3
2023-12-08T07:14:10Z
2023-12-08T15:16:20Z
https://github.com/langchain-ai/langchain/issues/14431
2,032,065,241
14,431
[ "hwchase17", "langchain" ]
### System Info Python ==3.11.3 pymilvus== 2.3.1 langchain==0.0.327 openai==0.28.1 ### Who can help? _No response_ ### Information - [X] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [X] Vector Stores / Retrievers - [ ] Memory - [X] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction Description: The issue encountered revolves around the inability of the retrieval process to filter data based on metadata, specifically focusing on the 'file_name' object in the metadata. The context pertains to utilizing Milvius as the database for the vector store. Details: Metadata Structure: The metadata comprises a list of dictionaries, where each dictionary holds key-value pairs containing metadata information. The 'file_name' attribute is utilized for filtration purposes. Example Metadata List: [{'page_number': '4', 'file_name': 'Apple_history.pdf', 'source_path': '.../Apple_history.pdf'}, ...] Applied Options and Observations: Attempted a approach by directly using a 'filter_query' with the 'file_name'. filter_query = {"filter": {"file_name": 'samsung.pdf'}, "k": self.top} retriever = vectorstore.as_retriever(search_kwargs=filter_query) # Retrieval of top k results result = retriever.get_relevant_documents(agent_query) Similar observations were made where the retrieval fetched data unrelated to the specified 'samsung.pdf' filename. ### Expected behavior The anticipated functionality was to filter the retrieval process based on the 'file_name' metadata attribute. In scenarios where there are no chunks associated with the specified 'file_name', the retrieval should ideally return no data.
Retrieval Inability to Filter Based on Metadata in Milvius Database
https://api.github.com/repos/langchain-ai/langchain/issues/14429/comments
2
2023-12-08T04:37:35Z
2024-03-17T16:10:02Z
https://github.com/langchain-ai/langchain/issues/14429
2,031,914,421
14,429
[ "hwchase17", "langchain" ]
### System Info Langchain Version: 0.0.348 Output from `poetry env info` **Virtualenv** Python: 3.9.12 Implementation: CPython Path: /Users/peternf/Desktop/langchain/libs/langchain/.venv Executable: /Users/peternf/Desktop/langchain/libs/langchain/.venv/bin/python Valid: True **System** Platform: darwin OS: posix Python: 3.9.12 Path: /Users/peternf/opt/miniconda3 Executable: /Users/peternf/opt/miniconda3/bin/python3.9 ### Who can help? @eyurtsev ### Information - [X] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [X] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction ### Reason to Install litellm @xavier-xia-99 and I are collaborating on finishing the tasks of #12165 for _libs/langchain/langchain/chat_models/litellm.py_. We have finished the implementation and is adding unit tests to verify our logic when we discovered that we need to include litellm for the unit tests, or `make test` will just skip our newly added unit tests. Yet as we try to add the optional dependency litellm according to the instructions provided in langchain/.github/CONTRIBUTING.md, it shows the following dependency conflict. Hence, we will appreciate any help in addressing this conflict or running the CI. Thank you! ### Steps to Reproduce the Behavior 1. Run `poetry add --optional litellm` 2. Error Message: <img width="720" alt="Screenshot 2023-12-07 at 5 50 43 PM" src="https://github.com/langchain-ai/langchain/assets/98713019/3e0d5730-2134-4e11-b901-fbda927bc796"> ### Expected behavior Output of litellm downloads in progress and successful installation of relevant packages.
Dependency Conflict between litellm and tiktoken
https://api.github.com/repos/langchain-ai/langchain/issues/14419/comments
1
2023-12-07T23:10:21Z
2024-03-16T16:13:31Z
https://github.com/langchain-ai/langchain/issues/14419
2,031,666,536
14,419
[ "hwchase17", "langchain" ]
### Issue you'd like to raise. im trying to create a metadata to separate user id fields vectors, I searched a lot and couldn't find anything , im using zilliz vector store , I tried using the userid as memory key but it's not possible , any ideas ? Im using the docs is automatically saved and retrieved from the saved collection on zilliz , so I can't edit the docs too , im just trying to add meta fields when user chats and when retrieved for context. ``` vectordb = Milvus.from_documents( {} , embeddings, connection_args={ "uri": ZILLIZ_CLOUD_URI, "token": ZILLIZ_CLOUD_API_KEY, # API key, for serverless clusters which can be used as replacements for user and password "secure": True, }, ) # vectordb. retriever = Milvus.as_retriever(vectordb,search_kwargs= {"k":15 , 'user_id': "a3"} ) # here we use userid with "a" for retreiving memory # print(retriever) memory= VectorStoreRetrieverMemory(retriever=retriever , memory_key="history" , metadata={"user_id": "a3"} ) chain = ConversationChain(llm=self.llm, memory=memory, verbose=True,prompt = PROMPT , metadata={"user_id": "a3"}) res = chain.predict(input=input_text) # with chain_recorder as recording: # llm_response = chain(input_text) return res ``` ### Suggestion: _No response_
Issue: <Zilliz and Milvus metadata field and memory seperation>
https://api.github.com/repos/langchain-ai/langchain/issues/14412/comments
1
2023-12-07T20:08:44Z
2024-03-16T16:13:26Z
https://github.com/langchain-ai/langchain/issues/14412
2,031,444,278
14,412
[ "hwchase17", "langchain" ]
### System Info LangChain version : Latest ```python def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> Optional[bool]: ``` [This check](https://github.com/langchain-ai/langchain/blob/54040b00a4a05e81964a1a7f7edbf0b830d4395c/libs/langchain/langchain/vectorstores/faiss.py#L798) causes the issue. ### Who can help? @eyurtsev ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [X] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction ```python db = FAISS.from_documents(text_pages, embeddings) db.delete() ``` Error ``` --------------------------------------------------------------------------- ValueError Traceback (most recent call last) Cell In[51], line 1 ----> 1 db.delete() File /opt/conda/lib/python3.10/site-packages/langchain/vectorstores/faiss.py:799, in FAISS.delete(self, ids, **kwargs) 789 """Delete by ID. These are the IDs in the vectorstore. 790 791 Args: (...) 796 False otherwise, None if not implemented. 797 """ 798 if ids is None: --> 799 raise ValueError("No ids provided to delete.") 800 missing_ids = set(ids).difference(self.index_to_docstore_id.values()) 801 if missing_ids: ValueError: No ids provided to delete. ``` ### Expected behavior The index should be deleted without needing to pass an index `id`.
FAISS db.delete() says `ids` is required even when it is Optional
https://api.github.com/repos/langchain-ai/langchain/issues/14409/comments
1
2023-12-07T19:22:56Z
2024-03-17T16:09:57Z
https://github.com/langchain-ai/langchain/issues/14409
2,031,382,949
14,409
[ "hwchase17", "langchain" ]
### System Info from langchain.llms import GooglePalm from sqlalchemy import create_engine from langchain.utilities import SQLDatabase from langchain.llms import GooglePalm from langchain_experimental.sql import SQLDatabaseChain ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction st.write(db_chain_sql_out.return_sql) is returning bool value "True" Instead of the model generated actual SQL Statement - I am using Google Palm - is this the normal output? ### Expected behavior Expecting model generated SQL
db_chain_sql_out.return_sql
https://api.github.com/repos/langchain-ai/langchain/issues/14404/comments
4
2023-12-07T15:55:01Z
2024-03-17T16:09:52Z
https://github.com/langchain-ai/langchain/issues/14404
2,031,053,527
14,404
[ "hwchase17", "langchain" ]
### Issue you'd like to raise. I am using langchain 0.0.316 an trying to create a ElasticsearchStore to do some similarity_search However, whenever I try to create it (using from_documents or not) I get the following error : `raise ValueError("check_hostname requires server_hostname")` This is a SSL error, and I suspect it to be the problem as I cannot use SSL. Somewhere else in the project, I cannot with Python to Elastic using verify_certs = False and everything works perfectly. Thus I tried to create ElasticsearchStore with the following arguments : ``` db = ElasticsearchStore(texts, es_url='...', index_name = '...', embedding, ssl_verify = {'verify_certs'=False'}) ``` But I still have the error and nothing has changed. How can I make langchain to initialize Elastic without checking for ssl certificates ? ### Suggestion: _No response_
Issue: ElasticsearchStore with ssl_verify = {'verify_certs':False} does not work
https://api.github.com/repos/langchain-ai/langchain/issues/14403/comments
5
2023-12-07T15:46:15Z
2024-02-05T02:29:06Z
https://github.com/langchain-ai/langchain/issues/14403
2,031,037,974
14,403
[ "hwchase17", "langchain" ]
### System Info Langchain: 0.0.346 OpenAI: 1.3.7 ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [X] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction Simple script to authenticate to Azure with RBAC ``` from langchain.embeddings import AzureOpenAIEmbeddings from azure.identity import DefaultAzureCredential, get_bearer_token_provider token_provider = get_bearer_token_provider(DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default") embeddings = AzureOpenAIEmbeddings(azure_endpoint='xxxxxxx', azure_ad_token_provider=token_provider) ``` ### Expected behavior Should authenticate, but is seems like the `azure_ad_token_provider` is not added to the values dict langchain/embeddings/azure_openai.py line 80-86 ``` values["azure_endpoint"] = values["azure_endpoint"] or os.getenv( "AZURE_OPENAI_ENDPOINT" ) values["azure_ad_token"] = values["azure_ad_token"] or os.getenv( "AZURE_OPENAI_AD_TOKEN" ) ``` Other parameters are added to values, but not `azure_ad_token_provider`
AzureOpenAIEmbeddings cannot authenticate with azure_ad_token_provider
https://api.github.com/repos/langchain-ai/langchain/issues/14402/comments
10
2023-12-07T15:35:19Z
2024-03-21T08:22:18Z
https://github.com/langchain-ai/langchain/issues/14402
2,031,016,359
14,402
[ "hwchase17", "langchain" ]
### Issue you'd like to raise. Hello community, I'm currently working on a project that involves using the langchain library for natural language processing. I'm encountering an issue with the LLMChain class, and I'm hoping someone can help me troubleshoot. I've initialized a Hugging Face pipeline and constructed a prompt using PromptTemplate. However, when I attempt to load a QA chain using the load_qa_chain function, I get a ValidationError related to the Runnable type. The error suggests that an instance of Runnable is expected, but it seems there's a mismatch. Here's a simplified version of my code: ``` from langchain.prompts import PromptTemplate from langchain import load_qa_chain from transformers import HuggingFacePipeline hf = HuggingFacePipeline.from_model_id( model_id="ai-forever/rugpt3large_based_on_gpt2", task="text-generation", pipeline_kwargs={"max_new_tokens": 10}, ) prompt = """Question: {question} Answer: {text}""" # The next line is where the error occurs chain = load_qa_chain(hf(prompt=prompt), chain_type="stuff") ``` ``` ValidationError: 2 validation errors for LLMChain llm instance of Runnable expected (type=type_error.arbitrary_type; expected_arbitrary_type=Runnable) llm instance of Runnable expected (type=type_error.arbitrary_type; expected_arbitrary_type=Runnable) ``` I have checked the documentation and versions of the libraries, but I'm still having trouble understanding and resolving the issue. Could someone please provide guidance on what might be causing this ValidationError and how I can address it? Thank you in advance for your help! ### Suggestion: _No response_
Issue: <Trouble with langchain Library: Error in LLMChain Validation>
https://api.github.com/repos/langchain-ai/langchain/issues/14401/comments
1
2023-12-07T15:17:37Z
2024-03-17T16:09:46Z
https://github.com/langchain-ai/langchain/issues/14401
2,030,975,852
14,401
[ "hwchase17", "langchain" ]
### System Info Hi Team, Am trying to connect to SQL/CSV using the HuggingFaceHUb and I get value error. This value error occurs even when I use the same example as given in https://python.langchain.com/docs/use_cases/qa_structured/sql except that instead of openAI am using huggingfaceHub ### Who can help? _No response_ ### Information - [X] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [X] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [X] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction st.title("SQL DB with Langchain") #entering input through streamlit into the app for querying input_text = st.text_input("enter the text for search") #input_period = st.text_input("enter the period for which you need summarization") #connecting to hugging face API os.environ["HUGGINGFACEHUB_API_TOKEN"] = huggingface_write_key #SQL db = SQLDatabase.from_uri("sqlite:///Chinook.db") repo_id = "google/flan-t5-xxl" llm = HuggingFaceHub( repo_id=repo_id, model_kwargs={"temperature": 0.2} ) db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True) if input_text: st.write(db_chain.run(input_text)) ### Expected behavior Expected to give output for the query thats run
value error when using huggingfacehub API
https://api.github.com/repos/langchain-ai/langchain/issues/14400/comments
10
2023-12-07T14:05:46Z
2024-04-25T11:22:42Z
https://github.com/langchain-ai/langchain/issues/14400
2,030,832,959
14,400
[ "hwchase17", "langchain" ]
### Issue you'd like to raise. python3.10/site-packages/langchain/llms/bedrock.py:315: RuntimeWarning: coroutine 'AsyncCallbackManagerForLLMRun.on_llm_new_token' was never awaited ### Suggestion: _No response_
Issue:python3.10/site-packages/langchain/llms/bedrock.py:315: RuntimeWarning: coroutine 'AsyncCallbackManagerForLLMRun.on_llm_new_token' was never awaited
https://api.github.com/repos/langchain-ai/langchain/issues/14399/comments
4
2023-12-07T13:59:30Z
2023-12-08T02:24:40Z
https://github.com/langchain-ai/langchain/issues/14399
2,030,821,733
14,399
[ "hwchase17", "langchain" ]
### System Info langchain: 0.0.346 python: 3.11.7 ### Who can help? _No response_ ### Information - [X] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [X] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction Error when run: ``` from langchain.tools import DuckDuckGoSearchRun search = DuckDuckGoSearchRun() search.run("Obama's first name?") ``` ### Expected behavior I got this error when run: ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) [c:\Users\thenh\OneDrive\M](file:///C:/Users/thenh/OneDrive/M)áy tính\demo\test.ipynb Cell 13 line 4 ..... File [c:\Program](file:///C:/Program) Files\Python311\Lib\site-packages\langchain_core\tools.py:337, in BaseTool.run(self, tool_input, verbose, start_color, color, callbacks, tags, metadata, run_name, **kwargs) [334](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain_core/tools.py:334) try: [335](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain_core/tools.py:335) tool_args, tool_kwargs = self._to_args_and_kwargs(parsed_input) [336](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain_core/tools.py:336) observation = ( --> [337](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain_core/tools.py:337) self._run(*tool_args, run_manager=run_manager, **tool_kwargs) [338](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain_core/tools.py:338) if new_arg_supported [339](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain_core/tools.py:339) else self._run(*tool_args, **tool_kwargs) [340](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain_core/tools.py:340) ) [341](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain_core/tools.py:341) except ToolException as e: [342](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain_core/tools.py:342) if not self.handle_tool_error: File [c:\Program](file:///C:/Program) Files\Python311\Lib\site-packages\langchain\tools\ddg_search\tool.py:37, in DuckDuckGoSearchRun._run(self, query, run_manager) [31](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/tools/ddg_search/tool.py:31) def _run( [32](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/tools/ddg_search/tool.py:32) self, [33](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/tools/ddg_search/tool.py:33) query: str, [34](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/tools/ddg_search/tool.py:34) run_manager: Optional[CallbackManagerForToolRun] = None, [35](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/tools/ddg_search/tool.py:35) ) -> str: [36](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/tools/ddg_search/tool.py:36) """Use the tool.""" ---> [37](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/tools/ddg_search/tool.py:37) return self.api_wrapper.run(query) File [c:\Program](file:///C:/Program) Files\Python311\Lib\site-packages\langchain\utilities\duckduckgo_search.py:81, in DuckDuckGoSearchAPIWrapper.run(self, query) [79](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/utilities/duckduckgo_search.py:79) """Run query through DuckDuckGo and return concatenated results.""" [80](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/utilities/duckduckgo_search.py:80) if self.source == "text": ---> [81](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/utilities/duckduckgo_search.py:81) results = self._ddgs_text(query) [82](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/utilities/duckduckgo_search.py:82) elif self.source == "news": [83](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/utilities/duckduckgo_search.py:83) results = self._ddgs_news(query) File [c:\Program](file:///C:/Program) Files\Python311\Lib\site-packages\langchain\utilities\duckduckgo_search.py:48, in DuckDuckGoSearchAPIWrapper._ddgs_text(self, query, max_results) [45](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/utilities/duckduckgo_search.py:45) from duckduckgo_search import DDGS [47](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/utilities/duckduckgo_search.py:47) with DDGS() as ddgs: ---> [48](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/utilities/duckduckgo_search.py:48) ddgs_gen = ddgs.text( [49](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/utilities/duckduckgo_search.py:49) query, [50](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/utilities/duckduckgo_search.py:50) region=self.region, [51](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/utilities/duckduckgo_search.py:51) safesearch=self.safesearch, [52](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/utilities/duckduckgo_search.py:52) timelimit=self.time, [53](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/utilities/duckduckgo_search.py:53) max_results=max_results or self.max_results, [54](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/utilities/duckduckgo_search.py:54) backend=self.backend, [55](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/utilities/duckduckgo_search.py:55) ) [56](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/utilities/duckduckgo_search.py:56) if ddgs_gen: [57](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/utilities/duckduckgo_search.py:57) return [r for r in ddgs_gen] TypeError: DDGS.text() got an unexpected keyword argument 'max_results' ``` After remove _`max_results=max_results or self.max_results`_, i still got another error: ``` --------------------------------------------------------------------------- HTTPError Traceback (most recent call last) [c:\Users\thenh\OneDrive\M](file:///C:/Users/thenh/OneDrive/M)áy tính\demo\test.ipynb Cell 13 line 4 [1](vscode-notebook-cell:/c%3A/Users/thenh/OneDrive/M%C3%A1y%20t%C3%ADnh/demo/test.ipynb#X15sZmlsZQ%3D%3D?line=0) from langchain.tools import DuckDuckGoSearchRun [2](vscode-notebook-cell:/c%3A/Users/thenh/OneDrive/M%C3%A1y%20t%C3%ADnh/demo/test.ipynb#X15sZmlsZQ%3D%3D?line=1) search = DuckDuckGoSearchRun() ----> [4](vscode-notebook-cell:/c%3A/Users/thenh/OneDrive/M%C3%A1y%20t%C3%ADnh/demo/test.ipynb#X15sZmlsZQ%3D%3D?line=3) search.run("who is newjeans") File [c:\Program](file:///C:/Program) Files\Python311\Lib\site-packages\langchain_core\tools.py:365, in BaseTool.run(self, tool_input, verbose, start_color, color, callbacks, tags, metadata, run_name, **kwargs) [363](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain_core/tools.py:363) except (Exception, KeyboardInterrupt) as e: [364](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain_core/tools.py:364) run_manager.on_tool_error(e) --> [365](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain_core/tools.py:365) raise e [366](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain_core/tools.py:366) else: [367](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain_core/tools.py:367) run_manager.on_tool_end( [368](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain_core/tools.py:368) str(observation), color=color, name=self.name, **kwargs [369](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain_core/tools.py:369) ) File [c:\Program](file:///C:/Program) Files\Python311\Lib\site-packages\langchain_core\tools.py:337, in BaseTool.run(self, tool_input, verbose, start_color, color, callbacks, tags, metadata, run_name, **kwargs) [334](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain_core/tools.py:334) try: [335](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain_core/tools.py:335) tool_args, tool_kwargs = self._to_args_and_kwargs(parsed_input) [336](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain_core/tools.py:336) observation = ( --> [337](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain_core/tools.py:337) self._run(*tool_args, run_manager=run_manager, **tool_kwargs) [338](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain_core/tools.py:338) if new_arg_supported [339](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain_core/tools.py:339) else self._run(*tool_args, **tool_kwargs) [340](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain_core/tools.py:340) ) [341](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain_core/tools.py:341) except ToolException as e: [342](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain_core/tools.py:342) if not self.handle_tool_error: File [c:\Program](file:///C:/Program) Files\Python311\Lib\site-packages\langchain\tools\ddg_search\tool.py:37, in DuckDuckGoSearchRun._run(self, query, run_manager) [31](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/tools/ddg_search/tool.py:31) def _run( [32](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/tools/ddg_search/tool.py:32) self, [33](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/tools/ddg_search/tool.py:33) query: str, [34](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/tools/ddg_search/tool.py:34) run_manager: Optional[CallbackManagerForToolRun] = None, [35](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/tools/ddg_search/tool.py:35) ) -> str: [36](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/tools/ddg_search/tool.py:36) """Use the tool.""" ---> [37](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/tools/ddg_search/tool.py:37) return self.api_wrapper.run(query) File [c:\Program](file:///C:/Program) Files\Python311\Lib\site-packages\langchain\utilities\duckduckgo_search.py:81, in DuckDuckGoSearchAPIWrapper.run(self, query) [79](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/utilities/duckduckgo_search.py:79) """Run query through DuckDuckGo and return concatenated results.""" [80](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/utilities/duckduckgo_search.py:80) if self.source == "text": ---> [81](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/utilities/duckduckgo_search.py:81) results = self._ddgs_text(query) [82](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/utilities/duckduckgo_search.py:82) elif self.source == "news": [83](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/utilities/duckduckgo_search.py:83) results = self._ddgs_news(query) File [c:\Program](file:///C:/Program) Files\Python311\Lib\site-packages\langchain\utilities\duckduckgo_search.py:57, in DuckDuckGoSearchAPIWrapper._ddgs_text(self, query, max_results) [48](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/utilities/duckduckgo_search.py:48) ddgs_gen = ddgs.text( [49](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/utilities/duckduckgo_search.py:49) query, [50](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/utilities/duckduckgo_search.py:50) region=self.region, (...) [54](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/utilities/duckduckgo_search.py:54) backend=self.backend, [55](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/utilities/duckduckgo_search.py:55) ) [56](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/utilities/duckduckgo_search.py:56) if ddgs_gen: ---> [57](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/utilities/duckduckgo_search.py:57) return [r for r in ddgs_gen] [58](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/utilities/duckduckgo_search.py:58) return [] File [c:\Program](file:///C:/Program) Files\Python311\Lib\site-packages\langchain\utilities\duckduckgo_search.py:57, in <listcomp>(.0) [48](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/utilities/duckduckgo_search.py:48) ddgs_gen = ddgs.text( [49](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/utilities/duckduckgo_search.py:49) query, [50](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/utilities/duckduckgo_search.py:50) region=self.region, (...) [54](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/utilities/duckduckgo_search.py:54) backend=self.backend, [55](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/utilities/duckduckgo_search.py:55) ) [56](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/utilities/duckduckgo_search.py:56) if ddgs_gen: ---> [57](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/utilities/duckduckgo_search.py:57) return [r for r in ddgs_gen] [58](file:///C:/Program%20Files/Python311/Lib/site-packages/langchain/utilities/duckduckgo_search.py:58) return [] File [c:\Program](file:///C:/Program) Files\Python311\Lib\site-packages\duckduckgo_search\duckduckgo_search.py:150, in DDGS.text(self, keywords, region, safesearch, timelimit, backend) [134](file:///C:/Program%20Files/Python311/Lib/site-packages/duckduckgo_search/duckduckgo_search.py:134) """DuckDuckGo text search generator. Query params: https://duckduckgo.com/params [135](file:///C:/Program%20Files/Python311/Lib/site-packages/duckduckgo_search/duckduckgo_search.py:135) [136](file:///C:/Program%20Files/Python311/Lib/site-packages/duckduckgo_search/duckduckgo_search.py:136) Args: (...) [147](file:///C:/Program%20Files/Python311/Lib/site-packages/duckduckgo_search/duckduckgo_search.py:147) [148](file:///C:/Program%20Files/Python311/Lib/site-packages/duckduckgo_search/duckduckgo_search.py:148) """ [149](file:///C:/Program%20Files/Python311/Lib/site-packages/duckduckgo_search/duckduckgo_search.py:149) if backend == "api": --> [150](file:///C:/Program%20Files/Python311/Lib/site-packages/duckduckgo_search/duckduckgo_search.py:150) yield from self._text_api(keywords, region, safesearch, timelimit) [151](file:///C:/Program%20Files/Python311/Lib/site-packages/duckduckgo_search/duckduckgo_search.py:151) elif backend == "html": [152](file:///C:/Program%20Files/Python311/Lib/site-packages/duckduckgo_search/duckduckgo_search.py:152) yield from self._text_html(keywords, region, safesearch, timelimit) File [c:\Program](file:///C:/Program) Files\Python311\Lib\site-packages\duckduckgo_search\duckduckgo_search.py:203, in DDGS._text_api(self, keywords, region, safesearch, timelimit) [201](file:///C:/Program%20Files/Python311/Lib/site-packages/duckduckgo_search/duckduckgo_search.py:201) for s in ("0", "20", "70", "120"): [202](file:///C:/Program%20Files/Python311/Lib/site-packages/duckduckgo_search/duckduckgo_search.py:202) payload["s"] = s --> [203](file:///C:/Program%20Files/Python311/Lib/site-packages/duckduckgo_search/duckduckgo_search.py:203) resp = self._get_url( [204](file:///C:/Program%20Files/Python311/Lib/site-packages/duckduckgo_search/duckduckgo_search.py:204) "GET", "https://links.duckduckgo.com/d.js", params=payload [205](file:///C:/Program%20Files/Python311/Lib/site-packages/duckduckgo_search/duckduckgo_search.py:205) ) [206](file:///C:/Program%20Files/Python311/Lib/site-packages/duckduckgo_search/duckduckgo_search.py:206) if resp is None: [207](file:///C:/Program%20Files/Python311/Lib/site-packages/duckduckgo_search/duckduckgo_search.py:207) break File [c:\Program](file:///C:/Program) Files\Python311\Lib\site-packages\duckduckgo_search\duckduckgo_search.py:89, in DDGS._get_url(self, method, url, **kwargs) [87](file:///C:/Program%20Files/Python311/Lib/site-packages/duckduckgo_search/duckduckgo_search.py:87) logger.warning(f"_get_url() {url} {type(ex).__name__} {ex}") [88](file:///C:/Program%20Files/Python311/Lib/site-packages/duckduckgo_search/duckduckgo_search.py:88) if i >= 2 or "418" in str(ex): ---> [89](file:///C:/Program%20Files/Python311/Lib/site-packages/duckduckgo_search/duckduckgo_search.py:89) raise ex [90](file:///C:/Program%20Files/Python311/Lib/site-packages/duckduckgo_search/duckduckgo_search.py:90) sleep(3) [91](file:///C:/Program%20Files/Python311/Lib/site-packages/duckduckgo_search/duckduckgo_search.py:91) return None File [c:\Program](file:///C:/Program) Files\Python311\Lib\site-packages\duckduckgo_search\duckduckgo_search.py:82, in DDGS._get_url(self, method, url, **kwargs) [78](file:///C:/Program%20Files/Python311/Lib/site-packages/duckduckgo_search/duckduckgo_search.py:78) resp = self._client.request( [79](file:///C:/Program%20Files/Python311/Lib/site-packages/duckduckgo_search/duckduckgo_search.py:79) method, url, follow_redirects=True, **kwargs [80](file:///C:/Program%20Files/Python311/Lib/site-packages/duckduckgo_search/duckduckgo_search.py:80) ) [81](file:///C:/Program%20Files/Python311/Lib/site-packages/duckduckgo_search/duckduckgo_search.py:81) if self._is_500_in_url(str(resp.url)) or resp.status_code == 202: ---> [82](file:///C:/Program%20Files/Python311/Lib/site-packages/duckduckgo_search/duckduckgo_search.py:82) raise httpx._exceptions.HTTPError("") [83](file:///C:/Program%20Files/Python311/Lib/site-packages/duckduckgo_search/duckduckgo_search.py:83) resp.raise_for_status() [84](file:///C:/Program%20Files/Python311/Lib/site-packages/duckduckgo_search/duckduckgo_search.py:84) if resp.status_code == 200: HTTPError: ```
TypeError: DDGS.text() got an unexpected keyword argument 'max_results' AND HTTPError:
https://api.github.com/repos/langchain-ai/langchain/issues/14397/comments
1
2023-12-07T13:55:48Z
2023-12-07T14:38:09Z
https://github.com/langchain-ai/langchain/issues/14397
2,030,814,970
14,397
[ "hwchase17", "langchain" ]
### Issue you'd like to raise. def delete_confluence_embeddings(file_path, persist_directory,not_uuid): chroma_db = chromadb.PersistentClient(path=persist_directory) collection = chroma_db.get_or_create_collection(name="langchain") project_instance = ProjectName.objects.get(not_uuid=not_uuid) confluence_data = json.loads(project_instance.media).get('confluence', []) confluence_url = project_instance.url username = project_instance.confluence_username api_key = base64.b64decode(project_instance.api_key).decode('utf-8') space_keys = [space_data['space_key'] for space_data in project_instance.space_key] documents = [] loader = ConfluenceLoader( url=confluence_url, username=username, api_key=api_key ) for space_key in space_keys: documents.extend(loader.load(space_key=space_key, limit=100)) page_info = [] for document in documents: page_id = document.metadata.get('id') page_title = document.metadata.get('title') formatted_title = page_title.replace(' ', '+') page_info.append({"id": page_id, "title": formatted_title}) # print(f"Page ID: {page_id}, Page Title: {formatted_title}") for entry in page_info: entry_file_path = f"{file_path}/pages/{entry['id']}" ids = collection.get(where={"source": entry_file_path})['ids'] collection.delete(where={"source": entry_file_path}, ids=ids) for entry in page_info: entry_file_path = f"{file_path}/pages/{entry['id']}/{entry['title']}" ids = collection.get(where={"source": entry_file_path})['ids'] collection.delete(where={"source": entry_file_path}, ids=ids) chroma_db.delete_collection(name="langchain") print("Delete successfully") how can i delete for particular space. file path is url/spaces/space_key/ ### Suggestion: _No response_
Issue: How to delete particular space embeddings for a confluence projects
https://api.github.com/repos/langchain-ai/langchain/issues/14396/comments
1
2023-12-07T13:14:35Z
2024-03-16T16:13:01Z
https://github.com/langchain-ai/langchain/issues/14396
2,030,741,752
14,396
[ "hwchase17", "langchain" ]
### System Info when i am using Retrieval QA with custom prompt on official llama2 model it gives back an empty result even though retriever has worked but LLM failed to give back the response but if i directly pass the query to chain without any prompt it works as expected ## Versions Python - 3.10 Langchain - 0.0.306 @hwchase17 and @agola11 please take a loot at this issue ### Who can help? _No response_ ### Information - [X] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [X] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction `qa_chain = RetrievalQA.from_chain_type( llm=llm, chain_type="stuff", retriever=final_retriever, chain_type_kwargs={"prompt": prompt_template}, return_source_documents=True )` if i initialize the chain like this it is failing without ### Expected behavior `qa_chain = RetrievalQA.from_chain_type( llm=llm, chain_type="stuff", retriever=final_retriever, return_source_documents=True )` if i initialize the chain like this it is working as expected
Retrieval QA chain does not work
https://api.github.com/repos/langchain-ai/langchain/issues/14395/comments
1
2023-12-07T13:08:45Z
2024-03-16T16:12:56Z
https://github.com/langchain-ai/langchain/issues/14395
2,030,731,840
14,395
[ "hwchase17", "langchain" ]
### System Info Google colab ### Who can help? @agola ### Information - [X] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction ``` !pip install lmql==0.0.6.6 langchain==0.0.316 openai==0.28.1 -q import lmql import aiohttp import os os.environ["OPENAI_API_KEY"] = "" from langchain import LLMChain, PromptTemplate from langchain.chat_models import ChatOpenAI from langchain.prompts.chat import ( ChatPromptTemplate, SystemMessagePromptTemplate, AIMessagePromptTemplate, HumanMessagePromptTemplate, ) from langchain.llms import OpenAI # Setup the LM to be used by langchain llm = OpenAI(temperature=0.9) human_message_prompt = HumanMessagePromptTemplate( prompt=PromptTemplate( template="What is a good name for a company that makes {product}?", input_variables=["product"], ) ) chat_prompt_template = ChatPromptTemplate.from_messages([human_message_prompt]) chat = ChatOpenAI(temperature=0.9) chain = LLMChain(llm=chat, prompt=chat_prompt_template) # Run the chain chain.run("colorful socks") ``` gives error: ``` --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) [<ipython-input-17-c7d901a7e281>](https://localhost:8080/#) in <cell line: 25>() 23 24 # Run the chain ---> 25 chain.run("colorful socks") 11 frames [/usr/local/lib/python3.10/dist-packages/langchain/chat_models/openai.py](https://localhost:8080/#) in _create_retry_decorator(self) 300 301 def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict: --> 302 overall_token_usage: dict = {} 303 for output in llm_outputs: 304 if output is None: AttributeError: module 'openai' has no attribute 'error' ``` ### Expected behavior show results
AttributeError: module 'openai' has no attribute 'error'
https://api.github.com/repos/langchain-ai/langchain/issues/14394/comments
1
2023-12-07T12:47:28Z
2024-03-18T16:07:39Z
https://github.com/langchain-ai/langchain/issues/14394
2,030,689,372
14,394
[ "hwchase17", "langchain" ]
### Issue you'd like to raise. How does conversationbuffermemory works with routerchain for suppose if I wanted to create a chat application I need memory to store the conversations how does that thing work with routerchain? I'm currently using the same implementation that has shown in the documentation please respond as soon as possible thank you:)) ### Suggestion: _No response_
Issue: <how does langchain's routerchain work with conversationbuffermemory>
https://api.github.com/repos/langchain-ai/langchain/issues/14392/comments
14
2023-12-07T10:39:13Z
2024-06-13T16:07:42Z
https://github.com/langchain-ai/langchain/issues/14392
2,030,447,654
14,392
[ "hwchase17", "langchain" ]
### System Info langchain v 0.0.344 pydantic v 2.5.2 pydantic_code v 2.14.5 python v 3.10.13 ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [X] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction I noticed that the **OutputFixingParser** class does not work when applied to a **PydanticOutputParser** class. Something has probably changed in **Pydantic**. Doing step-by-step debugging, I saw that in **PydanticOutputParser**, at line 32 (see below)... ![code](https://github.com/langchain-ai/langchain/assets/16083297/b59a8d15-2e21-41e3-baf4-180904d1f3ec) ...the exception caught is indeed a **ValidationError**, but _it is not the same_ **ValidationError**... The **ValidationError** expected from that `try..except` block is of this type. ![valerr01](https://github.com/langchain-ai/langchain/assets/16083297/17ea8ec2-4b5f-4820-8f95-3481d687cfba) While the **ValidationError** raised is of this other type. ![valerr02](https://github.com/langchain-ai/langchain/assets/16083297/d2ef0fa4-0fba-4234-a6a8-b65c1fd4ac76) ### Expected behavior I therefore imagine that the **LangChain** code needs to be updated to also handle the new exception (since the old one belongs to a "**v1**" package).
OutputFixingParser does not work with PydanticOutputParser
https://api.github.com/repos/langchain-ai/langchain/issues/14387/comments
2
2023-12-07T09:01:20Z
2024-03-17T16:09:31Z
https://github.com/langchain-ai/langchain/issues/14387
2,030,232,645
14,387
[ "hwchase17", "langchain" ]
### System Info langchain==0.0.346 python==3.11.6 ### Who can help? @eyurtsev ### Information - [ ] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [X] Document Loaders - [X] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction Hi there! I'm currently exploring the index feature in Langchain to load documents into the vector store upon my app startup. However, I've encountered an issue where the index doesn't delete old documents when utilizing Redis as the vector store. After some investigation, I discovered that the `delete` function in `langchain.vectorstores.redis.base.Redis` is a static method, which poses a limitation—it cannot access instance variables, including the essential `key_prefix`. Without the `key_prefix`, Redis is unable to delete documents correctly. This leads me to question why the `delete` method of the Redis vector store is static. I've noticed that other vector stores, such as Pinecone, do not have a static `delete` function and seem to handle this differently. ### Expected behavior index with Redis as vector store should delete documents correctly.
index with redis as vector store cannot delete documents
https://api.github.com/repos/langchain-ai/langchain/issues/14383/comments
2
2023-12-07T08:09:30Z
2024-03-13T21:06:56Z
https://github.com/langchain-ai/langchain/issues/14383
2,030,135,709
14,383