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PR_kwDOIPDwls5hi0QE

docs: add missing imports for local_llms

closed

CONTRIBUTOR

<!-- Thank you for contributing to LangChain! Replace this entire comment with: - **Description:** a description of the change, - **Issue:** the issue # it fixes (if applicable), - **Dependencies:** any dependencies required for this change, - **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below), - **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out! Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally. See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/extras` directory. If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. --> Keeping it consistent with everywhere else in the docs and adding the missing imports to be able to copy paste and run the code example.

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PR_kwDOIPDwls5hiWF0

Andrewreed

closed

CONTRIBUTOR

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PR_kwDOIPDwls5hiUt8

Corrected the argument alias from 'base_store' to 'docstore'

closed

CONTRIBUTOR

- **Description:** The sample code in the documentation about MultiVectorRetriever throws an error. The reason is an incorrect reference to an argument alias (`base_store` instead of `docstore`) The definition of the MultiVectorRetriever class shows that the correct argument alias is `docstore`. This fixes the error.,

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I_kwDOIPDwls55Ksmr

Web Scraping with ChatOllama gives SSL: WRONG_VERSION_NUMBER

open

NONE

### 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_

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PR_kwDOIPDwls5hhoeK

Fix: redundant casting to SecretStr API keys

open

CONTRIBUTOR

- **Description:** This PR serves as standardization of the masking and testing. - Some API keys were not really masked when stored in the `values` dict. They should be masked and cast back to string only during the `POST` request. I fixed 1 occurance of this. - Some tests were not covering this flaw because they were repeating the back-and-forth pattern of casting to `SecretStr` and invoking `.get_secret_value()` immediately. This pattern is confusing, and in order to clear this out I added an `extract_secret_str : SecretStr | Str | None -> str` method. - The code-base was filled with `secretstr, secret_str, secret_string` names. I replaced all of them with `secretstr`. - **Issue:** fixes https://github.com/langchain-ai/langchain/issues/14445, related https://github.com/langchain-ai/langchain/issues/12165 - **Dependencies:** None - **Tag maintainer:** @eyurtsev @hwchase17

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I_kwDOIPDwls55KEtZ

Issue: Flawed implementations of SecretStr for API keys

open

CONTRIBUTOR

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

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PR_kwDOIPDwls5hhZA6

Update with new VertexAIModelGarden output format

open

NONE

On December 1st, the Google Cloud Vertex AI Model Garden models runtime received an update in the form of `Prompt:\n\t{prompt}\nOutput:\n\t{prompt}` and now streams tokens throughout the second level list. This change will break the current behavior of your applications if adequate fixes are not applied. We are promoting these changes based on our experiences with LLaMA, Falcon, Mistral, and additional models that are distributed directly by Vertex AI Model Garden. Probably fixes #13370 #10012

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I_kwDOIPDwls55JwGm

Unexpected end of JSON

open

NONE

### 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) ```

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I_kwDOIPDwls55JKDt

Issue: Opensearch manually assigned shards and replicas while using vector_db.add_text(page_contents,metatas)

open

NONE

### 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.  ### Suggestion: _No response_

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I_kwDOIPDwls55I8Wu

Issue: How Can I use Prompt Template?

open

NONE

### 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_

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I_kwDOIPDwls55I3a6

custom_table_info along with sql_db_schema while using create_sql_agent

open

NONE

### 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

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PR_kwDOIPDwls5hgdD7

Support `score_threshold` in SupabaseVectorStore similarity search

closed

CONTRIBUTOR

Description: Add support for setting the `score_threshold` for similarity search in SupabaseVectoreStore. This pull request addresses issue #14438

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I_kwDOIPDwls55IvBA

DOC: `SupabaseVectorStore` support for similarity `score_threshold` filtering in `as_retriever`

open

CONTRIBUTOR

### 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.

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I_kwDOIPDwls55Ifxf

DoctranTextTranslator Is Not Working

open

NONE

### 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: john.doe@example.com) 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 security@example.com. 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: michael.johnson@example.com). 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: david.rodriguez@example.com) 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 jason@psychic.dev """ 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.

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I_kwDOIPDwls55Ifcy

import error in elasticsearch memory module: Module cannot be used as a type

open

NONE

### 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

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PR_kwDOIPDwls5hf77O

Improvement: Allow passing parameters to the underlying es_client. Closes: #14403

closed

CONTRIBUTOR

### Description In https://github.com/langchain-ai/langchain/issues/14403, the user mentioned that he hopes not to verify ssl and needs to pass more parameters I found that the `Elasticsearch` class [has very many parameters](https://github.com/elastic/elasticsearch-py/blob/98f2af2134dfc3f2b2054b87a4056e2508b9b8db/elasticsearch/_sync/client/__init__.py#L131-L191 ): <img width="1097" alt="Screenshot 2023-12-08 at 4 24 39 PM" src="https://github.com/langchain-ai/langchain/assets/10000925/f2201554-b41a-4388-a8e8-c14a2d0466d4"> In order to adapt to more situations, I want to add the kwargs parameter so that users can enter more `Elasticsearch` parameters. Like [redis](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/vectorstores/redis/base.py#L253), [tair](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/vectorstores/tair.py#L32), [myscale](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/vectorstores/myscale.py#L112) and so on.

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I_kwDOIPDwls55IEHg

StructuredChatAgent did not provide the correct action input.

closed

NONE

### 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" } } ```

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PR_kwDOIPDwls5hfpUb

Add option to preserve headers in MarkdownHeaderTextSplitter

closed

CONTRIBUTOR

- **Description:** `MarkdownHeaderTextSplitter` currently strips header lines from chunked content. Many applications require these header lines are preserved. This adds an optional parameter to preserve those headers in the chunked content. - **Issue:** #2836 (relevant) - **Dependencies:** - - **Tag maintainer:** @baskaryan - **Twitter handle:** @finnless Unit tests and new examples in notebook included. cc @rlancemartin

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PR_kwDOIPDwls5hfnDf

Improvement: Correct type annotation for azure_ad_token_provider Closed: #14402

closed

CONTRIBUTOR

Description Fix https://github.com/langchain-ai/langchain/issues/14402, Similar changes: https://github.com/langchain-ai/langchain/pull/14166 Twitter handle [lin_bob57617](https://twitter.com/lin_bob57617)

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I_kwDOIPDwls55HtrZ

Bug: ImportError for ElasticsearchStore

closed

NONE

### 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`

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PR_kwDOIPDwls5hfMqr

fix imports from core

closed

COLLABORATOR

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I_kwDOIPDwls55HI21

Retrieval Inability to Filter Based on Metadata in Milvius Database

open

NONE

### 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.

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PR_kwDOIPDwls5he84P

Fix scheduled testing

closed

COLLABORATOR

- integration tests in pyproject - integration test fixes

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PR_kwDOIPDwls5he8FA

Simplify ensemble retriever

open

NONE

- **Description:** code simplification to improve readability and remove unnecessary memory allocations. - **Tag maintainer**: @baskaryan, @eyurtsev, @hwchase17.

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PR_kwDOIPDwls5he5aw

docs `networkx`update

closed

COLLABORATOR

Added setting up instruction, package description and link

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PR_kwDOIPDwls5he0I8

Update mongodb_atlas docs for GA

closed

CONTRIBUTOR

Updated the MongoDB Atlas Vector Search docs to indicate the service is Generally Available, updated the example to use the new index definition, and added an example that uses metadata pre-filtering for semantic search

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PR_kwDOIPDwls5hetIi

GITLAB_URL should take default https://gitlab.com instead of error

closed

CONTRIBUTOR

The fix #14221 has broken default gitlab url which is forcing the users to specify GITLAB_URL for default one. With this fix if GITLAB_URL is not set, the default gitlab url will be taken. <!-- Thank you for contributing to LangChain! Replace this entire comment with: - **Description:** Add a Default URL with https://gitlab.com instead of None - **Issue:** The fix #14221 has broken default gitlab url which is forcing the users to specify GITLAB_URL - **Dependencies:** None - **Tag maintainer:** @hwchase17 - **Twitter handle:** @manjunath_shiva Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally. See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/extras` directory. If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. -->

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PR_kwDOIPDwls5heoCj

feat: patch chroma.similarity_search_with_score to return vector id

open

NONE

**Description:** In chroma, return the vector_ids for associated vectors when using the `similarity_search_with_score` and `similarity_search_by_vector_with_relevance_scores` so that turning around to manage the vector (update/delete) is instantly available. Right now, there is no easy way to get similarity and get the associated vector id for the results. Given the id on creation can be created in-library or _optionally_ set by the user we should greedily return the id so the user can always manage their vectors easily. **Issue:** #11592, **Dependencies:** N/A **Tag maintainer:** N/A, **Twitter handle:** @mintplexlabs

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PR_kwDOIPDwls5hefCc

manual mapping

closed

COLLABORATOR

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PR_kwDOIPDwls5heMDE

feat: masking api key for litellm

open

NONE

Description: Masking API key for LiteLLM Chat Model to protect user secrets. Issue: https://github.com/langchain-ai/langchain/issues/12165 Dependencies: Litellm and LangChain Tag maintainer: @eyurtsev `make test` skips current unit_test and we are unable to install the dependency `litellm` due to a conflict with `tiktoken` See #14419

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I_kwDOIPDwls55GMVo

Dependency Conflict between litellm and tiktoken

open

NONE

### 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.

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PR_kwDOIPDwls5heJpq

experimental[patch]: Release 0.0.45

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PR_kwDOIPDwls5heHVq

langchain[patch]: Release 0.0.348

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PR_kwDOIPDwls5heF0d

docs `promptlayer` pages update

closed

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Updated provider page by adding LLM and ChatLLM references; removed a content that is duplicate text from the LLM referenced page. Updated the collback page

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PR_kwDOIPDwls5heEgl

core[patch]: Release 0.0.12

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PR_kwDOIPDwls5hd3QW

core[patch], langchain[patch], experimental[patch]: import CI

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PR_kwDOIPDwls5hdtmI

Update prompts.py

closed

NONE

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I_kwDOIPDwls55FWE2

Issue: <Zilliz and Milvus metadata field and memory seperation>

open

NONE

### 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_

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PR_kwDOIPDwls5hdNxN

'on_llm_new_token' was never awaited: resolved

closed

NONE

Issue #14399 - **Description:** Added async to `on_llm_new_token` function in `CallbackManagerForLLMRun` class in `manager.py` file present in `libs/core/langchain_core`, - **Issue:** It fixed the RuntimeWarning: coroutine 'AsyncCallbackManagerForLLMRun.on_llm_new_token' was never awaited ChatGPT', - **Twitter handle:** https://twitter.com/Triyambaka58284 It is passing the `make format`, `make lint` and `make test` to check this locally. Kindly reviews my PR @baskaryan, @eyurtsev, @hwchase17.

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I_kwDOIPDwls55FHGl

FAISS db.delete() says `ids` is required even when it is Optional

open

CONTRIBUTOR

### 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`.

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PR_kwDOIPDwls5hdJ2A

Vectara summarization

closed

CONTRIBUTOR

- **Description:** Adding Summarization to Vectara, to reflect it provides not only vector-store type functionality but also can return a summary. Also added: * MMR capability (in the Vectara platform side) * Updated templates * Updated documentation and IPYNB examples - **Tag maintainer:** @baskaryan - **Twitter handle:** @ofermend

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PR_kwDOIPDwls5hc-lQ

core dependencies update

closed

COLLABORATOR

Updated `pyproject.toml` with missed dependencies. Several dependencies are missed in the `pyproject.toml`. They are explicitly imported in code. I listed the files and code lines below. Those packages are still presented in the `poetry.lock`. So, those missed dependencies do not break anything. This update is for consistency. Every explicitly imported package should be presented in the dependency configuration. Note: `poetry.lock` is updated because `anyio` version here (=>4.0 - from poetry.lock) conflicts with `langchain` version (<4.0) > langchain_core/language_models/llms.py: > langchain_core/prompts/base.py: > langchain_core/prompts/loading.py: > import yaml > > langchain_core/output_parsers/base.py: > from typing_extensions import get_args > > langchain_core/runnables/base.py: > from typing_extensions import Literal, get_args > > langchain_core/runnables/config.py: > langchain_core/runnables/router.py: > from typing_extensions import TypedDict > > langchain_core/utils/iter.py: > from typing_extensions import Literal > > langchain_core/tracers/langchain_v1.py: > langchain_core/utils/loading.py: > import requests > > langchain_core/utils/utils.py: > from requests import HTTPError, Response > > langchain_core/tracers/log_stream.py: > from anyio import create_memory_object_stream > > langchain_core/utils/utils.py: > from packaging.version import parse

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PR_kwDOIPDwls5hckaS

feat(llms): support more tasks in HuggingFaceHub LLM and remove deprecated dep

open

CONTRIBUTOR

- **Description:** this PR upgrades the `HuggingFaceHub` LLM: * support more tasks (`translation` and `conversational`) * replaced the deprecated `InferenceApi` with `InferenceClient` * adjusted the overall logic to use the "recommended" model for each task when no model is provided, and vice-versa. - **Tag mainter(s)**: @baskaryan @hwchase17

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PR_kwDOIPDwls5hcFOq

update LanguageModelInput from List to Sequence

closed

CONTRIBUTOR

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I_kwDOIPDwls55D2rX

db_chain_sql_out.return_sql

open

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### 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

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I_kwDOIPDwls55Dy4W

Issue: ElasticsearchStore with ssl_verify = {'verify_certs':False} does not work

closed

NONE

### 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_

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I_kwDOIPDwls55Dtmn

AzureOpenAIEmbeddings cannot authenticate with azure_ad_token_provider

closed

NONE

### 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`

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I_kwDOIPDwls55Djts

Issue: <Trouble with langchain Library: Error in LLMChain Validation>

open

NONE

### 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_

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I_kwDOIPDwls55DA0_

value error when using huggingfacehub API

open

NONE

### 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

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I_kwDOIPDwls55C-Fl

Issue:python3.10/site-packages/langchain/llms/bedrock.py:315: RuntimeWarning: coroutine 'AsyncCallbackManagerForLLMRun.on_llm_new_token' was never awaited

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### 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_

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PR_kwDOIPDwls5hbQ9Y

Fix Bedrock broad error catching

closed

CONTRIBUTOR

Fixes #14347 <!-- Thank you for contributing to LangChain! Replace this entire comment with: - **Description:** Added the traceback of the previous error to keep the initial error type, - **Issue:** #14347 , - **Dependencies:** None, - **Tag maintainer:** Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally. See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/extras` directory. If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. -->

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I_kwDOIPDwls55C8b6

TypeError: DDGS.text() got an unexpected keyword argument 'max_results' AND HTTPError:

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### 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: ```

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I_kwDOIPDwls55Cqj4

Issue: How to delete particular space embeddings for a confluence projects

open

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### 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_

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I_kwDOIPDwls55CoJA

Retrieval QA chain does not work

open

CONTRIBUTOR

### 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

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I_kwDOIPDwls55Cdxc

AttributeError: module 'openai' has no attribute 'error'

open

NONE

### 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

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PR_kwDOIPDwls5haSqu

add api_base to _client_params

closed

CONTRIBUTOR

- **Description:** This PR adds `api_base` to `_client_params` in the `chat_model` of LiteLLM to ensure it's included in API calls. Previously, `api_base` was set on the client but was not included in the parameters passed to the completion function. This change ensures that `api_base` is correctly passed to all API calls. - **Issue:** #14338 - **Tag maintainer:** @hwchase17 @agola11 - **Twitter handle:** @LMS_David_RS

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I_kwDOIPDwls55Biwm

Issue: <how does langchain's routerchain work with conversationbuffermemory>

open

NONE

### 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_

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PR_kwDOIPDwls5hZoe_

Rollback multiple keys in Qdrant

closed

CONTRIBUTOR

This reverts commit 38813d7090294c0c96d4963a2a230db4fef5e37e. This is a temporary fix, as I don't see a clear way on how to use multiple keys with `Qdrant.from_texts`. Context: #14378

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PR_kwDOIPDwls5hZhX9

Azure DocumentIntelligenceLoader/Parser support update with latest SDK

closed

CONTRIBUTOR

- **Description:** Add DocumentIntelligenceLoader & DocumentIntelligenceParser implementation using the latest Azure Document Intelligence SDK with markdown support. The core logic resides in DocumentIntelligenceParser and DocumentIntelligenceLoader is a mere wrapper of the parser. The parser will takes api_endpoint and api_key and creates DocumentIntelligenceClient for the user. 4 parsing modes are supported: 1. Markdown (default) 2. Single 3. Page 4. Object UT and notebook are also updated accordingly. - **Dependencies:** Azure Document Intelligence SDK: azure-ai-documentintelligence [azure-sdk-for-python/sdk/documentintelligence/azure-ai-documentintelligence at 7c42462ac662522a6fd21b17d2a20f4cd40d0356 · Azure/azure-sdk-for-python (github.com)](https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2FAzure%2Fazure-sdk-for-python%2Ftree%2F7c42462ac662522a6fd21b17d2a20f4cd40d0356%2Fsdk%2Fdocumentintelligence%2Fazure-ai-documentintelligence&data=05%7C01%7CZifei.Qian%40microsoft.com%7C298225aa3e31468a863108dbf07374ff%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C638368150928704292%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=oE0Sl4HERnMKdbkV9KgBV46Z2xytcQAShdTWf7ZNl%2Bs%3D&reserved=0).

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PR_kwDOIPDwls5hZY1U

infra: Fix test filesystem paths incompatible with windows

closed

CONTRIBUTOR

- **Description:** This PR fixes test failures on Windows caused by path handling differences and unescaped special characters in regex. The failing tests are: ``` FAILED tests/unit_tests/storage/test_filesystem.py::test_yield_keys - AssertionError: assert ['key1', 'subdir\\key2'] == ['key1', 'subdir/key2'] FAILED tests/unit_tests/test_imports.py::test_importable_all - ModuleNotFoundError: No module named 'langchain_community.langchain_community\\adapters' FAILED tests/unit_tests/tools/file_management/test_utils.py::test_get_validated_relative_path_errs_on_absolute - re.error: incomplete escape \U at position 53 FAILED tests/unit_tests/tools/file_management/test_utils.py::test_get_validated_relative_path_errs_on_parent_dir - re.error: incomplete escape \U at position 69 FAILED tests/unit_tests/tools/file_management/test_utils.py::test_get_validated_relative_path_errs_for_symlink_outside_root - re.error: incomplete escape \U at position 64 ``` - **Issue:** fixes https://github.com/langchain-ai/langchain/issues/11775 (partially) - **Dependencies:** none

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I_kwDOIPDwls55AuRF

OutputFixingParser does not work with PydanticOutputParser

open

NONE

### 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)...  ...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.  While the **ValidationError** raised is of this other type.  ### 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).

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PR_kwDOIPDwls5hZBPE

Fix `llms.Mlflow` example

closed

CONTRIBUTOR

<!-- Thank you for contributing to LangChain! Replace this entire comment with: - **Description:** a description of the change, - **Issue:** the issue # it fixes (if applicable), - **Dependencies:** any dependencies required for this change, - **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below), - **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out! Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally. See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/extras` directory. If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. --> The example code for `llms.Mlflow` is outdated.

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PR_kwDOIPDwls5hZAMC

Remove unused `Params`

closed

CONTRIBUTOR

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PR_kwDOIPDwls5hY9lm

Update minimax.py

open

NONE

MiniMaxChat class _generate method shoud return a ChatResult object not str <!-- Thank you for contributing to LangChain! Replace this entire comment with: - **Description:** a description of the change, - **Issue:** the issue # it fixes (if applicable), - **Dependencies:** any dependencies required for this change, - **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below), - **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out! Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally. See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/extras` directory. If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. -->

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I_kwDOIPDwls55AWmd

index with redis as vector store cannot delete documents

open

CONTRIBUTOR

### 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.

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I_kwDOIPDwls54_-Yv

Allow ChatAnthropic to receive Raw prompt or don't deprecate llms.Anthropic

open

CONTRIBUTOR

### Feature request GitLab is currently trying to adopt LangChain in https://gitlab.com/gitlab-org/modelops/applied-ml/code-suggestions/ai-assist/-/merge_requests/475 for Anthropic usage. It's just simply passing a raw prompt to the Anthropic client, however, LangChain seems not supporting it because: - `langchain.chat_models.ChatAnthropic` class can't take a raw prompt. It expects [`List[BaseMessage]`](https://github.com/dosuken123/langchain/blob/master/libs/langchain/langchain/chat_models/anthropic.py#L45C5-L45C41) and constructs the new messages. - `langchain.llms.Anthropic` class was deprecated by @hwchase17 in https://github.com/langchain-ai/langchain/commit/52d95ec47dbb06a1bcc3f0ff30cadc50135351db. We don't want to use a deprecated class. It sounds like we should add an option to `ChatAnthropic` to allow raw prompt, or recover `langchain.llms.Anthropic` from deprecated state. ### Motivation Increasing the adoption of LangChain in GitLab ### Your contribution I can contribute to this issue as LangChain contributor.

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I_kwDOIPDwls54_7Dd

How can we integrate tools with custom prompt?

open

NONE

### Issue you'd like to raise. I am using ConversationChain with a custom prompt, and now I am looking to integrate tools into it. How can we do that? ### Suggestion: _No response_

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I_kwDOIPDwls54_vIw

Issue: How to remove citation from langchain results

open

NONE

### Issue you'd like to raise. How to remove citation from langchain results and can we get the image out of it? ### Suggestion: How to remove citation from langchain results and can we get the image out of it?

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I_kwDOIPDwls54_oPN

create_sql_agent Suffix error

open

NONE

### System Info Langchain version = 0.0.344 Python version = 3.11.5 @agola11 @hwchase17 ### 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 - [ ] Vector Stores / Retrievers - [ ] Memory - [X] Agents / Agent Executors - [X] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction Here is my code and it is giving me below error for create_sql_agent whe i use suffix variable. A single string input was passed in, but this chain expects multiple inputs (set()). When a chain expects multiple inputs, please call it by passing in a dictionary, eg `chain({'foo': 1, 'bar': 2}) agent_inputs = { 'prefix': MSSQL_AGENT_PREFIX, 'format_instructions': MSSQL_AGENT_FORMAT_INSTRUCTIONS, 'suffix': MSSQL_AGENT_SUFFIX, 'llm': llm, 'toolkit': toolkit, 'top_k': 30, 'early_stopping_method': 'generate', 'handle_parsing_errors': True, 'input_variables': ['question'] } agent_executor_sql = create_sql_agent(**agent_inputs) i also used suffix': [MSSQL_AGENT_SUFFIX], and suffix': str(MSSQL_AGENT_SUFFIX). Yet the error persist. Kindly help. ### Expected behavior It should take suffix and work.

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I_kwDOIPDwls54_ipB

Issue with Qdrant: TypeError: unhashable type: 'list'

closed

NONE

### System Info langchain==0.0.347 langchain-core==0.0.11 ### Who can help? @JeanBaptiste-dlb @hwchase17 @kacperlukawski ### 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 - [X] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction The code I'm trying is based from: https://python.langchain.com/docs/integrations/vectorstores/qdrant ``` import os directory_path = 'data/' from langchain.document_loaders import TextLoader from langchain.embeddings.openai import OpenAIEmbeddings from langchain.text_splitter import CharacterTextSplitter from langchain.vectorstores import Qdrant from langchain.embeddings import OpenAIEmbeddings embeddings = OpenAIEmbeddings(openai_api_key="sk-XXX") loader = TextLoader(os.path.join(directory_path, "concept-note.md")) documents = loader.load() text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) docs = text_splitter.split_documents(documents) qdrant = Qdrant.from_documents( docs, embeddings, location=":memory:", # Local mode with in-memory storage only collection_name="my_documents", ) ``` The error: ``` Created a chunk of size 1893, which is longer than the specified 1000 Created a chunk of size 1728, which is longer than the specified 1000 Created a chunk of size 1317, which is longer than the specified 1000 Created a chunk of size 1464, which is longer than the specified 1000 Created a chunk of size 2119, which is longer than the specified 1000 Created a chunk of size 1106, which is longer than the specified 1000 Created a chunk of size 1822, which is longer than the specified 1000 Created a chunk of size 3658, which is longer than the specified 1000 Created a chunk of size 1233, which is longer than the specified 1000 Created a chunk of size 1522, which is longer than the specified 1000 --------------------------------------------------------------------------- TypeError Traceback (most recent call last) Cell In[3], line 20 16 docs = text_splitter.split_documents(documents) 17 docs ---> 20 qdrant = Qdrant.from_documents( 21 docs, 22 embeddings, 23 location=":memory:", # Local mode with in-memory storage only 24 collection_name="my_documents", 25 ) File /opt/conda/lib/python3.11/site-packages/langchain_core/vectorstores.py:510, in VectorStore.from_documents(cls, documents, embedding, **kwargs) 508 texts = [d.page_content for d in documents] 509 metadatas = [d.metadata for d in documents] --> 510 return cls.from_texts(texts, embedding, metadatas=metadatas, **kwargs) File /opt/conda/lib/python3.11/site-packages/langchain/vectorstores/qdrant.py:1345, in Qdrant.from_texts(cls, texts, embedding, metadatas, ids, location, url, port, grpc_port, prefer_grpc, https, api_key, prefix, timeout, host, path, collection_name, distance_func, content_payload_key, metadata_payload_key, vector_name, batch_size, shard_number, replication_factor, write_consistency_factor, on_disk_payload, hnsw_config, optimizers_config, wal_config, quantization_config, init_from, on_disk, force_recreate, **kwargs) 1210 """Construct Qdrant wrapper from a list of texts. 1211 1212 Args: (...) 1311 qdrant = Qdrant.from_texts(texts, embeddings, "localhost") 1312 """ 1313 qdrant = cls.construct_instance( 1314 texts, 1315 embedding, (...) 1343 **kwargs, 1344 ) -> 1345 qdrant.add_texts(texts, metadatas, ids, batch_size) 1346 return qdrant File /opt/conda/lib/python3.11/site-packages/langchain/vectorstores/qdrant.py:190, in Qdrant.add_texts(self, texts, metadatas, ids, batch_size, **kwargs) 174 """Run more texts through the embeddings and add to the vectorstore. 175 176 Args: (...) 187 List of ids from adding the texts into the vectorstore. 188 """ 189 added_ids = [] --> 190 for batch_ids, points in self._generate_rest_batches( 191 texts, metadatas, ids, batch_size 192 ): 193 self.client.upsert( 194 collection_name=self.collection_name, points=points, **kwargs 195 ) 196 added_ids.extend(batch_ids) File /opt/conda/lib/python3.11/site-packages/langchain/vectorstores/qdrant.py:2136, in Qdrant._generate_rest_batches(self, texts, metadatas, ids, batch_size) 2122 # Generate the embeddings for all the texts in a batch 2123 batch_embeddings = self._embed_texts(batch_texts) 2125 points = [ 2126 rest.PointStruct( 2127 id=point_id, 2128 vector=vector 2129 if self.vector_name is None 2130 else {self.vector_name: vector}, 2131 payload=payload, 2132 ) 2133 for point_id, vector, payload in zip( 2134 batch_ids, 2135 batch_embeddings, -> 2136 self._build_payloads( 2137 batch_texts, 2138 batch_metadatas, 2139 self.content_payload_key, 2140 self.metadata_payload_key, 2141 ), 2142 ) 2143 ] 2145 yield batch_ids, points File /opt/conda/lib/python3.11/site-packages/langchain/vectorstores/qdrant.py:1918, in Qdrant._build_payloads(cls, texts, metadatas, content_payload_key, metadata_payload_key) 1912 raise ValueError( 1913 "At least one of the texts is None. Please remove it before " 1914 "calling .from_texts or .add_texts on Qdrant instance." 1915 ) 1916 metadata = metadatas[i] if metadatas is not None else None 1917 payloads.append( -> 1918 { 1919 content_payload_key: text, 1920 metadata_payload_key: metadata, 1921 } 1922 ) 1924 return payloads TypeError: unhashable type: 'list' ``` ### Expected behavior It should not raise an error.

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I_kwDOIPDwls54_iF4

OpenAIEmbeddings bug

closed

NONE

### System Info langchain==0.0.344 langchain-experimental>=0.0.42 python==3.10.12 ``` embeddings = OpenAIEmbeddings(model_name=model, openai_api_key=get_model_path(model), chunk_size=CHUNK_SIZE) ``` error ``` WARNING! model_name is not default parameter. model_name was transferred to model_kwargs. Please confirm that model_name is what you intended. warnings.warn( 2023-12-07 12:36:12,645 - embeddings_api.py[line:39] - ERROR: Embeddings.create() got an unexpected keyword argument 'model_name' AttributeError: 'NoneType' object has no attribute 'conjugate' ``` ### Who can help? @hwchase17 ### 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 ``` from langchain.embeddings.openai import OpenAIEmbeddings embeddings = OpenAIEmbeddings(model_name="text-embedding-ada-002", openai_api_key='xxxx', chunk_size=512) data = embeddings.embed_documents(texts) ``` ### Expected behavior Normal loading

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https://github.com/langchain-ai/langchain/pull/14376

PR_kwDOIPDwls5hXu4q

Triyam/bedrock Exception bug resolved

closed

NONE

Issue #14347 - **Description:** There a minor bug in error catching in `bedrockbase` class `langchain/llms/bedrock.py` file, - **Issue:** exception was providing a broad error - **Changes:** >pytest = "^7.4.3", >content-hash = "fea3d6ece1f52d59a144dd626f61be20bda6306dfe4f786b815d7a2f1e82d486" > This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. - **Twitter handle:** https://twitter.com/Triyambaka58284?t=X5gMJFL7PcijWDzP5zgp6g ```diff Removed: -except Exception as e: - raise ValueError(f"Error raised by bedrock service: {e}") Added: +except Exception as e: + if "ThrottlingException" in str(e): + raise ValueError( + f"ThrottlingException raised by bedrock service: {e}" + ) from e + else: + raise ValueError(f"Error raised by bedrock service: {e}") from e ```

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PR_kwDOIPDwls5hXtPC

[Feature] RecursiveURLLoader: Custom PreventOutside

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COLLABORATOR

For websites where you start at a .html file, you aren't able to load sibling paths. If we allow prevent_outside to be another url, you can do things like: start at `https://api.python.langchain.com/en/latest/core_api_reference.html` and prevent_outside to be `https://api.python.langchain.com/en/latest/`

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