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delayedkarma

/

langchain-issues

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I_kwDOIPDwls54Kde4

Issue: how to increase the conversation chain response length?

open

NONE

### Issue you'd like to raise. When I call Llam2-70b-chat model using conversation chain, I got very short response from the model. But if I use LLMChain to call the model, it can give me a long response. So, is there any thing cut off or limit the response length in conversation chain? The model parameter about max_new_tokens I set as 4096, so I don't think that is caused by the model. <img width="1589" alt="截屏2023-11-29 15 47 33" src="https://github.com/langchain-ai/langchain/assets/27841780/34eb62c5-ce1f-4296-9ff8-6298c0c031d6"> ### Suggestion: _No response_

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I_kwDOIPDwls54KbhN

UserWarning: WARNING! repetition_penalty is not default parameter.

open

NONE

### Issue you'd like to raise. Hi, I am using Langchain and LlamaCpp to load my models. I have set "`mirostat`" and "`repitition_penalty`" in my model params and recently I am getting the following UserWarning: ``` UserWarning: WARNING! repetition_penalty is not default parameter. repetition_penalty was transferred to model_kwargs. Please confirm that repetition_penalty is what you intended. ``` and ``` UserWarning: WARNING! mirostat is not default parameter. mirostat was transferred to model_kwargs. Please confirm that mirostat is what you intended. ``` Here is my code: ``` import box import yaml from langchain.llms import LlamaCpp from langchain.callbacks.manager import CallbackManager from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler def build_llm(model_path): callback_manager = CallbackManager([StreamingStdOutCallbackHandler()]) n_gpu_layers = 1 # Metal set to 1 is enough. n_batch = 512 # Should be between 1 and n_ctx, consider the amount of RAM of your Apple Silicon Chip. if model_path == "/Users/mweissenba001/Documents/llama2/llama.cpp/models/7B/ggml-model-q4_0.bin": context_size = 4000 if model_path == "/Users/mweissenba001/Documents/rag_example/Modelle/llama-2-13b-german-assistant-v2.Q5_K_M.gguf": context_size = 4000 else: context_size = 7000 llm = LlamaCpp( max_tokens =cfg.MAX_TOKENS, model_path=model_path, temperature=cfg.TEMPERATURE, f16_kv=True, n_ctx=context_size, # 8k aber mann muss Platz lassen für Instruction, History etc. n_gpu_layers=n_gpu_layers, n_batch=n_batch, callback_manager=callback_manager, verbose=True, # Verbose is required to pass to the callback manager top_p=0.75, top_k=40, repetition_penalty=1.1, mirostat = 2, ) return llm llm = build_llm(model_path) ``` My current Langchain Version is: `langchain-0.0.339` Upgrading to `langchain-0.0.341` didn't help. So what do I have to do to prevent the warnings? Where in model_kwargs to I have to set mirostat and repitition_penalty? ### Suggestion: _No response_

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PR_kwDOIPDwls5gowxf

Mask API key for ForeFrontAI LLM

closed

CONTRIBUTOR

- **Description:** Mask API key for ForeFrontAI LLM and associated unit tests - **Issue:** https://github.com/langchain-ai/langchain/issues/12165 - **Dependencies:** N/A - **Tag maintainer:** @eyurtsev - **Twitter handle:** `__mmahmad__` I made the API key non-optional since linting required adding validation for None, but the key is required per documentation: https://python.langchain.com/docs/integrations/llms/forefrontai

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PR_kwDOIPDwls5govhj

Added Slacktoolkit

closed

CONTRIBUTOR

- **Description:** This PR introduces the Slack toolkit to LangChain, which allows users to read and write to Slack using the Slack API. Specifically, we've added the following tools. 1. get_channel: Provides a summary of all the channels in a workspace. 2. get_message: Gets the message history of a channel. 3. send_message: Sends a message to a channel. 4. schedule_message: Sends a message to a channel at a specific time and date. - **Issue:** This pull request addresses [Add Slack Toolkit #11747](https://github.com/langchain-ai/langchain/issues/11747) - **Dependencies:** package`slack_sdk` Note: For this toolkit to function you will need to add a Slack app to your workspace. Additional info can be found [here](https://slack.com/help/articles/202035138-Add-apps-to-your-Slack-workspace).

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PR_kwDOIPDwls5goukv

Update MLflow and Databricks docs

closed

CONTRIBUTOR

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PR_kwDOIPDwls5goe7w

feat: mask api_key for konko

closed

CONTRIBUTOR

for https://github.com/langchain-ai/langchain/issues/12165

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PR_kwDOIPDwls5goW0m

Ziqian/doc intelligence

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_kwDOIPDwls5goSxx

Add: Steam API tool

closed

CONTRIBUTOR

- **Description:** Our PR is an integration of a Steam API Tool that makes recommendations on steam games based on user's Steam profile and provides information on games based on user provided queries. - **Issue:** the issue # our PR implements: https://github.com/langchain-ai/langchain/issues/12120 - **Dependencies:** python-steam-api library, steamspypi library and decouple library - **Tag maintainer:** @baskaryan, @hwchase17 - **Twitter handle:** N/A Hello langchain Maintainers, We are a team of 4 University of Toronto students contributing to langchain as part of our course [CSCD01 (link to course page)](https://cscd01.com/work/open-source-project). We hope our changes help the community. We have run make format, make lint and make test locally before submitting the PR. To our knowledge, our changes do not introduce any new errors. Our PR integrates the python-steam-api, steamspypi and decouple packages. We have added integration tests to test our python API integration into langchain and an example notebook is also provided. Our amazing team that contributed to this PR: @JohnY2002, @shenceyang, @andrewqian2001 and @muntaqamahmood Thank you in advance to all the maintainers for reviewing our PR!

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PR_kwDOIPDwls5goSTW

add validation error handler to `BaseTool`

open

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. --> - **Description:** add a ValidationError handler as a field of [`BaseTool`](https://github.com/langchain-ai/langchain/blob/master/libs/core/langchain_core/tools.py#L101) and add unit tests for the code change. - **Issue:** #12721 #13662 - **Dependencies:** None - **Tag maintainer:** - **Twitter handle:** @hmdev3 - **NOTE:** - I'm wondering if the update of document is required.

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PR_kwDOIPDwls5goNsX

doc: update langchain version for mongodb integration

closed

CONTRIBUTOR

- **Description:** update minimal supported langchain version for [mongodb atlast integration webpage](https://python.langchain.com/docs/integrations/vectorstores/mongodb_atlas) - **Issue:** none - **Dependencies:** none ----- Just fixing a typo. In [mongodb atlas vectorstore integration page](https://python.langchain.com/docs/integrations/vectorstores/mongodb_atlas), `langchain` support for `$vectorSearch MQL stage` should be `0.0.305` rather than `0.0.35`

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PR_kwDOIPDwls5gn6-2

Improve file system blob loader and generic loader

closed

COLLABORATOR

* Add support for passing a specific file to the file system blob loader * Allow specifying a class parameter for the parser for the generic loader ```python class AudioLoader(GenericLoader): @staticmethod def get_parser(**kwargs): return MyAudioParser(**kwargs): ``` The intent of the GenericLoader is to provide on-ramps from different sources (e.g., web, s3, file system). An alternative is to use pipelining syntax or creating a Pipeline ``` FileSystemBlobLoader(...) | MyAudioParser ```

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I_kwDOIPDwls54JV88

`Rephrase and Respond`

open

COLLABORATOR

### Feature request [Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves](https://arxiv.org/abs/2311.04205) ",,,RaR is complementary to CoT and can be combined with CoT to achieve even better performance,,," - interesting prompt technique. Could be implemented with LangChain. ### Motivation to make LangChain more powerful? ### Your contribution I can help with documentation

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PR_kwDOIPDwls5gn0Q3

Adding new StackExchange API integration

closed

CONTRIBUTOR

Implements [#12115](https://github.com/langchain-ai/langchain/issues/12115) Who can review? @baskaryan , @eyurtsev , @hwchase17 Integrated Stack Exchange API into Langchain, enabling access to diverse communities within the platform. This addition enhances Langchain's capabilities by allowing users to query Stack Exchange for specialized information and engage in discussions. The integration provides seamless interaction with Stack Exchange content, offering content from varied knowledge repositories. A notebook example and test cases were included to demonstrate the functionality and reliability of this integration. - Add StackExchange as a tool. - Add unit test for the StackExchange wrapper and tool. - Add documentation for the StackExchange wrapper and tool. If you have time, could you please review the code and provide any feedback as necessary! My team is welcome to any suggestions.

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PR_kwDOIPDwls5gnou8

docs `OpenAI` platform page update

closed

COLLABORATOR

Missed the OpenAI adapter reference in the OpenAI platform page - Added this reference

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PR_kwDOIPDwls5gnlyx

docs `microsoft` page updates

closed

COLLABORATOR

The Excel, PowerPoint and SharePoint document loaders were missed in the `Microsoft` platform page. - added these references

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PR_kwDOIPDwls5gnd6v

New model parameters and dynamic batching for VertexAIEmbeddings

closed

CONTRIBUTOR

- **Description:** VertexAIEmbeddings performance improvements - **Twitter handle:** @vladkol ## Improvements - Dynamic batch size, starting from 250, lowering down to 5. Batch size varies across regions. Some regions support larger batches, and it significantly improves performance. When running large batches of texts in `us-central1`, performance gain can be up to 3.5x. The dynamic batching also makes sure every batch is below 20K token limit. - New model parameter `embeddings_type` that translates to `task_type` parameter of the API. Newer model versions support [different embeddings task types](https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-text-embeddings#api_changes_to_models_released_on_or_after_august_2023).

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PR_kwDOIPDwls5gncQZ

infra[patch]: add base deps

closed

COLLABORATOR

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PR_kwDOIPDwls5gnbiT

docs[patch]: format notebooks

closed

COLLABORATOR

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PR_kwDOIPDwls5gnZ5d

Adding Initial Support for NVIDIA AI Playground LLMs

closed

CONTRIBUTOR

- **Description:** Added NVIDIA AI Playground Initial support for a selection of models (Llama models, Mistral, etc.) - **Dependencies:** These models do depend on the AI Playground services in NVIDIA NGC. API keys with a significant amount of trial compute are available (10K queries as of the time of writing). - Some additional tests can be ran in [this colab notebook](https://colab.research.google.com/drive/1I2xcp8Mq1x36BizAlzRCYDsEbCiQqRBL#revisionId=0B3KSICnsqLpGNHJVWTlVN1E4YkZyR3JLNG1yOG8xUDVkU0VvPQ) - [A quick link to get a key](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/ai-foundation/models/codellama-13b/api) when you have an NGC account. Generate Key button at the top right of the code window. Thanks so much for the help, and please let me know if you need anything :D

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I_kwDOIPDwls54It_Y

DOC: streamlit memory parameters - memory_key and chat_history

open

NONE

### Issue with current documentation: Streamlit tutorial suggests ``` from langchain.memory import ConversationBufferMemory from langchain.memory.chat_message_histories import StreamlitChatMessageHistory # Optionally, specify your own session_state key for storing messages msgs = StreamlitChatMessageHistory(key="special_app_key") memory = ConversationBufferMemory(memory_key="history", chat_memory=msgs) if len(msgs.messages) == 0: msgs.add_ai_message("How can I help you?") ``` but in version 0.340, definition of ConversationBufferMemory only includes parameter memory_key but not chat_history. I have noticed my model outputs being less accurate for the same script when streamlit is incorporated and I suspect this is the issue. ### Idea or request for content: Please clarify how to best implement Streamlit with chat history.

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I_kwDOIPDwls54Io1R

SQLDatabaseChain returning Question and SQL Query instead of answer

open

NONE

### System Info Code db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True, return_sql=False, use_query_checker=True, prompt=prompt_template) db_chain.run("What are some recently added dockets and their title?") Verbose > Entering new SQLDatabaseChain chain... What are some recently added dockets and their title? SQLQuery:SELECT id, title, modifyDate FROM docket ORDER BY modifyDate DESC LIMIT 5; SQLResult: [('CMS-2023-0184', 'CY 2024 Inpatient Hospital Deductible and Hospital and Extended Care Services Coinsurance Amounts. CMS-8083-N', datetime.datetime(2023, 11, 1, 15, 34, 1)), ('CMS-2023-0183', '(CMS-10143) State Data for the Medicare Modernization Act (MMA)', datetime.datetime(2023, 11, 1, 10, 34, 24)), ('CMS-2023-0181', 'CHIP State Plan Eligibility (CMS-10398 #17)', datetime.datetime(2023, 11, 1, 10, 25, 35)), ('CMS-2023-0182', '(CMS-10434 #77) Medicaid and Continuous Eligibility for Children', datetime.datetime(2023, 11, 1, 10, 24, 56)), ('CMS-2023-0180', 'Virtual Groups for Merit Based Incentive Payment System (MIPS) (CMS-10652)', datetime.datetime(2023, 10, 31, 13, 8, 24))] Answer:SELECT id, title, modifyDate FROM docket ORDER BY modifyDate DESC LIMIT 5; > Finished chain. Output: 'SELECT id, title, modifyDate FROM docket ORDER BY modifyDate DESC LIMIT 5;' The chain is not returning the SQLResult in the chat format even though the query is executed correctly. ### 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 - [X] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [X] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction prompt_suffix = """ If asked for recent dockets, give 5 most recent ones. Make sure the table name is in the database. Table name: docket, Use only these columns when selecting: id, title, modifyDate """ prompt_template = PromptTemplate.from_template(prompt_suffix) db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True, return_sql=False, use_query_checker=True, prompt=prompt_template) db_chain.run("What are some recently added dockets and their title?") ### Expected behavior Example output: The recently added dockets with their dates are:\n\n* CMS-2023-0181 - CY 2024 Inpatient Hospital Deductible and Hospital and Extended Care Services Coinsurance Amounts (November 1st, 2023'

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PR_kwDOIPDwls5gm4vH

langchain[patch]: contributing test guide update

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PR_kwDOIPDwls5gm3Ts

langchain[patch]: release 0.0.342

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PR_kwDOIPDwls5gm2fB

Add MongoDB database agent

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<!-- 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. --> **Description:** Created an agent designed to interact with and query a `MongoDB` database using PyMongo. The agent is very similar to [SQL Database](https://python.langchain.com/docs/integrations/toolkits/sql_database). Currently, it only supports one database per instance `MongoDatabase` class using URI, and the database name must be appended to the end of the URI. See `docs/docs/integrations/toolkits/mongo_database.ipynb` for an example of usage. **Note:** To run PyMongo queries, it checks the input string using regex that it follows the `db.collection.find ...` format and runs it using `eval()`, which is bad practice and may be dangerous (if anyone finds a better solution, feel free change that). Additionally, the tests pass locally due to having MongoDB installed since the tests use the `tmp/mongodb-27017.sock` Unix socket, but do not pass Github Actions test workflows for the same reason, and since we weren't sure whether it would be good practice to edit the workflow and haven't figured out a good way to mock PyMongo's MongoClient/Database that isn't unnecessarily complex or makes the unit tests superficial, we left it as is. **Issue:** Closes [Connection with mongo db #3399](https://github.com/langchain-ai/langchain/issues/3399) **Dependencies:** pymongo (dependency already existed, but changed from optional to not)

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PR_kwDOIPDwls5gmz68

fix docs build

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PR_kwDOIPDwls5gmu4k

core[patch]: release 0.0.7

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PR_kwDOIPDwls5gmubp

Audio file loader implemented with Azure speech service

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I_kwDOIPDwls54IDqK

Issue: Introduce Pinecone into my PDF reader LLM

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### Issue you'd like to raise. Hi there, Before I was using local pickle files as the source of storage of my PDFs and chat history. And now I am moving into next step where I want to using Pinecone as my vector database to store these. I have made couple changes and they are not working and giving my error message. Especially for the part where I check if the embeddings are already stored in Pinecone and only create new embeddings for new files. Here is my code: ## Imports import streamlit as st import os from apikey import apikey import pickle from PyPDF2 import PdfReader # Streamlit - user interface from streamlit_extras.add_vertical_space import add_vertical_space # Langchain from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.embeddings.openai import OpenAIEmbeddings from langchain.vectorstores import FAISS from langchain.llms import OpenAI from langchain.chains.question_answering import load_qa_chain from langchain.callbacks import get_openai_callback from langchain.chat_models.openai import ChatOpenAI from langchain.schema import (SystemMessage, HumanMessage, AIMessage) # Pinecone from langchain.vectorstores import Pinecone import pinecone os.environ['OPENAI_API_KEY'] = apikey ## User Interface # Side Bar with st.sidebar: st.title('🚀 Zi-GPT Version 2.0') st.markdown(''' ## About This app is an LLM-powered chatbot built using: - [Streamlit](https://streamlit.io/) - [LangChain](https://python.langchain.com/) - [OpenAI](https://platform.openai.com/docs/models) LLM model ''') add_vertical_space(5) st.write('Made with ❤️ by Zi') # Main Page def main(): st.header("Zi's PDF Helper: Chat with PDF") # upload a PDF file pdf = st.file_uploader("Please upload your PDF here", type='pdf') # st.write(pdf) # read PDF if pdf is not None: pdf_reader = PdfReader(pdf) # split document into chunks # also can use text split: good for PDFs that do not contains charts and visuals sections = [] for page in pdf_reader.pages: # Split the page text by paragraphs (assuming two newlines indicate a new paragraph) page_sections = page.extract_text().split('\n\n') sections.extend(page_sections) chunks = sections # st.write(chunks) ## embeddings # Set up Pinecone pinecone.init(api_key='d8d78cba-fbf1-42c6-a761-9e89a5ed24eb', environment='gcp-starter') index_name = 'langchainresearch' if index_name not in pinecone.list_indexes(): pinecone.create_index(index_name, dimension=1536, metric="cosine") # Adjust the dimension as per your embeddings index = pinecone.Index(index_name) file_name = pdf.name[:-4] # Check if embeddings are already stored in Pinecone if index.exists(id=file_name): # Fetch embeddings from Pinecone VectorStore = index.fetch(ids=[file_name])[file_name] st.write('Embeddings Loaded from Pinecone') else: # Compute embeddings embeddings = OpenAIEmbeddings(model="text-embedding-ada-002") VectorStore = FAISS.from_texts(chunks, embedding=embeddings) # Store embeddings in Pinecone vectors = VectorStore.get_all_vectors() index.upsert(vectors={(file_name, vectors)}) st.write('Embeddings Computation Completed and Stored in Pinecone') # Create chat history # Pinecone Setup for Chat History chat_history_index_name = 'chat_history' if chat_history_index_name not in pinecone.list_indexes(): pinecone.create_index(chat_history_index_name, dimension=1) # Dimension is 1 as we're not storing vectors here chat_history_index = pinecone.Index(chat_history_index_name) # Create or Load Chat History from Pinecone if pdf: # Check if chat history exists in Pinecone if chat_history_index.exists(id=pdf.name): # Fetch chat history from Pinecone chat_history = chat_history_index.fetch(ids=[pdf.name])[pdf.name] st.write('Chat History Loaded from Pinecone') else: # Initialize empty chat history chat_history = [] # Initialize chat_history in session_state if not present if 'chat_history' not in st.session_state: st.session_state.chat_history = [] # Check if 'prompt' is in session state if 'last_input' not in st.session_state: st.session_state.last_input = '' # User Input current_prompt = st.session_state.get('user_input', '') prompt_placeholder = st.empty() prompt = prompt_placeholder.text_area("Ask questions about your PDF:", value=current_prompt, placeholder="Send a message", key="user_input") submit_button = st.button("Submit") if submit_button and prompt: # Update the last input in session state st.session_state.last_input = prompt docs = VectorStore.similarity_search(query=prompt, k=3) #llm = OpenAI(temperature=0.9, model_name='gpt-3.5-turbo') chat = ChatOpenAI(model='gpt-4', temperature=0.7, max_tokens=3000) message = [ SystemMessage(content="You are a helpful assistant"), HumanMessage(content=prompt) ] chain = load_qa_chain(llm=chat, chain_type="stuff") with get_openai_callback() as cb: response = chain.run(input_documents=docs, question=message) print(cb) # st.write(response) # st.write(docs) # Process the response using AIMessage schema # ai_message = AIMessage(content="AI message content") # ai_message.content = response.generations[0].message.content # Add to chat history chat_entry = { "user_message": prompt, "bot_response": response } # Save chat history # Generate a unique ID for the chat entry, e.g., using a timestamp or a UUID chat_entry_id = generate_unique_id() pinecone_upsert(chat_history_index, {chat_entry_id: chat_entry}) # Clear the input after processing prompt_placeholder.text_area("Ask questions about your PDF:", value='', placeholder="Send a message", key="pdf_prompt") # Display the entire chat chat_history = pinecone_query(chat_history_index, query_params) chat_content = "" for entry in chat_history: user_msg = entry["user_message"] bot_resp = entry["bot_response"] chat_content += f"<div style='background-color: #222222; color: white; padding: 10px;'>**You:** {user_msg}</div>" chat_content += f"<div style='background-color: #333333; color: white; padding: 10px;'>**Zi GPT:** {bot_resp}</div>" st.markdown(chat_content, unsafe_allow_html=True) if __name__ == '__main__': main() ### Suggestion: _No response_

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PR_kwDOIPDwls5gmnMI

Update writer.py

closed

NONE

fixed typos <!-- 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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PR_kwDOIPDwls5gmLl-

fix: InfoSQLDatabaseTool on openai functions mode sometimes calling tables instead of table_names as function arg

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PR_kwDOIPDwls5gmEMF

fix test deps

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PR_kwDOIPDwls5gl99Q

Nc/28nov/revert ci changes

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PR_kwDOIPDwls5glznI

[Evals] Add Project Tags

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Add them to project extra

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PR_kwDOIPDwls5glvk8

Mask api key for Together LLM

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- **Description:** Add unit tests and mask api key for Together LLM - **Issue:** the issue https://github.com/langchain-ai/langchain/issues/12165 , - **Dependencies:** N/A - **Tag maintainer:** ?, - **Twitter handle:** N/A

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PR_kwDOIPDwls5glrB7

Add retriever for Knowledge Bases for Amazon Bedrock

closed

CONTRIBUTOR

- **Description:** Adds a retriever implementation for [Knowledge Bases for Amazon Bedrock](https://aws.amazon.com/bedrock/knowledge-bases/), a new service announced at AWS re:Invent, shortly before this PR was opened. This depends on the `bedrock-agent-runtime` service, which will be included in a future version of `boto3` and of `botocore`. We will open a follow-up PR documenting the minimum required versions of `boto3` and `botocore` after that information is available. - **Issue:** N/A - **Dependencies:** `boto3>=1.33.2, botocore>=1.33.2` - **Tag maintainer:** @baskaryan - **Twitter handles:** `@pjain7` `@dead_letter_q` This PR includes a documentation notebook under `docs/docs/integrations/retrievers`, which I (@dlqqq) have verified independently. EDIT: `bedrock-agent-runtime` service is now included in `boto3>=1.33.2`: https://github.com/boto/boto3/commit/5cf793f49369607f2557fd11741c357df866e4a5

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PR_kwDOIPDwls5gljE0

Add Couchbase document loader

closed

CONTRIBUTOR

**Description:** Adds the document loader for [Couchbase](http://couchbase.com/), a distributed NoSQL database. **Dependencies:** Added the Couchbase SDK as an optional dependency. **Twitter handle:** nithishr

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I_kwDOIPDwls54GszA

Using langchain and LLaMA2 to QA with a large SQL DB

open

NONE

I was working with a sqlite DB that I created for a large dataset (~150k rows). Code snippets: `db = SQLDatabase.from_uri("sqlite:///MLdata.sqlite")` `SQLITE_PROMPT_TEXT = '''You are a SQLite expert. Given an input question, first create a syntactically correct SQLite query to run, then look at the results of the query and return the answer to the input question. Unless the user specifies in the question a specific number of examples to obtain, query for at most {top_k} results using the LIMIT clause as per SQLite. You can order the results to return the most informative data in the database. Never query for all columns from a table. You must query only the columns that are needed to answer the question. Wrap each column name in double quotes (") to denote them as delimited identifiers. Pay attention to use only the column names you can see in the tables below. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table. Use the following format: Question: Question here SQLQuery: SQL Query to run SQLResult: Result of the SQLQuery Answer: Final answer here Only use the following tables: {table_info} Question: {input}'''` `SQLITE_PROMPT = PromptTemplate(input_variables=['input', 'table_info', 'top_k'], template=SQLITE_PROMPT_TEXT) sql_chain = SQLDatabaseChain(llm=local_llm, database=db, prompt=SQLITE_PROMPT, return_direct=False, return_intermediate_steps=False, verbose=False) res=sql_chain("How many rows is in this db?")` Response: 'There are 142321 rows in the input_table of this db.' Second query `res=sql_chain("Count rows with 'Abdominal pain', VAX_TYPE='COVID19', SEX= 'F' and HOSPITAL= 'Y' is in the input_table of this db")` Response: 'There are 115 rows in the input_table where Abdominal pain is present, VAX_TYPE is COVID19, Sex is Female, and Hospital is Yes.' Third query I was trying to find the patient ID only instead of the count. But I am not able to get the patient ID. `res=sql_chain("What is the VAERS_ID with 'Abdominal pain', VAX_TYPE='COVID19', SEX= 'F' and HOSPITAL= 'Y' in this db. ")` But the output generated is same as my second query. Seems like the counting is working fine but nothing more. Can anyone help me in displaying table like output from sqlDbchain via langchain and llama2?

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PR_kwDOIPDwls5gk6XW

Nvidia Triton + TRT-LLM Connector

closed

CONTRIBUTOR

**Description**: This PR includes a connector for using the [Nvidia TensorRT LLM](https://developer.nvidia.com/tensorrt) which allows for accelerated LLM optimization and performance. **Issue**: This closes #13975 **Dependencies**: triton-client library was added to pyproject.toml

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I_kwDOIPDwls54F8tb

Nvidia TensorRT LLM Connector

open

CONTRIBUTOR

### Feature request NVIDIA TensorRT is an open-source SDK for high-performance deep learning inference, includes a deep learning inference optimizer and runtime that delivers low latency and high throughput for inference applications. I propose that a connector be added to langchain allowing users to use TensorRT with minimal configuration. ### Motivation There are several implementations of this connector floating around the internet. Lots of folks seem to want this. Rather than have everyone need to add that connector manually it seems to make sense to natively include it in Langchain. ### Your contribution I am happy to open the PR for this and will do so shortly.

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PR_kwDOIPDwls5gksLU

Eval Error Key

closed

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PR_kwDOIPDwls5gkVTk

RELEASE: core 0.0.7

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PR_kwDOIPDwls5gkKV0

Track RunnableAssign as a separate run trace

closed

CONTRIBUTOR

Addressing incorrect order being sent to callbacks / tracers, due to the nature of threading

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I_kwDOIPDwls54E_A6

Singel object extraction

open

CONTRIBUTOR

### Feature request Currently, the extraction chain only supports the extraction of an Array of object. For example ```python from typing import Optional from langchain.chains import create_extraction_chain_pydantic from langchain.pydantic_v1 import BaseModel # Pydantic data class class Properties(BaseModel): person_name: str person_height: int person_hair_color: str dog_breed: Optional[str] dog_name: Optional[str] # Extraction chain = create_extraction_chain_pydantic(pydantic_schema=Properties, llm=llm) # Run inp = """Alex is 5 feet tall. Claudia is 1 feet taller Alex and jumps higher than him. Claudia is a brunette and Alex is blonde.""" chain.run(inp) # Results in # # [Properties(person_name='Alex', person_height=5, person_hair_color='blonde', dog_breed=None, dog_name=None), # Properties(person_name='Claudia', person_height=6, person_hair_color='brunette', dog_breed=None, dog_name=None)] # ```` There is currently no option available to just get one `Properties` object. It would be nice if you could define at the beginning if you are interested in one object or an array of objects For example ```` python chain = create_extraction_chain_pydantic(pydantic_schema=List[Properties], llm=llm) -> Array of Properties chain = create_extraction_chain_pydantic(pydantic_schema=Properties] llm=llm) -> Just one Propertie ````` ### Motivation It would just make life easier when you knew that you were only dealing with one object. It might also improve the response and prevent wrong or incomplete responses. ### Your contribution I would create and submit a PR that contains this feature.

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I_kwDOIPDwls54E3Mr

AttributeError: module 'typing_extensions' has no attribute 'deprecated' when using ChatOpenAI

open

NONE

### System Info hi! im getting this error 👍 `AttributeError Traceback (most recent call last) File <command-3819272873890469>, line 7 1 # gpt-3.5-turbo-0613 2 # gpt-3.5-turbo-1106 3 # gpt-4 4 # gpt-4-1106-preview 5 llm_model = "gpt-3.5-turbo-1106" ----> 7 llm = ChatOpenAI( 8 temperature=0, 9 model=llm_model 10 ) File /local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.10/site-packages/langchain/load/serializable.py:97, in Serializable.__init__(self, **kwargs) 96 def __init__(self, **kwargs: Any) -> None: ---> 97 super().__init__(**kwargs) 98 self._lc_kwargs = kwargs File /databricks/python/lib/python3.10/site-packages/pydantic/main.py:339, in pydantic.main.BaseModel.__init__() File /databricks/python/lib/python3.10/site-packages/pydantic/main.py:1102, in pydantic.main.validate_model() File /local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.10/site-packages/langchain/chat_models/openai.py:291, in ChatOpenAI.validate_environment(cls, values) 284 values["openai_proxy"] = get_from_dict_or_env( 285 values, 286 "openai_proxy", 287 "OPENAI_PROXY", 288 default="", 289 ) 290 try: --> 291 import openai 293 except ImportError: 294 raise ImportError( 295 "Could not import openai python package. " 296 "Please install it with `pip install openai`." 297 ) File /local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.10/site-packages/openai/__init__.py:11 9 from ._types import NoneType, Transport, ProxiesTypes 10 from ._utils import file_from_path ---> 11 from ._client import ( 12 Client, 13 OpenAI, 14 Stream, 15 Timeout, 16 Transport, 17 AsyncClient, 18 AsyncOpenAI, 19 AsyncStream, 20 RequestOptions, 21 ) 22 from ._version import __title__, __version__ 23 from ._exceptions import ( 24 APIError, 25 OpenAIError, (...) 37 APIResponseValidationError, 38 ) File /local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.10/site-packages/openai/_client.py:12 8 from typing_extensions import override 10 import httpx ---> 12 from . import resources, _exceptions 13 from ._qs import Querystring 14 from ._types import ( 15 NOT_GIVEN, 16 Omit, (...) 21 RequestOptions, 22 ) File /local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.10/site-packages/openai/resources/__init__.py:6 4 from .chat import Chat, AsyncChat, ChatWithRawResponse, AsyncChatWithRawResponse 5 from .audio import Audio, AsyncAudio, AudioWithRawResponse, AsyncAudioWithRawResponse ----> 6 from .edits import Edits, AsyncEdits, EditsWithRawResponse, AsyncEditsWithRawResponse 7 from .files import Files, AsyncFiles, FilesWithRawResponse, AsyncFilesWithRawResponse 8 from .images import ( 9 Images, 10 AsyncImages, 11 ImagesWithRawResponse, 12 AsyncImagesWithRawResponse, 13 ) File /local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.10/site-packages/openai/resources/edits.py:24 19 from .._client import OpenAI, AsyncOpenAI 21 __all__ = ["Edits", "AsyncEdits"] ---> 24 class Edits(SyncAPIResource): 25 with_raw_response: EditsWithRawResponse 27 def __init__(self, client: OpenAI) -> None: File /local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.10/site-packages/openai/resources/edits.py:31, in Edits() 28 super().__init__(client) 29 self.with_raw_response = EditsWithRawResponse(self) ---> 31 @typing_extensions.deprecated( 32 "The Edits API is deprecated; please use Chat Completions instead.\n\nhttps://openai.com/blog/gpt-4-api-general-availability#deprecation-of-the-edits-api\n" 33 ) 34 def create( 35 self, 36 *, 37 instruction: str, 38 model: Union[str, Literal["text-davinci-edit-001", "code-davinci-edit-001"]], 39 input: Optional[str] | NotGiven = NOT_GIVEN, 40 n: Optional[int] | NotGiven = NOT_GIVEN, 41 temperature: Optional[float] | NotGiven = NOT_GIVEN, 42 top_p: Optional[float] | NotGiven = NOT_GIVEN, 43 # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs. 44 # The extra values given here take precedence over values defined on the client or passed to this method. 45 extra_headers: Headers | None = None, 46 extra_query: Query | None = None, 47 extra_body: Body | None = None, 48 timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN, 49 ) -> Edit: 50 """ 51 Creates a new edit for the provided input, instruction, and parameters. 52 (...) 81 timeout: Override the client-level default timeout for this request, in seconds 82 """ 83 return self._post( 84 "/edits", 85 body=maybe_transform( (...) 99 cast_to=Edit, 100 ) AttributeError: module 'typing_extensions' has no attribute 'deprecated'` im using ChatOpenAI with the follwing libs:  and python version is Python 3.10.12 until some days these worked great. and i didnt touch the code. so, whats happening here? thanks ### 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 - [X] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction llm_model = "gpt-4-1106-preview" llm = ChatOpenAI( temperature=0, model=llm_model ) -- using databricks, with langchain and openai ### Expected behavior just an ok and chatopenai working

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I_kwDOIPDwls54E2Vg

OutputParserException in extraction use case

open

NONE

### System Info Hi, i'm obtaining this error trace when performing a basic `create_extraction_chain` example.I'm using CTransformers (llama2 type), more specifically, this model here: https://huggingface.co/clibrain/Llama-2-13b-ft-instruct-es-gguf Python version: 3.11 Environment: ``` accelerate 0.24.1 aiofiles 22.1.0 aiohttp 3.9.0 aiosignal 1.3.1 aiosqlite 0.19.0 alembic 1.9.4 anyio 3.7.1 appnope 0.1.3 argilla 1.19.0 argon2-cffi 23.1.0 argon2-cffi-bindings 21.2.0 arrow 1.3.0 asttokens 2.4.1 async-lru 2.0.4 attrs 23.1.0 Babel 2.13.1 backoff 1.11.1 bcrypt 4.0.1 beautifulsoup4 4.12.2 bleach 6.1.0 Brotli 1.1.0 brotli-asgi 1.2.0 certifi 2023.11.17 cffi 1.16.0 charset-normalizer 3.3.2 click 8.1.7 coloredlogs 15.0.1 comm 0.2.0 commonmark 0.9.1 cryptography 41.0.5 ctransformers 0.2.27 dataclasses-json 0.6.3 datasets 2.15.0 debugpy 1.8.0 decorator 5.1.1 defusedxml 0.7.1 Deprecated 1.2.14 dill 0.3.7 diskcache 5.6.3 ecdsa 0.18.0 elastic-transport 8.10.0 elasticsearch8 8.7.0 executing 2.0.1 fastapi 0.104.1 fastjsonschema 2.19.0 filelock 3.13.1 fqdn 1.5.1 frozenlist 1.4.0 fsspec 2023.10.0 greenlet 3.0.1 h11 0.14.0 httpcore 0.16.3 httptools 0.6.1 httpx 0.23.3 huggingface-hub 0.19.4 humanfriendly 10.0 idna 3.4 ipykernel 6.27.0 ipython 8.17.2 isoduration 20.11.0 jedi 0.19.1 Jinja2 3.1.2 joblib 1.3.2 json5 0.9.14 jsonpatch 1.33 jsonpointer 2.4 jsonschema 4.20.0 jsonschema-specifications 2023.11.1 jupyter_client 8.6.0 jupyter_core 5.5.0 jupyter-events 0.9.0 jupyter-lsp 2.2.0 jupyter_server 2.11.0 jupyter_server_terminals 0.4.4 jupyterlab 4.0.9 jupyterlab-pygments 0.2.2 jupyterlab_server 2.25.2 langchain 0.0.341 langchain-core 0.0.6 langsmith 0.0.67 llama_cpp_python 0.2.20 Mako 1.3.0 markdown-it-py 3.0.0 MarkupSafe 2.1.3 marshmallow 3.20.1 matplotlib-inline 0.1.6 mdurl 0.1.2 mistune 3.0.2 monotonic 1.6 mpmath 1.3.0 multidict 6.0.4 multiprocess 0.70.15 mypy-extensions 1.0.0 nbclient 0.9.0 nbconvert 7.11.0 nbformat 5.9.2 nest-asyncio 1.5.8 networkx 3.2.1 nltk 3.8.1 notebook_shim 0.2.3 numpy 1.23.5 opensearch-py 2.0.1 optimum 1.14.1 overrides 7.4.0 packaging 23.2 pandas 1.5.3 pandocfilters 1.5.0 parso 0.8.3 passlib 1.7.4 pexpect 4.8.0 Pillow 10.1.0 pip 23.3.1 platformdirs 4.0.0 prometheus-client 0.19.0 prompt-toolkit 3.0.41 protobuf 4.25.1 psutil 5.9.6 ptyprocess 0.7.0 pure-eval 0.2.2 py-cpuinfo 9.0.0 pyarrow 14.0.1 pyarrow-hotfix 0.6 pyasn1 0.5.1 pycparser 2.21 pydantic 1.10.13 Pygments 2.17.2 python-dateutil 2.8.2 python-dotenv 1.0.0 python-jose 3.3.0 python-json-logger 2.0.7 python-multipart 0.0.6 pytz 2023.3.post1 PyYAML 6.0.1 pyzmq 25.1.1 referencing 0.31.0 regex 2023.10.3 requests 2.31.0 rfc3339-validator 0.1.4 rfc3986 1.5.0 rfc3986-validator 0.1.1 rich 13.0.1 rpds-py 0.13.1 rsa 4.9 safetensors 0.4.0 scikit-learn 1.3.2 scipy 1.11.4 segment-analytics-python 2.2.0 Send2Trash 1.8.2 sentence-transformers 2.2.2 sentencepiece 0.1.99 setuptools 68.2.2 six 1.16.0 smart-open 6.4.0 sniffio 1.3.0 soupsieve 2.5 SQLAlchemy 2.0.23 stack-data 0.6.3 starlette 0.27.0 sympy 1.12 tenacity 8.2.3 terminado 0.18.0 threadpoolctl 3.2.0 tinycss2 1.2.1 tokenizers 0.15.0 torch 2.1.1 torchvision 0.16.1 tornado 6.3.3 tqdm 4.66.1 traitlets 5.13.0 transformers 4.35.2 typer 0.9.0 types-python-dateutil 2.8.19.14 typing_extensions 4.8.0 typing-inspect 0.9.0 uri-template 1.3.0 urllib3 1.26.18 uvicorn 0.20.0 uvloop 0.19.0 watchfiles 0.21.0 wcwidth 0.2.12 webcolors 1.13 webencodings 0.5.1 websocket-client 1.6.4 websockets 12.0 wrapt 1.14.1 xxhash 3.4.1 yarl 1.9.3 ``` ### 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 - [X] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction Hi, i'm obtaining this error trace when performing a basic `create_extraction_chain` example.I'm using CTransformers (llama2 type), more specifically, this model here: https://huggingface.co/clibrain/Llama-2-13b-ft-instruct-es-gguf ``` OutputParserException Traceback (most recent call last) Cell In[9], line 17 12 chain = create_extraction_chain(schema, llm) 14 inp = """Alex is 5 feet tall. Claudia is 1 feet taller Alex and jumps higher than him. Claudia is a brunette and Alex is blonde. 15 Willow is a German Shepherd that likes to play with other dogs and can always be found playing with Milo, a border collie that lives close by.""" ---> 17 chain.run(inp) File ~/Desktop/green-jobs/genv/lib/python3.11/site-packages/langchain/chains/base.py:507, in Chain.run(self, callbacks, tags, metadata, *args, **kwargs) 505 if len(args) != 1: 506 raise ValueError("`run` supports only one positional argument.") --> 507 return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[ 508 _output_key 509 ] 511 if kwargs and not args: 512 return self(kwargs, callbacks=callbacks, tags=tags, metadata=metadata)[ 513 _output_key 514 ] File ~/Desktop/green-jobs/genv/lib/python3.11/site-packages/langchain/chains/base.py:312, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info) 310 except BaseException as e: 311 run_manager.on_chain_error(e) --> 312 raise e 313 run_manager.on_chain_end(outputs) 314 final_outputs: Dict[str, Any] = self.prep_outputs( 315 inputs, outputs, return_only_outputs 316 ) File ~/Desktop/green-jobs/genv/lib/python3.11/site-packages/langchain/chains/base.py:306, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info) 299 run_manager = callback_manager.on_chain_start( 300 dumpd(self), 301 inputs, 302 name=run_name, 303 ) 304 try: 305 outputs = ( --> 306 self._call(inputs, run_manager=run_manager) 307 if new_arg_supported 308 else self._call(inputs) 309 ) 310 except BaseException as e: 311 run_manager.on_chain_error(e) File ~/Desktop/green-jobs/genv/lib/python3.11/site-packages/langchain/chains/llm.py:104, in LLMChain._call(self, inputs, run_manager) 98 def _call( 99 self, 100 inputs: Dict[str, Any], 101 run_manager: Optional[CallbackManagerForChainRun] = None, 102 ) -> Dict[str, str]: 103 response = self.generate([inputs], run_manager=run_manager) --> 104 return self.create_outputs(response)[0] File ~/Desktop/green-jobs/genv/lib/python3.11/site-packages/langchain/chains/llm.py:258, in LLMChain.create_outputs(self, llm_result) 256 def create_outputs(self, llm_result: LLMResult) -> List[Dict[str, Any]]: 257 """Create outputs from response.""" --> 258 result = [ 259 # Get the text of the top generated string. 260 { 261 self.output_key: self.output_parser.parse_result(generation), 262 "full_generation": generation, 263 } 264 for generation in llm_result.generations 265 ] 266 if self.return_final_only: 267 result = [{self.output_key: r[self.output_key]} for r in result] File ~/Desktop/green-jobs/genv/lib/python3.11/site-packages/langchain/chains/llm.py:261, in <listcomp>(.0) 256 def create_outputs(self, llm_result: LLMResult) -> List[Dict[str, Any]]: 257 """Create outputs from response.""" 258 result = [ 259 # Get the text of the top generated string. 260 { --> 261 self.output_key: self.output_parser.parse_result(generation), 262 "full_generation": generation, 263 } 264 for generation in llm_result.generations 265 ] 266 if self.return_final_only: 267 result = [{self.output_key: r[self.output_key]} for r in result] File ~/Desktop/green-jobs/genv/lib/python3.11/site-packages/langchain/output_parsers/openai_functions.py:130, in JsonKeyOutputFunctionsParser.parse_result(self, result, partial) 129 def parse_result(self, result: List[Generation], *, partial: bool = False) -> Any: --> 130 res = super().parse_result(result, partial=partial) 131 if partial and res is None: 132 return None File ~/Desktop/green-jobs/genv/lib/python3.11/site-packages/langchain/output_parsers/openai_functions.py:68, in JsonOutputFunctionsParser.parse_result(self, result, partial) 66 generation = result[0] 67 if not isinstance(generation, ChatGeneration): ---> 68 raise OutputParserException( 69 "This output parser can only be used with a chat generation." 70 ) 71 message = generation.message 72 try: OutputParserException: This output parser can only be used with a chat generation. ``` Im using the following example: ``` schema = { "properties": { "person_name": {"type": "string"}, "person_height": {"type": "integer"}, "person_hair_color": {"type": "string"}, "dog_name": {"type": "string"}, "dog_breed": {"type": "string"}, }, "required": [], } chain = create_extraction_chain(schema, llm) inp = """Alex is 5 feet tall. Claudia is 1 feet taller Alex and jumps higher than him. Claudia is a brunette and Alex is blonde. Willow is a German Shepherd that likes to play with other dogs and can always be found playing with Milo, a border collie that lives close by.""" chain.run(inp) ``` ### Expected behavior I expect not to get an error when executing the code

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I_kwDOIPDwls54EubA

Caused by NewConnectionError when using ZERO_SHOT_REACT_DESCRIPTION

closed

NONE

### System Info **python:** 3.11.6 **langchain:** 0.0.335 **openai:** 0.28.1 ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [X] Agents / Agent Executors - [X] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction I tried to create custom tools for Agent type ZERO_SHOT_REACT_DESCRIPTION but after executing the first chain, it keeps getting error Caused by NewConnectionError on the next chain. The tools work fine because I tested them with Agent type OPENAI.FUNTIONS My first tool ``` def get_thing(u: str) -> str: c, l = get_thing_ori(u, True) r = c + '\n' + l return r class GetThingCheckInput(BaseModel): u: str = Field(..., description='thing to get') def get_thing_tool(): GetThingTool = StructuredTool.from_function( name="get_thing", description="Useful to get thing", func=get_thing, args_schema=GetThingCheckInput ) return GetThingTool ``` My second tool ``` def get_S(t: str, l=str, c=str) -> str: u_s, d_s = get_so() result = '' for u, d in zip(u_s, d_s): result += f'U: {u}\nD: {d}\n\n' return result def parsing_get_S(string: str): t, l, c = string.split(', ') return (get_S(t, l, c)) class ParsingGetSCheckInput(BaseModel): string: str = Field(..., description='A string contain a data in format: "T, L, C"') def get_parsing_get_S_tool(): ParsingGetSTool = StructuredTool.from_function( name = 'parsing_get_S', description="Useful to get S. Input is a comma-seperated list contain data in format: 'T, L, C'", func=parsing_get_S, args_schema=ParsingGetSCheckInput ) return ParsingGetSTool ``` This is my main ``` if __name__ == '__main__': llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0, max_retries=5, timeout=100) GetThingTool = get_thing_tool() ParsingGetSTool = get_parsing_get_S_tool() tools = [GetThingTool, ParsingGetSTool] agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True) agent.run("Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?") ``` I try to add both `os.environ["LANGCHAIN_TRACING"] = "true"` and `os.environ["LANGCHAIN_TRACING"] = "true"` but it not working. Full error is: ``` ERROR:root:download error: 'https://www.dailymail.co.uk/tvshowbiz/article-12792715/Leonardo-DiCaprio-low-key-glamorous-girlfriend-Vittoria-Ceretti-family-London.html' HTTPConnectionPool(host="'https", port=80): Max retries exceeded with url: //[www.dailymail.co.uk/tvshowbiz/article-12792715/Leonardo-DiCaprio-low-key-glamorous-girlfriend-Vittoria-Ceretti-family-London.html](https://file+.vscode-resource.vscode-cdn.net/d%3A/Python%20Project/www.dailymail.co.uk/tvshowbiz/article-12792715/Leonardo-DiCaprio-low-key-glamorous-girlfriend-Vittoria-Ceretti-family-London.html)' (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x0000018BEA481B90>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed')) ``` ### Expected behavior As I use Agent type OPENAI.FUNTIONS, it completely returns a full answer, so that means there is nothing wrong with the tools, i guess. It should work for agent type ZERO_SHOT_REACT_DESCRIPTION. The reason I use zero shot is because the thinking part works better in different languages than agent-type OPENAI.FUNTIONS.

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I_kwDOIPDwls54Egcz

Issue: Passing context(previous question and answer) to the create_pandas_dataframe_agent function.

open

NONE

### Issue you'd like to raise. Hello Team, We are finding a way to pass the context, previous question and answer to **create_pandas_dataframe_agent**. Can u please help me understand how i can pass the context(previous question and answer) to the **create_pandas_dataframe_agent**. it will be helpful if you have any such example implementation Thanks, Akash ### Suggestion: _No response_

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PR_kwDOIPDwls5gjbed

Add doc for the development of core and experimental sections

closed

CONTRIBUTOR

### **Description** Hi, I just started learning the source code of `langchain` and hope to contribute code. However, according to the instructions in the [CONTRIBUTING.md](https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md) document, I could not run the test command `make test` to run normally. I found that many modules did not exist after [splitting `langchain_core`](https://github.com/langchain-ai/langchain/discussions/13823), so I updated the document. ### **Twitter handle** lin_bob57617

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PR_kwDOIPDwls5gjSA9

Update CONTRIBUTING.md doc

closed

CONTRIBUTOR

- **Description:** The new demo notebook should be placed in [docs/docs/modules](https://github.com/langchain-ai/langchain/tree/master/docs/docs/modules) - **Twitter handle:** lin_bob57617

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I_kwDOIPDwls54ENbw

Why does FAISS similarity search not fetch data with respect to alphanumeric keys like ORA-14300?

open

NONE

### Issue you'd like to raise. **Summary:** The FAISS similarity search in LangChain is encountering difficulties when processing alpha-numeric queries that involve numeric integers. While the search performs well for queries like "What are resolutions to problems related to SAF," it exhibits suboptimal behavior when processing queries such as "Give me complete details of L2-resolution against ORA-14300," which involve alpha-numeric combinations. Note that we have already successfully embedded and indexed the above documents that includes alpha numeric key as well such as "ORA-14300". **Expected Behavior:** The FAISS similarity search should accurately and effectively retrieve relevant information for alpha-numeric queries, providing precise results even when numeric integers are included in the query. **Current Behavior:** The search is not functioning correctly when processing alpha-numeric queries with numeric integers. It fails to accurately identify and retrieve relevant documents, leading to a suboptimal user experience.   **Steps to Reproduce:** Index CSV data containing both text and numerical values, and subsequently execute a query that includes an alphanumeric question. **Additional Information:** Environment: Langchain version (0.0.284) **Impact:** This issue affects the accuracy and reliability of the FAISS similarity search, particularly when handling alpha-numeric queries that include numeric integers. Users relying on LangChain for information retrieval may experience challenges when seeking relevant documents related to such queries. **Priority:** High Are FAISS and Redis similarity searches capable of providing such high similarity search over the index? If not, please guide me on where I should turn to achieve better and more accurate results Thank you for your attention to this matter. Feel free to request additional information if needed. ### Suggestion: _No response_

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I_kwDOIPDwls54D-9y

Issue: Chroma.from_documents does not save metadata properly

open

NONE

### Issue you'd like to raise. I create my vector db using the following code: ``` db = Chroma.from_documents( chunked_documents, embeddings, persist_directory=db_path + '/' + db_type, client_settings=chroma_settings,) ``` `chunked_documents` is a list of elements of type Document. I have added metadata which is a simple numerical id: `{'id': 1}` ``` embeddings = HuggingFaceInstructEmbeddings( model_name=args.embedding_model, model_kwargs={"device": args.device}, ) ``` ``` CHROMA_SETTINGS = Settings( anonymized_telemetry=False, is_persistent=True, ) ``` What happens is that I run `db.similarity_search(query, k=3)` and for part of the answers, the metadata dict is empty. Has anyone encountered such an issue? Just to point out, when I create the db using the `from_texts()` method where I add raw texts and metadata separately I do not encounter the issue and when running `db.similarity_search()` the returned answer, contains the respective metadata. ### Suggestion: _No response_

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PR_kwDOIPDwls5gi6da

Remove `python_repl` from _BASE_TOOLS

closed

CONTRIBUTOR

### **Description:** Previously `python_repl` was a built-in tool, but now it has been moved to `langchain_experimental`. When I use `load_tools` I get an error: ```python In [1]: from langchain.agents import load_tools In [2]: load_tools(["python_repl"]) --------------------------------------------------------------------------- ImportError Traceback (most recent call last) Cell In[2], line 1 ----> 1 load_tools(["python_repl"]) File ~/workspace/langchain/libs/langchain/langchain/agents/load_tools.py:530, in load_tools(tool_names, llm, callbacks, **kwargs) 528 tool_names.extend(requests_method_tools) 529 elif name in _BASE_TOOLS: --> 530 tools.append(_BASE_TOOLS[name]()) 531 elif name in _LLM_TOOLS: 532 if llm is None: File ~/workspace/langchain/libs/langchain/langchain/agents/load_tools.py:84, in _get_python_repl() 83 def _get_python_repl() -> BaseTool: ---> 84 raise ImportError( 85 "This tool has been moved to langchain experiment. " 86 "This tool has access to a python REPL. " 87 "For best practices make sure to sandbox this tool. " 88 "Read https://github.com/langchain-ai/langchain/blob/master/SECURITY.md " 89 "To keep using this code as is, install langchain experimental and " 90 "update relevant imports replacing 'langchain' with 'langchain_experimental'" 91 ) ImportError: This tool has been moved to langchain experiment. This tool has access to a python REPL. For best practices make sure to sandbox this tool. Read https://github.com/langchain-ai/langchain/blob/master/SECURITY.md To keep using this code as is, install langchain experimental and update relevant imports replacing 'langchain' with 'langchain_experimental' ``` In this case, it will be very confusing. I think it is no longer a built-in tool now, so it can be removed from `_BASE_TOOLS` ### **Issue:** https://github.com/langchain-ai/langchain/issues/13858, https://github.com/langchain-ai/langchain/issues/13859, https://github.com/langchain-ai/langchain/issues/13856 ### **Twitter handle:** [lin_bob57617](https://twitter.com/lin_bob57617)

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I_kwDOIPDwls54Dzqx

Enable Limit False in Self Query Retriever doesn't have the expected behavior

open

NONE

### System Info I am using the SelfQueryRetriever class to make questions about a set of documents. It seems that there is an issue with the use of the `enable_limit` argument Versions: Langchain Version: 0.0.340 openai 1.3.5 ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [X] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [X] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction enable_limit = False: ```python retriever = SelfQueryRetriever.from_llm( llm, vectorstore, document_content_description, metadata_field_info, enable_limit=False ) query = "I want indentify thousand repairs with xxxx" result = retriever.invoke(query) len(result) #Result: 4 ``` If change enable_limit = True: ```python retriever = SelfQueryRetriever.from_llm( llm, vectorstore, document_content_description, metadata_field_info, enable_limit=True ) query = "I want indentify thousand repairs with xxxx" result = retriever.invoke(query) len(result) #Result: 1000 ``` If enable_limit=True and change in query for "All": ```python retriever = SelfQueryRetriever.from_llm( llm, vectorstore, document_content_description, metadata_field_info, enable_limit=True ) query = "I want indentify All repairs with xxxx" result = retriever.invoke(query) len(result) #Result: 4 ``` ### Expected behavior The expected behaviour with "enable_limit = False" was to show more than 1000 documents. As there is no defined limit, all documents were expected as a result.

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PR_kwDOIPDwls5giuKz

Fixes for opengpts release

closed

COLLABORATOR

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I_kwDOIPDwls54C6jE

ImportError: cannot import name 'agenerate_from_stream' from 'langchain_core.language_models.chat_models'

closed

CONTRIBUTOR

### System Info Current LangChain master commit: https://github.com/langchain-ai/langchain/commits/391f200 ### Who can help? @hwchase17 @baskaryan ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction Just run ``` from langchain.chat_models import BedrockChat ``` Stacktrace: ``` tests/e2e-tests/test_compatibility_matrix.py:10: in <module> from langchain.chat_models import ChatOpenAI, AzureChatOpenAI, ChatVertexAI, BedrockChat /tmp/venv/lib/python3.11/site-packages/langchain/chat_models/__init__.py:20: in <module> from langchain.chat_models.anthropic import ChatAnthropic /tmp/venv/lib/python3.11/site-packages/langchain/chat_models/anthropic.py:18: in <module> from langchain.chat_models.base import ( /tmp/venv/lib/python3.11/site-packages/langchain/chat_models/base.py:1: in <module> from langchain_core.language_models.chat_models import ( E ImportError: cannot import name 'agenerate_from_stream' from 'langchain_core.language_models.chat_models' (/tmp/venv/lib/python3.11/site-packages/langchain_core/language_models/chat_models.py) ``` ### Expected behavior To being able to import the required modules

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PR_kwDOIPDwls5ghy90

Use langchain core in-tree as a dev dependency

closed

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Using the published version means master is broken for contributors whenever we make changes in one lib that depend on the other. <!-- 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_kwDOIPDwls54CcBH

Issue: Question about Deletion of Embeddings for Confluence Spaces

open

NONE

### Issue you'd like to raise. I'm currently working on a project and have encountered an issue with the deletion functionality for Confluence Spaces. I've implemented a function delete_embeddings that is supposed to delete embeddings based on the Confluence space key, but it doesn't seem to be working as expected. Here's the relevant code snippet: def delete_embeddings(file_path, persist_directory): chroma_db = chromadb.PersistentClient(path=persist_directory) collection = chroma_db.get_or_create_collection(name="langchain") ids = collection.get(where={"source": file_path})['ids'] collection.delete(where={"source": file_path},ids=ids) # chroma_db.delete_collection(name="langchain") print("delete successfully") And I'm calling this function as follows: delete_embeddings(names, persist_directory) I want to delete embeddings of Confluence Spaces when user request deletion confluence space. ### Suggestion: _No response_

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I_kwDOIPDwls54CX3F

Advance metadata filtering for PGVector

open

NONE

### Feature request PGVector in Langchain does not support advance metadata filtering such as "OR" clause. For now, there is no way to perform filters such as: ``` { "$or": [ {"uploaded_by": {"$eq": "USER1"}}, {"org": {"$eq": "ORG"}}, ] } ``` ### Motivation Our team is unable to use langchain with PGVector due to its lack of support for "OR" filter. Having advanced metadata filtering like that in Pinecone/Qdrant would really help https://docs.pinecone.io/docs/metadata-filtering ### Your contribution For now, I see existing PR: https://github.com/langchain-ai/langchain/pull/12977 This could possibly solve the issue.

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I_kwDOIPDwls54B99F

Issue: Question about Deletion Functionality for Confluence Spaces

open

NONE

### Issue you'd like to raise. I'm currently working on a project and have encountered an issue with the deletion functionality for Confluence Spaces. I've implemented a function `delete_embeddings` that is supposed to delete embeddings based on the Confluence space key, but it doesn't seem to be working as expected. Here's the relevant code snippet: ```python def delete_embeddings(file_path, persist_directory): chroma_db = chromadb.PersistentClient(path=persist_directory) collection = chroma_db.get_or_create_collection(name="langchain") ids = collection.get(where={"source": file_path})['ids'] collection.delete(where={"source": file_path},ids=ids) # chroma_db.delete_collection(name="langchain") print("delete successfully") And I'm calling this function as follows: delete_embeddings(names, persist_directory) ### Suggestion: _No response_

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I_kwDOIPDwls54BzK-

DOC: Dropbox document loader functionality

closed

NONE

### Issue with current documentation: I was hoping to use the Dropbox document loader for a large number of pdf and some docx documents, however I am not sure whether this loader supports these file types. I followed the instructions on https://python.langchain.com/docs/integrations/document_loaders/dropbox and installed the "unstructured[all-docs]" package but I keep getting the message that the loader skips these files. > xxx.docx could not be decoded as text. Skipping. > yyy.pdf could not be decoded as text. Skipping. Does this loader only support .txt files? Is there an alternative? I see the Unstructured loader only works for individual files, is that the best alternative? Many thanks! ### Idea or request for content: File formats the loader supports needs to be clarified unstructured package was given as a prerequisite for pdf files but I was getting missing package/method errors until I installed the "unstructured[all-docs]" package and still not able to load pdf files

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I_kwDOIPDwls54Bomx

PineconeProtocolError: Failed to connect; did you specify the correct index name?

open

NONE

### System Info To train data in Pinecone, I used the function. _pinecone = Pinecone.from_documents(docs, self.embeddings, index_name=self.index_name) all the parameters have values always:   But, this error occurs often like this:  @hwchase17 , I am looking forward to fixing this issue asap. Thank you. ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [X] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction class PineConeIndexer: def __init__(self): self.embeddings = OpenAIEmbeddings() # initialize pinecone pinecone.init( api_key=os.environ.get('PINECONE_API_KEY'), environment=os.environ.get('PINECONE_ENV') ) self.index_name = os.environ.get('PINECONE_INDEX_NAME') def upsert_index_from_task(self, task): try: # get doc .... _pinecone = Pinecone.from_documents(docs, self.embeddings, index_name=self.index_name) return {"success": True, "error": None} except Exception as e: return {"success": False, "error": str(e)} ### Expected behavior sometimes, this error occurs: PineconeProtocolError Failed to connect; did you specify the correct index name? ProtocolError ('Connection aborted.', ConnectionResetError(104, 'Connection reset by peer'))

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PR_kwDOIPDwls5gg68L

Mathpix PDF loader supports arbitrary extra params

closed

CONTRIBUTOR

- **Description:** Support providing whatever extra parameters you want to the Mathpix PDF loader API request. - **Issue:** #12773 - **Dependencies:** None

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PR_kwDOIPDwls5gg3Zz

Fix Mathpix PDF loader integration

closed

CONTRIBUTOR

- **Description:** Fixes the Mathpix PDF loader API integration. Specifically, ensures that Mathpix auth headers are provided for every request, and ensures that we recognize all errors that can occur during a request. Also, the option to provide API keys as kwargs never actually worked before, but now that's fixed too. - **Issue:** #11249 - **Dependencies:** None

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PR_kwDOIPDwls5ggp_4

Tmp

closed

CONTRIBUTOR

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I_kwDOIPDwls54BTK6

can you tell me how to calculate what will be cost for this input tokens in prompt and output tokens in prompt

open

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### Issue with current documentation: this is intput token (import pandas as pd import json from IPython.display import Markdown, display from langchain.agents import create_csv_agent from langchain.chat_models import ChatOpenAI from langchain.llms import OpenAI import os os.environ["OPENAI_API_KEY"] = "" # Load the dataset df = pd.read_csv('Loan Collections - Sheet1.csv') # Function to convert date and time columns to float def convert_date_time_columns_to_float(df): for column in df.select_dtypes(include=['object']).columns: # Check if the column name contains 'date' or 'time' if 'date' in column.lower() or 'time' in column.lower(): try: # Convert the column to datetime df[column] = pd.to_datetime(df[column], errors='coerce') # Convert datetime to numerical representation (e.g., days since a reference date) reference_date = pd.to_datetime('1900-01-01') df[column] = (df[column] - reference_date).dt.total_seconds() / (24 * 60 * 60) except ValueError: # Handle errors during conversion print(f"Error converting column '{column}' to float.") # Convert 'date' and 'time' columns to float convert_date_time_columns_to_float(df) # Extract unique values for each column unique_values_per_column = {} for column in df.select_dtypes(include=['object']).columns: unique_values_per_column[column] = df[column].unique().tolist() # Convert the dictionary to JSON json_data_train = json.dumps(unique_values_per_column, indent=4) testData_fname = "Sample Retail Stores Data.csv" # Load the dataset df2 = pd.read_csv(testData_fname) convert_date_time_columns_to_float(df2) # Extract unique values for each column unique_values_per_column = {} for column in df2.select_dtypes(include=['object']).columns: unique_values_per_column[column] = df2[column].unique().tolist() # Convert the dictionary to JSON json_data_test = json.dumps(unique_values_per_column, indent=4) # Define user's question user_question = "Percentage share of State by Value?" # Define the prompt template prompt_template = f'''If the dataset has the following columns: {json_data_train}'''+''' Understand user questions with different column names and convert them to a JSON format. Question might not even mentioned column name at all, it would probably mention value of the column. so it has to figure it out columnn name based on that value. Example1: User Question1: top zone in the year 2019 with Loan Amt between 10k and 20k and tenure > 12 excluding Texas region? { "start_date": "01-01-2019", "end_date": "31-12-2019", "time_stamp_col": "Due Date", "agg_columns": [], "trend": "null", "to_start_date": "null", "to_end_date": "null", "growth": "null", "variables_grpby": ["Zone"], "filters": {}, "not_in": {"Region": ["Texas"]}, "num_filter": { "gt": [ ["Loan Tenure", 12], ["Loan Amount", 10000] ], "lt": [ ["Loan Amount", 20000] ] }, "percent": "false", "top": "1", "bottom": "null" } Note the following in the above example - The word "top" in the User Question made the "top" key have the value as "1". If "highest" is mentioned in the User Question, even then "top" would have the value as "1". If "top" is not mentioned or not implied in the User Question, then it takes on the value "null". Similarly for "bottom" key in the System Response. - The word "zone" in the User Question refers to a column "Zone" in the dataset and since it is a non-numeric column and we have to group by that column, the system response has it as one of the values of the list of the key "variables_grpby" - The key "start_date" and "end_date" Since it is mentioned 2019 in the User Question as the timeframe, the "start_date" assumes the beginning of the year 2019 and "end_date" assumes the end of the year 2019. If no date related words are mentioned in the question, "start_date" would be "null" and "end_date" would be "null". - The key "time_stamp_col" in the System Response should mention the relevant time related column name from the dataset according to the question if the question mentions a time related word. - The key "agg_columns" in the System Response is a list of columns to be aggregated which should mention the numeric column names on which the question wants us to aggregate on. - The key "trend" in the System Response, "trend" is set to "null" since the user question doesn't imply any trend analysis . If the question were about trends over time, this key would contain information about the trend, such as "upward," "downward," or "null" if no trend is specified. - The key "filters" An empty dictionary in this case, as there are no explicit filters mentioned in the user question. If the user asked to filter data based on certain conditions (e.g. excluding a specific region), this key would contain the relevant filters. - The key "to_start_date" and "to_end_date" Both set to "null" in this example because the user question specifies a single timeframe (2019). If the question mentioned a range (e.g. "from January 2019 to March 2019"), these keys would capture the specified range. - The key "growth" Set to "null" in this example as there is no mention of growth in the user question. If the user inquired about growth or change over time, this key would provide information about the type of growth (e.g."monthly","yearly"," "absolute") or be set to "null" if not applicable. - The key "not_in" Contains information about exclusion criteria based on the user's question. In this example, it excludes the "Texas" region. If the user question doesn't involve exclusions, this key would be an empty dictionary. - The key "num_filter" Specifies numerical filters based on conditions in the user question. In this example, it filters loans with a tenure greater than 12 and loan amounts between 10k and 20k. If the user question doesn't involve numerical filters, this key would be an empty dictionary. - The key "percent" Set to "false" in this example as there is no mention of percentage in the user question. If the user inquired about percentages, this key would contain information about the use of percentages in the response. Similarly, below are more examples of user questions and their corresponding expected System Responses. Example 2: User Question: What is the Highest Loan Amount and Loan Outstanding by RM Name James in January 2020 { "start_date": "01-01-2020", "end_date": "31-01-2020", "time_stamp_col": "Due Date", "agg_columns": ["Loan Amount", "Loan Outstanding"], "trend": "null", "to_start_date": "null", "to_end_date": "null", "growth": "null", "variables_grpby": [], "filters": {"RM Name": ["James"]}, "not_in": {}, "num_filter": {}, "percent": "false", "top": "1", "bottom": "null" } Example 3: User Question: Which RM Name with respect to Region has the Highest Interest Outstanding and Principal Outstanding in the year 2019 { "start_date": "01-01-2019", "end_date": "31-12-2019", "time_stamp_col": "Due Date", "agg_columns": ["Interest Outstanding", "Principal Outstanding"], "trend": "null", "to_start_date": "null", "to_end_date": "null", "growth": "null", "variables_grpby": ["RM Name", "Region"], "filters": {}, "not_in": {}, "num_filter": {}, "percent": "false", "top": "1", "bottom": "null" } Example 4: User Question: Which Branch in North Carolina with respect to Cibil Score Bucket has the Highest Cibil Score in 2019 { "start_date": "01-01-2019", "end_date": "31-12-2019", "time_stamp_col": "Due Date", "agg_columns": ["Cibil Score", "DPD Bucket"], "trend": "null", "to_start_date": "null", "to_end_date": "null", "growth": "null", "variables_grpby": ["Branch"], "filters": {"Region": ["North Carolina"]}, "not_in": {}, "num_filter": {}, "percent": "false", "top": "1", "bottom": "null" } '' Example 5: User Question: With respect to Zone, Region, Branch, RM Name what is the Highest Loan Amount, Loan Tenure, Loan Outstanding, EMI Pending, Principal Outstanding { "start_date": "null", "end_date": "null", "time_stamp_col": "null", "agg_columns": ["Loan Amount", "Loan Tenure", "Loan Outstanding", "EMI Pending", "Principal Outstanding"], "trend": "null", "to_start_date": "null", "to_end_date": "null", "growth": "null", "variables_grpby": ["Zone", "Region", "Branch", "RM Name"], "filters": {}, "not_in": {}, "num_filter": {}, "percent": "false", "top": "1", "bottom": "null" } Example 6: User Question: Top 2 zones by Housing Loan in the year 2019 { "start_date": "01-01-2019", "end_date": "31-12-2019", "time_stamp_col": "Due Date", "agg_columns": ["Housing Loan"], "trend": "null", "to_start_date": "null", "to_end_date": "null", "growth": "null", "variables_grpby": ["Zone"], "filters": {"Product": ["Home Loan"]}, "not_in": {}, "num_filter": {}, "percent": "false", "top": "2", "bottom": "null" } '''+ f'''Our test dataset has the following columns: {json_data_test} User Question (to be converted): {user_question}''' # Load the agent agent = create_csv_agent(OpenAI(temperature=0), testData_fname, verbose=True) gpt4_agent = create_csv_agent(ChatOpenAI(temperature=0, model_name="gpt-4-1106-preview"), testData_fname) # Use the formatted question as the input to your agent response = gpt4_agent.run(prompt_template) # Print the response print(user_question) print(response)) and this is output token ( Percentage share of State by Value? { "start_date": "null", "end_date": "null", "time_stamp_col": "null", "agg_columns": ["Value"], "trend": "null", "to_start_date": "null", "to_end_date": "null", "growth": "null", "variables_grpby": ["State"], "filters": {}, "not_in": {}, "num_filter": {}, "percent": "true", "top": "null", "bottom": "null" }) ### Idea or request for content: _No response_

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PR_kwDOIPDwls5gghYn

secretStr for baichuan chat model api key

closed

CONTRIBUTOR

Description: This PR removes secrets present in raw format, Issue: Baichuan api key was exposed when printing out the langchain object(https://github.com/langchain-ai/langchain/issues/12165) Tag maintainer: @eyurtsev

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PR_kwDOIPDwls5ggeut

feat: mask api key for nlpcloud

closed

CONTRIBUTOR

for https://github.com/langchain-ai/langchain/issues/12165

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PR_kwDOIPDwls5ggeHM

Secret str for baichuan

closed

CONTRIBUTOR

- **Description:** This PR removes secrets present in raw format, - **Issue:** Baichuan api key was exposed when printing out the langchain object(#12165) - **Tag maintainer:** @eyurtsev

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I_kwDOIPDwls54BEkG

'HTTPClient' object has no attribute 'configuration'

open

NONE

### System Info machine: mackbook pro Sonoma 14.1.1 package: python = "3.10.13" openai = "^0.28.1" pandas = "^2.1.1" ipython = "^8.16.0" langchain = "^0.0.306" python-dotenv = "^1.0.0" seaborn = "^0.13.0" tqdm = "^4.66.1" torch = "^2.1.0" transformers = "^4.35.2" accelerate = "^0.24.1" sentencepiece = "^0.1.99" openllm = "^0.4.27" ### 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 follow the instruction https://python.langchain.com/docs/integrations/llms/openllm 1)openllm start dolly-v2 2) from langchain.llms import OpenLLM server_url = "http://localhost:3000" # Replace with remote host if you are running on a remote server llm = OpenLLM(server_url=server_url) llm("what is the meaning of life") error: line 229 /langchain/llms/openllm.py 220 AtrributionError: "HttpClient" object has not attribute 'configuration' ### Expected behavior return a response from LLM

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PR_kwDOIPDwls5ggav8

add volcengine endpoint for LLM

closed

CONTRIBUTOR

- **Description:** Volc Engine MaaS serves as an enterprise-grade, large-model service platform designed for developers. You can visit its homepage at https://www.volcengine.com/docs/82379/1099455 for details. This change will facilitate developers to integrate quickly with the platform. - **Issue:** None - **Dependencies:** volcengine - **Tag maintainer:** @baskaryan - **Twitter handle:** @he1v3tica

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PR_kwDOIPDwls5ggWG9

Added Slack Toolkit

closed

CONTRIBUTOR

This pull request addresses #11747 We added slack toolkit to lang chain. Slack tools: - get_message: get message history in a channel - send_message: send a message on user's or bot's behalf to a channel - get_channel: get a summary of all channels in workspace Example: docs/integrations/toolkits/slack.ipynb

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PR_kwDOIPDwls5ggV5t

Split out openai

closed

COLLABORATOR

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I_kwDOIPDwls54AveH

To support open v1 SDK proxy setting

open

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### System Info langchain 0.0.340 ### Who can help? @hwchase17 @agola11 ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [X] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction After openai v1 sdk the previous method openai.proxy = {xxx} is no longer supported. But langchain is still using it. It seems openai v1 sdk only support to set proxy on client level. see this issue on open ai sdk repo https://github.com/openai/openai-python/issues/825#issuecomment-1826047567 ### Expected behavior Should implement the way openai sdk required to set proxy

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