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Human Input tool is not useable in production
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2023-06-11T05:52:55
2023-10-22T16:13:06
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### Issue you'd like to raise. The human input tool only works with agents run from the command line. As far as I can see, it is impossible to use it when the conversation is being displayed in a web UI (or, for that matter, over any other channel). ### Suggestion: Implement a callback for handling requests for human input and a way to return the input to the tool/agent. This would allow eg requests for input to be sent to an open WebSocket chat and optionally be added to the message history.
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DOC: ChatOpenAI parameters to get it to respond like ChatGPT Plus
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2023-06-11T02:42:21
2023-09-18T16:08:34
2023-09-18T16:08:33
NONE
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### Issue with current documentation: I know this is a pretty general question, but in my testing, the ChatGPT console (with GPT-4) is performing much better for text summarization than `ChatOpenAI(model='gpt-4')` is. Are the default settings intended to be as similar as possible, or are there other known arguments, model_kwargs, system prompts, etc. that will adjust the performance to be more in line with the console experience?
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change _agenerate in to use gather
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Harrison/arxiv fix
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Add a new vector store - AwaDB (#5971)
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2023-06-10T22:04:55
2023-06-10T22:42:34
2023-06-10T22:42:33
COLLABORATOR
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Added AwaDB vector store, which is a wrapper over the AwaDB, that can be used as a vector storage and has an efficient similarity search. Added integration tests for the vector store Added jupyter notebook with the example Delete a unneeded empty file and resolve the conflict(https://github.com/hwchase17/langchain/pull/5886) Please check, Thanks! @dev2049 @hwchase17 --------- <!-- Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution. Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change. After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost. Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle! --> <!-- Remove if not applicable --> Fixes # (issue) #### Before submitting <!-- If you're adding a new integration, please include: 1. a test for the integration - favor unit tests that does not rely on network access. 2. an example notebook showing its use See contribution guidelines for more information on how to write tests, lint etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md --> #### Who can review? Tag maintainers/contributors who might be interested: <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @vowelparrot VectorStores / Retrievers / Memory - @dev2049 -->
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Fixed typo missing "use"
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2023-06-10T20:03:48
2023-06-10T21:31:59
2023-06-10T21:31:59
CONTRIBUTOR
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<!-- Fixed a simple typo on https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/vectorstore.html where the word "use" was missing. #### Who can review? Tag maintainers/contributors who might be interested: <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @vowelparrot VectorStores / Retrievers / Memory - @dev2049 -->
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support kwargs
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2023-06-10T19:11:48
2023-06-11T17:09:24
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add test for structured tools
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bump version to 196
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2023-06-10T15:43:07
2023-06-10T16:06:36
2023-06-10T16:06:36
COLLABORATOR
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null
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1,751,010,738
I_kwDOIPDwls5oXk2y
5,987
Implement nice pythonic @decorators [see image]
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2023-06-10T15:27:35
2023-09-16T16:06:27
2023-09-16T16:06:27
CONTRIBUTOR
null
### Feature request I think it would be a nice idea to implement the langchain logic in a minimalistic way using python decorators to quickly prototype stuff. ### Motivation <img width="462" alt="Screenshot 2023-06-10 at 17 22 29" src="https://github.com/hwchase17/langchain/assets/34897716/a22aa5fd-be7a-4eaa-a479-cbbeeb08a15c"> like here https://github.com/srush/MiniChain ### Your contribution Currently I do not have the time to do this but I just wanted to pop the idea :)
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I_kwDOIPDwls5oXcq9
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pydantic validation error when creating a HumanMessagePromptTemplate from a PipelinePromptTemplate
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2023-06-10T13:47:36
2023-09-17T17:13:27
2023-09-17T17:13:26
NONE
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### System Info >>> langchain.__version__ '0.0.194' Python 3.11.0 (main, Oct 24 2022, 18:26:48) [MSC v.1933 64 bit (AMD64)] on win32 ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [X] 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 ``` from langchain.prompts.pipeline import PipelinePromptTemplate from langchain.prompts.prompt import PromptTemplate from langchain.prompts.chat import HumanMessagePromptTemplate pipeline_prompt = PipelinePromptTemplate(final_prompt=PromptTemplate.from_template(""), pipeline_prompts=[]) human_message_prompt = HumanMessagePromptTemplate(prompt=pipeline_prompt) ``` ### Expected behavior Code should run without raising any errors. Instead it gives this error: ``` pydantic.error_wrappers.ValidationError: 1 validation error for HumanMessagePromptTemplate prompt Can't instantiate abstract class StringPromptTemplate with abstract method format (type=type_error) ``` Which comes from the fact that HumanMessagePromptTemplate inherits from BaseStringMessagePromptTemplate and BaseStringMessagePromptTemplate requires a prompt of type StringPromptTemplate: https://github.com/hwchase17/langchain/blob/f3e7ac0a2c0ad677e91571f59b03b55c5af52db2/langchain/prompts/chat.py#L67 I solved it this way: ``` class ExtendedHumanMessagePromptTemplate(HumanMessagePromptTemplate): prompt: BasePromptTemplate human_message_prompt = ExtendedHumanMessagePromptTemplate(prompt=pipeline_prompt) ```
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PR_kwDOIPDwls5Srdrl
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Fix handling of missing action & input for async MRKL agent
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2023-06-10T11:58:33
2023-06-10T21:38:20
2023-06-10T21:38:20
CONTRIBUTOR
null
Hi, This is a fix for https://github.com/hwchase17/langchain/pull/5014. This PR forgot to add the ability to self solve the ValueError(f"Could not parse LLM output: {llm_output}") error for `_atake_next_step`.
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1,750,931,497
I_kwDOIPDwls5oXRgp
5,984
ConversationalRetrievalChain did not look into chat histroy while making an answer
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2023-06-10T11:32:30
2023-06-13T01:56:12
2023-06-13T01:56:11
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I am trying to make a chatbot which remember the existing chat and can answer from that as well as from document. Here is what I have tried. ``` memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True) qa = ConversationalRetrievalChain.from_llm( ChatOpenAI(model_name='gpt-3.5-turbo'), retriever=docsearch, memory=memory, verbose=True) result = qa({"question": "My name is Talha. Ali is my friend. What is CNN?"}) ``` My document is based on CNN, so it fetched what is CNN Then I ask another question. ``` result = qa({"question": "Who is Ali?"}) ``` this is what happened behind the scene ``` > Entering new LLMChain chain... Prompt after formatting: Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language. Chat History: Human: My name is Talha. Ali is my friend. What is CNN? Assistant: CNN is a tool for deep learning and machine learning algorithms used in artificial neural networks for image recognition, object detection, and segmentation. Follow Up Input: Who is Ali? Standalone question: > Finished chain. > Entering new StuffDocumentsChain chain... > Entering new LLMChain chain... Prompt after formatting: Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer. _few paragraphs from documents i have ingested in vector database_ Question: Ali is Talha's friend. Answer: ``` But when i print `result['answer']` It say `"I don't know."` Here is notebook for reproducibility https://colab.research.google.com/drive/1UTjdpAAjoZ_ccpdAEr9gpfpqlC3pwtmT?usp=sharing
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1,750,888,737
I_kwDOIPDwls5oXHEh
5,980
Get number of vectors in Vector Store
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2023-06-10T09:20:56
2023-11-16T19:10:08
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### Feature request What the title says. A method in the `VectorStore` class that allows the size of the store to be retrieved. ### Motivation This could be useful for certain applications of continuous storage. ### Your contribution Happy to contribute with some guidance.
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Add additional parameters to Graph Cypher Chain
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2023-06-10T09:02:27
2023-06-11T00:43:19
2023-06-10T21:39:56
CONTRIBUTOR
null
Based on the inspiration from the SQL chain, the following three parameters are added to Graph Cypher Chain. - top_k: Limited the number of results from the database to be used as context - return_direct: Return database results without transforming them to natural language - return_intermediate_steps: Return intermediate steps
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STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION with gpt-4 doesn't follow through the tasks, while gpt-3.5-turbo does.
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2023-12-06T17:45:35
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### System Info langchain 0.0.195 ### Who can help? @vowelparrot @hwchase17 ### Information - [X] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [X] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction ``` SendEmail = StructuredTool.from_function(sendEmail) UpdateClientProfile = StructuredTool.from_function(updateClientProfile) tools = [SendEmail, UpdateClientProfile] #llm=ChatOpenAI(openai_api_key=os.environ['OPENAI_API_KEY_GPT4'], model=pd.steps["code"]["$return_value"]["model"], temperature=0.8) llm=ChatOpenAI(openai_api_key=os.environ['OPENAI_API_KEY_GPT4'], model="gpt-4", temperature=0.8) prompt = pd.steps["code"]["$return_value"]["prompt"] gpt3 = initialize_agent(tools, llm, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True) res = gpt3.run(prompt) gpt4 = initialize_agent(tools, llm, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True) res = gpt4.run(prompt) ``` Here's the prompt: <img width="714" alt="prompt" src="https://github.com/hwchase17/langchain/assets/7931903/2d15bbdf-a81d-4742-beae-0664acce53bc"> gpt-3.5-turbo correctly handle the tasks: <img width="884" alt="gpt3 5" src="https://github.com/hwchase17/langchain/assets/7931903/0599f839-4a2e-44b5-9978-df758ea8e69c"> gpt-4 only do the first one: <img width="880" alt="gpt4" src="https://github.com/hwchase17/langchain/assets/7931903/1f6017a4-a31c-48b6-b06b-0bf6214cddf5"> ### Expected behavior Both agents should complete two tasks : 1 - Send an email 2 - Update the customer profile gpt-3.5-turbo correctly completes the tasks, while gpt-4 stops after the first one and outputs what it should do next: "Now I will update the customer profile..."
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ZapierToolkit Automatically Performing Unrequested Tasks
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2023-06-10T06:47:07
2023-09-16T16:06:38
2023-09-16T16:06:37
CONTRIBUTOR
null
### System Info The current behavior of the ZapierToolkit in the LangChain tool occasionally results in the execution of tasks that the user did not specifically request. For example, when a user asks the ZapierToolkit to read the most recent email, it sometimes replies to the email even when the user did not explicitly specify this action. This unexpected behavior occurs when the AgentExecutor chain automatically decides to reply to the email, regardless of whether the email requested the reply or not. It is safe for the ZapierToolkit to adhere to the user's explicit instructions to avoid potential mishaps or undesired outcomes. ### 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 The issue was tested with the GPT-3.5 models (openai chat completion model and text-davinci-003) with temperature=0.7, and Gmail-related Zapier NLA Development actions, such as "Gmail: Reply to Email," "Gmail: Find Email," and "Gmail: Send Email.", enabled. Code Sample: ```Python # get from https://platform.openai.com/ os.environ["OPENAI_API_KEY"] = os.environ.get("OPENAI_API_KEY", "") # get from https://nla.zapier.com/demo/provider/debug (under User Information, after logging in): os.environ["ZAPIER_NLA_API_KEY"] = os.environ.get("ZAPIER_NLA_API_KEY", "") llm = OpenAI() zapier = ZapierNLAWrapper() toolkit = ZapierToolkit.from_zapier_nla_wrapper(zapier) agent = initialize_agent(toolkit.get_tools(), llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True); try: agent.run("Read the most recent email") except Exception as e: print(e) exit(1) ``` ### Expected behavior The ZapierToolkit should only perform tasks that are explicitly requested by the user. If the user does not specify that the most recent email should be replied to, the ZapierToolkit should not automatically decide to perform this action.
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Add embaas embeddings api endpoints
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2023-06-10T06:22:41
2023-06-11T20:15:09
2023-06-11T20:15:09
CONTRIBUTOR
null
# Introduces embaas embeddings endpoints In this PR, we add support for embaas embeddings endpoints to Text Embedding Models (with LLMs, and Doc loaders in different PRs coming). We currently offer the MTEB leaderboard top performers, will continue to add top embedding models and soon add support for customers to deploy thier own models.Additional Documentation + Infomation can be found [here](https://embaas.io). While developing this integration, I closely followed the patterns established by other langchain integrations. Nonetheless, if there are any aspects that require adjustments or if there's a better way to present a new integration, let me know! :) #### Who can review? - @hwchase17 - @agola11
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Allow use of non-default QDrant vector key
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2023-06-10T04:22:43
2023-06-28T17:00:02
2023-06-28T16:59:54
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- Context #2594 - This is continuation of #2751 which diverged so much from origin and was better to be re-started. <!-- Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution. Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change. After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost. Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle! --> <!-- Remove if not applicable --> Fixes #2594 #### Who can review? @kacperlukawski
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pydantic.error_wrappers.ValidationError for args_schema
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2023-06-10T03:33:28
2023-11-05T07:01:46
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### System Info Latest langchain & openai 0.27.5 https://python.langchain.com/en/latest/modules/agents/tools/custom_tools.html ### 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 https://python.langchain.com/en/latest/modules/agents/tools/custom_tools.html class CalculatorInput(BaseModel): question:str = Field() tools.append( Tool.from_function( func=llm_math_chain.run, name="Calculator", description="useful for when you need to answer questions about math", args_schema=CalculatorInput # coroutine= ... <- you can specify an async method if desired as well ) Got this error msg: File "pydantic\main.py", line 341, in pydantic.main.BaseModel.__init__ pydantic.error_wrappers.ValidationError: 1 validation error for Tool args_schema extra fields not permitted (type=value_error.extra) ### Expected behavior Should enter Agent excution
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Issue: Is there some detail documents about chroma.from_documents args?
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2023-06-10T02:40:35
2023-09-18T16:08:39
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### Issue you'd like to raise. I was trying to store different vectorstore to chroma with different ids like "vectorstore = Chroma.from_documents(documents, embeddings, ids="test1",)", but it keeps telling Number of embeddings 9 must match number of ids 1 and how i can erase some of vectorstore inside chroma, if i can store many into it? ### Suggestion: _No response_
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Get start index and end index of a chunk in the original doc with recursivecharactersplit.
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2023-06-10T02:38:21
2023-09-18T16:08:43
2023-09-18T16:08:43
CONTRIBUTOR
null
### Feature request make recursivecharactersplit return start index and end index of a chunk in the original doc then user can get snnipt by `original_doc[start_index:end_index]` ### Motivation This is important info of the trunk. ### Your contribution I can not do it now, but if I have time, I will try to do it.
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Add a new vector store - AwaDB
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2023-06-10T02:31:28
2023-06-10T21:43:07
2023-06-10T21:43:07
CONTRIBUTOR
null
Added AwaDB vector store, which is a wrapper over the AwaDB, that can be used as a vector storage and has an efficient similarity search. Added integration tests for the vector store Added jupyter notebook with the example Delete a unneeded empty file and resolve the conflict(https://github.com/hwchase17/langchain/pull/5886) Please check, Thanks! @dev2049 @hwchase17
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Create Loaders for Major Music Streaming Services
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2023-06-10T02:22:02
2023-06-11T13:32:32
2023-06-10T12:10:29
NONE
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Fixes # (issue) Create loaders for library endpoints of major music streaming services #### Before submitting | - | - | | ----------- | ----------- | | a test for the integration | ❌ REST APIs | | an example notebook showing its use | ✅ | [twitter](https://twitter.com/JamesSJackson) #### Who can review? Tag maintainers/contributors who might be interested: @eyurtsev
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`UnstructuredPDFLoader` suddenly can't parse scanned PDFs
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2023-06-10T02:03:23
2023-09-16T16:06:53
2023-09-16T16:06:52
CONTRIBUTOR
null
### Issue you'd like to raise. I'm using `Unstructured` for parsing PDFs and have it installed through a docker dev container. It was working a few months ago but after I rebuilt the container for deployment, it suddenly breaks. Here's my dockerfile: ``` FROM python:3.9-slim-buster # Update package lists RUN apt-get update && apt-get install ffmpeg libsm6 libxext6 gcc g++ git build-essential libpoppler-cpp-dev libmagic-dev pkg-config poppler-utils tesseract-ocr libtesseract-dev -y # Make working directories RUN mkdir -p /app WORKDIR /app # Copy the requirements.txt file to the container COPY requirements.txt . # Install dependencies RUN pip install --upgrade pip RUN pip install torch torchvision torchaudio RUN pip install unstructured-inference RUN pip install -r requirements.txt RUN pip install 'git+https://github.com/facebookresearch/detectron2.git@e2ce8dc#egg=detectron2' # Copy the .env file to the container COPY .env . # Copy every file in the source folder to the created working directory COPY . . # Expose the port that the application will run on EXPOSE 8080 # Start the application CMD ["python3.9", "-m", "uvicorn", "main:app", "--proxy-headers", "--host", "0.0.0.0", "--port", "8080", "--workers", "4"] ``` and `requirements.txt`: ``` fastapi uvicorn langchain python-poppler pytesseract unstructured[local-inference] psycopg2-binary pgvector openai tiktoken python-dotenv pypdf ``` I've verified that detectron is in my system but it seems like it's not being used by unstructured as it defaults to using `pdfminer` ### Suggestion: _No response_
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Fix: SnowflakeLoader returning empty documents
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2023-06-10T01:17:18
2023-06-10T20:03:51
2023-06-10T20:03:51
CONTRIBUTOR
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**Fix SnowflakeLoader's Behavior of Returning Empty Documents** **Description:** This PR addresses the issue where the SnowflakeLoader was consistently returning empty documents. After investigation, it was found that the query method within the SnowflakeLoader was not properly fetching and processing the data. **Changes:** 1. Modified the query method in SnowflakeLoader to handle data fetch and processing more accurately. 2. Enhanced error handling within the SnowflakeLoader to catch and log potential issues that may arise during data loading. **Impact:** This fix will ensure the SnowflakeLoader reliably returns the expected documents instead of empty ones, improving the efficiency and reliability of data processing tasks in the LangChain project. Before Fix: `[ Document(page_content='', metadata={}), Document(page_content='', metadata={}), Document(page_content='', metadata={}), Document(page_content='', metadata={}), Document(page_content='', metadata={}), Document(page_content='', metadata={}), Document(page_content='', metadata={}), Document(page_content='', metadata={}), Document(page_content='', metadata={}), Document(page_content='', metadata={}) ]` After Fix: `[Document(page_content='CUSTOMER_ID: 1\nFIRST_NAME: John\nLAST_NAME: Doe\nEMAIL: [email protected]\nPHONE: 555-123-4567\nADDRESS: 123 Elm St, San Francisco, CA 94102', metadata={}), Document(page_content='CUSTOMER_ID: 2\nFIRST_NAME: Jane\nLAST_NAME: Doe\nEMAIL: [email protected]\nPHONE: 555-987-6543\nADDRESS: 456 Oak St, San Francisco, CA 94103', metadata={}), Document(page_content='CUSTOMER_ID: 3\nFIRST_NAME: Michael\nLAST_NAME: Smith\nEMAIL: [email protected]\nPHONE: 555-234-5678\nADDRESS: 789 Pine St, San Francisco, CA 94104', metadata={}), Document(page_content='CUSTOMER_ID: 4\nFIRST_NAME: Emily\nLAST_NAME: Johnson\nEMAIL: [email protected]\nPHONE: 555-345-6789\nADDRESS: 321 Maple St, San Francisco, CA 94105', metadata={}), Document(page_content='CUSTOMER_ID: 5\nFIRST_NAME: David\nLAST_NAME: Williams\nEMAIL: [email protected]\nPHONE: 555-456-7890\nADDRESS: 654 Birch St, San Francisco, CA 94106', metadata={}), Document(page_content='CUSTOMER_ID: 6\nFIRST_NAME: Emma\nLAST_NAME: Jones\nEMAIL: [email protected]\nPHONE: 555-567-8901\nADDRESS: 987 Cedar St, San Francisco, CA 94107', metadata={}), Document(page_content='CUSTOMER_ID: 7\nFIRST_NAME: Oliver\nLAST_NAME: Brown\nEMAIL: [email protected]\nPHONE: 555-678-9012\nADDRESS: 147 Cherry St, San Francisco, CA 94108', metadata={}), Document(page_content='CUSTOMER_ID: 8\nFIRST_NAME: Sophia\nLAST_NAME: Davis\nEMAIL: [email protected]\nPHONE: 555-789-0123\nADDRESS: 369 Walnut St, San Francisco, CA 94109', metadata={}), Document(page_content='CUSTOMER_ID: 9\nFIRST_NAME: James\nLAST_NAME: Taylor\nEMAIL: [email protected]\nPHONE: 555-890-1234\nADDRESS: 258 Hawthorn St, San Francisco, CA 94110', metadata={}), Document(page_content='CUSTOMER_ID: 10\nFIRST_NAME: Isabella\nLAST_NAME: Wilson\nEMAIL: [email protected]\nPHONE: 555-901-2345\nADDRESS: 963 Aspen St, San Francisco, CA 94111', metadata={})] ` **Tests:** All unit and integration tests have been run and passed successfully. Additional tests were added to validate the new behavior of the SnowflakeLoader. **Checklist:** - [x] Code changes are covered by tests - [x] Code passes `make format` and `make lint` - [x] This PR does not introduce any breaking changes Please review and let me know if any changes are required.
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feat: :sparkles: Added filtering option to FAISS vectorstore
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2023-06-10T01:07:26
2023-10-08T15:06:39
2023-06-11T20:20:04
CONTRIBUTOR
null
Inspired by the filtering capability available in ChromaDB, added the same functionality to the FAISS vectorestore as well. Since FAISS does not have an inbuilt method of filtering used the approach suggested in this [thread](https://github.com/facebookresearch/faiss/issues/1079) Langchain Issue inspiration: https://github.com/hwchase17/langchain/issues/4572 - [x] Added filtering capability to semantic similarly and MMR - [x] Added test cases for filtering in `tests/integration_tests/vectorstores/test_faiss.py` #### Who can review? Tag maintainers/contributors who might be interested: VectorStores / Retrievers / Memory - @dev2049 - @hwchase17
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SQLiteCache does not cache logprobs
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2023-06-10T00:26:29
2023-09-18T16:08:49
2023-09-18T16:08:48
NONE
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### System Info Langchain version: 0.0.192, Python: 3.10 ``` from langchain.cache import SQLiteCache langchain.llm_cache = SQLiteCache(database_path='.langchain.db') from langchain.llms import OpenAI my_llm = OpenAI(model_name='text-davinci-003', temperature=0, max_tokens=1, logprobs=5) result=my_llm.generate(['2+2=']) result.generations[0][0] ``` In the above, the logprobs will only be generated the first time and never again. Interestingly, the InMemoryCache does successfully persist the logprobs (and other generation_info) upon a cache hit. @hwchase17 @yuert ### Who can help? Any contributor. ### 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 ``` from langchain.cache import SQLiteCache langchain.llm_cache = SQLiteCache(database_path='.langchain.db') from langchain.llms import OpenAI my_llm = OpenAI(model_name='text-davinci-003', temperature=0, max_tokens=1, logprobs=5) result=my_llm.generate(['2+2=']) result.generations[0][0] ``` ### Expected behavior I expect the following output every time I run this ``` Generation(text='4', generation_info={'finish_reason': 'length', 'logprobs': <OpenAIObject at 0x7fbcb6ab1710> JSON: { "tokens": [ "4" ], "token_logprobs": [ -0.5794853 ], "top_logprobs": [ { "4": -0.5794853, "\n": -1.6557268, "5": -2.264629, "?": -3.518741, "3": -4.042759 } ], "text_offset": [ 4 ] }}) ``` But instead (upon the second or more times I run this), I get this: Generation(text='4', generation_info=None)
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Add Proxy to GoogleSearchAPIWrapper
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2023-06-09T23:38:23
2023-11-07T16:07:53
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### Feature request Need to add proxy to GoogleSearchAPIWrapper() Perhaps using this [approach](https://github.com/googleapis/google-api-python-client/issues/1078#issuecomment-718919158) and/or this [approach](https://github.com/googleapis/google-api-python-client/issues/1260#issuecomment-802728649)? ``` import httplib2 import google_auth_httplib2 http = httplib2.Http(proxy_info=httplib2.ProxyInfo( httplib2.socks.PROXY_TYPE_HTTP, PROXY_IP, PROXY_PORT )) authorized_http = google_auth_httplib2.AuthorizedHttp(credentials, http=http) service = discovery.build(http=authorized_http) ``` ### Motivation Add Proxy to GoogleSearchAPIWrapper ### Your contribution I can write a PR but perhaps with some additional help
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Issue: How to stop an AgentExecutor after has been dispatched/ran.
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### Issue you'd like to raise. Sometimes, the agent took too long to respond, stopping it and trying again would be a good option, So, How Can I Stop and Agent? Thanks in advance. ### Suggestion: _No response_
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Add New Retriever Interface with Callbacks
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2023-06-09T21:54:37
2023-07-03T03:42:33
2023-06-30T21:44:03
CONTRIBUTOR
null
Handle the new retriever events in a way that (I think) is entirely backwards compatible? Needs more testing for some of the chain changes and all. This creates an entire new run type, however. We could also just treat this as an event within a chain run presumably (same with memory) Adds a subclass initializer that upgrades old retriever implementations to the new schema, along with tests to ensure they work. First commit doesn't upgrade any of our retriever implementations (to show that we can pass the tests along with additional ones testing the upgrade logic). Second commit upgrades the known universe of retrievers in langchain. - [X] Add callback handling methods for retriever start/end/error (open to renaming to 'retrieval' if you want that) - [X] Update BaseRetriever schema to support callbacks - [X] Tests for upgrading old "v1" retrievers for backwards compatibility - [X] Update existing retriever implementations to implement the new interface - [X] Update calls within chains to .{a]get_relevant_documents to pass the child callback manager - [X] Update the notebooks/docs to reflect the new interface - [X] Test notebooks thoroughly Not handled: - Memory pass throughs: retrieval memory doesn't have a parent callback manager passed through the method
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Using batches for Chroma update_document
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2023-06-09T20:26:59
2023-08-11T17:52:14
2023-08-11T17:52:14
CONTRIBUTOR
null
https://github.com/hwchase17/langchain/blob/2a4b32dee24c22159805f643b87eece107224951/langchain/vectorstores/chroma.py#L355-L375 Currently, the defined update_document function only takes a single document and its ID for updating. However, Chroma can update multiple documents by taking a list of IDs and documents for batch updates. I update the Chroma vectorstore with refreshed information from my website every 20 minutes. Updating the update_document function to perform simultaneous updates for each changed piece of information would significantly reduce the update time in such use cases. For my case I update a total of 8810 chunks. Updating these 8810 individual chunks using the current function takes a total of 8.5 minutes. However, if we process the inputs in batches and update them collectively, all 8810 separate chunks can be updated in just 1 minute. This significantly reduces the time it takes for users of actively used chatbots to access up-to-date information. I also updated the integration test and the examples provided in the documentation for the new update_document function. @hwchase17 my twitter: [berkedilekoglu](https://twitter.com/berkedilekoglu)
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Move schema to directory
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2023-06-09T19:11:01
2023-07-12T06:49:58
2023-07-12T06:49:58
CONTRIBUTOR
null
As @nfcampos [pointed out](https://github.com/hwchase17/langchain/pull/5918#discussion_r1223894534), moving the schema to a directory will both let us keep it more organized and help us get around some cyclic dependencies. Added a public API test as well. Also realized we were importing the "~private" function `message_to_dict()` in several memory classes so made that public (along with the `message_from_dict()` function since it seems weird to only export one. I could alias it with the private one as well in case people were using it to make it completely backwards compatible but haven't done that yet
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Update arize_callback.py
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2023-06-09T18:59:51
2023-06-19T18:00:43
2023-06-19T18:00:38
CONTRIBUTOR
null
Arize released a new Generative LLM Model Type, adjusting the callback function to new logging. Added arize imports, please delete if not necessary. Specifically, this change makes sure that the prompt and response pairs from LangChain agents are logged into Arize as a Generative LLM model, instead of our previous categorical model. In order to do this, the callback functions collects the necessary data and passes the data into Arize using Python Pandas SDK. Arize library, specifically pandas.logger is an additional dependency. Notebook For Test: https://docs.arize.com/arize/resources/integrations/langchain #### Who can review? Tag maintainers/contributors who might be interested: @hwchase17 - project lead Tracing / Callbacks - @agola11
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Create Airtable loader
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2023-06-09T18:48:09
2023-06-10T22:43:19
2023-06-10T22:43:18
COLLABORATOR
null
Create document loader for Airtable
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DOC: Documented example doesn't work
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2023-06-09T18:21:14
2023-11-21T10:55:03
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### Issue with current documentation: This is for the documentation page: https://python.langchain.com/en/latest/modules/agents/agents/examples/structured_chat.html This does not work in Jupyter or if you download it to a .py file and run it. You do get 2 different errors though. When you run this in Jupyter you get this error: ``` --------------------------------------------------------------------------- ConfigError Traceback (most recent call last) Cell In[29], line 3 1 async_browser = create_async_playwright_browser() 2 # sync_browser = None # create_sync_playwright_browser() ----> 3 browser_toolkit = PlayWrightBrowserToolkit.from_browser(async_browser=async_browser) 4 tools = browser_toolkit.get_tools() File ~/Google Drive/PycharmProjectsLocal/pythonProject2/venv/lib/python3.8/site-packages/langchain/agents/agent_toolkits/playwright/toolkit.py:83, in PlayWrightBrowserToolkit.from_browser(cls, sync_browser, async_browser) 81 # This is to raise a better error than the forward ref ones Pydantic would have 82 lazy_import_playwright_browsers() ---> 83 return cls(sync_browser=sync_browser, async_browser=async_browser) File ~/Google Drive/PycharmProjectsLocal/pythonProject2/venv/lib/python3.8/site-packages/pydantic/main.py:339, in pydantic.main.BaseModel.__init__() File ~/Google Drive/PycharmProjectsLocal/pythonProject2/venv/lib/python3.8/site-packages/pydantic/main.py:1076, in pydantic.main.validate_model() File ~/Google Drive/PycharmProjectsLocal/pythonProject2/venv/lib/python3.8/site-packages/pydantic/fields.py:860, in pydantic.fields.ModelField.validate() ConfigError: field "sync_browser" not yet prepared so type is still a ForwardRef, you might need to call PlayWrightBrowserToolkit.update_forward_refs(). ``` When you run it as a .py file you get this error: ``` Connected to pydev debugger (build 231.9011.38) Traceback (most recent call last): File "/Applications/PyCharm.app/Contents/plugins/python/helpers/pydev/pydevd.py", line 1496, in _exec pydev_imports.execfile(file, globals, locals) # execute the script File "/Applications/PyCharm.app/Contents/plugins/python/helpers/pydev/_pydev_imps/_pydev_execfile.py", line 18, in execfile exec(compile(contents+"\n", file, 'exec'), glob, loc) File "/Users/greggwcasey/Google Drive/PycharmProjectsLocal/pythonProject2/structured_chat.py", line 76 response = await agent_chain.arun(input="Hi I'm Erica.") ^ SyntaxError: 'await' outside function python-BaseException ``` ### Idea or request for content: I would like to have a working example that I could download and run on my Jupyter server or Colab and it work as-is.
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Add load() to snowflake loader
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2023-06-09T18:18:59
2023-06-09T18:27:29
2023-06-09T18:27:29
COLLABORATOR
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Quick fix for recently added [snowflake data loader](https://github.com/hwchase17/langchain/pull/5825/files).
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feat: Add `UnstructuredXMLLoader` for `.xml` files
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2023-06-09T18:10:59
2023-06-10T23:24:42
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CONTRIBUTOR
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# Unstructured XML Loader Adds an `UnstructuredXMLLoader` class for .xml files. Works with unstructured>=0.6.7. A plain text representation of the text with the XML tags will be available under the `page_content` attribute in the doc. ### Testing ```python from langchain.document_loaders import UnstructuredXMLLoader loader = UnstructuredXMLLoader( "example_data/factbook.xml", ) docs = loader.load() ``` ## Who can review? @hwchase17 @eyurtsev
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Clarifai integration
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2023-06-09T17:47:07
2023-06-22T15:00:16
2023-06-22T15:00:15
NONE
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<!-- Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution. Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change. After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost. Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle! --> <!-- Remove if not applicable --> # Changes This PR adds [Clarifai](https://www.clarifai.com/) integration to Langchain. Clarifai is an end-to-end AI Platform. Clarifai offers user the ability to use many types of LLM (OpenAI, cohere, ect and other open source models). As well, a clarifai app can be treated as a vector database to upload and retrieve data. The integrations includes: - Clarifai LLM integration: Clarifai supports many types of language model that users can utilize for their application - Clarifai VectorDB: A Clarifai application can hold data and embeddings. You can run semantic search with the embeddings #### Before submitting - [x] Added integration test for LLM - [x] Added integration test for VectorDB - [x] Added notebook for LLM - [x] Added notebook for VectorDB #### Who can review? - @hwchase17 - @agola11 - @dev2049
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BadRequestError: BadRequestError(400, 'search_phase_execution_exception', 'runtime error') using ElasticVectorSearch
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2023-06-09T17:26:11
2023-09-15T16:08:07
2023-09-15T16:08:06
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### System Info langchain version = '0.0.191' elasticsearch version='(8, 8, 0)' ### Who can help? @hwchase17 @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 - [ ] Document Loaders - [X] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction I am trying ElasticVectorSearchon langchain with my Azure elastic instance and I am getting `BadRequestError: BadRequestError(400, 'search_phase_execution_exception', 'runtime error')` ``` from langchain import ElasticVectorSearch from langchain.embeddings import OpenAIEmbeddings embedding = OpenAIEmbeddings() elastic_host = "hostid.westeurope.azure.elastic-cloud.com" elasticsearch_url = f"https://user:password-elastic@{elastic_host}:9243" elastic_vector_search = ElasticVectorSearch( elasticsearch_url=elasticsearch_url, index_name="test_langchain", embedding=embedding ) ``` `doc = elastic_vector_search.similarity_search("simple query")` The response is : `BadRequestError: BadRequestError(400, 'search_phase_execution_exception', 'runtime error')` ### Expected behavior It should return a list of document that match the query from elastic search
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Ability to pass in ids for opensearch docs
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2023-06-09T17:06:34
2023-06-26T15:58:45
2023-06-26T15:58:45
CONTRIBUTOR
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### Feature request There is no way to update existing vectors in the opensearch implementation for example if some existing text was changed by passing in custom ids. https://github.com/hwchase17/langchain/blob/master/langchain/vectorstores/opensearch_vector_search.py#L95 ### Motivation Pinecone implementation has this basic functionality: https://github.com/hwchase17/langchain/blob/master/langchain/vectorstores/pinecone.py#L85 ### Your contribution I can try to make a PR to mirror the pincone implementation but it would be pretty naive.
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Update to Vectara integration
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2023-06-09T16:34:42
2023-06-10T23:27:02
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CONTRIBUTOR
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This PR updates the Vectara integration (@hwchase17 ): * Adds reuse of requests.session to imrpove efficiency and speed. * Utilizes Vectara's low-level API (instead of standard API) to better match user's specific chunking with LangChain * Now add_texts puts all the texts into a single Vectara document so indexing is much faster. * updated variables names from alpha to lambda_val (to be consistent with Vectara docs) and added n_context_sentence so it's available to use if needed. * Updates to documentation and tests
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bump ver to 195
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enhancment: optional file loader in `GoogleDriveLoader`
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2023-06-09T15:42:46
2023-06-18T17:48:16
2023-06-18T17:48:15
CONTRIBUTOR
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### Summary Gives users the ability to specify a `file_loader_cls` for processing files in Google Drive that are not Google Documents or Google Sheets. Fixes #5791. See also [this Twitter thread](https://twitter.com/klaudioz/status/1666124861482184708?s=46&t=UImYT1AvX587tYTQ2i1sIQ) where a user requested this capability. ### Testing For individual files (use a file id from your own GDrive): ```python from langchain.document_loaders import GoogleDriveLoader from langchain.document_loaders import UnstructuredFileIOLoader file_id="1x9WBtFPWMEAdjcJzPScRsjpjQvpSo_kz" loader = GoogleDriveLoader( file_ids=[file_id], file_loader_cls=UnstructuredFileIOLoader, file_loader_kwargs={"mode": "elements"} ) loader.load() ``` For a folder: ```python from langchain.document_loaders import GoogleDriveLoader from langchain.document_loaders import UnstructuredFileIOLoader folder_id="1asMOHY1BqBS84JcRbOag5LOJac74gpmD" loader = GoogleDriveLoader( folder_id=folder_id, file_loader_cls=UnstructuredFileIOLoader, file_loader_kwargs={"mode": "elements"} ) loader.load() ``` #### Who can review? @hwchase17 @eyurtsev
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Expose full params in Qdrant
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2023-06-09T15:12:22
2023-06-09T15:56:33
2023-06-09T15:56:32
CONTRIBUTOR
null
# Expose full params in Qdrant There were many questions regarding supporting some additional parameters in Qdrant integration. Qdrant supports many vector search optimizations that were impossible to use directly in Qdrant before. That includes: 1. Possibility to manipulate collection params while using `Qdrant.from_texts`. The PR allows setting things such as quantization, HNWS config, optimizers config, etc. That makes it consistent with raw `QdrantClient`. 2. Extended options while searching. It includes HNSW options, exact search, score threshold filtering, and read consistency in distributed mode. After merging that PR, #4858 might also be closed. ## Who can review? VectorStores / Retrievers / Memory @dev2049 @hwchase17
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5,945
VectorStore add embedded texts interface
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2023-06-09T14:33:34
2023-11-06T15:33:06
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CONTRIBUTOR
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### Feature request Add a way to pass pre-embedded texts into the VectorStore interface. Options: - Standardize the add_embeddings function that has been added to some of the implementations. e.g. ``` def add_embeddings( self, texts: List[str], embeddings: List[List[float]], metadatas: Optional[List[dict]] = None, **kwargs: Any, ) -> List[str]: ``` - Add embeddings kwarg to the add_texts interface ``` def add_texts( self, texts: Iterable[str], metadatas: Optional[List[dict]] = None, embeddings: Optional[List[List[float]]] = None, **kwargs: Any, ) -> List[str]: ``` First option is nice in that it leaves each function with a very distinct role, but has added overhead for implementing the interface. Results in very similar code between add_texts and add_embeddings and/or a third private method to handle the actual add operation. Second option should be pretty straightforward to add to all the implementations, but adds some clutter to the add_texts interface. No too bad IMO as it would be handled almost the same as metadatas. ### Motivation Embedding a large body of text (e.g. pubmed) takes a long time, and it is too restrictive to rely on each VectorStore implementation calling embedding functions in the most optimal way every use case. By having a defined way to pass in embeddings directly the interface becomes much more flexible. For example I've been using huggingface's Datasets.map for processing texts, running embeddings on multiple gpus, etc. Would like to be able to save the final embedded dataset, and then insert into the vector store. ### Your contribution Happy to help work on it. I know there's quite a few vector store implementations that would have to be updated for this.
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Not able to set k top documents in Chroma DB based Retriever
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2023-06-09T14:29:00
2023-09-27T16:06:25
2023-09-27T16:06:24
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### System Info I7 32 GBRAM , ASUS dynabook protege ### Who can help? @hwchase17 @agola11 @vow ### 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 ``` db = Chroma.from_texts(chunks, embeddings, persist_directory='db', client_settings=CHROMA_SETTINGS) chat_llm = ChatOpenAI(model_name = 'gpt-3.5-turbo', callbacks=callbacks, verbose=True, temperature=0, streaming = True ) question_generator = LLMChain(llm=chat_llm, prompt=CONDENSE_QUESTION_PROMPT) prompt = load_prompt(model_name='gpt') doc_chain = load_qa_chain(llm=chat_llm,chain_type="stuff",prompt=prompt) chain = ConversationalRetrievalChain(retriever=vectorstore.as_retriever(search_kwargs={"k": 2}), question_generator=question_generator, combine_docs_chain=doc_chain, memory=memory, return_source_documents=True, get_chat_history=lambda h :h) ``` ### Expected behavior I wish to change the `search_kwargs` more specifically `top k` relevant documents which are retrieved but I am not able to change it ! it is repeatedly providing the default 4 documents. I have tried all the required method. I am using Chroma DB as retriever on bunch of pdfs.
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Improvement: Do not run map on a single document in MapReduceDocumentsChain
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2023-06-09T14:12:24
2023-07-17T13:05:41
2023-07-17T13:05:41
CONTRIBUTOR
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If a single document is provided, the mapping step in `acombine_docs()` will be skipped. Before, the document was summarized using the map_prompt and then again using the combine_prompt. Assumption is, that this single document fits into the context window. Replaced self._process_results() with an async version and call with asyncio.run() from the sync methods Fixes #1937 #### Who can review? @hwchase17 Tag maintainers/contributors who might be interested: @agola11
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Gorilla examples
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2023-06-09T13:50:08
2023-11-15T16:09:23
2023-11-15T16:09:22
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### Feature request Hi, I'd love to request examples/documentation for how to use [Gorilla](https://github.com/ShishirPatil/gorilla#faqs) with LangChain. ### Motivation This looks like a great model to serve correct API calls. ### Your contribution 🤷🏻‍♂️
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Update dataframe.py
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2023-06-09T13:43:12
2023-11-07T04:15:18
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The original DataFrameLoader class has a limitation that only allows it to load one column out of all other columns in a dataframe. To overcome this limitation, a new class called DataFrameContentLoader has been implemented. The DataFrameContentLoader class allows for the loading of all the columns in a dataframe by setting the index as the metadata, dropping the index from the iteration variable row, and then converting the row to a dictionary. Since the Document class does not expect a dictionary, the text is converted to a string that looks like a JSON object. This implementation allows the DataFrameContentLoader to load all the contents of the dataframe rather than just one column. This is particularly useful when dealing with datasets that have multiple columns of relevant information. <!-- Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution. Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change. After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost. Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle! --> <!-- Remove if not applicable --> Fixes # (issue) #### Before submitting <!-- If you're adding a new integration, please include: 1. a test for the integration - favor unit tests that does not rely on network access. 2. an example notebook showing its use See contribution guidelines for more information on how to write tests, lint etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md --> #### Who can review? Tag maintainers/contributors who might be interested: <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @vowelparrot VectorStores / Retrievers / Memory - @dev2049 -->
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fix: use model token limit not tokenizer ditto
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2023-06-09T13:34:58
2023-06-11T06:07:28
2023-06-10T23:36:04
CONTRIBUTOR
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This fixes a token limit bug in the SentenceTransformersTokenTextSplitter. Before the token limit was taken from tokenizer used by the model. However, for some models the token limit of the tokenizer (from `AutoTokenizer.from_pretrained`) does not equal the token limit of the model. This was a false assumption. Therefore, the token limit of the text splitter is now taken from the sentence transformers model token limit. Twitter: @plasmajens #### Before submitting #### Who can review? @hwchase17 and/or @dev2049
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Added matching async load func to PlaywrightURLLoader
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CONTRIBUTOR
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<!-- Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution. Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change. After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost. Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle! --> <!-- Remove if not applicable --> Fixes # (issue) The existing PlaywrightURLLoader load() function uses a synchronous browser which is not compatible with jupyter. This PR adds a sister function aload() which can be run insisde a notebook. #### Before submitting <!-- If you're adding a new integration, please include: 1. a test for the integration - favor unit tests that does not rely on network access. 2. an example notebook showing its use See contribution guidelines for more information on how to write tests, lint etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md --> #### Who can review? Tag maintainers/contributors who might be interested: <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @vowelparrot VectorStores / Retrievers / Memory - @dev2049 -->
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Streaming final agent response callbacks
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2023-10-16T01:18:51
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CONTRIBUTOR
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This is a comprehensive version of callbacks when we want to streaming the only the last response of the agent. This fix issue #2483 but in a more comprehensive way. This is NOT duplicate of #4630 . I know there is a great work has been done by @UmerHA for **FinalStreamingStdOutCallbackHandler** class, and this PR work is inspired by him, but I want that class to be a simple use case for final streaming. What this PR does is a comprehensive version of final streaming with the following additional feature: - **Multiple answer prefix phrases**: Just like OpenAI they allows multiple stop sequences, this class can detect multiple stop sequences before it begin streaming. - **Post-process on-the-fly**: This utilize the above algorithm (matching multiple prefix phrases) so I think this is suitable to place it here. - **Easy-to-use Callback** on each new token **during streaming of final agent response**. - **Abnormal streaming detection**: if the streaming contains unexpected tokens generated by model, then raise an Exception. #### Quick setup ```python from langchain.agents import load_tools, initialize_agent, AgentType from langchain.llms import OpenAI from langchain.callbacks import StreamingLastResponseCallbackHandler llm = OpenAI(temperature=0, streaming=True) tools = load_tools(["serpapi", "llm-math"], llm=llm) agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=False) ``` #### Usage 1 - use as callback function for **last response new token** ```python stream = StreamingLastResponseCallbackHandler.from_agent_type(agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION) @stream.on_last_response_new_token() def on_new_token(token: str): if token is StopIteration: print("\n[Done]") return else: print(token, end="", flush=True) agent.run("Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?", callbacks=[stream]) ``` #### Usage 2 - use as iterator ```python stream = StreamingLastResponseCallbackHandler.from_agent_type(agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION) def _run(): agent.run("Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?", callbacks=[stream]) threading.Thread(target=_run).start() for token in stream: print(token, end="", flush=True) ``` #### Usage 3 - Post process on-the-fly ```python import tiktoken enc = tiktoken.get_encoding("cl100k_base") stream = StreamingLastResponseCallbackHandler.from_agent_type(agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION) @stream.postprocess(sliding_window_step=1, window_size=3) def postprocess_func(tokens: List[str]) -> List[str]: sentence = "".join(tokens).replace("LLM", "LangChain") words = [enc.decode([t]) for t in enc.encode(sentence)] # postprocess output can have different size! return words def _run(): agent.run("Say 'Large Language Model (LLM) is great!'", callbacks=[stream]) threading.Thread(target=_run).start() for token in stream: print(token, end="", flush=True) ``` #### Who can review? Hi @hwchase17 and @agola11, please review my PR, I do have an usage example in the docstring of this **StreamingLastResponseCallbackHandler** class. I appreciate any feedback, please review my PR soon. P/s: Updated using decorator approach.
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Update schema.py
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2023-08-11T01:30:32
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# Override add_user_message func to support saving dicts passed in through the agent.run(input={}) pattern. <!-- Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution. Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change. After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost. Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle! --> <!-- Remove if not applicable --> Fixes # (issue) #### Before submitting <!-- If you're adding a new integration, please include: 1. a test for the integration - favor unit tests that does not rely on network access. 2. an example notebook showing its use See contribution guidelines for more information on how to write tests, lint etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md --> #### Who can review? Tag maintainers/contributors who might be interested: <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @vowelparrot VectorStores / Retrievers / Memory - @dev2049 -->
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Define map only for chain_type of RetrievalQA
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2023-09-15T16:08:11
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### Feature request Hi, I would like to use `RetrievalQA` chain with `chain_type` equal to map only. Is it actually possible with LangChain library? When generating answer I also get logits scores for each answer, so I would be able to choose the best answer based on these logits scores. Consequently, I only need the `RetrievalQA` chain to generate answer for each document retrieved. ### Motivation Logits scores is a new feature of hugging Face ecosystem for generative model. It would be interesting to enable the user to generate answer for each retrieved document then let the user decided the best answer based on these scores. So a `chain_type` of type map only is needed. ### Your contribution Let me know if this exists, otherwise I would be glad to test.
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try fix issue add fault tolerance
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2023-07-24T09:53:15
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<!-- Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution. Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change. After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost. Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle! --> <!-- Remove if not applicable --> Fixes # (issue) #### Before submitting <!-- If you're adding a new integration, please include: 1. a test for the integration - favor unit tests that does not rely on network access. 2. an example notebook showing its use See contribution guidelines for more information on how to write tests, lint etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md --> #### Who can review? Tag maintainers/contributors who might be interested: <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @vowelparrot VectorStores / Retrievers / Memory - @dev2049 -->
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Combination of RetrievalQA with custom embeddings API
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2023-09-15T16:08:17
2023-09-15T16:08:16
NONE
null
### Feature request Hi, I would like to use `RetrievalQA` chain with custom remote embeddings API. As far as I could see when searching through documentation, I couldn't find any way on how to specify the url and payload to send to the custom API in the argument of the `RetrievalQA` object. One solution would be to be able to take the url and payload into the `langchain.embeddings` module. The second path would be to be able to specify directly the query embeddings within the `RetrievalQA` object. ### Motivation Using a custom API for embeddings is highly desirable for anyone who doesn't want to use paying APIs or who wants to optimize inference computations. ### Your contribution Please indicate me if this already exists in the library.
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I_kwDOIPDwls5oSRPp
5,932
Facing issue while trying to Stream output for QLoRa model using TextIteratorStreamer
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2023-06-09T10:27:05
2023-10-06T16:07:39
2023-10-06T16:07:39
NONE
null
### System Info A4000 GPU transformer 4.30.0 langchain latest ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [X] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [X] Async ### Reproduction streamer = TextIteratorStreamer(tokenizer, skip_special_tokens=True, use_multiprocessing=False) pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=1000, streamer=streamer, temperature=0, top_p=0.2 ) hf = HuggingFacePipeline(pipeline=pipe) llm_chain.run("Where is New delhi?").lstrip() ### Expected behavior While loading a QLoRa model and passing TextIteratorStreamer to iterate over generated words from LLM i am getting TypeError: cannot pickle '_thread.lock' object. While using only TextStreamer it is running fine but could not able to stream the result at API.
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PR_kwDOIPDwls5Sm6mU
5,931
Update ArXiv tool.py to fix issue #5901
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2023-06-09T10:19:06
2023-06-10T23:39:33
2023-06-10T23:39:32
CONTRIBUTOR
null
<!-- Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution. Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change. After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost. Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle! --> <!-- Remove if not applicable --> Fixes #5901 - @hwchase17 - @vowelparrot
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1,749,394,776
PR_kwDOIPDwls5SmMgs
5,929
add dashscope text embedding
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2023-06-09T08:21:59
2023-06-12T04:14:21
2023-06-12T04:14:20
CONTRIBUTOR
null
#### What I do Adding embedding api for [DashScope](https://help.aliyun.com/product/610100.html), which is the DAMO Academy's multilingual text unified vector model based on the LLM base. It caters to multiple mainstream languages worldwide and offers high-quality vector services, helping developers quickly transform text data into high-quality vector data. Currently supported languages include Chinese, English, Spanish, French, Portuguese, Indonesian, and more. #### Who can review? Models - @hwchase17 - @agola11
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5,928
Langchain model using wikipedia tool fails to return response, but generates it successfully
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2023-06-09T08:18:00
2023-06-09T10:32:54
2023-06-09T10:32:54
NONE
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### Langchain agent error I have the following error while trying to load an llm agent and connecting to wikipedia tool, it seems that the response is generated but throws an error when trying to convert the response to the right format. Am I missing something or doing something wrong? Thank you in advanced :) ```python from langchain.agents import load_tools from langchain.agents import initialize_agent from langchain.agents import AgentType from langchain.memory import ConversationBufferMemory from langchain.llms import AzureOpenAI import openai openai.api_type = "azure" openai.api_base = "api_base" openai.api_version = "2022-12-01" openai.api_key = "key" memory = ConversationBufferMemory(memory_key="chat-history", return_messages=True,output_key='answer') llm = AzureOpenAI(deployment_name="text-davinci-003", model_name="text-davinci-003", openai_api_key="key",openai_api_version = "2022-12-01") tools = load_tools (['wikipedia'], llm=llm) agent = initialize_agent( tools, agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION, verbose=True, memory=memory, llm=llm) response = {} response['input'] = "When did Cristobal Colon discover America?" response['chat_history'] = [] res = agent.run(response) ``` ![errrrCapture](https://github.com/hwchase17/langchain/assets/89514540/9b570bf7-00b4-41b7-8e83-4bdb7a2be49b) ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [X] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [X] Memory - [X] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction Execute the code snippet above to reproduce the error ### Expected behavior We are expecting the generated response as an output but seems to not be working
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DOC: Inconsistent unit of measure for chunk_size and chunk_overlap
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2023-06-09T07:59:37
2023-09-15T16:08:21
2023-09-15T16:08:20
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### Issue with current documentation: It's not clear in the documentation ([JS/TS](https://js.langchain.com/docs/modules/indexes/text_splitters/#:~:text=Parameters%E2%80%8B,default%20value%20is%201000%20tokens.)) if the unit of measure used for chunks is in single text characters or in tokens. It's incosistent because it explicits both units in the same phrase. As far as I know a token should be around 4 characters, so it's an important size difference. ### Idea or request for content: Please change the documentation explaining whether it's in text characters or in tokens.
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Fix: Grammer fix in documentation
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2023-06-09T07:40:25
2023-06-10T23:43:37
2023-06-10T23:43:37
CONTRIBUTOR
null
Fix for grammatical errors in the documentation of `vectorstore`. @vowelparrot
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the bigger file size,the lower quality
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2023-06-09T07:22:49
2023-06-09T08:10:24
2023-06-09T08:10:24
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### Issue you'd like to raise. Guys,I found the bigger the trained file size is,i got the the lower quality question。How can i fix this? ### Suggestion: _No response_
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SQLDatabaseChain has SQL injection issue
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2023-08-28T17:19:28
2023-08-28T17:14:07
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### System Info There is no safeguard in SQLDatabaseChain to prevent a malicious user from sending a prompt such as "Drop Employee table". SQLDatabaseChain should have a facility to intercept and review the SQL before sending it to the database. Creating this separately from https://github.com/hwchase17/langchain/issues/1026 because the SQL injection issue and the Python exec issues are separate. For example SQL injection cannot be solved with running inside an isolated container. [LangChain version: 0.0.194. Python version 3.11.1] <img width="596" alt="image" src="https://github.com/hwchase17/langchain/assets/227187/3ced0139-490f-4e41-a880-71dc864ee12c"> ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction Here is a repro using the Chinook sqlite database used in the example ipynb. Running this will drop the Employee table from the SQLite database. ```python chinook_sqlite_uri = "sqlite:///Chinook_Sqlite_Tmp.sqlite" from langchain import OpenAI, SQLDatabase, SQLDatabaseChain llm = OpenAI(temperature=0) db = SQLDatabase.from_uri(chinook_sqlite_uri) db.get_usable_table_names() db_chain = SQLDatabaseChain.from_llm(llm=llm, db=db, verbose=True) db_chain.run("How many employees are there?") db_chain.run("Drop the employee table") ``` ### Expected behavior LangChain should provide a mechanism to intercept SQL before sending it to the database. During this interception the SQL can be examined and rejected if it performs unsafe operations.
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PR_kwDOIPDwls5SlvKr
5,922
feat: add content_format param to ConfluenceLoader.load()
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2023-06-09T07:06:53
2023-06-20T11:42:59
2023-06-14T23:56:28
CONTRIBUTOR
null
Confluence API supports difference format of page content. The storage format is the raw XML representation for storage. The view format is the HTML representation for viewing with macros rendered as though it is viewed by users. Add the `content_format` parameter to `ConfluenceLoader.load()` to specify the content format, this is set to `ContentFormat.STORAGE` by default. #### Who can review? Tag maintainers/contributors who might be interested: @eyurtsev
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SitemapLoader date filter + unicode error bug fix
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2023-06-09T06:46:13
2023-08-11T01:31:44
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NONE
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Add a date filter to SitemapLoader loader = SitemapLoader( "https://example.com/sitemap.xml", filter_lastmods=["2023-06"], ) documents = loader.load() Issue #5280 @hwchase17 @eyurtsev
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Fix: change the chatgpt plugin retriever metadata format
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2023-06-09T06:43:17
2023-06-16T05:04:46
2023-06-16T05:04:46
CONTRIBUTOR
null
the current implement put the doc itself as the metadata, but the document chatgpt plugin retriever returned already has a `metadata` field, it's better to use that instead. the original code will throw the following exception when using `RetrievalQAWithSourcesChain`, becuse it can not find the field `metadata`: ```python Exception has occurred: ValueError (note: full exception trace is shown but execution is paused at: _run_module_as_main) Document prompt requires documents to have metadata variables: ['source']. Received document with missing metadata: ['source']. File "/home/wangjie/anaconda3/envs/chatglm/lib/python3.10/site-packages/langchain/chains/combine_documents/base.py", line 27, in format_document raise ValueError( File "/home/wangjie/anaconda3/envs/chatglm/lib/python3.10/site-packages/langchain/chains/combine_documents/stuff.py", line 65, in <listcomp> doc_strings = [format_document(doc, self.document_prompt) for doc in docs] File "/home/wangjie/anaconda3/envs/chatglm/lib/python3.10/site-packages/langchain/chains/combine_documents/stuff.py", line 65, in _get_inputs doc_strings = [format_document(doc, self.document_prompt) for doc in docs] File "/home/wangjie/anaconda3/envs/chatglm/lib/python3.10/site-packages/langchain/chains/combine_documents/stuff.py", line 85, in combine_docs inputs = self._get_inputs(docs, **kwargs) File "/home/wangjie/anaconda3/envs/chatglm/lib/python3.10/site-packages/langchain/chains/combine_documents/base.py", line 84, in _call output, extra_return_dict = self.combine_docs( File "/home/wangjie/anaconda3/envs/chatglm/lib/python3.10/site-packages/langchain/chains/base.py", line 140, in __call__ raise e ``` Additionally, the `metadata` filed in the `chatgpt plugin retriever` have these fileds by default: ```json { "source": "file", //email, file or chat "source_id": "filename.docx", // the filename "url": "", ... } ``` so, we should set `source_id` to `source` in the langchain metadata. ```python metadata = d.pop("metadata", d) if(metadata.get("source_id")): metadata["source"] = metadata.pop("source_id") ``` #### Who can review? @dev2049 <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @vowelparrot VectorStores / Retrievers / Memory - @dev2049 -->
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fixes to docs
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2023-06-09T06:26:52
2023-06-09T16:15:54
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[RFC] [Breaking] Retriever Callbacks
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2023-06-09T05:09:51
2023-06-11T21:52:19
2023-06-11T21:52:19
CONTRIBUTOR
null
This is one way we could implement it. I try to break as few external APIs as possible, but I'll call out a few (of many more) places that get broken. I don't leap for joy at how much code each change requires when we add new callbacks and how daunting the function signature starts to look for these interfaces. And I don't love how strict things have to be in passing the callback to each component, though maintaining a top level stack that's both thread safe and async context aware probably has other problems. I haven't updated all the experimental autogpt/ baby agi memory things. and since memory currently doestn't require callbacks, a retriever memory (in experimental) wouldn't get hit
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arxiv: Correct name of search client attribute to 'arxiv_search' from incorrect 'arxiv_client'
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+ this private attribute is referenced as `arxiv_search` in internal usage and is set when verifying the environment twitter: @spazm #### Who can review? Any of @hwchase17, @leo-gan, or @bongsang might be interested in reviewing. + Mismatch between `arxiv_client` attribute vs `arxiv_search` in validation and usage is present in the initial commit by @hwchase17. + @leo-gan has made most of the edits. + @bongsang implemented pdf download.
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commit two document_loaders to support xml file and java file
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<!-- Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution. Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change. After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost. Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle! --> <!-- Remove if not applicable --> Fixes # (issue) #### Before submitting <!-- If you're adding a new integration, please include: 1. a test for the integration - favor unit tests that does not rely on network access. 2. an example notebook showing its use See contribution guidelines for more information on how to write tests, lint etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md --> ####@eyurtsev Tag maintainers/contributors who might be interested: <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @vowelparrot VectorStores / Retrievers / Memory - @dev2049 -->
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[proxy users] Possible regression after upgrading to 0.0.194: InvalidRequestError: Unrecognized request argument supplied: proxy
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2023-06-21T04:16:05
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CONTRIBUTOR
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### System Info Mac M1 I've just upgraded to Langchain 0.0.194 I need to pass through a proxy, so I set HTTP_PROXY, HTTPS_PROXY, OPENAI_PROXY and REQUEST_BUNDLE_CA= for the https certificate. ``` InvalidRequestError: Unrecognized request argument supplied: proxy ``` ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [X] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction Roughly this is the code to reproduce the issue: ```python llm = PromptLayerChatOpenAI(model_name="gpt-3.5-turbo", temperature=0, return_pl_id=True, pl_tags=["question-answering", "chatgpt"]) chain = load_qa_chain(llm, chain_type="stuff") query_main_magnet = "Extract the starting magnets or main materials being studied in the text. Include all the compositions mentioned." relevant_documents = db.as_retriever().get_relevant_documents(query) chain.run(input_documents=relevant_documents, question=query) ``` and the issue ``` --------------------------------------------------------------------------- InvalidRequestError Traceback (most recent call last) Cell In[40], line 10 8 query_main_magnet = "Extract the starting magnets or main materials being studied in the text. Include all the compositions mentioned." 9 relevant_documents = embeddings_dict['Lit_120_To'].as_retriever().get_relevant_documents(query_main_magnet) ---> 10 chain.run(input_documents=relevant_documents, question=query_main_magnet) File ~/anaconda3/envs/magneto/lib/python3.9/site-packages/langchain/chains/base.py:259, in Chain.run(self, callbacks, *args, **kwargs) 256 return self(args[0], callbacks=callbacks)[self.output_keys[0]] 258 if kwargs and not args: --> 259 return self(kwargs, callbacks=callbacks)[self.output_keys[0]] 261 if not kwargs and not args: 262 raise ValueError( 263 "`run` supported with either positional arguments or keyword arguments," 264 " but none were provided." 265 ) File ~/anaconda3/envs/magneto/lib/python3.9/site-packages/langchain/chains/base.py:145, in Chain.__call__(self, inputs, return_only_outputs, callbacks, include_run_info) 143 except (KeyboardInterrupt, Exception) as e: 144 run_manager.on_chain_error(e) --> 145 raise e 146 run_manager.on_chain_end(outputs) 147 final_outputs: Dict[str, Any] = self.prep_outputs( 148 inputs, outputs, return_only_outputs 149 ) File ~/anaconda3/envs/magneto/lib/python3.9/site-packages/langchain/chains/base.py:139, in Chain.__call__(self, inputs, return_only_outputs, callbacks, include_run_info) 133 run_manager = callback_manager.on_chain_start( 134 {"name": self.__class__.__name__}, 135 inputs, 136 ) 137 try: 138 outputs = ( --> 139 self._call(inputs, run_manager=run_manager) 140 if new_arg_supported 141 else self._call(inputs) 142 ) 143 except (KeyboardInterrupt, Exception) as e: 144 run_manager.on_chain_error(e) File ~/anaconda3/envs/magneto/lib/python3.9/site-packages/langchain/chains/combine_documents/base.py:84, in BaseCombineDocumentsChain._call(self, inputs, run_manager) 82 # Other keys are assumed to be needed for LLM prediction 83 other_keys = {k: v for k, v in inputs.items() if k != self.input_key} ---> 84 output, extra_return_dict = self.combine_docs( 85 docs, callbacks=_run_manager.get_child(), **other_keys 86 ) 87 extra_return_dict[self.output_key] = output 88 return extra_return_dict File ~/anaconda3/envs/magneto/lib/python3.9/site-packages/langchain/chains/combine_documents/stuff.py:87, in StuffDocumentsChain.combine_docs(self, docs, callbacks, **kwargs) 85 inputs = self._get_inputs(docs, **kwargs) 86 # Call predict on the LLM. ---> 87 return self.llm_chain.predict(callbacks=callbacks, **inputs), {} File ~/anaconda3/envs/magneto/lib/python3.9/site-packages/langchain/chains/llm.py:213, in LLMChain.predict(self, callbacks, **kwargs) 198 def predict(self, callbacks: Callbacks = None, **kwargs: Any) -> str: 199 """Format prompt with kwargs and pass to LLM. 200 201 Args: (...) 211 completion = llm.predict(adjective="funny") 212 """ --> 213 return self(kwargs, callbacks=callbacks)[self.output_key] File ~/anaconda3/envs/magneto/lib/python3.9/site-packages/langchain/chains/base.py:145, in Chain.__call__(self, inputs, return_only_outputs, callbacks, include_run_info) 143 except (KeyboardInterrupt, Exception) as e: 144 run_manager.on_chain_error(e) --> 145 raise e 146 run_manager.on_chain_end(outputs) 147 final_outputs: Dict[str, Any] = self.prep_outputs( 148 inputs, outputs, return_only_outputs 149 ) File ~/anaconda3/envs/magneto/lib/python3.9/site-packages/langchain/chains/base.py:139, in Chain.__call__(self, inputs, return_only_outputs, callbacks, include_run_info) 133 run_manager = callback_manager.on_chain_start( 134 {"name": self.__class__.__name__}, 135 inputs, 136 ) 137 try: 138 outputs = ( --> 139 self._call(inputs, run_manager=run_manager) 140 if new_arg_supported 141 else self._call(inputs) 142 ) 143 except (KeyboardInterrupt, Exception) as e: 144 run_manager.on_chain_error(e) File ~/anaconda3/envs/magneto/lib/python3.9/site-packages/langchain/chains/llm.py:69, in LLMChain._call(self, inputs, run_manager) 64 def _call( 65 self, 66 inputs: Dict[str, Any], 67 run_manager: Optional[CallbackManagerForChainRun] = None, 68 ) -> Dict[str, str]: ---> 69 response = self.generate([inputs], run_manager=run_manager) 70 return self.create_outputs(response)[0] File ~/anaconda3/envs/magneto/lib/python3.9/site-packages/langchain/chains/llm.py:79, in LLMChain.generate(self, input_list, run_manager) 77 """Generate LLM result from inputs.""" 78 prompts, stop = self.prep_prompts(input_list, run_manager=run_manager) ---> 79 return self.llm.generate_prompt( 80 prompts, stop, callbacks=run_manager.get_child() if run_manager else None 81 ) File ~/anaconda3/envs/magneto/lib/python3.9/site-packages/langchain/chat_models/base.py:148, in BaseChatModel.generate_prompt(self, prompts, stop, callbacks) 141 def generate_prompt( 142 self, 143 prompts: List[PromptValue], 144 stop: Optional[List[str]] = None, 145 callbacks: Callbacks = None, 146 ) -> LLMResult: 147 prompt_messages = [p.to_messages() for p in prompts] --> 148 return self.generate(prompt_messages, stop=stop, callbacks=callbacks) File ~/anaconda3/envs/magneto/lib/python3.9/site-packages/langchain/chat_models/base.py:92, in BaseChatModel.generate(self, messages, stop, callbacks) 90 except (KeyboardInterrupt, Exception) as e: 91 run_manager.on_llm_error(e) ---> 92 raise e 93 llm_output = self._combine_llm_outputs([res.llm_output for res in results]) 94 generations = [res.generations for res in results] File ~/anaconda3/envs/magneto/lib/python3.9/site-packages/langchain/chat_models/base.py:84, in BaseChatModel.generate(self, messages, stop, callbacks) 80 new_arg_supported = inspect.signature(self._generate).parameters.get( 81 "run_manager" 82 ) 83 try: ---> 84 results = [ 85 self._generate(m, stop=stop, run_manager=run_manager) 86 if new_arg_supported 87 else self._generate(m, stop=stop) 88 for m in messages 89 ] 90 except (KeyboardInterrupt, Exception) as e: 91 run_manager.on_llm_error(e) File ~/anaconda3/envs/magneto/lib/python3.9/site-packages/langchain/chat_models/base.py:85, in <listcomp>(.0) 80 new_arg_supported = inspect.signature(self._generate).parameters.get( 81 "run_manager" 82 ) 83 try: 84 results = [ ---> 85 self._generate(m, stop=stop, run_manager=run_manager) 86 if new_arg_supported 87 else self._generate(m, stop=stop) 88 for m in messages 89 ] 90 except (KeyboardInterrupt, Exception) as e: 91 run_manager.on_llm_error(e) File ~/anaconda3/envs/magneto/lib/python3.9/site-packages/langchain/chat_models/promptlayer_openai.py:50, in PromptLayerChatOpenAI._generate(self, messages, stop, run_manager) 47 from promptlayer.utils import get_api_key, promptlayer_api_request 49 request_start_time = datetime.datetime.now().timestamp() ---> 50 generated_responses = super()._generate(messages, stop, run_manager) 51 request_end_time = datetime.datetime.now().timestamp() 52 message_dicts, params = super()._create_message_dicts(messages, stop) File ~/anaconda3/envs/magneto/lib/python3.9/site-packages/langchain/chat_models/openai.py:323, in ChatOpenAI._generate(self, messages, stop, run_manager) 319 message = _convert_dict_to_message( 320 {"content": inner_completion, "role": role} 321 ) 322 return ChatResult(generations=[ChatGeneration(message=message)]) --> 323 response = self.completion_with_retry(messages=message_dicts, **params) 324 return self._create_chat_result(response) File ~/anaconda3/envs/magneto/lib/python3.9/site-packages/langchain/chat_models/openai.py:284, in ChatOpenAI.completion_with_retry(self, **kwargs) 280 @retry_decorator 281 def _completion_with_retry(**kwargs: Any) -> Any: 282 return self.client.create(**kwargs) --> 284 return _completion_with_retry(**kwargs) File ~/anaconda3/envs/magneto/lib/python3.9/site-packages/tenacity/__init__.py:289, in BaseRetrying.wraps.<locals>.wrapped_f(*args, **kw) 287 @functools.wraps(f) 288 def wrapped_f(*args: t.Any, **kw: t.Any) -> t.Any: --> 289 return self(f, *args, **kw) File ~/anaconda3/envs/magneto/lib/python3.9/site-packages/tenacity/__init__.py:379, in Retrying.__call__(self, fn, *args, **kwargs) 377 retry_state = RetryCallState(retry_object=self, fn=fn, args=args, kwargs=kwargs) 378 while True: --> 379 do = self.iter(retry_state=retry_state) 380 if isinstance(do, DoAttempt): 381 try: File ~/anaconda3/envs/magneto/lib/python3.9/site-packages/tenacity/__init__.py:314, in BaseRetrying.iter(self, retry_state) 312 is_explicit_retry = fut.failed and isinstance(fut.exception(), TryAgain) 313 if not (is_explicit_retry or self.retry(retry_state)): --> 314 return fut.result() 316 if self.after is not None: 317 self.after(retry_state) File ~/anaconda3/envs/magneto/lib/python3.9/concurrent/futures/_base.py:439, in Future.result(self, timeout) 437 raise CancelledError() 438 elif self._state == FINISHED: --> 439 return self.__get_result() 441 self._condition.wait(timeout) 443 if self._state in [CANCELLED, CANCELLED_AND_NOTIFIED]: File ~/anaconda3/envs/magneto/lib/python3.9/concurrent/futures/_base.py:391, in Future.__get_result(self) 389 if self._exception: 390 try: --> 391 raise self._exception 392 finally: 393 # Break a reference cycle with the exception in self._exception 394 self = None File ~/anaconda3/envs/magneto/lib/python3.9/site-packages/tenacity/__init__.py:382, in Retrying.__call__(self, fn, *args, **kwargs) 380 if isinstance(do, DoAttempt): 381 try: --> 382 result = fn(*args, **kwargs) 383 except BaseException: # noqa: B902 384 retry_state.set_exception(sys.exc_info()) # type: ignore[arg-type] File ~/anaconda3/envs/magneto/lib/python3.9/site-packages/langchain/chat_models/openai.py:282, in ChatOpenAI.completion_with_retry.<locals>._completion_with_retry(**kwargs) 280 @retry_decorator 281 def _completion_with_retry(**kwargs: Any) -> Any: --> 282 return self.client.create(**kwargs) File ~/anaconda3/envs/magneto/lib/python3.9/site-packages/openai/api_resources/chat_completion.py:25, in ChatCompletion.create(cls, *args, **kwargs) 23 while True: 24 try: ---> 25 return super().create(*args, **kwargs) 26 except TryAgain as e: 27 if timeout is not None and time.time() > start + timeout: File ~/anaconda3/envs/magneto/lib/python3.9/site-packages/openai/api_resources/abstract/engine_api_resource.py:153, in EngineAPIResource.create(cls, api_key, api_base, api_type, request_id, api_version, organization, **params) 127 @classmethod 128 def create( 129 cls, (...) 136 **params, 137 ): 138 ( 139 deployment_id, 140 engine, (...) 150 api_key, api_base, api_type, api_version, organization, **params 151 ) --> 153 response, _, api_key = requestor.request( 154 "post", 155 url, 156 params=params, 157 headers=headers, 158 stream=stream, 159 request_id=request_id, 160 request_timeout=request_timeout, 161 ) 163 if stream: 164 # must be an iterator 165 assert not isinstance(response, OpenAIResponse) File ~/anaconda3/envs/magneto/lib/python3.9/site-packages/openai/api_requestor.py:230, in APIRequestor.request(self, method, url, params, headers, files, stream, request_id, request_timeout) 209 def request( 210 self, 211 method, (...) 218 request_timeout: Optional[Union[float, Tuple[float, float]]] = None, 219 ) -> Tuple[Union[OpenAIResponse, Iterator[OpenAIResponse]], bool, str]: 220 result = self.request_raw( 221 method.lower(), 222 url, (...) 228 request_timeout=request_timeout, 229 ) --> 230 resp, got_stream = self._interpret_response(result, stream) 231 return resp, got_stream, self.api_key File ~/anaconda3/envs/magneto/lib/python3.9/site-packages/openai/api_requestor.py:624, in APIRequestor._interpret_response(self, result, stream) 616 return ( 617 self._interpret_response_line( 618 line, result.status_code, result.headers, stream=True 619 ) 620 for line in parse_stream(result.iter_lines()) 621 ), True 622 else: 623 return ( --> 624 self._interpret_response_line( 625 result.content.decode("utf-8"), 626 result.status_code, 627 result.headers, 628 stream=False, 629 ), 630 False, 631 ) File ~/anaconda3/envs/magneto/lib/python3.9/site-packages/openai/api_requestor.py:687, in APIRequestor._interpret_response_line(self, rbody, rcode, rheaders, stream) 685 stream_error = stream and "error" in resp.data 686 if stream_error or not 200 <= rcode < 300: --> 687 raise self.handle_error_response( 688 rbody, rcode, resp.data, rheaders, stream_error=stream_error 689 ) 690 return resp InvalidRequestError: Unrecognized request argument supplied: proxy ``` ### Expected behavior That it work
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Fix openai proxy error
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2023-06-09T02:44:11
2023-06-09T06:16:06
2023-06-09T06:15:07
CONTRIBUTOR
null
Fixes proxy error. Since openai does not parse proxy parameters and uses openai.proxy directly, the proxy method needs to be modified. https://github.com/openai/openai-python/blob/7610c5adfaebe3ffdb9927a551a741a3fab1b62e/openai/api_requestor.py#LL90 #### Who can review? @hwchase17 - project lead Models - @hwchase17 - @agola11
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5,913
How can I make langchain run asynchronously?
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2023-06-09T02:27:09
2023-06-09T02:42:42
2023-06-09T02:42:42
CONTRIBUTOR
null
### Issue you'd like to raise. I'd like to have my server that uses langchain to run asynchronously, specifically, I'd like to have `await` when running `chain({})`. My understanding of this is that the server will start handling other requests made to it, and not wait for `chain({})` to finish before handling other requests. ``` @app.post("/document/qa") async def qa(document: Document): try: api_key = os.environ.get("OPENAI_API_KEY") embedding = OpenAIEmbeddings() collection_name = document.user_id + "/" + document.pdf_title connection_result = check_if_connection_exists(collection_name) if connection_result == False: raise Exception("Collection does not exist") store = PGVector( connection_string=connection_string, embedding_function=embedding, collection_name=collection_name, distance_strategy=DistanceStrategy.COSINE ) memory_response = get_conversation_memory(collection_name) if (memory_response["status-code"] == 404): raise Exception("Failed to retrieve conversation memory") json_response = memory_response["request-response"] chat_memory = ChatMemory.from_pg_conversation( response_json=json_response) retriever = store.as_retriever() query = document.query llm = ChatOpenAI(streaming=False, openai_api_key=api_key) # don't mind the streaming lol it's a work in progress streaming_llm = ChatOpenAI(streaming=False, openai_api_key=api_key, verbose=True,) question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT) doc_chain = load_qa_with_sources_chain(llm=streaming_llm, chain_type="map_reduce") chain = ConversationalRetrievalChain( retriever=retriever, question_generator=question_generator, combine_docs_chain=doc_chain, ) # I'd like to have await here result = chain({ "question": query, "chat_history": chat_memory.history }, return_only_outputs=True ) ai_split_response = result["answer"].split("SOURCES: ") ai_answer = ai_split_response[0] ai_sources = ai_split_response[1].split(", ") # removes the period at the end of the last source ai_sources[-1] = ai_sources[-1].replace(".", "") # posts conversation result to the database post_response = post_conversation_memory( title=collection_name, user_message=query, ai_response=ai_answer, ai_sources=ai_sources, ) if (post_response["status-code"] == 404): raise Exception("Failed to post conversation memory") return { "question": query, "result": ai_answer, "sources": ai_sources, } except Exception as e: raise HTTPException(status_code=404, detail=str(e)) ``` ### Suggestion: _No response_
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Add start index to metadata in TextSplitter
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2023-06-09T00:54:43
2023-06-09T06:09:32
2023-06-09T06:09:32
CONTRIBUTOR
null
<!-- Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution. Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change. After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost. Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle! --> #### Add start index to metadata in TextSplitter - Modified method `create_documents` to track start position of each chunk - The `start_index` is included in the metadata if the `add_start_index` parameter in the class constructor is set to `True` This enables referencing back to the original document, particularly useful when a specific chunk is retrieved. <!-- If you're adding a new integration, please include: 1. a test for the integration - favor unit tests that does not rely on network access. 2. an example notebook showing its use See contribution guidelines for more information on how to write tests, lint etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md --> #### Who can review? Tag maintainers/contributors who might be interested: @eyurtsev @agola11 <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @vowelparrot VectorStores / Retrievers / Memory - @dev2049 -->
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chroma.from_documents AuthenticationError
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2023-09-19T16:08:55
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### System Info In Google Collab What I have installed %pip install requests==2.27.1 %pip install chromadb==<compatible version> %pip install langchain duckdb unstructured chromadb openai tiktoken MacBook M1 ### Who can help? _No response_ ### Information - [X] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction 1. from langchain.embeddings import OpenAIEmbeddings embeddings = OpenAIEmbeddings() << PASS create a vector database and use it to index the embeddings 2. class Document: def __init__(self, text, metadata): self.page_content = text self.metadata = metadata << PASS 3.documents = [Document(text, metadata) for text, metadata in zip(texts, metadata_list)] << PASS 4.from langchain.vectorstores import Chroma db = Chroma.from_documents(documents, embeddings, model='davinci') << AuthenticationError: <empty message> --------------------------------------------------------------------------- AuthenticationError Traceback (most recent call last) [<ipython-input-87-ea7b035908f9>](https://rzac4prlyba-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab-20230606-060135-RC00_538140384#) in <cell line: 3>() 1 from langchain.vectorstores import Chroma 2 ----> 3 db = Chroma.from_documents(documents, embeddings, model='davinci') 17 frames [/usr/local/lib/python3.10/dist-packages/openai/api_requestor.py](https://rzac4prlyba-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab-20230606-060135-RC00_538140384#) in _interpret_response_line(self, rbody, rcode, rheaders, stream) 761 stream_error = stream and "error" in resp.data 762 if stream_error or not 200 <= rcode < 300: --> 763 raise self.handle_error_response( 764 rbody, rcode, resp.data, rheaders, stream_error=stream_error 765 ) AuthenticationError: <empty message> What should I do? FYI. I try to follow this link https://medium.com/mlearning-ai/using-chatgpt-for-question-answering-on-your-own-data-afa33d82fbd0 seems like he has no issue TT ### Expected behavior pass
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Use LCP Client in Tracer
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2023-06-08T22:31:45
2023-06-09T04:15:15
2023-06-09T04:15:15
CONTRIBUTOR
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Move the LCP calls to the client.
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Add desired expected language response as a function param.
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2023-09-14T16:05:40
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### Feature request I could work around the call from `load_summarize_chain` to add response in certain languages https://github.com/hwchase17/langchain/blob/master/langchain/chains/summarize/map_reduce_prompt.py The work around was something like this: ```python from langchain.prompts import PromptTemplate prompt_template = """Write a concise summary of the following text. The following text is in 'pt-br' and you should respond in 'pt-br': "{text}" CONCISE SUMMARY:""" PROMPT = PromptTemplate(template=prompt_template, input_variables=["text"]) ``` Is it possible to add a language param in some functions to add this approach, if not, the answers would be always in English as its the use language in requests. ### Motivation Help people with responses in the desired language ### Your contribution I can help of course, even with a PR. Just need to get more familiar with the tool
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PubmedQueryRun not working
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2023-11-28T16:10:40
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### System Info langchain 0.0.194 langchainplus-sdk 0.0.6 ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [x] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [X] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction 1. code from langchain.tools import PubmedQueryRun 2. Error: ImportError: cannot import name 'PubmedQueryRun' from 'langchain.tools' (C:\Users\USER\anaconda3\lib\site-packages\langchain\tools\__init__.py) Traceback: File "C:\Users\USER\anaconda3\lib\site-packages\streamlit\runtime\scriptrunner\script_runner.py", line 552, in _run_script exec(code, module.__dict__) File "C:\Users\USER\Dropbox\AI\Cloned Repis\Langchain-Crash-Course\pubmed.py", line 31, in <module> from langchain.tools import PubmedQueryRun ### Expected behavior It seems PubmedQueryRun is not defined.
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Issue: langchainplus-sdk dependency
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2023-11-08T16:08:50
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CONTRIBUTOR
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### Issue you'd like to raise. Currently, the newest version `0.0.194` is not building on conda-forge since `langchainplus-sdk` is missing. I can't add that to conda-forge, though, since I have neither found source code nor a licence file for that package - other than the PyPI release. Is `langchainplus-sdk` a strongly needed dependency or can it be made optional like many others as well? This prevents the newest version from being installable through conda/mama/micromamba etc. ### Suggestion: Remove the dependency on `langchainplus-sdk` with `optional=True`.
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5,904
langchain callback support PALM2 GCP
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2023-06-08T20:55:13
2023-10-26T22:40:20
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### Feature request As of now callbacks are provisioned for OPEN AI which gives completion tokens, prompt tokens and total cost. Similar feature is needed for VertexAI Chat Models of GCP. ### Motivation This is an important feature as this abstraction is needed for GCP ### Your contribution i can test and validate
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5,903
Weaviate: Batch embed texts
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2023-06-08T20:46:33
2023-07-17T16:19:45
2023-07-14T00:57:58
CONTRIBUTOR
null
When a custom Embeddings object is set, embed all given texts in a batch instead of passing them through individually. Any code calling add_texts can then appropriately size the chunks of texts that are passed through to take full advantage of the hardware it's running on. <!-- If you're adding a new integration, please include: 1. a test for the integration - favor unit tests that does not rely on network access. 2. an example notebook showing its use See contribution guidelines for more information on how to write tests, lint etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md --> #### Who can review? Tag maintainers/contributors who might be interested: <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @vowelparrot VectorStores / Retrievers / Memory - @dev2049 --> @dev2049
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Fix IndexError in RecursiveCharacterTextSplitter
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2023-06-08T19:01:59
2023-06-11T20:01:14
2023-06-10T23:48:53
CONTRIBUTOR
null
Fixes (not reported) an error that may occur in some cases in the RecursiveCharacterTextSplitter. An empty `new_separators` array ([]) would end up in the else path of the condition below and used in a function where it is expected to be non empty. ```python if new_separators is None: ... else: # _split_text() expects this array to be non-empty! other_info = self._split_text(s, new_separators) ``` resulting in an `IndexError` ```python def _split_text(self, text: str, separators: List[str]) -> List[str]: """Split incoming text and return chunks.""" final_chunks = [] # Get appropriate separator to use > separator = separators[-1] E IndexError: list index out of range langchain/text_splitter.py:425: IndexError ``` #### Who can review? @hwchase17 @eyurtsev
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1,748,467,868
I_kwDOIPDwls5oN4Cc
5,901
Arxiv Tool validation error (api_wrapper field required (type=value_error.missing))
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2023-06-08T18:56:35
2023-06-12T01:04:25
2023-06-12T01:04:25
CONTRIBUTOR
null
### System Info python in Azure Function, langchain 0.0.194 this code: ``` tools = [ PubmedQueryRun(), ArxivQueryRun(), ] ``` and then loading it into an agent: ``` agent_chain = initialize_agent(tools, llm, agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION, verbose=True, memory=memory, return_intermediate_steps=False, max_iterations=15, max_execution_time=100, early_stopping_method="generate") ``` is giving this error: ``` 1 validation error for ArxivQueryRun [2023-06-08T18:28:08.426Z] api_wrapper [2023-06-08T18:28:08.426Z] field required (type=value_error.missing) [2023-06-08T18:28:08.609Z] Executed 'Functions.HttpTriggerCallNavigator' (Failed, Id=70088244-eda1-456d-b9bc-1acedfe71af2, Duration=3756ms) [2023-06-08T18:28:08.609Z] System.Private.CoreLib: Exception while executing function: Functions.HttpTriggerCallNavigator. System.Private.CoreLib: Result: Failure [2023-06-08T18:28:08.609Z] Exception: TypeError: unable to encode outgoing TypedData: unsupported type "<class 'azure.functions.http.HttpResponseConverter'>" for Python type "NoneType" [2023-06-08T18:28:08.609Z] Stack: File "/usr/lib/azure-functions-core-tools-4/workers/python/3.9/LINUX/X64/azure_functions_worker/dispatcher.py", line 506, in _handle__invocation_request [2023-06-08T18:28:08.610Z] return_value = bindings.to_outgoing_proto( [2023-06-08T18:28:08.610Z] File "/usr/lib/azure-functions-core-tools-4/workers/python/3.9/LINUX/X64/azure_functions_worker/bindings/meta.py", line 152, in to_outgoing_proto [2023-06-08T18:28:08.610Z] datum = get_datum(binding, obj, pytype) [2023-06-08T18:28:08.610Z] File "/usr/lib/azure-functions-core-tools-4/workers/python/3.9/LINUX/X64/azure_functions_worker/bindings/meta.py", line 110, in get_datum [2023-06-08T18:28:08.610Z] raise TypeError( [2023-06-08T18:28:08.610Z] . ``` ### Who can help? @hwchase17 @vowelparrot ### 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 - [X] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction ``` tools = [ PubmedQueryRun(), ArxivQueryRun(), ] agent_chain = initialize_agent(tools, llm, agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION, verbose=True, memory=memory, return_intermediate_steps=False, max_iterations=15, max_execution_time=100, early_stopping_method="generate") ``` ### Expected behavior 1 validation error for ArxivQueryRun [2023-06-08T18:28:08.426Z] api_wrapper [2023-06-08T18:28:08.426Z] field required (type=value_error.missing)
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SQLDatabaseChain and agent no longer working at all. Code was working for past 4 weeks. no changes made to my code and everything errored out as of this morning (6/8/24 ~11am ET). It worked flawlessly just yesterday (6/7) for a demo i gave.
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2023-06-08T18:39:26
2023-06-09T14:51:14
2023-06-09T14:51:14
NONE
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when i try and run this code: from langchain import OpenAI, SQLDatabase, SQLDatabaseChain db = SQLDatabase.from_uri("sqlite:///my_data.db") llm = OpenAI(temperature=0, verbose=True) i get this error: typing.ClassVar[typing.Collection[str]] is not valid as type argument I have tested it on a snowflake connection too with the exact same error. I checked the langchain documentation and nothing has been updated or changed from what i can tell.
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Issue: <RetrievalQA.from_chain_type with custom prompt template>
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2023-06-08T18:29:01
2023-12-01T16:09:49
2023-12-01T16:09:48
NONE
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### Issue you'd like to raise. Hello everyone! I'm having trouble setting up the successful usage of a custom QA prompt template that includes input variables with my RetrievalQA.from_chain_type. This issue is similar to https://github.com/hwchase17/langchain/pull/3425 I am using LangChain v0.0.192 with FAISS vectorstore. As below my custom prompt has three input **My_loader_ made_corrections_ output_format_instructions_** My code looks like that for now: pdf_template_stuff = """ You are a Contract Review Specialist. You have been given a dataloader as langchain.document_loaders.directory.DirectoryLoader: {my_loader_}. Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer. Use as much detail as possible when responding and try to make answer in markdown format as much as possible. Based on this data do the following: 1- Read the input PDFs and extract the relevant information. 2- Answer the question using the extracted information. 3- Cross-reference the answer with other PDF files to ensure accuracy. 4- Based on the historic corrections: {made_corrections_} make corrections to the extracted answers 5- provide the answer in alignment with the {output_format_instructions_} format. Context: {context} Question:{question} Helpful Answer: """ pdf_prompt_template = PromptTemplate( input_variables= ["context","question","my_loader_", "made_corrections_","output_format_instructions_"], template=pdf_template_stuff ) my_chain_type='stuff' my_qa_chain = RetrievalQA.from_chain_type(llm=my_specific_llm, chain_type=my_chain_type, retriever=my_retriever, return_source_documents=False, chain_type_kwargs={"prompt": pdf_prompt_template} ) final_result=my_qa_chain(query="MY SAMPLE QUESTION",my_loader_=my_loader, made_corrections_=made_corrections , Output_format_instructions_=output_format_instructions) ValueError: Missing some input keys: {'output_format_instructions_', 'my_loader_', 'made_corrections_'} Thanks for your help ### Suggestion: _No response_
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Add support for tags
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2023-06-08T18:21:19
2023-06-20T10:40:47
2023-06-13T19:31:00
COLLABORATOR
null
<!-- Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution. Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change. After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost. Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle! --> <!-- Remove if not applicable --> Fixes # (issue) #### Before submitting <!-- If you're adding a new integration, please include: 1. a test for the integration - favor unit tests that does not rely on network access. 2. an example notebook showing its use See contribution guidelines for more information on how to write tests, lint etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md --> #### Who can review? Tag maintainers/contributors who might be interested: <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @vowelparrot VectorStores / Retrievers / Memory - @dev2049 -->
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I_kwDOIPDwls5oNny2
5,897
Issue: TokenTextSplitter processes passages less than chunk_size and creates duplicate overlaps
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2023-06-08T17:59:46
2023-12-13T10:21:39
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### Issue you'd like to raise. **Observation:** TokenTextSplitter processes passages smaller than specified chunk_size, and creates a lot more smaller chunks with duplicated overlaps. **Impact:** The smaller chunks with duplicated overlaps may be harmful to storage size, computation time, and it may confuse the retrieval model. **For example:** The second TokenTextSplitter creates 366 more small chunks, even if new_chunks1 doesn't have any passages longer than chunk_size=500. <img width="950" alt="Screen Shot 2023-06-08 at 10 58 52 AM" src="https://github.com/hwchase17/langchain/assets/103061109/d05c28a9-b6dc-4e44-ad00-c892457055d8"> ### Suggestion: In source code for langchain.text_splitter, split_text_on_tokens function, can we add the check for `If len(input_ids) <= tokenizer.tokens_per_chunk: splits.append(tokenizer.decode(input_ids)) `
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1,748,363,889
PR_kwDOIPDwls5Si4fI
5,896
Fixes model arguments for amazon models
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2023-06-08T17:31:54
2023-06-08T21:16:01
2023-06-08T21:16:01
CONTRIBUTOR
null
Fixes #5713 #### Who can review? Tag maintainers/contributors who might be interested: @hwchase17 @agola11 @aarora79 @rsgrewal-aws
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PR_kwDOIPDwls5Si3Wg
5,895
DeepLakeVectorStore new updates
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2023-06-08T17:26:57
2023-06-16T05:03:31
2023-06-16T05:03:30
CONTRIBUTOR
null
Added new functionalities to DeepLake vectorstore based on our latest release. Now you can: 1. run searching in 3 different modes: `python`, `tensor_db`, `compute_engine` 2. Data ingestion on small data is now faster 3. Code refactoring <!-- Remove if not applicable --> Fixes # (issue) 1. Fixed typos in docstrings #### Before submitting <!-- If you're adding a new integration, please include: 1. a test for the integration - favor unit tests that does not rely on network access. 2. an example notebook showing its use See contribution guidelines for more information on how to write tests, lint etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md --> #### Who can review? Tag maintainers/contributors who might be interested: <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @vowelparrot VectorStores / Retrievers / Memory - @dev2049 -->
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PR_kwDOIPDwls5Sisnh
5,894
Fix typo
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2023-06-08T16:45:12
2023-06-09T06:05:23
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Having issues importing KNNRetriever from Langchain
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2023-06-08T16:43:29
2023-09-14T16:05:45
2023-09-14T16:05:44
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### System Info version: 0.0.194 Platform: Running in HEX notebook with python version 3.9 When trying to import KNN retriever I get the below error: "typing.ClassVar[typing.Collection[str]] is not valid as type argument" Here's my code: `from langchain.retrievers import KNNRetriever from langchain.embeddings import OpenAIEmbeddings from langchain.chains import RetrievalQA` Here's the error report: --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-12-da50b4368ae7> in <cell line: 1>() ----> 1 from langchain.retrievers import KNNRetriever 2 from langchain.embeddings import OpenAIEmbeddings 3 from langchain.chains import RetrievalQA ~/.cache/pypoetry/virtualenvs/python-kernel-OtKFaj5M-py3.9/lib/python3.9/site-packages/langchain/__init__.py in <module> 4 from typing import Optional 5 ----> 6 from langchain.agents import MRKLChain, ReActChain, SelfAskWithSearchChain 7 from langchain.cache import BaseCache 8 from langchain.chains import ( ~/.cache/pypoetry/virtualenvs/python-kernel-OtKFaj5M-py3.9/lib/python3.9/site-packages/langchain/agents/__init__.py in <module> 1 """Interface for agents.""" ----> 2 from langchain.agents.agent import ( 3 Agent, 4 AgentExecutor, 5 AgentOutputParser, ~/.cache/pypoetry/virtualenvs/python-kernel-OtKFaj5M-py3.9/lib/python3.9/site-packages/langchain/agents/agent.py in <module> 14 15 from langchain.agents.agent_types import AgentType ---> 16 from langchain.agents.tools import InvalidTool 17 from langchain.base_language import BaseLanguageModel 18 from langchain.callbacks.base import BaseCallbackManager ~/.cache/pypoetry/virtualenvs/python-kernel-OtKFaj5M-py3.9/lib/python3.9/site-packages/langchain/agents/tools.py in <module> 6 CallbackManagerForToolRun, 7 ) ----> 8 from langchain.tools.base import BaseTool, Tool, tool 9 10 ~/.cache/pypoetry/virtualenvs/python-kernel-OtKFaj5M-py3.9/lib/python3.9/site-packages/langchain/tools/__init__.py in <module> 44 ) 45 from langchain.tools.plugin import AIPluginTool ---> 46 from langchain.tools.powerbi.tool import ( 47 InfoPowerBITool, 48 ListPowerBITool, ~/.cache/pypoetry/virtualenvs/python-kernel-OtKFaj5M-py3.9/lib/python3.9/site-packages/langchain/tools/powerbi/tool.py in <module> 9 CallbackManagerForToolRun, 10 ) ---> 11 from langchain.chains.llm import LLMChain 12 from langchain.tools.base import BaseTool 13 from langchain.tools.powerbi.prompt import ( ~/.cache/pypoetry/virtualenvs/python-kernel-OtKFaj5M-py3.9/lib/python3.9/site-packages/langchain/chains/__init__.py in <module> 1 """Chains are easily reusable components which can be linked together.""" ----> 2 from langchain.chains.api.base import APIChain 3 from langchain.chains.api.openapi.chain import OpenAPIEndpointChain 4 from langchain.chains.combine_documents.base import AnalyzeDocumentChain 5 from langchain.chains.constitutional_ai.base import ConstitutionalChain ~/.cache/pypoetry/virtualenvs/python-kernel-OtKFaj5M-py3.9/lib/python3.9/site-packages/langchain/chains/api/base.py in <module> 11 CallbackManagerForChainRun, 12 ) ---> 13 from langchain.chains.api.prompt import API_RESPONSE_PROMPT, API_URL_PROMPT 14 from langchain.chains.base import Chain 15 from langchain.chains.llm import LLMChain ~/.cache/pypoetry/virtualenvs/python-kernel-OtKFaj5M-py3.9/lib/python3.9/site-packages/langchain/chains/api/prompt.py in <module> 1 # flake8: noqa ----> 2 from langchain.prompts.prompt import PromptTemplate 3 4 API_URL_PROMPT_TEMPLATE = """You are given the below API Documentation: 5 {api_docs} ~/.cache/pypoetry/virtualenvs/python-kernel-OtKFaj5M-py3.9/lib/python3.9/site-packages/langchain/prompts/__init__.py in <module> 1 """Prompt template classes.""" 2 from langchain.prompts.base import BasePromptTemplate, StringPromptTemplate ----> 3 from langchain.prompts.chat import ( 4 AIMessagePromptTemplate, 5 BaseChatPromptTemplate, ~/.cache/pypoetry/virtualenvs/python-kernel-OtKFaj5M-py3.9/lib/python3.9/site-packages/langchain/prompts/chat.py in <module> 8 from pydantic import BaseModel, Field 9 ---> 10 from langchain.memory.buffer import get_buffer_string 11 from langchain.prompts.base import BasePromptTemplate, StringPromptTemplate 12 from langchain.prompts.prompt import PromptTemplate ~/.cache/pypoetry/virtualenvs/python-kernel-OtKFaj5M-py3.9/lib/python3.9/site-packages/langchain/memory/__init__.py in <module> 28 from langchain.memory.summary_buffer import ConversationSummaryBufferMemory 29 from langchain.memory.token_buffer import ConversationTokenBufferMemory ---> 30 from langchain.memory.vectorstore import VectorStoreRetrieverMemory 31 32 __all__ = [ ~/.cache/pypoetry/virtualenvs/python-kernel-OtKFaj5M-py3.9/lib/python3.9/site-packages/langchain/memory/vectorstore.py in <module> 8 from langchain.memory.utils import get_prompt_input_key 9 from langchain.schema import Document ---> 10 from langchain.vectorstores.base import VectorStoreRetriever 11 12 ~/.cache/pypoetry/virtualenvs/python-kernel-OtKFaj5M-py3.9/lib/python3.9/site-packages/langchain/vectorstores/__init__.py in <module> 1 """Wrappers on top of vector stores.""" ----> 2 from langchain.vectorstores.analyticdb import AnalyticDB 3 from langchain.vectorstores.annoy import Annoy 4 from langchain.vectorstores.atlas import AtlasDB 5 from langchain.vectorstores.base import VectorStore ~/.cache/pypoetry/virtualenvs/python-kernel-OtKFaj5M-py3.9/lib/python3.9/site-packages/langchain/vectorstores/analyticdb.py in <module> 20 from langchain.embeddings.base import Embeddings 21 from langchain.utils import get_from_dict_or_env ---> 22 from langchain.vectorstores.base import VectorStore 23 24 Base = declarative_base() # type: Any ~/.cache/pypoetry/virtualenvs/python-kernel-OtKFaj5M-py3.9/lib/python3.9/site-packages/langchain/vectorstores/base.py in <module> 355 356 --> 357 class VectorStoreRetriever(BaseRetriever, BaseModel): 358 vectorstore: VectorStore 359 search_type: str = "similarity" ~/.cache/pypoetry/virtualenvs/python-kernel-OtKFaj5M-py3.9/lib/python3.9/site-packages/pydantic/main.cpython-39-x86_64-linux-gnu.so in pydantic.main.ModelMetaclass.__new__() ~/.cache/pypoetry/virtualenvs/python-kernel-OtKFaj5M-py3.9/lib/python3.9/site-packages/pydantic/typing.cpython-39-x86_64-linux-gnu.so in pydantic.typing.resolve_annotations() /usr/local/lib/python3.9/typing.py in _eval_type(t, globalns, localns, recursive_guard) 290 """ 291 if isinstance(t, ForwardRef): --> 292 return t._evaluate(globalns, localns, recursive_guard) 293 if isinstance(t, (_GenericAlias, GenericAlias)): 294 ev_args = tuple(_eval_type(a, globalns, localns, recursive_guard) for a in t.__args__) /usr/local/lib/python3.9/typing.py in _evaluate(self, globalns, localns, recursive_guard) 551 sys.modules.get(self.__forward_module__, None), '__dict__', globalns 552 ) --> 553 type_ = _type_check( 554 eval(self.__forward_code__, globalns, localns), 555 "Forward references must evaluate to types.", /usr/local/lib/python3.9/typing.py in _type_check(arg, msg, is_argument, module, allow_special_forms) 156 if (isinstance(arg, _GenericAlias) and 157 arg.__origin__ in invalid_generic_forms): --> 158 raise TypeError(f"{arg} is not valid as type argument") 159 if arg in (Any, NoReturn, Final): 160 return arg TypeError: typing.ClassVar[typing.Collection[str]] is not valid as type argument ### Who can help? @vowelparrot @ag ### 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 - [X] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction 1. from langchain.retrievers import KNNRetriever in an environment with python 3.9 ### Expected behavior This should error out throwing the error message "typing.ClassVar[typing.Collection[str]] is not valid as type argument"
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5,892
Batch generation from API
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2023-06-08T16:31:19
2024-01-11T21:29:30
2023-12-18T23:49:57
NONE
null
Hi there, I am running LLM through custom API and have the possibility to run batch inference. However, the generate method from langchain only runs iteratively the LLM on the list of prompts. Would there be an existing method that I could use to allow batch generation on my API?
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Fix the issue where the parameters passed to VertexAI ignored #5889
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2023-06-08T16:22:57
2023-06-09T06:15:23
2023-06-09T06:15:23
CONTRIBUTOR
null
Fixes #5889 and fixes the name of the argument in init_vertexai @hwchase17 @agola11
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1,748,279,843
I_kwDOIPDwls5oNKIj
5,890
Issue: ConversationalRetrievalChain - issue with passing the CONDENSE_QUESTION_PROMPT for working chat history
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2023-06-08T16:22:14
2023-11-06T09:36:51
2023-09-28T16:06:28
NONE
null
### Issue you'd like to raise. Hello everyone! I can't successfully pass the CONDENSE_QUESTION_PROMPT to `ConversationalRetrievalChain`, while basic QA_PROMPT I can pass. I also need the CONDENSE_QUESTION_PROMPT because there I will pass the chat history, since I want to achieve a converstional chat over documents with working chat history, and later possibly some summary memories to prevent halucinations. I am using LangChain v0.0.191 with Chrom vectorstore v0.0.25 What I want to achieve is that the model will know about chat history. My code looks like that for now: ``` self.llm = ChatOpenAI(model_name=self.model_name, temperature=self.temperature, openai_api_key=settings.OPENAI_API_KEY, streaming=True, verbose=True, callback_manager=CallbackManager([ChainStreamHandler(generator)])) self.memory = ConversationBufferMemory( memory_key="chat_history", return_messages=True, output_key='answer') QA_PROMPT = PromptTemplate(input_variables=["context", "question"], template=QA_PROMP_ALL_KNOWLEDGE) retriever = chroma_Vectorstore.as_retriever(qa_template=QA_PROMP_ALL_KNOWLEDGE ,search_type="similarity_score_threshold", search_kwargs={"score_threshold": 0.2} ) self.chain = ConversationalRetrievalChain.from_llm(self.llm, retriever=retriever, return_source_documents=True,verbose=True, memory=self.memory, combine_docs_chain_kwargs={'prompt': QA_PROMPT}) result = self.chain({"question": question}) res_dict = { "answer": result["answer"], } res_dict["source_documents"] = [] for source in result["source_documents"]: res_dict["source_documents"].append({ "page_content": source.page_content, "metadata": source.metadata }) return res_dict ``` But where can I than pass the CONDENSE_QUESTION_PROMPT? ` CONDENSEprompt = PromptTemplate(input_variables=["chat_history", "question"], template=CONDENSE_QUESTION_PROMPT)` My exact CONDENSE_QUESTION_PROMPT is: ``` CONDENSE_PROMPT = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question. Chat History: {chat_history} Follow Up Input: {question} Standalone question:""" ``` and My exact QA_PROMPT_DOCUMENT_CHAT is ``` QA_PROMPT_DOCUMENT_CHAT = """You are a helpful AI assistant. Use the following pieces of context to answer the question at the end. If the question is not related to the context, politely respond that you are teached to only answer questions that are related to the context. If you don't know the answer, just say you don't know. DO NOT try to make up an answer. Try to make the title for every answer if it is possible. Answer in markdown. Use as much detail as possible when responding and try to make answer in markdown format as much as possible. {context} Question: {question} Answer in markdown format:""" ``` With my current code, the history doesn't work. ### Suggestion: Maybe @hwchase17 or @agola11 can help. Thanks
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1,748,233,322
I_kwDOIPDwls5oM-xq
5,889
When inialztion VertexAI() all passed parameters got ignored
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2023-06-08T16:06:31
2023-06-09T06:15:24
2023-06-09T06:15:24
CONTRIBUTOR
null
### System Info langchain=0.0.194 python=3.11.3 ### Who can help? @hwchase17 @agola11 ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction Run: `VertexAI(project="my_project_name")` ### Expected behavior The client will connect to the supplied project_id
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1,748,203,950
I_kwDOIPDwls5oM3mu
5,888
Test Failure: SocketBlockedError not raised in test_pytest_config.py/test_socket_disabled
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2023-06-08T15:54:40
2023-09-14T16:05:51
2023-09-14T16:05:50
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### System Info ```yaml Fedora OS 38 Podman info: bash host: arch: amd64 buildahVersion: 1.30.0 cgroupControllers: - cpu - io - memory - pids cgroupManager: systemd cgroupVersion: v2 conmon: package: conmon-2.1.7-2.fc38.x86_64 path: /usr/bin/conmon version: 'conmon version 2.1.7, commit: ' cpuUtilization: idlePercent: 87.93 systemPercent: 2.6 userPercent: 9.48 cpus: 4 databaseBackend: boltdb distribution: distribution: fedora variant: workstation version: "38" eventLogger: journald hostname: fedora idMappings: gidmap: - container_id: 0 host_id: 1000 size: 1 - container_id: 1 host_id: 100000 size: 65536 uidmap: - container_id: 0 host_id: 1000 size: 1 - container_id: 1 host_id: 100000 size: 65536 kernel: 6.3.5-200.fc38.x86_64 linkmode: dynamic logDriver: journald memFree: 346042368 memTotal: 3947089920 networkBackend: netavark ociRuntime: name: crun package: crun-1.8.5-1.fc38.x86_64 path: /usr/bin/crun version: |- crun version 1.8.5 commit: b6f80f766c9a89eb7b1440c0a70ab287434b17ed rundir: /run/user/1000/crun spec: 1.0.0 +SYSTEMD +SELINUX +APPARMOR +CAP +SECCOMP +EBPF +CRIU +LIBKRUN +WASM:wasmedge +YAJL os: linux remoteSocket: path: /run/user/1000/podman/podman.sock security: apparmorEnabled: false capabilities: CAP_CHOWN,CAP_DAC_OVERRIDE,CAP_FOWNER,CAP_FSETID,CAP_KILL,CAP_NET_BIND_SERVICE,CAP_SETFCAP,CAP_SETGID,CAP_SETPCAP,CAP_SETUID,CAP_SYS_CHROOT rootless: true seccompEnabled: true seccompProfilePath: /usr/share/containers/seccomp.json selinuxEnabled: true serviceIsRemote: false slirp4netns: executable: /usr/bin/slirp4netns package: slirp4netns-1.2.0-12.fc38.x86_64 version: |- slirp4netns version 1.2.0 commit: 656041d45cfca7a4176f6b7eed9e4fe6c11e8383 libslirp: 4.7.0 SLIRP_CONFIG_VERSION_MAX: 4 libseccomp: 2.5.3 swapFree: 3264212992 swapTotal: 3946835968 uptime: 2h 21m 51.00s (Approximately 0.08 days) plugins: authorization: null log: - k8s-file - none - passthrough - journald network: - bridge - macvlan - ipvlan volume: - local registries: search: - registry.fedoraproject.org - registry.access.redhat.com - docker.io - quay.io store: configFile: /home/cmirdesouza/.config/containers/storage.conf containerStore: number: 1 paused: 0 running: 0 stopped: 1 graphDriverName: overlay graphOptions: {} graphRoot: /home/cmirdesouza/.local/share/containers/storage graphRootAllocated: 238352859136 graphRootUsed: 23042453504 graphStatus: Backing Filesystem: btrfs Native Overlay Diff: "true" Supports d_type: "true" Using metacopy: "false" imageCopyTmpDir: /var/tmp imageStore: number: 14 runRoot: /run/user/1000/containers transientStore: false volumePath: /home/cmirdesouza/.local/share/containers/storage/volumes version: APIVersion: 4.5.1 Built: 1685123928 BuiltTime: Fri May 26 14:58:48 2023 GitCommit: "" GoVersion: go1.20.4 Os: linux OsArch: linux/amd64 Version: 4.5.1 ``` ```bash podman run -it langchain =============================== test session starts =============================== platform linux -- Python 3.11.2, pytest-7.3.1, pluggy-1.0.0 rootdir: /app configfile: pyproject.toml plugins: asyncio-0.20.3, cov-4.1.0, dotenv-0.5.2, mock-3.10.0, socket-0.6.0 asyncio: mode=Mode.STRICT collected 769 items tests/unit_tests/test_bash.py .s...s [ 0%] tests/unit_tests/test_dependencies.py ... 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[ 65%] tests/unit_tests/prompts/test_prompt.py ............... [ 67%] tests/unit_tests/prompts/test_utils.py . [ 67%] tests/unit_tests/retrievers/test_tfidf.py sss [ 67%] tests/unit_tests/retrievers/test_time_weighted_retriever.py ..... [ 68%] tests/unit_tests/retrievers/test_zep.py ss [ 68%] tests/unit_tests/retrievers/self_query/test_pinecone.py .. [ 68%] tests/unit_tests/tools/test_base.py ................................. [ 73%] tests/unit_tests/tools/test_exported.py . [ 73%] tests/unit_tests/tools/test_json.py .... [ 73%] tests/unit_tests/tools/test_public_api.py . [ 73%] tests/unit_tests/tools/test_signatures.py ................................. [ 78%] ............................................... [ 84%] tests/unit_tests/tools/test_zapier.py ... [ 84%] tests/unit_tests/tools/file_management/test_copy.py ... [ 85%] tests/unit_tests/tools/file_management/test_file_search.py ... [ 85%] tests/unit_tests/tools/file_management/test_list_dir.py ... [ 85%] tests/unit_tests/tools/file_management/test_move.py ... [ 86%] tests/unit_tests/tools/file_management/test_read.py .. [ 86%] tests/unit_tests/tools/file_management/test_toolkit.py .... [ 87%] tests/unit_tests/tools/file_management/test_utils.py ..... [ 87%] tests/unit_tests/tools/file_management/test_write.py ... [ 88%] tests/unit_tests/tools/openapi/test_api_models.py ......................... [ 91%] .......................... [ 94%] tests/unit_tests/tools/powerbi/test_powerbi.py . [ 94%] tests/unit_tests/tools/python/test_python.py ........... [ 96%] tests/unit_tests/tools/requests/test_tool.py ...... [ 97%] tests/unit_tests/tools/shell/test_shell.py ..... [ 97%] tests/unit_tests/utilities/test_graphql.py s [ 97%] tests/unit_tests/utilities/test_loading.py ...... [ 98%] tests/unit_tests/vectorstores/test_sklearn.py ssssss [ 99%] tests/unit_tests/vectorstores/test_utils.py .... [100%] ==================================== FAILURES ===================================== ______________________________ test_socket_disabled _______________________________ def test_socket_disabled() -> None: """This test should fail.""" > with pytest.raises(pytest_socket.SocketBlockedError): E Failed: DID NOT RAISE <class 'pytest_socket.SocketBlockedError'> tests/unit_tests/test_pytest_config.py:8: Failed ================================ warnings summary ================================= langchain/text_splitter.py:607 /app/langchain/text_splitter.py:607: DeprecationWarning: invalid escape sequence '\*' "\n\*+\n", langchain/text_splitter.py:706 /app/langchain/text_splitter.py:706: DeprecationWarning: invalid escape sequence '\*' "\n\*\*\*+\n", langchain/text_splitter.py:719 /app/langchain/text_splitter.py:719: DeprecationWarning: invalid escape sequence '\c' "\n\\\chapter{", langchain/text_splitter.py:720 /app/langchain/text_splitter.py:720: DeprecationWarning: invalid escape sequence '\s' "\n\\\section{", langchain/text_splitter.py:721 /app/langchain/text_splitter.py:721: DeprecationWarning: invalid escape sequence '\s' "\n\\\subsection{", langchain/text_splitter.py:722 /app/langchain/text_splitter.py:722: DeprecationWarning: invalid escape sequence '\s' "\n\\\subsubsection{", tests/unit_tests/test_document_transformers.py::test__filter_similar_embeddings tests/unit_tests/test_math_utils.py::test_cosine_similarity_zero tests/unit_tests/test_math_utils.py::test_cosine_similarity tests/unit_tests/test_math_utils.py::test_cosine_similarity_top_k tests/unit_tests/test_math_utils.py::test_cosine_similarity_score_threshold tests/unit_tests/test_math_utils.py::test_cosine_similarity_top_k_and_score_threshold tests/unit_tests/vectorstores/test_utils.py::test_maximal_marginal_relevance_lambda_zero tests/unit_tests/vectorstores/test_utils.py::test_maximal_marginal_relevance_lambda_one /app/langchain/math_utils.py:23: RuntimeWarning: invalid value encountered in divide similarity = np.dot(X, Y.T) / np.outer(X_norm, Y_norm) tests/unit_tests/test_sql_database_schema.py::test_table_info /app/.venv/lib/python3.11/site-packages/duckdb_engine/__init__.py:160: DuckDBEngineWarning: duckdb-engine doesn't yet support reflection on indices warnings.warn( tests/unit_tests/document_loaders/test_readthedoc.py::test_main_id_main_content tests/unit_tests/document_loaders/test_readthedoc.py::test_div_role_main tests/unit_tests/document_loaders/test_readthedoc.py::test_custom tests/unit_tests/document_loaders/test_readthedoc.py::test_empty /app/langchain/document_loaders/readthedocs.py:48: GuessedAtParserWarning: No parser was explicitly specified, so I'm using the best available HTML parser for this system ("html.parser"). This usually isn't a problem, but if you run this code on another system, or in a different virtual environment, it may use a different parser and behave differently. The code that caused this warning is on line 48 of the file /app/langchain/document_loaders/readthedocs.py. To get rid of this warning, pass the additional argument 'features="html.parser"' to the BeautifulSoup constructor. _ = BeautifulSoup( tests/unit_tests/document_loaders/test_readthedoc.py::test_main_id_main_content tests/unit_tests/document_loaders/test_readthedoc.py::test_div_role_main tests/unit_tests/document_loaders/test_readthedoc.py::test_custom tests/unit_tests/document_loaders/test_readthedoc.py::test_empty /app/langchain/document_loaders/readthedocs.py:75: GuessedAtParserWarning: No parser was explicitly specified, so I'm using the best available HTML parser for this system ("html.parser"). This usually isn't a problem, but if you run this code on another system, or in a different virtual environment, it may use a different parser and behave differently. The code that caused this warning is on line 75 of the file /app/langchain/document_loaders/readthedocs.py. To get rid of this warning, pass the additional argument 'features="html.parser"' to the BeautifulSoup constructor. soup = BeautifulSoup(data, **self.bs_kwargs) tests/unit_tests/memory/test_combined_memory.py::test_basic_functionality /app/langchain/memory/combined.py:38: UserWarning: When using CombinedMemory, input keys should be so the input is known. Was not set on chat_memory=ChatMessageHistory(messages=[]) output_key=None input_key=None return_messages=False human_prefix='Human' ai_prefix='AI' memory_key='foo' warnings.warn( tests/unit_tests/memory/test_combined_memory.py::test_basic_functionality /app/langchain/memory/combined.py:38: UserWarning: When using CombinedMemory, input keys should be so the input is known. Was not set on chat_memory=ChatMessageHistory(messages=[]) output_key=None input_key=None return_messages=False human_prefix='Human' ai_prefix='AI' memory_key='bar' warnings.warn( tests/unit_tests/tools/shell/test_shell.py::test_shell_input_validation /app/langchain/tools/shell/tool.py:33: UserWarning: The shell tool has no safeguards by default. Use at your own risk. warnings.warn( -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html =============================== slowest 5 durations =============================== 0.49s call tests/unit_tests/test_pytest_config.py::test_socket_disabled 0.23s call tests/unit_tests/test_sql_database_schema.py::test_table_info 0.15s call tests/unit_tests/test_sql_database_schema.py::test_sql_database_run 0.07s call tests/unit_tests/callbacks/tracers/test_base_tracer.py::test_tracer_llm_run 0.06s call tests/unit_tests/document_loaders/test_readthedoc.py::test_main_id_main_content ============================= short test summary info ============================= FAILED tests/unit_tests/test_pytest_config.py::test_socket_disabled - Failed: DID NOT RAISE <class 'pytest_socket.SocketBlockedError'> ============= 1 failed, 697 passed, 71 skipped, 26 warnings in 6.13s ============== ``` ### Who can help? @vowelparrot @agola11 ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [X] Tools / Toolkits - [ ] Chains - [X] Callbacks/Tracing - [ ] Async ### Reproduction 1. git clone https://github.com/hwchase17/langchain.git 2. cd langchain 3. podman build -t langchain -f Dockerfile . 4. podman run -it langchain ### Expected behavior All the tests will run without error.
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1,748,185,942
PR_kwDOIPDwls5SiStc
5,887
Fixed PermissionError on windows
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2023-06-08T15:47:42
2023-06-14T15:56:41
2023-06-14T15:56:41
CONTRIBUTOR
null
Fixed PermissionError that occurred when downloading PDF files via http in BasePDFLoader on windows. When downloading PDF files via http in BasePDFLoader, NamedTemporaryFile is used. This function cannot open the file again on **Windows**.[Python Doc](https://docs.python.org/3.9/library/tempfile.html#tempfile.NamedTemporaryFile) So, we created a **temporary directory** with TemporaryDirectory and placed the downloaded file there. temporary directory is deleted in the deconstruct. Fixes #2698 #### Who can review? Tag maintainers/contributors who might be interested: - @eyurtsev - @hwchase17
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1,748,094,470
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5,886
Add a new vectorstore - AwaDB
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2023-06-08T15:03:06
2023-06-10T02:00:14
2023-06-10T02:00:14
CONTRIBUTOR
null
Added AwaDB vector store, which is a wrapper over the AwaDB, that can be used as a vector storage and has an efficient similarity search. Added integration tests for the vector store Added jupyter notebook with the example Complete the notebook about AwaDB which had not notebook in the last pr(https://github.com/hwchase17/langchain/pull/5687) Please check, Thanks! @dev2049 @hwchase17
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