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https://api.github.com/repos/langchain-ai/langchain/issues/13909

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I_kwDOIPDwls537dav

MultiVector Retriever BaseStore

open

CONTRIBUTOR

### Feature request MultiVectorRetriever is really helpful to add summary and hypothetical queries of our documents to improve the retrievers but only these two are stored in the vectorstore, instead the entire document is within a BaseStore (Memory or Local). The main issue is that: - the Memory one is not going to persist across restarts - the File one is going to create tons of files Why not keeping the original document in the vectorstore as well instead of using external file/memory? ### Motivation Keep documents, questions and summaries on the same vectorstore. ### Your contribution I could work on that but I would like to know your point of view.

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PR_kwDOIPDwls5gbY6x

fix: fix qianfan validate_environment typo

closed

CONTRIBUTOR

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PR_kwDOIPDwls5gbQvy

feat: mask api_key for jina

closed

CONTRIBUTOR

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

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I_kwDOIPDwls537SBG

imports of OpenAI and SQLDatabase don't work in .py file

open

NONE

### System Info Ubuntu 23.10 ### Who can help? @hwchase17 @agola11 ### 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 import OpenAI, SQLDatabase from snowflake.snowpark import Session from langchain.chains import create_sql_query_chain from dotenv import load_dotenv import os from urllib.parse import quote load_dotenv() # use the env vars in comments above to set the vars below OpenAI_API_KEY = os.getenv("OPENAI_API_KEY") snowflake_account = os.getenv("ACCOUNT") username = os.getenv("USER") password = os.getenv("SNOWSQL_PWD") warehouse = os.getenv("WAREHOUSE") database = 'LANGCHAIN_DEMO_DB' #os.getenv("DATABASE") schema = 'PUBLIC' #os.getenv("SCHEMA") role = os.getenv("ROLE") # print out all env vars using f-strings each on a separate line but x out password print(f"OpenAI_API_KEY: {'x' * len(OpenAI_API_KEY)}") print(f"snowflake_account: {snowflake_account}") #print(f"username: {username}") #print(f"password: {password}") print(f"warehouse: {warehouse}") print(f"database: {database}") print(f"schema: {schema}") print(f"role: {role}") encoded_password = quote(password, safe='') ``` but it works in my Jupyter notebook  https://medium.com/@muriithicliffernest/snowflake-langchain-generating-sql-queries-from-natural-language-queries-12c4e2918631 is the tutorial I followed for the .ipynb. ``` pip install --upgrade pip pip install "snowflake-snowpark-python[pandas]" snowflake-sqlalchemy pip install langchain openai langchain-experimental jupyter ``` are the instructions to install packages in that Medium article so I matched versions for both the conda env I'm using for the .py and .ipynb. Even if I use the same `langchain-snowlfake` env for both the error is still there. See the red line under `from langchain import OpenAI, SQLDatabase ` in the right half of the image which is showing `lanchain-sql.py` ### Expected behavior The import should work, no red line.

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PR_kwDOIPDwls5galRN

IMPROVEMENT: import Document from core

closed

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PR_kwDOIPDwls5gaU08

Support for indexes that do not return a 'metadata' field in '_source'

closed

CONTRIBUTOR

Description: Some Elastic indexes do not return a 'metadata' field in '_source'. However, prior to this PR, the code assumed there always is a 'metadata' field. This PR adds support for cases where the field is missing by adding it manually. Issue: #13869

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PR_kwDOIPDwls5gaNF-

DOCS: update stack diagram

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PR_kwDOIPDwls5gaLHO

AnthropicFunctions function_call compatibility

closed

CONTRIBUTOR

- **Description:** Updates to `AnthropicFunctions` to be compatible with the OpenAI `function_call` functionality. - **Issue:** The functionality to indicate `auto`, `none` and a forced function_call was not completely implemented in the existing code. - **Dependencies:** None - **Tag maintainer:** @baskaryan , and any of the other maintainers if needed. - **Twitter handle:** None I have specifically tested this functionality via AWS Bedrock with the Claude-2 and Claude-Instant models.

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I_kwDOIPDwls535ztq

Issue: Retrieval QA Chain not giving response after Entering new RetrievalQA chain...

open

NONE

### Issue you'd like to raise. Sometimes when interacting with the bot using Retrieval QA chain, it just stops at Entering new RetrievalQA chain... No response, it doesn't give the response, it just stops, I am using qa.acall and using the async callback handler how to fix that, as that is unnacceptable ### Suggestion: _No response_

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I_kwDOIPDwls535Krw

Issue: AgentExecutor stopping before reaching the set max_iteration and max_execution_time limits without meeting the stop condition

closed

NONE

### Issue you'd like to raise. ```python llm = ChatOpenAI(model=gpt_4, temperature=0, api_key=os.environ['OPENAI_API_KEY']) llm_chain = LLMChain(llm=llm, prompt=react_prompt) tool_names = [tool.name] agent = LLMSingleActionAgent( llm_chain=llm_chain, output_parser=react_output_parser, stop=["\nObservation:"], allowed_tools=tool_names, max_execution_time=240, max_iterations=120, handle_parsing_errors=True ) agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=[tool], verbose=True) response = agent_executor.run(textual_description) ``` This is my setup for AgentExecutor. It is prompted to solve OpenAI gym's Taxi problem and only stop after the passenger is dropped off at the destination. But as the title suggests the AgentExecutor chain finishes before reaching the stopping limits or achieving the stopping condition. Also when I use GPT-3 model sometimes it stops following the ReAct template occasionally and raises errors as my output parser cannot process the output correctly. I wonder if there is a way to change that. ### Suggestion: _No response_

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I_kwDOIPDwls535ERy

Error raised during decoding response from inference endpoint when using ChatGLM

open

NONE

### System Info langchain==0.0.316 python==3.10.13 ### 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 i'm using the code here:https://python.langchain.com/docs/integrations/llms/chatglm Here is the full error output: Traceback (most recent call last): File "C:\Users\vic\Desktop\chatGLM\.conda\lib\site-packages\requests\models.py", line 971, in json return complexjson.loads(self.text, **kwargs) File "C:\Users\vic\Desktop\chatGLM\.conda\lib\json\__init__.py", line 346, in loads return _default_decoder.decode(s) File "C:\Users\vic\Desktop\chatGLM\.conda\lib\json\decoder.py", line 337, in decode obj, end = self.raw_decode(s, idx=_w(s, 0).end()) File "C:\Users\vic\Desktop\chatGLM\.conda\lib\json\decoder.py", line 355, in raw_decode raise JSONDecodeError("Expecting value", s, err.value) from None json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0) During handling of the above exception, another exception occurred: Traceback (most recent call last): File "C:\Users\vic\AppData\Roaming\Python\Python310\site-packages\langchain\llms\chatglm.py", line 107, in _call parsed_response = response.json() File "C:\Users\vic\Desktop\chatGLM\.conda\lib\site-packages\requests\models.py", line 975, in json raise RequestsJSONDecodeError(e.msg, e.doc, e.pos) requests.exceptions.JSONDecodeError: Expecting value: line 1 column 1 (char 0) During handling of the above exception, another exception occurred: Traceback (most recent call last): File "c:\Users\vic\Desktop\chatGLM\test_server.py", line 36, in <module> print(llm_chain.run(question)) File "C:\Users\vic\AppData\Roaming\Python\Python310\site-packages\langchain\chains\base.py", line 503, in run return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[ File "C:\Users\vic\AppData\Roaming\Python\Python310\site-packages\langchain\chains\base.py", line 308, in __call__ raise e File "C:\Users\vic\AppData\Roaming\Python\Python310\site-packages\langchain\chains\base.py", line 302, in __call__ self._call(inputs, run_manager=run_manager) File "C:\Users\vic\AppData\Roaming\Python\Python310\site-packages\langchain\chains\llm.py", line 93, in _call response = self.generate([inputs], run_manager=run_manager) File "C:\Users\vic\AppData\Roaming\Python\Python310\site-packages\langchain\chains\llm.py", line 103, in generate return self.llm.generate_prompt( File "C:\Users\vic\AppData\Roaming\Python\Python310\site-packages\langchain\llms\base.py", line 497, in generate_prompt return self.generate(prompt_strings, stop=stop, callbacks=callbacks, **kwargs) File "C:\Users\vic\AppData\Roaming\Python\Python310\site-packages\langchain\llms\base.py", line 646, in generate output = self._generate_helper( File "C:\Users\vic\AppData\Roaming\Python\Python310\site-packages\langchain\llms\base.py", line 534, in _generate_helper raise e File "C:\Users\vic\AppData\Roaming\Python\Python310\site-packages\langchain\llms\base.py", line 521, in _generate_helper self._generate( File "C:\Users\vic\AppData\Roaming\Python\Python310\site-packages\langchain\llms\base.py", line 1043, in _generate self._call(prompt, stop=stop, run_manager=run_manager, **kwargs) File "C:\Users\vic\AppData\Roaming\Python\Python310\site-packages\langchain\llms\chatglm.py", line 120, in _call raise ValueError( ValueError: Error raised during decoding response from inference endpoint: Expecting value: line 1 column 1 (char 0). ### Expected behavior output of ChatGLM's response is missing print(response) <Response [200]>

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PR_kwDOIPDwls5gZCwl

Improve exclude path filesystem logic

open

NONE

- **Description:** Improving the use of the exclude arguments for `FileSystemBlobLoader` - **Issue:** #13751 - **Dependencies:** n/a - **Tag maintainer:** - **Twitter handle:** this PR won't get announced, but @jlarks32 See the issue above for more detail, but I don't think that the `exclude` parameter is being handled really like what the API is indicating / what we were thinking (largely due to annoyances with pathlib's `Path.match`). Let me know if you all want me to add some more unit tests and build this out a little bit more. <!-- Thank you for contributing to LangChain! Replace this entire comment with: Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally. See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/extras` directory. If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. -->

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PR_kwDOIPDwls5gZB_B

Add multi-input Reddit search tool

closed

CONTRIBUTOR

- **Description:** Added a tool called RedditSearchRun and an accompanying API wrapper, which searches Reddit for posts with support for time filtering, post sorting, query string and subreddit filtering. - **Issue:** #13891 - **Dependencies:** `praw` module is used to search Reddit - **Tag maintainer:** @baskaryan , and any of the other maintainers if needed - **Twitter handle:** None. Hello, This is our first PR and we hope that our changes will be helpful to the community. We have run `make format`, `make lint` and `make test` locally before submitting the PR. To our knowledge, our changes do not introduce any new errors. Our PR integrates the `praw` package which is already used by RedditPostsLoader in LangChain. Nonetheless, we have added integration tests and edited unit tests to test our changes. An example notebook is also provided. These changes were put together by me, @Anika2000, @CharlesXu123, and @Jeremy-Cheng-stack Thank you in advance to the maintainers for their time.

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I_kwDOIPDwls534uke

Adding a multi-input Reddit search tool

closed

CONTRIBUTOR

### Feature request We are working on a way to add a multi-input tool to LangChain for searching Reddit posts. Integrating the API as a tool will allow agents to search for posts using a specific search query and some query parameters like sort, time_filter, subreddit etc. to respond to prompts. The tool will use search functionality provided by [the `praw` package](https://praw.readthedocs.io/en/stable/code_overview/models/subreddit.html#praw.models.Subreddit.search). ### Motivation Although LangChain currently has a document loader for Reddit (RedditPostsLoader), it is more centred around subreddit and username to load posts and we want to create our tool to provide more functionalities. Our tool will offer functionality for sorting and filtering by time, which is currently not handled by RedditPostsLoader. With this tool, agents can respond to prompts by interacting with the API without the user having to manually load the Reddit posts. The multi-input nature of the tool will make it useful for responding to more diverse prompts and we hope that users can use it to better leverage [multi-input tool](https://python.langchain.com/docs/modules/agents/tools/multi_input_tool) and [shared memory](https://python.langchain.com/docs/modules/agents/how_to/sharedmemory_for_tools) functionalities already provided by LangChain. ### Your contribution We have our code already prepared and we will be submitting a PR soon. As encouraged by contributing.md, we have added integration tests, a notebook example, and edits for documentation generation. `praw` has also been added as an optional dependency.

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PR_kwDOIPDwls5gY8_x

DOC: Fix Sample Code for Compatibility with Pydantic 2.0

closed

CONTRIBUTOR

- **Description:** I encountered an issue while running the existing sample code on the page https://python.langchain.com/docs/modules/agents/how_to/agent_iter in an environment with Pydantic 2.0 installed. The following error was triggered: ```python ValidationError Traceback (most recent call last) <ipython-input-12-2ffff2c87e76> in <cell line: 43>() 41 42 tools = [ ---> 43 Tool( 44 name="GetPrime", 45 func=get_prime, 2 frames /usr/local/lib/python3.10/dist-packages/pydantic/v1/main.py in __init__(__pydantic_self__, **data) 339 values, fields_set, validation_error = validate_model(__pydantic_self__.__class__, data) 340 if validation_error: --> 341 raise validation_error 342 try: 343 object_setattr(__pydantic_self__, '__dict__', values) ValidationError: 1 validation error for Tool args_schema subclass of BaseModel expected (type=type_error.subclass; expected_class=BaseModel) ``` I have made modifications to the example code to ensure it functions correctly in environments with Pydantic 2.0.

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PR_kwDOIPDwls5gY20h

Add retriever for Outline

closed

CONTRIBUTOR

- **Description:** Added a retriever for the Outline API to ask questions on knowledge base - **Issue:** resolves #11814 - **Dependencies:** None - **Tag maintainer:** @baskaryan

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PR_kwDOIPDwls5gY0L0

Fixes error loading Obsidian templates

closed

CONTRIBUTOR

- **Description:** Obsidian templates can include [variables](https://help.obsidian.md/Plugins/Templates#Template+variables) using double curly braces. `ObsidianLoader` uses PyYaml to parse the frontmatter of documents. This parsing throws an error when encountering variables' curly braces. This is avoided by temporarily substituting safe strings before parsing. - **Issue:** #13887 - **Tag maintainer:** @hwchase17

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I_kwDOIPDwls534iM2

ObsidianLoader fails when encountering template variables in the properties frontmatter of a file

open

CONTRIBUTOR

### System Info langchain version 0.0.340 Python version: 3.11.5 ### 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 - [X] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction 1. Create an Obsidian template with a [template variable](https://help.obsidian.md/Plugins/Templates#Template+variables) in the [properties](https://help.obsidian.md/Editing+and+formatting/Properties#Property+format) section of the file. 2. Attempt to load a directory containing that template file using [ObsidianLoader](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/document_loaders/obsidian.py). ```shell $ echo -e "---\nyear: {{date:YYYY}}\n---" > vault/template.md $ python >>> from langchain.document_loaders.obsidian import ObsidianLoader >>> loader = ObsidianLoader('vault') >>> loader.load() Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/user/.conda/envs/localai/lib/python3.11/site-packages/langchain/document_loaders/obsidian.py", line 115, in load front_matter = self._parse_front_matter(text) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/user/.conda/envs/localai/lib/python3.11/site-packages/langchain/document_loaders/obsidian.py", line 48, in _parse_front_matter front_matter = yaml.safe_load(match.group(1)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/user/.conda/envs/localai/lib/python3.11/site-packages/yaml/__init__.py", line 125, in safe_load return load(stream, SafeLoader) ^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/user/.conda/envs/localai/lib/python3.11/site-packages/yaml/__init__.py", line 81, in load return loader.get_single_data() ^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/user/.conda/envs/localai/lib/python3.11/site-packages/yaml/constructor.py", line 51, in get_single_data return self.construct_document(node) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/user/.conda/envs/localai/lib/python3.11/site-packages/yaml/constructor.py", line 60, in construct_document for dummy in generator: File "/home/user/.conda/envs/localai/lib/python3.11/site-packages/yaml/constructor.py", line 413, in construct_yaml_map value = self.construct_mapping(node) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/user/.conda/envs/localai/lib/python3.11/site-packages/yaml/constructor.py", line 218, in construct_mapping return super().construct_mapping(node, deep=deep) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/user/.conda/envs/localai/lib/python3.11/site-packages/yaml/constructor.py", line 141, in construct_mapping raise ConstructorError("while constructing a mapping", node.start_mark, yaml.constructor.ConstructorError: while constructing a mapping in "<unicode string>", line 1, column 7: year: {{date:YYYY}} ^ found unhashable key in "<unicode string>", line 1, column 8: year: {{date:YYYY}} ``` ### Expected behavior [Template variables](https://help.obsidian.md/Plugins/Templates#Template+variables) are a feature in Obsidian and including them in the properties section of a file is perfectly valid, so [ObsidianLoader](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/document_loaders/obsidian.py) should have no issue loading a directory that includes a file that has template variable in its properties.

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PR_kwDOIPDwls5gYvPF

export more types

closed

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PR_kwDOIPDwls5gYt0q

Mask API keys for various LLM/ChatModel Modules

open

NONE

**Description:** - Added masking of the API Keys for the modules: - `langchain/chat_models/openai.py` - `langchain/llms/openai.py` - `langchain/llms/google_palm.py` - `langchain/chat_models/google_palm.py` - `langchain/llms/edenai.py` - Updated the modules to utilize `SecretStr` from pydantic to securely manage API key. - Added unit/integration tests - `langchain/chat_models/asure_openai.py` used the `open_api_key` that is derived from the `ChatOpenAI` Class and it was assuming `openai_api_key` is a str so we changed it to expect `SecretStr` instead. **Issue:** https://github.com/langchain-ai/langchain/issues/12165 , **Dependencies:** none, **Tag maintainer:** @eyurtsev

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PR_kwDOIPDwls5gYqIj

Extend OpenAIEmbeddings class to support non-`tiktoken` based embeddings

closed

CONTRIBUTOR

<!-- Thank you for contributing to LangChain! Replace this entire comment with: - **Description:** This extends `OpenAIEmbeddings` to add support for non-`tiktoken` based embeddings, specifically for use with the new `text-generation-webui` API (`--extensions openai`) which does not support `tiktoken` encodings, but rather strings - **Issue:** Not found, - **Dependencies:** HuggingFace `transformers.AutoTokenizer` is new dependency for running the model without `tiktoken` - **Tag maintainer:** @baskaryan based on last commit for `langchain-core` refactor - **Twitter handle:** @xychelsea Modified the tokenization process to be model-agnostic, allowing for both OpenAI and non-OpenAI model tokenizations, by setting the new default `bool` flag `tiktoken_enabled` to `False`. This requeires HuggingFace’s AutoTokenizer and handling tokenization for models requiring different preprocessing steps to generate a chunked string request rather than a list of integers. Updated the embeddings generation process to accommodate non-OpenAI models. This includes converting tokenized text into embeddings using OpenAI’s and Hugging Face’s model architectures. -->

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PR_kwDOIPDwls5gYpST

add skeleton of thought

closed

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PR_kwDOIPDwls5gYm-9

Add progress bar to OllamaEmbeddings

closed

CONTRIBUTOR

- **Description:** Adds a tqdm progress bar to OllamaEmbeddings when embedding a list. - **Issue:** Related to #13637, but extended to Ollama. - **Dependencies:** `tqdm` made a necessary dependency. Thanks to @ugm2 for helping identify a common problem. Embeddings take a very long time to finish on local machines, and require a progress bar to help identify if one should even attempt the workload.

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PR_kwDOIPDwls5gYhbz

Mask api key for googlepalm chat models

closed

NONE

**Description**: masked Google Palm - Chat_Models module API key and added unit tests, **Issue**: the issue https://github.com/langchain-ai/langchain/issues/12165,

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PR_kwDOIPDwls5gYWV9

Make contributing to docs easier

closed

NONE

With this PR readers can suggest changes to the docs with an easy to use WYSIWYG markdown editor. [This](https://holocron.so/github/pr/langchain-ai/langchain/master/editor) is how the editor looks like for this repo. https://github.com/remorses/docusaurus-example/assets/31321188/43c8bc68-3668-4a25-bf1c-a70eceab7bcb

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I_kwDOIPDwls533qPr

storing metadata with the VectorStoreRetrieverMemory memory module

open

NONE

### Feature request If I want to use VectorStoreRetrieverMemory to store my users' chat memories, I need to search and store them using user_id and session_id. However, memory.save_context doesn't have a 'metadata' option. ### Motivation I want to associate chat merory with single user ### Your contribution I can't submit PR

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PR_kwDOIPDwls5gX5ry

max length attribute for spacy splitter for large docs

closed

CONTRIBUTOR

For large size documents spacy splitter doesn't work it throws an error as shown in below screenshot. Reason its default max_length is 1000000 and there is no option to increase it. So i added it in this PR. 

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PR_kwDOIPDwls5gX4tr

Fix: (issue #13825) Getting an error with DallEAPIWrapper

closed

CONTRIBUTOR

- **Description:** As of OpenAI's Python package 1.0, the existing DallEAPIWrapper does not work correctly, so the example in the LangChain Documentation link below does not work either. https://python.langchain.com/docs/integrations/tools/dalle_image_generator Also, since OpenAI only supports DALL-E version 2 or version 3, I modified the DallEAPIWrapper to support it. - **Issue:** #13825 - **Twitter handle:** ggeutzzang

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PR_kwDOIPDwls5gX3gV

docs: fix link to `local_retrieval_qa`

closed

CONTRIBUTOR

<!-- Thank you for contributing to LangChain! Replace this entire comment with: - **Description:** a description of the change, - **Issue:** the issue # it fixes (if applicable), - **Dependencies:** any dependencies required for this change, - **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below), - **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out! Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally. See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/extras` directory. If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. --> The original link in [this section](https://python.langchain.com/docs/use_cases/question_answering/#:~:text=locally%2Drunning%20models-,here,-.): https://python.langchain.com/docs/modules/use_cases/question_answering/local_retrieval_qa After fix: https://python.langchain.com/docs/use_cases/question_answering/local_retrieval_qa

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PR_kwDOIPDwls5gXzcD

Fix: getting an error with DallEAPIWrapper #13825

closed

CONTRIBUTOR

- **Description:** Existing DallEAPIWrapper code will throw errors in OpenAI 1.0 and later packages. Also, OpenAI now only supports DALL-E2 or DALL-E3. I modified the existing DallEAPIWrapper to accommodate this. - **Issue:** #13825 - **Twitter handle:** ggeutzzang

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I_kwDOIPDwls533Q44

KENDRA: issue with user_context parameter when using get_relevant_documents method (langchain.retrievers.kendra.AmazonKendraRetriever)

closed

NONE

### System Info Python version: 3.11.5 Langchain version: 0.0.316 ### Who can help? @3coins @hw ### 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 using Amazon Kendra as vector store to retrieve relevant documents as part of a Q&A application. As `UserContext` I am using User token: ``` def get_kendra_json_token(user_name: str, groups: List[str]): kendra_json_token = { 'username': user_name, 'groups': groups } return kendra_json_token ``` This output is subsequently converted to: `'user_context': {'Token': json.dumps(kendra_json_token)}` Everything is fine when I build the Retriever: ``` def get_kendra_doc_retriever(inputs: KendraRequest) -> AmazonKendraRetriever: try: kendra_client = boto3.client("kendra", os.environ.get('AWS_REGION')) retriever = AmazonKendraRetriever( index_id=inputs.kendra_index_id, top_k=get_param(AIAssistantParam.NB_KENDRA_DOCS), client=kendra_client, attribute_filter=inputs.attribute_filter, user_context=inputs.user_context ) logger.info(f'Kendra retriever successfully instantiated') return retriever ``` But then, when I call `get_relevant_documents`: ``` def ask_question( chain: Chain, retriever: AmazonKendraRetriever, question: str ) -> Response: try: context = retriever.get_relevant_documents(question) ``` I get this exception: `An error occurred (AccessDeniedException) when calling the Retrieve operation: The provided JSON token isn't valid. The username couldn't be parsed. Generate a new token with username as an array of strings and try your request again.` Of course, `username` should be an string. If I change the code doing this (swapping the content of `user_name` and `groups` in the user token): ``` def get_kendra_json_token(user_name: List[str], groups: str): kendra_json_token = { 'username': groups, 'groups': user_name } return kendra_json_token ``` everything works fine. It is like `user_name` and `groups` inputs parameters are messed up somewhere. ### Expected behavior No exception should be raised when creating the user token as explained in the description above.

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I_kwDOIPDwls533PUG

Support for elastic index without metadata field

open

NONE

### Feature request Currently the `_search` function in `ElasticsearchStore` assumes that the `hit` object returned in the search has a `metadata` field under `_source`: ```python hit["_source"]["metadata"][field] = hit["_source"][field] ``` However, this is not the case in the index I work with - it does not have a `metadata` field. Due to that, an exception is raised. Note that the following code does not help - ```python if "metadata" not in fields: fields.append("metadata") ``` The index still does not return any `metadata`. I assume that in indexes created by `ElasticsearchStore` the `metadata` field is forced, and therefor there is no such issue. However, when using indexes created by external tools, it is better not to assume that the field exists, and support the case where it doesn't. ### Motivation I'd prefer to re-use the existing `ElasticsearchStore` instead of my own implementation of it. ### Your contribution I think I can contribute a PR handling this issue, if the admins confirm the feature request.

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PR_kwDOIPDwls5gXuBx

Added score_threshold parameter to actually filter when retrieval search type is 'similarity_score_threshold'

closed

CONTRIBUTOR

Replace this entire comment with: - **Description:** currently similarity_score_threshold mode doesn't filter properly. This fixes it by adding the parameter and the relative filter - **Issue:** not sure, I think a few,

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I_kwDOIPDwls533KoU

bug: When running by example getting error: TypeError: 'dict' object is not callable

open

NONE

### System Info #### Environment variable ```bash BENTOML_DEBUG='' BENTOML_QUIET='' BENTOML_BUNDLE_LOCAL_BUILD='' BENTOML_DO_NOT_TRACK='' BENTOML_CONFIG='' BENTOML_CONFIG_OPTIONS='' BENTOML_PORT='' BENTOML_HOST='' BENTOML_API_WORKERS='' ``` #### System information `bentoml`: 1.1.10 `python`: 3.11.5 `platform`: Linux-6.2.0-37-generic-x86_64-with-glibc2.35 `uid_gid`: 1000:1000 `conda`: 23.7.4 `in_conda_env`: True <details><summary><code>conda_packages</code></summary> <br> ```yaml name: openllm channels: - defaults dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - bzip2=1.0.8=h7b6447c_0 - ca-certificates=2023.08.22=h06a4308_0 - ld_impl_linux-64=2.38=h1181459_1 - libffi=3.4.4=h6a678d5_0 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - libuuid=1.41.5=h5eee18b_0 - ncurses=6.4=h6a678d5_0 - openssl=3.0.12=h7f8727e_0 - pip=23.3.1=py311h06a4308_0 - python=3.11.5=h955ad1f_0 - readline=8.2=h5eee18b_0 - setuptools=68.0.0=py311h06a4308_0 - sqlite=3.41.2=h5eee18b_0 - tk=8.6.12=h1ccaba5_0 - wheel=0.41.2=py311h06a4308_0 - xz=5.4.2=h5eee18b_0 - zlib=1.2.13=h5eee18b_0 - pip: - accelerate==0.24.1 - aiohttp==3.9.0 - aiosignal==1.3.1 - anyio==3.7.1 - appdirs==1.4.4 - asgiref==3.7.2 - attrs==23.1.0 - beautifulsoup4==4.12.2 - bentoml==1.1.10 - bitsandbytes==0.41.2.post2 - build==0.10.0 - cattrs==23.1.2 - certifi==2023.11.17 - charset-normalizer==3.3.2 - circus==0.18.0 - click==8.1.7 - click-option-group==0.5.6 - cloudpickle==3.0.0 - coloredlogs==15.0.1 - contextlib2==21.6.0 - cuda-python==12.3.0 - dataclasses-json==0.6.2 - datasets==2.15.0 - deepmerge==1.1.0 - deprecated==1.2.14 - dill==0.3.7 - distlib==0.3.7 - distro==1.8.0 - einops==0.7.0 - fastapi==0.104.1 - fastcore==1.5.29 - filelock==3.13.1 - filetype==1.2.0 - frozenlist==1.4.0 - fs==2.4.16 - fsspec==2023.10.0 - ghapi==1.0.4 - greenlet==3.0.1 - h11==0.14.0 - httpcore==1.0.2 - httptools==0.6.1 - httpx==0.25.2 - huggingface-hub==0.19.4 - humanfriendly==10.0 - idna==3.6 - importlib-metadata==6.8.0 - inflection==0.5.1 - jinja2==3.1.2 - jsonpatch==1.33 - jsonpointer==2.4 - jsonschema==4.20.0 - jsonschema-specifications==2023.11.1 - langchain==0.0.340 - langsmith==0.0.66 - markdown-it-py==3.0.0 - markupsafe==2.1.3 - marshmallow==3.20.1 - mdurl==0.1.2 - mpmath==1.3.0 - msgpack==1.0.7 - multidict==6.0.4 - multiprocess==0.70.15 - mypy-extensions==1.0.0 - networkx==3.2.1 - ninja==1.11.1.1 - numpy==1.26.2 - nvidia-cublas-cu12==12.1.3.1 - nvidia-cuda-cupti-cu12==12.1.105 - nvidia-cuda-nvrtc-cu12==12.1.105 - nvidia-cuda-runtime-cu12==12.1.105 - nvidia-cudnn-cu12==8.9.2.26 - nvidia-cufft-cu12==11.0.2.54 - nvidia-curand-cu12==10.3.2.106 - nvidia-cusolver-cu12==11.4.5.107 - nvidia-cusparse-cu12==12.1.0.106 - nvidia-ml-py==11.525.150 - nvidia-nccl-cu12==2.18.1 - nvidia-nvjitlink-cu12==12.3.101 - nvidia-nvtx-cu12==12.1.105 - openllm==0.4.28 - openllm-client==0.4.28 - openllm-core==0.4.28 - opentelemetry-api==1.20.0 - opentelemetry-instrumentation==0.41b0 - opentelemetry-instrumentation-aiohttp-client==0.41b0 - opentelemetry-instrumentation-asgi==0.41b0 - opentelemetry-sdk==1.20.0 - opentelemetry-semantic-conventions==0.41b0 - opentelemetry-util-http==0.41b0 - optimum==1.14.1 - orjson==3.9.10 - packaging==23.2 - pandas==2.1.3 - pathspec==0.11.2 - pillow==10.1.0 - pip-requirements-parser==32.0.1 - pip-tools==7.3.0 - platformdirs==4.0.0 - prometheus-client==0.19.0 - protobuf==4.25.1 - psutil==5.9.6 - pyarrow==14.0.1 - pyarrow-hotfix==0.6 - pydantic==1.10.13 - pygments==2.17.2 - pyparsing==3.1.1 - pyproject-hooks==1.0.0 - python-dateutil==2.8.2 - python-dotenv==1.0.0 - python-json-logger==2.0.7 - python-multipart==0.0.6 - pytz==2023.3.post1 - pyyaml==6.0.1 - pyzmq==25.1.1 - ray==2.8.0 - referencing==0.31.0 - regex==2023.10.3 - requests==2.31.0 - rich==13.7.0 - rpds-py==0.13.1 - safetensors==0.4.0 - schema==0.7.5 - scipy==1.11.4 - sentencepiece==0.1.99 - simple-di==0.1.5 - six==1.16.0 - sniffio==1.3.0 - soupsieve==2.5 - sqlalchemy==2.0.23 - starlette==0.27.0 - sympy==1.12 - tenacity==8.2.3 - tokenizers==0.15.0 - torch==2.1.0 - tornado==6.3.3 - tqdm==4.66.1 - transformers==4.35.2 - triton==2.1.0 - typing-extensions==4.8.0 - typing-inspect==0.9.0 - tzdata==2023.3 - urllib3==2.1.0 - uvicorn==0.24.0.post1 - uvloop==0.19.0 - virtualenv==20.24.7 - vllm==0.2.2 - watchfiles==0.21.0 - websockets==12.0 - wrapt==1.16.0 - xformers==0.0.22.post7 - xxhash==3.4.1 - yarl==1.9.3 - zipp==3.17.0 prefix: /home/lolevsky/anaconda3/envs/openllm ``` </details> <details><summary><code>pip_packages</code></summary> <br> ``` accelerate==0.24.1 aiohttp==3.9.0 aiosignal==1.3.1 anyio==3.7.1 appdirs==1.4.4 asgiref==3.7.2 attrs==23.1.0 beautifulsoup4==4.12.2 bentoml==1.1.10 bitsandbytes==0.41.2.post2 build==0.10.0 cattrs==23.1.2 certifi==2023.11.17 charset-normalizer==3.3.2 circus==0.18.0 click==8.1.7 click-option-group==0.5.6 cloudpickle==3.0.0 coloredlogs==15.0.1 contextlib2==21.6.0 cuda-python==12.3.0 dataclasses-json==0.6.2 datasets==2.15.0 deepmerge==1.1.0 Deprecated==1.2.14 dill==0.3.7 distlib==0.3.7 distro==1.8.0 einops==0.7.0 fastapi==0.104.1 fastcore==1.5.29 filelock==3.13.1 filetype==1.2.0 frozenlist==1.4.0 fs==2.4.16 fsspec==2023.10.0 ghapi==1.0.4 greenlet==3.0.1 h11==0.14.0 httpcore==1.0.2 httptools==0.6.1 httpx==0.25.2 huggingface-hub==0.19.4 humanfriendly==10.0 idna==3.6 importlib-metadata==6.8.0 inflection==0.5.1 Jinja2==3.1.2 jsonpatch==1.33 jsonpointer==2.4 jsonschema==4.20.0 jsonschema-specifications==2023.11.1 langchain==0.0.340 langsmith==0.0.66 markdown-it-py==3.0.0 MarkupSafe==2.1.3 marshmallow==3.20.1 mdurl==0.1.2 mpmath==1.3.0 msgpack==1.0.7 multidict==6.0.4 multiprocess==0.70.15 mypy-extensions==1.0.0 networkx==3.2.1 ninja==1.11.1.1 numpy==1.26.2 nvidia-cublas-cu12==12.1.3.1 nvidia-cuda-cupti-cu12==12.1.105 nvidia-cuda-nvrtc-cu12==12.1.105 nvidia-cuda-runtime-cu12==12.1.105 nvidia-cudnn-cu12==8.9.2.26 nvidia-cufft-cu12==11.0.2.54 nvidia-curand-cu12==10.3.2.106 nvidia-cusolver-cu12==11.4.5.107 nvidia-cusparse-cu12==12.1.0.106 nvidia-ml-py==11.525.150 nvidia-nccl-cu12==2.18.1 nvidia-nvjitlink-cu12==12.3.101 nvidia-nvtx-cu12==12.1.105 openllm==0.4.28 openllm-client==0.4.28 openllm-core==0.4.28 opentelemetry-api==1.20.0 opentelemetry-instrumentation==0.41b0 opentelemetry-instrumentation-aiohttp-client==0.41b0 opentelemetry-instrumentation-asgi==0.41b0 opentelemetry-sdk==1.20.0 opentelemetry-semantic-conventions==0.41b0 opentelemetry-util-http==0.41b0 optimum==1.14.1 orjson==3.9.10 packaging==23.2 pandas==2.1.3 pathspec==0.11.2 Pillow==10.1.0 pip-requirements-parser==32.0.1 pip-tools==7.3.0 platformdirs==4.0.0 prometheus-client==0.19.0 protobuf==4.25.1 psutil==5.9.6 pyarrow==14.0.1 pyarrow-hotfix==0.6 pydantic==1.10.13 Pygments==2.17.2 pyparsing==3.1.1 pyproject_hooks==1.0.0 python-dateutil==2.8.2 python-dotenv==1.0.0 python-json-logger==2.0.7 python-multipart==0.0.6 pytz==2023.3.post1 PyYAML==6.0.1 pyzmq==25.1.1 ray==2.8.0 referencing==0.31.0 regex==2023.10.3 requests==2.31.0 rich==13.7.0 rpds-py==0.13.1 safetensors==0.4.0 schema==0.7.5 scipy==1.11.4 sentencepiece==0.1.99 simple-di==0.1.5 six==1.16.0 sniffio==1.3.0 soupsieve==2.5 SQLAlchemy==2.0.23 starlette==0.27.0 sympy==1.12 tenacity==8.2.3 tokenizers==0.15.0 torch==2.1.0 tornado==6.3.3 tqdm==4.66.1 transformers==4.35.2 triton==2.1.0 typing-inspect==0.9.0 typing_extensions==4.8.0 tzdata==2023.3 urllib3==2.1.0 uvicorn==0.24.0.post1 uvloop==0.19.0 virtualenv==20.24.7 vllm==0.2.2 watchfiles==0.21.0 websockets==12.0 wrapt==1.16.0 xformers==0.0.22.post7 xxhash==3.4.1 yarl==1.9.3 zipp==3.17.0 ``` </details> ### Who can help? _No response_ ### Information - [X] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction I am following the example and wrote the code: ``` llm = OpenLLM(server_url=server_url, server_type='http') llm("What is the difference between a duck and a goose? And why there are so many Goose in Canada?") ``` Seems like First request is hitting the server ``` (scheme=http,method=POST,path=/v1/metadata,type=application/json,length=2) (status=200 ``` Till now its look promising, but then I am getting error ```TypeError: 'dict' object is not callable```. As attached in the trace: ``` Traceback (most recent call last): File "/home/lolevsky/Github/Zodiac/main.py", line 24, in <module> run_zodiac() File "/home/lolevsky/Github/Zodiac/main.py", line 9, in run_zodiac resA = llm("What is the difference between a duck and a goose? And why there are so many Goose in Canada?") File "/usr/local/lib/python3.10/dist-packages/langchain/llms/base.py", line 876, in __call__ self.generate( File "/usr/local/lib/python3.10/dist-packages/langchain/llms/base.py", line 626, in generate params = self.dict() File "/usr/local/lib/python3.10/dist-packages/langchain/llms/base.py", line 974, in dict starter_dict = dict(self._identifying_params) File "/usr/local/lib/python3.10/dist-packages/langchain/llms/openllm.py", line 220, in _identifying_params self.llm_kwargs.update(self._client._config()) TypeError: 'dict' object is not callable ``` ### To reproduce This is how I had setup the envirment: - conda create --name openllm python=3.11 - conda activate openllm - pip install openllm - pip install langchain ### Expected behavior Should not get errors, should hit the server for prompting

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PR_kwDOIPDwls5gXqGN

Update PGVector __query_collection with extended filters

closed

CONTRIBUTOR

- **Description:** current SQL filtering capabilities of PGVector document matching are limited to "is equal" and "is in", this extends it and generalise the code to any (potential) future other matching type - **Dependencies:** none - **Tag maintainer:** @hwchase17 ,

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I_kwDOIPDwls533BOX

DOC: How langchain_experimental.rl_chain works

open

NONE

### Issue with current documentation: Hi, can we get more documentation on `langchain_experimental.rl_chain`? I'm having trouble wrapping my head around how it works, and the documentation is sparse. From the notebook intro, originally I thought it was going to tune the human written prompt template and then output a new and improved prompt template that it found was better. However it seems to be doing something else. ### Idea or request for content: _No response_

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PR_kwDOIPDwls5gXhqm

Add object parsing functionality

closed

CONTRIBUTOR

* **Description:** Parses huggingface dataset Sequence objects into strings for Document loading. * **Issue:** Fixes #10674 * **Tag maintainter:** @baskaryan @eyurtsev

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I_kwDOIPDwls5322b6

CalledProcessError: bash command for LLAVA in Multimodal giving error

open

NONE

### System Info RTX 3090 ``` Here is notebook for reference: https://colab.research.google.com/drive/1Rwdrji34CV4QJofVl9jAT7-EwodvphA4?usp=sharing ``` ### Who can help? @agola11 @ey ### 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 ``` !wget -O /content/models/ggml-model-f16.gguf https://huggingface.co/mys/ggml_llava-v1.5-7b/resolve/main/ggml-model-f16.gguf !wget -O /content/models/ggml-model-q5_k.gguf https://huggingface.co/mys/ggml_llava-v1.5-7b/resolve/main/ggml-model-q5_k.gguf ``` ``` %%bash # Define the directory containing the images IMG_DIR=/content/LLAVA/ # Loop through each image in the directory for img in "${IMG_DIR}"*.jpg; do # Extract the base name of the image without extension base_name=$(basename "$img" .jpg) # Define the output file name based on the image name output_file="${IMG_DIR}${base_name}.txt" # Execute the command and save the output to the defined output file /content/llama.cpp/bin/llava -m /content/models/ggml-model-q5_k.gguf --mmproj /content/models//mmproj-model-f16.gguf --temp 0.1 -p "Describe the image in detail. Be specific about graphs, such as bar plots." --image "$img" > "$output_file" done ``` gives error: ``` bash: line 14: /content/llama.cpp/bin/llava: No such file or directory bash: line 14: /content/llama.cpp/bin/llava: No such file or directory bash: line 14: /content/llama.cpp/bin/llava: No such file or directory bash: line 14: /content/llama.cpp/bin/llava: No such file or directory bash: line 14: /content/llama.cpp/bin/llava: No such file or directory bash: line 14: /content/llama.cpp/bin/llava: No such file or directory bash: line 14: /content/llama.cpp/bin/llava: No such file or directory bash: line 14: /content/llama.cpp/bin/llava: No such file or directory bash: line 14: /content/llama.cpp/bin/llava: No such file or directory bash: line 14: /content/llama.cpp/bin/llava: No such file or directory --------------------------------------------------------------------------- CalledProcessError Traceback (most recent call last) [<ipython-input-51-e049cdfbb7ce>](https://localhost:8080/#) in <cell line: 1>() ----> 1 get_ipython().run_cell_magic('bash', '', '\n# Define the directory containing the images\nIMG_DIR=/content/LLAVA/\n\n# Loop through each image in the directory\nfor img in "${IMG_DIR}"*.jpg; do\n # Extract the base name of the image without extension\n base_name=$(basename "$img" .jpg)\n\n # Define the output file name based on the image name\n output_file="${IMG_DIR}${base_name}.txt"\n\n # Execute the command and save the output to the defined output file\n /content/llama.cpp/bin/llava -m /content/models/ggml-model-q5_k.gguf --mmproj /content/models//mmproj-model-f16.gguf --temp 0.1 -p "Describe the image in detail. Be specific about graphs, such as bar plots." --image "$img" > "$output_file"\n\ndone\n') 4 frames <decorator-gen-103> in shebang(self, line, cell) [/usr/local/lib/python3.10/dist-packages/IPython/core/magics/script.py](https://localhost:8080/#) in shebang(self, line, cell) 243 sys.stderr.flush() 244 if args.raise_error and p.returncode!=0: --> 245 raise CalledProcessError(p.returncode, cell, output=out, stderr=err) 246 247 def _run_script(self, p, cell, to_close): CalledProcessError: Command 'b'\n# Define the directory containing the images\nIMG_DIR=/content/LLAVA/\n\n# Loop through each image in the directory\nfor img in "${IMG_DIR}"*.jpg; do\n # Extract the base name of the image without extension\n base_name=$(basename "$img" .jpg)\n\n # Define the output file name based on the image name\n output_file="${IMG_DIR}${base_name}.txt"\n\n # Execute the command and save the output to the defined output file\n /content/llama.cpp/bin/llava -m /content/models/ggml-model-q5_k.gguf --mmproj /content/models//mmproj-model-f16.gguf --temp 0.1 -p "Describe the image in detail. Be specific about graphs, such as bar plots." --image "$img" > "$output_file"\n\ndone\n'' returned non-zero exit status 127. ``` ### Expected behavior it should run , but do not understand ``` from langchain.llms import LlamaCpp llm = LlamaCpp(model_path="/path/to/llama/model") ```

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PR_kwDOIPDwls5gXYVv

Docs `adapters` moved

closed

COLLABORATOR

Current docs for adapters are in the `Guides/Adapters which is not a good place. - moved Adapters into `Integratons/Components/Adapters/ - simplified the OpenAI adapter notebook - rerouted the old OpenAI adapter page URL to a new one.

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I_kwDOIPDwls532tQL

TypeError using Baseten in a RAG

open

NONE

### System Info Hello, I am trying to use a Baseten base LLM in a RAG pipeline. ``` from operator import itemgetter from langchain.llms import Baseten from langchain.schema.runnable import RunnableMap llm = Baseten(model="MODEL_ID", verbose=True) rag_chain_from_docs = ( { "context": lambda input: format_docs(input["documents"]), "question": itemgetter("question"), } | rag_prompt_custom | llm | StrOutputParser() ) rag_chain_with_source = RunnableMap( {"documents": retriever, "question": RunnablePassthrough()} ) | { "documents": lambda input: [doc.metadata for doc in input["documents"]], "answer": rag_chain_from_docs, } rag_chain_with_source.invoke("What is Task Decomposition") ``` I am using a FAISS retriever and I am getting the following error on the `.invoke()` method: ``` File "/Users/usr/miniconda3/envs/langchain/lib/python3.11/site-packages/langchain/llms/baseten.py", line 69, in _call response = model.predict({"prompt": prompt, **kwargs}) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/usr/miniconda3/envs/langchain/lib/python3.11/site-packages/baseten/common/core.py", line 67, in wrapper return func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ File "/Users/usr/miniconda3/envs/langchain/lib/python3.11/site-packages/baseten/baseten_deployed_model.py", line 124, in predict raise TypeError('predict can be called with either a list, a pandas DataFrame, or a numpy array.') TypeError: predict can be called with either a list, a pandas DataFrame, or a numpy array. ``` It seems the `model.predict()` method is expecting a list. Does anyone already encountered this error? Thank you in advance ! ### Who can help? @hwchase17 @agola11 ### 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 - [X] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction ``` from operator import itemgetter from langchain.llms import Baseten from langchain.schema.runnable import RunnableMap llm = Baseten(model="MODEL_ID", verbose=True) rag_chain_from_docs = ( { "context": lambda input: format_docs(input["documents"]), "question": itemgetter("question"), } | rag_prompt_custom | llm | StrOutputParser() ) rag_chain_with_source = RunnableMap( {"documents": retriever, "question": RunnablePassthrough()} ) | { "documents": lambda input: [doc.metadata for doc in input["documents"]], "answer": rag_chain_from_docs, } rag_chain_with_source.invoke("What is Task Decomposition") ``` ### Expected behavior It seems the `model.predict()` method is expecting a list. Could you fix this issue?

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PR_kwDOIPDwls5gXUrK

Serpapi google jobs tool

closed

CONTRIBUTOR

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

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I_kwDOIPDwls532sUh

Issue: <Please write a comprehensive title after the 'Issue: ' prefix>

closed

NONE

### Issue you'd like to raise. ```# Load Tools tools = load_tools(["serpapi","langchain_experimental_python_repl"], llm=llm) ``` error ``` Exception has occurred: ImportError This tool has been moved to langchain experiment. This tool has access to a python REPL. For best practices make sure to sandbox this tool. Read To keep using this code as is, install langchain experimental and update relevant imports replacing 'langchain' with 'langchain_experimental' File "/home/isayahc/projects/buy-bot/react_agent.py", line 49, in create_agent_executor tools = load_tools(["serpapi","python_repl"], llm=llm) File "/home/isayahc/projects/buy-bot/react_agent.py", line 88, in <module> agent_executor = create_agent_executor() ImportError: This tool has been moved to langchain experiment. This tool has access to a python REPL. For best practices make sure to sandbox this tool. Read h To keep using this code as is, install langchain experimental and update relevant imports replacing 'langchain' with 'langchain_experimental' ``` how do i fix this? ### Suggestion: _No response_

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I_kwDOIPDwls532rTA

Issue: Load tools from experimental langchain module

open

NONE

### Issue you'd like to raise. Exception has occurred: ImportError This tool has been moved to langchain experiment. This tool has access to a python REPL. For best practices make sure to sandbox this tool. Read https://github.com/langchain-ai/langchain/blob/master/SECURITY.md To keep using this code as is, install langchain experimental and update relevant imports replacing 'langchain' with 'langchain_experimental' File "/home/isayahc/projects/buy-bot/react_agent.py", line 43, in create_agent_executor tools = load_tools(["serpapi","python_repl"], llm=llm) File "/home/isayahc/projects/buy-bot/react_agent.py", line 81, in agent_executor = create_agent_executor() ImportError: This tool has been moved to langchain experiment. This tool has access to a python REPL. For best practices make sure to sandbox this tool. Read https://github.com/langchain-ai/langchain/blob/master/SECURITY.md To keep using this code as is, install langchain experimental and update relevant imports replacing 'langchain' with 'langchain_experimental' I am trying to use the python repl, and load it to my tools ### Suggestion: _No response_

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I_kwDOIPDwls532qIP

Issue: what string works for experimental tool

closed

NONE

### Issue you'd like to raise. Exception has occurred: ImportError This tool has been moved to langchain experiment. This tool has access to a python REPL. For best practices make sure to sandbox this tool. Read https://github.com/langchain-ai/langchain/blob/master/SECURITY.md To keep using this code as is, install langchain experimental and update relevant imports replacing 'langchain' with 'langchain_experimental' File "/home/isayahc/projects/buy-bot/react_agent.py", line 43, in create_agent_executor tools = load_tools(["serpapi","python_repl"], llm=llm) File "/home/isayahc/projects/buy-bot/react_agent.py", line 81, in <module> agent_executor = create_agent_executor() ImportError: This tool has been moved to langchain experiment. This tool has access to a python REPL. For best practices make sure to sandbox this tool. Read https://github.com/langchain-ai/langchain/blob/master/SECURITY.md To keep using this code as is, install langchain experimental and update relevant imports replacing 'langchain' with 'langchain_experimental' ### Suggestion: _No response_

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PR_kwDOIPDwls5gXN3W

Migrating Langchain's openai-python usage to v1.0.0

open

NONE

- **Description:** - Converting Langchain to support openai-python v1.0.0 as per the [Official OpenAI v1.0.0 Migration Guide](https://github.com/openai/openai-python/discussions/742) and the [OpenAI v1.0.0 Migration Guide by Microsoft](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/migration?tabs=python%2Cdalle-fix) - **TODO:** - [x] Run `openai migrate ` - [x] Audit changes - [x] `libs/langchain/chains/moderation.py` - [x] `libs/langchain/chat_models/jinachat.py` - [x] `libs/langchain/chat_models/openai.py` - [x] `libs/langchain/document_loaders/parsrs/audio.py` - [x] `libs/langchain/embeddings/localai.py` - [x] `libs/langchain/embeddings/openai.py` - [x] `libs/langchain/llms/gooseai.py` - [x] `libs/langchain/llms/openai.py` - [x] `libs/langchain/utilities/dalle_image_generator.py` - [x] `tests/integration_tests/adapters/test_openai.py` - [x] `tests/integration_tests/embeddings/test_azure_openai.py` - [x] `tests/integration_tests/embeddings/test_openai.py` - [x] `tests/integration_tests/unit_tests/llms/test_openai.py` - **Issue:** #11755 - **Dependencies:** [openai-python v1.0.0](https://github.com/openai/openai-python) - **Tag maintainer:** @baskaryan @eyurtsev @hwchase17 - **Twitter handle:** [@Joshua_Concon](https://twitter.com/joshua_concon)

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PR_kwDOIPDwls5gXGxC

RELEASE: core 0.0.6

closed

COLLABORATOR

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PR_kwDOIPDwls5gXGfp

bump 0.0.339rc3

closed

COLLABORATOR

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PR_kwDOIPDwls5gW6sT

Reduce the number of tokens required to describe a Cypher/Neo4j schema

closed

CONTRIBUTOR

Instead of using JSON-like syntax to describe node and relationship properties we changed to a shorter and more concise schema description Old: ``` Node properties are the following: [{'properties': [{'property': 'name', 'type': 'STRING'}], 'labels': 'Movie'}, {'properties': [{'property': 'name', 'type': 'STRING'}], 'labels': 'Actor'}] Relationship properties are the following: [] The relationships are the following: ['(:Actor)-[:ACTED_IN]->(:Movie)'] ``` New: ``` Node properties are the following: Movie {name: STRING},Actor {name: STRING} Relationship properties are the following: The relationships are the following: (:Actor)-[:ACTED_IN]->(:Movie) ```

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PR_kwDOIPDwls5gW5pK

Allow users to pass local embeddings to Weaviate Hybrid Search Retriever #5539

open

NONE

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PR_kwDOIPDwls5gWviR

Fix bugs with Cypher validation

closed

CONTRIBUTOR

Fixes https://github.com/langchain-ai/langchain/issues/13803. Thanks to @sakusaku-rich

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PR_kwDOIPDwls5gWn1B

🎞️ add json format to OllamaCommon class

closed

CONTRIBUTOR

The Ollama Client now allows a pure [json mode](https://github.com/jmorganca/ollama/blob/main/docs/api.md#request-json-mode) which I just added to the OllamaCommon class. This allows using it like in this example: <img width="876" alt="Screenshot 2023-11-25 at 14 11 19" src="https://github.com/langchain-ai/langchain/assets/34897716/1e47584e-58e0-497d-806d-00ef8e3ea6a5">

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I_kwDOIPDwls5312lV

create_extraction_chain does not work with other LLMs?i try with llama_api

open

NONE

### System Info Python 3.9.18 ### Who can help? _No response_ ### Information - [X] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [x] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction Hi i try this example, but doesnt work ``` from llamaapi import LlamaAPI from langchain.chains import create_extraction_chain llama = LlamaAPI("My Api KEy") from langchain_experimental.llms import ChatLlamaAPI model = ChatLlamaAPI(client=llama) schema = { "properties": { "name": {"type": "string"}, "height": {"type": "integer"}, "hair_color": {"type": "string"}, }, "required": ["name", "height"], } inp = """ Alex is 5 feet tall. Claudia is 1 feet taller Alex and jumps higher than him. Claudia is a brunette and Alex is blonde. """ chain = create_extraction_chain(schema, model) chain.run(inp) ``` File "C:\mini\envs\py39\lib\site-packages\langchain\output_parsers\openai_func tions.py", line 136, in parse_result return res.get(self.key_name) if partial else res[self.key_name] TypeError: string indices must be integers ### Expected behavior Extraction from text

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I_kwDOIPDwls531yv7

Add memeory to langchain AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION agent

open

NONE

### System Info I am write a code and i want to add history to my langchain agent. History is present in chats list ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [x] My own modified scripts ### Related Components - [x] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [x] Memory - [x] Agents / Agent Executors - [x] Tools / Toolkits - [x] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction `async def chat_with_agent(user_input, formatting_data, chats: list): """ Initiates a chat with the agent based on the user input. """ try: # Initialize the chat model llm_model = "gpt-4-1106-preview" llm = ChatOpenAI(temperature=0.3, model=llm_model) # Load necessary tools tool = StructuredTool.from_function(get_human_input) tools = load_tools(["serpapi"], llm=llm) tools_list = [tool, exposure, get_user_profile, get_user_risk_profile, get_stock_technical_analysis, get_stock_fundamental_analysis, get_mutual_fund_exposure, get_stock_based_news, user_agent_chat_history] # Initialize the agent agent = initialize_agent( tools + tools_list, llm, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, handle_parsing_errors=True, verbose=True, max_execution_time=1800, max_iterations=300, agent_kwargs={ 'prefix': "Answer the questions as best you can. Use tools when needed. First task is check user " "If the query requires and domain specific experts and mention it in response there are other" "experts in system like stock expert, tax expert, mutual fund expert " "First: you task is to answer only financial question only" }, return_intermediate_steps=True ) # Add additional prompt extra_prompt = ("You are Relationship Manager. All values are in Indian Rupees. Answers or tasks always lie " "in the capacity of the tools. So ensure you are not expecting anything outside of it." ) final_input = "This is user input " + user_input + " This is helping prompt " + extra_prompt try: logger.info(f"User input + extra prompt: {user_input + extra_prompt}") # Run the agent result = agent(final_input) except Exception as e: logger.exception(f"Error while running the agent: {e}") result = str(e) logger.info(f"Agent chat result: {result['output']}") response = personalised_response_from_ai(final_input, str(result['output']), str(result["intermediate_steps"]), formatting_data) """report = report_writing_tool(user_input, str(result['output']), str(result["intermediate_steps"]))""" logger.info(f"Response from GPT: {response}") # return f" {response}, Report: {report}" if response: return response else: return str(result['output']) except Exception as e: logger.error(f"Error while talking with RM Agent: {str(e)}") raise HTTPException(status_code=500, detail=str(e))` ### Expected behavior I want add history to my agent

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PR_kwDOIPDwls5gWfZn

feat: allow users to pass local embeddings to Weaviate Hybrid Search …

closed

NONE

…Retriever #5539 <!-- Thank you for contributing to LangChain! Replace this entire comment with: - **Description:** a description of the change, - **Issue:** the issue # it fixes (if applicable), - **Dependencies:** any dependencies required for this change, - **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below), - **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out! Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally. See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/extras` directory. If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. -->

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PR_kwDOIPDwls5gWdH0

DOCS: fixed error in the docstring of RunnablePassthrough class

closed

CONTRIBUTOR

This pull request addresses an issue found in the example code within the docstring of `libs/core/langchain_core/runnables/passthrough.py` The original code snippet caused a `NameError` due to the missing import of `RunnableLambda`. The error was as follows: ``` 12 return "completion" 13 ---> 14 chain = RunnableLambda(fake_llm) | { 15 'original': RunnablePassthrough(), # Original LLM output 16 'parsed': lambda text: text[::-1] # Parsing logic NameError: name 'RunnableLambda' is not defined ``` To resolve this, I have modified the example code to include the necessary import statement for `RunnableLambda`. Additionally, I have adjusted the indentation in the code snippet to ensure consistency and readability. The modified code now successfully defines and utilizes `RunnableLambda`, ensuring that users referencing the docstring will have a functional and clear example to follow. There are no related GitHub issues for this particular change. Modified Code: ```python from langchain_core.runnables import RunnablePassthrough, RunnableParallel from langchain_core.runnables import RunnableLambda runnable = RunnableParallel( origin=RunnablePassthrough(), modified=lambda x: x+1 ) runnable.invoke(1) # {'origin': 1, 'modified': 2} def fake_llm(prompt: str) -> str: # Fake LLM for the example return "completion" chain = RunnableLambda(fake_llm) | { 'original': RunnablePassthrough(), # Original LLM output 'parsed': lambda text: text[::-1] # Parsing logic } chain.invoke('hello') # {'original': 'completion', 'parsed': 'noitelpmoc'} ```

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I_kwDOIPDwls531ltH

Does only openai support SystemMessage?

open

NONE

### System Info None ### Who can help? _No response_ ### Information - [X] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction My chat model is ERNIE-Bot, running the following code reported an error：  But after I removed SystemMessage it works fine. So I want to know, do all models support SystemMessage?  ### Expected behavior None

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PR_kwDOIPDwls5gWXZS

Add OneNote document loader

closed

CONTRIBUTOR

- **Description:** Added OneNote document loader - **Issue:** #12125 - **Dependencies:** msal

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PR_kwDOIPDwls5gWV9W

Update llamacpp.ipynb

closed

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

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PR_kwDOIPDwls5gWSz9

The experimental package needs to be compatible with the usage of importing agents

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CONTRIBUTOR

- **Description:** The experimental package needs to be compatible with the usage of importing agents For example, if i use `from langchain.agents import create_pandas_dataframe_agent`, running the program will prompt the following information: ``` Traceback (most recent call last): File "/Users/dongwm/test/main.py", line 1, in <module> from langchain.agents import create_pandas_dataframe_agent File "/Users/dongwm/test/venv/lib/python3.11/site-packages/langchain/agents/__init__.py", line 87, in __getattr__ raise ImportError( ImportError: create_pandas_dataframe_agent has been moved to langchain experimental. See https://github.com/langchain-ai/langchain/discussions/11680 for more information. Please update your import statement from: `langchain.agents.create_pandas_dataframe_agent` to `langchain_experimental.agents.create_pandas_dataframe_agent`. ``` But when I changed to `from langchain_experimental.agents import create_pandas_dataframe_agent`, it was actually wrong: ```python Traceback (most recent call last): File "/Users/dongwm/test/main.py", line 2, in <module> from langchain_experimental.agents import create_pandas_dataframe_agent ImportError: cannot import name 'create_pandas_dataframe_agent' from 'langchain_experimental.agents' (/Users/dongwm/test/venv/lib/python3.11/site-packages/langchain_experimental/agents/__init__.py) ``` I should use `from langchain_experimental.agents.agent_toolkits import create_pandas_dataframe_agent`. In order to solve the problem and make it compatible, I added additional import code to the langchain_experimental package. Now it can be like this Used `from langchain_experimental.agents import create_pandas_dataframe_agent` - **Twitter handle:** [lin_bob57617](https://twitter.com/lin_bob57617)

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I_kwDOIPDwls531cQg

Unable to get page count. Is poppler installed and in PATH

open

NONE

### System Info im running it on google Collab ### Who can help? trying the example of mult-modal rag - I tried everything no matter what if still getting this error please tell if if there is any alternative way or how can we install it? @bas ### Information - [X] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [X] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction just run it on collab & we will not able to get output from partitions https://github.com/langchain-ai/langchain/blob/master/cookbook/Multi_modal_RAG.ipynb  ### Expected behavior it should work normal without error on collab

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PR_kwDOIPDwls5gWE6i

Astra DB, chat message history

closed

CONTRIBUTOR

This PR adds a chat message history component that uses Astra DB for persistence through the JSON API. The `astrapy` package is required for this class to work. I have added tests and a small notebook, and updated the relevant references in the other docs pages. (@rlancemartin this is the counterpart of the Cassandra equivalent class you so helpfully reviewed back at the end of June) Thank you!

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PR_kwDOIPDwls5gWA3X

IMPROVEMENT: langchain anthropic nits

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PR_kwDOIPDwls5gV_Cu

Astra DB, LLM cache classes (exact-match and semantic cache)

closed

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This PR provides idiomatic implementations for the exact-match and the semantic LLM caches using Astra DB as backend through the database's HTTP JSON API. These caches require the `astrapy` library as dependency. Comes with integration tests and example usage in the `llm_cache.ipynb` in the docs. @baskaryan this is the Astra DB counterpart for the Cassandra classes you merged some time ago, tagging you for your familiarity with the topic. Thank you!

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I_kwDOIPDwls531CUW

lang chain Azure vector search not working neither on its direct fields nor on its metadata fields

open

NONE

Please help , I am blocked on it from many days . **I am trying to filter question of answer from pdf doc based on uploaded email filter . Only if email match happen answer the question from that document else just empty . I tried following code its not working rather even in case of OTHER email id also it give answer from my email uploaded document which is wrong , seems like filtering not working .** First i tried by putting email as metadata it did not work then i added as independent email field its not working there either . I am instantiating vector store like below , it has email field in it . I am able to create and successfully able to upload pdf doc with all the fields including email is populated . I checked created index and email field is filterable , searchable . - Creating azure instance as below : ` `self.vector_store = AzureSearch(azure_search_endpoint=endpoint, azure_search_key=admin_key, index_name=index_name, embedding_function=embedding_function,fields=fields)` - After doc uploaded as shown below it has email in it . ``` "@odata.context": "https://ops-documents.search.windows.net/indexes('index-new-5')/$metadata#docs(*)", "value": [ { "@search.score": 1, "id": "NjQ1YWViNWQtNDJkNy00NTcxLTlkMTktMDIzZTc0NTZlNDhm", "content": "ICAO TRIP 2023 – .", "metadata": "{\"source\": \"7-pl\", \"page\": 7, \"file_id\": \"65612fd773a9aa51a0939c96\", \"upload_document_name\": \"SITA Lab Furhat Backoffice IMM officer Dialog - v1.pdf\", \"email\": \"savita.raghuvanshi@st.com\", \"company\": \"sita\"}", "email": "savita.raghuvanshi@st.com" ``` - - Using following way to search : ``` my_retriever = self.vector_store.as_retriever(search_kwargs={'filter': { 'email': email }}) qa = RetrievalQA.from_chain_type(llm=self.llm,chain_type="stuff",retriever=my_retriever,chain_type_kwargs={"prompt": self.PROMPT}, return_source_documents=True, ) results = qa({"query": question}) ``` ``` Installations using as : **I am using OPENAI_API_VERSION="2023-10-01-preview" with gpt4 model** azure-common==1.1.28 azure-core==1.29.5 azure-identity==1.15.0 azure-search==1.0.0b2 azure-search-documents==11.4.0b8 langchain==0.0.326 ``` Kindly let me know if anything needed from me . Thanks so much for your help .

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PR_kwDOIPDwls5gV7QT

Added title metadata to GoogleDriveLoader for optional File Loaders

closed

CONTRIBUTOR

- **Description:** Simple change, I just added title metadata to GoogleDriveLoader for optional File Loaders - **Dependencies:** no dependencies - **Tag maintainer:** @hwchase17

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PR_kwDOIPDwls5gV6Xy

docs: `Hugging Face` platform page

closed

COLLABORATOR

`Hugging Face` is definitely a platform. It includes many integrations for many modules (LLM, Embedding, DocumentLoader, Tool) So, a doc page was added that defines Hugging Face as a platform.

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I_kwDOIPDwls530_bZ

Unable to implement memory in RAG agent, asking for chat_history

open

NONE

### Issue you'd like to raise. Hi I am trying to implement memory in an RAG agent and am following documentation, but I get the following error: ValueError: variable chat_history should be a list of base messages, got It seems that I should be passing in a chat_history, but all the notebook examples I have seen only pass the question. I have seen some implementations use initialize_agent(), while others use AgentExecutor(). Any help on how to implement memory with the agent would be greatly appreciated. this is my implementation: ```from langchain.vectorstores import Pinecone from langchain.llms import Cohere from langchain.retrievers.multi_query import MultiQueryRetriever from langchain.prompts import PromptTemplate import logging from langchain.chains import LLMChain, ConversationChain from langchain.chains.conversation.memory import ConversationBufferWindowMemory from langchain.chains import RetrievalQA from langchain.agents import Tool from langchain.retrievers import ContextualCompressionRetriever from langchain.retrievers.document_compressors import CohereRerank index = pinecone.Index(index_name) vectorstore = Pinecone(index, embeddings, "text") llm = Cohere(cohere_api_key=cohere_api_key) retriever_from_llm = MultiQueryRetriever.from_llm( retriever=vectorstore.as_retriever(search_kwargs={"k": 10, 'filter': {'user_id_str': '42', 'internal_mydocai_id_str': {"$in":["4", "5"]}}}), llm=llm ) from langchain.prompts import PromptTemplate QUERY_PROMPT = PromptTemplate( input_variables=["question"], template="""You are an AI language model assistant. Your task is to output the original query and four different versions of the given user query to retrieve relevant documents from a vector database. By generating multiple perspectives on the user question, your goal is to help the user overcome some of the limitations of the distance-based similarity search, while staying in the scope of the original question. Provide the original query and the alternative questions separated by newlines. Do not output anything else. Original question: {question}""", ) logging.basicConfig() logging.getLogger("langchain.retrievers.multi_query").setLevel(logging.INFO) compressor = CohereRerank(model= "rerank-multilingual-v2.0", cohere_api_key=cohere_api_key, client=co, user_agent="mydocument", top_n=5 ) compression_retriever = ContextualCompressionRetriever( base_compressor=compressor, base_retriever=retriever_from_llm ) compressed_docs = compression_retriever.get_relevant_documents( question memory = ConversationBufferWindowMemory(k=2, memory_key="chat_history", input_key='input', output_key="output") # retrieval qa chain qa = RetrievalQA.from_chain_type( llm=llm, chain_type="stuff", retriever=compression_retriever, ) tools = [ Tool( name='Knowledge Base', func=qa.run, description=( 'use this tool when answering general knowledge queries to get ' 'more information about the topic' ) ) ] agent = initialize_agent( agent='chat-conversational-react-description', tools=tools, llm=llm, verbose=True, max_iterations=3, early_stopping_method='generate', memory=memory ) agent(question) ``` Error: ``` ValueError: variable chat_history should be a list of base messages, got ### Suggestion: _No response_

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PR_kwDOIPDwls5gV2wi

BUGFIX: Import types and typevars from core

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PR_kwDOIPDwls5gVuzB

Simplified Docugami cookbook to remove code now available in docugami library

closed

CONTRIBUTOR

The cookbook had some code to upload files, and wait for the processing to finish. This code is now moved to the `docugami` library so removing from the cookbook to simplify. Thanks @rlancemartin for suggesting this when working on evals.

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PR_kwDOIPDwls5gVlZS

Multitenant adddocs

closed

CONTRIBUTOR

<!-- Thank you for contributing to LangChain! Replace this entire comment with: - **Description:** Added a line to pass the tenant parameter to add_data_object - **Issue:** An extra line added from the fix for #9956 - **Dependencies:** n/a - **Tag maintainer:** @baskaryan Tested locally, works as expected with the line change.

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I_kwDOIPDwls530eNC

Langchain Database Agent with local LLM

open

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I am using local LLM with langchain: openhermes-2.5-mistral-7b.Q8_0.gguf When using database agent this is how I am initiating things: `db = SQLDatabase.from_uri(sql_uri) model_path = "./openhermes-2.5-mistral-7b.Q8_0.gguf" n_gpu_layers = 1 # Change this value based on your model and your GPU VRAM pool. n_batch = 512 # Should be between 1 and n_ctx, consider the amount of VRAM in your GPU. n_ctx=50000 callback_manager = CallbackManager([StreamingStdOutCallbackHandler()]) llm = LlamaCpp( model_path=model_path, #temperature=0, n_gpu_layers=n_gpu_layers, n_batch=n_batch, n_ctx=n_ctx, callback_manager=callback_manager, verbose=True, ) #toolkit = CustomSQLDatabaseToolkit(db=db, llm=llm) toolkit = SQLDatabaseToolkit(db=db, llm=llm) toolkit.get_tools() PREFIX = '''You are a SQL expert. You have access to a Microsoft SQL Server database. Identify which tables can be used to answer the user's question and write and execute a SQL query accordingly. ''' FORMAT_INSTRUCTIONS = """RESPONSE FORMAT INSTRUCTIONS ---------------------------- When responding please, please output a response in this format: thought: Reason about what action to take next, and whether to use a tool. action: The tool to use. Must be one of: {tool_names} action_input: The input to the tool For example: thought: I need to get all tables from database action: sql_db_list_tables action_input: Empty string """ agent_executor = create_sql_agent( llm=llm, toolkit=toolkit, agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, handle_parsing_errors=True, agent_kwargs={ 'prefix': PREFIX, 'format_instructions': FORMAT_INSTRUCTIONS, } ) now = datetime.datetime.now() print("Starting executor : ") print(now.strftime("%Y-%m-%d %H:%M:%S")) agent_executor.run("Who is oldest user")` When Entering chain I usually get error "Could not parse LLM output" as despite the instructions Action Input part is not created by LLM. > Entering new AgentExecutor chain... Action: sql_db_list_tables Traceback (most recent call last): File "/Users/dino/Codings/python/LLM_test1/.venv/lib/python3.9/site-packages/langchain/agents/agent.py", line 1032, in _take_next_step output = self.agent.plan( File "/Users/dino/Codings/python/LLM_test1/.venv/lib/python3.9/site-packages/langchain/agents/agent.py", line 636, in plan return self.output_parser.parse(full_output) File "/Users/dino/Codings/python/LLM_test1/.venv/lib/python3.9/site-packages/langchain/agents/mrkl/output_parser.py", line 70, in parse raise OutputParserException( langchain.schema.output_parser.OutputParserException: Could not parse LLM output: `Action: sql_db_list_tables` Any Idea how to fix this ?

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I_kwDOIPDwls530Vzd

getting an error with DallEAPIWrapper

open

NONE

### System Info Hello! I got this error while trying to run code from [docs](https://python.langchain.com/docs/integrations/tools/dalle_image_generator). I have Python 3.11.3, openai 1.3.5 and langchain 0.0.340 . ``` You tried to access openai.Image, but this is no longer supported in openai>=1.0.0 - see the README at https://github.com/openai/openai-python for the API. You can run `openai migrate` to automatically upgrade your codebase to use the 1.0.0 interface. Alternatively, you can pin your installation to the old version, e.g. `pip install openai==0.28` A detailed migration guide is available here: https://github.com/openai/openai-python/discussions/742 ``` ### Who can help? _No response_ ### Information - [X] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction Write code from [docs](https://python.langchain.com/docs/integrations/tools/dalle_image_generator) and run it using python3. ```python from langchain.chains import LLMChain from langchain.llms import OpenAI from langchain.prompts import PromptTemplate from langchain.utilities.dalle_image_generator import DallEAPIWrapper llm = OpenAI(temperature=0.9) prompt = PromptTemplate( input_variables=["image_desc"], template="Generate a detailed prompt to generate an image based on the following description: {image_desc}", ) chain = LLMChain(llm=llm, prompt=prompt) image_url = DallEAPIWrapper().run(chain.run("halloween night at a haunted museum")) print(image_url) ``` ### Expected behavior get an image url

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PR_kwDOIPDwls5gVVDa

add more imports

closed

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