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Implements [langchain-ai#12115](langchain-ai#12115) Who can review? @baskaryan , @eyurtsev , @hwchase17 Integrated Stack Exchange API into Langchain, enabling access to diverse communities within the platform. This addition enhances Langchain's capabilities by allowing users to query Stack Exchange for specialized information and engage in discussions. The integration provides seamless interaction with Stack Exchange content, offering content from varied knowledge repositories. A notebook example and test cases were included to demonstrate the functionality and reliability of this integration. - Add StackExchange as a tool. - Add unit test for the StackExchange wrapper and tool. - Add documentation for the StackExchange wrapper and tool. If you have time, could you please review the code and provide any feedback as necessary! My team is welcome to any suggestions. --------- Co-authored-by: Yuval Kamani <[email protected]> Co-authored-by: Aryan Thakur <[email protected]> Co-authored-by: Manas1818 <[email protected]> Co-authored-by: aryan-thakur <[email protected]> Co-authored-by: Bagatur <[email protected]>
…Cypher/Neo4j schema (langchain-ai#13851) 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) ```
- **Description:** Updated to remove deprecated parameter penalty_alpha, and use string variation of prompt rather than json object for better flexibility. - **Issue:** the issue # it fixes (if applicable), - **Dependencies:** N/A - **Tag maintainer:** @eyurtsev - **Twitter handle:** @Symbldotai --------- Co-authored-by: toshishjawale <[email protected]> Co-authored-by: Harrison Chase <[email protected]>
…angchain-ai#13274) - **Description:** Update 5 pdf document loaders in `langchain.document_loaders.pdf`, to store a url in the metadata (instead of a temporary, local file path) if the user provides a web path to a pdf: `PyPDFium2Loader`, `PDFMinerLoader`, `PDFMinerPDFasHTMLLoader`, `PyMuPDFLoader`, and `PDFPlumberLoader` were updated. - The updates follow the approach used to update `PyPDFLoader` for the same behavior in langchain-ai#12092 - The `PyMuPDFLoader` changes required additional work in updating `langchain.document_loaders.parsers.pdf.PyMuPDFParser` to be able to process either an `io.BufferedReader` (from local pdf) or `io.BytesIO` (from online pdf) - The `PDFMinerPDFasHTMLLoader` change used a simpler approach since the metadata is assigned by the loader and not the parser - **Issue:** Fixes langchain-ai#7034 - **Dependencies:** None ```python # PyPDFium2Loader example: # old behavior >>> from langchain.document_loaders import PyPDFium2Loader >>> loader = PyPDFium2Loader('https://arxiv.org/pdf/1706.03762.pdf') >>> docs = loader.load() >>> docs[0].metadata {'source': '/var/folders/7z/d5dt407n673drh1f5cm8spj40000gn/T/tmpm5oqa92f/tmp.pdf', 'page': 0} # new behavior >>> from langchain.document_loaders import PyPDFium2Loader >>> loader = PyPDFium2Loader('https://arxiv.org/pdf/1706.03762.pdf') >>> docs = loader.load() >>> docs[0].metadata {'source': 'https://arxiv.org/pdf/1706.03762.pdf', 'page': 0} ```
…ain-ai#14029) - **Description:** use post field validation for `CohereRerank` - **Issue:** langchain-ai#12899 and langchain-ai#13058 - **Dependencies:** - **Tag maintainer:** @baskaryan --------- Co-authored-by: Bagatur <[email protected]>
- **Description:** Mask API key for ForeFrontAI LLM and associated unit tests - **Issue:** langchain-ai#12165 - **Dependencies:** N/A - **Tag maintainer:** @eyurtsev - **Twitter handle:** `__mmahmad__` I made the API key non-optional since linting required adding validation for None, but the key is required per documentation: https://python.langchain.com/docs/integrations/llms/forefrontai
- **Description:** Volc Engine MaaS serves as an enterprise-grade, large-model service platform designed for developers. You can visit its homepage at https://www.volcengine.com/docs/82379/1099455 for details. This change will facilitate developers to integrate quickly with the platform. - **Issue:** None - **Dependencies:** volcengine - **Tag maintainer:** @baskaryan - **Twitter handle:** @he1v3tica --------- Co-authored-by: lvzhong <[email protected]>
- **Description:** Added some of the more endpoints supported by serpapi that are not suported on langchain at the moment, like google trends, google finance, google jobs, and google lens - **Issue:** [Add support for many of the querying endpoints with serpapi langchain-ai#11811](langchain-ai#11811) --------- Co-authored-by: zushenglu <[email protected]> Co-authored-by: Erick Friis <[email protected]> Co-authored-by: Ian Xu <[email protected]> Co-authored-by: zushenglu <[email protected]> Co-authored-by: KevinT928 <[email protected]> Co-authored-by: Bagatur <[email protected]>
- **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:** langchain-ai#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. --------- Co-authored-by: What-Is-A-Username <[email protected]> Co-authored-by: Anika2000 <[email protected]> Co-authored-by: Jeremy Cheng <[email protected]> Co-authored-by: Harrison Chase <[email protected]>
- **Description:** Update the document for drop box loader + made the messages more verbose when loading pdf file since people were getting confused - **Issue:** langchain-ai#13952 - **Tag maintainer:** @baskaryan, @eyurtsev, @hwchase17, --------- Co-authored-by: Erick Friis <[email protected]>
# Description We implemented a simple tool for accessing the Merriam-Webster Collegiate Dictionary API (https://dictionaryapi.com/products/api-collegiate-dictionary). Here's a simple usage example: ```py from langchain.llms import OpenAI from langchain.agents import load_tools, initialize_agent, AgentType llm = OpenAI() tools = load_tools(["serpapi", "merriam-webster"], llm=llm) # Serp API gives our agent access to Google agent = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True ) agent.run("What is the english word for the german word Himbeere? Define that word.") ``` Sample output: ``` > Entering new AgentExecutor chain... I need to find the english word for Himbeere and then get the definition of that word. Action: Search Action Input: "English word for Himbeere" Observation: {'type': 'translation_result'} Thought: Now I have the english word, I can look up the definition. Action: MerriamWebster Action Input: raspberry Observation: Definitions of 'raspberry': 1. rasp-ber-ry, noun: any of various usually black or red edible berries that are aggregate fruits consisting of numerous small drupes on a fleshy receptacle and that are usually rounder and smaller than the closely related blackberries 2. rasp-ber-ry, noun: a perennial plant (genus Rubus) of the rose family that bears raspberries 3. rasp-ber-ry, noun: a sound of contempt made by protruding the tongue between the lips and expelling air forcibly to produce a vibration; broadly : an expression of disapproval or contempt 4. black raspberry, noun: a raspberry (Rubus occidentalis) of eastern North America that has a purplish-black fruit and is the source of several cultivated varieties —called also blackcap Thought: I now know the final answer. Final Answer: Raspberry is an english word for Himbeere and it is defined as any of various usually black or red edible berries that are aggregate fruits consisting of numerous small drupes on a fleshy receptacle and that are usually rounder and smaller than the closely related blackberries. > Finished chain. ``` # Issue This closes langchain-ai#12039. # Dependencies We added no extra dependencies. <!-- 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. --> --------- Co-authored-by: Lara <[email protected]> Co-authored-by: Harrison Chase <[email protected]>
# Description This PR implements Self-Query Retriever for MongoDB Atlas vector store. I've implemented the comparators and operators that are supported by MongoDB Atlas vector store according to the section titled "Atlas Vector Search Pre-Filter" from https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-stage/. Namely: ``` allowed_comparators = [ Comparator.EQ, Comparator.NE, Comparator.GT, Comparator.GTE, Comparator.LT, Comparator.LTE, Comparator.IN, Comparator.NIN, ] """Subset of allowed logical operators.""" allowed_operators = [ Operator.AND, Operator.OR ] ``` Translations from comparators/operators to MongoDB Atlas filter operators(you can find the syntax in the "Atlas Vector Search Pre-Filter" section from the previous link) are done using the following dictionary: ``` map_dict = { Operator.AND: "$and", Operator.OR: "$or", Comparator.EQ: "$eq", Comparator.NE: "$ne", Comparator.GTE: "$gte", Comparator.LTE: "$lte", Comparator.LT: "$lt", Comparator.GT: "$gt", Comparator.IN: "$in", Comparator.NIN: "$nin", } ``` In visit_structured_query() the filters are passed as "pre_filter" and not "filter" as in the MongoDB link above since langchain's implementation of MongoDB atlas vector store(libs\langchain\langchain\vectorstores\mongodb_atlas.py) in _similarity_search_with_score() sets the "filter" key to have the value of the "pre_filter" argument. ``` params["filter"] = pre_filter ``` Test cases and documentation have also been added. # Issue langchain-ai#11616 # Dependencies No new dependencies have been added. # Documentation I have created the notebook mongodb_atlas_self_query.ipynb outlining the steps to get the self-query mechanism working. I worked closely with [@Farhan-Faisal](https://github.com/Farhan-Faisal) on this PR. --------- Co-authored-by: Bagatur <[email protected]>
langchain-ai#13297) Response_if_no_docs_found is not implemented in ConversationalRetrievalChain for async code paths. Implemented it and added test cases Co-authored-by: Harrison Chase <[email protected]>
grammar correction <!-- 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. --> Co-authored-by: Harrison Chase <[email protected]>
**Description:** When using Vald, only insecure grpc connection was supported, so secure connection is now supported. In addition, grpc metadata can be added to Vald requests to enable authentication with a token. <!-- Thank you for contributing to LangChain! Replace this entire comment with: - **Description:** a description of the change, - **Issue:** the issue # it fixes (if applicable), - **Dependencies:** any dependencies required for this change, - **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below), - **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out! Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally. See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/extras` directory. If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. -->
**Description:** Added support for a Pandas DataFrame OutputParser with format instructions, along with unit tests and a demo notebook. Namely, we've added the ability to request data from a DataFrame, have the LLM parse the request, and then use that request to retrieve a well-formatted response. Within LangChain, it seamlessly integrates with language models like OpenAI's `text-davinci-003`, facilitating streamlined interaction using the format instructions (just like the other output parsers). This parser structures its requests as `<operation/column/row>[<optional_array_params>]`. The instructions detail permissible operations, valid columns, and array formats, ensuring clarity and adherence to the required format. For example: - When the LLM receives the input: "Retrieve the mean of `num_legs` from rows 1 to 3." - The provided format instructions guide the LLM to structure the request as: "mean:num_legs[1..3]". The parser processes this formatted request, leveraging the LLM's understanding to extract the mean of `num_legs` from rows 1 to 3 within the Pandas DataFrame. This integration allows users to communicate requests naturally, with the LLM transforming these instructions into structured commands understood by the `PandasDataFrameOutputParser`. The format instructions act as a bridge between natural language queries and precise DataFrame operations, optimizing communication and data retrieval. **Issue:** - langchain-ai#11532 **Dependencies:** No additional dependencies :) **Tag maintainer:** @baskaryan **Twitter handle:** No need. :) --------- Co-authored-by: Wasee Alam <[email protected]> Co-authored-by: Harrison Chase <[email protected]>
<!-- Thank you for contributing to LangChain! Replace this entire comment with: - **Description:** a description of the change, - **Issue:** the issue # it fixes (if applicable), - **Dependencies:** any dependencies required for this change, - **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below), - **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out! Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally. See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/extras` directory. If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. --> ### Description Hello, The [integration_test README](https://github.com/langchain-ai/langchain/tree/master/libs/langchain/tests) was indicating incorrect paths for the `.env.example` and `.env` files. `tests/.env.example` ->`tests/integration_tests/.env.example` While it’s a minor error, it could **potentially lead to confusion** for the document’s readers, so I’ve made the necessary corrections. Thank you!☺️ ### Related Issue - langchain-ai#2806
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