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pavelkashkarov
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…ist (langchain-ai#22817) Some minor fixes in the documentation: - ElasticsearchCache initilization is now correct - List of integrations for ES updated
The `ReAct` is used all across LangChain but it is not referenced properly. Added references to the original paper.
…#22821) - **Description:** Implement ZhipuAIEmbeddings interface, include: - The `embed_query` method - The `embed_documents` method refer to [ZhipuAI Embedding-2](https://open.bigmodel.cn/dev/api#text_embedding) --------- Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
…mple (langchain-ai#22833) Description: Adjusting the syntax for creating the vectorstore collection (in the case of automatic embedding computation) for the most idiomatic way to submit the stored secret name. Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
This PR adds the feature add Prem Template feature in ChatPremAI. Additionally it fixes a minor bug for API auth error when API passed through arguments.
…hain-ai#22764) ## Description Currently, the Qdrant integration relies on exceptions raised by [`get_collection` ](https://qdrant.tech/documentation/concepts/collections/#collection-info) to check if a collection exists. Using [`collection_exists`](https://qdrant.tech/documentation/concepts/collections/#check-collection-existence) is recommended to avoid missing any unhandled exceptions. This PR addresses this. ## Testing All integration and unit tests pass. No user-facing changes.
Updated ChatGroq doc string as per issue https://github.com/langchain-ai/langchain/issues/22296:"langchain_groq: updated docstring for ChatGroq in langchain_groq to match that of the description (in the appendix) provided in issue langchain-ai#22296. " Issue: This PR is in response to issue langchain-ai#22296, and more specifically the ChatGroq model. In particular, this PR updates the docstring for langchain/libs/partners/groq/langchain_groq/chat_model.py by adding the following sections: Instantiate, Invoke, Stream, Async, Tool calling, Structured Output, and Response metadata. I used the template from the Anthropic implementation and referenced the Appendix of the original issue post. I also noted that: `usage_metadata `returns none for all ChatGroq models I tested; there is no mention of image input in the ChatGroq documentation; unlike that of ChatHuggingFace, `.stream(messages)` for ChatGroq returned blocks of output. --------- Co-authored-by: lucast2021 <lucast2021@headroyce.org> Co-authored-by: Bagatur <baskaryan@gmail.com>
…angchain-ai#22721) Anthropic tool results can contain image data, which are typically represented with content blocks having `"type": "image"`. Currently, these content blocks are passed as-is as human/user messages to Anthropic, which raises BadRequestError as it expects a tool_result block to follow a tool_use. Here we update ChatAnthropic to nest the content blocks inside a tool_result content block. Example: ```python import base64 import httpx from langchain_anthropic import ChatAnthropic from langchain_core.messages import AIMessage, HumanMessage, ToolMessage from langchain_core.pydantic_v1 import BaseModel, Field # Fetch image image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" image_data = base64.b64encode(httpx.get(image_url).content).decode("utf-8") class FetchImage(BaseModel): should_fetch: bool = Field(..., description="Whether an image is requested.") llm = ChatAnthropic(model="claude-3-sonnet-20240229").bind_tools([FetchImage]) messages = [ HumanMessage(content="Could you summon a beautiful image please?"), AIMessage( content=[ { "type": "tool_use", "id": "toolu_01Rn6Qvj5m7955x9m9Pfxbcx", "name": "FetchImage", "input": {"should_fetch": True}, }, ], tool_calls=[ { "name": "FetchImage", "args": {"should_fetch": True}, "id": "toolu_01Rn6Qvj5m7955x9m9Pfxbcx", }, ], ), ToolMessage( name="FetchImage", content=[ { "type": "image", "source": { "type": "base64", "media_type": "image/jpeg", "data": image_data, }, }, ], tool_call_id="toolu_01Rn6Qvj5m7955x9m9Pfxbcx", ), ] llm.invoke(messages) ``` Trace: https://smith.langchain.com/public/d27e4fc1-a96d-41e1-9f52-54f5004122db/r
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **PR title**: [community] add chat model llamacpp
- **PR message**:
- **Description:** This PR introduces a new chat model integration with
llamacpp_python, designed to work similarly to the existing ChatOpenAI
model.
+ Work well with instructed chat, chain and function/tool calling.
+ Work with LangGraph (persistent memory, tool calling), will update
soon
- **Dependencies:** This change requires the llamacpp_python library to
be installed.
@baskaryan
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
…n-ai#22888) [Fixed typo](docs: Fix typo in tutorial about structured data extraction)
…vant score,relevant score> pairs. (langchain-ai#22578) - **Description:** Some of the Cross-Encoder models provide scores in pairs, i.e., <not-relevant score (higher means the document is less relevant to the query), relevant score (higher means the document is more relevant to the query)>. However, the `HuggingFaceCrossEncoder` `score` method does not currently take into account the pair situation. This PR addresses this issue by modifying the method to consider only the relevant score if score is being provided in pair. The reason for focusing on the relevant score is that the compressors select the top-n documents based on relevance. - **Issue:** langchain-ai#22556 - Please also refer to this [comment](UKPLab/sentence-transformers#568 (comment))
- rerun to remove warnings following langchain-ai#23363 - `raise` -> `return`
…torstore history with a token buffer (langchain-ai#22155) **langchain: ConversationVectorStoreTokenBufferMemory** -**Description:** This PR adds ConversationVectorStoreTokenBufferMemory. It is similar in concept to ConversationSummaryBufferMemory. It maintains an in-memory buffer of messages up to a preset token limit. After the limit is hit timestamped messages are written into a vectorstore retriever rather than into a summary. The user's prompt is then used to retrieve relevant fragments of the previous conversation. By persisting the vectorstore, one can maintain memory from session to session. -**Issue:** n/a -**Dependencies:** none -**Twitter handle:** Please no!!! - [X] **Add tests and docs**: I looked to see how the unit tests were written for the other ConversationMemory modules, but couldn't find anything other than a test for successful import. I need to know whether you are using pytest.mock or another fixture to simulate the LLM and vectorstore. In addition, I would like guidance on where to place the documentation. Should it be a notebook file in docs/docs? - [X] **Lint and test**: I am seeing some linting errors from a couple of modules unrelated to this PR. If no one reviews your PR within a few days, please @-mention one of baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17. --------- Co-authored-by: Lincoln Stein <lstein@gmail.com> Co-authored-by: isaac hershenson <ihershenson@hmc.edu>
fixed potential `IndexError: list index out of range` in case there is
no title
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: 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/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
**Description:** Add support passing extra_body parameter Some OpenAI compatible API's have additional parameters (for example [vLLM](https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html#extra-parameters)) that can be passed thought `extra_body`. Same question in openai/openai-python#767 <!-- If no one reviews your PR within a few days, please @-mention one of baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17. -->
…ain-ai#23516) - **Description:** This PR fixes an issue with SAP HANA Cloud QRC03 version. In that version the number to indicate no length being set for a vector column changed from -1 to 0. The change in this PR support both behaviours (old/new). - **Dependencies:** No dependencies have been introduced. - **Tests**: The change is covered by previous unit tests.
Thank you for contributing to LangChain! - [X] **PR title**: "community: fix code example in ZenGuard docs" - [X] **PR message**: - **Description:** corrected the docs by indicating in the code example that the tool accepts a list of prompts instead of just one - [X] **Lint and test**: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. See contribution guidelines for more: https://python.langchain.com/docs/contributing/ Thank you for review --------- Co-authored-by: Baur <baur.krykpayev@gmail.com>
- **Description:** Update Tongyi ChatModel rich docstring - **Issue:** the issue langchain-ai#22296
…angchain-ai#19359) ## Description Created a helper method to make vector search indexes via client-side pymongo. **Recent Update** -- Removed error suppressing/overwriting layer in favor of letting the original exception provide information. ## ToDo's - [x] Make _wait_untils for integration test delete index functionalities. - [x] Add documentation for its use. Highlight it's experimental - [x] Post Integration Test Results in a screenshot - [x] Get review from MongoDB internal team (@ShaneHarvey, @blink1073 , @NoahStapp , @caseyclements) - [x] **Add tests and docs**: If you're adding a new integration, please include 1. Added new integration tests. Not eligible for unit testing since the operation is Atlas Cloud specific. 2. an example notebook showing its use. It lives in `docs/docs/integrations` directory.  - [x] **Lint and test**: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. See contribution guidelines for more: https://python.langchain.com/docs/contributing/
…ngchain-ai#23256) Description: update agent and chains modules Pydantic root_validators. Issue: the issue langchain-ai#22819 --------- Co-authored-by: gongwn1 <gongwn1@lenovo.com> Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com> Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
Added missed docstrings. Formatted docstrings to the consistent form.
Added missed docstrings. Formatted docstrings to the consistent form.
- Updates chat few shot prompt tutorial to show off a more cohesive example - Fix async Chromium loader guide - Fix Excel loader install instructions - Reformat Html2Text page - Add install instructions to Azure OpenAI embeddings page - Add missing dep install to SQL QA tutorial @baskaryan
Added missed docstrings. Formatted docstrings to the consistent form.
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