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https://api.github.com/repos/langchain-ai/langchain/issues/11706
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| 1,939,853,669 |
I_kwDOIPDwls5zn9Fl
| 11,706 |
Issue: Invalid prompt schema: Missing value for input [GraphCypherQAChain][cypherPrompt]
|
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[] | null | 2 | 2023-10-12T12:12:12 | 2023-10-16T11:55:48 | 2023-10-16T11:55:10 |
NONE
| null |
### Issue you'd like to raise.
Having issue with cypherPrompt in GraphCypherQAChain .
I am trying to use GraphCypherQAChain and Bedrock to generate an answer from Neo4j database.
I am using local Neo4j Movies database and the code example from: https://js.langchain.com/docs/modules/chains/additional/cypher_chain
Here is the code:
```
const { PromptTemplate } = require("langchain/prompts");
const { GraphCypherQAChain } = require("langchain/chains/graph_qa/cypher");
const { Neo4jGraph } = require("langchain/graphs/neo4j_graph");
const { Bedrock } = require("langchain/llms/bedrock");
const url = "bolt://localhost:7687";
const username = "neo4j";
const password = "***";
const aiModelId = "ai21.j2-mid-v1";
module.exports.chainRun = async (event) => {
const graph = await Neo4jGraph.initialize({ url, username, password });
const model = new Bedrock({
model: aiModelId,
region: "***",
maxTokens: 200,
temperature: 0.5,
stopSequences: ["\n\nHuman:"],
credentials: {
accessKeyId: "***",
secretAccessKey: "***",
},
});
await graph.query(
"CREATE (a:Actor {name:'Bruce Willis'})" +
"-[:ACTED_IN]->(:Movie {title: 'Pulp Fiction'})"
);
const cypherTemplate = `Task:Generate Cypher statement to query a graph database.
Instructions:
Use only the provided relationship types and properties in the schema.
Do not use any other relationship types or properties that are not provided.
Schema:
{schema}
Note: Do not include any explanations or apologies in your responses.
Do not respond to any questions that might ask anything else than for you to construct a Cypher statement.
Do not include any text except the generated Cypher statement.
Follow these Cypher example when Generating Cypher statements:
# How many actors played in Top Gun?
MATCH (m:Movie {title:"Top Gun"})<-[:ACTED_IN]-()
RETURN count(*) AS result
The question is:
{question}`;
const cypherPrompt = new PromptTemplate({
template: cypherTemplate,
inputVariables: ["schema", "question"],
});
const chain = GraphCypherQAChain.fromLLM({
llm: model,
graph,
cypherPrompt,
});
const res = await chain.run("Who played in Pulp Fiction?");
return {
statusCode: 200,
body: JSON.stringify(res),
};
};
```
The error that I am getting:
<img width="926" alt="Screenshot 2023-10-12 at 2 08 52 PM" src="https://github.com/langchain-ai/langchain/assets/42175061/3f40f1e1-09fa-4ebf-ae29-35f6dc28e0b3">
If I run that query directly it returns result, also if I remove that part from cypher prompt there are no issues.
Can you help me to find what is the issue?
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5coAuC
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SingleStoreDBChatMessageHistory: Add singlestoredb support for ChatMessageHistory
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CONTRIBUTOR
| null |
**Description**
- Added the `SingleStoreDBChatMessageHistory` class that inherits `BaseChatMessageHistory` and allows to use of a SingleStoreDB database as a storage for chat message history.
- Added integration test to check that everything works (requires `singlestoredb` to be installed)
- Added notebook with usage example
- Removed custom retriever for SingleStoreDB vector store (as it is useless)
|
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PR_kwDOIPDwls5cn09T
| 11,703 |
Add YandexGPT LLM and Chat model
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[] | null | 5 | 2023-10-12T11:19:38 | 2023-10-17T03:30:08 | 2023-10-17T03:30:08 |
CONTRIBUTOR
| null |
<!-- 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:** Introducing an ability to work with the [YandexGPT](https://cloud.yandex.com/en/services/yandexgpt) language model.
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PR_kwDOIPDwls5cnzLw
| 11,702 |
Fix default impl of aparse_result
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[] | null | 1 | 2023-10-12T11:14:25 | 2023-10-12T13:14:00 | 2023-10-12T13:13:59 |
COLLABORATOR
| null |
Should delegate to parse_result, not to aparse, as parse_result is a method that some output parsers override
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
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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_kwDOIPDwls5zncxN
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Allow API_KEY to be optional when API_BASE is selected for OpenAI.
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| false | null |
[] | null | 3 | 2023-10-12T10:47:50 | 2023-10-13T10:01:13 | null |
NONE
| null |
### Feature request
I propose a feature that allows the **API key** to be set as an optional **parameter** when changing the `openai_api_base` in LangChain's implementation of OpenAI. This feature would make it possible to set the API key to an empty string or any arbitrary string, similar to how it's done in **LiteLLM**.
Relevant resources:
- LangChain OpenAI: [GitHub Repo](https://github.com/langchain-ai/langchain)
- LiteLLM Proxy: [Documentation](https://docs.litellm.ai/docs/proxy_server#create-a-proxy-for-multiple-llms)
### Motivation
The motivation for this proposal is to facilitate testing and development scenarios where the `openai_api_base` is pointed to a local or mock server that doesn't require an API key. Currently, when the API key is set to empty or an arbitrary string, LangChain OpenAI throws an `AuthenticationError`. This behavior is frustrating when trying to test or develop without a valid OpenAI API key.
### Your contribution
I am willing to help implement this feature and submit a PR, but I would appreciate guidance from the maintainers or community to ensure the changes are made correctly and in line with the project's standards and practices.
|
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I_kwDOIPDwls5znAoL
| 11,699 |
Agent stopped due to iteration limit or time limit.
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[] | null | 4 | 2023-10-12T09:38:36 | 2023-10-12T12:56:48 | null |
NONE
| null |
### Issue you'd like to raise.
如何获取打印的日志呢？不知道怎么关闭这些打印的日志
### Suggestion:
_No response_
|
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| 1,939,531,951 |
I_kwDOIPDwls5zmuiv
| 11,698 |
LocalAI embeddings require OpenAI key
|
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[] | null | 0 | 2023-10-12T08:58:04 | 2023-10-12T09:00:12 | null |
CONTRIBUTOR
| null |
### System Info
Current master, all python versions
### Who can help?
@hwchase17
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The LocalAI embeddings class required the openai api key to be set even though this might not be required by the locally hosted server: https://github.com/langchain-ai/langchain/blob/44da27c07b2bd0ccac355c8236a3ab1dd26870eb/libs/langchain/langchain/embeddings/localai.py#L200
Initializing the class like this fails:
```
from langchain.embeddings.localai import LocalAIEmbeddings
LocalAIEmbeddings(openai_api_key=None)
# Did not find openai_api_key, please add an environment variable `OPENAI_API_KEY` which contains it, or pass
`openai_api_key` as a named parameter. (type=value_error)
```
### Expected behavior
it should work fine, the key is not required for the class to function.
|
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| 1,939,487,505 |
I_kwDOIPDwls5zmjsR
| 11,697 |
Understanding the Role and Functionality of Metadata and Page_Content in Langchain
|
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[] | null | 1 | 2023-10-12T08:31:39 | 2023-10-12T08:37:56 | null |
NONE
| null |
### Issue you'd like to raise.
Dear Langchain Developers,
Thank you very much for developing Langchain.
I am writing to seek clarification on a few aspects of Langchain that I find intriguing. Specifically, I am interested in understanding more about the role and functionality of 'metadata' and 'page_content' within the system.
Could you kindly explain what the purpose of these two components is within Langchain? I am curious to know how they contribute to the overall functionality of the system. Additionally, I am interested in understanding how these elements are stored in vectors.
Furthermore, when a user question is used to match the most suitable text chunk, how does the system process this? After the user question is converted into a vector, does the system search the 'metadata' or the 'page_content'?
Your insights would be greatly appreciated. I am sure that understanding these aspects will enhance my appreciation of Langchain even more. Thank you in advance for your time and assistance.
Best regards,
### Suggestion:
_No response_
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| 1,939,352,699 |
I_kwDOIPDwls5zmCx7
| 11,696 |
Issue: GPT4AllEmbeddings - dont compare with - OpenAIEmbeddings
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[] | null | 1 | 2023-10-12T07:12:54 | 2023-10-12T07:17:56 | null |
NONE
| null |
### Issue you'd like to raise.
How to get the same values of the Float numbers generated as embeddings -
1/ Am comparing values generated from OpenAI - from langchain.embeddings.openai import OpenAIEmbeddings
embedding = OpenAIEmbeddings()
2/ comparing with the values generated from -- gpt4all
from langchain.embeddings import GPT4AllEmbeddings
gpt4all_embd = GPT4AllEmbeddings()
```python
def get_embeddings_gpt4all():
"""
"""
from langchain.embeddings import GPT4AllEmbeddings
gpt4all_embd = GPT4AllEmbeddings()
#text = "This is a test document."
text = "i like dogs"
query_result = gpt4all_embd.embed_query(text)
print("--query_result-",type(query_result))
print("--query_result-",len(query_result))
print("--query_result-",query_result[0:10])
print("--query_result-",query_result[11])
print("--query_result-",query_result[15])
doc_result = gpt4all_embd.embed_documents([text])
print("--doc_result-",type(doc_result)) # List of LIST --
print("--doc_result-",len(doc_result)) # 1
print("--doc_result-",type(doc_result[0])) # List of Floats --
print("--doc_result-",len(doc_result[0])) # 384
```
- terminal prints as below --
```
Found model file at C:\\\\Users\\\\rohit.dhankar\\\\.cache\\\\gpt4all\\ggml-all-MiniLM-L6-v2-f16.bin
--query_result- <class 'list'>
--query_result- 384
--query_result- [-0.04856949672102928, -0.04622294753789902, 0.060556311160326004, 0.043546244502067566, -0.05463598296046257, -0.004457559902220964, 0.05100328475236893, -0.02644169144332409, 0.08410052955150604, 0.05480821430683136]
--query_result- -0.07515878230333328
--query_result- 0.05370383337140083
--doc_result- <class 'list'>
--doc_result- 1
--doc_result- <class 'list'>
--doc_result- 384
```
### Suggestion:
_No response_
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| 1,939,343,638 |
I_kwDOIPDwls5zmAkW
| 11,695 |
agent是否支持llama2系列及其衍生模型
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[] | null | 2 | 2023-10-12T07:07:11 | 2023-10-12T07:44:50 | 2023-10-12T07:44:50 |
NONE
| null |
### Issue you'd like to raise.
_No response_
### Suggestion:
_No response_
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APIError: HTTP code 200 from API ("{\"rate_limit_usage\": {\)
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[] | null | 7 | 2023-10-12T04:24:29 | 2023-10-12T07:43:45 | 2023-10-12T07:43:44 |
NONE
| null |
### System Info
APIError: HTTP code 200 from API ("{\"rate_limit_usage\": {\)
File "[...]/anaconda3/envs/sjj_env/lib/python3.10/site-packages/streamlit/runtime/scriptrunner/script_runner.py", line 541, in _run_script
exec(code, module.__dict__)
File "[...]/gitlab/run_finQA_webUI.py", line 158, in <module>
generate_answer(clicked)
File "[...]/gitlab/run_finQA_webUI.py", line 123, in generate_answer
response = rq_agent.finQAinConversation(user_query, callbacks=[stream_handler])
File "[...]/gitlab/report_query/report_query.py", line 187, in finQAinConversation
response = self._convR_qa(query, callbacks=callbacks)
File "[...]/anaconda3/envs/sjj_env/lib/python3.10/site-packages/langchain/chains/base.py", line 306, in __call__
raise e
File "[...]/anaconda3/envs/sjj_env/lib/python3.10/site-packages/langchain/chains/base.py", line 300, in __call__
self._call(inputs, run_manager=run_manager)
File "[...]/gitlab/report_query/custom_intention_chain.py", line 131, in _call
answer = self.combine_docs_chain.run(
File "[...]/anaconda3/envs/sjj_env/lib/python3.10/site-packages/langchain/chains/base.py", line 506, in run
return self(kwargs, callbacks=callbacks, tags=tags, metadata=metadata)[
File "[...]/anaconda3/envs/sjj_env/lib/python3.10/site-packages/langchain/chains/base.py", line 306, in __call__
raise e
File "[...]/anaconda3/envs/sjj_env/lib/python3.10/site-packages/langchain/chains/base.py", line 300, in __call__
self._call(inputs, run_manager=run_manager)
File "[...]/anaconda3/envs/sjj_env/lib/python3.10/site-packages/langchain/chains/combine_documents/base.py", line 119, in _call
output, extra_return_dict = self.combine_docs(
File "[...]/anaconda3/envs/sjj_env/lib/python3.10/site-packages/langchain/chains/combine_documents/stuff.py", line 171, in combine_docs
return self.llm_chain.predict(callbacks=callbacks, **inputs), {}
File "[...]/anaconda3/envs/sjj_env/lib/python3.10/site-packages/langchain/chains/llm.py", line 257, in predict
return self(kwargs, callbacks=callbacks)[self.output_key]
File "[...]/anaconda3/envs/sjj_env/lib/python3.10/site-packages/langchain/chains/base.py", line 306, in __call__
raise e
File "[...]/anaconda3/envs/sjj_env/lib/python3.10/site-packages/langchain/chains/base.py", line 300, in __call__
self._call(inputs, run_manager=run_manager)
File "[...]/anaconda3/envs/sjj_env/lib/python3.10/site-packages/langchain/chains/llm.py", line 93, in _call
response = self.generate([inputs], run_manager=run_manager)
File "[...]/anaconda3/envs/sjj_env/lib/python3.10/site-packages/langchain/chains/llm.py", line 103, in generate
return self.llm.generate_prompt(
File "[...]/anaconda3/envs/sjj_env/lib/python3.10/site-packages/langchain/chat_models/base.py", line 469, in generate_prompt
return self.generate(prompt_messages, stop=stop, callbacks=callbacks, **kwargs)
File "[...]/anaconda3/envs/sjj_env/lib/python3.10/site-packages/langchain/chat_models/base.py", line 359, in generate
raise e
File "[...]/anaconda3/envs/sjj_env/lib/python3.10/site-packages/langchain/chat_models/base.py", line 349, in generate
self._generate_with_cache(
File "[...]/anaconda3/envs/sjj_env/lib/python3.10/site-packages/langchain/chat_models/base.py", line 501, in _generate_with_cache
return self._generate(
File "[...]/anaconda3/envs/sjj_env/lib/python3.10/site-packages/langchain/chat_models/openai.py", line 357, in _generate
return _generate_from_stream(stream_iter)
File "[...]/anaconda3/envs/sjj_env/lib/python3.10/site-packages/langchain/chat_models/base.py", line 60, in _generate_from_stream
for chunk in stream:
File "[...]/anaconda3/envs/sjj_env/lib/python3.10/site-packages/langchain/chat_models/openai.py", line 326, in _stream
for chunk in self.completion_with_retry(
File "[...]/anaconda3/envs/sjj_env/lib/python3.10/site-packages/openai/api_resources/abstract/engine_api_resource.py", line 166, in <genexpr>
return (
File "[...]/anaconda3/envs/sjj_env/lib/python3.10/site-packages/openai/api_requestor.py", line 693, in <genexpr>
self._interpret_response_line(
File "[...]/anaconda3/envs/sjj_env/lib/python3.10/site-packages/openai/api_requestor.py", line 755, in _interpret_response_line
raise error.APIError(
Seems like this error just appears starting from oct 12. The answer can be directly generated with an HTTP code 200 in Openai response, but an error appears at the end of generation. @hwchase17
### 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
answer = self.combine_docs_chain.run(
input_documents=docs, callbacks=_run_manager.get_child(), **new_inputs
)
### Expected behavior
No error happens.
|
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| 1,939,013,958 |
PR_kwDOIPDwls5clQvx
| 11,693 |
fix: incorrect arguments in clickhouse docstring
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[] | null | 1 | 2023-10-12T01:32:21 | 2023-10-12T01:41:21 | 2023-10-12T01:41:21 |
CONTRIBUTOR
| null |
- **Description:** fix docstring for clickhouse,
- **Issue:** N/A,
- **Dependencies:** none,
- **Tag maintainer:** N/A,
- **Twitter handle:**
<!-- 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/hwchase17/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_kwDOIPDwls5clQKT
| 11,692 |
Improve InfoSQLDatabaseTool because it doesn't work well with qwen
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NONE
| null |
1. Remove single quotation marks from the string, because qwen may return a string within single quotation marks.
2. Make the splition more robust, because qwen may return a list without blank after comma.
|
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I_kwDOIPDwls5zkvZ8
| 11,691 |
text split by llm?
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[] | null | 4 | 2023-10-12T01:29:23 | 2023-11-20T15:01:02 | null |
CONTRIBUTOR
| null |
### Feature request
Would it make sense to use an llm to split a large document into smaller documents that have complete context?
### Motivation
My best guess is that most of the existing splitters may drop context ?
### Your contribution
Could try
|
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PR_kwDOIPDwls5clLLL
| 11,690 |
add plan execute cookbook
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[] | null | 1 | 2023-10-12T01:03:07 | 2023-10-12T01:03:19 | 2023-10-12T01:03:13 |
COLLABORATOR
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PR_kwDOIPDwls5ck75Q
| 11,689 |
fix tool async
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Fix chains/loading.py error messages
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CONTRIBUTOR
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- **Description:** make the error messages consistent in chains/loading.py
- **Dependencies:** None
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PR_kwDOIPDwls5ckq8y
| 11,687 |
Adds GetByID Elasticsearch store strategy
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CONTRIBUTOR
| null |
- **Description:**
- Add GetByID strategy to allow retrieving exact documents when needed
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malformed.
- **Dependencies:** None
- **Twitter handle:** Void
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LLaMA2 SQL Chat cookbook
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PR_kwDOIPDwls5ckj1u
| 11,684 |
Feat: Add support for OpenAI function call
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CONTRIBUTOR
| null |
- **Description:** This PR proposes a simple implementation to add support for the Open AI `function_call` parameter and modifies the `with_function` for the `plan` method of the `ChatOpenAI`class accordingly.
- **Issue:** Right now, there is no way to force the model to generate a function call as enabled by the Open AI API.
@baskaryan
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I_kwDOIPDwls5zj12m
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A way to use csv_agent to simply return JSON
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NONE
| null |
### Feature request
At the moment the agent spends additional rounds of agent <> LLM to format the JSON data I request to be in a sentence format. Is there a way to avoid this?
### Motivation
If the user is asking for JSON the agent should provide JSON and not a properly formatted sentence(that costs more token>money)
### Your contribution
Test, Deploy to production workload
|
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PR_kwDOIPDwls5ckbCM
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Add `type` to Agent actions
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[] | null | 1 | 2023-10-11T21:27:53 | 2023-10-12T01:33:25 | 2023-10-12T01:33:25 |
COLLABORATOR
| null |
Add `type` to agent actions.
|
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PR_kwDOIPDwls5cka0p
| 11,681 |
evaluation criteria
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[] | null | 5 | 2023-10-11T21:27:11 | 2023-10-17T15:31:18 | 2023-10-17T04:05:08 |
COLLABORATOR
| null |
the updated value was:
` Criteria.MISOGYNY: "Is the submission misogynistic? If so, respond Y." `
The " If so, respond Y." should not be here. This sub-string is not presented in any other criteria and should not be presented here.
I also added a synonym to "misogynistic" as it done in many other criteria.
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PR_kwDOIPDwls5ckNYA
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add input type for convo retrieval chain
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PR_kwDOIPDwls5ckMZF
| 11,678 |
include_df_in_prompt should be True or False and not None
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[] | null | 3 | 2023-10-11T20:49:24 | 2023-11-13T07:10:58 | 2023-11-13T07:10:57 |
CONTRIBUTOR
| null |
Replace this entire comment with:
- **Description:** `include_df_in_prompt` does now also work with `False` instade of `None`
- **Issue:** #11677
- **Dependencies:** None
- **Tag maintainer:**
- **Twitter handle:**
<!-- 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_kwDOIPDwls5zjj0P
| 11,677 |
include_df_in_prompt should be compared as boolean value and not None
|
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[] | null | 1 | 2023-10-11T20:47:09 | 2023-12-22T07:34:59 | 2023-12-22T07:34:59 |
CONTRIBUTOR
| null |
### System Info
Version `0.0.312`
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
# Description
The parameter `include_df_in_prompt` on the method `create_pandas_dataframe_agent` is a boolean value which should be eigther `True` or `False`.
If you specify your own suffix in the create_pandas_dataframe_agent method it results in an error saying that the variable include_df_in_prompt should be False.
## For example
```python
agent = create_pandas_dataframe_agent(ChatOpenAI(temperature=0),df, AgentType.OPENAI_FUNCTIONS, suffix=sufix)
```
Results in
```
ValueError: If suffix is specified, include_df_in_prompt should not be.
```
This is also the correct behavior. However, if you set `include_df_in_prompt` to `False`, the error still occurs.
## For example
```python
agent = create_pandas_dataframe_agent(ChatOpenAI(temperature=0),df, AgentType.OPENAI_FUNCTIONS, suffix=sufix, include_df_in_prompt=False)
```
But everything just works fine if I use `None` instade of `False`
## For example
```python
agent = create_pandas_dataframe_agent(ChatOpenAI(temperature=0),df, AgentType.OPENAI_FUNCTIONS, suffix=sufix, include_df_in_prompt=None)
``
### Expected behavior
It should also work when I use `False` instade of `None`
|
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PR_kwDOIPDwls5ckHKo
| 11,676 |
Update kay.ipynb
|
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[] | null | 2 | 2023-10-11T20:33:31 | 2023-10-11T21:02:12 | 2023-10-11T21:02:12 |
CONTRIBUTOR
| null |
Fixed title display
|
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PR_kwDOIPDwls5cj0ni
| 11,675 |
Fix runnable docs link
|
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[] | null | 1 | 2023-10-11T19:43:46 | 2023-10-11T20:11:25 | 2023-10-11T20:11:24 |
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| null | null |
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PR_kwDOIPDwls5cjw6q
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Add COBOL parser and splitter
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CONTRIBUTOR
| null |
- **Description:** Add COBOL parser and splitter
- **Issue:** n/a
- **Dependencies:** n/a
- **Tag maintainer:** @baskaryan
- **Twitter handle:** erhartford
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| 1,938,566,368 |
I_kwDOIPDwls5zjCzg
| 11,673 |
Integrating `create_citation_fuzzy_match_chain` with `ConversationalRetrievalChain`
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NONE
| null |
### Feature request
Similar to the discussion in this thread https://github.com/langchain-ai/langchain/issues/7239, I would like to have a way to show inline citations in QA tasks.
Specially, I haven't had a whole lot of success integrating `create_citation_fuzzy_match_chain` with `ConversationalRetrievalChain`
### Motivation
The point of this change is to enable QA based chains (e.g. ConversationalRetrievalChain) to easily do inline citations, and surface the citations as part of metadata fields
### Your contribution
TBD
|
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PR_kwDOIPDwls5cjso7
| 11,672 |
Track ChatFireworks time to first_token
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I_kwDOIPDwls5zi_eJ
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DELETE
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NONE
| null |
### Issue you'd like to raise.
Similar to the discussion in this thread https://github.com/langchain-ai/langchain/issues/7239, I would like to have a way to show inline citations in QA tasks.
Specially, I haven't had a whole lot of success integrating `create_citation_fuzzy_match_chain` with `ConversationalRetrievalChain`
### Suggestion:
_No response_
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PR_kwDOIPDwls5cjD6l
| 11,670 |
Fix output types for BaseChatModel
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COLLABORATOR
| null |
* Should use non chunked messages for Invoke/Batch
* After this PR, stream output type is not represented, do we want to use the union?
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added trafilatura to webbaseloader
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NONE
| null |
- **Description:** The WebBaseLoader has some problems when it came to handling page content properly. These issues ranged from unwanted or missing spaces, to unwanted characters like dots, multiple newlines, and even the addition of header, footer, ads, and other unwanted data in the page content. It was a roadblock that needed a solution. I made changes to extract the page_content using another python package named as trafilatura, which handles all the issues I described earlier. The BeautifulSoup package is still used for metadata extraction, just the text part is extracted using trafilatura.,
- **Dependencies:** trafilatura,
- **Tag maintainer:** @baskaryan , @eyurtsev , @hwchase17 ,
- **Twitter handle:** abrehmaaan
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| 1,938,174,193 |
I_kwDOIPDwls5zhjDx
| 11,668 |
Add async streaming for Bedrock
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[] | null | 2 | 2023-10-11T16:06:06 | 2023-10-19T21:08:09 | null |
NONE
| null |
### Feature request
Currently, the Bedrock `chat_model` does not support async streaming, which is odd, as the previous [PR](https://github.com/langchain-ai/langchain/pull/10393/files#diff-9874347f7fa335df661ff4089b0922b3214e08a92e9879610424522f806358f7R62) included it; seems that the async method was not added properly.
### Motivation
Without async streaming you cannot pretty much build chatbot infra.
### Your contribution
Not sure, possibly just copying-pasting what was [here](https://github.com/langchain-ai/langchain/pull/10393/files#diff-9874347f7fa335df661ff4089b0922b3214e08a92e9879610424522f806358f7R62) already?
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PR_kwDOIPDwls5ciLxy
| 11,667 |
Fix typo in baidu_qianfan_endpoint.ipynb
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[] | null | 1 | 2023-10-11T15:40:24 | 2023-10-11T20:01:19 | 2023-10-11T20:01:19 |
CONTRIBUTOR
| null |
enviroment -> environment
<!-- 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,
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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:
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If you're adding a new integration, please include:
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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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| 1,938,100,043 |
I_kwDOIPDwls5zhQ9L
| 11,666 |
ConversationalRetrievalChain Vectorstore cannot apply filter to a key with given multiple values
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[] | null | 4 | 2023-10-11T15:29:24 | 2023-10-13T14:34:38 | null |
NONE
| null |
### System Info
on Ubuntu with Python 3.11.5, Langchain 0.0.307
### Who can help?
@hwchase17
### 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
What my code basically does is returns an answer to a given question using conversational retrieval chain with a qdrant vectorstore that stores publications. I want the chain to use only the documents published in specified years.
1. Create vectorstore
qdrant_client = QdrantClient(QDRANT_HOST, port=int(QDRANT_PORT))
retriever = Qdrant(client=qdrant_client, collection_name=QDRANT_COLLECTION, embeddings=get_encoder(ENCODER))
2. Create chain:
rag_chain = ConversationalRetrievalChain.from_llm(llm=llm,
retriever = retriever.as_retriever(search_kwargs={"filter": {"year": ["2009","2010"]}),
return_source_documents=return_source_documents,
verbose=verbose)
3. When I run the chain it retrieves no documents even though it should.
### Expected behavior
The chain should return documents related to a given question. When I checked the implementation of qdrant.py under langchain vectorstores I saw that _qdrant_filter_from_dict function makes a "must" match; however in my case I need a "should" match since no document can have more than one publication date.
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| 1,937,873,604 |
I_kwDOIPDwls5zgZrE
| 11,665 |
`Chroma.from_documents` adds docs to same collection creating duplicates
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[] | null | 5 | 2023-10-11T14:05:47 | 2023-10-11T20:53:10 | 2023-10-11T20:52:06 |
NONE
| null |
### System Info
langchain==0.0.306
chromadb==0.4.13
Python 3.11.5
### Who can help?
@hwchase17 @atroyn
### 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
```python
from langchain.vectorstores import Chroma
from langchain.docstore.document import Document
from langchain.embeddings import GPT4AllEmbeddings
docs = [
Document(page_content="This is a demo document"),
Document(page_content="This is another demo document"),
]
other_docs = [
Document(page_content="Unrelated document"),
Document(page_content="Another unrelated document"),
]
# Create two (independent) vector stores
vectorstore = Chroma.from_documents(documents=docs, embedding=GPT4AllEmbeddings())
other_vectorstore = Chroma.from_documents(documents=other_docs, embedding=GPT4AllEmbeddings())
# Expect two stores to be independent
assert len(vectorstore.similarity_search("document", k=999)) == len(docs) # OK
assert len(other_vectorstore.similarity_search("document", k=999)) == len(other_docs) # FAILS
# Documents from the first vector store are actually retrieved in the `other_vectorstore`
print(other_vectorstore.similarity_search("document"))
# Actually the two vector stores contain the union of `docs` and `other_docs`
assert len(vectorstore.get()["documents"]) == 2 # FAILS
assert len(other_vectorstore.get()["documents"]) == 2 # FAILS
```
### Expected behavior
When I initialize two separate Chroma in-memory vectorstores with `Chroma.from_documents` using two distinct groups of documents, the vector store that was initialized last will contain the union of the two groups of documents.
In summary, running this code:
```python
vectorstore = Chroma.from_documents(documents=documents, …)
other_vectorstore = Chroma.from_documents(documents=other_documents, …)
```
the `other_vectorstore` will contain both `documents` and `other_documents`.
---
It seems that the two vectorstores are sharing the same chroma client & collection.
This could be a side effect of commit https://github.com/langchain-ai/langchain/commit/822cdb161b6631d8255e341f3321d8f0b9e28e8a which made the `chromadb.Client` shared among all instances of the `Chroma` class.
One can avoid the bug by explicitly passing two different `collection_name` values when initializing the vector store, but I thought this would have been the default behaviour (or at least I expected a warning telling that the new vectorstore was reusing an existing collection).
Moreover, it is not clear to me why the first vector store will return the documents it was initialized with if I use `vectorstore.similarity_search(…)`, but shows the union of all documents if I use `vectorstore.get()`.
If you confirm what is the intended behaviour I am available to to work a PR to fix this.
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PR_kwDOIPDwls5chGM1
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lakeFS Document loader
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[] | null | 3 | 2023-10-11T13:42:41 | 2023-10-29T20:56:27 | 2023-10-29T20:56:27 |
CONTRIBUTOR
| null |
## Description
This PR adds support for a lakeFS document loader.
[lakeFS](https://lakefs.io/) provides version control over the data lake, and uses Git-like semantics to create and access those versions.
---
@hwchase17,
- **Twitter handle:** JonLaRose
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PR_kwDOIPDwls5chBdm
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Fix typos
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PR_kwDOIPDwls5cg-XZ
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Add patch_config(configurable=) arg, make with_config(configurable=) merge it with existing
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[] | null | 1 | 2023-10-11T13:27:23 | 2023-10-11T13:45:33 | 2023-10-11T13:45:32 |
COLLABORATOR
| null |
<!-- 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_kwDOIPDwls5zgARC
| 11,661 |
db = FAISS.from_documents(docs, embeddings) ;how to get docs's index or content from db?
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[] | null | 6 | 2023-10-11T13:20:45 | 2023-10-11T14:46:52 | 2023-10-11T14:31:53 |
NONE
| null |
### System Info
0.0.3
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.faiss.FAISS.html
### Expected behavior
get concrete docs's index or content
|
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PR_kwDOIPDwls5cgrzU
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#11655 Add SQLAlchemyMd5Cache implementation
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[] | null | 2 | 2023-10-11T12:51:37 | 2023-10-11T22:28:15 | 2023-10-11T22:28:09 |
CONTRIBUTOR
| null |
- **Description:** Add SQLAlchemyMd5Cache implementation,
- **Issue:** the issue # #11655,
- **Dependencies:** no deps,
- **Tag maintainer:** @markowanga
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PR_kwDOIPDwls5cgQrT
| 11,658 |
Qa with anonymization
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[] | null | 1 | 2023-10-11T11:44:04 | 2023-10-11T22:38:09 | 2023-10-11T22:38:08 |
CONTRIBUTOR
| null |
Added demo for QA system with anonymization. It will be part of LangChain's privacy webinar.
@hwchase17 @baskaryan @nfcampos
Twitter handle: @MaksOpp
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I_kwDOIPDwls5zfNrd
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Chat model answering incorrectly and weirdly
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[] | null | 2 | 2023-10-11T11:39:43 | 2023-10-12T09:38:26 | null |
NONE
| null |
I am using the ConversationalRetrievalChain along with DirectoryLoader from LangChain to load PDF files only and answer questions based on the different files in the directory along with returning the source of the answer from the document it got it from. As shown in the 2 pictures, in the first picture I asked it to answer in 1 single sentence but it kept answering and answering then repeated the answer. In the second picture, it rewrote the question again and then answered along with repeating the answers. In the 3rd picture, it answered a question thats not in any of the files in the Directory and it then shows the error of exceeding the rate limit.
I'd like help on how can I make it "listen" to me and how do I fix the rate limit error? I am using OpenAI embeddings for my Q&A chatbot. Does anyone recommend other embeddings that are not OpenAI which I could use for this use case? I plan on later moving my embeddings to the cloud and deploy it.
Thanks in advance
|
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I_kwDOIPDwls5zfJrL
| 11,656 |
Prompt invalid with ConversationalRetrievalChain
|
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[] | null | 5 | 2023-10-11T11:29:12 | 2023-11-29T17:42:20 | null |
NONE
| null |
I am trying to use a prompt with my chain but I am getting an error which I'm not sure how to solve. Also, I was wondering is it possible to use a constitutional principle to apply it to the ConversationalRetrievalChain? If so, can someone guide me on how to do so? Sorry for beginner questions but I am new to all of this including coding. Thanks in advance!
Code:
from dotenv import load_dotenv
import csv
import PyPDF2
from PyPDF2 import PdfReader
from langchain.document_loaders import DirectoryLoader, PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.callbacks import get_openai_callback
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationBufferMemory
from langchain.prompts import PromptTemplate
import time
from langchain.chains.qa_with_sources import load_qa_with_sources_chain
from langchain.vectorstores import Chroma
from langchain.callbacks import StreamingStdOutCallbackHandler
import pandas as pd
from docx import Document
from nltk.tokenize import sent_tokenize, word_tokenize
from collections import Counter
from nltk.corpus import stopwords
import os
def print_letter_by_letter(text):
for char in text:
print(char, end='', flush=True)
time.sleep(0.02)
def main():
load_dotenv()
# directory_path = input("Copy your directory path here or upload a file: ")
directory_path = "C:\\Users\\Asus\\OneDrive\\Documents\\Vendolista"
pdf_loader = DirectoryLoader(directory_path,
glob="**/*.pdf",
show_progress=True,
use_multithreading=True,
silent_errors=True,
loader_cls = PyPDFLoader)
documents = pdf_loader.load()
print(str(len(documents))+ " documents loaded")
llm = ChatOpenAI(temperature = 0, model_name='gpt-3.5-turbo', callbacks=[StreamingStdOutCallbackHandler()], streaming = True)
# Split into chunks
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
)
chunks = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
persist_directory = "C:\\Users\\Asus\\OneDrive\\Documents\\Vendolista"
knowledge_base = Chroma.from_documents(chunks, embeddings, persist_directory = persist_directory)
#save to disk
knowledge_base.persist()
#To delete the DB we created at first so that we can be sure that we will load from disk as fresh db
knowledge_base = None
new_knowledge_base = Chroma(persist_directory = persist_directory, embedding_function = embeddings)
custom_template = ("""
Help the users with answering questions based on the document.
"""
)
# prompt = PromptTemplate(input_variables=[], template=custom_template)
prompt = PromptTemplate.from_template(custom_template)
memory = ConversationBufferMemory(memory_key = "chat_history", return_messages = True)
model = ConversationalRetrievalChain.from_llm(llm = llm,
chain_type = "refine",
retriever = new_knowledge_base.as_retriever(),
memory = memory,
combine_docs_chain_kwargs = {"prompt":prompt}
)
while True:
question = input("Ask me anything about the files (type 'exit' to quit): ")
if question.lower() in ["exit"] and len(question) == 4:
end_chat = "Thank you for visiting us! Have a nice day"
print_letter_by_letter(end_chat)
break
if question:
chat_history = []
# with get_openai_callback() as cb:
response = model({"question": question}, return_only_outputs = True)
chat_history.append(('user', question))
chat_history.append(('AI', response))
print("AI:", response)
if __name__ == '__main__':
main()
Error:
86 documents loaded
Traceback (most recent call last):
File "C:\Users\Asus\Documents\Vendolista\app.py", line 100, in <module>
main()
File "C:\Users\Asus\Documents\Vendolista\app.py", line 73, in main
model = ConversationalRetrievalChain.from_llm(llm = llm,
File "C:\Users\Asus\AppData\Local\Programs\Python\Python310\lib\site-packages\langchain\chains\conversational_retrieval\base.py", line 341, in from_llm
doc_chain = load_qa_chain(
File "C:\Users\Asus\AppData\Local\Programs\Python\Python310\lib\site-packages\langchain\chains\question_answering\__init__.py", line 249, in load_qa_chain
return loader_mapping[chain_type](
File "C:\Users\Asus\AppData\Local\Programs\Python\Python310\lib\site-packages\langchain\chains\question_answering\__init__.py", line 206, in _load_refine_chain
return RefineDocumentsChain(
File "C:\Users\Asus\AppData\Local\Programs\Python\Python310\lib\site-packages\langchain\load\serializable.py", line 97, in __init__
super().__init__(**kwargs)
File "pydantic\main.py", line 341, in pydantic.main.BaseModel.__init__
pydantic.error_wrappers.ValidationError: 1 validation error for RefineDocumentsChain
prompt
extra fields not permitted (type=value_error.extra)
|
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I_kwDOIPDwls5ze3hZ
| 11,655 |
Using hash for indexing prompts SQLAlchemyCache
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[] | null | 3 | 2023-10-11T10:56:25 | 2023-10-23T05:39:08 | 2023-10-23T05:39:07 |
CONTRIBUTOR
| null |
### Feature request
I want to make similar implementation of SQLAlchemyCache that can index prompts by md5 hash.
### Motivation
Postgres have limitation for 2704 bytes on index. Processing on simple index can be faster.
### Your contribution
I have prepared cache implementation – I will add it in contributing PR
|
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I_kwDOIPDwls5zesH8
| 11,654 |
Issue: Inability to serialize langchain modules such as conversational retrieval chain, SQL database
|
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| false | null |
[] | null | 2 | 2023-10-11T10:35:10 | 2023-10-11T11:07:58 | null |
NONE
| null |
### Issue you'd like to raise.
In creating an app, ideally, you would like to let's say
db = SQLDatabase.from_uri(DATABASE_URI)
in one endpoint, and use the db in other endpoints,
The second endpoint could be what users get access to, so in getting responses, it should be quick, hence only accessing the already loaded db
but I cannot serialize db from the first endpoint, hence i have to include
db = SQLDatabase.from_uri(DATABASE_URI)
in the user endpoint, meaning anytime they hit the endpoint, db is run which delays significantly, and doesn't make for a good user experience, how do I solve this issue
### Suggestion:
_No response_
|
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Hallucinations, ignoring data in vector store and returning all documents as sources
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[] | null | 1 | 2023-10-11T10:27:20 | 2023-10-11T10:37:38 | null |
NONE
| null |
### System Info
IDE==PyCharm
Python==3.11.4
langchain==0.0.306
pinecone-client==2.2.2
### 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
- [X] Vector Stores / Retrievers
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The following function in my code with **ConversationalRetrievalChain** runs perfectly. It responds to the question "Hello" appropriately. It answers the questions related to the data in the vector_store:
```python
def get_conversation_chain(vector_store):
llm = ChatOpenAI()
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
conversation_chain = ConversationalRetrievalChain.from_llm(
llm=llm,
retriever=vector_store.as_retriever(),
memory=memory
)
return conversation_chain
```
When the chain is changed to **RetrievalQAWithSourcesChain** with new arguments then the bot responds to the user prompt "hello" with statements about Michael Jackson. It also does not answer questions to data that is contained within the vector store:
```python
def get_conversation_chain(vector_store):
llm = ChatOpenAI()
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True, output_key="answer")
conversation_chain = RetrievalQAWithSourcesChain.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=vector_store.as_retriever(),
# return_source_documents=True,
memory=memory
)
return conversation_chain
```
Everything else in my code remains the same when switching between the 2 functions. The issue only arises when I use the RetrievalQAWithSourcesChain function above. Also when the return_source_documents=True argument is active, it returns all documents within vector_store instead of only the documents being referenced in the answer.
### Expected behavior
I would expect that the answers from the RetrievalQAWithSourcesChain are the same as with ConversationalRetrievalChain:
- responds appropriately to the user prompt "hello"
- provides an answer referencing data that is contained within the vector_store
- plus returning relevant document sources related to answer.
|
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| 1,937,331,454 |
PR_kwDOIPDwls5cfbvr
| 11,652 |
Update models.py
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[] | null | 1 | 2023-10-11T09:43:45 | 2023-10-11T09:43:58 | null |
CONTRIBUTOR
| null |
Updated comments for better understanding of the search mechanism.
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I_kwDOIPDwls5zeSiW
| 11,651 |
DOC: HuggingFaceEmbeddings vs. HuggingFaceHubEmbeddings
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[] | null | 3 | 2023-10-11T09:37:15 | 2023-10-11T12:53:35 | null |
NONE
| null |
### Issue with current documentation:
Hello folks,
I am not quite sure if my issue is an issue regarding the docs or rather the implementation.
### What is the differnece between HuggingFaceEmbeddings and HuggingFaceHubEmbeddings?
Currently in [v0.0.312](https://github.com/langchain-ai/langchain/releases/tag/v0.0.312), there are two pretty similar modules in the `langchain` package, namely `libs/langchain/langchain/embeddings/huggingface.py` and `libs/langchain/langchain/embeddings/huggingface_hub.py` (compare https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/embeddings/huggingface.py and https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/embeddings/huggingface_hub.py).
Moreover, these modules provide pretty similar classes: `HuggingFaceEmbeddings` and `HuggingFaceHubEmbeddings`.
When should I use which one? This question arises as a consequence of the described example below.
### Relevant Example
Considering the task of using a LLM with additional documents, you could consider the following code:
```
from langchain.llms.huggingface_hub import HuggingFaceHub
from langchain.prompts import PromptTemplate
from langchain.vectorstores.faiss import FAISS
from langchain.chains import LLMChain
from langchain.embeddings import HuggingFaceHubEmbeddings, HuggingFaceEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
model = HuggingFaceHub(repo_id=llm, model_kwargs={"temperature": 0.2, "max_length": 64, "raw_response": True}, huggingfacehub_api_token=HUGGINGFACEHUB_API_KEY)
prompt = PromptTemplate(input_variables=["question", "docs"],
template="""
Answer the following question: {question}
By searching the following documents: {docs}
If you feel like you don't have enough information to answer the question, say "I don't know".
Your answers should be verbose and detailed.
""")
chain = LLMChain(llm=model, prompt=prompt)
docs = "bla bli blub ..."
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=5,
chunk_overlap=0
)
documents = text_splitter.split_text(document)
# A)
embeddings = HuggingFaceEmbeddings()
# B) alternatively
# embeddings = HuggingFaceHubEmbeddings(huggingfacehub_api_token=self.huggingfacehub_api_key)
self.documents_db = FAISS.from_texts(documents, embeddings)
docs = self.documents_db.similarity_search(prompt, k=4)
docs_page_content = " ".join([d.page_content for d in docs])
response = self.llm_chain.run(question=prompt, docs=docs_page_content)
response = response.replace("\n", "")
```
### Observed Behaviour
#### A)
Everything seems to work reliable and smooth without any errors or unexpected outputs.
#### B)
I did not conduct a study, but in my empirical testings I could observe the following.
Sometimes it works like A) and everything seems to work smooth and reliable,
However, sometimes (approximately 50% of the time i am running this script), I am getting one of the following two different errors:
##### KeyError
```
You're using a different task than the one specified in the repository. Be sure to know what you're doing :)
Traceback (most recent call last):
File "/qa_docs.py", line 59, in <module>
qa.add_document_to_context(document)
File "/qa.py", line 79, in add_document_to_context
self.documents_db = FAISS.from_texts(documents, embeddings)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/lib/python3.11/site-packages/langchain/vectorstores/faiss.py", line 603, in from_texts
return cls.__from(
^^^^^^^^^^^
File "/lib/python3.11/site-packages/langchain/vectorstores/faiss.py", line 562, in __from
index = faiss.IndexFlatL2(len(embeddings[0]))
~~~~~~~~~~^^^
KeyError: 0
```
##### ChunkedEncodingError
```
You're using a different task than the one specified in the repository. Be sure to know what you're doing :)
Traceback (most recent call last):
File "/lib/python3.11/site-packages/urllib3/response.py", line 710, in _error_catcher
yield
File "/lib/python3.11/site-packages/urllib3/response.py", line 1073, in read_chunked
self._update_chunk_length()
File "/lib/python3.11/site-packages/urllib3/response.py", line 1008, in _update_chunk_length
raise InvalidChunkLength(self, line) from None
urllib3.exceptions.InvalidChunkLength: InvalidChunkLength(got length b'', 0 bytes read)
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/lib/python3.11/site-packages/requests/models.py", line 816, in generate
yield from self.raw.stream(chunk_size, decode_content=True)
File "/lib/python3.11/site-packages/urllib3/response.py", line 933, in stream
yield from self.read_chunked(amt, decode_content=decode_content)
File "/lib/python3.11/site-packages/urllib3/response.py", line 1061, in read_chunked
with self._error_catcher():
File "/lib/python3.11/contextlib.py", line 155, in __exit__
self.gen.throw(typ, value, traceback)
File "/lib/python3.11/site-packages/urllib3/response.py", line 727, in _error_catcher
raise ProtocolError(f"Connection broken: {e!r}", e) from e
urllib3.exceptions.ProtocolError: ("Connection broken: InvalidChunkLength(got length b'', 0 bytes read)", InvalidChunkLength(got length b'', 0 bytes read))
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "qa_docs.py", line 59, in <module>
qa.add_document_to_context(document)
File "qa.py", line 79, in add_document_to_context
self.documents_db = FAISS.from_texts(documents, embeddings)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/lib/python3.11/site-packages/langchain/vectorstores/faiss.py", line 602, in from_texts
embeddings = embedding.embed_documents(texts)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/lib/python3.11/site-packages/langchain/embeddings/huggingface_hub.py", line 90, in embed_documents
responses = self.client(inputs=texts, params=_model_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/lib/python3.11/site-packages/huggingface_hub/inference_api.py", line 190, in __call__
response = get_session().post(self.api_url, headers=self.headers, json=payload, data=data)
File "/lib/python3.11/site-packages/requests/sessions.py", line 637, in post
return self.request("POST", url, data=data, json=json, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/lib/python3.11/site-packages/requests/sessions.py", line 589, in request
resp = self.send(prep, **send_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/lib/python3.11/site-packages/requests/sessions.py", line 747, in send
r.content
File "/lib/python3.11/site-packages/requests/models.py", line 899, in content
self._content = b"".join(self.iter_content(CONTENT_CHUNK_SIZE)) or b""
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/lib/python3.11/site-packages/requests/models.py", line 818, in generate
raise ChunkedEncodingError(e)
requests.exceptions.ChunkedEncodingError: ("Connection broken: InvalidChunkLength(got length b'', 0 bytes read)", InvalidChunkLength(got length b'', 0 bytes read))
```
### Remarks about the documents
Important note: the value of param `documents` is calculated by another script and is not fixed. It is a textual inference of another model. I already thought about that being the cause of the errors I encounter in some script executions but I have no idea how this could be. I can assure that there is always at least 100 characters that are being the value of `documents`.
However, I think that this problem already may be answered if I fully understand the differences between the two classes `HuggingFaceHubEmbeddings` and `HuggingFaceEmbeddings`.
### Related langchain documentation
- https://python.langchain.com/docs/integrations/vectorstores/faiss
- https://python.langchain.com/docs/integrations/providers/huggingface
- https://python.langchain.com/docs/integrations/llms/huggingface_hub.html
- https://python.langchain.com/docs/integrations/text_embedding/huggingfacehub.html
If there is more information needed, feel free to ask. I am looking forward for every help I can get!
### Idea or request for content:
When to use HuggingFaceEmbeddings vs. when to use HuggingFaceHubEmbeddings.
|
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| 1,937,199,873 |
I_kwDOIPDwls5zd1MB
| 11,650 |
ConversationalRetrievalChain + Memory ( New issue for me)
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[] | null | 8 | 2023-10-11T08:41:59 | 2023-10-27T05:33:48 | null |
NONE
| null |
### Issue you'd like to raise.
I've tried building a Bot and now I see the following issue.
Architecture:
1. Cloud Run service 1 ; Streamlit app which accepts the user_input (question) on the topic and sends it to Flask API part of service 2.
2. Cloud Run service 2: Flask code which calls the LLM code and returns the response to streamlit.
I'm using qa as below.
qa = ConversationalRetrievalChain.from_llm(
llm,
retriever,
condense_question_prompt=CONDENSE_QUESTION_PROMPT,
verbose=False,
memory=memory,
condense_question_llm =llm,
return_generated_question= True,
combine_docs_chain_kwargs={"prompt": promptHist},
return_source_documents=True,
)
Issue: If different Users are asking questions through various streamlit sessions, it is using the same chat_history buffer and mixing responses.
Question: Is there a way to separate the "qa" sessions specific to User and avoid common memory/chat buffer being used ? Is there a utility in Langchain for this?
### Suggestion:
_No response_
|
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GPT4All Model Serializable.__init__ Error
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[] | null | 1 | 2023-10-11T07:41:44 | 2023-10-11T07:53:52 | 2023-10-11T07:53:52 |
NONE
| null |
### System Info
Langchain version : 0.0.312
python version: 3.11.4
conda env platform: osx-arm64 / x86
system : macos M1 13.2.1 (22D68)
### Who can help?
@hwchase17 @agola11
### Information
- [x] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
template = """Question: {question}
Answer: Let's think step by step."""
prompt = PromptTemplate(template=template, input_variables=["question"])
local_path = ("/Users/langchain-usecase/04-gpt4all/llama-2-7b-chat.ggmlv3.q4_0.bin")
# Callbacks support token-wise streaming
callbacks = [StreamingStdOutCallbackHandler()]
# Verbose is required to pass to the callback manager
llm = GPT4All(model=local_path, callbacks=callbacks, verbose=True)
llm_chain = LLMChain(prompt=prompt, llm=llm)
question = "What NFL team won the Super Bowl in the year Justin Bieber was born?"
llm_chain.run(question)
```
bash output
``` bash
Traceback (most recent call last):
File "/Users/mac/EasyGithub/test/gpt4all.py", line 45, in <module>
llm = GPT4All(model=local_path, callbacks=callbacks, verbose=True)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/mac/miniforge3/envs/langchain/lib/python3.11/site-packages/langchain/load/serializable.py", line 97, in __init__
super().__init__(**kwargs)
File "/Users/mac/miniforge3/envs/langchain/lib/python3.11/site-packages/pydantic/v1/main.py", line 341, in __init__
raise validation_error
pydantic.v1.error_wrappers.ValidationError: 1 validation error for GPT4All
__root__ -> __root__
Serializable.__init__() takes 1 positional argument but 2 were given (type=type_error)
```
### Expected behavior
The same code runs on my other M2 MacMini and generates a reply. I don't know what caused the error.
M2 MacMini env info:
python==3.11.4
langchain==0.0.312
conda env platform: osx-arm64
macos==13.4.1
|
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| 11,648 |
ValueError: "Minimax" object has no field "_client"
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[] | null | 1 | 2023-10-11T06:52:49 | 2023-10-11T07:02:08 | null |
NONE
| null |
### System Info
langchain=0.0.312
### code:
from langchain.llms import Minimax
minimax = Minimax(minimax_api_key=minimax_api_key, minimax_group_id=minimax_group_id)
print(minimax("What is the difference between panda and bear?"))
### or:
from langchain.chat_models import MiniMaxChat
from langchain.schema import HumanMessage
chat = MiniMaxChat()
res = chat(
[
HumanMessage(
content="Translate this sentence from English to French. I love programming."
)
]
)
print(res)
### error log:
File "E:\AI\Projects\AIProject\AIApiTest\langchain_minimax.py", line 43, in <module>
llm = Minimax()
File "D:\anaconda3\envs\ai\lib\site-packages\langchain\llms\minimax.py", line 113, in __init__
self._client = _MinimaxEndpointClient(
File "pydantic\main.py", line 357, in pydantic.main.BaseModel.__setattr__
ValueError: "Minimax" object has no field "_client"
### 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
### code:
from langchain.llms import Minimax
minimax = Minimax(minimax_api_key=minimax_api_key, minimax_group_id=minimax_group_id)
print(minimax("What is the difference between panda and bear?"))
### or:
from langchain.chat_models import MiniMaxChat
from langchain.schema import HumanMessage
chat = MiniMaxChat()
res = chat(
[
HumanMessage(
content="Translate this sentence from English to French. I love programming."
)
]
)
print(res)
### Expected behavior
Fix this bug
|
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| 11,647 |
make tools conditional
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COLLABORATOR
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make utils conditional
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| 1,936,721,958 |
I_kwDOIPDwls5zcAgm
| 11,645 |
FAISS similarity search with score
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| false | null |
[] | null | 1 | 2023-10-11T03:21:59 | 2023-10-11T03:29:47 | null |
NONE
| null |
### System Info
langchain==0.0.311
python==3.9
I am trying to find out the similarity search score. but I got the score is abnormal. I hope the score is between 0 and 1.
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
from torch import cuda
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from langchain.vectorstores import (FAISS, Chroma)
from langchain.document_loaders import CSVLoader
embed_model_id = 'E:/modelscope/model/hggingface/text2vec-large-chinese'
device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'
embedding = HuggingFaceEmbeddings(
model_name=embed_model_id,
model_kwargs={'device': device},
encode_kwargs={'device': device, 'batch_size': 8}
)
def save_faiss_index(file_path: str = './label_remark_openai.csv', encoding: str = 'utf-8',
index_name: str = 'label_remark_openai_index',
save_path: str = "E:/pythonProject/information_demo/faiss_data"):
document = CSVLoader(file_path=file_path, encoding=encoding).load()
FAISS.normalize_L2 = True
db = FAISS.from_documents(documents=document, embedding=embedding, normalize_L2=True)
db.save_local(index_name=index_name, folder_path=save_path)
def get_faiss_index(index_name: str = 'label_remark_openai_index',
folder_path: str = "E:/pythonProject/information_demo/faiss_data"):
local_db = FAISS.load_local(index_name=index_name, embeddings=embedding, folder_path=folder_path, normalize_L2=True)
return local_db
if __name__ == '__main__':
index = get_faiss_index()
docs_and_scores = index.similarity_search_with_score("query text", k=10)
for doc, score in docs_and_scores:
print(f"Content: {doc.page_content}, Metadata: {doc.metadata}, Score: {score}")
```
### Expected behavior
I hope the score is between 0 and 1.
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| 1,936,650,375 |
I_kwDOIPDwls5zbvCH
| 11,644 |
ArxivLoader always returns 1 document
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[] | null | 5 | 2023-10-11T02:24:01 | 2023-10-14T04:35:52 | 2023-10-14T04:35:52 |
NONE
| null |
### System Info
Python version - Python 3
Langchain version - I am not completely sure, but I think 0.0.196
### Who can help?
@eyurtsev - can you help with the above issue as I see you look after DataLoader Abstractions?
### 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
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Code I wrote:
!pip install arxiv
!pip install pymupdf
from langchain.document_loaders import ArxivLoader
docs = ArxivLoader(query="2303.10130", load_max_docs=5, load_all_available_meta='FALSE').load()
### Expected behavior
Problem:
The problem is that I expected 'docs' variable to have at least 5 pages (as the article has 20+ pages) given i have passed the parameter load_max_docs = 5, but it only has 1. I tried with different numbers as well, but to no avail.
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PR_kwDOIPDwls5ccz3T
| 11,640 |
docstrings cleanup
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[] | null | 1 | 2023-10-11T01:05:41 | 2023-10-11T03:15:09 | 2023-10-11T02:56:47 |
COLLABORATOR
| null |
Added missed docstrings. Some reformatting.
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PR_kwDOIPDwls5ccq3_
| 11,639 |
collapse sidebar peer items
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| 1,936,527,305 |
PR_kwDOIPDwls5ccqBI
| 11,638 |
start cookbook
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| 1,936,494,704 |
I_kwDOIPDwls5zbJBw
| 11,637 |
create_csv_agent cannot read files hosted on Vercel blob
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[] | null | 4 | 2023-10-11T00:00:18 | 2023-10-11T03:26:55 | 2023-10-11T03:26:55 |
NONE
| null |
### System Info
Flask==3.0.0
Flask-Cors==4.0.0
langchain==0.0.312
openai==0.28.1
pandas==2.1.1
tabulate==0.9.0
numpy==1.26.0
### Who can help?
@eyurtsev
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
agent = create_csv_agent(
ChatOpenAI(temperature=0, model="gpt-4", openai_api_key=api_key, verbose=True),
["https://loyp2qzippf1q4hd.public.blob.vercel-storage.com/Mock%20Data%20_%20Dumping%20Costs%20-%20Dumping%20Cost-SIU4feVnqtCwoYg2g5Rgy9K5ejfKx0.csv"],
verbose=True,
agent_type=AgentType.OPENAI_FUNCTIONS,
return_intermediate_steps=True,
agent_executor_kwargs={"handle_parsing_errors": True},
)
```
### Expected behavior
The CSV file should be loaded accordingly
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PR_kwDOIPDwls5ccZzp
| 11,636 |
Start cookbook and move stuff from use cases
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[] | null | 1 | 2023-10-10T23:18:22 | 2023-10-11T19:27:14 | 2023-10-11T19:27:14 |
COLLABORATOR
| null |
<!-- 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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| 1,936,432,030 |
I_kwDOIPDwls5za5ue
| 11,635 |
How to debug vercel failures?
|
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[] | null | 5 | 2023-10-10T23:10:20 | 2023-10-12T01:31:50 | 2023-10-12T01:31:50 |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
I submitted a PR. Vercel 'deployment' failed. There is no other info. I dont seem to have access for this PR on vercel. How do i debug this?
### Suggestion:
_No response_
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| 1,936,416,962 |
PR_kwDOIPDwls5ccTMK
| 11,633 |
Add MMR functionality to elasticsearch retriever
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[] | null | 5 | 2023-10-10T22:57:50 | 2023-10-12T15:42:33 | 2023-10-12T15:42:33 |
CONTRIBUTOR
| null |
Allows MMR functionality only for the case where we have access to the embedding function. Also allows for users to request for fields from elasticsearch store. These are added to the document metadata.
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PR_kwDOIPDwls5ccPGs
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Add Support for Azure Cosmos DB MongoDB vCore Vector Store #11627
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[] | null | 3 | 2023-10-10T22:49:07 | 2023-10-11T20:56:46 | 2023-10-11T20:56:46 |
CONTRIBUTOR
| null |
This PR adds support for the Azure Cosmos DB MongoDB vCore Vector Store
https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/vcore/
https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/vcore/vector-search
Summary:
- **Description:** added vector store integration for Azure Cosmos DB MongoDB vCore Vector Store,
- **Issue:** the issue # it fixes #11627,
- **Dependencies:** pymongo dependency,
- **Tag maintainer:** @hwchase17,
- **Twitter handle:** @izzyacademy
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I_kwDOIPDwls5zaowE
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feat: implement ChatPromptTemplate.save
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[] | null | 2 | 2023-10-10T22:20:50 | 2023-10-11T05:28:21 | null |
NONE
| null |
### Feature request
Currently, the `save` method for `ChatPromptTemplate` is not implemented, preventing developers from saving templates as files.
### Motivation
This is a core feature that should be supported by all templates.
### Your contribution
If I have time I can make a PR.
|
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PR_kwDOIPDwls5ccEHq
| 11,630 |
Use `chunk_size` in openai embeddings
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[] | null | 1 | 2023-10-10T22:08:15 | 2023-10-11T03:26:28 | null |
NONE
| null |
Currently the `embed_documents` (and async version) ignore the `chunk_size` param.
Update passes it along to the relevant function.
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I_kwDOIPDwls5zak8E
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Issue as a result of Chroma.from_documents, OpenAI and AWS API Gateway
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[] | null | 1 | 2023-10-10T22:04:33 | 2023-10-10T22:10:45 | null |
NONE
| null |
### System Info
langchain version: 0.0.268
python version: 3.11
MacOS: Ventura 13.6
### Who can help?
@agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Using a Chroma vector store's from_documents method through an AWS API Gateway. The response size appears to be greater than 6MB. Is there a way to send a request whos response becomes smaller than 6MB?
Retrying langchain.embeddings.openai.embed_with_retry.<locals>._embed_with_retry in 10.0 seconds as it raised APIError: HTTP code 501 from API (Response size larger than Lambda 6MB Limit).
Traceback (most recent call last):
File "/Users/john_ramos/mckinsey-git/policy-y/.venv/lib/python3.11/site-packages/openai/api_requestor.py", line 753, in _interpret_response_line
data = json.loads(rbody)
^^^^^^^^^^^^^^^^^
File "/usr/local/Cellar/[email protected]/3.11.4_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/__init__.py", line 346, in loads
return _default_decoder.decode(s)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/Cellar/[email protected]/3.11.4_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/decoder.py", line 337, in decode
obj, end = self.raw_decode(s, idx=_w(s, 0).end())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/Cellar/[email protected]/3.11.4_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/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)
### Expected behavior
Embeddings are stored in the data store after the response comes back through API gateway.
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PR_kwDOIPDwls5cb0WC
| 11,628 |
Fixed the assignment of custom_llm_provider argument
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[] | null | 2 | 2023-10-10T21:22:19 | 2023-10-11T03:29:25 | 2023-10-11T03:29:25 |
CONTRIBUTOR
| null |
- **Description:** Assigning the custom_llm_provider to the default params function so that it will be passed to the litellm
- **Issue:** Even though the custom_llm_provider argument is being defined it's not being assigned anywhere in the code and hence its not being passed to litellm, therefore any litellm call which uses the custom_llm_provider as required parameter is being failed. This parameter is mainly used by litellm when we are doing inference via Custom API server. https://docs.litellm.ai/docs/providers/custom_openai_proxy
- **Dependencies:** No dependencies are required
@krrishdholakia , @baskaryan
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| 1,936,230,465 |
I_kwDOIPDwls5zaIhB
| 11,627 |
Add AzureCosmosDBVectorSearch VectorStore
|
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[] | null | 1 | 2023-10-10T20:55:53 | 2023-10-11T20:56:47 | 2023-10-11T20:56:47 |
CONTRIBUTOR
| null |
### Feature request
### Feature request
Azure Cosmos DB for MongoDB vCore enables users to efficiently store, index, and query high dimensional vector data stored directly in Azure Cosmos DB for MongoDB vCore. It contains similarity measures such as COS (cosine distance), L2 (Euclidean distance) or IP (inner product) which measures the distance between the data vectors and your query vector. The data vectors that are closest to your query vector are the ones that are found to be most similar semantically and retrieved during query time. The accompanying PR would add support for Langchain Python users to store vectors from document embeddings generated from APIs such as Azure OpenAI Embeddings or Hugging Face on Azure.
[Azure Cosmos DB for MongoDB vCore](https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/vcore/vector-search)
### Motivation
This capability described in the feature request is currently not available for Langchain Python.
### Your contribution
I will be submitting a PR for this feature request.
|
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I_kwDOIPDwls5zZ1qg
| 11,626 |
How to integrate Langchain's Human Tool into Streamlit
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[] | null | 3 | 2023-10-10T20:26:57 | 2024-01-10T13:58:57 | null |
NONE
| null |
### System Info
I'm using Langchain's Human Tool as part of my application. However, I am having difficulty integrating it into streamlit.
I'm mainly trying to change the input_func argument in the function:
`Human_Tool =load_tools(["human"], lm=llm, input_func=get_input)`
I've tried many different approaches but have not had success yet.
**I want to ask what might be a `get_input` function for langchains' Human Tool that integrates successfully with streamlit.**
### Who can help?
@hwchase17
@agola11
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Here are some of the things I've tried:
```
import streamlit as st
def get_input() -> str:
st.write("Insert your text below.")
# Updated this line to include a label and hide it using label_visibility
user_input = st.text_area(label="Your Text", value="", height=100, key=None, help=None, on_change=None, args=None, kwargs=None, label_visibility="hidden")
submit_button = st.button("Submit")
if submit_button:
return user_input.strip() # This will return the text entered by the user
if not submit_button:
with st.empty():
for remaining in range(3600, 0, -1):
st.write(f"You have {remaining//60} minutes and {remaining%60} seconds to enter the text.")
time.sleep(1)
st.empty()
return "" # Returning empty if no input after an hour or not submitted
```
The code above opens a streamlit box that and accepts the user's input but it does not pass the users input to the agent.
import streamlit as st
import uuid
def get_input() -> str:
unique_key = str(uuid.uuid4())
user_input = st.text_area(
"Insert your text below:",
key=f"uniqueTextAreaKey_{unique_key}",
value="",
height=200,
)
if st.button("Submit", key=f"uniqueButtonKey_{unique_key}"):
if user_input: # check if user input is not empty
st.session_state['user_input'] = user_input
st.session_state['submitted'] = True
st.success("Input submitted successfully!")
return user_input.strip()
elif 'submitted' in st.session_state and st.session_state['submitted']:
st.success("Input already submitted.")
return st.session_state['user_input'].strip()
else:
st.warning("Please enter text and press submit.")
return ""
This code also opens up a streamlit text box but gets stuck in an infinite loop.
### Expected behavior
I want the human tool to integrate with streamlit.
|
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I_kwDOIPDwls5zZ0yu
| 11,625 |
similaritySearchWithScore and similaritySearch method signatures are identical in javascript
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[] | null | 3 | 2023-10-10T20:24:24 | 2023-10-11T06:24:30 | null |
NONE
| null |
### System Info
The method signatures of similaritySearchWithScore and similaritySearch are identical in Javascript. I assume this is a mistake.
```
export declare abstract class VectorStore extends Serializable {
...
similaritySearch(query: string, k?: number, filter?: this["FilterType"] | undefined, _callbacks?: Callbacks | undefined): Promise<Document[]>;
similaritySearchWithScore(query: string, k?: number, filter?: this["FilterType"] | undefined, _callbacks?: Callbacks | undefined): Promise<[Document, number][]>;
```
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Open the base.d.ts file and search for the abstract class VectorStore. It contains these methods.
### Expected behavior
I would assume the signature for similaritySearchWithScore should contain some kind of Score parameter that we could pass to it to only return documents with a certain score.
|
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I_kwDOIPDwls5zZwxc
| 11,624 |
Release 3.0.4 breaks compatibility with ddtrace.
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[] | null | 2 | 2023-10-10T20:12:25 | 2023-10-12T15:04:28 | null |
NONE
| null |
### System Info
https://github.com/langchain-ai/langchain/releases/tag/v0.0.304
Reverting to 3.0.3 or under fixes it. The error looks something like (truncated confidential parts of the trace)
```
Traceback (most recent call last):
...
File "/Users/sid.gupta/.pyenv/versions/3.11.3/lib/python3.11/site-packages/ddtrace/_monkey.py", line 205, in patch_all
patch(raise_errors=False, **modules)
File "/Users/sid.gupta/.pyenv/versions/3.11.3/lib/python3.11/site-packages/ddtrace/_monkey.py", line 245, in patch
when_imported(module)(
File "/Users/sid.gupta/.pyenv/versions/3.11.3/lib/python3.11/site-packages/ddtrace/vendor/wrapt/importer.py", line 284, in register
register_post_import_hook(hook, name)
File "/Users/sid.gupta/.pyenv/versions/3.11.3/lib/python3.11/site-packages/ddtrace/vendor/wrapt/decorators.py", line 470, in _synchronized
return wrapped(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/sid.gupta/.pyenv/versions/3.11.3/lib/python3.11/site-packages/ddtrace/vendor/wrapt/importer.py", line 82, in register_post_import_hook
hook(module)
File "/Users/sid.gupta/.pyenv/versions/3.11.3/lib/python3.11/site-packages/ddtrace/_monkey.py", line 168, in on_import
imported_module.patch()
File "/Users/sid.gupta/.pyenv/versions/3.11.3/lib/python3.11/site-packages/ddtrace/contrib/langchain/patch.py", line 763, in patch
if hasattr(langchain.embeddings, text_embedding_model):
^^^^^^^^^^^^^^^^^^^^
File "/Users/sid.gupta/.pyenv/versions/3.11.3/lib/python3.11/site-packages/langchain/__init__.py", line 332, in __getattr__
raise AttributeError(f"Could not find: {name}")
AttributeError: Could not find: embeddings
```
### 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
Be on `ddtrace 1.17.6` and a langchain version >= 3.0.4. Running an webapp on hypercorn and fastAPI.
### Expected behavior
Don't throw this trace and block the application.
|
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PR_kwDOIPDwls5cbNrU
| 11,623 |
add ls guide redirect
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[] | null | 1 | 2023-10-10T19:57:58 | 2023-10-10T21:56:27 | 2023-10-10T19:58:04 |
COLLABORATOR
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PR_kwDOIPDwls5cbHGt
| 11,622 |
Use endpoint_url if provided with boto3 session for dynamodb
|
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[] | null | 2 | 2023-10-10T19:37:50 | 2023-11-13T18:31:16 | 2023-11-13T18:31:16 |
CONTRIBUTOR
| null |
- **Description:** Uses `endpoint_url` if provided with a boto3 session. When running dynamodb locally, credentials are required even if invalid. With this change, it will be possible to pass a boto3 session with credentials and specify an endpoint_url
|
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PR_kwDOIPDwls5cbD38
| 11,621 |
bump 312
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[] | null | 1 | 2023-10-10T19:27:36 | 2023-10-10T19:34:50 | 2023-10-10T19:34:49 |
COLLABORATOR
| null | null |
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PR_kwDOIPDwls5cbBd-
| 11,620 |
Restructure docs
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[] | null | 1 | 2023-10-10T19:20:54 | 2023-10-10T19:55:20 | 2023-10-10T19:55:19 |
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PR_kwDOIPDwls5cayN6
| 11,619 |
Remove LLM Bash and related bash utilities
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[] | null | 2 | 2023-10-10T18:42:53 | 2023-10-10T18:54:10 | 2023-10-10T18:54:09 |
COLLABORATOR
| null |
Deprecate LLMBash and related bash utilities
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I_kwDOIPDwls5zY4T4
| 11,618 |
AWS Sagemaker "code":424,
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open
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[] | null | 2 | 2023-10-10T17:47:53 | 2023-10-24T13:51:14 | null |
NONE
| null |
### System Info
from langchain.docstore.document import Document
from typing import Dict
from langchain import PromptTemplate, SagemakerEndpoint
from langchain.llms.sagemaker_endpoint import LLMContentHandler
from langchain.chains.question_answering import load_qa_chain
import json
example_doc_1 = """
Peter and Elizabeth took a taxi to attend the night party in the city. While in the party, Elizabeth collapsed and was rushed to the hospital.
Since she was diagnosed with a brain injury, the doctor told Peter to stay besides her until she gets well.
Therefore, Peter stayed with her at the hospital for 3 days without leaving.
"""
docs = [
Document(
page_content=example_doc_1,
)
]
query = """How long was Elizabeth hospitalized?
"""
prompt_template = """Use the following pieces of context to answer the question at the end.
{context}
Question: {question}
Answer:"""
PROMPT = PromptTemplate(
template=prompt_template, input_variables=["context", "question"]
)
class ContentHandler(LLMContentHandler):
content_type = "application/json"
accepts = "application/json"
def transform_input(self, prompt: str, model_kwargs: Dict) -> bytes:
input_dict = {"inputs": prompt, "parameters": model_kwargs}
return json.dumps(input_dict).encode('utf-8')
def transform_output(self, output: bytes) -> str:
response_json = json.loads(output.read().decode("utf-8"))
return response_json[0]["generated_text"]
content_handler = ContentHandler()
chain = load_qa_chain(
llm=SagemakerEndpoint(
endpoint_name="endpoint",
region_name="ap-southeast-2",
credentials_profile_name="dev",
model_kwargs={"temperature": 1e-10},
endpoint_kwargs={"CustomAttributes": 'accept_eula=true'},
content_handler=ContentHandler(),
),
prompt=PROMPT,
)
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
Stacktrace :
ValueError: Error raised by inference endpoint: An error occurred (ModelError) when calling the InvokeEndpoint operation: Received client error (424) from primary with message "{
"code":424,
"message":"prediction failure",
"error":"string indices must be integers"
### 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
run the same python code to reproduce.
### Expected behavior
I am expecting a response generated by the chain.
|
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PR_kwDOIPDwls5caDuC
| 11,617 |
Make metadata from the url_selenium loader match that of the web_base loader
|
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[] | null | 2 | 2023-10-10T16:43:40 | 2023-10-11T03:32:45 | 2023-10-11T03:32:45 |
CONTRIBUTOR
| null |
**Description:** I noticed the metadata returned by the url_selenium loader was missing several values included by the web_base loader. (The former returned `{source: ...}`, the latter returned `{source: ..., title: ..., description: ..., language: ...}`.) This change fixes it so both loaders return all 4 key value pairs.
Files have been properly formatted and all tests are passing. Note, however, that I am not much of a python expert, so that whole "Adding the imports inside the code so that tests pass" thing seems weird to me. Please LMK if I did anything wrong.
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I_kwDOIPDwls5zYWj8
| 11,616 |
Self-querying retriever for MongoDBAtlasVectorSearch
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[] | null | 1 | 2023-10-10T16:37:06 | 2023-10-10T16:39:28 | null |
CONTRIBUTOR
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### Feature request
Add self-querying capability for MongoDBAtlasVectorSearch so that any natural language query could be converted to a MongoDB query and then be used to search the MongoDB vector store
### Motivation
I was testing self-quering using MongoDBAtlasVectorSearch when I got an error saying that self-querying is not supported. I think it will be useful to have that functionality since MongoDB vector store is well used.
### Your contribution
We will work on this feature and submit a pull request in the coming 1-2 months.
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PR_kwDOIPDwls5cZr3c
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Deprecate LLMSymbolicMath from langchain core
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Deprecate LLMSymbolicMath from langchain core package.
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PR_kwDOIPDwls5cZm5_
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Docs to use LLMSymbolicMath and LLMBash + utilities from experimental
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[] | null | 1 | 2023-10-10T15:28:29 | 2023-10-10T17:11:47 | 2023-10-10T17:11:46 |
COLLABORATOR
| null |
Update docs in lieu of:
https://github.com/langchain-ai/langchain/discussions/11352
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PR_kwDOIPDwls5cZf-7
| 11,613 |
Add version to langchain_experimental
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COLLABORATOR
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Add version to langchain experimental
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PR_kwDOIPDwls5cZdtX
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Add yandex llm and chat model
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[] | null | 1 | 2023-10-10T15:07:41 | 2023-10-10T15:08:51 | 2023-10-10T15:08:05 |
CONTRIBUTOR
| null |
<!-- 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_kwDOIPDwls5cZXgk
| 11,610 |
rm slack from community.md
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PR_kwDOIPDwls5cZQAH
| 11,609 |
Fix: invalid link to chat model in openai platform docs
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| false | null |
[] | null | 2 | 2023-10-10T14:44:46 | 2023-10-10T17:22:40 | 2023-10-10T17:22:40 |
CONTRIBUTOR
| null |
<!-- 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.
-->
There is some invalid link in open ai platform [docs](https://python.langchain.com/docs/integrations/platforms/openai).
So i fixed it to valid links.
- `/docs/integrations/chat_models/openai` -> `/docs/integrations/chat/openai`
- `/docs/integrations/chat_models/azure_openai` -> `/docs/integrations/chat/azure_chat_openai`
Thanks! ☺️
|
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PR_kwDOIPDwls5cZMrI
| 11,608 |
Add support for ChatOpenAI models in Infino callback handler
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[] | null | 2 | 2023-10-10T14:40:23 | 2023-10-11T21:00:55 | 2023-10-11T21:00:55 |
CONTRIBUTOR
| null |
**Description:** This PR adds support for ChatOpenAI models in the Infino callback handler. In particular, this PR implements `on_chat_model_start` callback, so that ChatOpenAI models are supported. With this change, Infino callback handler can be used to track latency, errors, and prompt tokens for ChatOpenAI models too (in addition to the support for OpenAI and other non-chat models it has today). The existing example notebook is updated to show how to use this integration as well. cc/ @naman-modi @savannahar68
**Issue:** https://github.com/langchain-ai/langchain/issues/11607
**Dependencies:** None
**Tag maintainer:** @hwchase17
**Twitter handle:** [@vkakade](https://twitter.com/vkakade)
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I_kwDOIPDwls5zXYE4
| 11,607 |
Support ChatOpenAI models in Infino callback manager
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[] | null | 2 | 2023-10-10T14:32:26 | 2023-10-10T14:46:14 | null |
CONTRIBUTOR
| null |
### Feature request
[Infino callback handler](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/callbacks/infino_callback.py) as of this writing does not support ChatOpenAI models, as it does not support `on_chat_model_start` callback.
Adding this callback will enable users to track latency, errors and token usage for ChatOpenAI models (in addition to existing support for OpenAI and other non-chat models).
### Motivation
Infino customers have requested for this integration, as this increases Infino callback handler's coverage to OpenAI chat models.
Customer request GitHub issue for Infino - https://github.com/infinohq/infino/issues/93
### Your contribution
I have a working code change for this issue, and will submit a PR shortly.
|
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I_kwDOIPDwls5zXOPu
| 11,606 |
langchain is not working on AWS , It is always giving "ImportError: libmariadb.so.3: cannot open shared object file: No such file or directory" , no where i am using mariadb
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[] | null | 2 | 2023-10-10T14:13:21 | 2023-10-10T15:06:44 | null |
NONE
| null |
### System Info
openai==0.27.6
urllib3==1.26.15
pandas==2.0.1
slack-sdk==3.21.3
pydantic==2.4.2
langchain==0.0.311
SQLAlchemy==2.0.11
mysqlclient==2.2.0
pymysql==1.1.0
### 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 am using AWS lambda
### Expected behavior
it should run fine without MariaDB
|
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PR_kwDOIPDwls5cY5XO
| 11,605 |
Add baidu cloud vector search in vectorstore and fix some unit test in vectorstores
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[] | null | 4 | 2023-10-10T14:01:58 | 2023-11-03T11:01:23 | 2023-10-25T20:44:20 |
CONTRIBUTOR
| null |
**Description:**
Add baidu cloud vector search in vectorstore
|
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PR_kwDOIPDwls5cY0JA
| 11,604 |
FIX: 'from_texts' method in Weaviate with non-existent kwargs param
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[] | null | 1 | 2023-10-10T13:50:17 | 2023-11-13T18:32:21 | 2023-11-13T18:32:20 |
CONTRIBUTOR
| null |
Due to the possibility of external inputs including UUIDs, there may be additional values in **kwargs, while Weaviate's `__init__` method does not support passing extra **kwarg parameters.
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PR_kwDOIPDwls5cYcBI
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Fix mutation bugs in callback manager configure
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<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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PR_kwDOIPDwls5cYb2m
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Update azureml_chat_endpoint code exemple
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PR_kwDOIPDwls5cYOFy
| 11,601 |
Add configurable fields with options
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<!-- Thank you for contributing to LangChain!
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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_kwDOIPDwls5cX1q2
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Add id in the metadata of the Documents returned in Similarity Search
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NONE
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The documents returned in any similarity search function doesnt returns the id of the document. This id can later be use to update the page_content or the metadata of the stored document in the vector store.
@hwchase17
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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- **Issue:** the issue # it fixes (if applicable),
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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:
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If you're adding a new integration, please include:
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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_kwDOIPDwls5zV5FW
| 11,599 |
Using a self-hosted LLM in an offline environment
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[] | null | 3 | 2023-10-10T11:22:02 | 2023-10-10T12:06:35 | null |
NONE
| null |
### Issue you'd like to raise.
I built a self-hosted LLM and applied Langchain's HuggingFaceTextGenInference for use in an offline environment, but an error occurred because the tokenizer was forced to call online in the code using the map_reduce type of summarize_chain.
I would like to solve this problem to use self-hosted LLM in an offline environment
The error message is as follows:
OSError: Can't load tokenizer for 'gpt2'. If you were trying to load it from 'https://huggingface.co/models', make sure you don't have a local directory with the same name. Otherwise, make sure 'gpt2' is the correct path to a directory containing all relevant files for a GPT2TokenizerFast tokenizer.
Output is truncated. View as a [scrollable element](command:cellOutput.enableScrolling?8199cda9-1c5c-4995-ba50-f58f326273c3) or open in a [text editor](command:workbench.action.openLargeOutput?8199cda9-1c5c-4995-ba50-f58f326273c3). Adjust cell output [settings](command:workbench.action.openSettings?%5B%22%40tag%3AnotebookOutputLayout%22%5D)...
### Suggestion:
I would like to know how to change the tokenizer used online in HuggingFaceTextGenInference to offline or how to utilize self-hosted LLM without using HuggingFaceTextGenInference.
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PR_kwDOIPDwls5cXVjk
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[Issue#11595] Multithreading support for TextSplitter
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] | null | 6 | 2023-10-10T10:26:11 | 2023-10-19T14:42:08 | 2023-10-19T14:42:07 |
NONE
| null |
### Feature request
Add optional multithreading support for `TextSplitter`, e.g for the loop in `TextSplitter.create_documents`:
https://github.com/langchain-ai/langchain/blob/e2a9072b806b1a45b0e4c107b30dddd0f67a453f/libs/langchain/langchain/text_splitter.py#L138-L153
Question: Is there anything opposing this idea / preventing it from a technical perspective?
### Motivation
Text splitting can take up significant time and resources if a custom length function is used to measure chunk length (e.g. based on a huggingface tokenizer's encode method), especially for the `RecursiveCharacterTextSplitter`.
Therefore we want to introduce multithreading support on a document level.
### Your contribution
Feature Request: https://github.com/langchain-ai/langchain/issues/11595
PR: https://github.com/langchain-ai/langchain/pull/11598
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PR_kwDOIPDwls5cXUHf
| 11,597 |
langchain-experimental: Add allow_list support in experimental/data_anonymizer
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[] | null | 4 | 2023-10-10T10:23:40 | 2023-10-11T21:51:37 | 2023-10-11T21:50:42 |
CONTRIBUTOR
| null |
- **Description:** Add allow_list support in langchain experimental data-anonymizer package
- **Issue:** no
- **Dependencies:** no
- **Tag maintainer:** @hwchase17
- **Twitter handle:**
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