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] | closed | false | null | [] | null | 1 | 2023-12-04T16:23:18 | 2023-12-04T19:43:05 | 2023-12-04T19:43:05 | CONTRIBUTOR | null | **Description:** The way the condition is checked in the `return_stopped_response` function of `OpenAIAgent` may not be correct, when the value returned is `AgentFinish` from the tools it does not work properly.
Thanks for review, @baskaryan, @eyurtsev, @hwchase17. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14234 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14234/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14234/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14234/events | https://github.com/langchain-ai/langchain/pull/14234 | 2,024,219,747 | PR_kwDOIPDwls5hEudl | 14,234 | deepinfra support for chat_models | {
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] | closed | false | null | [] | null | 6 | 2023-12-04T16:11:34 | 2024-01-09T23:50:57 | 2024-01-09T23:50:56 | CONTRIBUTOR | null | Add deepinfra chat models support.
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https://api.github.com/repos/langchain-ai/langchain/issues/14233 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14233/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14233/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14233/events | https://github.com/langchain-ai/langchain/issues/14233 | 2,024,212,346 | I_kwDOIPDwls54pwd6 | 14,233 | Issue: Recently, the DuckduckGo search tool seems not working. VQDExtractionException: Could not extract vqd. keywords="keywords" | {
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] | open | false | null | [] | null | 6 | 2023-12-04T16:08:09 | 2023-12-27T15:30:49 | null | NONE | null | ### Issue you'd like to raise.
from langchain.agents import AgentType, initialize_agent
from langchain.chat_models import ChatOpenAI
from langchain.tools import DuckDuckGoSearchRun
llm = ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo-16k")
web_search = DuckDuckGoSearchRun()
tools = [
web_search,
# other tools
]
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
agent.run("<Some question>")
VQDExtractionException: Could not extract vqd. keywords="blah blah blah"
---
The code above worked fine several weeks ago, but it is not working any more. I tried every version of LangChain released after September.
Maybe there is a change in DuckDuckGo's API. Please help me, thanks.
### Suggestion:
_No response_ | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14232 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14232/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14232/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14232/events | https://github.com/langchain-ai/langchain/issues/14232 | 2,024,210,192 | I_kwDOIPDwls54pv8Q | 14,232 | similarity_search_with_score does not accept "!" in the query | {
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] | closed | false | null | [] | null | 1 | 2023-12-04T16:07:02 | 2023-12-13T17:09:52 | 2023-12-13T17:09:52 | NONE | null | ### System Info
python 3.11.6
langchain==0.0.345
langchain-core==0.0.9
jupyter_client==8.6.0
jupyter_core==5.5.0
ipykernel==6.27.0
ipython==8.17.2
on mac M2
### Who can help?
@baskaryan @tomasonjo @hwchase17
### 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
1. The setup is the same as in https://github.com/langchain-ai/langchain/issues/14231, although I don't think it matters.
2. Run `existing_graph.similarity_search_with_score("It is the end of the world. Take shelter!")`
3. It returns the following error
---------------------------------------------------------------------------
ClientError Traceback (most recent call last)
[/Users/josselinperrus/Projects/streetpress/neo4j.ipynb](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/neo4j.ipynb) Cell 17 line 1
----> [1](vscode-notebook-cell:/Users/josselinperrus/Projects/streetpress/neo4j.ipynb#X21sZmlsZQ%3D%3D?line=0) existing_graph.similarity_search_with_score("It is the end of the world. Take shelter !")
File [~/Projects/streetpress/venv/lib/python3.11/site-packages/langchain/vectorstores/neo4j_vector.py:550](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/langchain/vectorstores/neo4j_vector.py:550), in Neo4jVector.similarity_search_with_score(self, query, k)
[540](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/langchain/vectorstores/neo4j_vector.py:540) """Return docs most similar to query.
[541](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/langchain/vectorstores/neo4j_vector.py:541)
[542](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/langchain/vectorstores/neo4j_vector.py:542) Args:
(...)
[547](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/langchain/vectorstores/neo4j_vector.py:547) List of Documents most similar to the query and score for each
[548](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/langchain/vectorstores/neo4j_vector.py:548) """
[549](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/langchain/vectorstores/neo4j_vector.py:549) embedding = self.embedding.embed_query(query)
--> [550](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/langchain/vectorstores/neo4j_vector.py:550) docs = self.similarity_search_with_score_by_vector(
[551](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/langchain/vectorstores/neo4j_vector.py:551) embedding=embedding, k=k, query=query
[552](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/langchain/vectorstores/neo4j_vector.py:552) )
[553](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/langchain/vectorstores/neo4j_vector.py:553) return docs
File [~/Projects/streetpress/venv/lib/python3.11/site-packages/langchain/vectorstores/neo4j_vector.py:595](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/langchain/vectorstores/neo4j_vector.py:595), in Neo4jVector.similarity_search_with_score_by_vector(self, embedding, k, **kwargs)
[586](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/langchain/vectorstores/neo4j_vector.py:586) read_query = _get_search_index_query(self.search_type) + retrieval_query
[587](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/langchain/vectorstores/neo4j_vector.py:587) parameters = {
[588](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/langchain/vectorstores/neo4j_vector.py:588) "index": self.index_name,
[589](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/langchain/vectorstores/neo4j_vector.py:589) "k": k,
(...)
[592](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/langchain/vectorstores/neo4j_vector.py:592) "query": kwargs["query"],
[593](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/langchain/vectorstores/neo4j_vector.py:593) }
--> [595](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/langchain/vectorstores/neo4j_vector.py:595) results = self.query(read_query, params=parameters)
[597](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/langchain/vectorstores/neo4j_vector.py:597) docs = [
[598](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/langchain/vectorstores/neo4j_vector.py:598) (
[599](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/langchain/vectorstores/neo4j_vector.py:599) Document(
(...)
[607](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/langchain/vectorstores/neo4j_vector.py:607) for result in results
[608](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/langchain/vectorstores/neo4j_vector.py:608) ]
[609](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/langchain/vectorstores/neo4j_vector.py:609) return docs
File [~/Projects/streetpress/venv/lib/python3.11/site-packages/langchain/vectorstores/neo4j_vector.py:242](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/langchain/vectorstores/neo4j_vector.py:242), in Neo4jVector.query(self, query, params)
[240](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/langchain/vectorstores/neo4j_vector.py:240) try:
[241](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/langchain/vectorstores/neo4j_vector.py:241) data = session.run(query, params)
--> [242](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/langchain/vectorstores/neo4j_vector.py:242) return [r.data() for r in data]
[243](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/langchain/vectorstores/neo4j_vector.py:243) except CypherSyntaxError as e:
[244](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/langchain/vectorstores/neo4j_vector.py:244) raise ValueError(f"Cypher Statement is not valid\n{e}")
File [~/Projects/streetpress/venv/lib/python3.11/site-packages/langchain/vectorstores/neo4j_vector.py:242](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/langchain/vectorstores/neo4j_vector.py:242), in <listcomp>(.0)
[240](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/langchain/vectorstores/neo4j_vector.py:240) try:
[241](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/langchain/vectorstores/neo4j_vector.py:241) data = session.run(query, params)
--> [242](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/langchain/vectorstores/neo4j_vector.py:242) return [r.data() for r in data]
[243](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/langchain/vectorstores/neo4j_vector.py:243) except CypherSyntaxError as e:
[244](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/langchain/vectorstores/neo4j_vector.py:244) raise ValueError(f"Cypher Statement is not valid\n{e}")
File [~/Projects/streetpress/venv/lib/python3.11/site-packages/neo4j/_sync/work/result.py:270](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/neo4j/_sync/work/result.py:270), in Result.__iter__(self)
[268](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/neo4j/_sync/work/result.py:268) yield self._record_buffer.popleft()
[269](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/neo4j/_sync/work/result.py:269) elif self._streaming:
--> [270](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/neo4j/_sync/work/result.py:270) self._connection.fetch_message()
[271](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/neo4j/_sync/work/result.py:271) elif self._discarding:
[272](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/neo4j/_sync/work/result.py:272) self._discard()
File [~/Projects/streetpress/venv/lib/python3.11/site-packages/neo4j/_sync/io/_common.py:178](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/neo4j/_sync/io/_common.py:178), in ConnectionErrorHandler.__getattr__.<locals>.outer.<locals>.inner(*args, **kwargs)
[176](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/neo4j/_sync/io/_common.py:176) def inner(*args, **kwargs):
[177](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/neo4j/_sync/io/_common.py:177) try:
--> [178](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/neo4j/_sync/io/_common.py:178) func(*args, **kwargs)
[179](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/neo4j/_sync/io/_common.py:179) except (Neo4jError, ServiceUnavailable, SessionExpired) as exc:
[180](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/neo4j/_sync/io/_common.py:180) assert not asyncio.iscoroutinefunction(self.__on_error)
File [~/Projects/streetpress/venv/lib/python3.11/site-packages/neo4j/_sync/io/_bolt.py:849](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/neo4j/_sync/io/_bolt.py:849), in Bolt.fetch_message(self)
[845](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/neo4j/_sync/io/_bolt.py:845) # Receive exactly one message
[846](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/neo4j/_sync/io/_bolt.py:846) tag, fields = self.inbox.pop(
[847](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/neo4j/_sync/io/_bolt.py:847) hydration_hooks=self.responses[0].hydration_hooks
[848](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/neo4j/_sync/io/_bolt.py:848) )
--> [849](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/neo4j/_sync/io/_bolt.py:849) res = self._process_message(tag, fields)
[850](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/neo4j/_sync/io/_bolt.py:850) self.idle_since = perf_counter()
[851](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/neo4j/_sync/io/_bolt.py:851) return res
File [~/Projects/streetpress/venv/lib/python3.11/site-packages/neo4j/_sync/io/_bolt5.py:374](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/neo4j/_sync/io/_bolt5.py:374), in Bolt5x0._process_message(self, tag, fields)
[372](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/neo4j/_sync/io/_bolt5.py:372) self._server_state_manager.state = self.bolt_states.FAILED
[373](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/neo4j/_sync/io/_bolt5.py:373) try:
--> [374](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/neo4j/_sync/io/_bolt5.py:374) response.on_failure(summary_metadata or {})
[375](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/neo4j/_sync/io/_bolt5.py:375) except (ServiceUnavailable, DatabaseUnavailable):
[376](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/neo4j/_sync/io/_bolt5.py:376) if self.pool:
File [~/Projects/streetpress/venv/lib/python3.11/site-packages/neo4j/_sync/io/_common.py:245](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/neo4j/_sync/io/_common.py:245), in Response.on_failure(self, metadata)
[243](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/neo4j/_sync/io/_common.py:243) handler = self.handlers.get("on_summary")
[244](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/neo4j/_sync/io/_common.py:244) Util.callback(handler)
--> [245](https://file+.vscode-resource.vscode-cdn.net/Users/josselinperrus/Projects/streetpress/~/Projects/streetpress/venv/lib/python3.11/site-packages/neo4j/_sync/io/_common.py:245) raise Neo4jError.hydrate(**metadata)
ClientError: {code: Neo.ClientError.Procedure.ProcedureCallFailed} {message: Failed to invoke procedure `db.index.fulltext.queryNodes`: Caused by: org.apache.lucene.queryparser.classic.ParseException: Encountered "<EOF>" at line 1, column 42.
Was expecting one of:
<BAREOPER> ...
"(" ...
"*" ...
<QUOTED> ...
<TERM> ...
<PREFIXTERM> ...
<WILDTERM> ...
<REGEXPTERM> ...
"[" ...
"{" ...
<NUMBER> ...
<TERM> ...
"*" ...
}
### Expected behavior
No error | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14231 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14231/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14231/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14231/events | https://github.com/langchain-ai/langchain/issues/14231 | 2,024,195,747 | I_kwDOIPDwls54psaj | 14,231 | similarity_search_with_relevance_scores returns incoherent relevance scores with Neo4jVector | {
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] | open | false | null | [] | null | 3 | 2023-12-04T16:00:03 | 2023-12-05T10:43:22 | null | NONE | null | ### System Info
python 3.11.6
langchain==0.0.345
langchain-core==0.0.9
jupyter_client==8.6.0
jupyter_core==5.5.0
ipykernel==6.27.0
ipython==8.17.2
on mac M2
### Who can help?
@baskaryan @tomasonjo @hwchase17
### 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
1. Add a first node to the graph
`first_node = Node(
id="1",
type="Sentence",
properties={
"description": "This is my first node"
}
)
graph_document = GraphDocument(
nodes= [first_node],
relationships= [],
source= Document(page_content="my first document")
)
graph.add_graph_documents([graph_document])`
2. Add a second node to the graph
`second_node = Node(
id="2",
type="Sentence",
properties={
"description": "I love eating spinach"
}
)
graph_document = GraphDocument(
nodes= [second_node],
relationships= [],
source= Document(page_content="second doc")
)
graph.add_graph_documents([graph_document])`
3. Create a hybrid index
`existing_graph = Neo4jVector.from_existing_graph(
embedding=OpenAIEmbeddings(),
url=url,
username=username,
password=password,
index_name="sentence_index",
keyword_index_name="sentence_kindex",
node_label="Sentence",
text_node_properties=["description"],
embedding_node_property="embedding",
search_type="hybrid"
)`
4. Do a similarity search
`existing_graph.similarity_search_with_score("It is the end of the world. Take shelter")`
5. This yields a relevance score of 1 for the 1st result
`[(Document(page_content='\ndescription: This is my first node'), 1.0),
(Document(page_content='\ndescription: I love eating spinach'),
0.8576263189315796)]`
6. Test the strategy in use:
`existing_graph._distance_strategy`
which returns `<DistanceStrategy.COSINE: 'COSINE'>`
### Expected behavior
The relevance score should return the cosine similarity score.
In this particular case the cosine similarity score is 0.747
`def get_embedding(text):
response = openai.embeddings.create(input=text, model="text-embedding-ada-002")
return response.data[0].embedding
def cosine_similarity(vec1, vec2):
return dot(vec1, vec2) / (norm(vec1) * norm(vec2))
embedding1 = get_embedding("This is my first node")
embedding2 = get_embedding("It is the end of the world. Take shelter")
similarity = cosine_similarity(embedding1, embedding2)
print(f"Cosine Similarity: {similarity}")`
returns
`Cosine Similarity: 0.7475260325549817` | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14230 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14230/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14230/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14230/events | https://github.com/langchain-ai/langchain/pull/14230 | 2,024,180,853 | PR_kwDOIPDwls5hEmEv | 14,230 | Fix `config` arg detection for wrapped lambdarunnable | {
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] | null | 1 | 2023-12-04T15:52:57 | 2023-12-04T22:18:30 | 2023-12-04T22:18:30 | CONTRIBUTOR | null | **Description:**
When a RunnableLambda only receives a synchronous callback, this callback is wrapped into an async one since #13408. However, this wrapping with `(*args, **kwargs)` causes the `accepts_config` check at [/libs/core/langchain_core/runnables/config.py#L342](https://github.com/langchain-ai/langchain/blob/ee94ef55ee6ab064da08340817955f821dfa6261/libs/core/langchain_core/runnables/config.py#L342) to fail, as this checks for the presence of a "config" argument in the method signature.
Adding a `functools.wraps` around it, resolves it.
**Issue:**
None, writing this PR already took longer than the fix ;)
**Tag maintainer:**
@nbondoux @efriis (participants of the original PR)
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https://api.github.com/repos/langchain-ai/langchain/issues/14229 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14229/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14229/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14229/events | https://github.com/langchain-ai/langchain/issues/14229 | 2,024,080,225 | I_kwDOIPDwls54pQNh | 14,229 | DOC: How to create a custom filtered retriever | {
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] | open | false | null | [] | null | 3 | 2023-12-04T15:07:02 | 2023-12-05T13:39:52 | null | CONTRIBUTOR | null | ### Issue with current documentation:
I cant find documentattion about filtered retrievers, basically I want to fix this issue: https://github.com/langchain-ai/langchain/issues/14227
### Idea or request for content:
And according to other github issues, the way to fix it is with custom filtered retrievers.
So I tried the following:
```
class FilteredRetriever:
def __init__(self, retriever, title):
self.retriever = retriever
self.title = title
def retrieve(self, *args, **kwargs):
results = self.retriever.retrieve(*args, **kwargs)
return [doc for doc in results if doc['title'].startswith(self.title)]
filtered_retriever = FilteredRetriever(vector_store.as_retriever(), '25_1_0.pdf')
llm = AzureChatOpenAI(
azure_deployment="chat",
openai_api_version="2023-05-15",
)
retriever = vector_store.as_retriever(search_type="similarity", kwargs={"k": 3})
chain = RetrievalQA.from_chain_type(llm=llm,
chain_type="stuff",
retriever=filtered_retriever,
return_source_documents=True)
result = chain({"query": 'Can Colleagues contact their managers??'})
for res in result['source_documents']:
print(res.metadata['title'])
```
However I get this error:
```
ValidationError: 1 validation error for RetrievalQA
retriever
value is not a valid dict (type=type_error.dict)
```
but as I cant find documentation about it, I am not sure how to solve it | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14228 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14228/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14228/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14228/events | https://github.com/langchain-ai/langchain/pull/14228 | 2,024,077,529 | PR_kwDOIPDwls5hEPec | 14,228 | Clarifying descriptions in `GuardrailsOutputParser` | {
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] | closed | false | null | [] | null | 1 | 2023-12-04T15:05:45 | 2023-12-04T19:43:07 | 2023-12-04T19:33:22 | CONTRIBUTOR | null | Upstreaming knowledge from https://github.com/guardrails-ai/guardrails/discussions/473 to LangChain | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14227 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14227/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14227/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14227/events | https://github.com/langchain-ai/langchain/issues/14227 | 2,024,062,849 | I_kwDOIPDwls54pL-B | 14,227 | Filters dont work with Azure Search Vector Store retriever | {
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] | open | false | null | [] | null | 1 | 2023-12-04T14:58:48 | 2023-12-04T15:09:12 | null | CONTRIBUTOR | null | ### System Info
langchain 0.0.342
langchain-core 0.0.7
azure-search-documents 11.4.0b8
Python: 3.10
### Who can help?
@hw
### 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
The following code works fine:
```
from langchain_core.vectorstores import VectorStore, VectorStoreRetriever
index_name: str = "langchain-vector-demo"
vector_store: AzureSearch = AzureSearch(
azure_search_endpoint=vector_store_address,
azure_search_key=vector_store_password,
index_name="vector-1701341754619",
embedding_function=embeddings.embed_query
)
res = vector_store.similarity_search(
query="Can Colleagues contact their managers?", k=20, search_type="hybrid", filters="title eq '25_1_0.pdf'")
The res object contains the chunkcs where title is 25_1_0.pdf' ONLY
```
However when using it with an LLM:
```
llm = AzureChatOpenAI(
azure_deployment="chat",
openai_api_version="2023-05-15",
)
retriever = vector_store.as_retriever(search_type="similarity", filters="title eq '25_1_0.pdf'", kwargs={"k": 3})
chain = RetrievalQA.from_chain_type(llm=llm,
chain_type="stuff",
retriever=retriever,
return_source_documents=True)
result = chain({"query": 'Can Colleagues contact their managers??'})
for res in result['source_documents']:
print(res.metadata['title'])
```
My output has chunks which dont respect the filter:
142_2_0.pdf
99_9_0.docx
99_9_0.docx
142_2_0.pdf
### Expected behavior
The answer generated with source_documents, should contain chunks which respects the given filters. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14226 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14226/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14226/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14226/events | https://github.com/langchain-ai/langchain/issues/14226 | 2,023,977,308 | I_kwDOIPDwls54o3Fc | 14,226 | partially initialized module 'requests' has no attribute 'Response' | {
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] | open | false | null | [] | null | 1 | 2023-12-04T14:24:15 | 2023-12-04T14:33:55 | null | CONTRIBUTOR | null | ### System Info
ml.g5.48xlarge EC2 instance on AWS with:
- Langchain 0.0.305
- Python 3.10
### Who can help?
@hwchase17 @agola11
### 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
I run into the above error when importing `HuggingFaceEmbeddings`.
```py
from langchain.embeddings import HuggingFaceEmbeddings
```
I believe this is due to the fact that we have a file named `requests.py` in the root folder of the project, which conflicts with the `requests` package.
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[19], line 1
----> 1 from langchain.embeddings import HuggingFaceEmbeddings
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/langchain/embeddings/__init__.py:17
14 import logging
15 from typing import Any
---> 17 from langchain.embeddings.aleph_alpha import (
18 AlephAlphaAsymmetricSemanticEmbedding,
19 AlephAlphaSymmetricSemanticEmbedding,
20 )
21 from langchain.embeddings.awa import AwaEmbeddings
22 from langchain.embeddings.azure_openai import AzureOpenAIEmbeddings
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/langchain/embeddings/aleph_alpha.py:6
3 from langchain_core.embeddings import Embeddings
4 from langchain_core.pydantic_v1 import BaseModel, root_validator
----> 6 from langchain.utils import get_from_dict_or_env
9 class AlephAlphaAsymmetricSemanticEmbedding(BaseModel, Embeddings):
10 """Aleph Alpha's asymmetric semantic embedding.
11
12 AA provides you with an endpoint to embed a document and a query.
(...)
30
31 """
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/langchain/utils/__init__.py:14
7 from langchain_core.utils.formatting import StrictFormatter, formatter
8 from langchain_core.utils.input import (
9 get_bolded_text,
10 get_color_mapping,
11 get_colored_text,
12 print_text,
13 )
---> 14 from langchain_core.utils.utils import (
15 check_package_version,
16 convert_to_secret_str,
17 get_pydantic_field_names,
18 guard_import,
19 mock_now,
20 raise_for_status_with_text,
21 xor_args,
22 )
24 from langchain.utils.env import get_from_dict_or_env, get_from_env
25 from langchain.utils.math import cosine_similarity, cosine_similarity_top_k
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/langchain_core/utils/__init__.py:14
7 from langchain_core.utils.formatting import StrictFormatter, formatter
8 from langchain_core.utils.input import (
9 get_bolded_text,
10 get_color_mapping,
11 get_colored_text,
12 print_text,
13 )
---> 14 from langchain_core.utils.loading import try_load_from_hub
15 from langchain_core.utils.utils import (
16 build_extra_kwargs,
17 check_package_version,
(...)
23 xor_args,
24 )
26 __all__ = [
27 "StrictFormatter",
28 "check_package_version",
(...)
41 "build_extra_kwargs",
42 ]
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/langchain_core/utils/loading.py:10
7 from typing import Any, Callable, Optional, Set, TypeVar, Union
8 from urllib.parse import urljoin
---> 10 import requests
12 DEFAULT_REF = os.environ.get("LANGCHAIN_HUB_DEFAULT_REF", "master")
13 URL_BASE = os.environ.get(
14 "LANGCHAIN_HUB_URL_BASE",
15 "[https://raw.githubusercontent.com/hwchase17/langchain-hub/{ref}/](https://raw.githubusercontent.com/hwchase17/langchain-hub/%7Bref%7D/)",
16 )
File ~/SageMaker/langchain/libs/langchain/langchain/requests.py:2
1 """DEPRECATED: Kept for backwards compatibility."""
----> 2 from langchain.utilities import Requests, RequestsWrapper, TextRequestsWrapper
4 __all__ = [
5 "Requests",
6 "RequestsWrapper",
7 "TextRequestsWrapper",
8 ]
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/langchain/utilities/__init__.py:8
1 """**Utilities** are the integrations with third-part systems and packages.
2
3 Other LangChain classes use **Utilities** to interact with third-part systems
4 and packages.
5 """
6 from typing import Any
----> 8 from langchain.utilities.requests import Requests, RequestsWrapper, TextRequestsWrapper
11 def _import_alpha_vantage() -> Any:
12 from langchain.utilities.alpha_vantage import AlphaVantageAPIWrapper
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/langchain/utilities/requests.py:10
6 import requests
7 from langchain_core.pydantic_v1 import BaseModel, Extra
---> 10 class Requests(BaseModel):
11 """Wrapper around requests to handle auth and async.
12
13 The main purpose of this wrapper is to handle authentication (by saving
14 headers) and enable easy async methods on the same base object.
15 """
17 headers: Optional[Dict[str, str]] = None
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/langchain/utilities/requests.py:27, in Requests()
24 extra = Extra.forbid
25 arbitrary_types_allowed = True
---> 27 def get(self, url: str, **kwargs: Any) -> requests.Response:
28 """GET the URL and return the text."""
29 return requests.get(url, headers=self.headers, auth=self.auth, **kwargs)
AttributeError: partially initialized module 'requests' has no attribute 'Response' (most likely due to a circular import)
```
### Expected behavior
To be able to correctly import and use the embeddings. | {
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] | open | false | null | [] | null | 2 | 2023-12-04T12:21:19 | 2023-12-04T18:07:30 | null | NONE | null | ### System Info
Python: 3.10
LangChain: 0.0.344
OpenSearch: Amazon OpenSearch Serverless Vector Engine
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I have loaded documents with metadata into OpenSearch which includes a date value. When generating a SelfQuerying Retriever the generated query specifies the date type as "date", and results in an invalid query being generated and sent to OpenSearch that then returns an error. I have set the AttributeInfo to "string", but even if it was "date" the query would be invalid.
```
document_content_description = "Feedback from users"
metadata_field_info = [
AttributeInfo(
name="timestamp",
description="A string representing the date that the feedback was submitted in the 'yyyy-MM-dd' format",
type="string",
)
]
retriever = SelfQueryRetriever.from_llm(
llm, vectorstore, document_content_description, metadata_field_info, verbose=True
)
retriever.get_relevant_documents("Summarize feedback submitted on 2023-06-01")
```
Output Logs:
```
"repr": "StructuredQuery(query=' ', filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='timestamp', value={'date': '2023-06-01', 'type': 'date'}), limit=None)"
...
{
"size": 4,
"query": {
"bool": {
"filter": {
"term": {
"metadata.timestamp": {
"date": "2023-06-01",
"type": "date"
}
}
},
...
}
{
"error": {
"root_cause": [
{
"type": "parsing_exception",
"reason": "[term] query does not support [date]",
"line": 1,
"col": 76
}
],
"type": "x_content_parse_exception",
"reason": "[1:76] [bool] failed to parse field [filter]",
"caused_by": {
"type": "parsing_exception",
"reason": "[term] query does not support [date]",
"line": 1,
"col": 76
}
},
"status": 400
}
```
### Expected behavior
LangChain should generate a valid OpenSearch query such as:
```
{
"size": 4,
"query": {
"bool": {
"filter": {
"term": {
"metadata.timestamp.keyword": "2023-06-01"
}
},
...
}
``` | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14224 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14224/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14224/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14224/events | https://github.com/langchain-ai/langchain/pull/14224 | 2,023,730,229 | PR_kwDOIPDwls5hDCXv | 14,224 | Adds `llm_chain_kwargs` to `BaseRetrievalQA.from_llm` | {
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] | closed | false | null | [] | null | 2 | 2023-12-04T12:20:03 | 2023-12-04T19:34:02 | 2023-12-04T19:34:01 | CONTRIBUTOR | null | - **Description:** Adds `llm_chain_kwargs` to `BaseRetrievalQA.from_llm` so these can be passed to the LLM at runtime,
- **Issue:** https://github.com/langchain-ai/langchain/issues/14216,
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https://api.github.com/repos/langchain-ai/langchain/issues/14223 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14223/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14223/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14223/events | https://github.com/langchain-ai/langchain/issues/14223 | 2,023,542,482 | I_kwDOIPDwls54nM7S | 14,223 | Dall-E Image Generator return url without authentication information | {
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] | open | false | null | [] | null | 1 | 2023-12-04T10:41:50 | 2023-12-04T10:48:15 | null | CONTRIBUTOR | null | ### System Info
MacOS
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
As described in the [docs page](https://python.langchain.com/docs/integrations/tools/dalle_image_generator):
```
from langchain.agents import initialize_agent, load_tools
tools = load_tools(["dalle-image-generator"])
agent = initialize_agent(tools, llm, agent="zero-shot-react-description", verbose=True)
output = agent.run("Create an image of a halloween night at a haunted museum")
```
Below is output:
```
> Entering new AgentExecutor chain...
I need to generate an image from a text description
Action: Dall-E-Image-Generator
Action Input: "Halloween night at a haunted museum"
Observation: https://oaidalleapiprodscus.blob.core.windows.net/private/org-yt03sAlJZ8YRfqIcNivAAqZu/user-xV6mgISZftMROz9SukNqCHqH/img-EKpmrjqlb1988YrkkBm0vgjr.png?st=2023-12-04T08%3A30%3A50Z&se=2023-12-04T10%3A30%3A50Z&sp=r&sv=2021-08-06&sr=b&rscd=inline&rsct=image/png&skoid=6aaadede-4fb3-4698-a8f6-684d7786b067&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2023-12-03T23%3A02%3A03Z&ske=2023-12-04T23%3A02%3A03Z&sks=b&skv=2021-08-06&sig=9TvprwW3Wl3ZHj%2B2ga6juBT1KQLJIc9TUz%2BDIVcd3XA%3D
Thought: I now know the final answer
Final Answer: https://oaidalleapiprodscus.blob.core.windows.net/private/org-yt03sAlJZ8YRfqIcNivAAqZu/user-xV6mgISZftMROz9SukNqCHqH/img-EKpmrjqlb1988YrkkBm0vgjr.png?st=2023-12-04T08%3A30%3A50Z&se=2023-12-04T10%3A30%3A50Z&sp=r&sv=2021-08-06&sr=b&rscd=inline&rsct=image/png&skoid=6aaadede-4fb3-4698-a8f6-684d7786b067&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2023-12-03T23%3A02%3A03Z&ske=2023-12-04T23%
> Finished chain.
```
Seems ok? But the url has been cut, it not origin! So you will not get the right picture.
Instead got this:
<img width="1472" alt="image" src="https://github.com/langchain-ai/langchain/assets/19658300/f856e606-9d9e-466d-bd8f-de4eed70f15c">
### Expected behavior
Dalle is a import tool in openai.
I want get the picture which I can see it. So it should give me the origin link.
Just like this:
```
> Entering new AgentExecutor chain...
I can use the Dall-E-Image-Generator to generate an image of a volcano island based on the text description.
Action: Dall-E-Image-Generator
Action Input: "A volcano island"
Observation: https://oaidalleapiprodscus.blob.core.windows.net/private/org-yt03sAlJZ8YRfqIcNivAAqZu/user-xV6mgISZftMROz9SukNqCHqH/img-H3m0wSNxDXVUkUKiE9kOKgvg.png?st=2023-12-04T09%3A39%3A05Z&se=2023-12-04T11%3A39%3A05Z&sp=r&sv=2021-08-06&sr=b&rscd=inline&rsct=image/png&skoid=6aaadede-4fb3-4698-a8f6-684d7786b067&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2023-12-03T22%3A42%3A28Z&ske=2023-12-04T22%3A42%3A28Z&sks=b&skv=2021-08-06&sig=WSEO5/OX5GgYaNTWxZhNmsK%2BqeaDLMEsDdGEnHX18BY%3D
Thought:I now know the final answer.
Final Answer: The image of a volcano island can be found at the following link: https://oaidalleapiprodscus.blob.core.windows.net/private/org-yt03sAlJZ8YRfqIcNivAAqZu/user-xV6mgISZftMROz9SukNqCHqH/img-H3m0wSNxDXVUkUKiE9kOKgvg.png?st=2023-12-04T09%3A39%3A05Z&se=2023-12-04T11%3A39%3A05Z&sp=r&sv=2021-08-06&sr=b&rscd=inline&rsct=image/png&skoid=6aaadede-4fb3-4698-a8f6-684d7786b067&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2023-12-03T22%3A42%3A28Z&ske=2023-12-04T22%3A42%3A28Z&sks=b&skv=2021-08-06&sig=WSEO5/OX5GgYaNTWxZhNmsK%2BqeaDLMEsDdGEnHX18BY%3D
> Finished chain.
The image of a volcano island can be found at the following link: https://oaidalleapiprodscus.blob.core.windows.net/private/org-yt03sAlJZ8YRfqIcNivAAqZu/user-xV6mgISZftMROz9SukNqCHqH/img-H3m0wSNxDXVUkUKiE9kOKgvg.png?st=2023-12-04T09%3A39%3A05Z&se=2023-12-04T11%3A39%3A05Z&sp=r&sv=2021-08-06&sr=b&rscd=inline&rsct=image/png&skoid=6aaadede-4fb3-4698-a8f6-684d7786b067&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2023-12-03T22%3A42%3A28Z&ske=2023-12-04T22%3A42%3A28Z&sks=b&skv=2021-08-06&sig=WSEO5/OX5GgYaNTWxZhNmsK%2BqeaDLMEsDdGEnHX18BY%3D
``` | {
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- **Description:** Increased compatibility with all versions openai for dalle,
This pr add support for openai version from 0 ~ 1.3.
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https://api.github.com/repos/langchain-ai/langchain/issues/14220 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14220/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14220/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14220/events | https://github.com/langchain-ai/langchain/pull/14220 | 2,023,464,151 | PR_kwDOIPDwls5hCHdh | 14,220 | add(feat): Text Embeddings by Cloudflare Workers AI | {
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Trying to align it with its langchain-js version counterpart [here](https://api.js.langchain.com/classes/embeddings_cloudflare_workersai.CloudflareWorkersAIEmbeddings.html).
- Dependencies: N/A
- Done `make format` `make lint` `make spell_check` `make integration_tests` and all my changes was passed
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] | open | false | null | [] | null | 2 | 2023-12-04T10:01:18 | 2024-01-09T22:06:58 | null | NONE | null | - **Description:** Bedrock API now returns [token usage](https://docs.aws.amazon.com/bedrock/latest/userguide/monitoring-cw.html) details across all hosted models which can be used to collect statistics on token usage. This can be used to implement a similar [token usage callback](https://python.langchain.com/docs/modules/model_io/llms/token_usage_tracking) as OpenAI.
- **Issue:** #14120
- **Dependencies:** None
- **Tag maintainer:** @3coins
Token counting (as for OpenAI's callback) only works for the non-stream variant currently and hence the warning has been included.
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https://api.github.com/repos/langchain-ai/langchain/issues/14218 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14218/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14218/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14218/events | https://github.com/langchain-ai/langchain/issues/14218 | 2,023,436,264 | I_kwDOIPDwls54my_o | 14,218 | Table data extraction from HTML files. | {
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I am working on extracting data from HTML files. I need to extract table data to store in a data frame as a table. With the help of langchain document loader I can extract the data row wise but the headers of columns are not getting extracted. How to extract column headers along with the data.
### Suggestion:
_No response_ | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14217 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14217/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14217/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14217/events | https://github.com/langchain-ai/langchain/pull/14217 | 2,023,422,028 | PR_kwDOIPDwls5hB-L2 | 14,217 | [Revised] Virtual column name for self-querying retrievers | {
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Replace this entire comment with:
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- **Issue:** the issue # it fixes (if applicable),
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Self query retrievers are powerful and it can be even stronger than before. Taking MyScale as an example, sometimes the user may add complex SQL function to a column name. So we add a `VirtualColumnName` for self-query retriever to reduce token usage with extra functionality.
1. a function that dynamically creates function mapped column names, which becomes handy when the user want to compare between current time / location or other attributes along with a column.
2. complex function call in plain string can now be replaced with a shorter nickname in prompt, which saves number of token for very long function names.
3. refined the logic in MyScale translator to meet this standard.
This change will not affect other self query retrievers as it preserves the original plain string interface under comparison. ~~The actual difference happens under `QueryTransformer` in `query_construct.parser` for `Lark` parser.~~
`Comparator.attribute` can now be either a string or a `VirtualColumnName`. ~~I have defined the default behaviour which stops the user to use `VirtualColumnName` under other vectorstores than MyScale.~~ Parser for `VirtualColumnName`s are chained with original structured query parser, using LCEL.
This functionality will boost the self-query retrievers as a bridge between text and simple SQL query. And we believe this will help users to expand their usage to this retriever. Other SQL vector database is theoretically compatible to this new feature as well.
Hope you find this interesting.
@hwchase17 @baskaryan @efriis
Our contact on twitter: @MyScaleDB @mpsk_liu
=========
Updates:
* Separate from `StructuredQueryOutputParser` and created new `VirtualColumnParser`
* Rewritten with LCEL
* Won't hurt any inheritance / API signatures
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https://api.github.com/repos/langchain-ai/langchain/issues/14216 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14216/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14216/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14216/events | https://github.com/langchain-ai/langchain/issues/14216 | 2,023,415,515 | I_kwDOIPDwls54mt7b | 14,216 | Add `llm_kwargs` to `BaseRetrievalQA.from_llm` | {
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] | open | false | null | [] | null | 2 | 2023-12-04T09:38:10 | 2023-12-04T11:06:33 | null | CONTRIBUTOR | null | https://github.com/langchain-ai/langchain/blob/ee94ef55ee6ab064da08340817955f821dfa6261/libs/langchain/langchain/chains/llm.py#L71
In my humble opinion, I think `llm_kwargs` variable should be a parameter declared in the LLM class rather than the LLM Chain. One of the reasons for this, is that when you are declaring, for instance, a `RetrievalQA` chain `from_chain_type` or `from_llm` , you cannot specify these `llm_kwargs`.
You can workaround that by calling the inner `llm_chain` with (given that you're using a `combine_documents_chain`):
```python
chain.combine_documents_chain.llm_chain.llm_kwargs = {'test': 'test'}
```
But seems very odd that you have to do that. And the kwargs seems that it should be a responsibility of the LLM, not the LLM Chain.
Happy to be proven wrong! 🙂 | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14215 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14215/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14215/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14215/events | https://github.com/langchain-ai/langchain/pull/14215 | 2,023,332,519 | PR_kwDOIPDwls5hBqpx | 14,215 | fixed: ModuleNotFoundError: No module named 'clarifai.auth' | {
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] | closed | false | null | [] | null | 1 | 2023-12-04T08:51:08 | 2023-12-04T19:53:35 | 2023-12-04T19:53:35 | CONTRIBUTOR | null | Updated the clarifai imports
fixed: #14175
@efriis
@baskaryan | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14214 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14214/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14214/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14214/events | https://github.com/langchain-ai/langchain/pull/14214 | 2,023,186,118 | PR_kwDOIPDwls5hBKrx | 14,214 | Fix issue(#14213): BiliBiliLoader Credential No Sessdata error | {
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- **Description:** Fix the BiliBiliLoader that can receive cookie parameters; Add example in docs
- **Issue:** [#14213]
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https://api.github.com/repos/langchain-ai/langchain/issues/14213 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14213/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14213/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14213/events | https://github.com/langchain-ai/langchain/issues/14213 | 2,023,161,496 | I_kwDOIPDwls54lv6Y | 14,213 | BiliBiliLoader Credential No Sessdata error | {
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] | open | false | null | [] | null | 2 | 2023-12-04T06:58:42 | 2023-12-04T09:07:10 | null | NONE | null | ### System Info
Mac os
Python3.9
### Who can help?
@Jiaaming
I'm working on this issue.
### 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
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
when you run the BiliBiliLoader example from the official [docs](https://python.langchain.com/docs/integrations/document_loaders/bilibili)
```
loader = BiliBiliLoader(
[
"https://www.bilibili.com/video/BV1g84y1R7oE/",
]
)
docs = loader.load()
print(docs)
```
will get
```
bilibili_api.exceptions.CredentialNoSessdataException.CredentialNoSessdataException: Credential 类未提供 sessdata 或者为空。
Process finished with exit code 1
```
The is because the original [bilibili_api](https://nemo2011.github.io/bilibili-api/#/get-credential) require a Credential to fetch the info from the video
### Expected behavior
Should return a Document object
```
[Document(page_content="Video Title:...,description:....)]
``` | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14212 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14212/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14212/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14212/events | https://github.com/langchain-ai/langchain/pull/14212 | 2,023,131,973 | PR_kwDOIPDwls5hA-tr | 14,212 | Support `openai_proxy` compatible with openai>1.0.0 | {
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`openai` v1 no longer supports the method of setting `openai.proxy`. If using `OPENAI_PRPXY`, the user needs to initialize an http_client.
### **Issue:**
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] | closed | false | null | [] | null | 1 | 2023-12-04T05:36:46 | 2023-12-04T20:03:48 | 2023-12-04T20:03:48 | CONTRIBUTOR | null | - **Description:** The commands can be more efficient if the output name is set to the destined filename instead of renaming in the second command.
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] | open | false | null | [] | null | 4 | 2023-12-04T04:34:52 | 2023-12-05T11:31:16 | null | NONE | null | ### Issue you'd like to raise.
ValidationError: 2 validation errors for ConversationChain
advisor_summary
extra fields not permitted (type=value_error.extra)
__root__
Got unexpected prompt input variables. The prompt expects ['advisor_summary', 'history', 'input'], but got ['history'] as inputs from memory, and input as the normal input key. (type=value_error)
How to resolve this error?
### Suggestion:
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https://api.github.com/repos/langchain-ai/langchain/issues/14209 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14209/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14209/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14209/events | https://github.com/langchain-ai/langchain/issues/14209 | 2,022,958,852 | I_kwDOIPDwls54k-cE | 14,209 | Issue: How to avoid modifying user input and output in initialize_agent? | {
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Hi, I am trying to use `initialize_agent` to leverage custom tools but when the agent is accepting it, it is modifying the user's input in its own way and causing error in the output. For example, in SQLTool, it is automatically converting it into SQL query which is wrong and ended up giving me wrong answer. I want to know how can I ensure raw input is getting passed into the `action_input` of `initialize_agent`! An example below:
User input: How many users have purchased PlayStation from New York in October?
Expected flow of execution will start like this:
```
> Entering new AgentExecutor chain...
Action:
```
{
"action": "SQLTool",
"action_input": {
"raw_input": "How many users have purchased PlayStation from New York in October?"
}
}
```
```
Also, since I am using BaseTool in the custom tools, I had to ensure output format is in string - can we customize it like getting it in JSON format?
### Suggestion:
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https://api.github.com/repos/langchain-ai/langchain/issues/14208 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14208/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14208/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14208/events | https://github.com/langchain-ai/langchain/issues/14208 | 2,022,925,148 | I_kwDOIPDwls54k2Nc | 14,208 | Issue: <How to use the local huggingface models> | {
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I have a problem in this code `llm_chain = LLMChain(prompt=prompt,llm=local_llm)`, how to define the local huggingface models into this `local_llm`
### Suggestion:
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https://api.github.com/repos/langchain-ai/langchain/issues/14207 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14207/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14207/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14207/events | https://github.com/langchain-ai/langchain/issues/14207 | 2,022,901,487 | I_kwDOIPDwls54kwbv | 14,207 | Issue: I'd like to use Hugging Face's Chat UI frontend with LangChain. | {
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] | open | false | null | [] | null | 1 | 2023-12-04T02:38:47 | 2023-12-04T02:45:57 | null | NONE | null | ### Issue you'd like to raise.
I'd like to use Hugging Face's Chat UI frontend with LangChain.
https://github.com/huggingface/chat-ui
But it looks like the Chat UI is only available through Hugginf Face's Text Generation Inference endpoint.
https://github.com/huggingface/chat-ui/issues/466
How can I serve the chain I have configured with LangChain in TGI format so I can use Chat UI?
Thank you in advance.
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https://api.github.com/repos/langchain-ai/langchain/issues/14206 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14206/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14206/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14206/events | https://github.com/langchain-ai/langchain/pull/14206 | 2,022,865,130 | PR_kwDOIPDwls5hAEhg | 14,206 | Modify LLMs/Anyscale work with OpenAI API v1 | {
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] | null | 13 | 2023-12-04T01:55:14 | 2024-01-11T13:25:15 | null | CONTRIBUTOR | null | <!-- 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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If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
- **Description:**
1. Modify LLMs/Anyscale to work with OAI v1
2. Get rid of openai_ prefixed variables in Chat_model/ChatAnyscale
3. Modify `anyscale_api_base` to `anyscale_base_url` to follow OAI name convention (reverted) | {
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`{"filter": {"key": { "NIN" : ["list", "of", "values"]}}}`
"IN" is already supported, so this is a quick & related update to add "NIN"
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- Moved file to the `https://python.langchain.com/docs/integrations/providers/semadb`
- Added a redirect for the file URL | {
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Hi,
I am wondering if anyone has a work around using ConversationalRetrievalChain to retrieve documents with their sources, and prevent the chain from returning sources for questions without sources.
query = "How are you doing?"
result = chain({"question": query, "chat_history": chat_history})
result['answer']
"""
I'm doing well, thank you.
SOURCES: /content/xxx.pdf
"""
### Suggestion:
SOURCES: | {
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] | null | 2 | 2023-12-03T19:46:13 | 2023-12-04T22:43:34 | 2023-12-04T22:43:33 | CONTRIBUTOR | null | Allow users to pass a generic `BaseStore[str, bytes]` to MultiVectorRetriever, removing the need to use the `create_kv_docstore` method. This encoding will now happen internally.
@rlancemartin @eyurtsev | {
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CC @rlancemartin @eyurtsev | {
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also im not sure this is even really a test file? which is why i didnt move it
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https://api.github.com/repos/langchain-ai/langchain/issues/14199 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14199/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14199/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14199/events | https://github.com/langchain-ai/langchain/issues/14199 | 2,022,602,013 | I_kwDOIPDwls54jnUd | 14,199 | Can i save openai token usage information which get from get_openai_callback directlly to database with SQLChatMessageHistory | {
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] | open | false | null | [] | null | 1 | 2023-12-03T15:59:28 | 2023-12-03T16:06:05 | null | NONE | null | ### Issue you'd like to raise.
_No response_
### Suggestion:
_No response_ | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14198 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14198/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14198/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14198/events | https://github.com/langchain-ai/langchain/issues/14198 | 2,022,589,587 | I_kwDOIPDwls54jkST | 14,198 | Amazon Kendra: Full answer text not returned for FAQ type answers | {
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] | open | false | null | [] | null | 1 | 2023-12-03T15:31:50 | 2023-12-03T15:34:03 | null | NONE | null | ### System Info
Langchain version: 0.0.342
Python version: 3.9
OS: Mac OS
### Who can help?
@3coins
### 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
Use any Kendra index that contains FAQ style answers and try to query them as a retriever.
### Expected behavior
### Background
When using `AmazonKendraRetriever`, `get_relevant_documents` first calls the `retrieve` API and if nothing is returned, it calls the `query` API (see [here](https://github.com/langchain-ai/langchain/blob/0bdb4343838c4513d15cd9702868adf6f652421c/libs/langchain/langchain/retrievers/kendra.py#L391-L399)).
The `query` API has the capability to return answers from not just documents but also Kendra's FAQs (as described in [API docs for Kendra](https://docs.aws.amazon.com/kendra/latest/dg/query-responses-types.html#response-types)).
### Issue
While Kendra normally only returns snippets, it does return full text for FAQs. The problem is that it is ignored by langchain today hence return snippet for FAQs also. The problem lies in these line of code:
https://github.com/langchain-ai/langchain/blob/0bdb4343838c4513d15cd9702868adf6f652421c/libs/langchain/langchain/retrievers/kendra.py#L225-L244
The Key `AnswerText` is always assumed to be at the 0th index when it can often be in fact on the 1st index. No such assumption can made also based on the [documentation](https://docs.aws.amazon.com/kendra/latest/APIReference/API_QueryResultItem.html#API_QueryResultItem_Contents) of the `QueryResultItem` structure.
For the Kendra index I am testing with, this is indeed the case which is how I discovered the bug.
This is easily fixable where we loop over all indices to search for the key `AnswerText`. I can make the PR if this is deemed as the desired behavior.
Side note: I am happy to add some tests for `kendra.py` which I realized has no testing impemented. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14197 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14197/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14197/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14197/events | https://github.com/langchain-ai/langchain/pull/14197 | 2,022,446,765 | PR_kwDOIPDwls5g-uA2 | 14,197 | Fix Html2TextTransformer for shallow copy | {
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] | closed | false | null | [] | null | 1 | 2023-12-03T09:53:16 | 2023-12-03T16:45:35 | 2023-12-03T16:45:35 | 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),
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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.
-->
Hi,
There is some unintended behavior in Html2TextTransformer.
The current code is **directly modifying the original documents that are passed as arguments to the function.**
Therefore, not only the return of the function but also the input variables are being modified simultaneously.
**To resolve this, I added unit test code as well.**
reference link: [Shallow vs Deep Copying of Python Objects](https://realpython.com/copying-python-objects/)
Thanks! ☺️
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https://api.github.com/repos/langchain-ai/langchain/issues/14196 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14196/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14196/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14196/events | https://github.com/langchain-ai/langchain/issues/14196 | 2,022,412,641 | I_kwDOIPDwls54i5Fh | 14,196 | TypeError: 'HumanMessage' object is not subscriptable | {
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] | open | false | null | [] | null | 6 | 2023-12-03T07:57:32 | 2024-01-11T03:49:30 | null | NONE | null | Hey @dosubot, im using this code
memory = ConversationBufferMemory(
return_messages=True, output_key="answer", input_key="question"
)
retriever = load_emdeddings(cfg.faiss_persist_directory, cfg.embeddings).as_retriever(search_type="similarity_score_threshold",
search_kwargs={"score_threshold": .65,
"k": 2})
memory.load_memory_variables({})
_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.
Chat History:
{chat_history}
Follow Up Input: {question}
Standalone question:"""
CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
template = """Answer the question based only on the following context:
{context}
Question: {question}
"""
ANSWER_PROMPT = ChatPromptTemplate.from_template(template)
DEFAULT_DOCUMENT_PROMPT = PromptTemplate.from_template(template="{page_content}")
def _combine_documents(
docs, document_prompt=DEFAULT_DOCUMENT_PROMPT, document_separator="\n\n"
):
doc_strings = [format_document(doc, document_prompt) for doc in docs]
return document_separator.join(doc_strings)
def _format_chat_history(chat_history: List[Tuple[str, str]]) -> str:
# chat history is of format:
# [
# (human_message_str, ai_message_str),
# ...
# ]
# see below for an example of how it's invoked
buffer = ""
for dialogue_turn in chat_history:
human = "Human: " + dialogue_turn[0]
ai = "Assistant: " + dialogue_turn[1]
buffer += "\n" + "\n".join([human, ai])
return buffer
_inputs = RunnableParallel(
standalone_question=RunnablePassthrough.assign(
chat_history=lambda x: _format_chat_history(x["chat_history"])
)
| CONDENSE_QUESTION_PROMPT
| ChatOpenAI(temperature=0)
| StrOutputParser(),
)
_context = {
"context": itemgetter("standalone_question") | retriever | _combine_documents,
"question": lambda x: x["standalone_question"],
}
conversational_qa_chain = _inputs | _context | ANSWER_PROMPT | ChatOpenAI()
loaded_memory = RunnablePassthrough.assign(
chat_history=RunnableLambda(memory.load_memory_variables) | itemgetter("history")
)
# Now we calculate the standalone question
standalone_question = {
"standalone_question": {
"question": lambda x: x["question"],
"chat_history": lambda x: _format_chat_history(x["chat_history"]),
}
| CONDENSE_QUESTION_PROMPT
| ChatOpenAI(temperature=0)
| StrOutputParser(),
}
# Now we retrieve the documents
retrieved_documents = {
"docs": itemgetter("standalone_question") | retriever,
"question": lambda x: x["standalone_question"],
}
# Now we construct the inputs for the final prompt
final_inputs = {
"context": lambda x: _combine_documents(x["docs"]),
"question": itemgetter("question"),
}
# And finally, we do the part that returns the answers
answer = {
"answer": final_inputs | ANSWER_PROMPT | ChatOpenAI(),
"docs": itemgetter("docs"),
}
# And now we put it all together!
final_chain = loaded_memory | standalone_question | retrieved_documents | answer
inputs = {"question": "what is my name?"}
result = final_chain.invoke(inputs)
memory.save_context(inputs, {"answer": result["answer"].content})
result
**if i run this second time then im getting this error
TypeError: 'HumanMessage' object is not subscriptable**
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https://api.github.com/repos/langchain-ai/langchain/issues/14195 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14195/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14195/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14195/events | https://github.com/langchain-ai/langchain/pull/14195 | 2,022,390,395 | PR_kwDOIPDwls5g-jC1 | 14,195 | Asynchronous human-in-the-loop callback | {
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**Issue:** -
**Dependencies:** - | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14194 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14194/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14194/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14194/events | https://github.com/langchain-ai/langchain/pull/14194 | 2,022,386,286 | PR_kwDOIPDwls5g-iOy | 14,194 | Fix: (issue #14127) Volc Engine MaaS import error | {
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- **Issue:** [the issue # it fixes (if applicable),](https://github.com/langchain-ai/langchain/issues/14127)
- **Dependencies:** None
- **Tag maintainer:** @baskaryan
- **Twitter handle:** | {
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- **Issue:** https://github.com/langchain-ai/langchain/issues/12165
- **Dependencies:** N/A
- **Tag maintainer:** @eyurtsev
- **Twitter handle:** `eekaiboon` | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14192 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14192/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14192/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14192/events | https://github.com/langchain-ai/langchain/issues/14192 | 2,022,372,837 | I_kwDOIPDwls54ivXl | 14,192 | Passing BaseModel type data from agent to tool | {
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We create an agent using initialize_agent, register the tool, and configure it to be selected through the agent.
However, in the agent, regardless of which tool is selected, I want to pass a data object called request of BaseModel type when executing the tool.
I would appreciate it if you could tell me how to configure and connect the agent and tool.
### Suggestion:
_No response_ | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14191 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14191/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14191/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14191/events | https://github.com/langchain-ai/langchain/issues/14191 | 2,022,366,141 | I_kwDOIPDwls54itu9 | 14,191 | How do i use system prompt template inside conversational retrieval chain? | {
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] | open | false | null | [] | null | 8 | 2023-12-03T04:30:52 | 2024-01-12T10:09:38 | null | NONE | null | @dosubot , how do i use system prompt inside conversational retreival chain?
prompt = ChatPromptTemplate(
messages=[
SystemMessagePromptTemplate.from_template(
"You are a nice chatbot named James-AI having a conversation with a human."
),
MessagesPlaceholder(variable_name="chat_history"),
HumanMessagePromptTemplate.from_template("{question}")]
)
memory = ConversationBufferWindowMemory(k=5, memory_key="chat_history", return_messages=True)
**retriever = new_db.as_retriever(search_type="similarity_score_threshold",
search_kwargs={"score_threshold": .65,
"k": 2})**
**qa = ConversationalRetrievalChain.from_llm(cfg.llm, verbose=True,retriever=retriever, memory=memory, prompt=prompt)**
if i use like that then it says
ValidationError: 1 validation error for ConversationalRetrievalChain
prompt
extra fields not permitted (type=value_error.extra) | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14190 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14190/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14190/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14190/events | https://github.com/langchain-ai/langchain/pull/14190 | 2,022,345,993 | PR_kwDOIPDwls5g-Zmn | 14,190 | Added Python `logging` tracer | {
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] | closed | false | null | [] | null | 1 | 2023-12-03T04:03:29 | 2023-12-03T17:37:10 | 2023-12-03T17:36:31 | CONTRIBUTOR | null | This PR creates a logging handler and adds a simple unit test of it
Supercedes https://github.com/langchain-ai/langchain/pull/12862 | {
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] | open | false | null | [] | null | 2 | 2023-12-03T02:55:24 | 2024-01-12T00:23:43 | null | NONE | null | ### Issue you'd like to raise.
When I use an existing library that includes langsmith (such as https://github.com/langchain-ai/weblangchain), but I don't have access to langsmith API as I'm still on the waitlist. How can I quickly disable the langsmith functionality without commenting out the code?
### Suggestion:
_No response_ | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14186 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14186/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14186/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14186/events | https://github.com/langchain-ai/langchain/pull/14186 | 2,022,254,418 | PR_kwDOIPDwls5g-GIO | 14,186 | Multi-modal RAG template | {
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* GPT-4V | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14185 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14185/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14185/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14185/events | https://github.com/langchain-ai/langchain/issues/14185 | 2,022,245,713 | I_kwDOIPDwls54iQVR | 14,185 | Issue: Unable to properly parse output of custom chat agent. | {
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] | open | false | null | [] | null | 4 | 2023-12-02T22:59:44 | 2023-12-21T02:53:58 | null | NONE | null | ### Issue you'd like to raise.
pip list:
- langchain 0.0.314
- langchain-core 0.0.8
```python
prompt_template = """
### [INST]
Assistant is a large language model trained by Mistral.
Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.
Assistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.
Overall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.
Context:
------
Assistant has access to the following tools:
{tools}
To use a tool, please use the following format:
'''
Thought: Do I need to use a tool? Yes
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
'''
When you have a response to say to the Human, or if you do not need to use a tool, you MUST use the format:
'''
Thought: Do I need to use a tool? No
Final Answer: [your response here]
'''
Begin!
Previous conversation history:
{chat_history}
New input: {input}
Current Scratchpad:
{agent_scratchpad}
[/INST]
"""
# Create prompt from prompt template
prompt = PromptTemplate(
input_variables=['agent_scratchpad', 'chat_history', 'input', 'tool_names', 'tools'],
template=prompt_template,
)
prompt = prompt.partial(
tools=render_text_description(tools),
tool_names=", ".join([t.name for t in tools]),
)
# Create llm chain
llm_chain = LLMChain(llm=mistral_llm, prompt=prompt)
from langchain.agents import AgentOutputParser
from langchain_core.agents import AgentAction, AgentFinish
import re
from typing import List, Union
class CustomOutputParser(AgentOutputParser):
def parse(self, output) -> Union[AgentAction, AgentFinish]:
print(output)
output = ' '.join(output.split())
# Check if the output contains 'Final Answer:'
if 'Final Answer:' in output:
# Extract the final answer from the output
final_answer = output.split('Final Answer:')[1].strip()
print(final_answer)
agent_finish = AgentFinish(
# Return values is generally always a dictionary with a single `output` key
# It is not recommended to try anything else at the moment :)
return_values={"output": final_answer},
log=output,
)
print(agent_finish)
return agent_finish
else:
# Handle other cases or raise an error
raise ValueError("Unexpected output format")
output_parser = CustomOutputParser()
# Create an agent with your LLMChain
agent = ConversationalAgent(llm_chain=llm_chain , output_parser=output_parser)
memory = ConversationBufferMemory(memory_key="chat_history")
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True, memory=memory)
agent_executor.run("Hi, I'm Madhav?")
```
I get the following output:
```python
> Entering new AgentExecutor chain...
Error in StdOutCallbackHandler.on_agent_action callback: 'tuple' object has no attribute 'log'
Thought: Do I need to use a tool? No
Final Answer: Hello, Madhav! How can I assist you today?
Hello, Madhav! How can I assist you today?
return_values={'output': 'Hello, Madhav! How can I assist you today?'} log='Thought: Do I need to use a tool? No Final Answer: Hello, Madhav! How can I assist you today?'
File /opt/conda/lib/python3.10/site-packages/langchain/agents/agent.py:1141, in AgentExecutor._call(self, inputs, run_manager)
1139 # We now enter the agent loop (until it returns something).
1140 while self._should_continue(iterations, time_elapsed):
-> 1141 next_step_output = self._take_next_step(
1142 name_to_tool_map,
1143 color_mapping,
1144 inputs,
1145 intermediate_steps,
1146 run_manager=run_manager,
1147 )
1148 if isinstance(next_step_output, AgentFinish):
1149 return self._return(
1150 next_step_output, intermediate_steps, run_manager=run_manager
1151 )
File /opt/conda/lib/python3.10/site-packages/langchain/agents/agent.py:983, in AgentExecutor._take_next_step(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager)
981 run_manager.on_agent_action(agent_action, color="green")
982 # Otherwise we lookup the tool
--> 983 if agent_action.tool in name_to_tool_map:
984 tool = name_to_tool_map[agent_action.tool]
985 return_direct = tool.return_direct
AttributeError: 'tuple' object has no attribute 'tool'
...
```
I'm having trouble understading what is happening here. I'm certain my output contains `Final Answer` so I'd assume I need an `ActionFinish` as per [this tutorial](https://python.langchain.com/docs/modules/agents/how_to/custom_llm_agent#output-parser).
Any tips would be much appreciated!
### Suggestion:
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https://api.github.com/repos/langchain-ai/langchain/issues/14184 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14184/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14184/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14184/events | https://github.com/langchain-ai/langchain/pull/14184 | 2,022,243,720 | PR_kwDOIPDwls5g-EGy | 14,184 | langchain[patch]: Release langchain 0.0.345 | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14183 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14183/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14183/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14183/events | https://github.com/langchain-ai/langchain/pull/14183 | 2,022,243,160 | PR_kwDOIPDwls5g-D_v | 14,183 | Pass kwargs to chat fireworks | {
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] | closed | false | null | [] | null | 1 | 2023-12-02T22:48:39 | 2023-12-03T23:12:03 | 2023-12-03T23:12:02 | COLLABORATOR | null | Otherwise `.bind()` isn't really any good | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14182 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14182/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14182/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14182/events | https://github.com/langchain-ai/langchain/pull/14182 | 2,022,231,025 | PR_kwDOIPDwls5g-Bca | 14,182 | core[patch]: Release 0.0.9 | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14181 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14181/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14181/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14181/events | https://github.com/langchain-ai/langchain/pull/14181 | 2,022,229,114 | PR_kwDOIPDwls5g-BD6 | 14,181 | docs: moved `vectorstore` notebook file | {
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- Moved the file
- Rerouted the page URL | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14180 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14180/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14180/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14180/events | https://github.com/langchain-ai/langchain/issues/14180 | 2,022,198,845 | I_kwDOIPDwls54iE49 | 14,180 | Add new Document Loader for JIRA | {
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] | open | false | null | [] | null | 1 | 2023-12-02T20:48:41 | 2023-12-02T20:50:52 | null | NONE | null | ### Feature request
I think Langchain should have a Document Loader that would allow reading all issues from provided project as documents.
### Motivation
Langchain currently has agent for JIRA, but there is no Document Loader that would allow to read all issues from specified project and store them in Vector database. This could be a very useful tool for RAG usecases and building internal knowledge bases in the companies.
### Your contribution
I can create a PR for this issue. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14179 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14179/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14179/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14179/events | https://github.com/langchain-ai/langchain/issues/14179 | 2,022,197,127 | I_kwDOIPDwls54iEeH | 14,179 | Cloudflare Vectorize Support | {
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] | open | false | null | [] | null | 2 | 2023-12-02T20:43:58 | 2023-12-04T11:51:10 | null | NONE | null | ### Feature request
Looks like it should be doable to have Cloudflare Vectorize support, as there's a HTTP API for Vectorize.
https://developers.cloudflare.com/api/operations/vectorize-list-vectorize-indexes
### Motivation
Similar to langchain-js, it would be nice if we could use Cloudflare Vectorize when using LangChain with Python. It would allow us to use the same vector store for both our TypeScript Workers and Python Lambdas.
https://js.langchain.com/docs/integrations/vectorstores/cloudflare_vectorize
### Your contribution
N/A sadly | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14178 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14178/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14178/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14178/events | https://github.com/langchain-ai/langchain/pull/14178 | 2,022,192,929 | PR_kwDOIPDwls5g95l- | 14,178 | Fix: (issue #14177) OpenAI functions Agent official example Error | {
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] | closed | false | null | [] | null | 2 | 2023-12-02T20:29:21 | 2023-12-05T02:11:12 | 2023-12-04T20:04:29 | CONTRIBUTOR | null | - **Issue:** #14177
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https://api.github.com/repos/langchain-ai/langchain/issues/14177 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14177/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14177/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14177/events | https://github.com/langchain-ai/langchain/issues/14177 | 2,022,175,407 | I_kwDOIPDwls54h_Kv | 14,177 | Issues: OpenAI functions Agent official example Error | {
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] | closed | false | null | [] | null | 1 | 2023-12-02T19:29:23 | 2023-12-05T04:53:44 | 2023-12-05T04:53:44 | CONTRIBUTOR | null | ### System Info
- LangChain - 0.0.344
- python version - 3.11.6
- platform - windows11
### Who can help?
https://python.langchain.com/docs/modules/agents/agent_types/openai_functions_agent
When running the above example, the following error occurs.
-----------------------------------------------------------------
```shell
> Entering new AgentExecutor chain...
Invoking: `Search` with `Leo DiCaprio girlfriend`
Vittoria Ceretti
Invoking: `Calculator` with `age ^ 0.43`
> Entering new LLMMathChain chain...
age ^ 0.43```text
Traceback (most recent call last):
File "C:\Users\hyung\AppData\Local\pypoetry\Cache\virtualenvs\langchainstudy-esJm8fcP-py3.11\Lib\site-packages\numexpr\necompiler.py", line 760, in getArguments
a = local_dict[name]
~~~~~~~~~~^^^^^^
KeyError: 'age'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:\Users\hyung\AppData\Local\pypoetry\Cache\virtualenvs\langchainstudy-esJm8fcP-py3.11\Lib\site-packages\langchain\chains\llm_math\base.py", line 89, in _evaluate_expression
numexpr.evaluate(
File "C:\Users\hyung\AppData\Local\pypoetry\Cache\virtualenvs\langchainstudy-esJm8fcP-py3.11\Lib\site-packages\numexpr\necompiler.py", line 975, in evaluate
raise e
File "C:\Users\hyung\AppData\Local\pypoetry\Cache\virtualenvs\langchainstudy-esJm8fcP-py3.11\Lib\site-packages\numexpr\necompiler.py", line 874, in validate
arguments = getArguments(names, local_dict, global_dict, _frame_depth=_frame_depth)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\hyung\AppData\Local\pypoetry\Cache\virtualenvs\langchainstudy-esJm8fcP-py3.11\Lib\site-packages\numexpr\necompiler.py", line 762, in getArguments
a = global_dict[name]
~~~~~~~~~~~^^^^^^
KeyError: 'age'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:\Users\hyung\GitHubProjects\LangChainStudy\agent\openai_functions.py", line 76, in <module>
agent_executor.invoke(
File "C:\Users\hyung\AppData\Local\pypoetry\Cache\virtualenvs\langchainstudy-esJm8fcP-py3.11\Lib\site-packages\langchain\chains\base.py", line 89, in invoke
return self(
^^^^^
File "C:\Users\hyung\AppData\Local\pypoetry\Cache\virtualenvs\langchainstudy-esJm8fcP-py3.11\Lib\site-packages\langchain\chains\base.py", line 312, in __call__
raise e
File "C:\Users\hyung\AppData\Local\pypoetry\Cache\virtualenvs\langchainstudy-esJm8fcP-py3.11\Lib\site-packages\langchain\chains\base.py", line 306, in __call__
self._call(inputs, run_manager=run_manager)
File "C:\Users\hyung\AppData\Local\pypoetry\Cache\virtualenvs\langchainstudy-esJm8fcP-py3.11\Lib\site-packages\langchain\agents\agent.py", line 1312, in _call
next_step_output = self._take_next_step(
^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\hyung\AppData\Local\pypoetry\Cache\virtualenvs\langchainstudy-esJm8fcP-py3.11\Lib\site-packages\langchain\agents\agent.py", line 1038, in _take_next_step
[
File "C:\Users\hyung\AppData\Local\pypoetry\Cache\virtualenvs\langchainstudy-esJm8fcP-py3.11\Lib\site-packages\langchain\agents\agent.py", line 1038, in <listcomp>
[
File "C:\Users\hyung\AppData\Local\pypoetry\Cache\virtualenvs\langchainstudy-esJm8fcP-py3.11\Lib\site-packages\langchain\agents\agent.py", line 1134, in _iter_next_step
observation = tool.run(
^^^^^^^^^
File "C:\Users\hyung\AppData\Local\pypoetry\Cache\virtualenvs\langchainstudy-esJm8fcP-py3.11\Lib\site-packages\langchain_core\tools.py", line 365, in run
raise e
File "C:\Users\hyung\AppData\Local\pypoetry\Cache\virtualenvs\langchainstudy-esJm8fcP-py3.11\Lib\site-packages\langchain_core\tools.py", line 337, in run
self._run(*tool_args, run_manager=run_manager, **tool_kwargs)
File "C:\Users\hyung\AppData\Local\pypoetry\Cache\virtualenvs\langchainstudy-esJm8fcP-py3.11\Lib\site-packages\langchain_core\tools.py", line 510, in _run
self.func(
File "C:\Users\hyung\AppData\Local\pypoetry\Cache\virtualenvs\langchainstudy-esJm8fcP-py3.11\Lib\site-packages\langchain\chains\base.py", line 507, in run
age ** 0.43
'''
...numexpr.evaluate("age ** 0.43")...
return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\hyung\AppData\Local\pypoetry\Cache\virtualenvs\langchainstudy-esJm8fcP-py3.11\Lib\site-packages\langchain\chains\base.py", line 312, in __call__
raise e
File "C:\Users\hyung\AppData\Local\pypoetry\Cache\virtualenvs\langchainstudy-esJm8fcP-py3.11\Lib\site-packages\langchain\chains\base.py", line 306, in __call__
self._call(inputs, run_manager=run_manager)
File "C:\Users\hyung\AppData\Local\pypoetry\Cache\virtualenvs\langchainstudy-esJm8fcP-py3.11\Lib\site-packages\langchain\chains\llm_math\base.py", line 158, in _call
return self._process_llm_result(llm_output, _run_manager)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\hyung\AppData\Local\pypoetry\Cache\virtualenvs\langchainstudy-esJm8fcP-py3.11\Lib\site-packages\langchain\chains\llm_math\base.py", line 112, in _process_llm_result
output = self._evaluate_expression(expression)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\hyung\AppData\Local\pypoetry\Cache\virtualenvs\langchainstudy-esJm8fcP-py3.11\Lib\site-packages\langchain\chains\llm_math\base.py", line 96, in _evaluate_expression
raise ValueError(
ValueError: LLMMathChain._evaluate("
age ** 0.43
") raised error: 'age'. Please try again with a valid numerical expression
Process finished with exit code 1
'''
```
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
https://python.langchain.com/docs/modules/agents/agent_types/openai_functions_agent
Run the example
### Expected behavior
```shell
> Entering new AgentExecutor chain...
Invoking: `Search` with `Leo DiCaprio girlfriend`
Vittoria Ceretti
Invoking: `Calculator` with `25 ^ 0.43`
> Entering new LLMMathChain chain...
25 ^ 0.43'''text
/25 ** 0.43
'''
...numexpr.evaluate("25 ** 0.43")...
Answer: 3.991298452658078
> Finished chain.
Answer: 3.991298452658078Leo DiCaprio's girlfriend is Vittoria Ceretti. Her current age raised to the power of 0.43 is approximately 3.99.
> Finished chain.
``` | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14176 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14176/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14176/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14176/events | https://github.com/langchain-ai/langchain/issues/14176 | 2,022,127,350 | I_kwDOIPDwls54hzb2 | 14,176 | DOC: Is there a way to conditionally create/update a Pandas DataFrame agent? | {
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] | open | false | null | [] | null | 2 | 2023-12-02T17:08:46 | 2023-12-02T21:56:38 | null | NONE | null | ### Issue with current documentation:
Is there a way to conditionally create a Pandas agent using `create_pandas_dataframe_agent` or update the DataFrame the agent has access to?
Say I have an agent that has access to tools like so:
```python
@tool()
def f(query: str) -> pd.DataFrame:
...
agent = initialize_agent(
tools=[f, PythonREPLTool()],
llm,
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True,
handle_parsing_errors=True,
)
```
Since `create_pandas_dataframe_agent` takes the `df` argument, I can't create this agent unless/until the tool `f` is called, producing the DataFrame. But without the specialized Pandas agent, the regular agent seems to be producing bad/bogus Python, trying to filter the DataFrame (either with syntax errors or just doing the wrong thing). Is there a way to either 1) call`create_pandas_dataframe_agent` ahead of time and then later update the contents of the `df` after the function call or 2) create a Pandas agent on-the-fly inside `f` to answer the question (I haven't found a way to pass the current `Thought` content to the nested agent in this case)?
### Idea or request for content:
It would be helpful to see how to work with Pandas data when reading dynamically created DataFrames coming from functions that agents may or may not call. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14175 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14175/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14175/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14175/events | https://github.com/langchain-ai/langchain/issues/14175 | 2,022,085,674 | I_kwDOIPDwls54hpQq | 14,175 | ModuleNotFoundError: No module named 'clarifai.auth' | {
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] | closed | false | null | [] | null | 1 | 2023-12-02T15:28:09 | 2023-12-18T20:34:37 | 2023-12-04T19:53:36 | NONE | null | ### System Info
platform: Vagrant - Ubuntu 2204
python: 3.9.18
langchain version: 0.0.344
langchain core: 0.0.8
clarifai: 9.10.4
### Who can help?
@hwchase17 @agola11
### Information
- [X] The official example notebooks/scripts
- [x] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Install the latest version of clarifai (9.10.4)
2. Run the example: https://python.langchain.com/docs/integrations/llms/clarifai
``` bash
Could not import clarifai python package. Please install it with `pip install clarifai`.
File 'clarifai.py', line 77, in validate_environment:
raise ImportError( Traceback (most recent call last):
File "/home/vagrant/.virtualenvs/env/lib/python3.9/site-packages/langchain/llms/clarifai.py", line 74, in validate_environment
from clarifai.auth.helper import ClarifaiAuthHelper
ModuleNotFoundError: No module named 'clarifai.auth'
```
### Expected behavior
I expect **ClarifaiAuthHelper** to import correctly.
In the latest version of clarifai **ClarifaiAuthHelper** is imported in this way:
``` python
from clarifai.client.auth.helper import ClarifaiAuthHelper
``` | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14174 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14174/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14174/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14174/events | https://github.com/langchain-ai/langchain/pull/14174 | 2,022,033,084 | PR_kwDOIPDwls5g9aAY | 14,174 | Enable Multi-tenancy for Weaviate | {
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] | open | false | null | [] | null | 13 | 2023-12-02T13:04:33 | 2024-01-16T14:51:07 | null | NONE | null | <!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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- **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),
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- **Description:** Added support for multi-tenancy when creating a schema and taking tenant name to index and retrieve data.
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- **Twitter handle:** https://twitter.com/khajavi
| {
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https://api.github.com/repos/langchain-ai/langchain/issues/14173 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14173/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14173/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14173/events | https://github.com/langchain-ai/langchain/issues/14173 | 2,022,026,571 | I_kwDOIPDwls54ha1L | 14,173 | BUG: csv loader exception, AttributeError: partially initialized module 'importlib.metadata' has no attribute 'version' (most likely due to a circular import) | {
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] | open | false | null | [] | null | 3 | 2023-12-02T12:42:14 | 2023-12-31T10:26:05 | null | NONE | null | ### System Info
langchain version ：
langchain 0.0.344
langchain-core 0.0.8
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
code:
from langchain.document_loaders.csv_loader import CSVLoader
loader = CSVLoader(file_path='./test.csv')
data = loader.load()
### Expected behavior
exception:
Traceback (most recent call last):
File "/Users/liucong/opt/miniconda3/envs/310-test/lib/python3.10/site-packages/langchain_core/__init__.py", line 4, in <module>
__version__ = metadata.version(__package__)
AttributeError: partially initialized module 'importlib.metadata' has no attribute 'version' (most likely due to a circular import)
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<frozen importlib._bootstrap>", line 1027, in _find_and_load
File "<frozen importlib._bootstrap>", line 992, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed
File "/Users/liucong/opt/miniconda3/envs/310-test/lib/python3.10/site-packages/langchain/__init__.py", line 4, in <module>
from importlib import metadata
File "/Users/liucong/opt/miniconda3/envs/310-test/lib/python3.10/importlib/metadata/__init__.py", line 4, in <module>
import csv
File "/Users/liucong/PycharmProjects/llm_learn/lang_chain/data_connection/csv.py", line 1, in <module>
from langchain.document_loaders.csv_loader import CSVLoader
File "/Users/liucong/opt/miniconda3/envs/310-test/lib/python3.10/site-packages/langchain/document_loaders/__init__.py", line 18, in <module>
from langchain.document_loaders.acreom import AcreomLoader
File "/Users/liucong/opt/miniconda3/envs/310-test/lib/python3.10/site-packages/langchain/document_loaders/acreom.py", line 5, in <module>
from langchain_core.documents import Document
File "/Users/liucong/opt/miniconda3/envs/310-test/lib/python3.10/site-packages/langchain_core/__init__.py", line 5, in <module>
except metadata.PackageNotFoundError:
AttributeError: partially initialized module 'importlib.metadata' has no attribute 'PackageNotFoundError' (most likely due to a circular import) | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14171 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14171/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14171/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14171/events | https://github.com/langchain-ai/langchain/pull/14171 | 2,021,998,538 | PR_kwDOIPDwls5g9TJ- | 14,171 | Fixes and Upgrades for the Tongyi LLM and ChatTongyi Model | {
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] | closed | false | null | [] | null | 6 | 2023-12-02T11:10:45 | 2023-12-12T06:50:17 | 2023-12-12T06:50:17 | CONTRIBUTOR | null | - **Description:** Upgrade the `Tongyi` LLM and `ChatTongyi` Model.
- Fixed typos; it should be `Tongyi`, not `OpenAI`.
- Fixed a bug in `stream_generate_with_retry`; it's a real stream generator now.
- Fixed a bug in `validate_environment`; the `dashscope_api_key` should be properly handled when set by environment variables or initialization parameters.
- Changed the `dashscope` response to incremental output by setting the parameter `incremental_output`, which eliminates the need for the prefix-removal trick.
- Removed some unused parameters, like `n`, `prefix_messages`.
- Added `_stream` method.
- Added async methods, such as `_astream`, `_agenerate`.
- **Issue:** No related issue.
- **Dependencies:** No new dependencies.
- **Tag maintainer:** @baskaryan
PS: Some may be confused about the terms `dashscope`, `tongyi`, and `Qwen`:
- `dashscope`: A platform to deploy LLMs and provide APIs to invoke the LLM.
- `tongyi`: A brand name or overall term about Alibaba Cloud's LLM/AI.
- `Qwen`: An LLM that is open-sourced and deployed in `dashscope`.
We use the `dashscope` SDK to interact with the `tongyi`-`Qwen` LLM.
~~And the fix and upgrade of the chat model `ChatTongyi` will be addressed in future PRs.~~ (Already included in this PR)
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https://api.github.com/repos/langchain-ai/langchain/issues/14170 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14170/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14170/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14170/events | https://github.com/langchain-ai/langchain/pull/14170 | 2,021,991,104 | PR_kwDOIPDwls5g9RoY | 14,170 | Mask API key for Tongyi ChatModel and LLM | {
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- **Issue:** https://github.com/langchain-ai/langchain/issues/12165
- **Dependencies:** dashscope
- **Tag maintainer:** @eyurtsev
- **Twitter handle:** FadilParves | {
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] | open | false | null | [] | null | 6 | 2023-12-02T09:07:35 | 2023-12-02T13:09:21 | null | NONE | null | ### Issue you'd like to raise.
Please help!
Error:
GGML_ASSERT: C:\Users\user\AppData\Local\Temp\pip-install-l0_yao9s\llama-cpp-python_686671b6d7b8440a98e23acb5bc6a41a\vendor\llama.cpp\ggml.c:15149: cgraph->nodes[cgraph->n_nodes - 1] == tensor
GGML_ASSERT: C:\Users\user\AppData\Local\Temp\pip-install-l0_yao9s\llama-cpp-python_686671b6d7b8440a98e23acb5bc6a41a\vendor\llama.cpp\ggml.c:4326: ggml_nelements(a) == (ne0ne1ne2*ne3)
Repeated trials yield same results.
Local llm: TheBloke/Llama-2-7B-Chat-GGUF (llama-2-7b-chat.Q6_K.gguf)
Embedding: BAAI/bge-large-en-v1.5
```
import os
os.environ['OPENAI_API_KEY'] = 'dummy_key'
# import paperscraper
from paperqa import Docs
from langchain.llms.llamacpp import LlamaCpp
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain.callbacks.manager import CallbackManager
from langchain.embeddings import LlamaCppEmbeddings
from langchain.embeddings import HuggingFaceBgeEmbeddings
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
# Make sure the model path is correct for your system!
llm = LlamaCpp(
model_path="C:/paper-qa/models/llama-2-7b-chat.Q6_K.gguf", callbacks=[StreamingStdOutCallbackHandler()],
)
model_name = "BAAI/bge-large-en-v1.5"
model_kwargs = {'device': 'cpu'}
encode_kwargs = {'normalize_embeddings': True}
embeddings = HuggingFaceBgeEmbeddings(
model_name=model_name,
# model_kwargs=model_kwargs,
# encode_kwargs=encode_kwargs,
cache_folder="C:\paper-qa\models"
)
# embeddings = LlamaCppEmbeddings(
# model_path="C:/paper-qa/models/bge-large-en-v1_5_pytorch_model.bin"
# )
# model_name = "BAAI/bge-large-en-v1.5"
# model_kwargs = {'device': 'cpu'}
# encode_kwargs = {'normalize_embeddings': False}
# hf = HuggingFaceEmbeddings(
# model_name=model_name,
# model_kwargs=model_kwargs,
# encode_kwargs=encode_kwargs
# )
docs = Docs(llm=llm, embeddings=embeddings)
# keyword_search = 'bispecific antibody manufacture'
# papers = paperscraper.search_papers(keyword_search, limit=2)
# for path,data in papers.items():
# try:
# docs.add(path,chunk_chars=500)
# except ValueError as e:
# print('Could not read', path, e)
path = "C:\\paper-qa\\Source\\The Encyclopedia of the Cold War_90-95.pdf"
print("Before adding document")
docs.add(path,chunk_chars=500)
print("Document added")
answer = docs.query("Summarize 2 interesting events of the cold war from the book.")
print("Queried.")
print(answer)
```
whole output
```
llama_model_loader: loaded meta data with 19 key-value pairs and 291 tensors from C:/paper-qa/models/llama-2-7b-chat.Q6_K.gguf (version GGUF V2)
llama_model_loader: - tensor 0: token_embd.weight q6_K [ 4096, 32000,
1, 1 ]
llama_model_loader: - tensor 1: blk.0.attn_norm.weight f32 [ 4096, 1,
1, 1 ]
llama_model_loader: - tensor 2: blk.0.ffn_down.weight q6_K [ 11008, 4096,
1, 1 ]
llama_model_loader: - tensor 3: blk.0.ffn_gate.weight q6_K [ 4096, 11008,
1, 1 ]
llama_model_loader: - tensor 4: blk.0.ffn_up.weight q6_K [ 4096, 11008,
1, 1 ]
llama_model_loader: - tensor 5: blk.0.ffn_norm.weight f32 [ 4096, 1,
1, 1 ]
llama_model_loader: - tensor 6: blk.0.attn_k.weight q6_K [ 4096, 4096,
1, 1 ]
llama_model_loader: - tensor 7: blk.0.attn_output.weight q6_K [ 4096, 4096,
1, 1 ]
llama_model_loader: - tensor 8: blk.0.attn_q.weight q6_K [ 4096, 4096,
1, 1 ]
llama_model_loader: - tensor 9: blk.0.attn_v.weight q6_K [ 4096, 4096,
1, 1 ]
llama_model_loader: - tensor 10: blk.1.attn_norm.weight f32 [ 4096, 1,
1, 1 ]
llama_model_loader: - tensor 11: blk.1.ffn_down.weight q6_K [ 11008, 4096,
1, 1 ]
llama_model_loader: - tensor 12: blk.1.ffn_gate.weight q6_K [ 4096, 11008,
1, 1 ]
llama_model_loader: - tensor 13: blk.1.ffn_up.weight q6_K [ 4096, 11008,
1, 1 ]
llama_model_loader: - tensor 14: blk.1.ffn_norm.weight f32 [ 4096, 1,
1, 1 ]
llama_model_loader: - tensor 15: blk.1.attn_k.weight q6_K [ 4096, 4096,
1, 1 ]
llama_model_loader: - tensor 16: blk.1.attn_output.weight q6_K [ 4096, 4096,
1, 1 ]
llama_model_loader: - tensor 17: blk.1.attn_q.weight q6_K [ 4096, 4096,
1, 1 ]
llama_model_loader: - tensor 18: blk.1.attn_v.weight q6_K [ 4096, 4096,
1, 1 ]
llama_model_loader: - tensor 19: blk.10.attn_norm.weight f32 [ 4096, 1,
1, 1 ]
llama_model_loader: - tensor 20: blk.10.ffn_down.weight q6_K [ 11008, 4096,
1, 1 ]
llama_model_loader: - tensor 21: blk.10.ffn_gate.weight q6_K [ 4096, 11008,
1, 1 ]
llama_model_loader: - tensor 22: blk.10.ffn_up.weight q6_K [ 4096, 11008,
1, 1 ]
llama_model_loader: - tensor 23: blk.10.ffn_norm.weight f32 [ 4096, 1,
1, 1 ]
llama_model_loader: - tensor 24: blk.10.attn_k.weight q6_K [ 4096, 4096,
1, 1 ]
llama_model_loader: - tensor 25: blk.10.attn_output.weight q6_K [ 4096, 4096,
1, 1 ]
llama_model_loader: - tensor 26: blk.10.attn_q.weight q6_K [ 4096, 4096,
1, 1 ]
llama_model_loader: - tensor 27: blk.10.attn_v.weight q6_K [ 4096, 4096,
1, 1 ]
llama_model_loader: - tensor 28: blk.11.attn_norm.weight f32 [ 4096, 1,
1, 1 ]
llama_model_loader: - tensor 29: blk.11.ffn_down.weight q6_K [ 11008, 4096,
1, 1 ]
llama_model_loader: - tensor 30: blk.11.ffn_gate.weight q6_K [ 4096, 11008,
1, 1 ]
llama_model_loader: - tensor 31: blk.11.ffn_up.weight q6_K [ 4096, 11008,
1, 1 ]
llama_model_loader: - tensor 32: blk.11.ffn_norm.weight f32 [ 4096, 1,
1, 1 ]
llama_model_loader: - tensor 33: blk.11.attn_k.weight q6_K [ 4096, 4096,
1, 1 ]
llama_model_loader: - tensor 34: blk.11.attn_output.weight q6_K [ 4096, 4096,
1, 1 ]
llama_model_loader: - tensor 35: blk.11.attn_q.weight q6_K [ 4096, 4096,
1, 1 ]
llama_model_loader: - tensor 36: blk.11.attn_v.weight q6_K [ 4096, 4096,
1, 1 ]
llama_model_loader: - tensor 37: blk.12.attn_norm.weight f32 [ 4096, 1,
1, 1 ]
llama_model_loader: - tensor 38: blk.12.ffn_down.weight q6_K [ 11008, 4096,
1, 1 ]
llama_model_loader: - tensor 39: blk.12.ffn_gate.weight q6_K [ 4096, 11008,
1, 1 ]
llama_model_loader: - tensor 40: blk.12.ffn_up.weight q6_K [ 4096, 11008,
1, 1 ]
llama_model_loader: - tensor 41: blk.12.ffn_norm.weight f32 [ 4096, 1,
1, 1 ]
llama_model_loader: - tensor 42: blk.12.attn_k.weight q6_K [ 4096, 4096,
1, 1 ]
llama_model_loader: - tensor 43: blk.12.attn_output.weight q6_K [ 4096, 4096,
1, 1 ]
llama_model_loader: - tensor 44: blk.12.attn_q.weight q6_K [ 4096, 4096,
1, 1 ]
llama_model_loader: - tensor 45: blk.12.attn_v.weight q6_K [ 4096, 4096,
1, 1 ]
llama_model_loader: - tensor 46: blk.13.attn_norm.weight f32 [ 4096, 1,
1, 1 ]
llama_model_loader: - tensor 47: blk.13.ffn_down.weight q6_K [ 11008, 4096,
1, 1 ]
llama_model_loader: - tensor 48: blk.13.ffn_gate.weight q6_K [ 4096, 11008,
1, 1 ]
llama_model_loader: - tensor 49: blk.13.ffn_up.weight q6_K [ 4096, 11008,
1, 1 ]
llama_model_loader: - tensor 50: blk.13.ffn_norm.weight f32 [ 4096, 1,
1, 1 ]
llama_model_loader: - tensor 51: blk.13.attn_k.weight q6_K [ 4096, 4096,
1, 1 ]
llama_model_loader: - tensor 52: blk.13.attn_output.weight q6_K [ 4096, 4096,
1, 1 ]
llama_model_loader: - tensor 53: blk.13.attn_q.weight q6_K [ 4096, 4096,
1, 1 ]
llama_model_loader: - tensor 54: blk.13.attn_v.weight q6_K [ 4096, 4096,
1, 1 ]
llama_model_loader: - tensor 55: blk.14.attn_norm.weight f32 [ 4096, 1,
1, 1 ]
llama_model_loader: - tensor 56: blk.14.ffn_down.weight q6_K [ 11008, 4096,
1, 1 ]
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llama_model_loader: - tensor 260: blk.28.attn_output.weight q6_K [ 4096, 4096,
1, 1 ]
llama_model_loader: - tensor 261: blk.28.attn_q.weight q6_K [ 4096, 4096,
1, 1 ]
llama_model_loader: - tensor 262: blk.28.attn_v.weight q6_K [ 4096, 4096,
1, 1 ]
llama_model_loader: - tensor 263: blk.29.attn_norm.weight f32 [ 4096, 1,
1, 1 ]
llama_model_loader: - tensor 264: blk.29.ffn_down.weight q6_K [ 11008, 4096,
1, 1 ]
llama_model_loader: - tensor 265: blk.29.ffn_gate.weight q6_K [ 4096, 11008,
1, 1 ]
llama_model_loader: - tensor 266: blk.29.ffn_up.weight q6_K [ 4096, 11008,
1, 1 ]
llama_model_loader: - tensor 267: blk.29.ffn_norm.weight f32 [ 4096, 1,
1, 1 ]
llama_model_loader: - tensor 268: blk.29.attn_k.weight q6_K [ 4096, 4096,
1, 1 ]
llama_model_loader: - tensor 269: blk.29.attn_output.weight q6_K [ 4096, 4096,
1, 1 ]
llama_model_loader: - tensor 270: blk.29.attn_q.weight q6_K [ 4096, 4096,
1, 1 ]
llama_model_loader: - tensor 271: blk.29.attn_v.weight q6_K [ 4096, 4096,
1, 1 ]
llama_model_loader: - tensor 272: blk.30.attn_norm.weight f32 [ 4096, 1,
1, 1 ]
llama_model_loader: - tensor 273: blk.30.ffn_down.weight q6_K [ 11008, 4096,
1, 1 ]
llama_model_loader: - tensor 274: blk.30.ffn_gate.weight q6_K [ 4096, 11008,
1, 1 ]
llama_model_loader: - tensor 275: blk.30.ffn_up.weight q6_K [ 4096, 11008,
1, 1 ]
llama_model_loader: - tensor 276: blk.30.ffn_norm.weight f32 [ 4096, 1,
1, 1 ]
llama_model_loader: - tensor 277: blk.30.attn_k.weight q6_K [ 4096, 4096,
1, 1 ]
llama_model_loader: - tensor 278: blk.30.attn_output.weight q6_K [ 4096, 4096,
1, 1 ]
llama_model_loader: - tensor 279: blk.30.attn_q.weight q6_K [ 4096, 4096,
1, 1 ]
llama_model_loader: - tensor 280: blk.30.attn_v.weight q6_K [ 4096, 4096,
1, 1 ]
llama_model_loader: - tensor 281: blk.31.attn_norm.weight f32 [ 4096, 1,
1, 1 ]
llama_model_loader: - tensor 282: blk.31.ffn_down.weight q6_K [ 11008, 4096,
1, 1 ]
llama_model_loader: - tensor 283: blk.31.ffn_gate.weight q6_K [ 4096, 11008,
1, 1 ]
llama_model_loader: - tensor 284: blk.31.ffn_up.weight q6_K [ 4096, 11008,
1, 1 ]
llama_model_loader: - tensor 285: blk.31.ffn_norm.weight f32 [ 4096, 1,
1, 1 ]
llama_model_loader: - tensor 286: blk.31.attn_k.weight q6_K [ 4096, 4096,
1, 1 ]
llama_model_loader: - tensor 287: blk.31.attn_output.weight q6_K [ 4096, 4096,
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llama_model_loader: - tensor 288: blk.31.attn_q.weight q6_K [ 4096, 4096,
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llama_model_loader: - tensor 289: blk.31.attn_v.weight q6_K [ 4096, 4096,
1, 1 ]
llama_model_loader: - tensor 290: output_norm.weight f32 [ 4096, 1,
1, 1 ]
llama_model_loader: - kv 0: general.architecture str = llama
llama_model_loader: - kv 1: general.name str = LLaMA v2
llama_model_loader: - kv 2: llama.context_length u32 = 4096
llama_model_loader: - kv 3: llama.embedding_length u32 = 4096
llama_model_loader: - kv 4: llama.block_count u32 = 32llama_model_loader: - kv 5: llama.feed_forward_length u32 = 11008
llama_model_loader: - kv 6: llama.rope.dimension_count u32 = 128
llama_model_loader: - kv 7: llama.attention.head_count u32 = 32llama_model_loader: - kv 8: llama.attention.head_count_kv u32 = 32llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 10: general.file_type u32 = 18llama_model_loader: - kv 11: tokenizer.ggml.model str = llama
llama_model_loader: - kv 12: tokenizer.ggml.tokens arr[str,32000] = ["<unk>", "<s>", "</s>", "<0x00>", "<...
llama_model_loader: - kv 13: tokenizer.ggml.scores arr[f32,32000] = [0.000000, 0.000000, 0.000000, 0.0000...
llama_model_loader: - kv 14: tokenizer.ggml.token_type arr[i32,32000] = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
llama_model_loader: - kv 15: tokenizer.ggml.bos_token_id u32 = 1
llama_model_loader: - kv 16: tokenizer.ggml.eos_token_id u32 = 2
llama_model_loader: - kv 17: tokenizer.ggml.unknown_token_id u32 = 0
llama_model_loader: - kv 18: general.quantization_version u32 = 2
llama_model_loader: - type f32: 65 tensors
llama_model_loader: - type q6_K: 226 tensors
llm_load_vocab: special tokens definition check successful ( 259/32000 ).
llm_load_print_meta: format = GGUF V2
llm_load_print_meta: arch = llama
llm_load_print_meta: vocab type = SPM
llm_load_print_meta: n_vocab = 32000
llm_load_print_meta: n_merges = 0
llm_load_print_meta: n_ctx_train = 4096
llm_load_print_meta: n_embd = 4096
llm_load_print_meta: n_head = 32
llm_load_print_meta: n_head_kv = 32
llm_load_print_meta: n_layer = 32
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_gqa = 1
llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-06
llm_load_print_meta: f_clamp_kqv = 0.0e+00
llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llm_load_print_meta: n_ff = 11008
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 10000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_yarn_orig_ctx = 4096
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: model type = 7B
llm_load_print_meta: model ftype = mostly Q6_K
llm_load_print_meta: model params = 6.74 B
llm_load_print_meta: model size = 5.15 GiB (6.56 BPW)
llm_load_print_meta: general.name = LLaMA v2
llm_load_print_meta: BOS token = 1 '<s>'
llm_load_print_meta: EOS token = 2 '</s>'
llm_load_print_meta: UNK token = 0 '<unk>'
llm_load_print_meta: LF token = 13 '<0x0A>'
llm_load_tensors: ggml ctx size = 0.11 MiB
llm_load_tensors: mem required = 5272.45 MiB
....................................................................................................
llama_new_context_with_model: n_ctx = 512
llama_new_context_with_model: freq_base = 10000.0
llama_new_context_with_model: freq_scale = 1
llama_new_context_with_model: kv self size = 256.00 MiB
llama_build_graph: non-view tensors processed: 740/740
llama_new_context_with_model: compute buffer total size = 4.16 MiB
AVX = 1 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | SSSE3 = 0 | VSX = 0
|
Before adding document
Abdullah I of Jordan. "Soldier, Diplomat, and King of Jordan." The Oxford Encyclopedia of the
Modern World, vol. 1, Oxford University Press, 2023, pp. 56-57.
llama_print_timings: load time = 7311.60 ms
llama_print_timings: sample time = 18.23 ms / 55 runs ( 0.33 ms per token,
3016.67 tokens per second)
llama_print_timings: prompt eval time = 53923.10 ms / 190 tokens ( 283.81 ms per token,
3.52 tokens per second)
llama_print_timings: eval time = 19552.25 ms / 54 runs ( 362.08 ms per token,
2.76 tokens per second)
llama_print_timings: total time = 73785.66 ms
Document added
Llama.generate: prefix-match hit
Llama.generate: prefix-match hit
Llama.generate: prefix-match hit
Llama.generate: prefix-match hit
Llama.generate: prefix-match hit
GGML_ASSERT: C:\Users\user\AppData\Local\Temp\pip-install-l0_yao9s\llama-cpp-python_686671b6d7b8440a98e23acb5bc6a41a\vendor\llama.cpp\ggml.c:15149: cgraph->nodes[cgraph->n_nodes - 1] == tensor
GGML_ASSERT: C:\Users\user\AppData\Local\Temp\pip-install-l0_yao9s\llama-cpp-python_686671b6d7b8440a98e23acb5bc6a41a\vendor\llama.cpp\ggml.c:4326: ggml_nelements(a) == (ne0*ne1*ne2*ne3)
PS C:\paper-qa>
```
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] | closed | false | null | [] | null | 4 | 2023-12-02T09:01:35 | 2023-12-04T02:10:15 | 2023-12-04T01:33:55 | NONE | null | ### Issue you'd like to raise.
```python
from langchain.chains import create_extraction_chain
from langchain.chat_models import ChatOpenAI
import dotenv
dotenv.load_dotenv()
# Schema
schema = {
"properties": {
"name": {"type": "string"},
"education": {"type": "string"},
"company": {"type": "string"},
},
"required": ["name", "education"],
}
# Input
inp = """小明毕业于北京大学，在微软上班\n小李毕业于清华大学在阿里巴巴上班\n小王毕业于浙江大学在烟草局上班"""
# Run chain
llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-1106")
chain = create_extraction_chain(schema, llm)
print(chain.run(inp)[0])
```
```shell
#output
{'name': 'å°\x8fæ\x98\x8e', 'education': 'å\x8c\x97京大å\xad¦', 'company': '微软'}
```
How to fix this Chinese `encoding/decoding` problem?
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Code:
retriever = BM25Retriever.from_documents(
[
Document(page_content="foo"),
Document(page_content="bar"),
Document(page_content="world"),
Document(page_content="hello"),
Document(page_content="foo bar"),
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)
Error:
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31 [
32 Document(page_content="foo"),
33 Document(page_content="bar"),
34 Document(page_content="world"),
35 Document(page_content="hello"),
36 Document(page_content="foo bar"),
37 ]
38 )
AttributeError: type object 'BM25Retriever' has no attribute 'from_documents'
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] | closed | false | null | [] | null | 1 | 2023-12-02T08:26:20 | 2023-12-03T16:55:26 | 2023-12-03T16:55:26 | CONTRIBUTOR | null | ### Description
Fix #14069
### Twitter handle
[lin_bob57617](https://twitter.com/lin_bob57617) | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14165 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14165/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14165/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14165/events | https://github.com/langchain-ai/langchain/pull/14165 | 2,021,876,944 | PR_kwDOIPDwls5g85l6 | 14,165 | Closed #14159 | {
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Fix: #14159
Use `from pydantic.v1 import BaseModel, Field` instead of `from pydantic import BaseModel, Field`
### [lin_bob57617](https://twitter.com/lin_bob57617) | {
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The `RateLimitError` initialization method has changed after openai v1, and the usage of `patch` needs to be changed.
### Twitter handle
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Use new [APIs](https://github.com/openai/openai-python/blob/main/api.md#finetuning)
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to make a pipeline that properly propagates metadata information
from raw content to the derived content.
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] | closed | false | null | [] | null | 1 | 2023-12-02T01:44:13 | 2023-12-02T01:52:08 | 2023-12-02T01:52:08 | NONE | null | ### System Info
langchain: 0.0.339
Python: 3.11.6
I followed instructions in
[https://docs.smith.langchain.com/evaluation/evaluator-implementations](https://docs.smith.langchain.com/evaluation/evaluator-implementations)
`from langsmith import Client
from langchain.smith import RunEvalConfig, run_on_dataset
from backend.tools.self_query_tool import do_self_query
evaluation_config = RunEvalConfig(
evaluators=[
"cot_qa",
]
)
client = Client()
run_on_dataset(
dataset_name="self_query_tool",
llm_or_chain_factory=do_self_query,
client=client,
evaluation=evaluation_config,
verbose=True,
project_name="default",
)`
got this blob code. I used the langsmith UI to create a dataset and a single example.
However when i run the test with
python test/self_query_tool_test.py
i get
`
File "/home/dharshana/.local/share/virtualenvs/turners-virtual-assistant-QSiRQ3G1/lib/python3.11/site-packages/langsmith/client.py", line 1113, in create_project
ls_utils.raise_for_status_with_text(response)
File "/home/dharshana/.local/share/virtualenvs/turners-virtual-assistant-QSiRQ3G1/lib/python3.11/site-packages/langsmith/utils.py", line 85, in raise_for_status_with_text
raise requests.HTTPError(str(e), response.text) from e
requests.exceptions.HTTPError: [Errno 409 Client Error: Conflict for url: https://api.langchain.plus/sessions] {"detail":"Session already exists."}
❯
`
I have never attempted an evaluation of my chain before. Im just calling a function which creates its own llm client and runs
`llm = ChatOpenAI(temperature=0, model="gpt-4-1106-preview")
retriever = SelfQueryRetriever.from_llm(
llm,
vectordb,
document_content_description,
metadata_field_info,
enable_limit=True,
verbose=True
)`
Is there an issue with doign this? I have been using langsmith successfully to log my llm calls and agent actions
### 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
Create a function which does
`
llm = ChatOpenAI(temperature=0, model="gpt-4-1106-preview")
retriever = SelfQueryRetriever.from_llm(
llm,
vectordb,
document_content_description,
metadata_field_info,
enable_limit=True,
verbose=True
)
`
then evaluate the function with
`
evaluation_config = RunEvalConfig(
evaluators=[
"cot_qa",
]
)
client = Client()
run_on_dataset(
dataset_name="self_query_tool",
llm_or_chain_factory=do_self_query,
client=client,
evaluation=evaluation_config,
verbose=True,
project_name="default",
)
`
### Expected behavior
The function is evaluated with test results in the UI | {
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- added missed integration references to the `AWS` platform page
- added/updated descriptions and links in the referenced notebooks
- renamed two notebook files. They have file names != page Title, which generate unordered ToC.
- reroute the URLs for renamed files
- fixed `amazon_textract` notebook: removed failed cell outputs | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14159 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14159/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14159/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14159/events | https://github.com/langchain-ai/langchain/issues/14159 | 2,021,770,513 | I_kwDOIPDwls54gcUR | 14,159 | create_tagging_chain_pydantic() pydantic schema validation errors | {
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] | closed | false | null | [] | null | 2 | 2023-12-02T00:35:53 | 2023-12-04T20:06:05 | 2023-12-04T20:06:05 | NONE | null | ### System Info
[pydantic_chain_test_code.txt](https://github.com/langchain-ai/langchain/files/13532796/pydantic_chain_test_code.txt)
[error_log.txt](https://github.com/langchain-ai/langchain/files/13532799/error_log.txt)
### Who can help?
@agola11 @hwchase17
The attached code is adopted from the official example from https://python.langchain.com/docs/use_cases/tagging. I am using the AzureChatOpenAI API, but I don't think the cause is related to that. I added an example prompt to ensure that the llm is working properly.
It appears that even when passing in a confirmed subclass of BaseModel, which is pydantic_schema, create_tagging_chain_pydantic(Tags, llm) is triggering validation errors. Please see the attached test code and error log.
### 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
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Run the attached code and match it with the attached error log, which shows the version numbers of Python, Langchain and Pydantic. I am running on WIndows 11.:
Python version: 3.11.5 | packaged by Anaconda, Inc. | (main, Sep 11 2023, 13:26:23) [MSC v.1916 64 bit (AMD64)]
Langchain version: 0.0.344
Pydantic version: 2.5.2
### Expected behavior
Expected behavior is as shown in the last example on https://python.langchain.com/docs/use_cases/tagging
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Also bumps us down to python3.8 because it's significantly faster in the vercel build step. Uses default openssl version instead of upgrading as well. | {
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] | open | false | null | [] | null | 10 | 2023-12-01T21:48:33 | 2024-01-04T18:40:07 | null | NONE | null | ### System Info
conda 23.3.1
Python 3.10.6
angchain 0.0.344 pypi_0 pypi
langchain-core 0.0.8 pypi_0 pypi
langchain-experimental 0.0.43 pypi_0 pypi
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
#This Notebook will show the integration between Generative AI with SAP HANA Cloud
!pip install -q --upgrade langchain boto3 awscli botocore
!pip install -q sqlalchemy-hana langchain_experimental hdbcli
import langchain
from langchain.sql_database import SQLDatabase
from langchain_experimental.sql import SQLDatabaseChain
from langchain.prompts.prompt import PromptTemplate
from langchain.llms.sagemaker_endpoint import LLMContentHandler
from langchain import SagemakerEndpoint
from langchain.llms import bedrock
from urllib.parse import quote
import sqlalchemy
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
from sqlalchemy import create_engine, select, Table, MetaData, Column, String
import hdbcli
import json
#Next step is to prepare the template for prompt and input to be used by the Generative AI
table_info = "Table Hotel has fields Name, Address, City, State, Zip code. \
Table Room has fields Free or Available, Price. \
Table Customer has fields Customer Number, title, first name, name, address, zip code. \
Table Reservation has fields Reservation Number,Arrival Date, Departure Date. \
Table Maintenance has fields Description, Date performed, Performed by."
_DEFAULT_TEMPLATE = """
Given an input question, create a syntactically correct {dialect} SQL query to run without comments, then provide answer to the question in english based on the result of SQL Query.
Always use schema USER1. You DO NOT need to check on the SQL Query for common mistake.
{table_info}
Question: {input}"""
PROMPT = PromptTemplate(
input_variables=["input", "table_info", "dialect"], template=_DEFAULT_TEMPLATE
)
#Next let's instatiate the bedrock and the handler treatment of input and output
class ContentHandler(LLMContentHandler):
content_type = "application/json"
accepts = "application/json"
def transform_input(self, prompt: str, model_kwargs: dict) -> bytes:
input_str = json.dumps({"text_inputs": prompt, **model_kwargs})
return input_str.encode('utf-8')
def transform_output(self, output: bytes) -> str:
response_json = json.loads(output.read().decode("utf-8"))
return response_json["generated_texts"][0]
content_handler = ContentHandler()
model_parameter = {"temperature": 0, "max_tokens_to_sample": 4000}
#Make sure your account has access to anthropic claude access. This can be enabled from Bedrock console. Access is auto approved.
llm = bedrock.Bedrock(model_id="anthropic.claude-v2:1", model_kwargs=model_parameter, region_name="us-west-2")
#Let's connect to the SAP HANA Database and then execute langchain SQL Database Chain to query from the Generative AI
db = SQLDatabase.from_uri("hana://USER:Password@<mydb>.hana.trial-us10.hanacloud.ondemand.com:443")
db_chain = SQLDatabaseChain.from_llm(llm=llm, db=db, prompt=PROMPT, verbose=True, use_query_checker=True, top_k=5 )
Execute the first query, this is a simple English to text SQL
db_chain.run("How many Hotels are there ?")
UNEXPECTED OUTPUT *****************
> Entering new SQLDatabaseChain chain...
How many Hotels are there ?
SQLQuery:I don't see any issues with the original SQL query. It looks good as written. Here is the query again:
```sql
SELECT COUNT(*) AS num_hotels
FROM hotel;
```
This counts the number of rows in the hotel table to get the number of unique hotels and returns the result in the num_hotels column alias. The query follows proper practices and does not contain any of the common mistakes listed.
---------------------------------------------------------------------------
ProgrammingError Traceback (most recent call last)
File /opt/conda/lib/python3.10/site-packages/sqlalchemy/engine/base.py:1819, in Connection._execute_context(self, dialect, constructor, statement, parameters, execution_options, *args, **kw)
1818 if not evt_handled:
-> 1819 self.dialect.do_execute(
1820 cursor, statement, parameters, context
1821 )
1823 if self._has_events or self.engine._has_events:
File /opt/conda/lib/python3.10/site-packages/sqlalchemy/engine/default.py:732, in DefaultDialect.do_execute(self, cursor, statement, parameters, context)
731 def do_execute(self, cursor, statement, parameters, context=None):
--> 732 cursor.execute(statement, parameters)
ProgrammingError: (257, 'sql syntax error: incorrect syntax near "I": line 1 col 1 (at pos 1)')
The above exception was the direct cause of the following exception:
ProgrammingError Traceback (most recent call last)
Cell In[43], line 2
1 #Execute the first query, this is a simple English to text SQL
----> 2 db_chain.run("How many Hotels are there ?")
File /opt/conda/lib/python3.10/site-packages/langchain/chains/base.py:507, in Chain.run(self, callbacks, tags, metadata, *args, **kwargs)
505 if len(args) != 1:
506 raise ValueError("`run` supports only one positional argument.")
--> 507 return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
508 _output_key
509 ]
511 if kwargs and not args:
512 return self(kwargs, callbacks=callbacks, tags=tags, metadata=metadata)[
513 _output_key
514 ]
File /opt/conda/lib/python3.10/site-packages/langchain/chains/base.py:312, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)
310 except BaseException as e:
311 run_manager.on_chain_error(e)
--> 312 raise e
313 run_manager.on_chain_end(outputs)
314 final_outputs: Dict[str, Any] = self.prep_outputs(
315 inputs, outputs, return_only_outputs
316 )
File /opt/conda/lib/python3.10/site-packages/langchain/chains/base.py:306, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)
299 run_manager = callback_manager.on_chain_start(
300 dumpd(self),
301 inputs,
302 name=run_name,
303 )
304 try:
305 outputs = (
--> 306 self._call(inputs, run_manager=run_manager)
307 if new_arg_supported
308 else self._call(inputs)
309 )
310 except BaseException as e:
311 run_manager.on_chain_error(e)
File /opt/conda/lib/python3.10/site-packages/langchain_experimental/sql/base.py:198, in SQLDatabaseChain._call(self, inputs, run_manager)
194 except Exception as exc:
195 # Append intermediate steps to exception, to aid in logging and later
196 # improvement of few shot prompt seeds
197 exc.intermediate_steps = intermediate_steps # type: ignore
--> 198 raise exc
File /opt/conda/lib/python3.10/site-packages/langchain_experimental/sql/base.py:168, in SQLDatabaseChain._call(self, inputs, run_manager)
162 _run_manager.on_text(
163 checked_sql_command, color="green", verbose=self.verbose
164 )
165 intermediate_steps.append(
166 {"sql_cmd": checked_sql_command}
167 ) # input: sql exec
--> 168 result = self.database.run(checked_sql_command)
169 intermediate_steps.append(str(result)) # output: sql exec
170 sql_cmd = checked_sql_command
File /opt/conda/lib/python3.10/site-packages/langchain/utilities/sql_database.py:433, in SQLDatabase.run(self, command, fetch)
423 def run(
424 self,
425 command: str,
426 fetch: Union[Literal["all"], Literal["one"]] = "all",
427 ) -> str:
428 """Execute a SQL command and return a string representing the results.
429
430 If the statement returns rows, a string of the results is returned.
431 If the statement returns no rows, an empty string is returned.
432 """
--> 433 result = self._execute(command, fetch)
434 # Convert columns values to string to avoid issues with sqlalchemy
435 # truncating text
436 res = [
437 tuple(truncate_word(c, length=self._max_string_length) for c in r.values())
438 for r in result
439 ]
File /opt/conda/lib/python3.10/site-packages/langchain/utilities/sql_database.py:411, in SQLDatabase._execute(self, command, fetch)
409 else: # postgresql and other compatible dialects
410 connection.exec_driver_sql("SET search_path TO %s", (self._schema,))
--> 411 cursor = connection.execute(text(command))
412 if cursor.returns_rows:
413 if fetch == "all":
File /opt/conda/lib/python3.10/site-packages/sqlalchemy/engine/base.py:1306, in Connection.execute(self, statement, *multiparams, **params)
1302 util.raise_(
1303 exc.ObjectNotExecutableError(statement), replace_context=err
1304 )
1305 else:
-> 1306 return meth(self, multiparams, params, _EMPTY_EXECUTION_OPTS)
File /opt/conda/lib/python3.10/site-packages/sqlalchemy/sql/elements.py:332, in ClauseElement._execute_on_connection(self, connection, multiparams, params, execution_options, _force)
328 def _execute_on_connection(
329 self, connection, multiparams, params, execution_options, _force=False
330 ):
331 if _force or self.supports_execution:
--> 332 return connection._execute_clauseelement(
333 self, multiparams, params, execution_options
334 )
335 else:
336 raise exc.ObjectNotExecutableError(self)
File /opt/conda/lib/python3.10/site-packages/sqlalchemy/engine/base.py:1498, in Connection._execute_clauseelement(self, elem, multiparams, params, execution_options)
1486 compiled_cache = execution_options.get(
1487 "compiled_cache", self.engine._compiled_cache
1488 )
1490 compiled_sql, extracted_params, cache_hit = elem._compile_w_cache(
1491 dialect=dialect,
1492 compiled_cache=compiled_cache,
(...)
1496 linting=self.dialect.compiler_linting | compiler.WARN_LINTING,
1497 )
-> 1498 ret = self._execute_context(
1499 dialect,
1500 dialect.execution_ctx_cls._init_compiled,
1501 compiled_sql,
1502 distilled_params,
1503 execution_options,
1504 compiled_sql,
1505 distilled_params,
1506 elem,
1507 extracted_params,
1508 cache_hit=cache_hit,
1509 )
1510 if has_events:
1511 self.dispatch.after_execute(
1512 self,
1513 elem,
(...)
1517 ret,
1518 )
File /opt/conda/lib/python3.10/site-packages/sqlalchemy/engine/base.py:1862, in Connection._execute_context(self, dialect, constructor, statement, parameters, execution_options, *args, **kw)
1859 branched.close()
1861 except BaseException as e:
-> 1862 self._handle_dbapi_exception(
1863 e, statement, parameters, cursor, context
1864 )
1866 return result
File /opt/conda/lib/python3.10/site-packages/sqlalchemy/engine/base.py:2043, in Connection._handle_dbapi_exception(self, e, statement, parameters, cursor, context)
2041 util.raise_(newraise, with_traceback=exc_info[2], from_=e)
2042 elif should_wrap:
-> 2043 util.raise_(
2044 sqlalchemy_exception, with_traceback=exc_info[2], from_=e
2045 )
2046 else:
2047 util.raise_(exc_info[1], with_traceback=exc_info[2])
File /opt/conda/lib/python3.10/site-packages/sqlalchemy/util/compat.py:208, in raise_(***failed resolving arguments***)
205 exception.__cause__ = replace_context
207 try:
--> 208 raise exception
209 finally:
210 # credit to
211 # https://cosmicpercolator.com/2016/01/13/exception-leaks-in-python-2-and-3/
212 # as the __traceback__ object creates a cycle
213 del exception, replace_context, from_, with_traceback
File /opt/conda/lib/python3.10/site-packages/sqlalchemy/engine/base.py:1819, in Connection._execute_context(self, dialect, constructor, statement, parameters, execution_options, *args, **kw)
1817 break
1818 if not evt_handled:
-> 1819 self.dialect.do_execute(
1820 cursor, statement, parameters, context
1821 )
1823 if self._has_events or self.engine._has_events:
1824 self.dispatch.after_cursor_execute(
1825 self,
1826 cursor,
(...)
1830 context.executemany,
1831 )
File /opt/conda/lib/python3.10/site-packages/sqlalchemy/engine/default.py:732, in DefaultDialect.do_execute(self, cursor, statement, parameters, context)
731 def do_execute(self, cursor, statement, parameters, context=None):
--> 732 cursor.execute(statement, parameters)
ProgrammingError: (hdbcli.dbapi.ProgrammingError) (257, 'sql syntax error: incorrect syntax near "I": line 1 col 1 (at pos 1)')
[SQL: I don't see any issues with the original SQL query. It looks good as written. Here is the query again:
```sql
SELECT COUNT(*) AS num_hotels
FROM hotel;
```
This counts the number of rows in the hotel table to get the number of unique hotels and returns the result in the num_hotels column alias. The query follows proper practices and does not contain any of the common mistakes listed.]
(Background on this error at: https://sqlalche.me/e/14/f405)
### Expected behavior
The select statement is produced correct, it just that the LLM perform checks on the SQL Statement and comment it before passing it to SQLAlchemy for execution. When i used Claude v2, 75% SQLDatabaseChain is successful, however 25% always gets back to the behaviour providing comments about whether the SQL Query has common mistakes.
Now with Claude v2.1, ALL of the questions are checked, and failing when passing to SQLAlchemy. It seems there is a problem with the SQLDatabaseChain, either the prompt or something else. Please help to fix this.
[Lab2.ipynb.zip](https://github.com/langchain-ai/langchain/files/13532157/Lab2.ipynb.zip)
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Replace this entire comment with:
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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.
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If you're adding a new integration, please include:
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Adding [IBM Watson Discovery Service](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-getting-started) as an additional retriever might be a useful extension to enable RAG-use cases or supporting RetrievalChains.
IBM Watson Discovery Service can find relevant documents based on natural language queries and understand document structures such as tables.
### Motivation
Currently, there is no support for IBM Watson Discovery Service (WDS) as a retriever which requires external scripts to use WDS as a source for a RetrievalChain e.g.
### Your contribution
I'm able to contribute a IbM Watson Discovery Service retriever. | {
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Added this link. | {
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From what I can see, `poetry` will auto-include this. Please let me know if I am missing something here. | {
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] | closed | false | null | [] | null | 1 | 2023-12-01T20:37:00 | 2023-12-01T21:36:41 | 2023-12-01T21:36:40 | CONTRIBUTOR | null | - **Description:** Change instances of RunnableMap to RunnableParallel, as that should be the one used going forward. This makes it consistent across the codebase.
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] | closed | false | null | [] | null | 3 | 2023-12-01T20:31:24 | 2024-01-11T22:59:40 | 2024-01-11T22:59:40 | NONE | null | ### System Info
It seems the typing of my index is lost when I save and load it. This is me creating the FAISS vector store with the MAX_INNER_PRODUCT flag.
```python
# Saving
save_db = FAISS.from_texts(texts, embedding_function, metadatas=metadatas, distance_strategy="MAX_INNER_PRODUCT", normalize_L2=True)
save_db.save_local(db_directory)
print(save_db.index, save_db.distance_strategy)
# <faiss.swigfaiss_avx2.IndexFlatIP; proxy of <Swig Object of type 'faiss::IndexFlatIP *' at 0x7f4e0402e4b0> > MAX_INNER_PRODUCT
# Loading
load_db = FAISS.load_local(db_directory, embedding_function, distance_strategy="MAX_INNER_PRODUCT")
print(load_db .index, load_db .distance_strategy)
# <faiss.swigfaiss_avx2.IndexFlat; proxy of <Swig Object of type 'faiss::IndexFlat *' at 0x7f4dd0accdb0> > MAX_INNER_PRODUCT
```
I have looked through FAISS API documentation and can't tell if this is an abstracted class.
### 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
1. Create a FAISS vector store passing distance_strategy = "MAX_INNER_PRODUCT". This should create an index with FAISS.IndexFlatIP.
2. Save the FAISS vector store.
3. Load the FAISS vector store with the distance_strategy = "MAX_INNER_PRODUCT"
4. Compare the saved.index and loaded.index objects. One is IndexFlatIP and the other is an IndexFlat class.
### Expected behavior
When loading the index from load_local, it should still be an IndexFlatIP. | {
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] | open | false | null | [] | null | 7 | 2023-12-01T20:20:11 | 2024-01-10T19:59:42 | null | NONE | null | ### Feature request
Amazon DocumentDB has launched its vector search feature recently. Let us add Amazon DocumentDB as vector store in langchain.
### Motivation
This will help large customer base of Amazon DocumentDB to adopt langchain and seamlessly build AI/ML apps
### Your contribution
Amazon DocumentDB team can help with testing and technical help.
https://aws.amazon.com/about-aws/whats-new/2023/11/vector-search-amazon-documentdb/
https://docs.aws.amazon.com/documentdb/latest/developerguide/vector-search.html | {
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This PR creates a helper method for DRY code. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14136 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14136/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14136/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14136/events | https://github.com/langchain-ai/langchain/pull/14136 | 2,021,449,299 | PR_kwDOIPDwls5g7d1J | 14,136 | Removing extra `StdOutCallbackHandler` overridden methods | {
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] | closed | false | null | [] | null | 3 | 2023-12-01T19:18:00 | 2023-12-03T18:40:51 | 2023-12-03T17:38:50 | CONTRIBUTOR | null | Unnecessarily overridden methods:
- Give the idea the subclass is doing something special (when it isn't)
- Block CTRL-click to the actual method
This PR removes some unnecessarily overridden methods in `StdOutCallbackHandler`
Supercedes https://github.com/langchain-ai/langchain/pull/12858 | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14135 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14135/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14135/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14135/events | https://github.com/langchain-ai/langchain/issues/14135 | 2,021,388,401 | I_kwDOIPDwls54e_Bx | 14,135 | Bug: LCEL prompt template not logging | {
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] | open | false | null | [] | null | 2 | 2023-12-01T18:30:40 | 2023-12-02T19:00:00 | null | CONTRIBUTOR | null | ### System Info
Python 3.11, `langchain==0.0.340`
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
Before LCEL, there is lots of logging:
```python
from langchain.chains import LLMChain
from langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain.schema.callbacks.stdout import StdOutCallbackHandler
prompt = PromptTemplate.from_template(
"What is a good name for a company that makes {product}?"
)
chain = LLMChain(prompt=prompt, llm=ChatOpenAI(), callbacks=[StdOutCallbackHandler()])
chain.run(product="colorful socks")
```
```none
> Entering new LLMChain chain...
Prompt after formatting:
What is a good name for a company that makes colorful socks?
> Finished chain.
```
After LCEL, there is less logging:
```python
from langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain.schema.callbacks.stdout import StdOutCallbackHandler
prompt = PromptTemplate.from_template(
"What is a good name for a company that makes {product}?"
)
runnable = prompt | ChatOpenAI()
runnable.invoke(
input={"product": "colorful socks"}, config={"callbacks": [StdOutCallbackHandler()]}
)
```
Leads to this output:
```none
> Entering new RunnableSequence chain...
> Entering new PromptTemplate chain...
> Finished chain.
> Finished chain.
```
### Expected behavior
To migrate from `LLMChain` to LCEL, I want the logging statements/verbosity to remain in tact | {
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https://python.langchain.com/docs/modules/callbacks/
This doc doesn't mention how to use callbacks with LCEL. It would be good to add that, to help with the migration from `LLMChain` to `LCEL`.
### Idea or request for content:
https://python.langchain.com/docs/modules/chains/ does a nice job of showing code pre-LCEL and code post-LCEL.
https://github.com/langchain-ai/langchain/discussions/12670 shows how to pass the callback to `invoke`, but ideally the example shows how to `bind` the callback once (as opposed to passing to each `invoke`). | {
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