Merge remote-tracking branch 'origin/main'

docs/docs/installation/azure.md

@@ -1,6 +1,6 @@
 ## Azure DevOps Pipeline
 
-You can use a pre-built Action Docker image to run PR-Agent as an Azure devops pipeline.
+You can use a pre-built Action Docker image to run PR-Agent as an Azure DevOps pipeline.
 Add the following file to your repository under `azure-pipelines.yml`:
 
 ```yaml
@@ -8,12 +8,16 @@ Add the following file to your repository under `azure-pipelines.yml`:
 trigger: none
 
 # Configure PR trigger
-pr:
-  branches:
-    include:
-    - '*'
-  autoCancel: true
-  drafts: false
+# pr:
+#   branches:
+#     include:
+#     - '*'
+#   autoCancel: true
+#   drafts: false
+
+# NOTE for Azure Repos Git:
+# Azure Repos does not honor YAML pr: triggers. Configure Build Validation
+# via Branch Policies instead (see note below). You can safely omit pr:.
 
 stages:
 - stage: pr_agent
@@ -61,6 +65,19 @@ Make sure to give pipeline permissions to the `pr_agent` variable group.
 
 > Note that Azure Pipelines lacks support for triggering workflows from PR comments. If you find a viable solution, please contribute it to our [issue tracker](https://github.com/Codium-ai/pr-agent/issues)
 
+### Azure Repos Git PR triggers and Build Validation
+
+Azure Repos Git does not use YAML `pr:` triggers for pipelines. Instead, configure Build Validation on the target branch to run the PR Agent pipeline for pull requests:
+
+1. Go to Project Settings → Repositories → Branches.
+2. Select the target branch and open Branch Policies.
+3. Under Build Validation, add a policy:
+   - Select the PR Agent pipeline (the `azure-pipelines.yml` above).
+   - Set it as Required.
+4. Remove the `pr:` section from your YAML (not needed for Azure Repos Git).
+
+This distinction applies specifically to Azure Repos Git. Other providers like GitHub and Bitbucket Cloud can use YAML-based PR triggers.
+
 ## Azure DevOps from CLI
 
 To use Azure DevOps provider use the following settings in configuration.toml:

docs/docs/tools/implement.md

@@ -43,7 +43,7 @@ It leverages LLM technology to transform PR comments and review suggestions into
     You can reference and implement changes from any comment by:
 
     ```
-    /implement <link-to-review-comment>
+    /implement <link-to-an-inline-comment>
     ```
 
     ![implement3](https://codium.ai/images/pr_agent/implement3.png){width=640}
@@ -54,4 +54,4 @@ It leverages LLM technology to transform PR comments and review suggestions into
 
 - Use `/implement` to implement code change within and based on the review discussion.
 - Use `/implement <code-change-description>` inside a review discussion to implement specific instructions.
-- Use `/implement <link-to-review-comment>` to indirectly call the tool from any comment.
+- Use `/implement <link-to-an-inline-comment>` to respond to an inline comment by triggering the tool from anywhere in the thread.

docs/docs/tools/similar_issues.md

@@ -27,6 +27,7 @@ Choose from the following Vector Databases:
 
 1. LanceDB
 2. Pinecone
+3. Qdrant
 
 #### Pinecone Configuration
 
@@ -40,6 +41,25 @@ environment = "..."
 
 These parameters can be obtained by registering to [Pinecone](https://app.pinecone.io/?sessionType=signup/).
 
+#### Qdrant Configuration
+
+To use Qdrant with the `similar issue` tool, add these credentials to `.secrets.toml` (or set as environment variables):
+
+```
+[qdrant]
+url = "https://YOUR-QDRANT-URL" # e.g., https://xxxxxxxx-xxxxxxxx.eu-central-1-0.aws.cloud.qdrant.io
+api_key = "..."
+```
+
+Then select Qdrant in `configuration.toml`:
+
+```
+[pr_similar_issue]
+vectordb = "qdrant"
+```
+
+You can get a free managed Qdrant instance from [Qdrant Cloud](https://cloud.qdrant.io/).
+
 ## How to use
 
 - To invoke the 'similar issue' tool from **CLI**, run:

pr_agent/settings/.secrets_template.toml

@@ -25,6 +25,11 @@ key = ""  # Acquire through https://platform.openai.com
 api_key = "..."
 environment = "gcp-starter"
 
+[qdrant]
+# For Qdrant Cloud or self-hosted Qdrant
+url = ""  # e.g., https://xxxxxxxx-xxxxxxxx.eu-central-1-0.aws.cloud.qdrant.io
+api_key = ""
+
 [anthropic]
 key = "" # Optional, uncomment if you want to use Anthropic. Acquire through https://www.anthropic.com/
 

pr_agent/settings/configuration.toml

@@ -345,7 +345,7 @@ service_callback = []
 skip_comments = false
 force_update_dataset = false
 max_issues_to_scan = 500
-vectordb = "pinecone"
+vectordb = "pinecone" # options: "pinecone", "lancedb", "qdrant"
 
 [pr_find_similar_component]
 class_name = ""
@@ -363,6 +363,11 @@ number_of_results = 5
 [lancedb]
 uri = "./lancedb"
 
+[qdrant]
+# fill and place credentials in .secrets.toml
+# url = "https://YOUR-QDRANT-URL"
+# api_key = "..."
+
 [best_practices]
 content = ""
 organization_name = ""

pr_agent/tools/pr_similar_issue.py

@@ -174,6 +174,87 @@ class PRSimilarIssue:
                 else:
                     get_logger().info('No new issues to update')
 
+        elif get_settings().pr_similar_issue.vectordb == "qdrant":
+            try:
+                import qdrant_client
+                from qdrant_client.models import (Distance, FieldCondition,
+                                                  Filter, MatchValue,
+                                                  PointStruct, VectorParams)
+            except Exception:
+                raise Exception("Please install qdrant-client to use qdrant as vectordb")
+
+            api_key = None
+            url = None
+            try:
+                api_key = get_settings().qdrant.api_key
+                url = get_settings().qdrant.url
+            except Exception:
+                if not self.cli_mode:
+                    repo_name, original_issue_number = self.git_provider._parse_issue_url(self.issue_url.split('=')[-1])
+                    issue_main = self.git_provider.repo_obj.get_issue(original_issue_number)
+                    issue_main.create_comment("Please set qdrant url and api key in secrets file")
+                raise Exception("Please set qdrant url and api key in secrets file")
+
+            self.qdrant = qdrant_client.QdrantClient(url=url, api_key=api_key)
+
+            run_from_scratch = False
+            ingest = True
+
+            if not self.qdrant.collection_exists(collection_name=self.index_name):
+                run_from_scratch = True
+                ingest = False
+                self.qdrant.create_collection(
+                    collection_name=self.index_name,
+                    vectors_config=VectorParams(size=1536, distance=Distance.COSINE),
+                )
+            else:
+                if get_settings().pr_similar_issue.force_update_dataset:
+                    ingest = True
+                else:
+                    response = self.qdrant.count(
+                        collection_name=self.index_name,
+                        count_filter=Filter(must=[
+                            FieldCondition(key="metadata.repo", match=MatchValue(value=repo_name_for_index)),
+                            FieldCondition(key="id", match=MatchValue(value=f"example_issue_{repo_name_for_index}")),
+                        ]),
+                    )
+                    ingest = True if response.count == 0 else False
+
+            if run_from_scratch or ingest:
+                get_logger().info('Indexing the entire repo...')
+                get_logger().info('Getting issues...')
+                issues = list(repo_obj.get_issues(state='all'))
+                get_logger().info('Done')
+                self._update_qdrant_with_issues(issues, repo_name_for_index, ingest=ingest)
+            else:
+                issues_to_update = []
+                issues_paginated_list = repo_obj.get_issues(state='all')
+                counter = 1
+                for issue in issues_paginated_list:
+                    if issue.pull_request:
+                        continue
+                    issue_str, comments, number = self._process_issue(issue)
+                    issue_key = f"issue_{number}"
+                    point_id = issue_key + "." + "issue"
+                    response = self.qdrant.count(
+                        collection_name=self.index_name,
+                        count_filter=Filter(must=[
+                            FieldCondition(key="id", match=MatchValue(value=point_id)),
+                            FieldCondition(key="metadata.repo", match=MatchValue(value=repo_name_for_index)),
+                        ]),
+                    )
+                    if response.count == 0:
+                        counter += 1
+                        issues_to_update.append(issue)
+                    else:
+                        break
+
+                if issues_to_update:
+                    get_logger().info(f'Updating index with {counter} new issues...')
+                    self._update_qdrant_with_issues(issues_to_update, repo_name_for_index, ingest=True)
+                else:
+                    get_logger().info('No new issues to update')
+
 
     async def run(self):
         get_logger().info('Getting issue...')
@@ -246,6 +327,36 @@ class PRSimilarIssue:
                 score_list.append(str("{:.2f}".format(1-r['_distance'])))
             get_logger().info('Done')
 
+        elif get_settings().pr_similar_issue.vectordb == "qdrant":
+            from qdrant_client.models import FieldCondition, Filter, MatchValue
+            res = self.qdrant.search(
+                collection_name=self.index_name,
+                query_vector=embeds[0],
+                limit=5,
+                query_filter=Filter(must=[FieldCondition(key="metadata.repo", match=MatchValue(value=self.repo_name_for_index))]),
+                with_payload=True,
+            )
+
+            for r in res:
+                rid = r.payload.get("id", "")
+                if 'example_issue_' in rid:
+                    continue
+                try:
+                    issue_number = int(rid.split('.')[0].split('_')[-1])
+                except Exception:
+                    get_logger().debug(f"Failed to parse issue number from {rid}")
+                    continue
+                if original_issue_number == issue_number:
+                    continue
+                if issue_number not in relevant_issues_number_list:
+                    relevant_issues_number_list.append(issue_number)
+                if 'comment' in rid:
+                    relevant_comment_number_list.append(int(rid.split('.')[1].split('_')[-1]))
+                else:
+                    relevant_comment_number_list.append(-1)
+                score_list.append(str("{:.2f}".format(r.score)))
+            get_logger().info('Done')
+
         get_logger().info('Publishing response...')
         similar_issues_str = "### Similar Issues\n___\n\n"
 
@@ -458,6 +569,101 @@ class PRSimilarIssue:
         get_logger().info('Done')
 
 
+    def _update_qdrant_with_issues(self, issues_list, repo_name_for_index, ingest=False):
+        try:
+            import uuid
+
+            import pandas as pd
+            from qdrant_client.models import PointStruct
+        except Exception:
+            raise
+
+        get_logger().info('Processing issues...')
+        corpus = Corpus()
+        example_issue_record = Record(
+            id=f"example_issue_{repo_name_for_index}",
+            text="example_issue",
+            metadata=Metadata(repo=repo_name_for_index)
+        )
+        corpus.append(example_issue_record)
+
+        counter = 0
+        for issue in issues_list:
+            if issue.pull_request:
+                continue
+
+            counter += 1
+            if counter % 100 == 0:
+                get_logger().info(f"Scanned {counter} issues")
+            if counter >= self.max_issues_to_scan:
+                get_logger().info(f"Scanned {self.max_issues_to_scan} issues, stopping")
+                break
+
+            issue_str, comments, number = self._process_issue(issue)
+            issue_key = f"issue_{number}"
+            username = issue.user.login
+            created_at = str(issue.created_at)
+            if len(issue_str) < 8000 or \
+                    self.token_handler.count_tokens(issue_str) < get_max_tokens(MODEL):
+                issue_record = Record(
+                    id=issue_key + "." + "issue",
+                    text=issue_str,
+                    metadata=Metadata(repo=repo_name_for_index,
+                                      username=username,
+                                      created_at=created_at,
+                                      level=IssueLevel.ISSUE)
+                )
+                corpus.append(issue_record)
+                if comments:
+                    for j, comment in enumerate(comments):
+                        comment_body = comment.body
+                        num_words_comment = len(comment_body.split())
+                        if num_words_comment < 10 or not isinstance(comment_body, str):
+                            continue
+
+                        if len(comment_body) < 8000 or \
+                                self.token_handler.count_tokens(comment_body) < MAX_TOKENS[MODEL]:
+                            comment_record = Record(
+                                id=issue_key + ".comment_" + str(j + 1),
+                                text=comment_body,
+                                metadata=Metadata(repo=repo_name_for_index,
+                                                  username=username,
+                                                  created_at=created_at,
+                                                  level=IssueLevel.COMMENT)
+                            )
+                            corpus.append(comment_record)
+
+        df = pd.DataFrame(corpus.dict()["documents"])
+        get_logger().info('Done')
+
+        get_logger().info('Embedding...')
+        openai.api_key = get_settings().openai.key
+        list_to_encode = list(df["text"].values)
+        try:
+            res = openai.Embedding.create(input=list_to_encode, engine=MODEL)
+            embeds = [record['embedding'] for record in res['data']]
+        except Exception:
+            embeds = []
+            get_logger().error('Failed to embed entire list, embedding one by one...')
+            for i, text in enumerate(list_to_encode):
+                try:
+                    res = openai.Embedding.create(input=[text], engine=MODEL)
+                    embeds.append(res['data'][0]['embedding'])
+                except Exception:
+                    embeds.append([0] * 1536)
+        df["vector"] = embeds
+        get_logger().info('Done')
+
+        get_logger().info('Upserting into Qdrant...')
+        points = []
+        for row in df.to_dict(orient="records"):
+            points.append(
+                PointStruct(id=uuid.uuid5(uuid.NAMESPACE_DNS, row["id"]).hex, vector=row["vector"], payload={"id": row["id"], "text": row["text"], "metadata": row["metadata"]})
+            )
+        self.qdrant.upsert(collection_name=self.index_name, points=points)
+        get_logger().info('Done')
+
+
 class IssueLevel(str, Enum):
     ISSUE = "issue"
     COMMENT = "comment"

requirements.txt

@@ -36,6 +36,7 @@ giteapy==1.0.8
 # pinecone-client
 # pinecone-datasets @ git+https://github.com/mrT23/pinecone-datasets.git@main
 # lancedb==0.5.1
+# qdrant-client==1.15.1
 # uncomment this to support language LangChainOpenAIHandler
 # langchain==0.2.0
 # langchain-core==0.2.28